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1ForewordSAIS Review vol. XXXIV no. 1 (Winter–Spring 2014)
1© 2014 by The Johns Hopkins University Press
Foreword
Following the exposure of the U.S. National Security Administration’s
(NSA) controversial surveillance program, there has been heated debate
surrounding the collection and storage of personal data. Our latest issue of
The SAIS Review of International Affairs, “Policy by Numbers: How Big Data
is Transforming Security, Governance, and Development,” seeks to move
beyond the sensationalism that has accompanied the NSA revelations. We
hope to provide readers a more nuanced perspective on the role of data
in international affairs, with a diverse collection of interviews, essays, and
opinion editorials from scholars, technologists, and policymakers.
We explore the rise of big data, in which governments and profit-
seeking organizations make policies and predictions based upon correla-
tions among massive quantities of data. We examine the trend toward open
data, in which governments provide valuable datasets directly to the public.
We assess the impact of data—positive and negative, international and do-
mestic—on public policy, national security, international development, and
individual well-being.
While the rise of big and open data is associated with promising ap-
plications, there are still vast uncertainties regarding how best to exploit
this technology. We hope that readers from the academic and public policy
communities will feel empowered to enhance their understanding of techni-
cal tools and data analysis, in an age where technological innovation often
outpaces government policy.
We begin with a conversation with Robert Kirkpatrick, Director of
the United Nations Global Pulse Initiative. The UN Global Pulse Initiative
collects and analyzes real-time data to better protect populations from
socioeconomic shocks. Kirkpatrick explores the challenges associated with
big data analytics, the surprising correlations among seemingly unrelated
datasets, and the initiative’s effort to predict food price crises with data
from social media.
Human rights data often impacts policy decisions. The next three
articles explore the opportunities and risks associated with collecting and
analyzing this sensitive information. Megan Price and Patrick Ball use
case studies of violent conflicts in Syria and Iraq to evaluate data-gathering
methodologies in conflict scenarios. They warn that datasets from conflict
scenarios are often subject to bias, and should not be used in isolation
to draw conclusions. Monti Narayan Datta argues that the collection of
quantitative data on modern day slavery has generated discussion in media
and among policymakers on how to mitigate and eradicate slavery. Our
interview with Arch Puddington, Vice President for Research at Freedom
House, discusses worldwide trends in freedom, and the impact of Freedom
House’s annual reports and indices.

2 SAIS Review Winter–Spring 2014
The rise of big and open data has a powerful impact on government
policymaking. Pongkwan Sawasdipakdi frames data as an information
weapon in the context of Thai domestic politics. She examines the govern-
ment’s rice-pledging scheme, and argues that contrasting datasets from the
government and opposition parties are used to gain political power and
credibility. Ian Kalin describes the theory and practice of open data policy
in the United States, and explains how government leaders can replicate
successful open data initiatives. Joel Gurin argues that open government
and open data can improve economic growth, transparency, and citizen
engagement. He also notes the obstacles for implementing open data initia-
tives in developing countries.
How can policymakers craft policies and frameworks that best take
advantage of big data? Given the fast pace of innovation and the slow pace
of policy, Kord Davis discusses how to bridge the gap between policymak-
ers and innovators. He identifies spaces where the public and private sector
can collaborate to produce effective and balanced policy. Aniket Bhushan
argues that the rise of big data and open data has created an opportunity
for disruptive innovation in international affairs. He offers examples related
to real-time macroeconomic analysis, humanitarian response, and poverty
measurement.
Data impacts national security and individual privacy, as well. Chris
Poulin outlines the processes of data collection and analysis, using case
studies from the Arab Spring, medical risk analysis, and his work at the
Durkheim Project, a data analysis initiative that seeks to predict and prevent
veteran suicides. David Rubin, Kim Lynch, Jason Escaravage, and Hillary
Lerner explain how to balance the opposing forces of opportunity and risk,
collective security and individual privacy, and innovation and protection
when using data for national security programs.
Finally, we look to China for lessons on data infrastructure. Eric Hagt
traces the history of China’s satellite navigation system, Beidou, and com-
pares its potential as a tool for development versus domestic and national
security. Margaret Ross statistically analyzes the risk of potential disrup-
tions to the global undersea cable communications network.
We conclude with analyses of influential literature and scholarly re-
search. Ilaria Mazzocco reviews Big Data: A Revolution That Will Transform
How We Live, Work, and Think by Viktor Mayer-Schönberger and Kenneth
Cukier. Bartholomew Thanhauser reviews Evgeny Morozov’s To Save Ev-
erything, Click Here: The Folly of Technological Solutionism.
We would like to thank our advisory board for their guidance in shap-
ing our exploration of data, our excellent editorial staff for their dedication
and persistence, and our authors for their thoughtful work on complex
global challenges. Their combined contributions made the publication of
“Policy by Numbers” possible.
Meghan Kleinsteiber Lauren Caldwell
Editor-in-Chief Senior Editor

3A Conversation with Robert KirkpatrickSAIS Review vol. XXXIV no. 1 (Winter–Spring 2014)
A Conversation with Robert
Kirkpatrick, Director of United
Nations Global Pulse
You are director of United Nations Global Pulse, an initiative to
leverage real-time data and analytics to monitor impacts of inter-
national and local shocks. How did the idea of Global Pulse come
about? What is your mission statement?
The initial idea of Global Pulse came about in the aftermath of the global
financial crisis. There was a recognition that we live in a hyper-connected
world where information moves at the speed of light, and crises and vul-
nerabilities can emerge quickly, but we’re still using two- to three-year-
old statistics to make most policy decisions. It was clear that there were
swathes of people being pushed below the poverty line almost overnight,
and we needed to modernize
our systems and capacities for
absorbing real-time informa-
tion for decision-making.
As a result, United Nations
Secretary-General Ban Ki-moon
established Global Pulse in 2009
to act as an innovation lab and
catalyst for the United Nations.
We bring together global de-
velopment experts, as well as
experts from academia and the
private sector, to explore how
analysis of big data can reveal faster insights about human well-being and
emerging vulnerabilities, in order to better protect populations from hunger,
poverty, and disease.
So Global Pulse’s mission is to accelerate the use of data science for
sustainable development and humanitarian action, to address systemic bar-
riers to adoption, and to cultivate a robust innovation ecosystem.
Robert Kirkpatrick is the director of UN Global Pulse, an initiative of the Executive
Office of the United Nations Secretary-General. The Global Pulse initiative explores
how Big Data and real-time analytics technologies can power a more agile approach to
sustainable development.
We . . . explore how digital data
sources and real-time analytics
technologies can help reveal
insights about human well-being
and emerging vulnerabilities,
in order to better protect
populations from shocks.

As the New York Times wrote in its August 2013 profile of Global Pulse,
the United Nations is often perceived as a “sprawling bureaucracy.”
What makes the Global Pulse team unique? What qualities—personal
and professional—do you seek in a team member?
Global Pulse is unique because we have an “intrapreneurial” approach. It
requires risk-taking and innovation to discover and generate new tools,
techniques, and methodologies to help the UN system and wider community
leverage new sources of real-time information and insights in the service
of humanitarian response and development work. This also requires a real
blend of expertise from within and outside of the UN. Due to the experi-
mental nature of our work, we are set up as a network of labs.
We have multidisciplinary teams working at our Pulse Labs in New
York, Jakarta, and Kampala that include data scientists and analysts, social
scientists, legal experts, and communications and partnerships specialists.
Pulse Lab teams design, scope, and co-create projects with UN agencies
and national institutions that provide sectoral expertise, and with private
sector or academic partners who provide access to data or analytical and
engineering tools.
When building a team, I look for “T-shaped people”—that is, people with
a broad range of skills and a flexible attitude, as well as deep knowledge of
one discipline, whether it is data science, design, partnership management,
or legal and privacy matters.
From what range of sources do you derive the data used for your
analyses? Which datasets do you consider to be the most unique or
surprising? What are the challenges associated with data collection
and analysis?
Global Pulse is interested in trends that reveal something about human
well-being, which can be revealed from data produced by people as they
go about their daily lives (sometimes known as “data exhaust”). Broadly
speaking, we have been exploring two types of data in the Pulse Labs. The
first is data that reflects “what people say,” which includes publicly available
content from the open web, such as tweets, blog posts, news stories, and so
forth. The second is data that reflects “what people do,” which can include
information routinely generated for business intelligence and to optimize
sales in a private sector company. An example of “what people do” data is
anonymized mobile phone traffic information, which can reveal everything
from footfall in a shopping district during rush hour to how a population
migrates after a natural catastrophe.
A dataset that may be surprising is postal data (the traffic and volume
of packages being shipped), which can be used as a proxy for GDP and eco-
nomic activity in a country or region. We are beginning a series of research
projects with the Universal Postal Union (UPU), the United Nations special-
ized agency for the postal sector, to explore this relationship further.
There are several challenges associated with moving this kind of
analysis out of an innovation lab and into practice, including the need to

5A Conversation with Robert Kirkpatrick
build skills and capacity around data science, the formation of sustain-
able partnerships with potential data providers in the private sector, and
identifying where new data and
insights can fit into the planning
and decision-making processes.
And most importantly, we must
take data protection and privacy
norms, policies, and techniques
to a new level to mitigate the po-
tential for misuse. Our mission
to find responsible ways of using
big data for global development
purposes does not include analyzing private or confidential information.
We follow, and advocate for, robust privacy protection principles.
Does Global Pulse focus on certain sectors? If so, why?
Although we can and do work with any part of the UN system that has a de-
velopment problem that data science might contribute to solving, there are
certain areas that are particularly well-suited to big data analysis. This year,
we will focus in particular on public health, including attitudes to health
as expressed on social media, news media, and patterns in anonymized
search data. For example, in partnership with the World Health Organiza-
tion, we are exploring whether early warning of non-communicable disease
risk factors in a country or community could be understood via analysis of
key words in social media data. We continue to look at parental attitudes
to immunizing children as expressed on social media, in order to address
misinformation that stops parents from protecting their children against
preventable diseases.
Another research priority is food security. In Indonesia, our Pulse Lab
Jakarta research team is exploring whether big data can provide insights
about the impacts of food price changes, in order to support the social pro-
tection policies of the government of Indonesia. Other areas of focus this
year include supporting humanitarian action through new data analytics
techniques, finding new ways to measure economic well-being, and using
digital data mining to help shape the priority development agenda that will
replace the Millennium Development Goals after they expire in 2015. Across
all sectors, though, Global Pulse conducts a range of activities to strengthen
the big data for development (BD4D) ecosystem by guiding the development
of regulatory frameworks and technical standards to address data-sharing
and privacy protection challenges. We support an emerging community of
practice to accelerate public sector adoption through advocacy, policy guid-
ance, and technical assistance.
How do you identify and maintain relationships with your private sec-
tor partners?
Private sector partners are incredibly important in helping us leverage big
data as a resource for sustainable development. Using big data responsibly
We must take data protection
and privacy norms, policies,
and techniques to a new level
to mitigate the potential for
misuse.

and effectively requires several different elements, so there are different
areas of expertise, knowledge, and resources we look for when building
partnerships. The Global Pulse network of partners and collaborators in-
cludes forward-thinking private sector companies that are willing to engage
in “data philanthropy,” by granting access to data and technology tools to
the public sector. Our network also includes industry leaders, universities,
research institutes, and non-profit networks of researchers and innovators
who are ready to bring their skills and expertise to bear for advancing the
use of data science across the global development and humanitarian fields.
To establish and maintain these relationships, we have a partnership
manager and a privacy and legal expert, both of whom help guide potential
partners through the process. They work with counterparts in the com-
panies to ensure that safeguards, legal agreements, and data protection
principles are in place. Once collaboration is underway, our research team
will work closely with data analysts in the partner organization to initiate a
project or exploration. Often, the data never leaves the business that owns it;
rather, our data scientists guide the process and then the trends or results
are shared. This modality works well when the data is sensitive.
The experience with our partners, overall, is one of mutual learning.
The Pulse Lab network offers a safe “sandbox” for de-risking this type of
experimentation as we all learn together how the public and private sector
can responsibly harness big data for development.
Could you share a few Global Pulse success stories? Similarly, which
development challenges (such as particular regions or issues) are par-
ticularly difficult to tackle?
There are success stories on the data philanthropy front in which telecom-
munications companies have made anonymized datasets available as part
of a competition or challenge. For example, last year we collaborated with
Orange Telecom to host a “Data for Development Challenge” in which the
company opened up a dataset of anonymized mobile phone data to more
than eighty research teams from around the world to analyze. This research
garnered insights that the international development community can be
inspired by or learn from.
In terms of projects we are carrying out, as I mentioned previously, our
Pulse Lab in Indonesia is conducting research on mining tweets to under-
stand food price crises. Their research has provided new insights into the
very real problem of sudden increases in the price of staple foodstuffs, like
rice prices, pushing families below the poverty line and causing regional
economic instability. Real-time information about these impacts could help
policymakers and governments provide support to families who are suffer-
ing as a result of food price hikes.
Going forward, we plan to conduct further research on social media
analysis for food security and for crowdsourcing food prices, since these are
areas of focus for the government of Indonesia, and will be applicable in
many other parts of the world. Certainly, this is not a solution that can be

7A Conversation with Robert Kirkpatrick
applied universally. Social media analysis is of limited use in countries where
internet penetration is low, and even in regions of a country where the digi-
tal divide is vast.
There are also ana-
lytical challenges
yet to be resolved,
including the ne-
cessity of build-
ing technologies
that can support
diverse local lan-
guages.
We are in the
early days of dis-
covering how big data can be applied to development and humanitarian
contexts, and there are diverse challenges ranging from data access and the
capacity to use real-time data in decision-making to data privacy. These
challenges will be addressed over time.
As Viktor Mayer-Schönberger and Kenneth Cukier wrote, big data is
revolutionizing the way we solve problems. You have noted several ways
that data collection and analysis is helping Global Pulse address global
development challenges. But does the shift toward big data analytics
have drawbacks for the field of international development, as well?
There are risks rather than drawbacks. I hear a lot about the supposed fear
that all development decisions would be made by algorithms. This is not
a realistic fear, but rather a false dichotomy between quantitative use of
data in decision-making and policymakers using qualitative experiences to
decide a course of action. Big data will always be one part of a solution, not
the only solution. Of course, we need research, official statistics, and the
deep knowledge of field workers, communities, and practitioners, but big
data and data science represents a useful addition to the development and
humanitarian worker’s toolbox.
Another common misperception is that real-time data would replace
official statistics, but this assumption is unrealistic, as well. Official statis-
tics will continue to provide high-quality snapshots of progress that can
be benchmarked. But increasingly, between those annual, bi-annual, or
monthly updates, real-time data sources will provide valuable interim feed-
back and indicators. This feedback can enable course-correction when it is
evident that a program isn’t working. And real-time data can reveal shifts
in food-pricing, population changes, or disease outbreaks within a day or
an hour, rather than a month or a year.
We are in the early days of discovering how
big data can be applied to development
and humanitarian contexts, and there
are diverse challenges ranging from data
access and the capacity to use real-time
data in decision-making to data privacy.

Many graduate schools of international affairs, including the Johns
Hopkins School of Advanced International Studies (SAIS), offer cours-
es or concentrations in international development. Do you find that
these programs offer adequately rigorous quantitative or data analysis
requirements? What skills should students develop if they intend to
enter the field of international development?
There is a need for greater skills capacity for data analysis—this is something
that is needed across the board and not only in our field. The international
development practitioner of the future will be someone who is data literate,
and capable of using data analysis to inform his or her understanding and
decision-making. So yes, we’d like to see graduate schools covering data
for development
and the process-
es involved. Just
as the schools of
journalism are
now teaching data
journalism, all
students must un-
derstand how to
identify credible
informative sources, how to perform—or at least understand quantitative
statistical and data analysis, and how to appropriately use data to inform
their judgment. The good news is that I see a lot of appetite for these skills
from current students, so this change is beginning to happen.
The international development practitioner
of the future will be someone who is data
literate, and capable of using data analysis
to inform his or her understanding and
decision-making.

9SAIS Review vol. XXXVI no. 1 (Winter–Spring 2014)
Big Data, Selection Bias, and the
Statistical Patterns of Mortality in
Conflict
Megan Price and Patrick Ball
The notion of “big data” implies very specific technical assumptions. The tools that have made
big data immensely powerful in the private sector depend on having all (or nearly all) of the
possible data. In our experience, these technical assumptions are rarely met with data about
the policy and social world. This paper explores how information is generated about killings
in conflict, and how the process of information generation shapes the statistical patterns in
the observed data. Using case studies from Syria and Iraq, we highlight the ways in which
bias in the observed data could mislead policy. The paper closes with recommendations about
the use of data and analysis in the development of policy.
Introduction
Emerging technology has greatly increased the amount and availability
of data in a wide variety of fields. In particular, the notion of “big data”
has gained popularity in a number of business and industry applications,
enabling companies to track products, measure marketing results, and in
some cases, successfully predict customer behavior.1
These successes have,
understandably, led to excitement about the potential to apply these meth-
ods in an increasing number of disciplines.
Megan Price is the director of research at the Human Rights Data Analysis Group. She
has conducted data analyses for projects in a number of locales including Syria and
Guatemala. She recently served as the lead statistician and head author of two reports
commissioned by the Office of the United Nations High Commissioner of Human
Rights.
Patrick Ball is the executive director of the Human Rights Data Analysis Group.
Beginning in El Salvador in 1991, Patrick has designed technology and conducted
quantitative analyses for truth commissions, non-governmental organizations, domestic
and international criminal tribunals, and United Nations missions. Most recently, he
provided expert testimony in the trial of former de facto President of Guatemala, Gen.
José Efraín Ríos Montt.
The materials contained herein represent the opinions of the authors and editors and should not be
construed to be the view of HRDAG, any of HRDAG’s constituent projects, the HRDAG Board of
Advisers, the donors to HRDAG, or this project.

Although we share this excitement about the potential power of data
analysis, our decades of experience analyzing data about conflict-related
violence motivates us to proceed with caution. The data available to hu-
man rights researchers is fundamentally different from the data available
to business and industry. The difference is whether the data are complete.
In most business processes, an organization has access to all the data: every
item sold in the past twelve months, every customer who clicked through
their website, etc. In the exceptional cases where complete data are unavail-
able, industry analysts are often able to generate a representative sample of
the data of interest.2
In human rights, and more specifically in studies of conflict violence,
we rarely have access to complete data. What we have instead are snapshots
of violence: a few videos of public killings posted to YouTube, a particular
set of events retro-
spectively recorded by
a truth commission,
stories covered in the
local or international
press, protesters’ SMS
messages aggregated
onto a map, or victims’ testimonies recorded by non-governmental human
rights organizations (NGOs) are typical sources. Statistically speaking, these
snapshots are “convenience samples,” and they cover an unknown propor-
tion of the total number of cases of violence.3
It is mathematically difficult,
often impossible, to know how much is undocumented and, consequently,
missing from the sample.
Incompleteness is not a criticism of data—collecting complete or rep-
resentative data under conflict conditions is generally impossible. The chal-
lenge is that researchers and advocates naturally want to address questions
that require either the total number or a representative subset of cases of
violence. How many people have been killed? What proportion was from
a vulnerable population? Were more victims killed last week or this week?
Which perpetrator(s) are committing the majority of the violence? Basing
answers and policy decisions on analyses of partial datasets with unknown,
indeed unknowable, biases can prove to be misleading. These concerns
should not deter researchers from asking questions of data; rather, it should
caution them against basing conclusions on inadequate analyses of raw data.
We conclude by suggesting methods from several quantitative disciplines
to estimate the bias in direct observations.
The Problem of Bias
When people record data about events in the world, the records are almost
always partial; reasons why the observation of violence often misses some or
most of the violence are presented in the examples to follow. Most samples
are partial, and in samples not collected randomly, the patterns of omission
may have structure that influence the patterns observed in the data. For ex-
ample, killings in urban areas may be nearly always reported, while killings
In human rights, and more specifically
in studies of conflict violence, we rarely
have access to complete data.

11Big Data, Selection Bias, and Mortality in Conflict
in rural areas are rarely documented. Thus, the probability of an event be-
ing reported depends on where the event happened. Consequently, analysis
done directly from this data will suggest that violence is primarily urban.
This conclusion is incorrect because the data simply do not include many
(or at least proportionally fewer) cases from the rural areas. In this case, the
analysis is finding a pattern in the documentation that may appear to be a
pattern in true violence—but if analysts are unaware of the documentation
group’s relatively weaker coverage of the rural areas, they can be misled
by the quantitative result. In our experience, even when analysts are aware
of variable coverage in different areas, it is enormously difficult to draw a
meaningful conclusion from a statistical pattern that is affected by bias.
Statisticians call this problem “selection bias” because some events
(in this example, urban ones) are more likely to be “selected” for the sample
than other events (in this example, rural ones). Selection bias can affect
human rights data collection in many ways.4
We use the word “bias” in the
statistical sense, meaning a statistical difference between what is observed
and what is “truth” or reality. “Bias” in this sense is not used to connote
judgment. Rather, the point is to focus attention on empirical, calculable
differences between what is observed and what actually happened.
In this article, we focus on a particular kind of selection bias called
“event size bias.” Event size bias is the variation in the probability that a
given event is reported, related to the size of the event: big events are likely
to be known, small events are less likely to be known. In studies of conflict
violence, this kind of bias arises when events that involve only one victim
are less likely to be documented than events that involve larger groups of
victims. For example, a market bombing may involve the deaths of many
people. The very public nature of the attack means that the event is likely to
attract extensive attention from multiple media organizations. By contrast,
an assassination of a single person, at night, by perpetrators who hide the
victim’s body, may go unreported. The victim’s family may be too afraid to
report the event, and the body may not be discovered until much later, if at
all. These differences in the likelihood of observing information about an
event can skew the available data and result in misleading interpretations
about patterns of violence.5
Case Studies
We present here two examples from relatively well-documented conflicts.
Some analysts have argued that information about conflict-related killings
in Iraq and Syria is complete, or at least sufficient for detailed statistical
analysis. In contrast, our analysis finds that in both cases, the available data
are likely to be systematically biased in ways that are likely to confound
interpretation.
Syria
Many civilian groups are currently carrying out documentation efforts in the
midst of the ongoing conflict in Syria. In early 2012, the United Nations Of-
fice for the High Commissioner for Human Rights (OHCHR) commissioned

the Human Rights Data Analysis Group (HRDAG) to examine datasets from
several of these groups, and in two reports, Price et al. provide in-depth de-
scriptions of these sources.6
In this section, we focus our attention on four
sources—in essence, lists of people killed—which cover the entire length of
the ongoing conflict and which have continued to provide us with updated
records of victims. These sources are the Syrian Center for Statistics and Re-
search7
(CSR-SY), the Syrian Network for Human Rights8
(SNHR), the Syria
Shuhada website9
(SS) and the Violations Documentation Centre10
(VDC).
Figure 1 shows the number of victims documented by each of the four
sources over time within the Syrian governorate of Tartus. The large peak
visible in all four lines in May 2013 corresponds to an alleged massacre in
Banias.11
It appears that all four sources documented some portion of this
event. Many victims were recorded in the alleged massacre, this event was
very well reported, and all four of our sources reflect this event in their lists.
However, three out of the four sources document very little violence occur-
ring before or after May 2013 in Tartus. The fourth source, VDC, shows the
peak of violence in May as the culmination of a year of consistent month-
to-month increases in the number of reported killings.
When interpreting figures such as Figure 1, we should not aim to iden-
tify a single “correct” source. All of these sources are documenting different
snapshots of the violence, and all of them are contributing substantial num-
bers of unique records of victims undocumented by the other sources.12
The
presence of event size bias is detectable in this particular example because
all four of the sources obviously captured a similar event (or set of events)
in May 2013, while at the same time one of those sources captured a very
different subset of events during the preceding months. If we did not have
access to the VDC data, our analysis of conflict violence in Tartus would
incorrectly conclude that the alleged massacre in May 2013 was an isolated
event surrounded by relatively low levels of violence.
The conclusion from Figure 1 should not be that VDC is doing a
“better” job of documenting victims. VDC is clearly capturing some events
that are not captured by the other sources, but there is no way to tell how
many events are not being captured by VDC. From this figure alone we
cannot conclude what other biases may be present in the observed data.
For example, the relatively small peak in February 2012 could be as small
as it seems, or it could be as large as the later peak in May 2013. Without a
method of statistical estimation that uses a probability model to account
for the undocumented events, it is impossible to know.13
To underline this crucial point: despite the availability of a large
amount of data describing violence in Tartus, there is no mathematically
sound method to draw conclusions about the patterns of violence directly
from the data (though it is possible to use the data and statistical models to
estimate how many events are missing). The differences in the four sources
available to us make it possible to detect the event size bias occurring in May
2013, but what other biases might also be present in this observed data and
hidden from view? What new events might a fifth, sixth, or seventh source
document? Are there enough undocumented events such that if they were

13BIG DATA, SELECTION BIAS, AND MORTALITY IN CONFLICT
included, our interpretation of the patterns would change? These are the
crucial questions that must be examined when interpreting perceived pat-
terns in observed data.
Iraq
We detect a subtler form of event size bias in data from the Iraq Body Count
(IBC), which indexes media and other sources that report on violent deaths
in Iraq since the Allied invasion in March 2003.14
Our analysis is motivated
by a recent study by Carpenter et al., which found evidence of substantial
event size bias.15
Their approach was to compare the U.S. military’s “sig-
nificant acts” (SIGACTS) database to the IBC records. As they report, this
comparison showed that “[e]vents that killed more people were far more
likely to appear in both datasets, with 94.1% of events in which ≥20 people
were killed being likely matches, as compared with 17.4% of … killings [that
occurred one at a time].”16
This implies that IBC, SIGACTS, or both, capture
a higher fraction of large events than small events. Carpenter et al. go on
Figure 1. Number of Victims Documented by Four Sources, Over Time, in Tartus

to note that “[t]he possibility that large events, or certain kinds of events
(e.g., car bombs) are overrepresented might allow attribution that one side
in a conflict was more recklessly killing civilians, when in fact, that is just
an artifact of the data collection process.”17
Motivated by this analysis, we considered other ways to examine IBC
records for evidence of potential event size bias. Since IBC aggregates re-
cords from multiple sources, updated IBC data already incorporates many
records from SIGACTS.18
In contrast to the work of Carpenter et al., who
treated IBC and SIGACTS as two separate data sources and conducted their
own independent record linkage between the two sources, we examined only
records in the IBC database, including those labeled as from SIGACTS.
It should be noted that we conducted this analysis on a subset of the
data after filtering out very large events with more than fifty victims. We
made this choice because, on inspection, many of the records with larger
numbers of reported victims are data released in batches by institutions
such as morgues, or incidents aggregated over a period of time, rather than
specific, individual events.
We began by identifying the top one hundred data sources; one or
more of the top one hundred sources cover 99.4 percent of the incidents
in IBC.19
Given these sources, we counted the number of sources (up to
one hundred) for each event. Event size was defined as the mean (rounded
to the nearest integer) of the reported maximum and minimum event size
values. Then the data were divided into three categories: events with one
victim, events with two to five victims, and events with six to fifty victims.
The analysis was performed on these groups.
Figure 2 summarizes our findings. The shading of each bar in Figure
2 indicates the proportion of events of that size reported by one, two, or
three or more sources. For each category of event sizes, most events have
two sources. For events of size one, the second most frequent number of
sources is one, accounting for nearly a third of all events of this size; almost
no single-victim events have three or more sources. The number of events
with three or more sources increases quickly in medium-sized events and
in large events. Relatively few of the largest events are reported by a single
source. Thus there seems to be a relationship between event size and the
number of sources: larger events are captured by more sources. This rein-
forces the finding by Carpenter et al. that larger events are more likely to
be captured by both IBC and SIGACTS. We have generalized this finding to
the top one hundred sources; larger events are more likely to be captured
by multiple sources.
The number of sources covering an event is an indicator of how “inter-
esting” an event is to a community of documentation groups—in this case,
media organizations. The pattern shown in Figure 2 implies that media
sources are more interested in larger events than smaller events. Greater
interest in the larger events implies that larger events are more likely to be
reported (observed) by multiple sources relative to smaller events. Since a
larger proportion of small events are covered by only a single source, it is
likely that more small events are missed, and therefore excluded from IBC.20

As noted by Carpenter et al., the correlation between event attributes
and the likely reporting of those events can result in highly misleading in-
terpretation of apparent patterns in the data. As a relatively neutral example,
analysts might erroneously conclude that most victims in Iraq were killed
in large events, whereas this may actually be an artifact of the data collec-
tion. A potentially more damag-
ing, incorrect conclusion might
be reached if large events are
centered in certain geographic
regions or attributed to certain
perpetrators; in these cases,
reading the raw data directly
would mistake the event size
bias for a true pattern, thereby
misleading the analyst. Inap-
propriate interpretations could result in incorrect decisions regarding
security measures, intervention strategies, and ultimately, accountability.
The correlation between event
attributes and the likely reporting
of those events can result in
highly misleading interpretation
of apparent patterns in the data.
Figure 2. Proportion of Events Covered by One, Two, or Three or More Sources

Discussion
Event size bias is one of many kinds of selection and reporting biases that
are common to human rights data collection. It is important to recall that
we refer here to biases in the statistical sense: a measurable difference be-
tween the observed sample and the underlying population of interest. The
biases that worry us here affect statistics and quantitative analyses; we are
not implying that the political goals of the data collection groups have
influenced their work.
In the context of conflict violence, meaningful statistical analysis
involves comparisons to answer questions such as: Did more violence oc-
cur this month or last month? Were there more victims of ethnicity A or
B? Did the majority of the
violence occur in the north or
the south of the country? The
concern about bias focuses
on how the data collection
process may more effectively
document one month rela-
tive to another, creating the
appearance of a difference
between the months. Unfortu-
nately, the apparent difference is the result of changes in the documentation
process, not real changes in the patterns of violence.
To make sense of such comparisons, the observed data must in some
way be adjusted to represent the true rates. There are a number of methods
for making this adjustment if the observed data were collected at random,
but this is rarely the case. There are relatively few models that can adjust
data that were collected because it was simply available.
In order to compare nonrandom data across categories like months
or regions, the analyst must assume that the rate at which events from
each category are observed is the same. For example, 60 percent of the total
killings were collected in March, and 60 percent of the total killings were
collected in April. This rate is called the coverage rate, and it is unknown,
unless somehow the true number of events were known or estimated. If the
coverage rates for different categories are not the same, the observed data
tell only the story of the documentation; they do not indicate an accurate
pattern. For example, if victims of ethnicity A are killed in large-scale vio-
lent events with many witnesses, while victims of ethnicity B are killed in
targeted, isolated violent events, we may receive more reports of victims of
ethnicity A and erroneously conclude that the violence is targeted at eth-
nicity A. Until we adjust for the event size bias resulting in more reports of
victims of ethnicity A, we cannot draw conclusions about the true relation-
ship between the number of victims from ethnicity A versus B.
There are many other kinds of selection bias. As an example, when rely-
ing on media sources, journalists make decisions about what is considered
newsworthy. Sometimes their decisions may create event size bias, as large
. . . the apparent difference
is the result of changes in the
documentation process, not
real changes in the patterns of
violence.

events are frequently considered newsworthy. But the death of individual,
prominent members of a society are frequently also considered newswor-
thy. Conversely, media “fatigue” may result in under-documentation later
in a conflict, or when other newsworthy stories may limit the amount of
time and space available to cover victims of a specific conflict.21
Many other
characteristics of both the documentation groups and the conflict can result
in these kinds of biases such as logistical or budgetary limitations, trust or
affinity variations within the community, and the security and stability of
the situation on the ground.22
As each of these factors changes, coverage
rates are likely to change as well.
The fundamental reason why biases are so problematic for quantita-
tive analyses is that bias often correlates with other dimensions that are
interesting to analysts, such as trends over time, patterns over space, differ-
ences compared by the victims’ sex, or some other factor. As in the example
of ethnicities A and B above, the event size bias is correlated with the kind
of event. Failing to adjust for the reporting bias leads to the wrong conclu-
sion. As another example, consider the Iraq case described above: If event
size is correlated with the events’ perpetrators, then bias on event size means
bias on perpetrator, and a naïve reading of the data could lead to security
officials trying to solve the wrong security problems. Or, in the Syria case,
if decisions about resource allocation to Tartus were made on the basis of
the observed information, without taking into account the patterns of kill-
ings that were not observed, researchers may have inaccurately concluded
that violence documented in May 2013 represented an isolated event. One
could imagine that such a conclusion could lead to any number of incorrect
decisions: sending aid groups into Tartus under the erroneous assumption
of relative security, or failing to send aid and assistance before or after May
2013, assuming that such resources were more in need elsewhere.
It is important to note that these challenges frequently lack a scientific
solution.23
We do not need to simply capture more data. What we need is
to appropriately recognize and adjust for the biases present in the available
data. Indeed, as indicated in the Iraq example, where multiple media sources
appear to share similar biases, the addition of more data perpetuates and
in some cases amplifies the event size bias.
Detection of, and adjustment for, bias requires statistical estimation.
A wide variety of statistical methods can be used to adjust for bias and es-
timate what is missing from observed data. In our work we favor multiple
systems estimation, which has been developed under the name capture-
recapture in ecology, and used to study a variety of human populations
in research in demography and public health. Analysts more familiar with
traditional survey methods often prefer adjustments based on post-stratifi-
cation or “raking,” each of which involves scaling unrepresentative data to a
known representative sample or population.24
Each method has limitations
and requires assumptions, which may or may not be reasonable, but formal
statistical models provide a way to make those assumptions explicit, and in
some cases, to test whether they are appropriate. Comparisons from raw data
implicitly but necessarily assume that such snapshots are statistically repre-
sentative. This assumption may sometimes be true, but only by coincidence.

Conclusions
Carpenter et al. warn that “press members and scientists alike should be cau-
tious about assuming the completeness and representativeness of tallies for
which no formal evaluation of sensitivity has been conducted. Citing partial
tallies as if they were scientific samples confuses the public, and opens the
press and scholars to being manipulated in the interests of warring parties.”
In a back-of-the-envelope description elsewhere, we have shown that small
variations in coverage rates can lead to an exactly wrong conclusion from
raw data. 25
Groups such as the Iraq Body Count, the Syrian Center for Statistics
and Research, the Syrian Network for Human Rights, the Syria Shuhada
website, and the Violations Documentation Centre collect invaluable data,
and they do so systematically, and with principled discipline. These groups
should continue to collate and share it as a fundamental record of the past.
The data can also be used in qualitative research about specific cases, and in
some circumstances, in statistical models that can adjust for biases.
It is tempting, particularly in emotionally charged research such as
studies of conflict-related violence, to search available data for answers. It is
intuitive to create infographics, to draw maps, and to calculate statistics and
draft graphs to look for patterns in the data. Unfortunately, all people—even
statisticians—tend to draw conclusions even when we know that the data
are inadequate to support comparisons. Weakly founded statistics tend to
mislead the reader.
Statistics, graphs, and maps are seductive because they seem to prom-
ise a solid basis for conclusions. The current obsession with using data to
formulate evidence-based policy increases the pressure to use statistics, even
as new doubts emerge about whether “big data” predictions about social
conditions are accurate.26
When calculations are made in a way that enables
a mathematical foundation for statistical inference, these statistics deliver
on the promise of an objective
measurement in relation to a
specific question. But analysis
with inadequate data is very hard
even for subject matter experts
to interpret. In the worst case,
it offers a falsely precise view,
a view that may be completely
wrong. In the best case, it invites speculation about what’s missing and
what biases are uncontrolled, creating more questions than answers, and
ultimately, a distraction. When policymakers turn to statistical analysis to
address key questions, they must assure that the analysis gives the right
answers.
Statistics, graphs, and maps are
seductive because they seem
to promise a solid basis for
conclusions.

Notes
1
One extreme example includes Target successfully predicting a customer’s pregnancy, as
reported in the New York Times and Forbes. In particular, Target noticed that pregnant women
buy specific kinds of products at regular points in their pregnancy, and the company used
this information to build marketing campaigns.
2
However it is certainly worth noting that even in these contexts sometimes big data are
not big enough and may still be subject to the kinds of biases we worry about in this paper.
See Kate Crawford’s keynote at STRATA and Tim Harford’s recent post on Financial Times
for examples.
3
Specifically, “convenience samples” refer to data that is non-randomly collected, though
collecting such data is rarely convenient.
4
Another common kind of bias that affects human rights data is reporting bias. Whereas se-
lection bias focuses on how the data collection process identifies events to sample, reporting
bias describes how some points become hidden, while others become visible, as a result of
the actions and decisions of the witnesses and interviewees. For an overview of the impact
of selection bias on human rights data collection, see Jule Krüger, Patrick Ball, Megan Price,
and Amelia Hoover Green (2013). “It Doesn’t Add Up: Methodological and Policy Implica-
tions of Conflicting Casualty Data.” In Counting Civilian Casualties: An Introduction to Recording
and Estimating Nonmilitary Deaths in Conflict, ed. by Taylor B. Seybolt, Jay D. Aronson, and
Baruch Fischhoff. Oxford UP.
5
Christian Davenport and Patrick Ball. “Views to a Kill: Exploring the Implications of Source
Selection in the Case of Guatemalan State Terror, 1977–1996.” Journal of Conflict Resolution
46(3): 427–450. 2002.
6
Megan Price, Jeff Klingner, Anas Qtiesh, and Patrick Ball (2013). “Full Updated Statistical
Analysis of Documentation of Killings in the Syrian Arab Republic.” Human Rights Data
Analysis Group, commissioned by the United Nations Office of the High Commissioner
for Human Rights (OHCHR). Megan Price, Jeff Klingner, and Patrick Ball (2013). “Prelimi-
nary Statistical Analysis of Documentation of Killings in the Syrian Arab Republic.” The
Benetech Human Rights Program, commissioned by the United Nations Office of the High
Commissioner for Human Rights (OHCHR).
7
http://www.csr-sy.com
8
http://www.syrianhr.org
9
http://syrianshuhada.com
10
http://www.vdc-sy.info
11
See reports in the LA Times, BBC, and the Independent, among others.
12
Price. et al. 2013.
13
See https://hrdag.org/mse-the-basics/ for the first in a series of blog posts describing
Multiple Systems Estimation (MSE) or Kristian Lum , Megan Emily Price and David Banks
(2013). Applications of Multiple Systems Estimation in Human Rights Research. The Ameri-
can Statistician, 67:4, 191–200. DOI: 10.1080/00031305.2013.821093
14
http://www.iraqbodycount.org
15
Carpenter D, Fuller T, Roberts L. “WikiLeaks and Iraq Body Count: the sum of parts may
not add up to the whole—a comparison of two tallies of Iraqi civilian deaths.” Prehosp Disaster
Med. 2013;28(3):1–7. doi:10.1017/S1049023X13000113
16
Ibid.
17
Ibid.
18
We downloaded the ibc-incidents file on 14 Feb 2014, and processed it using the pandas
package in python.
19
The top 100 sources include, for example, AFP, AL-SHAR, AP, CNN, DPA, KUNA, LAT,
MCCLA, NINA, NYT, REU, VOI, WP, XIN, and US DOD VIA WIKILEAKS.
20
These assumptions can be formalized and tested within the framework of ‘species richness,’
which is a branch of ecology that estimates the number of different types of species within
a geographic area and/or time period of interest using models for data organized in a very
similar way to the IBC’s event records. See Wang, Ji-Ping. “Estimating species richness by a
Poisson-compound gamma model.” Biometrika 97.3 (2010): 727–740.

21
A research question to address this might be: Do media-reported killings in a globally-
interesting conflict like Iraq or Syria decline during periods when other stories attract
interest? Do reported killings decline during the Olympics?
22
Krüger et al. (2013)
23
Bias issues can sometimes be resolved with appropriate statistical models, that is, with
better scientific reasoning about the specific kind of data involved. However, we underline
that bias is not solvable with better technology. Indeed, some of the most severely biased
datasets we have studied are those collected by semi- or fully-automated, highly technologi-
cal methods. Technology tends to increase analytic confusion because it tends to amplify
selection bias.
24
For a description of multiple systems estimation, see Lum et al. 2013. For methods on
missing data in survey research which might be applicable to the adjustment of raw, non-
random data if population-level information is available, see Brick, J. Michael, and Graham
Kalton. “Handling missing data in survey research.” Statistical methods in medical research 5.3
(1996): 215–238. For an overview of species richness models which might be used to estimate
total populations from data organized like the IBC, see op. cit Wang. For an analysis of
sampling issues in “elusive” populations, see Johnston, Lisa G., and Keith Sabin. “Sampling
hard-to-reach populations with respondent driven sampling.” Methodological Innovations
Online 5.2 (2010): 38–48.
25
https://hrdag.org/why-raw-data-doesnt-support-analysis-of-violence/
26
Lazer, David and Kennedy, Ryan and King, Gary and Vespignani, Alessandro, Google Flu
Trends Still Appears Sick: An Evaluation of the 2013–2014 Flu Season (March 13, 2014).
Available at SSRN: http://ssrn.com/abstract=2408560

21SAIS Review vol. XXXIV no. 1 (Winter–Spring 2014)
Using Big Data and Quantitative
Methods to Estimate and Fight
Modern Day Slavery
Monti Narayan Datta
Given the hidden, criminal nature of contemporary slavery, empirically estimating the
proportion of the population enslaved at the national and global level is a challenge. At the
same time, little is understood about what happens to the lives of the survivors of slavery
once they are free. I discuss some data collection methods from two nongovernmental
organizations (NGOs) I have worked with that shed light on these issues. The first NGO,
the Walk Free Foundation, estimates that there are about 30 million enslaved in the world
today. The second NGO, Free the Slaves, employs a longitudinal analysis to chronicle the
lives of survivors. The acquisition and dissemination of such information is crucial because
policymakers and donors sometimes require hard data before committing time, political
will, and resources to the cause.
Unpacking the Problem of Contemporary Slavery
As Kevin Bales of the Wilberforce Institute for the Study of Slavery and
Emancipation explains, “Slavery is the possession and control of a
person in such a way as to significantly deprive that person of his or her
individual liberty, with the intent of exploiting that person through their
use, management, profit, transfer or disposal. Usually this exercise will be
achieved through means
such as violence or threats
of violence, deception and/
or coercion.”1
Thus, at its core,
slavery is a dynamic be-
tween two individuals, the
enslaved and the slave-
holder, in which the slave-
holder has a monopoly of
control and violence upon
Monti Narayan Datta is an assistant professor of political science at the University of
Richmond. His current book project, forthcoming with Cambridge University Press,
focuses on the consequences of anti-Americanism. He is working on several projects
on human trafficking and modern day slavery with Free the Slaves and Chab Dai and
the Walk Free Foundation. Along with Kevin Bales and Fiona David, he is a co-author
of the Global Slavery Index: http://www.globalslaveryindex.org.
. . . at its core, slavery is a dynamic
between two individuals, the
enslaved and the slaveholder,
in which the slaveholder has a
monopoly of control and violence
upon the enslaved.

the enslaved. The slaveholder can coerce the enslaved to perform a number
of abominable acts. This can include: sexual servitude on the streets of New
York City;2
adult labor in the coltan mines of the Congo;3
child slavery in the
shrimp farms of Bangladesh; or forced domestic servitude in the suburbs of
Los Angeles.4
Compounding the matter is that enslaved persons can spend
years—sometimes decades—under such conditions.5
This can sometimes lead
to slavery lasting across several generations. Short of homicide, slavery is
one of the most inhumane crimes one person can commit against another.
In recent years, a number of governments and international govern-
mental organizations have addressed modern day slavery at home and
abroad. In the United States, Congress passed the Victims of Trafficking
and Violence Protection Act (TVPA) in 2000. The TVPA established the
President’s Interagency Task Force to Monitor and Combat Trafficking—a
cabinet-level group whose mission is to coordinate efforts to combat traf-
ficking in persons—led by the U.S. State Department. Since then, the State
Department has produced its annual Trafficking in Persons (TIP) Report,
which has become “the U.S. Government’s principal diplomatic tool to en-
gage foreign governments on human trafficking.”6
Although not without
controversy, the TIP Report has educated many on the sources and impact
of modern day slavery.7
On the global stage, between 2000 and 2001 the United Nations Gen-
eral Assembly adopted three protocols to its Convention against Transna-
tional Organized Crime: (1) the Protocol to Prevent, Suppress and Punish
Trafficking in Persons, especially Women and Children; (2) the Protocol
against the Smuggling of Migrants by Land, Sea, and Air; and (3) the Pro-
tocol against the Illicit Manufacturing and Trafficking in Firearms. With
117 signatory countries, these protocols, known as the Palermo Protocols,
advanced the global discussion not only on what constitutes contemporary
slavery, but also on what the international community can do to mitigate
its spread.
Although some may argue that international agreements like the
Palermo Protocols and documents like the TIP Report matter only margin-
ally,8
others counter they catalyze change.9
Building upon a crest of public
awareness on human trafficking, U.S. President Barack Obama proclaimed
in 2012 at the Clinton Global Initiative, “We are turning the tables on the
traffickers. Just as they are now using technology and the Internet to exploit
their victims, we are going to harness technology to stop them.”10
Although he did not mention it explicitly, President Obama was re-
ferring to the idea of using big data to mitigate contemporary slavery. As
Pulitzer-prize winning journalist Steve Lohr explains, big data is “shorthand
for advancing trends in technology that open the door to a new approach
to understanding the world and making decisions.”11
This typically involves
using software to find trends and patterns in large amounts of aggregated
data from the Internet, sometimes from publicly available data, and other
times from clandestinely obtained data.
Along the lines of utilizing publicly available data, the tech-giant
Google announced in April 2013 a big data partnership with the Polaris

23Big Data and Quantitative Methods to Estimate Modern Day Slavery
Project, an antislavery NGO in Washington, D.C. The partnership, called the
Global Human Trafficking Hotline Network, aims to use data mining soft-
ware to identify human trafficking trends from the hotline that can even-
tually inform “eradication, prevention, and victim protection strategies.”12
Although using big data to fight trafficking is new, the idea has been
demonstrated by scholars like Mark Latonero of the Annenberg Center on
Communication Leadership & Policy at the University of Southern Califor-
nia. Latonero’s team partnered with local law enforcement agencies in Los
Angeles, explored trends in human trafficking on websites like Backpage.
com, and applied this information to target specific traffickers. This was
done by mining data from advertisements for the sexual services of domestic
minors on the adult section of Backpage in the Greater Los Angeles area, and
identifying the phone numbers from those ads that appeared in the greatest
frequencies. With this information, Latonero’s team was able to provide law
enforcement with data linking certain phone numbers to criminal networks.
The U.S. government is also using big data to mine private information
networks, not on the World Wide Web, but on what is called the Deep Web—
that part of the Internet that is not searchable on databases like Google.
The Defense Advanced Research Projects Agency (DARPA), a branch of the
U.S. military, recently launched a program called Memex to hunt criminal
networks on the Deep Web. The first domain DARPA intends to undercover
with this new technology is human trafficking.13
These developments in big data dovetail with a broader discussion
within academia about how social science researchers can apply quantitative
methods to estimate trends in contemporary slavery. Although rigorous,
many studies of modern day
slavery only exist in the pol-
icy and academic communi-
ties, and very few published
works actually employ
quantitative methods. In
a comprehensive review of
the research-based literature
on contemporary slavery,
Elżbieta M. Goździak and
Micah N. Bump of George-
town University found that,
of 218 research-based journal articles, only seven (about 3 percent) were
based on quantitative methods. Without hard data, it can be challenging
for scholars to make generalizable inferences to inform policy.
In this paper, using big data as a backdrop, I discuss some novel quan-
titative methods employed by two NGOs I have worked with that shed light
on contemporary slavery. The first NGO, the Walk Free Foundation, esti-
mates that there are about 30 million enslaved in the world today. The sec-
ond NGO, Free the Slaves, working with its local Indian partner, MSEMVS,
assesses the lives of survivors and how they are reintegrating into society.
The acquisition and dissemination of such information is crucial because
These developments in big data
dovetail with a broader discussion
within academia about how social
science researchers can apply
quantitative methods to estimate
trends in contemporary slavery.

policymakers and donors sometimes require hard data before committing
time, political will, and resources to the cause.
The Walk Free Foundation
Australian philanthropists Andrew and Nicola Forrest established the Walk
Free Foundation (Walk Free)14
three years ago to eradicate contemporary
slavery. After meeting with Microsoft co-founder Bill Gates, Andrew For-
rest was inspired to explore the underpinnings of contemporary slavery
using quantitative methods. As Forrest recounts, “Global modern slavery
is hard to measure, and Bill’s a measure kind of guy,” adding, “in manage-
ment speak, if you can’t measure it, it doesn’t exist.”15
For Forrest, it was
important to inform people in the business and policy worlds of the extent
to which slavery exists, country-by-country, to prompt action. Although
some quantitative assessments of contemporary slavery existed, very little
was publicly available. Forrest sought to collect more precise data to dis-
seminate freely and thus launched a Global Slavery Index (GSI), on which
I have been working since 2012.16
The 2013 GSI ranks 162 of the world’s nations in terms of their level
of contemporary slavery. Methodologically, these rankings are based on
several factors; the most novel is an estimation of the proportion of the
population enslaved in each country. For this measure, the GSI team (led
by Kevin Bales and Fiona David) has drawn upon secondary source data
analysis that Bales pioneered for his book, Disposable People, and later dis-
seminated in Scientific American.17
These secondary sources consisted of a
review of the public record, including materials from published reports from
governments, the investigations of NGOs and international organizations,
and journalistic reports. The GSI team has also drawn upon data from rep-
resentative random sample surveys to extrapolate the prevalence of slavery
for selected comparable countries. Figure 1 illustrates the 2013 GSI data for
the proportion of the population estimated to be enslaved.
In Figure 1 the countries with darker shades indicate a corresponding
higher proportion of enslavement. Some of the countries with the highest
proportions are Haiti (about 2.1 percent of the population enslaved), Mau-
ritania (about 4.0 percent of the population enslaved), Pakistan (about 1.2
percent of the population enslaved), and
India (about 1.1 percent of the popula-
tion enslaved).
Table 1 lists the 2013 GSI data in
terms of the total estimated number of
the enslaved, country-by-country. This is
a novel contribution compared to other
estimates of contemporary slavery. Such information can be useful to busi-
ness people, policymakers, and students who want a more informed under-
standing of where slavery occurs and with what frequency.
Overall, the 2013 GSI estimates about 29.8 million are enslaved among
the 162 countries under study. The country with the least number of esti-
. . . 29.8 million are
enslaved among the 162
countries under study.

mated enslaved in 2013 was Iceland (twenty-two enslaved), and the country
with the greatest number was India (13.9 million enslaved). The standard
deviation (or spread) was extremely large: about 1.2 million enslaved.
One important question is if the GSI has made a difference in the
real world. One way to shed light on this is to explore some of the statis-
tics achieved since the GSI’s launch in October 2013. To date, the GSI has
received over half a million website visits. There have been over thirteen
thousand downloads of the full report, available in English, Arabic, French,
and Spanish. Moreover, there have been over fifteen hundred media reports
about the GSI in over thirty-five countries, including The Economist,18
Die
Standaard,19
La Vanguardia,20
CNN,21
National Public Radio,22
and Time.23
Some of the media reports about the GSI illustrate how it can gener-
ate discussion on an underreported issue. In India, for instance, where the
GSI estimates the greatest number of the enslaved to be, media response
has been strong. The Times of India reported, “Sixty-six years after indepen-
dence, India has the dubious distinction of being home to half the number
of modern-day slaves in the world.”24
Perhaps due to such sentiments, the
Hindustan Times discussed the causes of slavery in India and observed, “Some
of the reasons for high numbers caught in slavery in India are the difficulty
in accessing protections and government entitlements, such as the food
rations card, corruption or non-performance of safety nets (such as the
National Employment Guarantee, primary health care and pensions) and
practices of land grabbing and asset domination by high-caste groups.”25
There is also some evidence that the GSI has begun to influence gov-
ernment policy. In January of this year, building upon the momentum of
the GSI, Andrew Forrest signed a memorandum of understanding (MOU)
Figure 1. Global Slavery Index (GSI)—Proportion of the Population Estimated Enslaved in 2013

Table 1. Global Slavery Index—Estimated Enslaved in 2013
Country Estimated Enslaved Country Estimated Enslaved
Afghanistan 86,089 Lebanon 4,028
Albania 11,372 Lesotho 14,560
Algeria 70,860 Liberia 29,504
Angola 16,767 Libya 17,683
Argentina 35,368 Lithuania 2,909
Armenia 10,678 Luxembourg 69
Australia 3,167 Macedonia 6,226
Austria 1,100 Madagascar 19,184
Azerbaijan 33,439 Malawi 110,391
Bahrain 2,679 Malaysia 25,260
Bangladesh 343,192 Mali 102,240
Barbados 46 Mauritania 151,353
Belarus 11,497 Mauritius 535
Belgium 1,448 Mexico 103,010
Benin 80,371 Moldova 33,325
Bolivia 29,886 Mongolia 4,729
Bosnia and
Herzegovina 13,789 Montenegro 2,234
Botswana 14,298 Morocco 50,593
Brazil 209,622 Mozambique 173,493
Brunei 417 Myanmar 384,037
Bulgaria 27,739 Namibia 15,729
Burkina Faso 114,745 Nepal 258,806
Burundi 71,146 Netherlands 2,180
Cambodia 106,507 New Zealand 495
Cameroon 153,258 Nicaragua 5,798
Canada 5,863 Niger 121,249
Cape Verde 3,688 Nigeria 701,032
Central African
Republic 32,174 Norway 652
Chad 86,329 Oman 5,739
Chile 37,846 Pakistan 2,127,132
China 2,949,243 Panama 548
Colombia 129,923 Papua New Guinea 6,131
Costa Rica 679 Paraguay 19,602
Côte d’Ivoire 156,827 Peru 82,272
Croatia 15,346 Philippines 149,973
Cuba 2,116 Poland 138,619
Czech Republic 37,817 Portugal 1,368
Democratic Republic
of the Congo 462,327 Qatar 4,168
Denmark 727 Republic of the Congo 30,889
Djibouti 2,929 Romania 24,141
Dominican Republic 23,183 Russia 516,217
Ecuador 44,072 Rwanda 80,284
Egypt 69,372 Saudi Arabia 57,504
El Salvador 10,490 Senegal 102,481

Equatorial Guinea 5,453 Serbia 25,981
Eritrea 44,452 Sierra Leone 44,644
Estonia 1,496 Singapore 1,105
Ethiopia 651,110 Slovakia 19,458
Finland 704 Slovenia 7,402
France 8,541 Somalia 73,156
Gabon 13,707 South Africa 44,545
Gambia 14,046 South Korea 10,451
Georgia 16,227 Spain 6,008
Germany 10,646 Sri Lanka 19,267
Ghana 181,038 Sudan 264,518
Greece 1,466 Suriname 1,522
Guatemala 13,194 Swaziland 1,302
Guinea 82,198 Sweden 1,237
Guinea-Bissau 12,186 Switzerland 1,040
Guyana 2,264 Syria 19,234
Haiti 209,165 Tajikistan 23,802
Honduras 7,503 Tanzania 329,503
Hong Kong,
SAR China 1,543 Thailand 472,811
Hungary 35,763 Timor-Leste 1,020
Iceland 22 Togo 48,794
India 13,956,010 Trinidad and Tobago 486
Indonesia 210,970 Tunisia 9,271
Iran 65,312 Turkey 120,201
Iraq 28,252 Turkmenistan 14,711
Ireland 321 Uganda 254,541
Israel 8,096 Ukraine 112,895
Italy 7,919 United Arab Emirates 18,713
Jamaica 2,386 United Kingdom 4,426
Japan 80,032 United States 59,644
Jordan 12,843 Uruguay 9,978
Kazakhstan 46,668 Uzbekistan 166,667
Kenya 37,349 Venezuela 79,629
Kuwait 6,608 Vietnam 248,705
Kyrgyzstan 16,027 Yemen 41,303
Laos 50,440 Zambia 96,175
Latvia 2,040 Zimbabwe 93,749
Source: The Global Slavery Index
with the Pakistani State of Punjab. In the business world, that a government
would sign a deal with a businessman to help eradicate slavery in its own
borders is atypical. Yet Forrest was able to leverage his influence in Pakistan
to encourage a conversation that aims to provide the state of Punjab with
inexpensive coal in exchange for assurances that the government will work
toward the liberation of its own people. 26
Although it is too early to see
how Pakistan will hold up to its promise, this agreement may herald future
MOUs between NGOs like Walk Free and governments that want to mitigate
slavery, and one day even eradicate it.

The GSI may also be influencing heads of state. Former U.S. President
Jimmy Carter references the GSI several times in his new bestselling book,
A Call to Action: Women, Religion, Violence, and Power. And the GSI has been
publicly endorsed by, among others, Hillary Clinton, Gordon Brown, Julia
Gillard, and Tony Blair.27
Free the Slaves
The GSI strives to use big data to count the number of slaves in the world.
Other NGOs have begun to employ longitudinal techniques to chronicle
the lives of survivors of slavery once they are free. One such NGO is Free
the Slaves (FTS), which Kevin Bales, Peggy Callahan, and Jolene Smith co-
founded in 2000 as the sister-organization of Anti-Slavery International (the
oldest international human rights organization in the world).28
Early in its evolution, FTS reasoned that the liberation of any slave
would be beneficial not only for that individual, but also for the commu-
nity, and thus produce a “freedom dividend,” multiplied by each additional
person freed. As FTS explains, “Local communities thrive when formerly
enslaved people start their own businesses; communities begin to flourish as
people come together to organize and watch out for one another; children
go to school—and the benefits extend for generations.” 29
For the past decade, FTS has partnered with different grassroots orga-
nizations in Haiti, India, Nepal, Ghana, the Democratic Republic of Congo,
and Brazil to empower local communities of the enslaved to seek liberation.
In India, FTS has worked with a local grassroots organization called Mina
Sansadham Evam Mahila Vikas Sansthan (MSEMVS).30
Through the ef-
forts of MSEMVS, over 150 villages have eradicated slavery and trafficking
in recent years and many more are beginning to experience liberation in
the North Indian States of Uttar Pradesh and Bihar, two of India’s poorest
states, as Figure 2 highlights.
In addition to empowering people in rural Uttar Pradesh to seek libera-
tion, MSEMVS has been among the first NGOs to begin several longitudinal
studies on the effects, in addition to quantitative studies of the predictive
factors of enslavement. The studies are intended to provide insight into:
(1) whether slavery and trafficking have been eradicated; and (2) whether
the socio-economic conditions of people living in these communities have
improved. I consulted with Free the Slaves at this time, and, along with
Ginny Baumann, Jody Sarich, Austin Choi-Fitzpatrick, and Jessica Leslie,
helped put together a follow-up report for the village of Kukrouthi in Ut-
tar Pradesh.
The follow-up report was conducted among the residents of three
hamlets in Kukrouthi village.31
There were two sources of information: The
first was a set of 120 household level surveys, and the second was a set of
focus group discussions. A total of 929 people were accounted for by the
surveys. The time periods under comparison were 2009 (when the libera-
tion process began) and 2011 (when the process of self-liberation by local
residents was completed).

29BIG DATA AND QUANTITATIVE METHODS TO ESTIMATE MODERN DAY SLAVERY
Some of the key findings between the 2009 and 2011 studies are as
follows, providing credence to FTS’s supposition of there being a “freedom
dividend” after liberation.
Growth in Childhood Education
One important indicator of a freedom dividend in Kukrouthi village is the
number of children in school. The underlying premise is that in free com-
munities children receive better education, which fuels a society’s human
capital. In Kukrouthi, the team from MSEVMS found evidence of significant
growth in childhood education rates. Whereas in 2009 only 69 percent of
the school-aged children were reported to be in school, by 2011, 91 percent
were enrolled, as Figure 3 illustrates.
Figure 2. Uttar Pradesh, North India

30 SAIS Review WINTER–SPRING 2014
Better Nutrition
Another key indicator illuminating the freedom dividend is access to ad-
equate nutrition. As with childhood education, the team from MSEVMS
reported a dramatic increase in the number of families that were able to eat
three meals a day, from 31 percent in 2009 to 71 percent in 2011. This was
more than a 200 percent increase, as Table 2 details.
Table 2. Number of Daily Meals By
Household in Kukrouthi, 2009 and 2011
Number of Meals Year Percentage
Two Meals Per Day 2011 22%
2009 31%
Three Meals Per Day 2011 71%
2009 31%
No Response 2011 8%
2009 3%
Figure 3. Percent Children in School in Kukrouthi, 2009 and 2011

Improved Access to Health Care
Yet another strong indicator of a freedom dividend is access to health care,
even if of rudimentary quality. In 2011, MSEVMS reported that almost the
entire population of Kukrouthi village had access to healthcare. This was
another dramatic increase compared to 2009, when MSEVMS found that
just 52 percent of families received health care treatment. Table 3 provides
a breakdown of this comparison.
Table 3. Comparison of Access to Health Care in
Kukrouthi, 2009 and 2011
Access to Health Care Year Percent
Yes 2011 96%
2009 57%
No 2011 3%
2009 43%
Don’t Know 2011 1%
2009 .
No Response 2011 1%
2009 1%
Improvement in Childhood Vaccinations
Lastly, in 2009, just one-third of children had the proper number of recom-
mended vaccinations (i.e., three vaccinations). By 2011, this had increased
to 90 percent, as Table 4 shows.
Table 4. Comparison of Child Vaccinations in
Kukrouthi, 2009 and 2011
Immunizations Year Percentage
None 2011 .
2009 49%
One 2011 3%
2009 7%
Two 2011 7%
2009 12%
Three 2011 90%
2009 33%

A World Without Slavery
Applying quantitative methods to the study of contemporary slavery could
contribute significantly to shedding more light on the phenomenon. In
collaboration with my colleagues at the Walk Free Foundation, I have
used quantitative methods to estimate the total number of enslaved in the
world today. This, in turn,
has generated discussion
among the media and pol-
icy community on how to
mitigate modern day slav-
ery, with an eye toward its
eradication. With Free the
Slaves and MSEVMS, we
have begun to chronicle systematically how communities can benefit from
freedom. This information provides preliminary evidence to policy makers
that liberating slaves provides a wide range of socioeconomic benefits.
The modern day anti-slavery movement is young. Moving forward, we
need more scholars and policy makers who want to explore what quantita-
tive methods and big data can do for the movement. We are at a point in
the world where everyone agrees that contemporary slavery is a wrong that
must be addressed. The time is ripe for further discussion on how to make
this a reality. I hope we can get there, at least in part, through employing
quantitative methods and exploring big data.
Notes
1
Kevin Bales, The Global Slavery Index, 2013. http://www.globalslaveryindex.org/report/#view-
online
2
For example: http://www.gems-girls.org/get-involved/very-young-girls
3
Congo. https://www.freetheslaves.net/congo
4
CNN Freedom Project, http://thecnnfreedomproject.blogs.cnn.com.
5
For example: Survivors of Slavery Speak Out, http://survivorsofslavery.org
6
Trafficking In Persons Report, http://www.state.gov/j/tip/rls/tiprpt
7
For example: http://www.coha.org/the-trafficking-in-persons-report-who-is-the-united-
states-to-judge
8
For example: John J. Mearsheimer, “The False Promise of International Institutions,” In-
ternational Security, Vol. 19, No. 3 (1995) pp. 5–49.
9
For example: Anne Marie-Slaughter, A New World Order, (Princeton University Press, 2005).
10
Barack Obama, “Remarks by the President to the Clinton Global Initiative,” September 25,
2012. http://www.whitehouse.gov/the-press-office/2012/09/25/remarks-president-clinton-
global-initiative
11
Steve Lohr, “The Age of Big Data,” The New York Times, February 11, 2012. http://
www.nytimes.com/2012/02/12/sunday-review/big-datas-impact-in-the-world.
html?pagewanted=all&_r=0
12
“Polaris Project Launches Global Human Trafficking Hotline Network.” http://www.
polarisproject.org/media-center/news-and-press/press-releases/767-polaris-project-launches-
global-human-trafficking-hotline-network
13
“Darpa Reinventing Search Engines to Fight Crime,” Wired, February 11, 2014. http://
www.wired.co.uk/news/archive/2014-02/11/darpa-memex-human-trafficking
14
The Walk Free Foundation, http://www.walkfreefoundation.org
Applying quantitative methods to the
study of contemporary slavery could
contribute significantly to shedding
more light on the phenomenon.

15
Elisabeth Behrmann, “Gates Helps Australia’s Richest Man in Bid to End Slavery,” Bloom-
berg, April 14, 2013. http://www.bloomberg.com/news/2013-04-10/gates-helps-australia-s-
richest-man-in-bid-to-end-slavery.html
16
The Global Slavery Index, http://www.globalslaveryindex.org
17
Kevin Bales, “The Social Psychology of Modern Slavery,” Scientific American, April 2002.
18
“Dry Bones,” The Economist, October 19, 2013. http://www.economist.com/news/
international/21588105-hateful-practice-deep-roots-still-flourishing-dry-bones
19
“Wereldwijd bijna 29 miljoen slaven [29 million people in slavery worldwide],” De Stan-
daard, October 17, 2013. http://nos.nl/artikel/563375-wereldwijd-bijna-30-miljoen-slaven.
html
20
“Casi 30 millones de personas son esclavos modernos [Almost 30 million people
are modern slaves],” La Vanguardia, October 18, 2013. http://www.lavanguardia.
com/20131018/54391301708/casi-30-millones-de-personas-son-esclavos-modernos-barce-
lona.html
21
Tim Hume, “India, China, Pakistan, Nigeria on Slavery’s List of Shame, Says Report,” CNN,
October 17, 2013. http://www.cnn.com/2013/10/17/world/global-slavery-index
22
Audie Cornish, “Report Estimates 30 Million People in Slavery Worldwide,” National Public
Radio, October 17, 2013, http://www.npr.org/templates/story/story.php?storyId=236407720
23
Nilanjana Bhowmick, “Report: Almost 14 Million Indians Live Like Slaves,” Time, October
17, 2013. http://world.time.com/2013/10/17/report-almost-14-million-indians-live-like-
slaves/
24
“India Has Half the World’s Modern Slaves: Study,” The Times of India, October 18, 2013.
http://timesofindia.indiatimes.com/india/India-has-half-the-worlds-modern-slaves-Study/
articleshow/24313244.cms
25
Abhijit Patnaik, “Modern Slavery Widespread in India,” Hindustan Times, October 17, 2013.
http://www.hindustantimes.com/India-news/NewDelhi/Modern-slavery-widespread-in-
India/Article1-1136431.aspx
26
Dennis Shanahan, “Andrew Forrest Strikes Cheap Coal Deal to End Pakistan Slav-
ery,” The Australian, January 23, 2014, http://www.theaustralian.com.au/business/
mining-energy/andrew-forrest-strikes-cheap-coal-deal-to-end-pakistan-slavery/story-
e6frg9df-1226808181875#
27
The Global Slavery Index. http://www.globalslaveryindex.org/endorsements
28
Anti-Slavery International. http://www.antislavery.org/english
29
FTS In India: Free a Village, Build a Movement. http://www.freetheslaves.net
30
https://www.ashanet.org/projects/project-view.php?p=907
31
Ginny Baumann, et al, “Follow Up Study of Slavery and Poverty In Kukrouthi village, St
Ravidas Nagar District, Uttar Pradesh,” June 2012, unpublished manuscript. Free the Slaves.

35A Conversation with Arch PuddingtonSAIS Review vol. XXXIV no. 1 (Winter–Spring 2014)
A Conversation with Arch
Puddington, Vice President for
Research at Freedom House
Who is the target audience of Freedom House reports?
From the beginning, we have sought to provide analysis that combines
scholarly rigor with a methodology and vocabulary that is accessible to the
general public. Obviously, there is a niche group of policymakers here and
in Europe, as well as journalists, scholars, political activists and dissidents,
who make up our core audience. But our data are also widely used by educa-
tors and students, including at the secondary level.
We have also developed a growing audience among foreign government
officials. This is in large measure due to the important role of democracy
and honest governance in the calculations of international development
agencies, financial institutions, and governments. Especially since Freedom
House findings have been formally incorporated into the foreign assistance
process of the American government, we have experienced a major increase
in communications with foreign diplomats, who want to discuss, or com-
plain about, our conclusions about their countries.
Freedom House’s 2013 Freedom on the Net report examined internet
activism and “increasingly sophisticated restrictions on internet free-
dom” by authoritarian regimes. Based on the report, what opportuni-
ties and obstacles do new technologies offer in promoting freedom?
New technologies offer a significant opportunity to advance democracy.
Throughout the world, online activists and ordinary social media users uti-
lize these tools to organize, lobby, and hold their governments accountable.
Women’s rights groups, free speech advocates, and human rights organiza-
tions have staged successful advocacy campaigns to overturn or prevent the
passage of oppressive laws. In many authoritarian states, such as China,
Saudi Arabia, and Bahrain, exposés by online and citizen journalists reveal-
ing corruption, police abuse, and pollution often force the authorities to
acknowledge the issue, and in some cases, hold the perpetrators accountable.
Unfortunately, the transformative power of digital media is not limited
Arch Puddington is vice president for research at Freedom House. He manages the
publication of Freedom in the World, an annual report assessing global political rights
and civil liberties, and is responsible for the development of new research and advocacy
programs.

to individuals fighting to promote freedom. Technological advances also
bring new tools to censor the web and intimidate citizens who are engaged
in online speech that
is deemed to threaten
the regime, insult the
dominant religion,
or sow social discord.
Authoritarian regimes
monitor the personal
communications of
their citizens for po-
litical reasons, with
the goal of identify-
ing and suppressing
government critics and human rights activists. Such monitoring can have
dire repercussions for the targeted individuals in those countries, including
imprisonment, torture, and even death.
In 2007, Freedom House published a report indicating a “profoundly
disturbing deterioration”—a greater number of countries were becom-
ing less free than were becoming more free. Could you share insights
on this finding?
According to our findings, more countries have experienced declines in
freedom than have experienced gains during each of the last eight years.
This is unprecedented in the forty-one-year history of Freedom in the World.
At the same time, this decline is not in itself a cause for alarm. Many of the
declines represent quite small
setbacks and not a pell-mell
retreat from the gains of pre-
vious decades. Many coun-
tries that embraced democ-
racy over the previous four
decades had little experience
with the institutions of free-
dom, and their adherence to
good government standards
is beginning to fray. Especially in times of relative scarcity, corruption is
emerging as a particular evil, especially as top-to-bottom graft and favorit-
ism erodes popular faith in democratic institutions.
A more serious problem that is reflected in our findings is the durabil-
ity of what we call modern authoritarian regimes. Russia’s Vladimir Putin
and the Chinese Communist Party leadership are the best examples of this
phenomenon, but there are others as well: Aliyev in Azerbaijan, the Iranian
clerics, Correa in Ecuador, the post-Chavez group in Venezuela. Modern
authoritarians preside over countries that are well-integrated into the
global economic and diplomatic systems and often possess energy riches.
Technological advances also bring new
tools to censor the web and intimidate
citizens who are engaged in online
speech that is deemed to threaten the
regime, insult the dominant religion, or
sow social discord.
. . . corruption is emerging as a
particular evil, especially as top-
to-bottom graft and favoritism
erodes popular faith in democratic
institutions.

37A Conversation with Arch Puddington
The leaders are unabashedly antidemocratic and anti-Western. They devote
their energies to the control of the political process, the press, civil society,
and the rule of law. They avoid the excesses and stupidities of communism,
especially in economic policy, but use nuanced and sophisticated methods
to control the levers of power. Modern authoritarianism has emerged over
the past fifteen years, and its practitioners have grown in power and even
international respectability over time. Modern authoritarianism today ranks
as the most worrying threat to freedom around the world.
The most recent Freedom in the World report noted that the number of
electoral democracies has risen, while the distribution of countries
in each of the “free,” “partly free,” and “not free” categories did not
change significantly in comparison to 2012. Why do you think this is
the case?
One way to think of a country with a designation as “free” is as a liberal or
consolidated democracy. In recent years, the number of free countries has
remained steady at eighty-seven to ninety, meaning that approximately 45
percent of the world’s sovereign states enjoy systems that guarantee com-
petitive elections and a broad range of civil liberties. On the other hand, the
number of electoral democracies has oscillated between 115 and 123. There
are thus some thirty countries that can be said to have met internationally
accepted standards for competitive elections but which fall short on other
indicators that measure liberal democracy—press freedom, minority rights,
gender equality, corruption, and so forth.
Freedom in the World ranks Mexico as an electoral democracy but also
places it in the “partly free” category because of the impact of uncontrolled
violence. Indonesia likewise qualifies as an electoral democracy but is ranked
as “partly free” because, among other problems, its government has been
unable to secure the rights of religious minorities. Given that the standards
for gaining a designation as an electoral democracy are less strict than for
achieving designation as a free country, it is not surprising that there is more
movement in and out of the electoral democracy category.
What do you make of recent articles from BBC and al-Jazeera (among
others) calling attention to corruption in the EU? Some have argued
that the quality of electoral democracies in the United States and in
Europe have been deteriorating. What are your thoughts on this as-
sertion? How closely do you think popular indices like those from
Freedom House mirror the reality on the ground?
I’m not overly exercised about the level of corruption in the EU. Every so-
ciety based on money transactions suffers from corruption to one degree
or another. The key here is whether corruption is pervasive, officially toler-
ated, and engaged in by the political leadership. The most damning report
on European corruption was commissioned by the EU itself, and most EU
countries have media which investigate corruption charges and an indepen-

dent judiciary which prosecutes corrupt officials. Europe should be con-
cerned when officials are intervening to prevent the press from uncovering
corrupt acts or prosecutors from bringing charges against officials accused
of graft. All too often accusations of widespread corruption in democracies
are advanced by people of bad faith from countries—Russia and Belarus, for
example—where corruption is a way of life.
As for the United States, there clearly are growing problems with its
political system. Gerrymandering has gotten worse and the new movement
for voter identification has been implemented in ways that suggest efforts
to weaken Democratic candidates. At the same time, the American system
retains a unique dynamism. It remains open to the emergence of new faces
(Barack Obama) and new forces (the Tea Party). Despite its multinational
character, the United States has managed to avoid the emergence of influ-
ential parties or movements that preach racism or xenophobia.
We place considerable effort on capturing these nuances in Freedom in
the World and other reports. Freedom in the World is not a report on governance
per se; we endeavor to reflect the level of freedom an individual experiences
on the ground, and zero in on the threats to freedom whether they come
from the state, terrorists, extremist movements, or other sources. We have
developed a methodology that looks at the broad set of institutions and val-
ues that make up human freedom while providing a flexibility that enables
us to highlight the qualitative differences between one society and another.
How does Freedom House collect the quantitative and qualitative in-
formation used in its reports? How do you extract significant insights
from this information?
We see our principal role as providing analysis, including scores and judg-
ments about democratic performance, to the policymaking community, the
media, and scholars. Our analysts make use of the vast sources of informa-
tion that are available these days, including government reports, the find-
ings of think tanks and NGOs, reports of multilateral institutions, press
accounts, interviews with officials and critics alike, and the many other
sources that have emerged in the data explosion era.
Freedom House is a source for analysis, not data. We see our role as
providing assessments on the state of freedom, identifying the principal
threats to freedom, and showcasing global and regional trends. Using data
from our country analysis, we are able to identify the global and regional
trajectory of freedom, broadly defined, as well as specific elements of free-
dom, such as freedom of expression and press, elections, corruption and
transparency, civil society, and rule of law. We can, in other words, illuminate
which institutions of democracy are most vulnerable to pressure from au-
thoritarian rulers, and which institutions have proved most durable. There
are other organizations that see their mission as providing data on elections,
corruption, assaults on journalists, economic freedom, and so forth. Free-
dom House, by contrast, works to inform the public about the gains and
setbacks in democratic government, civil liberties, and personal freedom.

39A Conversation with Arch Puddington
What do you make of the recent trend in which governments freely
release open data? What are the policy implications of this trend?
Clearly, enhanced transparency is preferable to less openness. My concern is
that some governments will be tempted to fudge or falsify data or decide to
stop publishing informa-
tion when the results are
embarrassing. For some
time now, Argentina has
been publishing inflation
figures that most experts
regard as bogus. After po-
litical attention was drawn
to spiraling crime rates, the
Venezuelan government
stopped publishing statis-
tics on violent crime. These
examples suggest that in the future, as in the past, the data world will be
divided between democracies that almost always publish honest statistics
and other countries whose data may or may not reflect reality.
For democracies, political leaderships will face a new challenge in
explaining the unwelcome news that will inevitably emerge from published
data. More data will mean a more informed citizenry, especially at the elite
level. But it will also mean more pressure on governments to communicate,
often in response to the arguments of demagogues, why unemployment
rates, inequality, traffic accidents, or test scores for children are moving in
the wrong direction.
Authoritarian regimes will have it easier. Their leaders will either quash
uncomfortable facts or distort them. Here it will be essential that interna-
tional financial institutions, transparency think tanks, and the global busi-
ness community weigh in by demanding honest accounting. It is instructive
that Argentina agreed to adjust its inflation figures after pressure from the
IMF.
The field of international affairs
has become more focused on
collecting and analyzing large
quantities of data. What would
you recommend to international
affairs students as the field be-
comes more data-driven?
I would urge students to remember that data and facts can be manipulated
and misused. Serious assessment of a society’s political well-being requires
facts, but it also demands honest interpretation. An overemphasis on data
can distort an analyst’s efforts to understand the true quality of freedom
as thoroughly as can outright bias.
. . . in the future, as in the past, the
data world will be divided between
democracies that almost always
publish honest statistics and other
countries whose data may or may
not reflect reality.
An overemphasis on data can
distort an analyst’s efforts to
understand the true quality of
freedom as thoroughly as can
outright bias.

41Corruption, Transparency, and Apathy in the Western WorldSAIS Review vol. XXXIV no. 1 (Winter–Spring 2014)
Of Note
A Deterioration of Democracy?
Corruption, Transparency, and Apathy in
the Western World
Rachel Ostrow
Arch Puddington, in his interview with the SAIS Review of International
Affairs, expresses a firm belief in the power of interpretation. “An over-
emphasis on data,” he says, “can distort an analyst’s efforts to understand
the true quality of freedom as thoroughly as can outright bias.” Freedom
House’s annual reports on freedom have made data on democracy accessible
for millions of people in the diplomatic, academic, and wider communities.
These analyses have criticized governments throughout the Middle East,
Africa, and Asia for anti-democratic and autocratic methods. However, Free-
dom House’s important work researching authoritarianism and democracy
throughout the world should start to focus once again on its birthplace—the
Western world.
Freedom House, based in the United States (and largely funded by
government agencies such as the State Department and the U.S. Agency
for International Development), could be—and has been—accused of hav-
ing Western biases. A quick look at Freedom in the World 2014 shows that the
United States and Canada, as well as the majority of European nations, are
classified as “free” right up to the Ukrainian border.1
However, several coun-
tries in Europe—as Freedom House rightly notes—have suffered democratic
backsliding. France, Switzerland, and Hungary have all passed laws or gone
through social movements seeking to limit the rights of migrants and ethnic
minorities. These occurrences, though noted in Freedom House’s analysis,
do not seriously affect the calculations within.
Hungary itself is an excellent example of where this analysis has
masked the more sinister undertones within an open democracy. Hungary’s
recent re-election of Viktor Orban—in an election widely seen as free and
fair—can be seen as a backwards turn for Hungarian democracy. As a member
of the right-wing, nationalist Fidesz party, Orban will likely have to make
concessions to the far-right, anti-Semitic, and anti-Roma Jobbik party, which
Rachel Ostrow is a second-year M.A. candidate at the Johns Hopkins University Paul H.
Nitze School of Advanced International Studies (SAIS) concentrating in Russian and
Eurasian Studies. She is Web Editor of The SAIS Review.

is gaining power and influence in Hungary. Orban has also courted the
governments of the authoritarian states of Russia, Azerbaijan, and China,
much to the consternation of
other European states.2
While
Freedom House notes these
events with concern, these
movements do not seem to
affect the calculations for
Hungary, and it remains clas-
sified as a “free” state in Free-
dom in the World 2013. In a
more objective, side-by-side
analysis of migrant rights and media access, countries like Hungary may
match more closely with China than their governments—or Freedom
House—would care to admit.
Despite investigations by the BBC and Al-Jazeera showing that corrup-
tion is an issue in the European Union, Puddington pushes back against
those allegations, and the wider implications for the deteriorating quality
of electoral democracies in the established democratic world. “Every soci-
ety based on money transactions suffers from corruption to one degree or
another,” he says, arguing that corruption within the political class is the
clearest sign that a society is undemocratic, and that such corruption is rare
within Western democratic societies.
Giovanni Sartori’s “conceptual stretching” theory would imply that
corruption cannot be the same everywhere. Dynastic authoritarian regimes
are considered inherently corrupt by Western scholars; however, the stability
they allow in some states (particularly those in a post-conflict state) ensures
their popularity on a domestic level. In the United States, the recent Su-
preme Court ruling in McCutcheon v. Federal Election Commission, enshrining
money as speech—and thus allowing unlimited amounts of private spending
in political campaigns—could be viewed as corrupt in countries where poli-
tics remain clan- or kinship-based.3
The effects that the McCutcheon ruling
will have on American democracy remain to be seen, but a likely outcome
will be the continued disenfranchisement of low-income and minority vot-
ers as special interests pay their way into office.
Puddington notes that the American political system has “growing
problems,” specifically pinpointing gerrymandering and voter ID laws as
obstacles to continued democracy in the United States.4
He argues, however,
that the United States still has a dynamic political system that prevents the
emergence of xenophobic or racist parties. Puddington is correct in assert-
ing that the United States is still a vibrant democracy. However, it could be
argued that the increasing polarity of the two-party system has drowned
out the rise of minority parties—both those considered dangerous (the xe-
nophobic and racist parties Puddington warns of) and those less so, namely
socialist and issue-specific parties.
A two-party system ignores pluralism, instead prioritizing electoral
duopoly; while this does not directly threaten democracy, it does stoke
Hungary itself is an excellent
example of where this analysis
has masked the more sinister
undertones within an open
democracy.

43Corruption, Transparency, and Apathy in the Western World
electoral indifference. Since 2008, youth participation in democratic insti-
tutions in the United States has fallen, and distrust in political institutions
among that age group has
risen.5
Though 52 percent
of young Americans polled
stated that they would re-
call every single member of
the United States Congress
following the 2013 govern-
ment shutdown, a quarter
of the respondents said they
did not plan to vote in the
2014 midterm elections.6
Revelations such as those disclosed in the Edward
Snowden National Security Administration (NSA) leaks continue to erode
trust in the government as well.7
Challenges to democracy exist throughout the Western world, not
just in Hungary and the United States. As Puddington notes, Mexico, de-
spite being an electoral democracy, is listed as only “partly free” due to the
threat of violence against its citizens. The Freedom in the World index is not
just about governance, says Puddington, but rather the “level of freedom
an individual experiences on the ground.” Thus, threats from outside the
government—such as from terrorist groups and rebels—can affect freedom
even in the most developed democracy. An excellent example of this extra-
governmental threat are the recent elections in Afghanistan, where threats
by the Taliban insurgency against the April 2014 elections were carried out
in the form of terrorist bombings and shootings throughout the country in
the weeks and months leading up to the elections. Defying all expectations,
however, the elections were considered to have been relatively free and fair,
and even featured significant female participation.8,9
The use of data on a large scale has been Freedom House’s method
since the institution began publishing the Freedom in the World index in 1973.
Advances made in the calculation and tabulation of big data in recent years
have allowed for evaluation of post-communist, developing, authoritarian,
and nascent countries’ democratic development. Indices like Freedom in the
World help to prioritize international development efforts, aid allocation,
and even sanctions. However, the data used in these indices is, as Pudding-
ton says, useless without qualitative evaluation. With democracy’s declining
quality throughout the
developed and developing
world, this kind of analy-
sis has become even more
important.
Freedom House,
in 2007, announced the
“profoundly disturbing
deterioration” of democracy—according to its numbers, from 2006 to 2007,
a greater number of countries had become less free than more free. This
A two-party system ignores
pluralism, instead prioritizing
electoral duopoly; while this does
not directly threaten democracy, it
does stoke electoral indifference.
. . . the fact that some of these
declines have been seen in the world’s
most established democracies is a
cause for concern.

has now been a trend for the past eight years. Puddington insists this is
not cause for alarm, and that tough economic times may be to blame for
some of this democratic backsliding. However, the fact that some of these
declines have been seen in the world’s most established democracies is a
cause for concern. Freedom House should more closely examine the data
for the world’s developed states and cast a more critical eye on their demo-
cratic institutions and norms, subjecting these developed states to the same
qualitative analysis experienced by developing democracies.
Notes
1
Freedom House, Freedom in the World 2014, January 23, 2014.
2
Charles Gati, “What Viktor Orban’s victory means for Hungary and the West,” The Wash-
ington Post, April 7, 2014.
3
“Before and After the Supreme Court’s Ruling,” The New York Times, April 2, 2014.
4
The interview with Mr. Puddington was held prior to the Supreme Court’s ruling in Mc-
Cutcheon v. FEC.
5
Benjamin Scuderi, “What are Millenials thinking?” Harvard Political Review, 2014.
6
Ian Kohnle, “Angry, Yet Apathetic: The Young American Voter,” Harvard Political Review,
December 3, 2013.
7
Harry Enlen, “Polls show Obama’s real worry: NSA leaks erode trust in government,” The
Guardian, June 13, 2013.
8
Rob Nordland and Matthew Rosenberg, “After ‘09 Fraud, Afghanistan Reports a Cleaner
Election,” The New York Times, April 8, 2014.
9
Rob Nordland, Azam Ahmed, and Matthew Rosenberg, “Afghan Turnout Is High as Voters
Defy the Taliban,” The New York Times, April 5, 2014.

45Controversy Surrounding the Thai Rice-pledging SchemeSAIS Review vol. XXXIV no. 1 (Winter–Spring 2014)
The Politics of Numbers:
Controversy Surrounding the
Thai Rice-pledging Scheme
Pongkwan Sawasdipakdi
Thailand’s rice-pledging scheme has been widely criticized for its unsustainability. While the
government argues that the program is beneficial for rice farmers, the opposition points out
that the program has resulted in significant losses. Over the past few years, data have been
used as a tool to support arguments from both the government and the opposition. However,
the reliability of data utilized by both sides is questionable. This paper discusses problems
surrounding data collection and usage, both of which have contributed to the controversy
surrounding the policy.
Introduction
One of the most controversial policies implemented by Prime Minister
Yingluck Shinawatra’s government is the rice-pledging scheme initi-
ated in 2011. The policy, often criticized for its populist appeal, intended to
improve the living standards of Thai rice farmers by increasing rice prices.
In order to increase the prices, the program allows farmers to directly pledge
rice to the government at a fixed price of 15,000 baht ($469) per ton.1
The
government believes that this higher-than-market price helps ease the bur-
den placed on the rice farmers due to the increasing cost of production.
However, the results have been starkly different than those the govern-
ment anticipated. Because the government has been unable to export the rice
it purchased from domestic farmers at elevated prices, its current surplus of
rice is unprecedented. While benefits to farmers are debatable, the program
is significantly over budget and is a heavy burden on government finances. In
June 2013, the opposition Democratic Party disclosed a classified document
from the audit committee overseeing the agricultural product-pledging
programs. This document concludes that the program was over budget
by 260 billion baht ($8.13 billion).2
The leaked information immediately
became the opposition’s political weapon to nurture anti-government sen-
timent. The reports also led to credit rating agency Moody’s downgrading
Thailand’s sovereign debt. However, instead of defending themselves by
Pongkwan Sawasdipakdi is a second-year M.A. candidate at the Johns Hopkins
Paul H. Nitze School of Advanced International Studies (SAIS) concentrating
in Southeast Asia Studies.

disclosing available data, Minister of Commerce Boonsong Teriyapirom and
Deputy Minister of Commerce Natawut Saikua repeatedly insisted that the
program would be over budget by less than 260 billion baht ($8.13 billion)
without referring to any spe-
cific numbers.3
This was an ineffective
way to respond to the claims
made by the opposition. In
a modern society, people in-
creasingly demand statistics
either to confirm or convey
their beliefs. The failure of the ministers to offer an alternative set of sta-
tistics damaged the credibility of the government. First, it showed that the
government did not have reliable data with which it could evaluate the
impact of the program and utilize as the basis for future decisions. Second,
it led to criticism that the government did not release the information
because it wanted to maintain a populist policy that targeted the ruling
party’s political base.
This paper discusses problems surrounding data collection and us-
age, both of which have contributed to the controversy surrounding the
policy. It argues that the confusion over the statistical method contributed
to the ambiguity of the program’s performance evaluation. Furthermore,
the government’s failure to disclose the available statistics to the public
has damaged the credibility of not only the program, but the government.
The first section of the paper will briefly explain the regulations and
procedures of the rice-pledging scheme. The following section will explore
controversies and claims made by the opposition that affect the credibility
of the program and the government. Finally, the paper analyzes how the
confusion over data collection and statistics usage reinforced the problem.
Regulations and Procedures of Thailand’s Rice-pledging Scheme
The current rice-pledging scheme allows rice farmers to pledge an unlim-
ited amount of their self-produced paddy to the government. This type of
agricultural program in Thailand is unusual. The Thai government’s other
agricultural policies generally set a quota for how much farmers can sell
directly to the government. Because the new policy allows farmers to, in
essence, sell all of their rice production to the government, it requires that
rice farmers intending to participate in the program obtain approval from
the Department of Agriculture and their community. Moreover, the farmers
have to certify that the rice they sell to the government is their own product.
In other words, neither farmers nor landlords are allowed to pledge rice
purchased from other owners or unknown origins to the government. These
regulations were designed to lower instances of corruption.4
With these basic requirements fulfilled, farmers are eligible to pledge
their product at any local rice miller. In return, the millers issue a warehouse
receipt for the farmers to claim funding from the Bank of Agriculture and
The failure of the ministers to
offer an alternative set of statistics
damaged the credibility of the
government.

47Controversy Surrounding the Thai Rice-pledging Scheme
Agricultural Cooperatives (BAAC). The BAAC is then obligated to process
a payment to the farmers within three business days. However, in reality,
the government has had difficulty fulfilling the time commitment. In some
cases, the government has failed to process the entire payment to local farm-
ers, mainly due to its inability to sell the rice on the international market
and secure loans from Thai banks.
Procurement prices vary based on the type and quality of rice, rang-
ing from 13,800 baht ($431) per ton to 20,000 baht ($625) per ton. The
announced 15,000 baht ($469) per ton price was used as a benchmark for
100 percent white rice. Besides differences in types, prices are also vary based
on moisture content. Rice with low moisture content is priced higher than
rice with higher moisture content. Table 1 shows detailed price data for
different types of rice.5
Table 1. Procurement Prices of the Rice-pledging Scheme
Type Pledging Prices
Baht US Dollar
White rice 25% 13,800 431.25
White rice 15% 14,200 443.75
White rice 10% 14,600 456.25
White rice 5% 14,800 462.50
White rice 100% 15,000 468.75
Sticky rice (short grain) 15,000 468.75
Sticky rice (long grain) 16,000 500.00
Thai Pathumthani fragrant rice 16,000 500.00
Province fragrant rice 18,000 562.50
Thai jasmine rice 20,000 625.00
Source: Department of Internal Trade, Ministry of Commerce
Each miller processes the pledged raw rice after procuring it from
farmers. These millers are paid by the government to mill and deliver
processed rice to the government’s central warehouses, controlled by the
Public Warehouse Organization and the Marketing Organization for Farm-
ers. A subcommittee overseeing rice sales is then responsible for drafting
a selling plan. The government, at first, planned to outsource rice sales to
a private company. However, the private company lacked market networks
both domestically and internationally, and could not sell rice at a profit-
making level. Instead, the government has accumulated a surplus of rice. As
a result, the government has signed contracts with seven other companies
to sell them rice at significantly discounted prices. This situation has been

worsened by the fact that the program is suffering from financial problems
and requires immediate additional revenues to pay the farmers.6
Criticisms of the Rice-pledging Scheme
The rice-pledging program has been criticized by the public, the opposition,
and both Thai and foreign economists for being unsustainable. After the
first year of implementation, in September 2012, the Thailand Develop-
ment Research Institute (TDRI), a prominent Thai economic think tank,
published a research paper on the advantages and disadvantages of the
program. The research indicated that the program is beneficial for the rice
farmers in two ways. First, the rice farmers participating in the program
have received additional income from fixed and market price differentials
totaling 72.71 billion baht ($2.27 billion). Second, farmers not participat-
ing in the program are able to sell their rice at higher prices. This is because
private merchants have to compete with the government by offering farmers
higher prices, thereby increasing domestic market rice prices.7
However, the
paper also criticizes many aspects of the policy. First, the program mostly
benefits high and middle-income rice farmers because they can afford high
costs of production and utilize economies of scale to produce rice on a
larger scale. In contrast, poorer rice farmers mainly grow rice for household
consumption. Second, the program is costly. Aside from the estimated cost
of over 300 billion baht ($9.38 billion) that the government paid in subsi-
dies to the farmers in the 2011–2012 production season, it paid the millers
approximately 20.64 billion baht ($645 million) for the milling costs. The
government has also had to rent warehouses and pay costs to preserve rice
quality. Moreover, the government had to hire surveyors to inspect quantity
and quality of the procured rice and pay interest and miscellaneous costs.
Table 2 shows the costs of the rice-pledging scheme for the 2011–2012 sea-
son estimated by the TDRI report.
The third backlash pointed out by the TDRI is corruption. The paper
raises a concern that millers participating in the program could smuggle rice
from neighboring countries (specifically mentioning Cambodia), and pledge
the smuggled rice to the government under the names of random farmers.
Moreover, the millers could deliver milled rice of sub-par quality and quan-
tity to the government. Fourth, if rice is stored for long periods of time, its
quality decreases, as does its value. This could affect the prestige of Thai rice
in the world market; Thai rice is globally renowned for its high quality and
sells for a higher price than rice produced by other countries. The changing
perception towards Thai rice can plausibly drive Thai rice prices down in
the future. Finally, the TDRI report criticizes the government’s vague rice
sales plan, which it alleges could accelerate the program’s financial prob-
lems. While the pledging and procurement processes are clearly elaborated
in the policy, the government only states that rice sales are handled by the
subcommittee on rice sales. This subcommittee is responsible for deter-
mining quantity, prices, methods, and conditions for selling rice stored in
government’s warehouses. However, no specific rice-selling guidelines have
yet been published.

Table2.Expenses,Income,andProfitoftheRice-pledgingScheme2011–2012(millionbaht)
Table2:Expenses,Income,andProfitoftheRice-pledgingScheme2011-2012
(millionbaht)
Contents
RicePricesbasedonAssumption1.2RicePricesbasedonAssumption1.3
WetSeason
2011-2012
Dry
Season
2012
TotalWetSeason
2011-2012
DrySeason
2012
Total
1.Loans
Calculatedfromtheamountof
unmilledriceinstockasof
September2012(milliontons)
118.593.68
6.95
179,294.50
12.10
297,888.18
19.08
118,593.68
6.95
179,294.50
12.10
297,888.18
19.08
2.Interests3,331.482,184.355,515.833,331.482,184.355,515.83
3.OperationalCosts
3.1Millingcosts
3.2Warehouserentsand
qualitypreservation(47
baht/tonmilledrice/3
months)
3.3Qualityinspection(14.5
baht/tonmilledrice)
3.4Operationalcostsof
governmentalagencies
3.5Deteriorationcosts(5%
yearlydecreaseofvalueof
milledstoredinthe
warehouses)
13,775.89
6,989.35
483.32
61.29
2,770.18
3,471.76
18,366.52
12,369.42
652.48
108.41
3,176.01
2,060.20
32,142.42
19,358.77
1,135.80
169.70
5,946.19
5,531.96
13,420.97
6,989.35
483.32
61.29
2,770.18
3,116.84
18,102.65
12,369.42
652.48
108.41
3,176.01
1,796.32
31,523.62
19,358.77
1,135.80
169.70
5,946.19
4,913.16
4.TotalExpenses(1+2+3)
TotalExpensesperTon
135,701.05
19,525
199,845.38
16,519
333,546.43
17,616
135,346.13
19,474
199.581.50
16,497
334,927.63
17,585
5.TotalIncome
TotalIncomeperTon
92,390.68
13,293
130,674.70
10,798
223,025.38
11,709
83,660.02
12,037
115,486.87
9,546
199,146.89
10,455
6.Loss
LossperTon
43,310.37
6,232
69,210.68
5,721
112,521.05
5,907
51,686.11
7,437
84,094.63
6,951
135,780.74
7,128
EstimationAssumptions:
1.Assumptionsforricesalesandsaleprices
1.1Assumethattheprogramstoredriceinthewarehousesforayear,startedfromOctober
2011andendedinSeptember2012.ThegovernmentsoldallriceinSeptember2012.
1.2Inthecasethatthegovernmentsoldallriceatthepricesequivalenttothebiddingprices
onSeptember5,2012
a)Whiterice5%ispricedat16,300baht/tonandJasminericeispriced29,800

Table 2. Continued
Estimation Assumptions:
1. Assumptions for rice sales and sale prices
1.1 Assume that the program stored rice in the warehouses for a year, started
from October 2011 and ended in September 2012. The government sold all
rice in September 2012.
1.2 In the case that the government sold all rice at the prices equivalent to the
bidding prices on September 5, 2012
a) White rice 5% is priced at 16,300 baht/ton and Jasmine rice is
priced 29,800 baht/ton (based on the bidding prices on September
5, 2012).
b) Prices of the other types of rice are based on domestic wholesale
prices in July 2012, subtracting operational costs of 1,500 baht/
ton.
1.3 In the case that sale prices have decreased
a) White rice 5% is priced at 13,470 baht/ton (decreasing 10% from
the bidding price and subtracting the operational costs of 1,200/
ton).
b) Jasmine rice is priced at 25,810 baht/ton (decreasing 5% from the
bidding price and subtracting the operational costs of 2,500 baht/
ton).
c) Domestic wholesale prices are equivalent to those in 1.2.
2. Assume that milling and delivering processes follow the period stated in the program,
so that there would not be unmilled rice stored in the warehouses and the govern-
ment did not have to rent miller’s warehouses.
3. The loan interests are based on value and period in which unmilled rice was received
by the program.
4. Warehouse rents for milled rice, quality preservation costs, and deterioration costs
are calculated based on the quantity of milled rice and period in which the rice was
delivered to the warehouses
Source: Thailand Development Research Institute
In June 2013, criticisms over the rice-pledging scheme once again oc-
cupied news headlines. A classified report by the audit committee for the
government’s agricultural product pledging programs was leaked to the op-
position Democratic Party. In the report, which was posted on the Twitter
of Korn Chatikawanich, one of the opposition’s most prominent leaders,
the program was estimated to cost the government at least 260 billion baht
($8.13 billion). The report includes budgets for the rice-pledging program in
2011–2012 and 2012–2013. It indicates that the government spent 661.22
billion baht ($20.66 billion) in the 2011–2012 production season and 408.75
billion baht ($12.79 billion) during the 2012–2013 production season. After
including a loan from the BAAC and income from rice sold by the Ministry
of Commerce, the balance for both seasons of the rice-pledging scheme
resulted in a
loss of 220.9
7 billion baht
($6.9 billion).
However, this
number only reflects the balance as of 31 January 2013. The committee in-
dicated that the program caused monthly losses of 10 billion baht ($312.5
. . .the total losses incurred by the program
could be as high as 260 billion baht.

million). As a result, as of May 2012, the total losses incurred by the program
could be as high as 260 billion baht.8
The report was part of government efforts to estimate losses resulting
from the rice-pledging scheme. The conclusions of the internal government
report were inconclusive. There are several other reports that show conflict-
ing data and illustrate that the government was in the process of choosing
the most logical way to estimate losses caused by the program.9
In response
to the criticisms, Minister of Commerce Boonsong Teriyapirom and Deputy
Minister of Commerce Natawut Saikua held a press conference on the bal-
ance of the rice-pledging scheme on 7 June 2013. The ministers insisted that
the program was over budget by less than 260 billion baht. Moreover, they
argued that since the program was still in progress, making conclusions
about the impact on the budget was impossible. However, the government
maintained that the program would be over budget by a maximum of 80
to 90 billion baht ($2.50 to $2.81 billion). The fact that the ministers could
not cite specific numbers further inflamed criticisms. Many journalists in
attendance at the press conference were furious over how unclear the state-
ment was and argued with the ministers.10
The inability of the ministers and
the government to adequately satisfy the demand for credible information
intensified criticisms directed at the government and exacerbated confusion
among the opposition and the public. This clearly affected the government’s
credibility.11
A day after the press conference, opposition leader Abhisit Vejjajiva
emphasized that Prime Minister Yingluck’s government had lost 42.96 bil-
lion baht ($1.34 billion) in the 2011–2012 wet season, the inaugural grow-
ing and harvest season of the program. According to Abhisit, the loss in the
first season was minimal, considering that Thailand’s rice production was
devastated by a massive flood in May-June 2012. However, in 2012–2013,
the government lost 178 billion baht ($55.63 billion). This number does
not include the operational and administrative costs of the program, esti-
mated to be approximately 40 billion baht ($1.25 billion). Therefore, the
rice-pledging program lost at least 200 billion baht ($6.25 billion) in one
production year.12
On 14 June 2013, the government disclosed a new set of numbers con-
cerning the rice-pledging scheme. The numbers show that in the 2011–2012
production year, the program was over budget by a maximum of 130 billion
baht ($4.63 billion), less than the estimated loss cited by the opposition.
However, the government accepted that there was confusion over numbers
used by different agencies.13
In October 2013, Pridiyathorn Devakula, former deputy prime minis-
ter and former minister of finance, estimated that the rice-pledging scheme
would result in losses as high as 425 billion baht ($13.28 billion).14
Pridi-
yathorn was the first person that publicly and explicitly explained his cal-
culation method. According to his calculations, the program’s costs include
payments to farmers, processing, delivery, storage, debt interest, and other
miscellaneous expenses. The income from the program includes income
from selling rice and expected income from selling the unsold rice at market

prices at the end of the year. He presented two methods of balance calcula-
tion. The first method involves a basic addition and subtraction of expenses
and income. Utilizing this method yields a loss of 425 billion baht ($13.28
billion). The second method includes interest rates from government loans
of 55 billion baht ($1.72 billion), deterioration costs of 60 billion baht ($1.88
billion), and preservation costs and other expenses of 20 billion baht ($625
million) in the calculation. Using data from the official government audit
committee for the agricultural product-pledging scheme, the second method
yields a loss of 470 billion baht ($14.69 billion).15
Information War: Data as a Legitimizing Tool
The rice-pledging scheme has clearly been an information war where numer-
ous sets of data are used to provide legitimacy for both sides. It is difficult
to verify the reliability of this data. First, the method of calculation is not
uniform across government agencies. Although different agencies share the
same raw data, such as the amount of pledged rice, subsidies, and milled
rice in stock, profits
and losses are usually
different due to dissim-
ilarities in calculations.
The government’s at-
tempt to calculate ac-
tual losses, in the wake
of charges by the op-
position in 2012, is a
good example of dissimilarities resulting from calculation methodologies.
Prime Minister Yingluck appointed Varathep Rattanakorn, the Minister in
the Office of the Prime Minister and the Deputy Minister of Commerce,
to investigate actual losses of the program. Varathep gathered data from
different agencies, including the Ministry of Commerce, the Ministry of
Agriculture and Cooperatives, the Ministry of Interior, the Office of the
Prime Minister, and the Office of the National Economic and Social Devel-
opment Board. He discovered differences between the agencies.16
Table 3
shows differences in the results of the calculations from the audit commit-
tee overseeing agricultural pledging scheme and the Ministry of Commerce.
This could potentially explain why the ministers and deputy ministers of
commerce insisted that the program’s total loss was indeed less than 280
billion baht. However, because of the government’s inability to provide of-
ficial data coordinated across all agencies to counter the opposition’s claims,
the opposition’s numbers were perceived as more reliable to the public.
This clearly affected the government’s credibility. Table 3 shows differences
between data held by the audit committee and the Ministry of Commerce.
The numbers presented in Table 3 were gathered as an attempt to
clarify the financial costs accrued by the program in the face of criticism
by both the opposition and the public. These two calculations in Table
3, while sharing the same set of raw data, show a significant difference in
Although different agencies share
the same raw data […] profits and
losses are usually different due to
dissimilarities in calculations.

Table3.PrimaryDataontheRice-pledgingScheme2011–2012
(WetSeason2011-2012—DrySeason2012)
ContentsTheAuditCommittee TheMinistryofCommerce
Amountofunmilledrice(milliontons) 21.7 21.7
Amountofmilledrice(milliontons) 13.5 13.5
Budgetused(baht) 337,322,000,000 337,322,000,000
Expenses(baht) 14,786,000,000 14,786,000,000
Totalexpense(2)+(3)(baht) 352,108,000,000 362,108,000,000
Valueofmilledriceremaininginstock(baht) 156,000,000,000 252,300,000,000
Valueofricesold(baht) 59,200,000,000 49,900,000,000
Loss(baht) 136,908,000,000 49,908,000,000
Source:CabinetResolutionJune18,2013

estimated loss. The two numbers differ by 87 billion baht ($2.72 billion).17
The cabinet resolution, however, does not include any information about
the calculation methodologies utilized by each agency. Therefore, the root
cause of differences in calculations is not known. This not only increased
confusion among the public, but also impacted the government’s decision-
making processes. For example, when the Ministry of Commerce relies on
the data that estimate a lower level of losses, it could alter the decision to
hold or sell rice. In fact, the government originally decided to stock rice until
the global prices of rice increased. However, this method proved ineffective
as world rice prices continued to decrease, primarily due to an increase in
rice production in India and Vietnam.18
Figure 1. Export Prices for Rice19
Source: Food and Agriculture Organization of the United Nations (FAO)
The data do not include numbers from the 2012–2013 production
year. This is because at the time this report was released, the 2012–2013
production cycle had not yet ended. Nevertheless, the cabinet resolution in
which the data was published includes an estimated budget for the 2012–
2013 production year provided by the Office of National Economic and
Social Development Board. Table 4 shows the data provided by this report.
The same report by the Office of National Economic and Social De-
velopment Board also points out expected negative effects of the program.
First, the government would accumulate debt of 159.69 billion baht ($4.951
billion). If the government specified a pledging quota of 15 million tons a
year from 2014–2017, the government would have an average debt of 80.62
billion baht ($2.5 billion) annually. This debt figure was estimated using
the difference between the pledging price and the market price trend for
rice. The figure also includes interest and procurement costs. Second, the
program increases export prices and costs of production which therefore
decreases Thailand’s relative competitiveness with other rice producing

Table4.BudgetEstimationfortheRice-pledgingScheme2011–2012and2012–2013
Contents 2011–20122012–2013Total
1. Amountofunmilledrice(milliontons) 21.68 18.79 40.47
2. Budgetused(baht) 337,246,000,000 251,462,000,000 588,708,000,000
3. Valueofricesold(baht) FromthebeginningoftheprogramtoMay2013:
76,001,000,000
RiceexpectedtobesolduntilSeptember2013:
73,082,000,000
4. Benefitsoftheprogram
4.1 Increaseinfarmer’sprofits(baht) 116,000,000,000 114,000,000,000 230,000,000,000
4.2 IncreaseinGDP(percent) 0.69 0.62
Source:CabinetResolutionJune18,2013

countries. Third, large and medium sized farmers have gained more benefits
from the program than small sized farmers. Forth, the government has a
limited ability to sell rice. Finally, the process of issuing certified documents
and warehouse receipts for farmers is bureaucratically slow.20
Aside from the calculation problem, it is also unclear in which stages
data are collected. For practical purposes, statistics on unmilled rice should
be collected when farmers deliver their products to millers. The numbers
should mirror the amount of unmilled rice specified in the warehouse
receipts that farmers deposit at the BAAC. The second set of data are col-
lected after the pledged rice has been milled and delivered to government
warehouses. Then,
the government has
to keep record of how
much milled rice has
been sold and how
much remained in the
warehouses. The com-
plexity of the process provides opportunities for miscalculations and cor-
ruption. Although the final data gathered and compared by Varathep show
cohesive raw data such as amount of unmilled and unmilled rice in stock,
their original data have been reportedly contradictory. Unfortunately, these
contradicting data have not been made available to the public. According
to Teerat Ratasevi, the Spokesperson of the Office of the Prime Minister,
before Prime Minister Yingluck appointed Varathep to investigate the data,
each agency had used uncoordinated sets of data. These include data on
the amount of unmilled and milled rice in stock, program’s income, and
amount of rice sold. This added to the contradictory estimations of losses.
Another problem affecting the data collection process is corruption.
Farmers or millers smuggle rice from neighboring countries to claim sub-
sidies; some millers also hide unqualified rice in a bulk of high quality rice.
When surveyors arrive to inspect quality and quantity of rice, the unqualified
rice can pass undetected. The corruption associated with the rice-pledging
scheme is publicly recognized. A nation-wide survey by the National Insti-
tute of Development Administration (NIDA) on farmers’ perspectives on
the rice-pledging scheme, which sampled 1,250 respondents, shows that
the popular perception is that the program failed mainly because of cor-
ruption.21
This public perception of corruption within the program, again,
impacted the credibility of both the program and the government. More
importantly, instances of corruption distort the data and subsequent poli-
cies enacted by the government.
Conclusion
Data are often used as a tool to shape public opinion. At the same time,
the public also demands data and numbers to confirm their beliefs. In a
country like Thailand, where data collection and calculation methodologies
are not uniform and not completely reliable, data have been used as a tool
The complexity of the process provides
opportunities for miscalculations and
corruption.

to gain legitimacy by both factions in Thai politics. In the case of the rice-
pledging scheme, differences in data collected and calculated by different
government agencies have led to confusion over actual losses accrued by
the program. When the opposition took advantage of data from one of the
agencies, interpreted the estimated loss, and turned the data into a political
weapon, the government remained confused over contradicting data from
different agencies and could not effectively respond to the opposition’s
claim. Later, the government tried to regain public confidence by providing
a more coordinated set of data, but the criticism has already been widely
spread throughout the population.
When numbers become political themselves, the question is no longer
about the precision and correctness of data. Rather, it becomes a question of
who can use the available data more effectively and appropriately. The Thai
government’s failure
in this political game
of numbers has con-
tributed immensely
to its credibility crisis.
Despite the fact that
this political game
is more about data
presentation than
data correctness, the
data collection and
calculation processes
should not be disregarded. The government has to make sure that its data
is reliable and the calculation reflects the reality. Thus, correct data will
contribute to the implementation of more realistic policies in the future.
Notes
1
Exchange rate 32 baht/USD as of April 13, 2014. Assanai Panyamang, “Nayobai Chamnam
Khao Pak Phue Thai” [Phue Thai Party’s Rice-pledging Policy], Voice TV, June 20, 2011.
http://news.voicetv.co.th/thaivote/12673.html (accessed April 10, 2014).
2
Bangkok Biz News, “Yan Rattaban Khadtoon Chamnam Khao 2.6 San Laan” [Confirmed,
the Government Lost 260 billion in the Rice-pledging Scheme], Bangkok Biz News, June 7,
2013. http://www.bangkokbiznews.com/home/detail/business/business/20130607/509898/
ยันรัฐบาลขาดทุนจำ�นำ�ข้าว2.6แสนล้าน.html (accessed April 10, 2014).
3
Matichon Online, “‘Poo’ Mai Doo ‘Boonsong-Natawut’ Tob Nak Khao Mai Dai Chamnam
Khao Tao Dai Aang Lerk Ngan Leaw Ma Chuoi ‘Sam’ Ha Sieng” [Yingluck Did Not Watch
When “Boonsong-Natawut” Could Not Answer Journalists on the Rice-pledging Scheme,
Says Currently Help “Sam” Run Campaign], Matichon Online, June 8, 2013. http://www.
matichon.co.th/news_detail.php?newsid=1370604260&grpid=01&catid=&subcatid= (ac-
cessed April 10, 2014).
4
Khaosod, “Khanton Chamnam Khao” [Rice-pledging Procedures], Khaosod, October 3,
2011. http://www.khaosod.co.th/view_news.php?newsid=TUROamIyd3hNREF6TVRBMU
5BPT0= (accessed April 10, 2014)
5
Department of Domestic Trade, “Raka Chamnam Khao Pluek Na Pee Pee Kan Palit
2554/2555” [Rice-pledging Prices for the Wet Season 2011/2012 Production Year], Depart-
ment of Domestic Trade, Ministry of Commerce. http://www.thairiceexporters.or.th/Interven-
tion%20program.htm (accessed April 10, 2014).
When numbers become political
themselves, the question is no longer
about the precision and correctness of
data. Rather, it becomes a question of
who can use the available data more
effectively and appropriately.

6
Bangkok Biz Online, “Rat Hom Rabai Khao Jai Nee Cao Na” [The Government Rushes to
Sell Rice to Pay Farmers], Bangkok Biz Online, February 4, 2014. http://www.bangkokbiznews.
com/home/detail/business/business/20140204/560825/รัฐโหมระบายข้าวจ่ายหนี้ชาวนา.html
(accessed April 10, 2014).
7
Thailand Development Research Institute, “Pol Dee Pol Sia Khong Karn Chamnam Khao
Took Med” [Advantages and Disadvantages of Pledging Unlimited Amount of Rice], Thai-
land Development Research Institute. http://tdri.or.th/tdri-insight/ar3/ (accessed April
10, 2014).
8
Bangkok Biz News, “Yan rattaban khadtoon chamnam khao 2.6 san laan,”
9
Thairath’s business team, “Scan Krongkarn Chamnam Khao Tang Aok Rattaban Kue
Thoi” [Scan the Rice-pledging Scheme, Government Should Back Off], Thairath, June 17,
2013. http://www.thairath.co.th/column/eco/ecoscoop/351544 (accessed April 10, 2014).
10
Matichon Online, “‘Poo mai doo Boonsong-Natawut.”
11
Phichit Likhitjitsomboon, “Krongkarn Rab Chamnam Khao Khadtoon 2.6 San Laan Baht
Jing Rue” [Did the Rice-pledging Scheme Really Lost 260 Billion Baht], Prachatai, June 21,
2013. http://prachatai.com/journal/2013/06/47311 (accessed April 10, 2014).
12
Thairath’s Political Team. “‘Mark’ Yum Khadtoon Chamnam Khao 2.6 San Laan Jee ‘Poo’
Thobthuan Duan” [Abhisit Insists the Rice-pledging Scheme Lost 260 Billion Baht, Urge
Yingluck to Urgently Review the Program], Thairath, June 8, 2013. http://www.thairath.co.th/
content/pol/349968 (accessed April 10, 2014).
13
Krob Krua Kao, “Varathorn Pei Chamnam Khao Kadtoon Kae 1.3 San Laan” [Varathorn
Points Out the Rice-pledging Scheme lost only 130 Billion Bhat], Krob Krua Kao, June 14,
2013. http://www.krobkruakao.com/ข่าวเศรษฐกิจ/74917/-วราเทพ-เผยจำ�นำ�ข้าวขาดทุนแค่-1-3-
แสนล้าน.html (accessed April 10, 2014).
14
Pridiyathorn is not a member of the Democratic Party, the current official opposition
party. However, he has criticized the Yingluck government on many issues ranging from
political to economics.
15
Thai Republica, “Mahakarp Chamnam Khao (2): ‘Mom Oui” Kang Vitee Khamnuan Kad-
toon lae Kwak Ngern Chang Borisat Samruat Chaona Pisoot Krai Dai Krai Sia” [Pridiyathorn
Shows Calculation Method for the Rice-pledging Scheme’s Loss and Hires a Company to
Investigate Gains and Loss from the Program], Thai Republica, October 18, 2013. http://
thaipublica.org/2013/10/pledge-rice-epic-2/ (accessed April 10, 2014).
16
Bangkok Biz News, “Kittirat Chaeng Khadtoon Chamnam Khao Mai Thueng 2.6 San Laan”
[Kittirat Explains the Rice-pledging Scheme is Less than 260 Billion Baht Over Budget],
Bangkok Biz News, June 10, 2013. http://www.bangkokbiznews.com/home/detail/busi-
ness/business/20130610/510340/กิตติรัตน์แจ้งขาดทุนจำ�นำ�ข้าวไม่ถึง2.6แสนล..html (accessed
April 13, 2014).
17
Prachatai, “Perd Tua Lek – Kho Sanernae Khong Sor Sor Cho Tor Khrongkarn Chamnam
Khao” [Reveal Numbers – Nesdb’s Recommendation for the Rice-pledgign Scheme], Pracha-
tai, June 18, 2013. http://prachatai.com/journal/2013/06/47270 (accessed April 10, 2014).
18
Vikram Nehru, “Thailand’s Rice Policy Gets Sticky,” East Asia Forum, June 13, 2012.
http://www.eastasiaforum.org/2012/06/13/thailand-s-rice-policy-gets-sticky/ (accessed
April 10, 2014).
19
Food and Agriculture Organization of the United Nations, “Rice Market Monitor,” Food
and Agriculture Organization of the United Nations, 17.1 (April 2014). http://www.fao.
org/fileadmin/templates/est/COMM_MARKETS_MONITORING/Rice/Images/RMM/
RMM_APR14_H.pdf (accessed April 13, 2014).
20
Prachatai, “Perd Tua Lek – Kho Sanernae Khong Sor Sor Cho Tor Khrongkarn Chamnam
Khao.”
21
Oryza, “Corruption Killed Thailand Pledging Scheme, Says Survey,” Oryza, February 13,
2014. http://oryza.com/news/rice-news/corruption-killed-thailand-rice-pledging-scheme-
says-survey (accessed April 10, 2014).

59Open Data Policy Improves DemocracySAIS Review vol. XXXIV no. 1 (Winter–Spring 2014)
Open Data Policy Improves
Democracy
Ian Kalin
Global organizations are increasingly adopting technology lessons from the commercial
sector—in particular, technology that facilitates “openness.” In a tangible sense, this open-
ness could be misunderstood as a global increase in government-managed websites. In
reality, governments all over the world are empowering people with greater access to pub-
lic information while also helping citizens extract value from this data. The popularity of
data liberation has been reflected in a portfolio of new policies that are increasingly being
coordinated among nations. In this article, one of the global leaders in open innovation,
Socrata, identifies where these open data initiatives are working well, the common trends
between them, and how government leaders and policymakers could replicate the success
for their own constituencies.
Data All Around Us
In October 2012, with Hurricane Sandy closing in on Manhattan, the
New York City government posted information about evacuation zones,
shelters, and food centers to its web-based, open data portal. Without any
formal mandate or procurement, civic developers and other government
response workers then developed dynamic digital maps that citizens could
access on their mobile devices. This public information was placed directly
in the hands of the people who needed it, and it helped them in clear and
measurable ways. After the storm, Rachael Haot, New York City’s chief digi-
tal officer, said, “We estimate that collectively we served and informed ten
times as many individuals—hundreds of thousands of people—by embracing
an open strategy.”1
This story illustrates a growing global trend toward government
transparency and accountability. Governments are finding innovative ways
to liberate data from antiquated “vaults” in order to help agencies perform
their core services. Whether for disaster response or more routine func-
tions—such as managing education systems, health care, housing services,
business permits, safety inspections, parking resources, or even fighting
crime—governments are using data to improve quality of life.
Of course, this evolving landscape would not function well if the
system only included publishers. Data consumers, ranging from scientists
fighting climate change to parents who are curious about local graduation
rates, are also contributing new ideas and approaches to the open data
Ian Kalin was formerly an advisor to the White House on technology policy as a
Presidential Innovation Fellow. Today, Ian is the Director of Open Data for Socrata.

movement. Indeed, a growing and self-perpetuating ecosystem of innova-
tion has arisen from the exchange between data producers and consumers.
Policymakers must understand and advance this ecosystem so that they can
take advantage of their integral role and improve the quality of government
services.
Open Data is Not New
With deference to history, the concept of open data is not new. The practice
of pooling tax revenues to provide free access to public information is as old
as the first library in human civilization. But changes in scale have become
changes in kind. The cost of cloud computing has dropped precipitously;
the technical barriers to entry for interaction with digital data have soft-
ened; and general democratic dissatisfaction has driven governments to be
more “open.” This confluence of factors means that the open data market
is ripe with opportunity. The first global leader to recognize this potential
was then-presidential candidate Barack Obama.
In 2008, the Obama campaign launched an effort to achieve unprec-
edented levels of transparency, following years of systemic obfuscation by
the previous administration. As a candidate, Obama practiced transparency
through actions such as disclosing political donations. As president, Obama
signed the “Memorandum on Transparency and Open Government” on his
first day in office.2
This memo ushered in a wave of “Open Government”
initiatives that have fundamentally transformed the way in which the federal
government collaborates with its citizens and people throughout the world.
Other governments, both local and international, soon followed the
precedent set by the U.S. federal government. For example, San Francisco
Mayor Gavin Newsom issued an executive directive on open government in
2009.3
In 2011, the British government established its Government Digital
Service to deliver on a “digital by default” strategy.4
More recent global
milestones in this movement include the European Union directive on the
“re-use of public sector information”5
and the adoption of the Open Data
Charter by the “Group of Eight” (G8) in 2013.6
Non-governmental organiza-
tions and private companies have also implemented open data policies. This
series of landmark legislative acts and international agreements affirm that
open data is both rooted in history and constantly evolving.
The Citizen Experience
Why is open data valuable for ordinary people? This is what open data
would look like if you could see the invisible connections:
• A student with a smartphone can predict when the next public bus will
arrive because the city has published bus schedules in an accessible data
format.
• A parent can learn whether a restaurant has a history of unsafe conditions
because the state’s inspection reports are embedded on Yelp.
• A senior citizen can access free assistance in choosing a new medical insur-
ance plan because a community organizer is able to access federal pricing
schedules from a number of different online networks.

61Open Data Policy Improves Democracy
These examples demonstrate that open data by itself is inert; it cannot
cure and it has no taste. But when put in the hands of innovators, open data
can do amazing things. Good
data design is essential for im-
proving citizens’ interactions
with data. As described by Cyd
Harrell in Beyond Transparency:
Open Data and the Future of Civic
Innovation, a book published by
Code for America:
Public data is rarely usable
by ordinary citizens in the
form in which it is first released. The release is a crucial early step, but it is
only one step in the process of maximizing the usefulness of public resources
for the people who own them. […] The real-time transit apps that are such
a strong early example of useful open data do more [emphasis added] than
offer a raw feed of bus positions. The best of them allow riders to search
arrivals on multiple lines of their choosing and adjust their commute plans
accordingly.7
Yet the previous examples also demonstrate that people rarely inter-
act directly with open data. Does this mean that governments must master
the art of mobile app design? Fortunately for governments everywhere, the
answer is simple: You don’t have to do it all yourself.
Governments as Wholesale, Innovators as Retail
Generally, governments are not very good at extracting all of the value from
their own data. Of course, there are some notable exceptions. For example,
when I worked at the U.S. Department of Energy, I learned that the federal
government created and managed some of the best computer simulators
in the world. But most city and state governments do not have the human
resources to build and ship software with the same efficiency as technology
companies. Therefore, in order for governments to truly take advantage of
open data, they must publicly engage non-governmental innovators.
The best analogy for how governments should approach the multi-
stage process of building on the value of data is to think of data as a fuel.
Governments effectively mine the fuel through their day-to-day operations,
whether through processing permits, conducting inspections, or other
activities. Next, governments must open up their data as wholesale, raw
information. Finally, innovators take this data-as-fuel and “burn it” in their
digital engines, propelling their retail products and services forward.
A single dataset further illustrates the process: The city of Chicago
actively publishes the city’s crime data on a web-based portal.8
Although
there are lightweight tools to visualize the crime data on Chicago’s website,
companies like the Chicago Tribune9
and Kramer Concepts enhanced the
effectiveness of the data with retail products that people were eager to use,
such as Kramer Concepts’ iPhone app, “Chicago Crime Watch.”10
The annual
[...] open data by itself is inert; it
cannot cure and it has no taste.
But when put in the hands of
innovators, open data can do
amazing things.

62 SAIS Review WINTER–SPRING 2014
report of the city of Chicago notes: “Open data has enabled developers to
create a wealth of applications for Chicago’s residents. These applications
range from browsing 311 requests, to reminding residents of street clean-
ings, to informing citizens on the location and nuances of zoning laws.”11
Stories like this one from Chicago are not rare. In fact, a study from
the New York University GovLab—titled the “Open Data 500”—lists five
hundred U.S. organizations that rely on federal government open data,
including datasets available at Data.gov, to sell their products.12
The Brit-
ish government lists more than three hundred apps that leverage their
equivalently centralized open data portal.13
All of this activity represents the
dawn of the open data market. A study by the McKinsey Global Institute
titled “Unlocking Innovation and Performance with Liquid Information”
estimated that between $3 trillion and $5 trillion of economic value could
be generated annually across just a handful of sectors through the smarter
use of public-domain data.14
This is how governments can turn megabytes into jobs. By treating
data as a fuel rather than as an end unto itself, governments can serve as
trusted platforms for fur-
thering innovation, as op-
posed to passive bystanders
in the emerging data revolu-
tion.
This is how governments can turn
megabytes into jobs.
Figure 1. The Data Ecosystem

Government Innovator’s Toolkit
Governments have a variety of tools that help them serve as platforms for
innovation. Among the most grassroots-level tools are events called “hack-
athons.” During these gatherings, which usually take place over a weekend,
software developers compete to develop new apps that serve a pre-estab-
lished goal—for example, finding creative uses for recently released govern-
ment datasets. The power of hackathons to attract brilliant, civic-minded
young people is widely documented. However, these events are usually not
effective in creating sustainable products of real value, given their short
time span and informal nature. For that reason, governments—ranging from
small cities to the White House—also host “data jams” and “datapaloozas.”
These initiatives are derivatives of the hackathon model, but their mission
is to engage diverse business leaders, such as CEOs, instead of the young
coders that usually attend hackathons.
Many more variations of public-private partnerships have developed in
recent years, including entrepreneur-in-residence programs, startup week-
ends, weekly hack nights, and innovation challenges. Much like hackathons,
data jams, and datapaloozas, these initiatives favor fast results—for better
or worse. For example, entrepreneur-in-residence programs, like the Fuse
Corps or Presidential Innovation Fellows, seek to place executive innovators
in government offices where they can impact local and national challenges.
These entrepreneur-in-residence programs typically range from six to twelve
months in length. Consequently, even if these innovators succeed in pro-
moting tremendous change, what happens after they complete their tour of
service? As should be expected from initiatives that favor fast results, many
of the tools in the government innovator’s toolkit are short-lived.
However, one significant tool can produce more sustainable results:
policy creation. Increasingly, governments are reaching to their oldest tool
to ensure that open data initiatives outlive a single administration and pen-
etrate deeper into the core operations of government services. Four types of
policy levers are most commonly used with regard to open data: executive
orders, new laws, new regulations, and non-binding resolutions.
The Four Types of Policy Tools and Lessons from Implementation
Policy Tool #1: Executive Orders
President Obama’s January 2009 memorandum on transparency and open
government demonstrated the administration’s commitment to enhanc-
ing openness in the federal government. Yet the real policy magic was an
executive order, the Open Government Directive of December 2009, which
required all U.S. federal agencies to comply with a detailed set of time-bound
actions. The directive’s requirements were published on a GitHub reposi-
tory, a hosting platform for open source software development projects,
where even non-government workers could offer suggestions to improve the
detailed policy guidance. The “Project Open Data” public revision notes on
GitHub include suggestions to clarify the strategic objectives, to correct mis-

steps in the prescribed licensing terms for legal data reuse, and to improve
the mandatory metadata fields for enterprise data inventories.15
Unfortunately, the president’s executive order did not work as planned.
The technology system selected to power the latest version of Data.gov
proved more difficult to work with than anticipated. Additionally, the ac-
tion timelines were widely perceived as too aggressive, yet federal agencies
that were unable to meet the deadlines did not suffer any liabilities, which
contributed to an overall lack of accountability. As a result of these factors,
more than half of federal agencies failed to comply with the executive order
by the stated deadlines. Of the two major problems—technology and time-
lines—the latter was more directly related to policy failure.
The Open Government Directive serves as a memorable benchmark
for this type of policy tool. All effective open data policies must include
some sort of timeline in order to be effective. Yet for an organization as
large as the U.S. federal government, six months was not enough to publish
comprehensive data catalogs, to create enterprise data inventories, and to
devise new internal management systems.
Policy Tool #2: Non-Binding Resolutions
Short and sweet, non-binding resolutions are town squire-like pronounce-
ments without legal weight that are used to express a community’s interests
or concerns. A timeless example of a non-binding resolution is the recent
proclamation on “Open Data by Default” from Palo Alto, California.16
Palo
Alto is a city to watch for fans of civic innovation because it is endowed with
a wealth of entrepreneurial people and businesses, including HP, Palantir,
Facebook executives, and Stanford University. Palo Alto has made great
strides with open data in recent years, despite the city’s small size and com-
paratively small budget.
Non-binding resolutions are usually brief—about one page in length—
and include declarative sentences that begin with words like “whereas.”
Below are diverse examples of non-binding resolutions, which could serve
as introductory articles for more robust legislation in the future:
• WHEREAS, the government is committed to using technology to foster
open, transparent, and accessible government; and
• WHEREAS, the adoption of open data improves provision of services,
increases transparency and access to public information, and enhances
coordination and efficiencies among departments and partner organiza-
tions across the public, nonprofit, and private sectors; and
• WHEREAS, it should be easy to do business with the government. On-
line government interactions mean more convenient services for citizens
and businesses and online government interactions improve the cost-
effectiveness and accuracy of government operations; and
• WHEREAS, the protection of privacy, confidentiality, and security will be
maintained as a paramount priority while also advancing the government’s
transparency and accountability through open data; and
• WHEREAS, by publishing structured standardized data in machine-
readable formats, the government seeks to encourage the local software
community to develop software applications and tools to collect, organize,
and share public data in new and innovative ways; and

• WHEREAS, in commitment to the spirit of open government, the gov-
ernment will consider public data to be an asset of the citizens that paid
for it and therefore operate under an “open by default” philosophy; and
• WHEREAS, proactively disclosing government data is a fundamental
element of an open government program and is consistent with existing
laws; and
• WHEREAS, the use of open data exchange standards improves transpar-
ency, access to public information, and improved coordination and ef-
ficiencies among organizations across the public, non-profit, and private
sectors.
This lightweight policy tool has the benefit of lasting longer than a sin-
gle government official. At a functional level, these resolutions also empower
individuals to work as “change agents” within government whenever they
encounter the stereotypical (and all-too-common) bureaucrat who rejects
proposals simply because they are unfamiliar. With a copy of an open data
resolution in hand, a government entrepreneur possesses tangible proof of
a chief executive’s support for transparency. However, with respect for the
numerous resolutions that have come out of small towns, resolutions are
only as timeless as residents’ memories.
Policy Tool #3: Internal Regulations
The third type of policy tool includes memorandums, handbooks, director’s
instructions, official guides, semi-formal outlines like a “Letter from the Of-
fice of the Governor,” and other internal regulations. The New York State
Handbook on Open Data is an example of an internal regulations success
story. Originating from the Office of Information Technology Resources,
the handbook is a comprehensive, clear, and authoritative guide on how
open data should work. Also available on GitHub, the handbook resembles
the federal open government initiative. Yet New York State deserves credit
for the manner in which it developed the guidelines. The state government
established a system in which counties can procure and quickly deploy the
same system used at the state level. In comparison, federal agencies were
encouraged to establish their own systems in response to the Open Govern-
ment Directive, but were not offered the same off-the-shelf resources or tools
that New York State has provided to its local governments.
Another example of effective internal open data regulations comes
from the U.S. Department of Interior (DOI). The DOI responded to the
president’s executive order on open data by creating a hierarchy of manuals
and internal policies to prescribe how the program should function on a
day-to-day level within the operationally diverse bureaus and offices within
the DOI.17
There are tiers of regulations, including: a strategic manual at
the top level, a council charter that assigns responsibilities at the next level,
policies at the third level, and at the lowest level, detailed plans. Although
this hierarchy might seem unnecessarily burdensome, those familiar with
large bureaucracies will recognize that these types of guidelines are often
necessary for governments to operate effectively.

Policy Tool #4: Codified Laws
Legislation that becomes codified law is the 800-pound gorilla of open
data policy tools. The archetypal example comes from San Francisco, where
Mayor Gavin Newsom drafted executive orders that called for California to
become a leader in “cloud-first” technology procurement, transparency, and
open data. Significantly, the executive orders later turned into legislation,
which brought the power of stronger department mandates and a decent
budget.
Once enacted, laws are generally difficult to revise. However, in the case
of San Francisco, the city council has already revised the law two times in
four years. The current law reads like an all-star list of elements that should
be included in open data policies, including:
• A strategic, long-term vision for resident empowerment
• Clear definitions and straightforward ownership or core responsibilities
• A mandate to identify data that should be open and clear direction on
how to open the data
• Strong technical standards for data interoperability and the use of Ap-
plication Programming Interfaces (APIs)
• Rigorous protection of privacy, confidentiality, and security
• Minimal license restrictions
• Building on other existing laws
• A mandate to engage the public for feedback while also improving cus-
tomer service18
Despite the strengths of its law, San Francisco has fallen behind other major
cities in terms of extracting value from its data. A simple comparison to the
success of Chicago’s Smart Chicago Collaborative or New York City’s Big
Apps Challenges demonstrates how, for San Francisco, a great law is simply
not good enough.
In the United States, twenty cities, as well as nine counties and states,
have passed open data laws. There are another ten to fifteen jurisdictions
with legislators that have publicly indicated that they will propose similar
legislation.19
As with many other waves of inventions, these laws have certain
elements in common, yet there are also city-level experiments that have ab-
solutely nothing in common with laws in other cities. Of course, time will
tell which policies ultimately prove to be the best.
Do You Really Need an Open Data Policy?
With this roster of policy successes and failures, it is important to take a
step back and ask whether open data policy is essential. Whether a govern-
ment is large or small, it is reasonable to conclude that an official policy is
not strictly necessary for empowering people with the public data they need.
However, any attorney general will advise a mayor or governor that there is
some risk involved in data publishing efforts, and that policies are the best
way to mitigate this risk.
When well-executed, policies provide benefits that other innovation
tools—such as hackathons or entrepreneur-in-residence programs—cannot.

These benefits include additional funding to make sure the programs have
the requisite infrastructure, longevity beyond any individual leader’s term,
and a wake-up call to
spur slow-moving bu-
reaucracies. But the ab-
sence of policy should
not prevent a govern-
ment leader from taking
the first steps toward
a successful open data
program. The greatest
barrier to getting started
is identifying responsible government leaders who recognize they should
give resources back to the taxpayers who pay for them.
The Protection of Privacy
Every policy leader who works on open government issues should place
top priority on the protection of privacy, confidentiality, and security. This
article will not address the intricate legal definitions that determine which
data is private or whether recent U.S. federal government activities were
justified. Instead, in the context of open data policy, the responsible catch-
all is to reiterate in policy documents the paramount importance of privacy
protection, and to consult with your government agency’s chief attorney,
chief privacy officer, or other relevant leader to determine agency-specific
privacy protection measures.
For example, there is an evolving risk area known as the “mosaic ef-
fect,” which refers to a process in which direct disclosures of information
alone are not violations of privacy, but the ability to combine several differ-
ent datasets can lead to such violations. The existence of this risk became
well-known when Netflix released what they thought was “anonymized”
information about a set of users, when in reality, it was possible for hackers
to “de-anonymize” the profiles.20
In the case of the U.S. federal government,
its policy acknowledges this risk, places responsibility for fighting the risk
on individual government agencies, and provides tools to help them do so.
Open data is not fundamentally at odds with privacy protection. In
my experience, the exercise of conducting an inventory, which is essen-
tial to managing an
effective open data
program, always led
to revelations for
leadership about the
data their govern-
ment was collecting. These revelations ultimately resulted in the creation
of more secure digital infrastructures. It is easy for an apathetic stakeholder
to mention the word “privacy” in order to restrict innovation in open data.
The protection of privacy must be paramount, but given the advanced tech-
Open data is not fundamentally at odds
with privacy protection.
The greatest barrier to getting started
is identifying responsible government
leaders who recognize they should
give resources back to the taxpayers
who pay for them.

nological solutions for protecting sensitive information, which are used by
hundreds of government agencies all throughout the world, privacy does
not have to be an excuse to halt the entire train.
A Global Movement
The opportunities and risks of government data are not restricted to any
one country. As mentioned before, the G8 recently adopted an open data
charter and is launching new programs now. Additionally, the World Bank
has one of the best open data programs among the major non-governmental
organizations. Furthermore, after Typhoon Haiyan struck the Philippines in
2013, killing over six thousand people and inflicting unmeasurable damage,
open data programs organically developed to assist with the reconstruction
efforts by better targeting aid relief.21
Open data initiatives do not develop in isolated cases; instead, there is
a global highway. Applications that were built to simplify the complexity of
city building permits in Ireland are expanding to San Francisco, given the
similarity of data availability. From one perspective, as new data becomes
available, the data serves as a kind of business development plan for innova-
tors to sell their products into new markets. On a global scale, these patterns
demonstrate that no single organization can extract all of open data’s value.
This maxim is best illustrated by Bill Joy, the co-founder of Sun Microsys-
tems. He advises that the smartest people in the world will always work for
somebody else.22
Therefore, to unlock the maximum potential from open
data, organizations must host data in ways that maximize discoverability,
value creation, and access through interoperability. For these reasons, there
is a self-fulfilling process in which the global growth of open data will also
improve the quality of its impact.
Your Local Playbook
You’re a policy leader who does not yet have an open data program. How can
you get started today? As a first step, I advise you to think about opening
datasets for people to use. Of course, choosing the data to start with can be
a cumbersome task. Most cities begin with datasets related to crime, zoning,
permits and licenses, taxes and budget, or general site locations. Prioritizing
the datasets that support the political executive’s top priorities may also be
a good place to start. It is also a good exercise to begin with the question,
“Am I wasting time trying to find data within my government instead of
spending my time actually working with the data?”
For most organizations that launch open data programs, the first set
of tangible returns they receive is often due to major efficiency improve-
ments. Open data provides direct benefits to publishing organizations. It
not only benefits external stakeholders, but when leveraged internally, it can
also help organizations make data-driven decisions, deliver core programs
and services, and achieve mission goals more efficiently. As a final sugges-
tion, simply research what open data leaders like Chicago, New York, and
San Francisco are doing for inspiration.

We all know that established government bureaucracies are in need
of transformation. We have witnessed how innovative ideas can arise when
the right information is connected with the right people. Therefore, policy
leaders must commit to using open data tools to empower their residents
and to improve the quality of government services.
Notes
1
Rachel Haot, “Open Government Initiatives Helped New Yorkers Stay Connected During
Hurricane Sandy,” TechCrunch, January 11, 2013, http://techcrunch.com/2013/01/11/data-
and-digital-saved-lives-in-nyc-during-hurricane-sandy/ (accessed March 2, 2014).
2
President Barack Obama to Heads of Executive Departments and Agencies, “Memorandum
on Transparency and Open Government,” The White House, January 21, 2009, http://www.
whitehouse.gov/the_press_office/Transparency_and_Open_Government/ (accessed March
2, 2014).
3
Gavin Newsom, “San Francisco Government and Technology: How We’re Innovating,”
Mashable, October 21, 2009, http://mashable.com/2009/10/21/san-francisco-government/
(accessed March 17, 2014).
4
“Government Digital Service,” Wikipedia, http://en.wikipedia.org/wiki/Government_Digi-
tal_Service, (accessed March 17, 2014).
5
European Commission, European legislation on reuse of public sector information, http://
ec.europa.eu/digital-agenda/en/european-legislation-reuse-public-sector-information (ac-
cessed March 17, 2014).
6
G8, Open Data Charter and Technical Annex (London, United Kingdom: UK Cabinet Office,
17 June 2013), https://www.gov.uk/government/publications/open-data-charter/g8-open-
data-charter-and-technical-annex#contents (accessed March 2, 2014).
7
Cyd Harrell, “The Beginning of a Beautiful Friendship: Data and Design in Innovative
Citizen Experiences,” Beyond Transparency: Open Data and the Future of Civic Innovation, ed.
Brett Goldstein and Lauren Dyson (San Francisco, CA: Code for America Press, 2013), 151.
8
City of Chicago, “Crimes - 2001 to Present,” City of Chicago Data Portal, https://data.
cityofchicago.org/Public-Safety/Crimes-2001-to-present/ijzp-q8t2 (accessed March 23,
2014).
9
City of Chicago, “Crime App,” City of Chicago Digital Portal, http://digital.cityofchicago.
org/index.php/crime-app/ (accessed March 17, 2014).
10
Kramer Concepts, LLC, Chicago Crime Watch, version 1.2.2 (iTunes, 2013), iPhone and iPad
app, https://itunes.apple.com/us/app/chicago-crime-watch/id549140902.
11
City of Chicago, A report on the status of open data in Chicago and actions for 2014, http://report.
cityofchicago.org/open-data-2013/ (accessed March 17, 2014).
12
GovLab, “Welcome to the Open Data 500 Pre-Launch,” Open Data 500, http://www.
opendata500.com/ (accessed March 23, 2014).
13
Data.gov.uk, “Apps,” Data.gov.uk, http://data.gov.uk/apps (accessed March 23, 2014).
14
McKinsey Global Institute, “Open data: Unlocking innovation and performance with liq-
uid information,” http://www.mckinsey.com/insights/business_technology/open_data_un-
locking_innovation_and_performance_with_liquid_information, October 2013 (accessed
March 17, 2014).
15
Github, “Project Open Data,” Revision Notes, https://github.com/project-open-data/
project-open-data.github.io/commits/master (accessed March 20, 2014).
16
City of Palo Alto, “Proclamation Open [Data] by Default,” https://www.cityofpaloalto.org/
civicax/filebank/documents/38803, February 3, 2014 (accessed March 17, 2014).
17
Bernard Mazer, Department of Labor Memorandum, “Implementation of Department of
Interior’s Open Data Policy”, http://project-open-data.github.io/assets/docs/MEMO_RE_
IMPLEMENTATION_OF_DOI_OPEN_DATA_POLICY.pdf, September 16, 2013 (accessed
March 20, 2014).
18
American Legal Publishing Corporation, San Francisco Codes, http://www.amlegal.com/
library/ca/sfrancisco.shtml (accessed March 20, 2014).

19
Sunlight Foundation, “Open Data Policies at Work,” http://sunlightfoundation.com/
policy/opendatamap/ (accessed March 20, 2014).
20
Taylor Buley, “Netflix Settles Privacy Lawsuit, Cancels Prize Sequel,” Forbes, http://www.
forbes.com/sites/firewall/2010/03/12/netflix-settles-privacy-suit-cancels-netflix-prize-two-
sequel/, March, 12, 2010 (accessed March 17, 2014).
21
Zuzana Stanton, “OpenStreeMap volunteers map Typhoon Haiyan-affected areas to
support Philippines relief and recovery efforts,” World Bank Blog, https://blogs.worldbank.
org/eastasiapacific/openstreetmap-volunteers-map-typhoon-haiyan-affected-areas-support-
philippines-relief-and-recovery, November 15, 2013 (accessed March 17, 2014).
22
McKinsey Global Institute, “Unleashing Government’s ‘innovation mojo,’” http://www.
mckinsey.com/insights/public_sector/unleashing_governments_innovation_mojo_todd_
park_interview, October 2012 (accessed on March 23, 2014).

71SAIS Review vol. XXXIV no. 1 (Winter–Spring 2014)
Open Governments, Open Data:
A New Lever for Transparency,
Citizen Engagement, and
Economic Growth
Joel Gurin
The international open data movement is beginning to have an impact on government
policy, business strategy, and economic development. Roughly sixty countries in the Open
Government Partnership have committed to principles that include releasing government
data as open data—that is, free public data in forms that can be readily used. Hundreds of
businesses are using open data to create jobs and build economic value. Up to now, however,
most of this activity has taken place in developed countries, with the United States and United
Kingdom in the lead. The use of open data faces more obstacles in developing countries, but
has growing promise there, as well.
The open data movement, an international movement to make data from
governments and other sources available for public use, has evolved
alongside big data and holds equally powerful implications. Expanding
our public stores of open data will allow users, from small-scale mobile
app developer to large financial or healthcare institutions, to find new, in-
novative, and productive applications that generate economic and social
value. Examples of the impact of the open data movement can be found
the world over:
• In the United States, more than five hundred companies are using free
government open data to build businesses of all kinds and sizes—in
healthcare, financial services, energy, education, and other fields.1
The
federal government encourages this trend by holding “Datapaloozas”
that bring hundreds or even thousands of policymakers, subject matter
experts, business leaders, and technologists together to celebrate the value
of government data and identify new applications.2
• In the United Kingdom, the government launched the Open Data Insti-
tute with an initial grant of 10 million pounds. This institute serves as
an incubator for new data-driven companies working on such issues as
sustainability, housing, and corporate transparency.3
Joel Gurin is senior advisor at the Governance Lab at New York University, where he
directs the Open Data 500 study. He is also author of the book Open Data Now, and
founder and editor of OpenDataNow.com. He previously chaired the White House
Task Force on Smart Disclosure and is the former editorial director and executive vice
president of Consumer Reports.

• In Russia, a real estate lawyer founded RosPil, a website that publishes
government contracts online for the public to scrutinize for signs of cor-
ruption. Many suspicious contracts were annulled after being flagged in
RosPil. The site saved Russians more than a billion dollars in its first few
years.4
• In Ghana, a young entrepreneur created Farmerline, a company that
increases access to government data on markets, weather, and more for
farmers and their families. Farmerline provides information by voice via
cellphone for illiterate farmers.5
• In about sixty countries around the world, members of the Open Govern-
ment Partnership have committed to making government data available
to their citizens in simple, practical formats.6
Defining Open Data and Big Data
Open data is distinct from big data, which describes large datasets pro-
cessed using advanced analytic techniques. Big data on national economies,
demographics, energy sources, social and political trends, and more can in-
fluence strategic approaches
to foreign policy and inter-
national relations. However,
this data is often inaccessible
to entrepreneurs, advocates,
or ordinary citizens. For ex-
ample, national security or
corporate and financial datasets are closely guarded, and only available to
a limited number of people for review and analysis.
In comparison, open data is designed for public consumption, and it
is democratic by nature. It can be defined as “accessible, public data that
people, companies, and organizations can use to launch new ventures, ana-
lyze patterns and trends, make data-driven decisions, and solve complex
problems.”7
Where big data used within government agencies aids leaders
in the formulation of foreign policy, open data can boost economic develop-
ment, improve trust in government, and fight corruption.
Big data, open data, and the broader idea of open government are
related and overlapping concepts, as demonstrated in the Venn diagram
below. Big data does not have a precise definition, but describes data that is
voluminous, varied, and rapidly changing. Big data must be managed and
analyzed with sophisticated technology. Open data consists of big data,
as well as smaller-scale data, which also has important applications. For
example, the participatory budgeting movement, now active in 1,500 cities
around the world, uses open data on city budgets to empower communities
to direct how their tax dollars are spent. The budget data is not especially
complex, but opening it up for citizens to use can have important results.8
Similarly, the World Bank, which has started publishing information on
international development as open data, provides a lot of small-scale data
that is nonetheless significant. Statistics like the number of women in
parliament or patterns in life expectancy can be displayed in simple but
meaningful tables and graphs.9
[…]open data can boost economic
development, improve trust in
government, and fight corruption.

73OPEN GOVERNMENTS, OPEN DATA
Sources and Uses of Open Data
While national, regional, and local governments are the main sources of
open data, other sources exist, as well. Civil society and the private sector
are both major generators of open data. In developing countries, because
government data systems are often incomplete or unreliable, these sources
are especially important.
Social media is one significant source of open data. Public activity on
Twitter, review websites like Yelp, or other outlets for public opinion can be
analyzed to identify market trends, societal issues, or political momentum.
For example, V.S. Subrahmanian at the University of Maryland used data
collected from social media to predict the outcome of national elections in
India,10
while a team at Imperial College in London reviewed Twitter pat-
terns to study how different groups influenced and directed each other in
the 2011 London riots.11
Open data can also be collected from citizens themselves. This “crowd-
sourcing” approach is the basis for RosPil’s anti-corruption work in Russia,
as well as a similar site in India, IPaidABribe.com. The international organi-
zation Global Integrity asks ordinary people in countries around the world
to gather information to increase government transparency and account-
ability.12
Crowdsourcing can also be used to gather field data on healthcare
issues, the activities of multinational corporations, or the environment,
which becomes open data when made available for public use.
Finally, more and more research scientists—even some in the pharma-
ceutical industry—are starting to share their research findings as open data
in the name of scientific progress. Often in international collaborations,
Figure 1. Big Data, Open Data, and Open Government

researchers studying Alzheimer’s Disease, Parkinson’s Disease, multiple my-
eloma, and other serious diseases are now working together to share data in
the early stages of experiments. Many foundations that support this research
insist on data-sharing as a condition for grants: focused on the search for
cures, they see shared data as a way to accelerate progress.13
In a recent significant step, Johnson Johnson agreed to make its
clinical trial data publicly available by sharing it with a Yale research cen-
ter. This is an important shift from the common practice of releasing only
those clinical trials that suggest a drug is effective. By releasing all clinical
trial data, Johnson Johnson will enable other researchers to determine
whether the positive clinical data actually outweighs negative trial results.14
In short, open data can be derived from a range of nongovernmental
and governmental sources. The result is an emerging ecosystem that is ro-
bust and complex. Considered as a whole, open data has a clear potential to
be a major force in politics, culture, and economic growth throughout the
world. This potential is already evident in a number of areas.
Open Data as an Economic Driver
As the open data movement developed over the last decade, its goals ex-
panded. At first, advocates focused on government transparency and ac-
countability. Those are still essential goals. However, governments now also
recognize open data as a tool for economic development. In a world where
economies are increasingly data-driven, the free, public nature of open data
makes it a powerful business resource.
Open data’s economic value was first realized in the United States and
the United Kingdom, the two countries with the most extensive and devel-
oped national open data policies. The U.S. government’s Open Data Policy,
announced by executive order in May 2013, is designed to make federal data
“open by default,” meaning that agencies will release it in usable forms to
the public unless there is a compelling reason not to, such as privacy or
national security.15
In announcing the policy, President Obama recognized the economic
value of open data. He declared that “we’re making even more govern-
ment data available,
and we’re making it
easier for people to
find and to use.” He
then predicted that
open data is “going
to help launch more
startups. It’s going to
help launch more businesses… It’s going to help more entrepreneurs come
up with products and services that we haven’t even imagined yet.”16
While making data “open by default” is the right goal, it is idealistic
to expect it to happen any time soon. Simply declaring that data should be
open will not make it soi the data will not open itself. The United States has
thousands of different federal data systems, many housing incomplete or
The United States has thousands of
different federal data systems, many
housing incomplete or inaccurate data
managed with outdated technology.

75Open Governments, Open Data
inaccurate data managed with outdated technology. And in other countries,
open data resources may be far more limited. How can we determine whether
opening this data will be worth the time, effort, and expense?
The most widely quoted study of open data’s potential value was pub-
lished by McKinsey and Company in October 2013.17
That study estimated
that unlocking open data could be worth 3 to 5 trillion dollars per year
across seven sectors of the global economy: education, transportation, con-
sumer products, electricity, oil and gas, health care, and consumer finance.
Other studies have provided regional and country-level estimates. Open
data has an estimated annual worth between 3 and 9 billion dollars in the
United Kingdom.18
For the European Union, the value has been estimated
to be 30 to 140 billion euros.19
Just as important as knowing the economic value of open data is
understanding how that economic value is produced. The Open Data 500
study, managed by New York University’s Governance Lab (GovLab), ex-
amines five hundred companies based in the United States to assess how
government open data is used as a key business resource.20
Companies in
fifteen different sectors were identified as open data users. They range from
information providers for consumers to those that manage data for other
businesses—companies that ranged in size from large organizations like
Bloomberg and Dun and Bradstreet to those as small as two-person startups
that create mobile apps.
The Open Data 500 survey asked companies to list the government
agencies whose data they use, in the interest of developing a new process
to make key government datasets more accessible and usable. The GovLab
plans to use information from the Open Data 500 as a foundation for a
series of open data roundtables, which will bring government agencies to-
gether with the companies that use their data. In these structured dialogues,
data-holders and data-users will work together to identify high-priority
datasets, discuss the obstacles to their use, and begin to figure out ways to
overcome those obstacles.
The Open Data 500 study also asked companies to describe how they
earn revenue, and found that open data businesses use a variety of operating
models. Several use the same kinds of revenue models that any web-based
business could consider, including advertising, subscriptions, and referral
fees for sending customers to other businesses. Others, in contrast, provide
more high-tech data management or data analysis services. Many use a mix
of models.
Other researchers have studied the ways that open data builds business
value. Research by Deloitte’s Insights Team identified five “archetypes” for
open data companies:21
• Suppliers publish their data as open data, not as a direct revenue source
but to increase customer loyalty, enhance the company’s reputation, or
help meet other goals.
• Aggregators collect and analyze open data, selling their insights or profit-
ing in other ways.
• Developers “design, build, and sell web-based, tablet, or smartphone ap-
plications” using open data as a free resource.

• Enrichers are “typically large, established businesses” that use open data
to “enhance their existing products and services,” for example by using
demographic data to reach customers in new ways.
• Enablers charge other companies to facilitate the access and use of open
data.
Understanding business needs and revenue models will help gauge the
value of open data, but may still not yield meaningful national numbers.
Estimating what a country’s entire corpus of government data is worth is
difficult and, in fact, may not
be the right approach. Not all
government data is equally valu-
able. For example, 10 percent of
a government’s data may hold
90 percent of its economic value.
Determining which datasets in
which sectors are most valuable
to the public may be a better ap-
proach. It is also important to
identify how the public and private sectors can cooperate to achieve greater
access.
Government Approaches to Open Data
Up to now, U.S. government agencies have determined the release process
for their data with little input from the people and companies that use
it. This process, essentially a supply-side approach to data, was based on
the hope that simply providing access to data would encourage its usage.
However, left to make their own judgments, government agencies may not
make the best decisions. They may avoid releasing data that is more diffi-
cult to process for public consumption, or data with embarrassing flaws in
completeness and accuracy. This could mean the information most valued
by the public remains behind closed doors. Or, in contrast, too much un-
important data could be published instead, simply because it happens to be
easy to release. Daniel Kaufmann of Revenue Watch has called this problem
“zombie data”—data published without purpose or any real value.22
A better approach is what can be called “demand-driven data disclo-
sure”: a systematic way to ensure that open data is driven by the needs of
potential users by involving many stakeholders (see diagram below). The U.S.
Open Data Policy requires each agency to release an inventory of all its data-
sets and designate a point of contact for public inquiries—both good steps to
connect data providers to data users. But agencies could do more to gather
input from data users directly. The GovLab’s open data roundtables are
designed to facilitate that process. Agencies could also create online forums
to discuss specific issues or establish public advisory groups on open data.
Other countries already engage with open data users in innovative
ways. In the United Kingdom, the government formed an Open Data User
Group in 2013 to provide ongoing feedback on its open data program.23
The
Not all government data is
equally valuable […] 10
percent of a government’s data
may hold 90 percent of its
economic value.

77OPEN GOVERNMENTS, OPEN DATA
French government asked a variety of open data activists to advise them on
how to structure Data.gouv.fr, the central government hub for open data.24
In Mexico, the government used a website it calls a “datatron” to gather
input on the open data that was most sought after by the public.25
As more governments launch open data programs, NGOs are develop-
ing methods to measure the effectiveness of these initiatives. The Open Data
Barometer, recently published by the Open Data Institute and the World
Wide Web Foundation, measures the “openness” of government data in
seventy-seven countries.26
A comparable assessment of seventy countries by
the Open Knowledge Foundation, called the Open Data Index, produced
generally similar country evaluations.27
Wealthier countries generally score higher on openness than develop-
ing countries, with the United Kingdom and the United States as the world
leaders. A recent analysis of the Open Knowledge Foundation’s index by the
Oxford Internet Institute found that national wealth accounts for about
a third of the variation in countries’ scores.28
To some extent, the reasons
may be technological: technology infrastructure is expensive and a country
without widespread internet access may find it difficult to take advantage
of open data.
In many countries, though, the problem is more complex. Open data
can challenge the established order in ways unfavorable to the incumbent
government. The founder of RosPil, Aleksei Navalny, faces ongoing legal
difficulties with the Russian government, including imprisonment on
charges of conspiring to steal from a state-owned lumber company.29
Many
governments will find open data a threat to their power. As the Washington
Post observed in reporting on the Oxford Internet Institute study mentioned
above, “If you’ve got a corrupt government, transparency’s probably not your
thing. If you’ve got high unemployment, deep poverty or a serious pollution
problem, you’re probably not inclined to hand over information about those
problems to the people who live with them.”30
Figure 2. Multi-stakeholder Engagement: “Demand-Driven Data Disclosure”

How to Make Data Work for Development
Put these issues aside for a minute and imagine that a government wants
to use open data in the cause of transparency, accountability, good govern-
ment, and economic development. Even in the best political environment,
putting open data to work in the developing world is difficult. Limited
resources, poor data collection, and political and legal issues all interfere
with open data’s effective usage.
A recent blog post by Prasanna Lal Das, a leader in open data initia-
tives at the World Bank, summarized the obstacles. First, many countries
lack key data on important issues. They also face logistical challenges in
releasing data, including data irregularity, technical gaps, and cost. In ad-
dition, Lal Das writes, “The policy/regulation environment around open
data in developing countries is patchy…There are human capital gaps in
the sector [and]… access to finance for emerging smart data firms is one of
the frontline issues.”31
It is a chicken and egg problem. National governments may have little
incentive to release data until they see the economic benefits, but compa-
nies cannot demonstrate those benefits until they have access to open data.
But some developing countries
are beginning to develop pro-
grams to release open data,
either because they have seen
its promise in the developed
world, or because they want
to increase government trans-
parency and credibility. The
World Bank is helping those
countries implement open
data policies with its Open
Data Readiness Assessment. The assessment helps national governments to
evaluate the state of their data resources, gaps in their expertise, and actions
required to release data effectively.32
Where governments have been slow to gather and release their own
open data, private data companies have begun to step in. As those companies
gather data on developing countries, the countries’ governments need to de-
cide whether to acknowledge and publish it. Their incentive is to gain credit
for transparency, with foreign investors and their own citizens, by publishing
the new data. Even though the data may include embarrassing information
on poor health, poverty, or a country’s other problems, acknowledging the
problems and promoting open data may be politically wise when a private
company starts to bring these issues to light.
One data-gathering company is Metabiota, a San Francisco-based
company that collects information on infectious disease internationally, in-
cluding in the United States. Metabiota merges data from the World Health
Organization and other sources with data from blood samples collected by
workers the company trains. The company then sells reports based on the
National governments may have
little incentive to release data
until they see the economic
benefits, but companies cannot
demonstrate those benefits until
they have access to open data.

data to U.S. government agencies, such as the Department of Defense, and
others that have an interest in knowing about infections disease risks.33
Based in New York, another innovative startup is Ulula, which means
“transparency” in Chichewe, a Southern African language. Ulula gathers
data about companies in the extractive industries in about a dozen different
countries. Using mobile technology, local citizens can anonymously report
information on company operations. The clients for this crowdsourced
open data include investors and companies themselves, who use Ulula to
monitor their operations and improve relationships with the communities
in which they operate.34
Applying open data in any country requires a deep knowledge of what
kind of business will best serve that country’s citizens. Take the case of
weather and agricultural data, and the ways it can be used to serve farmers
in different parts of the world.
First, consider a company that has become the best-known example of
a successful open data business. The Climate Corporation formed in 2006
with the goal of developing a better form of weather insurance: The com-
pany planned to analyze the risks posed by bad weather precisely enough
to offer better insurance benefits at reasonable premiums. Mathematicians
and data analysts from organizations like Google and Stanford were hired
to analyze vast amounts of data from U.S. government sources to achieve
the company’s goal.
As a result, the Climate Corporation developed such an extensive
analysis that it can now offer more than weather insurance: It can provide
American farmers precise guidance on crops and planting cycles to help
them adapt to climate change. In the fall of 2013, Monsanto bought the
Climate Corporation for just under one billion dollars. Their success is a
prominent example of how free public data can be turned into financial
gain.35
The Climate Corporation is a success in America, but it would not
work as well in Ghana, where more than half of the population relies on
farming for their livelihood. With the exception of a few large agribusinesses,
most farmers do not want highly sophisticated data. Instead, they seek
public data on weather, markets, and other relevant factors. The Ghanaian
farmer’s problem is that even this basic information is inaccessible and
many farmers are not literate enough to use what is available. Agricultural
extension officers can help farmers in theory, but there are major practical
limitations to their impact: There are about a thousand farmers for every
extension officer assigned to help them.
On the other hand, open data can be used to help these farmers in
ways that work in their country. Mobile phone penetration is high in Ghana
and there is a growing cadre of young tech-savvy entrepreneurs. One of these
innovators noticed the information gap and created Farmerline to deliver
farm-related data in a simplified but effective format. Using an applica-
tion available to anyone with a cell phone, Farmerline delivers information
and allows farmers to ask questions through voice as well as text in any of
Ghana’s many languages.36

Conclusion
The drive to open data is a global movement that will unfold in different
ways around the world. In developed countries, the economic value is now
clear enough to give a rationale for releasing open data, and the United
States’ and United Kingdom’s national policies are setting a model that
other countries have started to follow. In developing countries, open data
has been a lower priority. There isn’t yet strong evidence that open data can
quickly create great business value in developing economies today.
Still, there are clear reasons for developing countries to adopt open
data policies sooner rather than later. In countries with a history of corrup-
tion, open data can help a government establish transparency, credibility,
and trust. International investors will increasingly demand open data to
help them decide which countries are a good risk for them. And for young,
tech-savvy entrepreneurs, open data in any country can be a free resource
to develop new products and services at a low cost.
However the future plays out, open data is almost certain to be a sig-
nificant global force. It will help shape economic development, international
investment decisions, and international relations. Like big data, it will have
a profound effect on how we understand the world’s economic, political,
and demographic landscapes. But unlike most big data, open data will make
that understanding accessible to all, with far-reaching results.
Notes
1
http://www.OpenData500.com
2
Lauren Caldwell, “Open Government Data Fuels Private Sector Innovation: Data Jams,
Hackathons, and Datapaloozas,” SAIS Review (blog), March 12, 2014, http://saisreview.
org/2014/03/12/open-government-data-fuels-private-sector-innovation-data-jams-hack-
athons-and-datapaloozas/.
3
http://theodi.org/
4
Paul Healy, Karthik Ramanna, and Matthew Shaffer, “Rospil.Info,” HBS No. 112-033
(Boston: Harvard Business School Publishing, 2012), http://www.hbs.edu/faculty/Pages/
item.aspx?num=41530.
5
http://farmerline.org/
6
http://www.opengovpartnership.org/
7
Joel Gurin, Open Data Now, (New York: McGraw-Hill, 2014), 9.
8
For more on the Participatory Budgeting Project, see: http://www.participatorybudgeting.
org/about-participatory-budgeting/what-is-pb/.
9
See http://data.worldbank.org/.
10
V.S. Subrahmanian, “Forecasting the Spread of Sentiments and Emotion in Social Media.”
presentation, Sentiment Analysis Symposium from Alta Plana Corporation, New York, NY,
March 6, 2014.
11
Mariano Beguerisse-Diaz et al., “Communities, roles, and informational organigrams in
directed networks: the Twitter network of the UK riots,” arXiv:1311.6785v1 [physics.soc-ph],
November 26, 2013, http://arxiv.org/abs/1311.6785.
12
http://www.globalintegrity.org/.
13
See Gurin, Open Data Now, Chapter 10.
14
“Johnson Johnson announces clinical trial data sharing agreement with Yale School of
Medicine,” Johnson Johnson press release, January 31, 2014, https://www.jnj.com/news/
all/johnson-and-johnson-announces-clinical-trial-data-sharing-agreement-with-yale-school-
of-medicine.

15
Barack Obama, “Making Open and Machine-Readable the New Default for Government
Information,” Executive Order 13642, May 9, 2013, http://www.whitehouse.gov/the-press-
office/2013/05/09/executive-order-making-open-and-machine-readable-new-default-
government-.
16
Barack Obama, “President Obama Speaks on Innovation and Manufacturing,” remarks,
Applied Materials, Inc., Austin, TX, May 9, 2013, http://www.whitehouse.gov/photos-and-
video/video/2013/05/09/president-obama-speaks-innovation-and-manufacturing.
17
James Manyika et al., “Open Data: Unlocking Innovation and Performance With Liquid
Information,” McKinsey Company, October 2013, http://www.mckinsey.com/insights/
business_technology/open_data_unlocking_innovation_and_performance_with_liquid_in-
formation.
18
UK Department for Business Innovation and Skills, Market Assessment of Public Sector
Information, by Deloitte, London, UK: URN BIS/13/743, May 2013, https://www.gov.uk/
government/uploads/system/uploads/attachment_data/file/198905/bis-13-743-market-
assessment-of-public-sector-information.pdf.
19
European Commission, Review of Recent Studies on PSI [Public Sector Information Re-Use and
Recent Market Developments, by Graham Vickery, Information Economics, Paris, France: 2011,
http://www.umic.pt/images/stories/publicacoes6/psi_final_version_formatted-1.pdf.
20
The Open Data 500 study has both civic and economic goals, including: provide a basis
for assessing the economic value of government open data; encourage the development of
new open data companies; and, foster a dialogue between government and business on how
government data can be made more useful. The research model employed is applicable to
other countries, and we are investigating opportunities to replicate the study around the
world. For additional information, see http://www.opendata500.com.
21
Deloitte LLP and the Open Data Institute, “Open Growth: Stimulating the Demand for
Open Data in the UK,” Deloitte Analytics Briefing Note, 2012, 3. http://www.deloitte.com/
assets/Dcom-UnitedKingdom/Local%20Assets/Documents/Market%20insights/Deloitte%20
Analytics/uk-da-open-growth.pdf.
22
Daniel Kaufmann, “Zombie Data: Has Open Data Failed to Live Up to Its Hype?” round-
table discussion, from Thomson Reuters, London, UK, October 25, 2013, http://www.trust.
org/spotlight/Zombie-data-has-open-data-failed-to-live-up-to-its-hype/.
23
https://www.gov.uk/government/policy-advisory-groups/open-data-user-group
24
Romaine Dillet, “How France’s Open Data Team is Modernizing the French Government
Through Data,” TechCrunch, February 12, 2014, http://techcrunch.com/2014/02/12/how-
frances-open-data-team-is-modernizing-the-french-government-through-data/.
25
http://datos.gob.mx/.
26
“Open Data Barometer,” Open Data Research Network, http://www.opendataresearch.
org/project/2013/odb.
27
“Open Data Index,” Open Knowledge Foundation, https://index.okfn.org/.
28
“Open Data Index,” Oxford Internet Institute, http://geography.oii.ox.ac.uk/#open-data-
index.
29
Greg Brown, “Crowdsourcing to Fight Corruption: Aleksei Navalny and the RosPil
Experiment,” Sunlight Foundation (blog), August 6, 2013, https://sunlightfoundation.
com/blog/2013/08/06/crowdsourcing-to-fight-corruption-aleksei-navalny-and-the-rospil-
experiment/.
30
Emily Badger, “Why the Wealthiest Countries Are Also the Most Open With Their Data,”
Wonkblog (blog), Washington Post, March 14, 2014, http://www.washingtonpost.com/blogs/
wonkblog/wp/2014/03/14/why-the-wealthiest-countries-are-also-the-most-open-with-their-
data/.
31
Prasanna Lal Das and Alla Morrison, “From Open Data to Development Impact – the
Crucial Role of the Private Sector,” Open Data: The World Bank Data Blog (blog), January 8,
2014, http://blogs.worldbank.org/opendata/open-data-development-impact-crucial-role-
private-sector.
32
“Readiness Assessment Tool,” The World Bank, http://data.worldbank.org/about/open-
government-data-toolkit/readiness-assessment-tool.
33
http://metabiota.com/

34
http://ulula.com/
35
Gurin, Open Data Now, 27-31.
36
Laura Manley (project manager of the Open Data 500 and former Vittana Fellow in Ghana)
in discussion with the author, March 2014.

83Getting the Technological Toolkits RightSAIS Review vol. XXXIV no. 1 (Winter–Spring 2014)
Of Note
Open Governments, Open Data:
Getting the Technological Toolkits Right
Mike Nguyen
Around the world, governments and local authorities are beginning to
release their data for public consumption and reuse. As Joel Gurin writes
in his article, “Open Governments, Open Data,” open data promises to be
a “global force” that has the potential “to shape economic development,
international investment decisions, and international relations.” However,
as anyone who has worked with data knows, simply releasing or having
open data is not enough. The challenge comes in formatting the data to be
workable and computable, which allows the data to deliver insights. With
constant advances in computing and mobile technology, open data offers
even more promise in the hands of average end-users when combined with
the right tools.
On one end of the spectrum, some authorities have made great strides
in pushing for open data, but have developed limited means to visualize
or drive home the impact of their data. For example, NGOs like China’s
Institute of Public and Envi-
ronmental Affairs, which devel-
oped the China Pollution Map
Database, have pushed Chinese
government departments at all
levels and regions throughout
China to release 97,000 envi-
ronmental supervision records
of polluting enterprises in Chi-
na. However, a perusal of the
website demonstrates its limitations: visualization is only possible within
the website’s limited platform, and individual records can only be searched
and accessed on a one-by-one basis. These limitations become frustrating
when trying to gather aggregate data to tell a story about a particular region
or company.
Likewise, IPaidABribe.com, an Indian NGO that collects corruption
data, provides aggregated, summary-level data on corruption nationally and
Mike Nguyen is a second-year M.A. candidate at the Johns Hopkins University Paul H.
Nitze School of Advanced International Studies (SAIS) concentrating in China Studies.
He is an Assistant Editor for The SAIS Review.
...some authorities have made
great strides in pushing for open
data, but have developed limited
means to visualize or drive home
the impact of their data.

provincially, but offers only anecdotal evidence on the individual-level. The
end-user cannot plot the anecdotal evidence of corruption on a map, link
the data to specific offices or individuals, or research specific ministries or
individual public officials to monitor trends or patterns of corruption at
the micro-level. IPaidABribe.com has a wealth of information on corruption,
with over 25,000 reports covering 659 cities throughout India and totaling
over 71 crore rupees ($11.8 million) in bribes. Designing a tool or equipping
users with methods to compute or utilize the data more effectively would
make IPaidABribe.com’s platform even more powerful and impactful.
On the other end of the spectrum are entities such as the United States
Census Bureau, which balances the limitations of its “in-house” interactive
data visualization tools with the provision of raw, granular data that links to
geographic information system (GIS) shapefiles and other survey processing
software. Census data is a powerful resource when it is expertly visualized via
GIS, offering clear insights from colored overlays on maps. However, for the
average end-user, the process of visualizing this data—joining shapefiles on
GIS, carefully pruning immense datasets, learning functions such as pivot
tables, or software such as Microsoft Excel—requires a tremendous time
commitment and technical expertise not readily accessible. These tools are
useful for researchers, but for the average citizen, there is almost no desire
to manipulate complex data.
Some authorities have tried for a middle ground approach to data
availability and accessibility. For example, the World Bank’s World Databank
allows for the creation of simple reports directly on their website, with op-
tions to quickly access, download, and interact with the raw data (in various
formats) within minutes. This approach strikes a healthy balance between
the average end-user who may wish to generate simple charts and trends,
and the needs of researchers, who may wish to run more complicated regres-
sions using the detailed datasets. Promoters of open data should strive for
this middle ground, providing both ease of use for the average end-user and
more meaningful data for researchers.
However, the true potential of open data reaches beyond visualization
or accessibility. It rests with execution—when real problems are made solv-
able for real people through the use of open data. Imagine a world where
entire domains of
knowledge, from
arts to engineering
to medicine, are
connected on the
cloud, curated, and
can be accessed on a
mobile device. Imag-
ine an illiterate,
poor farmer in sub-
Saharan Africa switching on his smartphone, studying Ikea-like diagrams
of how to repair motorbikes, and then starting his own repair business
with the microfinance application he has accessed. Imagine a child teaching
The true potential of open data reaches
beyond visualization or accessibility.
It rests with execution—when real
problems are made solvable for real
people through the use of open data.

85Getting the Technological Toolkits Right
herself the skills to play an instrument, test for HIV, or operate a welding
machine—all from her smartphone and other mobile devices that tap into
curated domains of knowledge. Now, imagine that she can teach herself to
program any number of these devices (or others yet to be connected) to fill
knowledge or skills gaps in her community.
This is exactly the vision that Stephen Wolfram, inventor of Math-
ematica, has behind his highly ambitious new computational paradigm and
programming language.1
More promising than Google, which seeks to un-
derstand objects and things and their relationships so it can provide search
results, Wolfram Alpha wants to make all of the world’s data and domains
of knowledge computable. Wolfram Alpha has already generated thousands
of accessible datasets that are curated and updated in real-time on a variety
of disciplines, such as weather patterns, socioeconomic indicators, health
and medicine, engineering, transportation, and food and nutrition.
The project’s aim is to make these domains of knowledge accessible
and referenced, intuitively computable via the Wolfram programming lan-
guage, scalable to web applications, or embedded within mobile and con-
nected devices that run on credit card-sized, single-board computer chips
such as the Raspberry Pi. Put simply, the Wolfram programming language
(which has 11,000 pages of documentation) hopes to make the world’s data
and knowledge more accessible and computable. Then, it hopes to make
programs—and eventually objects—smart. Imagine the incredible potential
these smart objects would have if they could access all the world’s knowl-
edge, as well as the incredible amounts of open data hosted by governments,
corporations, and other organizations.
Governments and authorities that wish to join this mobile computing
revolution must do more than make open data available. They must properly
interface open data with the right devices, platforms, and programming
languages so that the knowledge hidden in open data can be intuitively pre-
sented, utilized, and implemented by end-users. Additionally, corporations,
non-governmental organizations, and other authorities that curate immense
stores of useful data should be encouraged to be more forthcoming and
overcome the privacy and other challenges inherent in releasing this data.
While there are technological limitations, realizing open data’s full
potential is not farfetched. According to Sony Ericsson’s 2013 “Mobility
Report,” by 2019 LTE will cover more than 65 percent of the world’s popu-
lation, and $20 smartphones and $35 tablets will soon be a global reality.2
When coupled with cloud computing capabilities and inexpensive mobile
devices, the revolution in open data is a lightning rod in a perfect storm of
technology, further democratizing and revolutionizing the way we interact
with, access, and make use of data. Whether governments can take advantage
of this revolution will depend on more than just making open data avail-
able. Instead, it depends on how innovative and committed authorities are
to ensuring that open data interfaces with the mobile, cloud, and technical
revolutions simultaneously occurring.

Notes
1
“Stephen Wolfram’s Introduction to the Wolfram Language,” [n.d.], video clip, YouTube,
https://www.youtube.com/watch?v=_P9HqHVPeik (accessed April 12, 2014).
2
Ericsson Mobility Report: On the Pulse of the Networked Society, November 2013, http://
www.ericsson.com/res/docs/2013/ericsson-mobility-report-november-2013.pdf

87Bridging the Innovation-Policy GapSAIS Review vol. XXXIV no. 1 (Winter–Spring 2014)
Bridging the Innovation-Policy
Gap
Kord Davis
The speed of technological progress has generated a gap in the dialogue between innovators
and policymakers, posing a danger for the future of both. As questions over data ethics
protocols gain prevalence, it will become critical to construct collaborative spaces where
the public and private sector can engage in mutually beneficial conversations and share
knowledge to design effective, balanced, and useful policy.
The pace of technological innovation far exceeds that of legislation.
Policymakers are challenged to keep up with the latest developments in
features, functionality, and business models. Meanwhile, technologists are
innovating on a daily basis, often ignoring the potential impact that future
legislation or policy might have on their endeavors.
This is an untenable state of affairs. The growing gap between rev-
enue-generating innovations in the business and technology worlds and
the policies and
regulations that
govern commerce
represents a mas-
sive risk to econo-
mies around the
world. It is truly
the Wild West of
data innovation.
What is legal and
ethical today might be constrained in unimaginable ways tomorrow unless
policymakers and technologists engage in a more productive dialogue.
Policymakers and Technologists: Challenges of Collaboration
There are many challenges to creating a space for generating dialogue be-
tween policymakers and technologists. First among these challenges are
borders. Political, cultural, social, and technological boundaries are shifting
rapidly, and each has historically proven to be difficult terrain on which to
map appropriate and useful policy. Data theft crimes have demonstrated
Kord Davis is a strategist and facilitator who helps organizations at the intersection
of people and data. He is author of Ethics of Big Data (O’Reilly Media 2012), one of the
first works to explore a framework for making decisions about data based in individual
personal and shared organizational values.
What is legal and ethical today might
be constrained in unimaginable ways
tomorrow unless policymakers and
technologists engage in a more productive
dialogue.

the incredible complexities in applying legal policy across multiple cross-
border jurisdictions.1
Similarly, the process of policy development poses significant challenges.
Special interest lobbyists, committees, debates, and legal procedures are in-
herently time-consuming influences on the development and enforcement
of good policy. The sheer amount of time and effort it takes to generate
consensus and to implement policy or legislation is one of the most stub-
born barriers to collaboration.
Knowledge gaps are a major obstacle, as well. Technology evolves at such
a rapid pace that keeping policymakers up to date on the latest innovation
is a full-time job. Worse, our collective understanding of the ethical issues
at play in the big data landscape is often too narrow or too broad to fully
inform our actions. This knowledge gap is most visible on the topic of in-
dividual privacy. Digital privacy does not exist outside the context of the
particular technological system in which it is defined. And it is inherently
bound to the technological architecture that defines individual identity.
In order to develop good policy, policymakers must understand the
complexities of how identity and privacy are technologically instantiated
in any given system. Furthermore, they must understand how identity and
privacy are managed generally across many different systems. This is com-
plex knowledge, yet it is essential to generate balanced and useful policy.
There is also a culture clash between technology and policy development.
One way to characterize this clash is that technologists are interested in
solving problems, while policymakers are interested in regulating solutions
(or the methods of solving problems). For technologists, legislation and
policy represent boundaries or constraints and feel like four-letter words.
For policy makers, unregulated innovation represents the risk of unintended
consequences. If we are to close the gap between innovation and legislation,
we must improve our understanding of this culture clash, as well as the driv-
ing forces that govern its shape and size, which include process, cultural,
and social components.
Among these cultural and social components are conflicts of interest be-
tween organizations and individuals. Businesses (and supporting technolo-
gies) are inherently interested in innovation and profit, while individuals and
organizations have a vested interest in their digital identity, privacy, prop-
erty, and reputations. Good, fair, and balanced policy provides protections
against business practices that are damaging to individuals, organizations,
and markets, while encouraging ongoing innovation in pursuit of profit.
Without a more comprehensive understanding of the technological
forces at play in these rapidly developing and innovative business practices
and technical solutions, policy will continue to be outdated and toothless—
or, worse, it will become a disruptive force that fails to do its intended job.
Technology and Social Values: The Cases of Robert Bork and Netflix
Technology will continue to evolve in ways and with outcomes that we
cannot fully anticipate. These innovations can have a deep influence that
impacts our social values and our body of law.

89Bridging the Innovation-Policy Gap
For example, in 1987, Robert Bork’s nomination to the Supreme Court
was hotly contested. The opposition used Bork’s video rental history as evi-
dence against his confirmation, and the resulting controversy led to federal
legislation enacted by Congress in 1988. Called the Video Privacy Protection
Act (VPPA), the VPPA made it illegal for any videotape rental service provider
to disclose rental history information outside the ordinary course of busi-
ness, and violators were liable for damages up to $2,500.
In September 2011, Netflix posted a public appeal, requesting that
customers contact their Congressional representatives to amend the VPPA.
The amendment would allow Netflix users to share their viewing history
with friends on Facebook.22
It had been a mere twenty-three years between
the passing of the VPPA, when Congress took action to protect consumers
from having their purchase history used to judge their professional capa-
bilities, and 2011, when a major American business asked its customers to
allow that very same information to be shared legally.
What would a federal appointee’s nomination look like today if his or
her Netflix queue were part of the standard confirmation process? Should
there be a special section in the process for “watches a lot of conspiracy
movies”? How many is too
many?
Without big data, no
business would be in a po-
sition to offer such a ca-
pability or to make such a
request. In the twenty-three
years between the VPAA and
the Netflix request, big data has influenced the mechanisms with which
we share information, as well as our desires and preferences about what is
important enough to share. The influence of big data on our daily lives has
motivated a call for more explicit discussion about the ethical use of big
data technologies.
Four Aspects of Data Ethics
Technology and policy do not exist independently. Especially in the realm of
big data, the complexities of technical infrastructure make many aspects of
data ethics interdependent in complicated ways. To more fully understand
these dependencies, we must first understand the key elements of big data’s
forcing function and their ethical implications. Four aspects of data ethics
are of particular importance in assessing the obstacles to developing col-
laborative policy: identity, privacy, ownership, and reputation.
First, digital identity is concerned with the relationship between our
offline identity and our online identity. It is, by definition, what an organi-
zation knows (or believes it knows) about you. It is also always necessarily
incomplete. While many people are working hard to close this gap, true 1:1
parity between our physical presence and our virtual presence is a long way
off. There will be gaps for a long time to come—and many argue the gap
will never close.
What would a federal appointee’s
nomination look like today if his
or her Netflix queue were part of
the standard confirmation process?

Second, privacy is concerned with the access, control, and usage of
personal data. One of the greatest challenges here is that we often have
different norms and definitions of what constitutes “privacy.” Today, you
might not care that Congress knows what movies you watch. In 1987,
Robert Bork cared very much. Helen Nissenbaum argues that privacy is
context dependent and that the “flow” or transmission of personal data
occurs within informational norms. Changing one of the norms results in
unexpected usage of personal data—which often feels like a breach of trust
and a violation of privacy.33
Third, ownership is concerned with identifying who generates and owns
data; what rights can transferred, sold, bought, shared, or stored (and for
how long); and the obligations of people who generate and use data. This is
a massive policy challenge. Entire new markets are being developed around
“data brokers” who actively seek, acquire, buy, and sell personal data for a
myriad of uses.44
Finally, reputation is concerned with the process of determining
whether data is trustworthy, and how we can use data to make value-based
judgments about individuals and organizations. Big data exponentially
increases the amount of information available and the number of ways we
can interact with it. This phenomenon increases the complexity of manag-
ing how individuals and organizations are perceived and judged. Organiza-
tions are increasingly using big data to make reputation-based assessments
on critical topics including credit-worthiness, insurance risk, employment
eligibility, and health status.
We are often myopic about these four elements, frequently focusing
on one to the exclusion of others. Good privacy policy cannot be devel-
oped without an understanding of the technical infrastructure and data
handling practices that define an individual’s identity in a given system.
Similarly, what an organization does with that data can directly impact an
individual’s or organization’s reputation. Furthermore, who benefits finan-
cially or otherwise from data ownership is an economic question of great
importance. There is an Internet meme that says, “If you’re not paying for
a service, you’re not the customer, you’re the product.” This concept turns
the question of data ownership on its head and fundamentally alters the
notion of property.55
Traditionally, an individual either owned or “sold” the results of their
labor (their output) to an employer or a customer. They had some influence
on who bought the results of their labor, and they typically received fair
market value for that output in the form of revenue or salary. You don’t
need to be an historian or an economist to understand that circumstances
are different today, and these four aspects of data ethics raise more ques-
tions than they answer.
When Facebook sells its users’ web browsing history to targeted mar-
keting agencies, this “output” has been explicitly given to Facebook through
its terms of service agreement. Users lose most of the control over what
happens to the data, how much it is worth, and who buys or accesses it. In
return for this loss of control, we can post funny pictures of cats, notes to
each other, and links to other places on the web.

91Bridging the Innovation-Policy Gap
A balanced perspective is essential to bridging the innovation-policy
gap. That balance is informed by an understanding of the complex inter-
actions among identity, privacy, ownership, and reputation. Policymakers
need help from technologists to ensure that policy is balanced, fair, timely,
and useful. Technologists need to engage policymakers in ongoing and
collaborative dialogue to help guide the outcomes. Bridging this gap is in
everyone’s best interest.
Overcoming Challenges and Finding Mutual Benefits
How are we to respond to this increasing gap between technological innova-
tion and the development of policy and legislation?
The first option available is to level the knowledge base. Policymakers
and technologists need to create a common, shared understanding of each
other’s perspectives. Each discipline is complex, nuanced, and takes years to
learn how to do well. Furthermore, these disciplines are becoming increas-
ingly intertwined.
Without policy guidelines, we face an uncertain future for technologi-
cal innovation. Without technical knowledge to inform those guidelines, we
may unnecessarily constrain
competitive markets and di-
minish the opportunity to
benefit from innovations.
Policymakers and technolo-
gists must learn to collabo-
rate more closely to ensure
that we find a good balance
between risk and innovation.
Another helpful ap-
proach is to create a space for
collaboration between poli-
cymakers and technologists. Too often, policymakers and technologists
operate too far remove from each other. Policy is frequently developed
with a superficial understanding of the implications on user experience,
infrastructure, access, bandwidth, usage patterns, and the true costs and
benefits associated with the amazing possibilities big data offers. Technical
innovations are often made with little to no discussion of the ethical, cul-
tural, legal, political, and social implications of building a product or service.
In their effort to bridge the innovation-policy gap, policymakers
and technologists should seek to find mutual benefits. The increase in data
breaches, continuing concerns over data privacy, complex privacy and usage
policies, difficult opt-out procedures, and a persistent lack of understanding
by the general public of how specific technologies work can all be reduced
or removed by working more closely together.
Policies benefit from having an accurate technical understanding of
the issues at hand, and technology benefits from well-informed policy that
provides protections and guidance. Mutual benefits encourage deeper brand
relationships and consumer confidence. This translates into faster adoption
by consumers, more vocal brand advocates, a reduction of risk from poorly
Policy is frequently developed with
a superficial understanding of the
implications on user experience,
[...] and the true costs and benefits
associated with the amazing
possibilities big data offers.

designed or unexpected legislation, and market leadership by promoting
the common good.
Each discipline can help the other by intentionally adopting these
practices. Sharing knowledge in collaborative spaces generates mutual ben-
efit for both disciplines, and ultimately, for the common and greater good.
Mutual benefit is created from a shared understanding of our common
values and aligning our collective actions with those values. This alignment
increases the pace of innovation, informs balanced and useful policy, and
helps make those benefits real in the world by turning the question “should
we do this” into “how might we do this”?
Who is Bridging the Gap Today?
Technologists are not the only voice in the policy discussion. A recent ini-
tiative announced by the White House exhibits many of the characteristics
described above. By hosting a series of public events to encourage discussion
amongst technologists, business leaders, civil society, and the academic com-
munity, the Office of Science and Technology Policy seeks to learn from a
wide range of experts and to engage the public in this discussion.5
There is also an emerging “think/do” tank in New York City called
the Data Society Research Institute. Their explicit purpose is “addressing
social, technical, ethical, legal, and policy issues that are emerging because of
data-centric technological development.”6
They are creating working groups,
developing and holding events, and inviting a wide variety of participants
to engage in an ongoing dialogue.
Both of these initiatives are intentionally seeking to level the knowl-
edge base, create spaces for collaboration, and find mutual benefits. We can
look to these initial efforts as examples and inspiration—but policymakers
and technologists should not stop there. As noted previously, we are living
in the Wild West of data innovation. It is an inspiring time that offers the
potential for amazing benefits. However, much like the expansion of the
American West, it is also fraught with danger, hardship, and few rules.
By working together to design a few guiding principles, we can help
ourselves reduce the risk of dangers, ease hardships, and improve our
chances of reaping the collective and common benefits of big data. Bridging
the innovation-policy gap is essential to achieving this goal.
Notes
1
An outstanding introduction to the complexities of this world is: Joseph Menn, Fatal Sys-
tem Error: The Hunt for the New Crime Lords Who are Bringing Down the Internet (New York City:
Public Affairs, 2010).
2
Netflix, “Blog: Help Us Bring Facebook Sharing to Netflix USA,” Netflix, http://blog.netflix.
com/2011/09/help-us-bring-facebook-sharing-to.html.
3
Helen Nissenbaum, “A Contextual Approach to Privacy Online,” Dædalus 140, no. 4 (2011),
http://www.amacad.org/publications/daedalus/11_fall_nissenbaum.pdf
4
IT Law Wiki, “Data Broker,” IT Law Wiki, http://itlaw.wikia.com/wiki/Data_broker.
5
John Podesta, “Big Data and the Future of Privacy,” The White House Blog, http://www.
whitehouse.gov/blog/2014/01/23/big-data-and-future-privacy
6
The Data and Society Institute, “Homepage,” The Data and Society Institute, http://www.
datasociety.net/

93Making the Most of Disruptive InnovationSAIS Review vol. XXXIV no. 1 (Winter–Spring 2014)
Fast Data, Slow Policy: Making
the Most of Disruptive Innovation
Aniket Bhushan
International affairs policy and practice are particularly ripe for disruptive innovation fu-
eled by the rise of big data and open data. While there are several loci of disruption, this paper
demonstrate how the speed and time dimension in policy-relevant research in international
affairs and international development are being disrupted. Three illustrative case studies—
real-time macroeconomic analysis, humanitarian response, and poverty measurement—are
discussed. Finally, the concluding section explores how successful policy entrepreneurs can
make the most of disruptive innovation in the age of big data.
Most trends are accompanied by a familiar cycle of hype. After the initial
trigger come inflated expectations, then a trough of disillusionment
followed by an upward slope of enlightenment, before finally resting on a
mainstream plateau.1
The heightened pace of open data and big data, and
their potential impact on international affairs, is following a similar pattern.
Whether one chooses to defend the hype or challenge “data fundamental-
ism,”2
enough fodder exists to fuel both sides of the debate.
Proponents argue the rise of big data and open data fundamentally
changes the way we think about the world. The sheer volume, velocity,
variety and veracity of big data3
means we can worry less about quality is-
sues associated with narrower information sources. We can reframe our
methodological orientation to focus on iterative learning and correlations
as opposed to obsessing over causality. Doing this allows us to embrace
the possibility of working with a plethora of untapped (and growing) data
feeds to address challenges not even fully articulated yet. Doing this also
means leaving in abeyance a host of new dilemmas in areas such as privacy,
autonomy, and asymmetric coverage.4
Detractors on the other hand are quick to point out that data is not
objective (indeed the term “raw data” is an oxymoron). Data cannot “speak
for itself,” as the proponents of big data would have us believe.5
There are
biases at all stages, from collection to analysis to presentation. Big data
may be unbeatable when it comes to forecasting, but it is “dumb” when it
Aniket Bhushan is senior researcher at the North-South Institute (NSI), a Canadian-
based policy research institution. Mr. Bhushan heads NSI’s work on the Canadian
International Development Platform (CIDP), a data and analytical platform on
Canada’s engagement with the developing world. Mr. Bhushan’s current research
focuses on the impact of big data and open data on public policy. His academic
background includes degrees in political science and commerce, and he completed his
M.A. in political science at Carleton University (Canada).

“comes to doing science,” as it is not underpinned by sophisticated research
designs that aim to identify causal relationships.6
The bigger the data, the
more we are prone to lull ourselves into a false sense of confidence in predic-
tive analytics. Indeed, big data accentuates the “signal to noise” problem.7
There are a multitude of other issues associated with the use of data.
Big data has its roots in the commercial sector.8
The main intention behind
generating sharper insights into customer behavior and profiles is to achieve
better targeting and segmentation; in other words, smarter discrimina-
tion to ultimately drive profitability. When examined from a public policy
perspective, this could be highly problematic. The kinds of targeting and
discrimination taken for granted in many commercial sectors, like adver-
tising, would be expressly forbidden in more regulated industries, like the
insurance industry, and may be contrary to the aims of public policy and
public service delivery.9
Whichever perspective one identifies with, the inescapable fact is that
big data and open data are already disrupting several industries. Policy
relevant research and analysis in international affairs is no exception. In
fact, international affairs—and international development as a subset—are
particularly ripe for data driven disruption. The speed of big data and open
data has inherent disruptive potential that is already impacting a number
of areas, from the highly mainstream business of macroeconomic indicators
to new approaches to poverty measurement in difficult country contexts.
The aim of this paper is to describe what the emerging paradigm of in-
ternational affairs in the age of big data looks like and the role of the policy
entrepreneur at the center of this process. Successful policy entrepreneurs,
both individuals and institutions, will be able to make the most of highly
disruptive trends in data that will fuel future analyses of international af-
fairs. Adapting to this emerging paradigm requires investing in new tools.
While there are several loci of disruption, the emphasis of this paper is on
the time and speed dimension.
International Affairs and Development are
Ripe for Disruptive Innovation
Disruptive innovation is a business school concept often overused with little
reference to the original idea. What does one mean by disruptive innovation
in international affairs? The disruptive innovation paradigm argues that
small, speculative innovations at the base of the pyramid can often leapfrog
and disrupt established domains because top-tier players pursue incremen-
tal innovation with their most important, but also most change-resistant
clients.10
There are several examples from big business—Amazon’s business
model disrupted brick and mortar retail, Skype disrupted long distance
telephony, Netflix is disrupting cable broadcasting.
The paradigm is also applicable to international affairs. The main
clients of policy analysis and research are bureaucrats or political decision
makers whether in government or international institutions. In this context,
the potential disrupters are analysts who can make the most of the rise of
big and open data.

95Making the Most of Disruptive Innovation
Both as a field of practice and analysis, international affairs and devel-
opment present an opportunity for disruption. Recent research into what
senior policymakers in international affairs want and expect from research-
ers is revealing in this regard. The survey, unique in its sampling, targeted
senior U.S. national defense policymakers from the George H.W. Bush, Bill
Clinton, and George W. Bush administrations.11
The findings paint an un-
flattering picture of the increasing gap between the scientific aspirations of
international affairs scholarship and the needs of policymakers. The gaps
are highest at the top. The study found that the more policymakers know
about a subject (especially through direct experience), the less likely they
are to believe the “experts.”
While the gap between direct experience and academic knowledge
may be unsurprising, the survey also points to just how wide some of the
generational gaps can be. For instance, it is alarming that, for a survey
conducted in 2013, senior policymakers still do not count the Internet as a
useful source of policy relevant information. Instead they prefer to gravitate
towards individual high profile scholars. A key take-away is that the ease
of access to information does not necessarily translate into greater efficacy.
Rather, the diffuse nature of the medium—usually seen as a positive in terms
of democratizing information—is itself its weakness. A plethora of sources,
many of questionable reliability and with no authoritative source among
them, can become a barrier for time constrained policymakers who have
little interest in “cutting-edge tools and rarified theory.”12
Senior policy-
makers instead prefer researchers and analysts act as informal advisers and
creators of knowledge.
Senior policymakers neither trust nor have time for big data and open
data. At the highest levels, most senior policymakers act precisely like in-
cumbent industry leaders or the large risk-averse, change-resistant clients
in Christensen’s disruptive innovation model. They are highly embedded
in the classical paradigm typified by clean, composite, largely linear, and
mostly backward-looking indicators, underpinned by methodologically rig-
orous, often expensive heuristic frameworks that remain firmly within the
closed-loop single source of truth paradigm. These clients are serviced by
well-heeled ad-
visors steeped
in high quality
(i.e. expensively
vetted) informa-
tion and data
flows. Think of
examples rang-
ing from official
unemployment
figures to infla-
tion to, worse,
composite in-
dexes of poverty,
Much of the data we rely on in international
affairs and international development
research and analysis is fraught with serious
problems and is so slow that it is almost
a historical caricature by the time it is
published, barely descriptive about the
present, let alone insightful about the
future.

and inherently fuzzy concepts like “governance.”13
This paradigm is already
being decisively disrupted by the rapid mainstreaming of big and open data,
as outlined in the next section.
Much of the data we rely on in international affairs and international
development research and analysis is fraught with serious problems and is
so slow that it is almost a historical caricature by the time it is published,
barely descriptive about the present, let alone insightful about the future.
Some of the most important data that we take for granted as “real-time” is
not only published with significant lags, but is subject to significant revision.
For example, gross domestic product (GDP) is, at best, a quarterly series
published with a two-month lag, and revised over the next four years.14
This,
along with the vastly asymmetric influence of a handful of influential senior
advisors privy to the closed inner circle of senior decision makers, makes
international affairs fertile ground for disruptive innovation.
The “MS Excel error heard around the world” provides a glimpse into
what disruption in international affairs research in the age of big data looks
like.15
The most telling aspect about the now infamous Reinhart-Rogoff
spreadsheet error, at least from the perspective of research in the age of
big data, was not the furor it created by questioning whether high public
debt (more than 90 percent of GDP) really has an unusually large effect on
growth prospects. The issue was that the researchers were conducting the
analysis manually in Excel instead of using reproducible code. Their specific
data was not initially “open,” but was made so when a graduate student
requested it to replicate results—and obviously could not. The story was
literally heard around the world, thanks not only to the speed of the spread
of information but also due to the high profile of the “experts” involved in
a highly polarized debate. A series of small, incremental and—when viewed
in isolation—unexpected trends at the base of the pyramid were able to
disrupt the asymmetric influence wielded by high profile individual incum-
bents, even in an unlikely domain such as international affairs research and
analysis.
How Big Data and Open Data are Disrupting International Affairs:
The Key Element is Time and Speed
In this section we discuss three highly summarized case studies on how
big and open data are disrupting research, analysis, and practice in three
very different data domains within international affairs: macroeconomic
analysis, humanitarian crisis response, and the measurement of poverty and
socioeconomic indicators. The common thread in each case is the focus on
the impact of big and open data on time and speed.
Real-time economic analysis
Current or near real-time economic analysis is a highly data dependant en-
terprise. It is highly conservative, in that it is dominated by central banks,
ministries of finance, and large private financial institutions. The more
timely, accurate, and relevant the data, the better the current assessment

and the more valuable it is from a policy perspective. Big data is already
disrupting how we collect, compute, and project basic real-time macroeco-
nomic indicators, ranging from GDP and inflation to financial, housing,
and labor market indicators.
Recently, many central banks, including the Bank of England, Eu-
ropean Central Bank, the Bank of Japan, and the Bank of Canada, have
looked into the possibility of leveraging big data to enhance the timeliness
of current economic analysis.16
An interesting innovation in Canada is the
use of big data to fill the gaps in the timeliness of official GDP statistics
by developing a new short term GDP indicator that provides daily updates
of real GDP growth forecasts. Existing monthly data is combined with big
data to predict GDP growth before official national accounts data are re-
leased for a given quarter, thus bridging the gap period.17
The example also
demonstrates how big data traverses the “official” and “unofficial” domains.
In the case of Japan, the Abe government needed immediate informa-
tion on time-sensitive policy changes, such as a major increase in the sales
tax. What analysts found was that under the existing system there was no
way to assess the situation until the household survey or sales data was
released and analyzed months later—an eternity in terms of real-time eco-
nomic analysis. In response, the government proposed the development of
a new composite index that would use big data, including online searches
and point-of-sale records that would shed immediate light on the impact of
policies, albeit not without significant methodological challenges.18
Similarly, the Billion Prices Project (BPP) at the Massachusetts Insti-
tute of Technology (MIT) demonstrates how big data can be leveraged to
provide a real-time gauge of inflation. BPP uses web-scrapers (a relatively
simple approach, but one that is highly extensible and adaptable to several
uses) to scour websites of online retailers for real-time prices on an enor-
mous range of products. After the collapse of Lehman Brothers in 2008,
BPP data showed how businesses started cutting prices immediately. In
contrast, official inflation figures did not show deflationary pressures until
November.19
Given the importance of inflation and timely assessment of
inflationary expectations from the perspective of monetary policy response,
this information represents a significant improvement in response time.20
To assume that these innovations are limited to advanced economies
would be a mistake. The UN Global Pulse initiative has partnered with
BPP and Price Stats to apply the same web-scraping approach in six Latin
American countries, specifically to monitor the price of bread and calculate
a new eBread Index.21
Nascent results from the project show the approach
can be extended to developing country contexts, and that, in general, the
eBread Index is highly correlated with the official consumer price index for
the food basket in these countries. However unlike official inflation data
that is available monthly, the eBread Index is available daily. This again is
a major improvement in country contexts where inflation and inflationary
expectations can change rapidly.
Big data has also been successfully leveraged for a range of other macro
indicators. For instance, online search data from Google has been success-

fully used to predict initial claims for unemployment benefits, consumer
sentiment indexes in the United States and United Kingdom, and even
car sales down to specific brands.22
These trends show that companies like
Google, Facebook, and Twitter23
are as important to the future data flow
that will fuel policy relevant international affairs research as any national
official statistical agency.
The implication is that these companies may be far more important
than multilateral data clearing houses such as the World Bank, OECD, or
UN bodies, on whose highly questionable traditional data—in terms of qual-
ity, coverage, granularity and timeliness—much of the current research and
analysis in international affairs and development depends. While academics
have often pontificated about new and alternative measures of progress,
such as the “happiness index,” lesser known firms than Google or Facebook,
like Jana, are experimenting with SMS based surveys on a global scale that
are able to deliver a real-time snapshot of societal well-being.24
Humanitarian crises and disaster relief 2.0
The tragic earthquake off the coast of Haiti’s capital in January 2010 marked
a watershed moment for the impact of big data and open data on disaster
relief. The earthquake “created a chasm between what the international
humanitarian community knew about Haiti prior to the quake and the
reality it faced in the immediate aftermath.”25
The response in Haiti dem-
onstrated an important change in how the huge information gap between
damage assessment and response planning was filled. For the first time, two
new data inflows were added to the typical crisis response data: one from
volunteer and technical communities around the world (principally open
source mapping communities like OpenStreetMap, Sahana, CrisisMappers,
and Ushahidi), and one directly from the affected community of Haitians.26
The experience in Haiti showed that the international humanitarian
community was not equipped to handle these new information channels, in
terms of both speed and complexity.27
The volunteer technical communities
approached the problems in ways that fundamentally challenged the status
quo of large humanitarian agencies leading the recovery efforts while the
smaller groups follow.
Criticism of the Haiti experience revolves around the overflowing
information pipeline.28
Yet, this is a far better problem than the opposite
situation. The ability to learn and rapidly apply lessons in future crises, as
discussed below, demonstrates the benefit of having “too much” informa-
tion. Before focusing on the lessons, it is important to emphasize that the
Haitian response proved the rise of big data and open data is not simply
about data or technical sophistication. One of the most useful roles played
by volunteers was language translation of a huge volume of SMS and other
messaging through social media channels. The disruptive innovation was
that a highly networked and highly technical, yet contextually aware, virtual
community emerged organically. Arguably, the creation of such a commu-
nity may not have been possible, no matter how many pilot projects were
funded by well-meaning donor agencies.29
One reason is that the problem-

solving, transparency-driven, open source mindset that underpins much of
the virtual community is not always shared by big bureaucracies and senior
policymakers.30
Lessons from the Haitian
earthquake have been applied in
other contexts. User generated crisis
maps have saved lives in subsequent
disasters.31
Volunteers involved in
the Haiti mapping project have
supported other crowd-sourced
mapping initiatives, including proj-
ects that emerged in the wake of
the earthquake in Chile, floods in
Pakistan, the crisis in Libya, the
earthquake and tsunami in Japan,
and the typhoon in the Philippines. With each experience, the work has
gotten better as lessons are rapidly shared within a likeminded, highly moti-
vated, and well organized community. The process of interlinking real-time,
geo-spatial crisis data with other relevant data feeds, such as traditional me-
dia, has grown exponentially in the past few years. The time taken between
crisis impact and information generation has shrunk dramatically compared
to historical response times. In the case of Japan, within two hours after the
earthquake and tsunami, real-time witness reports were being mapped and
shared. In a context where seconds and minutes can determine the difference
between life and death, the rise of big and open data and their associated
communities has disrupted how society plans humanitarian responses,
ensuring such tools will be leveraged in future crises.
The poverty of poverty measures
At the other end of the velocity spectrum are data on typically slow moving
measures like poverty. Not only are poverty trends relatively slow moving, at
least in comparison to the examples discussed above, but the reporting lags
are enormous. The significant lag time of the data bears repeating: When
the World Bank announced that 22 percent of the world’s population lived
on less than $1.25 a day in 2012—and, consequently, the first Millennium
Development Goal had been achieved—that data was four years old when
reported, dating from 2008.32
The data is the poorest where it matters the most. Recent analysis of
the state of widely used economic indicators, such as GDP in sub-Saharan
Africa, raises serious issues. While international databases like the World
Bank report time-series data for many countries, the countries themselves
were found to have not published their own data for many of the years cov-
ered. Many countries in the region have or are in the process of updating
their national income accounts methodology, making these more consistent
with what most countries use. In so doing, many are finding a very different
picture than they had been led to believe.
...the problem-solving,
t r a n s p a r e n c y - d r i v e n ,
open source mindset that
underpins much of the virtual
community is not always
shared by big bureaucracies
and senior policymakers.

For instance, Ghana’s 2010 revision showed that GDP was 60 percent
higher than expected, instantly catapulting a low income country to middle
income status. Research comparing GDP data from country sources with
GDP data from the World Bank is alarming. GDP estimates according to
national sources in some countries like Burundi (2007) were found to be
32 percent higher than the same reported by the World Bank. However, in
other cases the reverse was true, and for Guinea-Bissau in 2006, the World
Bank’s estimate was 43 percent higher than that of the national authority.33
It is important to understand that the problems underpinning these
data challenges are not merely an issue of technical capacity, competence, or
cost of collection. A far greater problem is perceived or actual interference,
whether from political authorities, donors, or other actors. These issues
have been aptly termed “the political economy of bad data,” which neatly de-
scribes the situation
in many developing
countries.34
Huge in-
centives to misreport
plague administra-
tive data systems on
many levels. For ex-
ample, when Kenya
decided to abolish fees in primary school, this radically changed the incen-
tives for reporting by school administrators, as schools are allocated more
teachers and funding if they attract more students. While administrative
data from the Ministry of Education shows a steady increase in primary
school enrollment rates, demographic survey and national statistical data
fails to confirm the trend and instead indicates enrollment rates have been
flat over the same time period.35
These findings, while extremely troubling, are made worse by added
issues that complicate incentives. For instance, a fast growing trend among
donors is cash-on-delivery or performance-based aid, a trend based on the
idea of paying for results instead of paying for inputs. Whatever one may
think about this conceptually as an aid modality, the fact is that these ap-
proaches greatly increase the data burden. In this approach donors pay for
development results or outcomes such as increased educational enrollment
and improved performance. For performance-based measures to work, or-
ganizations need better, more timely, and more granular data. The more
ingenuity society can throw at the problem the better.
How are big data and open data disrupting this landscape? Given the
context described above, tapping into passively generated and proxy data, if
only to triangulate results or provide baseline referential information, could
be a welcome innovation. Big data approaches have thrown up three inter-
esting possibilities. The first is analysis of anonymized call detail records
(CDRs). A recent project in Cote d’Ivoire, using five million anonymized
CDRs from Orange telecommunications customers collected over a five
month period, analyzed both the level and location of activity. The analysis
indicated that a wider range of calls and longer durations were good prox-
ies for wealth. Using this data, researchers were able to create a granular
. . . that the problems underpinning
these data challenges are not merely an
issue of technical capacity, competence,
or cost of collection.

geospatial estimate of poverty in Cote d’Ivoire—the first data pertaining to a
full survey of the country that has been available since the late 1990s due to
political strife and economic turmoil in recent years, which have hampered
traditional methods.36
Another interesting innovation in small scale poverty measurement
and prediction is an approach using night light illumination. This approach
rests on the assumption that poorer places are quite literally in the dark.
Using geospatial, night light, and census data for Bangladesh in 2001 and
2005, researchers showed that a regression model combining the data was
able to predict poverty at a granular level. The cost effective and non-intru-
sive nature of this approach makes it a useful source of proxy poverty data,
and makes up for potentially lower accuracy.37
The concept is also being
extended with application to other geographic regions, including in Africa.
A third avenue is high-frequency micro-surveys conducted using
mobile phones and other platforms. The World Bank’s Listening to Latin
America or L2LAC project was launched out of a frustration among policy-
makers looking for information on the impact of the 2008 economic crisis in
Latin America. Typically this sort of analysis depends on household survey
data collected and reported over years—and at a high cost. The L2LAC pilot
covered nationally representative samples in Peru and Honduras and dem-
onstrated that by using mobile platforms, small versions of wider household
surveys can be conducted on a monthly basis and at a fraction of the cost.
This provides much closer to “real-time” insights into poverty, employment,
inequality, and other trends essential for effective responses to fast moving
crises. L2LAC also provides a useful gauge of poverty dynamics and trends
between official reporting periods, which can be years apart.38
The model
has since been extended to pilot projects in Africa.39
Anonymized CDR analysis, proxy light source data, and mobile phone
based micro-surveys are big data innovations that are disrupting how we
measure and respond to poverty at various levels. Aspects of each approach
have the potential to be “mainstreamed,” which would have been unthink-
able just a few years ago.
Making the Most of Disruption Requires a
New Kind of Policy Entrepreneur
Simon Maxwell, formerly of the Overseas Development Institute, popular-
ized the term “policy entrepreneur” when he proposed a simple self-assess-
ment questionnaire that categorized analysts into four types: story-tellers who
are steeped in powerful grand narratives that often inform policy; networkers
who rely mostly on their connections with policymakers; engineers who are
grounded in testing ideas expected to have policy import; and fixers who
wield expert power around specific problems.40
In an age where big data and open data are fast becoming part of the
mainstream in several domains of international affairs, we need to add a
new, fifth type of policy entrepreneur: the disrupter. The policy entrepreneur
who focuses on making the most of disruptive innovation opportunities
ushered in by big data and open data typifies the role of the disrupter.

A set of key trends underpin our discussion and make international
affairs ripe for disruptive innovation. They are worth repeating. The majority
of analyses in international affairs rely on clean, composite, largely linear,
and mostly backward-looking indicators that remain firmly within the
closed-loop single source of truth paradigm. This, combined with the asym-
metric influence of a handful of influential senior advisors privy to the inner
circle of senior policymakers, makes international affairs fertile ground for
disruptive innovation fueled by the rapid mainstreaming of big data tools.
A key trend underlying the examples discussed in this paper is the
dramatically abbreviated learning, development and deployment curves
associated with these innovations, and their shrinking marginal costs. A
result of these improvements is that sophisticated analytical capacity is be-
coming more available to an ever wider range of users and analyzers, with
a much wider set of applications. The scope of who—whether individual or
institution—can be a data generator, aggregator, analyzer, and synthesizer
is expanding rapidly.
The rise of big data and open data has shrunk the time lag between
the start of a trend, when responders have access to essential information
needed to respond to the trend,
and the feedback loop generated
by those who are affected by
the response.41
Traces of emerg-
ing trends show up faster in
machine-level exchanges across
online data platforms—or in the
data stream generated by the
nearly 35,000 Facebook likes
that brands and organizations
receive every minute—than they
do in official statistics.42
As the
level of digital activity grows, as
social networks become more
ossified, and as literacy and
awareness around tools increase
across the developed and the
developing worlds, traditional
barriers to adoption of new tech-
nologies will fall rapidly. These trends create new analytical and engagement
opportunities.
The rise of big data is disrupting clean, composite, backward-looking
indicators and the result is often messy, probabilistic, but real-time and
forward looking dashboards. We are just at the beginning of a shift in the
nature of policy analysis in international affairs, from in-depth research
to on the fly analytics. What do these trends mean for a new generation of
policy entrepreneurs? How can the new policy entrepreneur make the most
of the disruptive innovation potential fueled by big data and open data?
The rise of big data is disrupting
clean, composite, backward-
looking indicators and the result
is often messy, probabilistic,
but real-time and forward
looking dashboards. We are
just at the beginning of a shift
in the nature of policy analysis
in international affairs, from
in-depth research to on the fly
analytics.

The successful policy entrepreneur will first and foremost invest in
staying on top of these rapidly moving trends and their associated tools
and technologies. The landscape is evolving fast. Take for instance crowd
sourced mapping, which only emerged at scale less than five years ago and
is already a mainstream tool in crisis response. To make the most of the
disruptive potential, the successful policy entrepreneur will work to break
down perceived dichotomies and distinctions, and get comfortable work-
ing in polarized terrains. Or consider the distinction between “public” and
“private” sources of information. As we have seen, Google and Twitter can
be as important a data and analytical resource as the World Bank or any
national statistical agency. Policy entrepreneurs that succeed in blurring
and breaking these dichotomies will be best placed to capitalize on the
disruptive potential.
Harnessing disparate sources requires investing in tools that help drive
interoperability. A lot of potentially disruptive data exists across disparate
levels and domains—for example, at the national, local, regional or even
neighbourhood levels. Potentially disruptive data also exists in both actual
and virtual communities and networks, as well as across government depart-
ments and agencies. Furthermore, disruptive data can be found across dif-
ferent data types that may not intuitively work well together—for instance,
well-structured data (such as numerical relational tables) and unstructured
data (such as Twitter hashtags or search data with multiple and complex in-
terrelationships). There are currently over 300 known open data sites hosted
at various levels of government across the world.43
As a starting point, there
remains significant unexploited potential to better link and leverage this
fast growing public sector open data, not only to increase efficiency, but to
drive entirely new business and service-delivery models.44
In addition to tools that stimulate interoperability across disparate
sources, types, and levels, the successful policy entrepreneurs will distinguish
themselves by having a keen eye on which combination of tools and infor-
mation source work best for which policy questions. The greatest potential
impact may be in areas where experimental alternatives, like triangulating
multiple big or open data sources, either significantly improves or funda-
mentally alters the view presented by standard single-source closed-loop
indicators.
With the expanded scope of who can be a data generator, aggregator,
or analyzer come inherent problems. These challenges will not only be a
major preoccupation of the new policy entrepreneur but will also represent
a significant opportunity. A key issue is validation of new and innovative ap-
proaches. Here, successful policy entrepreneurs will make the most of their
“analog” skillsets and deep contextual awareness. Big data, no matter how
powerful, can easily go astray without a domain expertise. Maintaining con-
textual awareness requires a great deal of offline investment. The importance
of building agile teams and maintaining loose but motivated networks with
diverse backgrounds and skillsets cannot be overemphasized. The new breed
of policy entrepreneur, while versed in data science, cannot be restricted to
data. Big data policy entrepreneurs will need to be obsessed with developing

innovative validation and verification solutions, with the aim of disrupting
the incumbent closed-loop single source of truth paradigm, still emblematic
of most international affairs research and policy analysis.
Notes
1
Gartner, Inc. Research Methodologies, “Hype Cycle Research Methodology,” Gartner, Inc.,
http://www.gartner.com/technology/research/methodologies/hype-cycle.jsp.
2
Kate Crawford, “The Hidden Biases in Big Data,” HBR Blog Network, posted April 1, 2013,
http://blogs.hbr.org/2013/04/the-hidden-biases-in-big-data/.
3
IBM, “The Four V’s of Big Data” IBM, Inc., http://www.ibmbigdatahub.com/sites/default/
files/infographic_file/4-Vs-of-big-data.jpg As recently as 2000, less than a quarter of the
world’s stored information was digital. Now, around 98 percent is digitally stored. There
are several similar statistics. A good reference for the scale of the rise of big data is WikiBon,
found at http://wikibon.org/blog/big-data-statistics/.
4
There are several big data evangelists and enthusiasts; see in particular: Kenneth Cukier
and Viktor Mayer-Schoenberger, “How It’s Changing the Way We Think About the World,”
Foreign Affairs 92, no. 3 (2013): 27-40; World Economic Forum 2012 Report, “Big Data, Big
Impact: New Possibilities for International Development,” World Economic Forum, http://
www.weforum.org/reports/big-data-big-impact-new-possibilities-international-development;
Harvard Business Review October 2012, “Getting Control of Big Data,” Harvard Business
Review; Aniket Bhushan, “Big Data, Democratized Analytics and Deep Context Will Change
How We Think and Do Development,” The North-South Institute, http://www.nsi-ins.ca/wp-
content/uploads/2013/04/2012-Big-Data-Democratized-Analytics-and-Deep-Context-will-
Change-How-We-Think-and-Do-Development.pdf.
5
Kate Crawford, “The Hidden Biases in Big Data,” HBR Blog Network, posted April 1, 2013,
http://blogs.hbr.org/2013/04/the-hidden-biases-in-big-data/.
6
Marc Bellemare, “Big Dumb Data,” Marc F. Bellemare Blog, posted May 16, 2013, http://
marcfbellemare.com/wordpress/2013/05/big-dumb-data/. The point is interesting, but
questionable. Much of the artificial intelligence and machine learning at the core of big
data is in fact underpinned by sophisticated and causal, if only iterative, research designs
and models. In fact, there is no reason one couldn’t apply more or bigger data to various
social science research designs. The greatest potential from a methodological and academic
perspective may be found by overcoming some of these gaps.
7
Konrad Yakabuski, “Big Data should inspire humility, not hype,” The Globe and Mail, March
4, 2013, http://www.theglobeandmail.com/globe-debate/big-data-should-inspire-humility-
not-hype/article9234569/.
8
Bernholz describes how big data hype has captured the imagination of the commercial and
to some extent the public policy domains, but virtually ignores civil society, a sector that
generates a great deal of significantly untapped data. See: Lucy Bernholz, “Civil Society and
Big Data,” Philanthropy 2173 Blog, posted February 24, 2014, http://philanthropy.blogspot.
ca/2014/02/civil-society-and-big-data.html?m=1.
9
Michael Schrage, “Big Data’s Dangerous New Era of Discrimination,” HBR Blog Network,
posted January 29, 2014, http://blogs.hbr.org/2014/01/big-datas-dangerous-new-era-of-
discrimination/.
10
Clayton Christensen, “Disruptive Innovation,” Clayton Christensen’s personal website,
http://www.claytonchristensen.com/key-concepts/.
11
Paul Avey and Michael Desch, “What Do Policymakers Want From Us? Results of a Sur-
vey of Current and Former Senior National Security Decision-makers,” International Studies
Quarterly 58, no. 4 (2014) Forthcoming.
12
Paul Avey and Michael Desch, “What Do Policymakers Want From Us? Results of a Sur-
vey of Current and Former Senior National Security Decision-makers,” International Studies
Quarterly 58, no. 4 (2014): 34. Forthcoming. (Page number may differ in final edition.)
13
Several examples come to mind, but think of highly composite measures like the Human
Development Index (HDI) or the World Governance Indicators (WGI) that are popular in

international affairs research and rely on such untimely data that they are more a historical
caricature than an insightful tool for understanding coming trends.
14
Similarly, inflation data is published monthly, but with a three week lag after the reporting
month. See: Nii Ayi Armah, “Big Data Analysis: The Next Frontier,” Bank of Canada Review,
Summer 2013, http://www.bankofcanada.ca/wp-content/uploads/2013/08/boc-review-
summer13-armah.pdf.
15
The Rachel Maddow Show, “The Excel Error Heard Around the World,” MSNBC, http://
www.msnbc.com/rachel-maddow-show/the-excel-error-heard-round-the-world. See also:
Thomas Herndon, Michael Ash and Robert Pollin, “Does High Public Debt Consistently
Stifle Economic Growth? A Critique of Reinhart and Rogoff,” (working paper no. 322, PERI
University of Massachusetts, Amherst).
16
See: Mitsuru Obe, “Japan Looks to Big Data for Timely Economic Indicator,” The Wall Street
Journal, September 24, 2013; L. Einav and J. D. Levin, “The Data Revolution and Economic
Analysis,” (working paper no. 19035, National Bureau of Economic Research); A. Binette and
J. Chang, “CSI: A Model for Tracking Short-Term Growth in Canadian Real GDP,” Bank of
Canada Review, Summer 2013, http://www.bankofcanada.ca/wp-content/uploads/2013/08/
boc-review-summer13-binette.pdf; Nii Ayi Armah, “Big Data Analysis: The Next Fron-
tier,” Bank of Canada Review, Summer 2013, http://www.bankofcanada.ca/wp-content/
uploads/2013/08/boc-review-summer13-armah.pdf.
17
Binette and J. Chang, “CSI: A Model for Tracking Short-Term Growth in Canadian Real
GDP,” Bank of Canada Review, Summer 2013, http://www.bankofcanada.ca/wp-content/
uploads/2013/08/boc-review-summer13-binette.pdf; Nii Ayi Armah, “Big Data Analysis:
The Next Frontier,” Bank of Canada Review, Summer 2013, http://www.bankofcanada.ca/
wp-content/uploads/2013/08/boc-review-summer13-armah.pdf.
18
Mitsuru Obe, “Japan Looks to Big Data for Timely Economic Indicator,” The Wall Street
Journal, September 24, 2013.
19
Nii Ayi Armah, “Big Data Analysis: The Next Frontier,” Bank of Canada Review, Summer
2013, http://www.bankofcanada.ca/wp-content/uploads/2013/08/boc-review-summer13-
armah.pdf.
20
A counterargument often made is that focusing on frequency tends to overestimate both
inflation and the variability of inflation. This is particularly the case in other measures
such as the Everyday Price Index, which measures inflation from the perspective of goods
that people purchase frequently. However, these critiques can be easily countered by sta-
tistically correcting for additional variability inherent in higher frequency data. For more
information, see: Matthew Yglesias, “Here’s a Deliberately Inaccurate Inflation Index,” April
9, 2013, http://www.slate.com/blogs/moneybox/2013/04/09/aier_s_everyday_inflation_in-
dex_is_terrible.html
21
UN Global Pulse, PriceStats, and the Billion Prices Project, “Daily Tracking of Commodity
Prices: the eBread Index,” http://www.unglobalpulse.org/projects/comparing-global-prices-
local-products-real-time-e-pricing-bread.
22
Hyunyoung Choi and Hal Varian, “Predicting Initial Claims for Unemployment Benefits,”
Google Inc., http://static.googleusercontent.com/media/research.google.com/en/us/archive/
papers/initialclaimsUS.pdf; Hyunyoung Choi and Hal Varian, “Predicting the Present with
Google Trends,” Google Inc., http://people.ischool.berkeley.edu/~hal/Papers/2011/ptp.
pdf; Paul Cheung, “Big Data, Official Statistics and Social Science Research: Emerging Data
Challenges,” (presentation at the World Bank, December 12, 2012).
23
Mining Twitter data on food related conversations has also been shown to be strongly
correlated with food price inflation. See: World Economic Forum 2012 Report, “Big Data,
Big Impact: New Possibilities for International Development,” World Economic Forum, http://
www.weforum.org/reports/big-data-big-impact-new-possibilities-international-development.
24
JANA and UN Global Pulse, “Global Snapshot of Well Being - Mobile Survey,” United Na-
tions, http://www.unglobalpulse.org/sites/default/files/Mobile%20Data%20for%20Develop
ment%20Primer_Oct2013.pdf.
25
UNOCHA et al., “Disaster Relief 2.0: The Future of Information Sharing in Humanitarian
Emergencies,” United Nations Foundation, http://issuu.com/unfoundation/docs/disaster_re-
lief20_report.

26
UNOCHA et al., “Disaster Relief 2.0: The Future of Information Sharing in Humanitarian
Emergencies,” United Nations Foundation, http://issuu.com/unfoundation/docs/disaster_re-
lief20_report; See also: Kim Rose, “The humanitarian power of big data,” Hortonworks Blog,
posted May 6, 2013, http://hortonworks.com/big-data-insights/the-humanitarian-power-of-
big-data/; Maja Bott, Björn-Sören Gigler, and Gregor Young, “The Role of Crowdsourcing
for Better Governance in Fragile State Contexts,” International Bank for Reconstruction
and Development/World Bank, https://wbi.worldbank.org/wbi/Data/wbi/wbicms/files/
drupal-acquia/wbi/crowdsourcing_final_0.pdf; Maja Bott and Gregor Young, “The Role of
Crowdsourcing for Better Governance in International Development,” The Fletcher Journal
of Human Security 27 (2012): 47-70.
27
During the first week, volunteers mapped some 1,600 reports from affected Haitians based
on information from Twitter, Facebook, and online news. Over 30,000 SMS messages from
affected Haitians were sent through the Ushahidi led Project 4636 in the first month alone.
28
Maja Bott, Björn-Sören Gigler, and Gregor Young, “The Role of Crowdsourcing for Better
Governance in Fragile State Contexts,” International Bank for Reconstruction and Development/
World Bank, https://wbi.worldbank.org/wbi/Data/wbi/wbicms/files/drupal-acquia/wbi/
crowdsourcing_final_0.pdf.
29
It is worth noting that once innovations like open source crowd mapping prove their
utility, donors and others see value in supporting and facilitating them from the “inside.”
A great example of this is USAID’s recent MapGive initiative, which encourages volunteers
to learn and get involved in crowd mapping using the OpenStreetMap platform: http://
mapgive.state.gov/index.html
30
The Standby Volunteer Task Force for Live Mapping (SBTF), an online volunteer initia-
tive for crisis mapping that was founded as a consequence of the various loosely connected
projects for Haiti’s recovery.
31
Jing Guo, “How User Generated Crisis Maps Save Lives in Disasters,” The World Bank Blog,
posted February 26, 2014, http://blogs.worldbank.org/publicsphere/how-user-generated-
crisis-maps-save-lives-disasters.
32
Johan Mistiaen, “What will it take to improve poverty data?” The World Bank: MDGs and
Beyond 2015 (2012): 2.
33
Jerven Morton, “African Growth Miracle or Statistical Tragedy? Interpreting trends in the
data over the past two decades,” (paper presented at UNU-WIDER conference on Inclusive
Growth in Africa, September 20-21, 2013). More worryingly, as the paper shows, in some
cases base year revisions are nearly three decades apart, implying that we should not be
surprised if we see a number of countries go through a statistical inflation in fundamental
data like GDP, as in the recent experience in Ghana.
34
Justin Sandefur and Amanda Glassman, “The Political Economy of Bad Data: Evidence
from African Survey and Administrative Statistics,” (paper presented at UNU-WIDER con-
ference on Inclusive Growth in Africa, September 20-21, 2013).
35
Justin Sandefur and Amanda Glassman, “The Political Economy of Bad Data: Evidence
from African Survey and Administrative Statistics,” (paper presented at UNU-WIDER con-
ference on Inclusive Growth in Africa, September 20-21, 2013). We should also note that
analyses such as these are made far easier by the spread of open data including administra-
tive level data. Kenya is viewed as a leader in this regard among developing countries.
36
Christopher Smith, Afra Mashhadi, and Licia Capra, “Ubiquitous Sensing for Mapping
Poverty in Developing Countries,” (paper submitted to the D4D session of the NetMob 2013
conference). UN Global Pulse, “Mobile Phone Network Data for Development,” October
2013, http://www.unglobalpulse.org/sites/default/files/Mobile%20Data%20for%20Develop-
ment%20Primer_Oct2013.pdf.
37
Prasanna Lal Das, “Scenes from the DC big data dive- the final report,” The World Bank
Blog, posted May 28, 2013, http://blogs.worldbank.org/opendata/scenes-dc-big-data-dive-
final-report.
38
A. Ballivian and J. Azevedo, “Listening to LAC: Using Mobile Phones for High Frequency
Data Collection,” The World Bank.
39
An example at the metropolis level is the Listening to Dar project in Dar es Salaam,
Tanzania.

40
Simon Maxwell, “Policy Influence: Policy entrepreneurs,” Overseas Development Institute Blog,
posted January 2009, http://www.odi.org.uk/publications/5896-simon-maxwell-engineer-
networker-fixer-storyteller-policy-entrpreneurship.
41
World Economic Forum 2012 Report, “Big Data, Big Impact: New Possibilities for Inter-
national Development,” World Economic Forum, http://www.weforum.org/reports/big-data-
big-impact-new-possibilities-international-development.
42
Nii Ayi Armah, “Big Data Analysis: The Next Frontier,” Bank of Canada Review, Summer
2013, http://www.bankofcanada.ca/wp-content/uploads/2013/08/boc-review-summer13-
armah.pdf.
43
For more see Open Data Sites: http://opendatasites.com/
44
A good example is the emergence of public sector open data based companies and busi-
ness models. In the United States, real estate sector companies Trulia and Zillow come to
mind. There are also innovative business models emerging around searching, indexing,
meta-tagging, and visualizing open public data, like Evision.io.

109Big Data Custodianship in a Global SocietySAIS Review vol. XXXIV no. 1 (Winter–Spring 2014)
Big Data Custodianship in a
Global Society
Chris Poulin
With the rise of big data, we are witnessing the unprecedented ability of organizations, public
and private, global and local, to collect data that is both broader in scope and more exacting
in detail. This new technology paradigm allows for revolutionary advances, improving our
daily lives in areas such as healthcare, national security, and consumer products and services.
However, with any emerging technological advance, there is potential for abuse. In the case
of big data, there are concerns about privacy, and even the potential for behavioral modi-
fication. This paper will discuss the process of collecting, analyzing, and applying big data,
using examples from international cases and the author’s work at the Durkheim Project.
What is Big Data?
Big data is a recently developed catchphrase in information technology
that refers to the rise of data collection at a scale previously unfeasible.
Any piece of measurable data that previously might have been discarded
can now be stored, and increasingly, everything that can be stored can also
be analyzed. Big data initiatives have emerged in a variety of disciplines, in-
cluding medical research, weather analysis, financial quantitative research,
targeted e-commerce, and government intelligence activities.1
Historical Development
The historical foundation of the big data trend was the democratization
of supercomputing-related technologies, otherwise known as “high per-
formance computing.” The tools used in supercomputing, which dates to
the 1960s, bear a striking similarity to many of the tools used in big data
analysis, albeit now with a greater emphasis on functionality and ease of use.
For example, many nuclear research programs required complex simulations
capabilities that outstripped simple computing resources. Combined with
an explosion in the amount of information available to scientists in other
areas, such as astronomical science, researchers pushed for the development
of tools that could do more with less. In the 1990s, these software tools
Chris Poulin is the principal partner of Patterns and Predictions, a big data prediction
company. He is also director of the Durkheim Project, a non-profit big data
collaboration with the U.S. Department of Veterans Affairs and Facebook. He recently
served as co-director of the Dartmouth Metalearning Working Group at Dartmouth
College, where he worked on large-scale machine learning. Poulin has also lectured on
artificial intelligence at the U.S. Naval War College.

found their way to commodity machines (or personal computers), which
formed the basis for the generation of tools we now know as big data.
Founded in 1998, Google was one of the first large commercial compa-
nies to embrace commodity distributed computing. For Google’s founders,
a primary goal was to provide better search engines, both in scope and so-
phistication, while spending fewer resources to process the data. Their initial
attempt was a home-brewed set of tools for distributed computing on low
cost machines. In 2004, Google launched a series of internal initiatives to
develop a reliable big data infrastructure. Their findings were subsequently
published in a series of high profile research papers.2, 3
As Google was one of the greatest success stories in the business, many
researchers took interest when these papers were released. One researcher,
Doug Cutting, was inspired to write his own implementation of a distrib-
uted search engine (called Nutch) and a distributed storage system (called
Hadoop). The influential Hadoop distributed storage system, with its open
source grid tools, and systems similar in functionality have further driven
the rise of big data.
Applications
Put simply, if you collect enough information, you can see things at a
level of detail that you could not see before. For example, if given access to
enough data about an anonymous individual, you can likely identify that
individual. With enough information over time, you can predict events in
that individual’s life. And if you observe how an individual reacts to these
events, you can predict that individual’s future behavior. Within this infor-
mation lies tremendous opportunity to help individuals avoid unnecessary
hardships and live better lives.
These opportunities are especially apparent in the field of medicine.
With the ability to collect and view more patient data, researchers and physi-
cians are able to better quantify risks to individuals.4
They can view details
that previously slipped past clinical practice and can identify potential
medical problems before they happen. Within the deployment of big data
systems in both research and clinical medical practice, the elusive prom-
ises of preventative medicine are
closer to being realized.
As director of the Dur-
kheim Project,5
a non-profit that
generates linguistic-driven mod-
els to predict veteran suicides,
I explored how big data analy-
sis could be used to save lives.
Our team spent over a decade
researching the cues in language
and the correlations between combinations of words and outcomes. While
teaching a machine to understand language is widely considered to be a
“hard AI” problem—that is, requiring an advanced level of artificial intelli-
gence—many have proven that increasingly sophisticated computer systems
Assessing suicide risk is a
particularly difficult diagnosis
for clinical professionals, and
one that is highly amenable to
big data.

111Big Data Custodianship in a Global Society
can predict correlations of language with events. We applied this system
to the challenge of estimating suicide risk among U.S. veterans. Assessing
suicide risk is a particularly difficult diagnosis for clinical professionals, and
one that is highly amenable to big data. Thus, the social utility of this type
of application is obvious.6
However, many entities collect large amounts of highly detailed
personal information for less purely altruistic purposes. Much of this in-
formation has helped power the growth of the commercial Internet giant.
Furthermore, some uses of big data are highly distressing when they go
wrong;7
misuse can cause public embarrassment8
or financial hardship for
an individual,9
and even pose challenges to government security.10
While it
is a broader discussion to debate the scope of national security activities by
state-run intelligence agencies, we do note that the capability to collect this
data on individuals is now widely known by the public.
Researchers, private corporations, and public institutions now have the
capability to collect and analyze data on a grand scale. With this newfound
and increasingly powerful analysis capability, the question therefore arises:
What actions should these actors undertake, and what actions should they
refrain from?
What Does a Big Data Project Look Like?
We will discuss six important implementation areas, drawing examples from
two hypothetical cases: (1) the highly public use of data during the Arab
Spring,11
and (2) a human subjects study using private medical data.12
We
will also draw lessons from our work at the Durkheim Project.
Collection
First, data must be collected. In the Arab Spring case, social media messages
are pulled from Twitter’s web services. The data is comprised of “tweets,”
which are electronic messages of 140 characters or less. Since the informa-
tion in this case is published publicly, the users have consented to fairly
unrestricted use of their data. In contrast, in the second hypothetical case,
medical data generally requires informed consent in the form of an opt-in
procedure, usually a formal consent agreement. The reader should note that
nearly all other data falls somewhere between these two examples in terms of
openness versus restrictedness. In the Durkheim Project, we follow clinical
standards, even when not strictly needed, in order to maintain the utmost
trust in the data collected by our system.
Storage
In both hypothetical examples, datasets are stored in large databases follow-
ing collection. The individual or institution storing the data may or may not
have a data governance or data privacy policy. In brief, a data governance
policy determines what to do with the data, while a data privacy policy
determines who can view the data. In the Twitter case, the data is of low
sensitivity, since the users and their published tweets are publicly known.

However, in the case of medical data, the information requires more careful
handling. For example, proper procedure restricts access to the dataset, and
requires that data be stored securely behind an IT firewall. In the Durkheim
Project, we first aggregated the data from social media and mobile applica-
tions worldwide on secure cloud services, like Amazon Web Services, and
then transmitted the data to our medical center partner for final storage.
Processing
To analyze the datasets, we must process the data via some sort of comput-
ing cluster. This cluster may be in a secure data center, or it may not. The
transmissions between the analyst who is sending the data and the machines
that are processing the data may or may not be encrypted. The data process-
ing machines may or may not be themselves secure. While this may not be
critical in the first hypothetical case, it is certainly critical to the second.
In fact, standards in the field of data processing are only now emerging.
The Durkheim Project facilities have a supercomputer (Cray grid) behind a
firewall that processes the data, before transmitting the results to another
system that can be accessed by medical analysts directly via the Internet.
Query
Once an initial data analysis has been conducted, it is necessary to specify
who has access to the data. The question now becomes, who has rights to
query the system? In both hypothetical cases, this is a concern. In the case
of the Arab Spring, what if the U.S. Department of State had witnessed an
increasing frequency of terms related to dissent in the weeks before the mass
protests? This information would have been valuable to many actors: West-
ern governments interested in anticipating regional upheaval or disaster;
NGOs that needed to estimate demand for foreign aid; and even dictators
that wanted to gauge the risk to their own regime. In the case of medical
risk analysis, the issue of query is strongly related to the privacy of the indi-
vidual. At the Durkheim Project, our medical protocol dictates that the raw
data contained in medical records of those who are suicide positive are never
seen by non-Veterans Affairs staff. However, once the data is “de-identified”
and analyzed, our internal team (bound by non-disclosure agreements and
HIPAA regulation compliance rules) can access the information.
Reporting
Analytics reports are usually a combination of numbers, qualitative visual-
izations, and written narratives intended to measure tested outcomes. The
end consumer might be a scientific journal, government research agency, or
a policymaker. This leads to the question, can the consumer of the report
trust the data? Does this consumer or decision maker understand how the
data was collected? Do they know whether the data was stored and pro-
cessed securely? How will the dissemination of the analytic report impact
the people, places and things that were analyzed?
In the case of Twitter and the Arab Spring, analytic reports have
been published publicly.13
In contrast, in the case of medical data, only

de-identified data is disseminated outside of the direct researchers. At the
Durkheim Project, we shared our preliminary results with other organiza-
tions under non-disclosure agreement, as well as with our funding agency
(DARPA). Final publication of results was in a peer-reviewed journal (PLOS
ONE), and any further sharing of our data would have required restrictions.
Dissemination of final results will vary highly by project, depending on the
type of intended analysis.
Action
Future leaders, having reviewed the data from the Arab Spring case, might
learn to better allocate resources based on conflicts detected via social media.
In the medical case, the proper use of risk metrics can enable triage of clini-
cal resources, and produce recommendations for risk reduction or lifestyle
changes. In considering these scenarios, it is important to ask, should the
person entrusted with these decisions also be trusted with their analysis?
We argue the answer is: only if they know (with great certainty) how the
information was collected, stored, processed, queried, and reported. Mak-
ing use of our own analysis, the Durkheim Project is currently engaged in
intervention efforts related to suicide and other mental health risks. Effec-
tive clinical interventions must first be based upon validated, peer-reviewed
medical research. No responsible clinical decision maker would assume
otherwise. Although we have published comprehensive methods, we have
only begun the validation of our methodology, and this process is ongoing.
Behavioral Modification
If big data can enable us to predict events, then the actions of large cor-
porations, NGOs and governments have the potential to affect behavioral
change on those populations whose future behavior is predicted. In the
cases described above, there are clear advantages to this predictive potential.
However, big data analysis can also be applied with malicious intent. For ex-
ample, consider a scenario
in which a big data analyst
can craft the rhetoric of
thought leaders (perhaps
on Twitter) to impact how
an event will unfold. Simi-
larly, consider a situation
in which an unregulated
medical insurance compa-
ny understands individual
risk values, and adjusts its
pricing accordingly, thus
adversely affecting the un-
healthy population. These capabilities, combined with other tools like geo-
location from mobile phone data, are incredibly powerful: they can predict
where an individual will go and what they will do.
If big data can enable us to
predict events, then the actions
of large corporations, NGOs and
governments have the potential to
affect behavioral change on those
populations whose future behavior
is predicted.

At the Durkheim Project, in our own behavioral modification studies,
we hope to enable interventions for individuals—not only before a negative
event occurs, but well before. For example, if an individual has acute risk
of suicide within days, then emergency services might need to be involved.
However, if an individual is exhibiting early stages of a suicidal thought
process, an intervener can offer softer cues that a more positive outcome is
possible, thus avoiding later acute risk. This is, in fact, our current research
effort.
Implications
When big data first emerged, early adopters embraced the mentality: “Now
we can collect everything, so let’s do that.” But as use of big data technol-
ogy evolves, institutions are becoming more careful of what they collect, on
whom, and why.
Consent of the Governed
Careful implementation of opt-in/consent-based data collection saves a
great deal of confusion and institutional heartache down the line. In this
context, careful means explicit—for example, avoiding long contracts with
fine print, and clearly describing the data’s current and future use. At the
Durkheim Project, we have made a concerted effort to make our consent
process easy to understand. Other organizations have taken this idea one
step further, arguing for data ownership contracts with individuals.14
In
the Arab Spring and Twitter case, while the data may be public, its analysis
remains dangerous to those who were analyzed. In the medical case, the
underlying data is already regulated and protected, but the meta-analysis
is a legal grey area.
Privacy
Publicly available information is, and will likely always be, considered part of
the information commons.15
At the same time, individuals will always value
privacy. One function of privacy (especially on the Internet) is to spare us
from embarrassment and societal exclusion. Privacy is also a protected right
in many developed nations. Privacy protection in the world of big data will
increasingly mean the difference between fair treatment and discrimination,
or in many cases, life and death. In the Arab Spring case, if a regime could
identify the dissenters by their
tweets, then their lives may
be in jeopardy. In suicide risk
prediction, we are fully aware
of the implications of false
positive signals—for example,
we might embarrass our users.
In many cases, even a true positive might exacerbate an individual’s risk if
the data is treated with improper care.
. . . it is the transparency of the
underlying process that will win
the trust of big data consumers.

Transparency and Trust
While some subjects are undoubtedly sensitive or proprietary, it is the
transparency of the underlying process that will win the trust of big data
consumers. Trust in systems is critical to maintaining a stable infrastruc-
ture, and systems must have checks that are formal and procedural to instill
trust. Furthermore, transparency on the part of data leaders will engender
trust in systems. Data leaders should seek to assure the public, as customer
or constituent, that their analysis will be used for constructive purposes.
The Durkheim Project team understands that trust is the core function of
our effort to reach out to, and stay in contact with, enough individuals to
make a difference.
Big Data Leadership Going Forward
Big data offers a world-changing paradigm. The same technology that drove
much of Google’s growth is now diffusing into the mainstream. This trend
is revolutionizing health care, promoting a greater understanding of finan-
cial risks, and a greater situational awareness in state security and military
applications. Our world is becoming more aware of its own comings and
goings, and as a result, has become more efficient in its capabilities for re-
source allocation.
However, we are only beginning to see the potential applications of big
data. Like the powerful paradigms before (for example, nuclear power), big
data will offer applications for the benefit of mankind, as well as applica-
tions that control others and harm societies. We are already beginning to
witness some of the implications of deep privacy and behavioral tracking
offered by big data.
In the rush to create and profit from these new technologies, many
companies have become quite successful, largely following the existing let-
ter of the law. However, as society increases its understanding of big data’s
potential power, there must be a greater focus on defining what are and are
not exploitive uses.
In this effort, we advocate more transparent and consent-based big
data systems. We hope that with the Durkheim Project, we lead with a
positive example of custodianship. We believe that systems with this design
philosophy can truly change the world for the better, while systems without
this philosophy tend to be adversarial and exploitive. Policymakers should
be aware of both the promise and peril of these new data systems.
Notes
1
“Liberty and Security in a Changing World,” Report and Recommendations of The President’s
Review Group on Intelligence and Communications Technologies, December 12, 2013, http://www.
whitehouse.gov/sites/default/files/docs/2013-12-12_rg_final_report.pdf
2
Jeffrey Dean Sanjay Ghemawat “MapReduce: simplified data processing on large clus-
ters,” Google, Inc. (2004), http://static.googleusercontent.com/media/research.google.com/en/us/
archive/mapreduce-osdi04.pdf
3
Andrew Fikes, Deborah A. Wallach, Fay Chang, Jeffrey Dean, Michael Burrows, Robert E
Gruber, Sanjay Ghemawat, Tushar Chandra, Wilson C. Hsieh, “Bigtable: A Distributed Stor-

age System for Structured Data,” Google Inc. (2006), http://static.googleusercontent.com/media/
research.google.com/en/us/archive/ bigtable- osdi06.pdf
4
Chris Poulin, Brian Shiner, Paul Thompson, Linas Vepstas, Yinong Young-Xu et al., “Pre-
dicting the Risk of Suicide by Analyzing the Text of Clinical Notes,” PLoS ONE 9, no. 1
(2004), http://www.plosone.org/article/fetchObject.action?uri=info%3Adoi%2F10.1371%2F journal.
pone.0085733representation=PDF
5
The Durkheim Project: www.durkheimproject.org
6
Neal Ungerleider, “This May Be The Most Vital Use Of ‘Big Data’ We’ve Ever Seen”, Fast
Company, http://www.fastcolabs.com/3014191/this-may-be-the-most-vital-use-of-big-data-
weve-ever-seen
7
Matt Pearce, “Dad gets OfficeMax mail addressed ‘Daughter Killed in Car Crash,’” Los
Angeles Times, January 19, 2014.
8
Charles Duhigg, “How Companies Learn Your Secrets,” New York Times, February 16, 2012,
http://www.nytimes.com/2012/02/19/magazine/shopping-habits.html?pagewanted=1_r=2hp
9
Bart Custers, Bart Schermer, Tal Zarsky, Toon Calders, eds., Discrimination and Privacy
in the Information Society: Data Mining and Profiling in Large Databases, (New York, NY:
Springer, 2012).
10
Denver Nicks, “NSA Memo Says Snowden Tricked Colleague to Get Password,” Time,
February 13, 2014, http://swampland.time.com/2014/02/13/nsa-leaks-edward-snowden-password/
11
David Wolman, “Facebook, Twitter Help the Arab Spring Blossom,” Wired, April 16, 2013,
http://www.wired.com/magazine/2013/04/arabspring/ (accessed March 17, 2014).
12
Dartmouth Committee For the Protection of Human Subjects (CPHS): Study #23781
13
Gilad Lotan, et al. “The Arab Spring: Revolutions Were Tweeted: Information Flows during
the 2011 Tunisian and Egyptian Revolutions,” International Journal of Communication, vol. 5
(2011), http://ijoc.org/index.php/ijoc/article/view/1246 (accessed March 9, 2014).
14
Jaron Lanier, Who Owns the Future? (New York, NY: Simon Schuster, 2013).
15
Lawrence Lessig, “The Public Domain,” Foreign Policy, August 30, 2005, http://www.foreign-
policy.com/articles/2005/08/30/the_public_domain (accessed March 17, 2014).

117The Limits of Big DataSAIS Review vol. XXXIV no. 1 (Winter–Spring 2014)
Of Note
The Limits of Big Data
Christine Croft
More Data, More Questions
Chris Poulin’s essay, “Big Data Custodianship in a Global Society,” es-
tablishes a foundation for the exploration of fascinating and difficult
questions surrounding the use and influence of big data. While Poulin’s
discussion ranges from the basics of big data to the implications of its use,
three main questions beg greater consideration. First, one must consider in
greater depth the “how” of big data: how it is collected, stored, analyzed, and
reported. This “how” becomes essential as policymakers and technologists
interact to create and inform policy. Second, one must also confront the
uncomfortable question of whether big data is actually capable of changing
the world. Will big data become the dominant force in helping policymakers
predict protests or doctors save lives, or should big data be recognized as one
of many metrics to consider? Finally, the question of whether big data will
serve to modify behavior based on the knowledge of its collection presents
a particularly troubling view of the future.
The Big Data Buzz
In recent years, big data has become a buzz word in the tech-savvy com-
munity, simultaneously holding promise and mystery. The “what” of big
data—what it can accomplish and the consequences of its use—has been
well documented. Yet
the “how” of big data is
less easily understood,
protected by technolo-
gists as if it were an
industry secret. While
the rise of big and open
data is purported to
offer transparency and
greater access, the pro-
Christine Croft is a first-year M.A. candidate at the Johns Hopkins University Paul
H. Nitze School of Advanced International Studies (SAIS) concentrating in Strategic
Studies. She is an Assistant Editor for The SAIS Review.
While the rise of big and open data is
purported to offer transparency and
greater access, the process of how to
analyze raw data to create actionable
insights is still a mystery to many in
the business and policy communities.

cess of how to analyze raw data to create actionable insights is still a mystery
to many in the business and policy communities.
In his essay, Poulin outlines the process of utilizing big data: collec-
tion, storage, processing, query, reporting, and action. Though he appro-
priately raises the issues of data privacy during the collection, storage, and
reporting phases, he skims over the processing and query phases, which
are arguably most important to the analysis of big data.1
The absence of a
clear, scientific understanding and resulting communication of how mas-
sive amounts of data are sorted and analyzed presents a major challenge to
policymakers.
David Weinbeger, a senior researcher at Harvard University’s Berkman
Center for Internet and Society and co-director of the Harvard Library In-
novation Laboratory, argues that “it is not at all clear that human brains
will be capable of understanding why the supercomputers have come up
with the answers that they have.”2
The risk, he explains, is that we create
“knowledge but no understanding.”3
If technologists cannot accurately
explain to policymakers and business leaders why the data has produced
a certain result, particularly in the case of an outlier, there is a chance the
results of the analysis will be discarded as illogical or unsound.
As industry leaders, consumers, and policymakers become increas-
ingly comfortable with the use of big data, we must increase our knowledge
about how it is queried, analyzed, and reported. Otherwise, we risk creating
statistical results that appear to lack a causal link, which only increases the
risk for missing the correlated outcomes that big data is hailed as capable
of predicting.
Should Big Data Analyze Everything?
The two case studies (the Arab Spring and the medical industry) discussed
by Poulin reflect the diversity of big data’s applications. While some may
laud the breadth of topics that big data can touch, one should be equally
cautious about the applicability of big data to topics that require more nu-
anced analysis and understanding than big data alone can deliver. The ease
with which big data can be acquired and analyzed inclines us to look for
answers where perhaps none exist.
The Arab Spring case is an excellent example of searching for an-
swers where none exist. Ambiguity exists in the world, and big data cannot
eliminate it; thus, policymakers must be comfortable operating within the
bounds of the known and unknown. However, Poulin offers the Arab Spring
as an example of a future in which data from Twitter feeds could be used
to predict dissent and anticipate regime change. Such an idea stretches the
limits of big data analysis; the events in Egypt cannot be reduced to measur-
ing how often certain words are used on Twitter. Surely, each tweet fueled a
dialogue, and in turn, a movement, but perhaps that is what big data misses.
Big data cannot account for the cumulative effect of a movement, it cannot
perceive intensity of human emotion, and it cannot predict free will.
Similarly, the case of suicide risk among U.S. veterans presents po-
tential risks of relying on data. There is no substitute for person to person

119The Limits of Big Data
interaction; we cannot replace doctors with computers that measure the
word count rather than the intonation of a distressed veteran. We must
rely upon individuals to collect the “raw data” provided by the human
condition—a veteran’s face, eyes,
and emotion. Data can be used to
make predictions, and it is one of
many tools that might decrease
the risk of veteran suicides, but it
cannot serve as a replacement for
human interaction.
The overwhelming range of
big data’s applicability in vari-
ous industries can provide a false
sense of security when it comes to searching for answers to perplexing
questions. The challenge is that, while data is excellent at making correla-
tions, it does not tell us why those correlations are significant or whether
they have a causal relationship.4
Furthermore, by glorifying big data as the
next paradigm for predicting the future, we sacrifice our understanding
and appreciation of organic, emotional action—the type that fuels protests
and saves lives.
Can Big Data Change Our Behavior?
One of Poulin’s strongest assertions about big data is its ability to predict
an individual’s future behavior. From this assumption, he draws a second-
ary conclusion—that organizations, NGOs, and governments can use that
information to effect behavioral change. Poulin considers a situation where
an analyst knows what will happen before the outcome and then modifies
a “thought leader’s” discussion on Twitter to affect the predicted outcome.
With this assertion, Poulin fails to fully analyze the problems with
manipulating human behavior. While studies have indicated that human
behavior changes in response to a feeling of being “watched,”5
the benefit of
big data is that it is collected at a meta-level, and individuals may not even
realize that data is being collected about their actions. Furthermore, Poulin
ignores the challenges inherent in data collection and analysis. The lack
of understanding surrounding how data is collected and why it produces
certain results may preclude governments and organizations from acting
on the information, thereby precluding individuals from being influenced
by the collection of data. Indeed, Columbia University statistician Victoria
Stodden argues that individuals may not even listen to the answers provided
by big data due to its complexity or the occurrence of outliers.6
The Orwellian notion that big data can modify behavior may be ex-
citing to big data analysts, but presents a much darker picture for society.
Big data cannot and should not provide the solution to every global crisis;
rather, it should inform decisions as one of many tools used by policymak-
ers or business leaders.
Big data cannot account for
the cumulative effect of a
movement, it cannot perceive
intensity of human emotion,
and it cannot predict free will.

Can Big Data Really Change the World?
Big data is often hailed as a world-changing paradigm, and perhaps it is. Yet
by blindly accepting big data without thoughtful and thorough consider-
ation of its limitations, society risks losing its appreciation for the human
condition. The more we rely on the alleged predictive capabilities of data,
the less we rely on our intuition, which reflects each individual’s own big
data analysis of past experiences and knowledge.
Big data must be recognized as a tool that offers analysis to pressing
questions and global challenges, but the results of big data analyses should
never be accepted outright as the final solution to these challenges. In the
future, it is likely that big data will be so ingrained in our analytical meth-
odologies that its use will be as ubiquitous as the Internet. If we can find
a way to utilize the merits of big data while appreciating its limits, society
will have developed an essential tool to make sense of an increasingly data-
filled world.
Notes
1
Christopher Versace, “The Big Deal about Big Data and What it Means for IT and You,”
Forbes, 28 January 2014, http://www.forbes.com/sites/chrisversace/2014/01/28/the-big-deal-
about-big-data-and-what-it-means-for-it-and-you/
2
David Weinberger, “The Machine That Would Predict the Future,” Scientific American,
December 2011, 6.
3
Ibid.
4
Gary Marcus and Ernest Davis, “Eight (No, Nine!) Problems With Big Data,” The New York
Times, 6 April 2014, http://www.nytimes.com/2014/04/07/opinion/eight-no-nine-problems-
with-big-data.html?action=clickcontentCollection=Opinionregion=Footermodule=M
oreInSectionpgtype=Blogs_r=0
5
Jason Goldman, “How being watched changes you – without you knowing,” BBC, 10 Feb-
ruary 2014, http://www.bbc.com/future/story/20140209-being-watched-why-thats-good.
6
Weinberger, 6.

121Harnessing Data for National SecuritySAIS Review vol. XXXIV no. 1 (Winter–Spring 2014)
Harnessing Data for National
Security
David Rubin, Kim Lynch, Jason Escaravage,
and Hillary Lerner
Since 9/11, the U.S. government has initiated efforts to enhance its information-sharing
capabilities and doubled its investment in counterterrorism, spending nearly $80 billion.
Sharing data and conducting analyses across the government’s legacy stovepipes of infor-
mation is challenging but mandatory to reduce redundancy, increase cost efficiency, and
improve national security mission performance. The challenges involved in harnessing
big data analytics for a more enlightened approach to national security center on striking
the optimal balance between complex opposing forces—opportunity versus risk, collective
security versus individual privacy, and innovation versus protection. While the government
has made progress in identifying existing data sources and sharing high-level metadata, it is
still in the early phases of the maturity curve in terms of enabling access across the federal
ecosystem to leverage the valuable analytics that inform evidence-driven decision-making.
This paper explores the strategies and frameworks to expedite effectively analyzing and
using data to drive national security activities.
Introduction
French philosopher Denis Diderot, prominent during the Age of Enlight-
enment, authored an early version of the encyclopedia to condense stores
of knowledge into one place, thereby democratizing access to insight. The
twenty-first century Internet is based on a similar premise.
David Rubin, a senior vice president at Booz Allen Hamilton, leads the Law Enforcement
and Intelligence Subaccount focusing on the Department of Justice, Federal Bureau of
Investigation, and Department of Homeland Security. He brings 27 years of professional
experience supporting analysis related to national security.
Kim Lynch, a principal at Booz Allen Hamilton, leads the firm’s work at the Department
of Homeland Security Office of Intelligence and Analysis along with other national
security clients. Ms. Lynch also serves as an adjunct professor at George Washington
University.
Jason Escaravage is a principal on Booz Allen Hamilton’s Strategic Innovations Group.
Since joining the firm in 2000, he has supported a diverse set of customers throughout
the Department of Defense and Intelligence Community with a focus in technology-
driven analytics to automate, enhance, and expand an organization’s ability to analyze
its mission critical information.
Hillary Lerner is a senior associate focused on policy, communications, and training
support for the Department of Homeland Security and other national security clients.

Our connected society continuously produces valuable data that is
ushering in a new era of enlightenment. The convergence of behavioral,
scientific, economic, and environmental data offers access to unimaginable
discoveries that have the power to transform our lives. When the data
holdings of the United States government can be more easily accessed and
shared across agencies and departments at the federal, state, and local levels,
perhaps the United States will achieve a modern-day vision of the collective
enlightenment Diderot conceived.
Advanced technologies allow information about our purchases, social
networks, movements, and physical identities to be collected, stored, ana-
lyzed, and used in unprecedented ways, sparking an ongoing, impassioned
debate about the tenuous intersection of national security and civil liber-
ties. “The immense volume, diversity, and potential value of data will have
profound implications for privacy, the economy, and public policy,” notes
John Podesta, Counselor to the President.2
Since 9/11, the U.S. government has initiated efforts to enhance its
information-sharing capabilities and doubled its investment in counterter-
rorism, spending nearly $80 billion. This included the establishment of
the National Counterterrorism Center (NCTC), an organization charged
with bringing together more than thirty data sources from across federal
departments and agencies to provide real-time and strategic intelligence
related to terrorism. Today, NCTC has established itself as the focal point
for sharing information on terrorism and driving activities across the Intel-
ligence Community.
As is often the case, the challenges involved in harnessing big data
analytics for a more enlightened approach to national security center on
striking the optimal balance between complex opposing forces—opportu-
nity versus risk, collective security versus individual privacy, and innovation
versus protection. Some level of individual exposure is required to pursue
the collective, societal benefit. And, protection measures must not have a
chilling effect on innovation that empowers us to stay a step ahead of ad-
versaries.
Setting aside bureaucratic turf wars over rigid silos of locked informa-
tion, today’s legislative and regulatory leaders must collaborate and engage
in conversations that are less about the inevitable, now largely accepted, col-
lection and storage of data in the contemporary ecosystem, and more about
developing and coordinating appropriate policies and governance frame-
works to manage the use of information.
Conventional data practices and mechanisms do not allow federal
agencies to effectively and securely integrate, share, and analyze segmented
data sources in order to draw the critical conclusions required for informed
national security. The data itself can be used to accelerate and enhance the
consolidation of disparate repositories of information and intelligence. And,
savvy investments in cloud analytics solutions will allow government leaders
to fully capitalize on the power and promise of big data, gaining real-time
situational awareness of constantly evolving threats to national security.

123Harnessing Data for National Security
Agencies must collaboratively create and carefully manage shared data
environment policies and governance frameworks that effectively balance
national security and privacy concerns. Their ability to accomplish these ob-
jectives will directly impact the government’s mission to safeguard America’s
borders, infrastructure, and citizens, while ensuring a commitment to the
civil liberties defined in the Constitution.
Data: The Most Powerful Asset in America’s Arsenal
Data impacts every facet of our personal and professional lives. Every day,
we willingly share information with government agencies, Internet service
providers, financial institutions, social networks, and other entities that
enhance and sustain our existence.
Big data and associated analytics deliver transformative value to soci-
ety across a broad range of activities in the commercial sector, from trans-
portation to energy to healthcare. In the public sector, data also drives nearly
every aspect of mission-critical national security—from intelligence analysis
to military operations, fraud detection, counterterrorism, diplomatic deci-
sions, and foreign policy initiatives.
The ability to aggregate and analyze large amounts of data can help
governments and organizations better address public health issues, learn
how economies work, and prevent fraud and other cyber crimes. Data can
help isolate the needle in the haystack. The White House’s Big Data Research
and Development Initiative underscores a growing recognition that big data
analytics can aid in solving some of the nation’s most complex problems.6
Big data can address pressing social, political, and economic changes; ac-
celerate the pace of discovery in science and engineering; and strengthen
national security.
Relevant information often comes from unlikely sources. Consider
Eliot Higgins, a self-taught munitions expert and blogger based in Leicester,
UK, credited with confirming Syria’s use of chemical weapons. Relying on
verified social media sources inside Syria, Higgins, known online as “Brown
Moses,” posted video evidence of civilian casualties on his blog and proved
to be an indispensable, authoritative source, especially given the Bashar al-
Assad regime’s effective ban of international press in Syria.7
Similarly, some
of the best, real-time information regarding Arab Spring came from social
media sites like Twitter and Facebook.
The challenge is monitoring the multitude of available information
sources. By the time government agencies produce classified intelligence
reports, people like Higgins may have already known about and reported
on the relevant international developments.
Information Sharing Saves Lives
Similar to the federal government, the retail and healthcare industries al-
ready possess a wealth of information that lends itself to advanced analytics.
Online retailers, for example, tap into every available data to unlock insights
about customers’ preferences and purchasing behavior, target their market-

ing efforts, and give consumers what they want before they know they want
it. Demanding, instant gratification-driven consumers agree to the tradeoff
of sacrificing some privacy in exchange for the perks of personalized delivery.
In healthcare, like national security, historical data and information related
to risk factors and outcomes are used to measure efficacy and inform criti-
cal decision-making.
Case studies from the healthcare industry illustrate the transformative
process and performance impact of data analytics. Using advanced analytic
techniques, Columbia University Medical Center shortened the time needed
to identify possible complications in brain-injured patients with potentially
lifesaving results.9
In 2011, researchers at Kaiser Permanente used the medi-
cal records of 3.2 million individuals to find a link between mothers’ use
of antidepressant drugs and autism spectrum disorders in their children,
determining that if a mother used antidepressants during pregnancy, her
child’s risk of developing autism doubled. The only reason researchers had
access to the medical records was because they had been collected earlier
and retained.10
Growing From Ground Zero to Big Data Analytics
Sharing data and conducting analyses across the government’s legacy stove-
pipes of information is challenging but mandatory to reduce redundancy,
increase cost efficiency, and improve national security mission performance.
Critical connections remain elusive because different agencies relying on
different databases are responsible for different aspects of securing and
facilitating the movement of data. A leaner, more productive government
enterprise will harness the power of existing data at its disposal.11
Lack of awareness of information assets may prevent an organization
from including critical data in its scope of analysis. Increased knowledge of
existing information resources is required in order to unlock these resources
for analysis and derive maximum value. It is a continuum that starts with
organizing and enabling access to current data; then expanding the size,
type, and scale of data that systems can process; and enhancing analytics
capabilities to better execute and manage enterprise priorities.
The Drug Enforcement Administration’s 2013 National Drug Threat
Assessment notes that the “heroin seized each year at the Southwest Border
increased 232 percent from 2008 (558.8 kilograms) to 2012 (1,855 kilo-
grams). The increase in Southwest Border seizures appears to correspond
with increasing levels of production of Mexican heroin and the expansion of
Mexican heroin traffickers into new U.S. markets.” This type of trend can be
further explored and addressed by the U.S. government through an opera-
tional lens to give front-line mission operators additional insight through
money laundering-related data, network analysis, and demand data. With
the right analytic tools, analysts can more effectively and efficiently use these
types of large-scale data repositories—not by combing through looking for a
needle in a haystack, but rather through tools that quickly identify patterns,
trends, and anomalies meant for further exploration.

While the government has made progress in identifying existing data
sources and sharing high-level metadata, it is still in the early phases of
the maturity curve in terms of enabling access across the federal ecosystem
to leverage the valuable analytics that inform evidence-driven decision-
making. In the past, foreign policy experts relied on small amounts of data
to implement national security activities. There will always be gaps in data
and analysis, but the future of data analytics provides the opportunity for
better decisions based on a more informed perspective.
“Public agencies are in the early days of their big data efforts. Chief
Information Officers (CIO) overwhelmingly report that they are just get-
ting started with big data efforts. While they see the value in synthesizing
databases and using analytics, the challenge of big data has overwhelmed
them.”12
Agency CIOs are aware of the value proposition of data analytics,
knowing that big data can revolutionize service delivery and streamline
business operations.
In a recent report by MeriTalk, Balancing the Cyber Big Data Equation,
which captured insights from federal IT experts in the interwoven disciplines
of big data and cybersecurity, “agencies agreed there is ‘tremendous value’ in
data the government collects, but note that agencies ‘lack both infrastruc-
ture and policy to enable correlation, dissemination and protection.’”13
In
short, the data is there—and will continue to grow—but federal policies will
have to change before that data is used in the most efficient way.
Strategies and Frameworks to Expedite the Transition
The Information Sharing Environment
The Office of the Director of National Intelligence’s (ODNI) Information
Sharing Environment (ISE) encompasses the people, projects, systems, and
agencies that enable responsible information sharing for national security.14
Supported by standards, policy, and governance, ISE partners develop and
sustain mission processes and activities that help frontline law enforcement,
intelligence, and defense personnel detect, prevent, and mitigate terrorist
activity. One of ISE’s mission objectives is to improve nationwide decision-
making by transforming from information ownership to stewardship. This
will be accomplished by:
• Achieving greater interoperability through standards-based acquisition
• Driving responsible information sharing by interconnecting existing net-
works and systems with strong identity, access, and discovery capabilities
• Standardizing, reusing, and automating information sharing policies and
agreements with strong protection of privacy, civil liberties, and civil rights
ODNI recently introduced a new distributed data aggregation reference ar-
chitecture designed to make information sharing between federal agencies
easier. “In a sense, it helps take the ‘big’ out of big data,”15
explains Ksh-
emendra Paul, a program manager at ISE. Instead of inefficiently sharing
data in bulk, which causes potential problems related to privacy, policy, and
security, enhanced correlated data will consistently be pushed to agencies.

For example, to assist in the processing of visa applications, limited data
regarding an individual’s criminal record could be exposed between the
departments of Justice and State.
Hierarchical Access Control
Building on the concept of multilevel access privileges, consider this ex-
ample: If there’s a “person of interest” identifier, such as a phone number,
in both a DEA database and an FBI database, even though detailed informa-
tion is only available to those with authorized access, it is helpful to agents
in both organizations to know the individual is on the radar of multiple
agencies as part of a drug or counterterrorism investigation.
Government organizations can establish alliances and validate the
data’s authoritative nature as part of sophisticated identity and access man-
agement. Business rules can dictate that alliance partners have full access,
for example, while others receive only the “executive summary” without
being able to see the granular data.
Big data has opened up new and broader access to sensitive informa-
tion. Organizations need to keep pace with new security concerns and bring
big data under a sound identity and access management umbrella.16
Data Policy Coordination through Governance Frameworks
“Next-generation governance provides a framework to help federal agency
CIOs and other leaders understand how they can use available tools and
data, not only to manage individual programs but also to coordinate pro-
gram portfolios within agencies and across the government. In particular,
the framework can help agencies build data-driven governance that ensures
compatibility, consistency and efficiency in federal IT investments,” explains
a senior Booz Allen Hamilton technology leader.17
CIOs say that poor data governance is the most critical factor holding
up agencies in their efforts to pursue big data. “We continue to build on a
fragile foundation…. Data governance is not sexy and no one wants to do
it, yet it is our Achilles’ heel.”18
A next-generation framework streamlines and strengthens decision
making by putting information, such as critical risk and cost data, at the
center of governance. Harnessing the capability of richer data sets and ro-
bust analytics tools gives government decisionmakers transparent insight
into mission performance, as well as enabling rapid information sharing
and real-time reporting to support a more agile process.19
Data-Powered Risk Management
Next-generation governance can help agencies achieve mission goals and
risk management objectives. A scalable and auditable governance framework
enables effective implementation of the policies and processes required to
ensure desired outcomes and manage inherent risk. The same data analyt-
ics tools used to streamline government processes can be applied to risk
mitigation efforts.

Sophisticated data-centric threat tracking allows government officials
to monitor activity in specific regions and quickly issue alerts in response
to real-time circumstances. Data can also help fiscally constrained law
enforcement and national security entities efficiently allocate, deploy, and
align resources with prioritized levels of risk. The key is leveraging data and
analytics to improve workflow and augment human expertise.
Tracking patterns and analyzing trends lead to relevant conclusions
and reliable predictions. When a shift in activity reveals a vulnerability, law
enforcement can intercede and initiate a proactive response to prevent an
emerging threat from morphing into a tangible risk. For example, cross
referencing ship manifests against known data from ongoing criminal in-
vestigations can be used to alert officials regarding which cargo containers
to search at ports of entry.
Chief Data Officers to Nurture Need-to-Share Model
The concept of establishing Chief Data Officers (CDOs), more often found
in commercial organizations, can help enterprises and government agen-
cies adopt governance structures, increase data transparency, and develop
information access strategies for accomplishing specific objectives. Data
assets—like IT resources aligned to the CIO—are critical to enterprise suc-
cess and therefore require senior-level accountability and management to
drive maturation. CDOs can nurture a transition from a need-to-know to a
need-to-share paradigm that manages data assets in compliance with privacy
policies while simultaneously removing any hurdles that prevent extracting
inherent value from information that can and should be exchanged.
Back to Basics: Balancing Risk and Rewards
Internally, big data empowers government leaders to track the success of
national security initiatives and allocate resources to those that are yield-
ing a measurable return on investment. From a broader perspective, effec-
tive management of data assets enables the government to proactively and
consistently protect American citizens, our border, and the U.S. economy.
As data structures continue to grow larger and more diverse, the chal-
lenges of integrating and protecting data will become even greater. “The
revolution in big data and cloud computing has ignited a ‘gold rush’ to ex-
tract value from the mountains of digital information collected and stored
by government. The benefit to agencies…will be substantial.”20
Failure to adopt big data analytics will constrict mission performance.
When evaluating potential privacy infringement, a holistic view of the com-
pelling advantages offered by big data analytics should be part of a balanced
risk equation. The consequences of not adopting big data analytics will
surely outweigh the risk.
When government organizations examine how these analytics tech-
niques are transforming enterprises in real-world scenarios, the value
becomes apparent via dramatic gains in the efficiency, efficacy, and perfor-
mance of mission-critical business processes. 21

The U.S. national security posture has a lot to gain from a rigorous
effort to integrate massive data troves and glean the valuable insights that
will help dictate America’s future.
Notes
1
Peter Daboll, “5 Reasons Why Big Data Will Crush Big Research,” Forbes, December 3, 2013,
http://www.forbes.com/sites/onmarketing/2013/12/03/5-reasons-why-big-data-will-crush-big-research/
2
John Podesta, Counselor to the President, “Big Data and the Future of Privacy,” The White
House Blog, January 23, 2014, http://www.whitehouse.gov/blog/2014/01/23/big-data-and-future-
privacy (accessed February 21, 2014).
3
Craig Mundie, Senior Advisor to CEO of Microsoft, “Privacy Pragmatism, Focus on Data
Use, Not Data Collection” Foreign Affairs, March/April 2014, http://www.foreignaffairs.com/
articles/140741/craig-mundie/privacy-pragmatism (accessed March 11, 2014).
4
Michael Allen, Majority Staff Director, House Permanent Select Committee on Intelligence,
Book Discussion on Blinking Red, Crisis and Compromise in American Intelligence after 9/11, C-
Span, December 5, 2013.
5
“Privacy Pragmatism.”
6
Jason Escaravage and Peter Guerra, Enabling Cloud Analytics with Data-Level Security: Tapping
the Full Value of Big Data and the Cloud, Booz Allen Hamilton, 2013.
7
Patrick Radden Keefe, “Rocket Man, How an unemployed blogger confirmed that Syria
had used chemical weapons,” The New Yorker, November 25, 2013, http://www.newyorker.com/
reporting/2013/11/25/131125fa_fact_keefe (accessed March 11, 2014).
8
“Rocket Man.”
9
Data-driven healthcare organizations use big data analytics for big gains, IBM, February 2013,
http://public.dhe.ibm.com/common/ssi/ecm/en/imw14682usen/IMW14682USEN.PDF (accessed
March 11, 2014.
10
“Privacy Pragmatism”
11
Suzanne Storc and Mike Delurey, Marshaling Data for Enterprise Insights, A 10-Year Vision for
the US Department of Homeland Security, Booz Allen Hamilton, 2012.
12 “
What CIOs say about big data,” FCW, February 24, 2014, http://fcw.com/articles/2014/02/24/
exectech-drill-down.aspx (accessed March 11, 2014) in reference to IBM Center for the Busi-
ness of Government report, Realizing the Promise of Big Data (http://is.gd/IBM_Center_BigData).
13
Frank Konkel, “Report: Big data, cybersecurity intrinsically linked,” FCW, February 25,
2014, http://fcw.com/articles/2014/02/25/crit-read-big-data-cyber.aspx (accessed March 11, 2014).
14
Information Sharing Environment, www.ise.gov.
15
Frank Konkel, “Three things to watch on information sharing,” FCW, January 31, 2014,
http://fcw.com/articles/2014/01/31/paul-on-info-sharing-2014.aspx (accessed March 11, 2014).
16
Jonathan Lewis, Big Data, Big Privacy Concerns: Identity Management in a Big Data World, M2M
Evolution, March 7, 2014, http://www.m2mevolution.com/topics/m2mevolution/articles/372521-
big-data-big-privacy-concerns-identity-management-a.htm (accessed March 11, 2014).
17
Fred Knops and Mike Isman, “Next-generation governance is the key to future IT success,”
GCN, April 13, 2011, http://gcn.com/Articles/2011/04/13/Commentary-next-generation-governance.
aspx (accessed March 5, 2014).
18 “
What CIOs say about big data.”
19
Next-Generation Governance, Enhanced Decisionmaking Through a Mission-Focused, Data-Driven
Approach, Booz Allen Hamilton, April 2011.
20
Jason Escaravage and Peter Guerra, Enabling Big Data with Data-Level Security, The Cloud
Analytics Reference Architecture, Booz Allen Hamilton, 2012.
21
Generating Value From Big Data Analytics, ISACA, January 2014, http://www.isaca.org/Knowl-
edge-Center/Research/ResearchDeliverables/Pages/Generating-Value-From-Big-Data-Analytics.aspx

129Implications for the Individual and the StateSAIS Review vol. XXXIV no. 1 (Winter–Spring 2014)
China’s Beidou: Implications for
the Individual and the State
Eric Hagt
China’s satellite navigation system, Beidou, has been elevated from a second-order consider-
ation to a place of prominence in China’s space program. This transformation in national
priorities is the result of a convergence of several state goals involving economic development,
industrial and technological innovation policies, as well as domestic and external security.
The implications of a completed Beidou system are significant for the development and
security of the Chinese state and the individual, in both beneficial and disquieting ways.
Introduction
The evolution of China’s Beidou Navigation Satellite System (北斗卫星
导航系统 beidou weixing daohang xitong) from a regional project into a
global capability reflects more than a loosening of fiscal constraints, but a
clear national policy agenda. Beidou (abbreviated as BDS) is a mega project
that bolsters national development through several means: it builds the nec-
essary infrastructure for a modernizing economy, propels China’s domestic
science and technology efforts,
and moves industrial activity up
the value-added chain. Though
more aspirational than reality
at this point, BDS has the po-
tential to tap into the lucrative
global satellite navigation mar-
ket, rivaling GPS and Galileo.
However, Beidou is also
the backbone for an informa-
tion system that provides the
Chinese state with an impressive array of military as well as internal moni-
toring and surveillance capabilities. This application has important con-
sequences for China’s strategic interaction with other countries, and for
individuals in Chinese society. The balance of priorities in the early years
of Beidou will determine whether it will be used primarily as a coercive tool
or to augment China’s national development.
Eric Hagt is a PhD candidate in the China Studies program at the Johns Hopkins
University Paul H. Nitze School of Advanced International Studies (SAIS).
Though more aspirational
than reality at this point, BDS
has the potential to tap into
the lucrative global satellite
navigation market, rivaling GPS
and Galileo.

Humble Beginnings
As of early 2014, China has launched sixteen satellites for its second genera-
tion BDS (Beidou-2). It has offered services to customers in the Asia-Pacific
region since December 2012, and China plans to serve customers worldwide
upon the system’s completion in 2020. At that time, the system will include
thirty-five satellites, comparable to the U.S. GPS and the planned European
Galileo. It consists of both civilian and military signals, the former with an
accuracy of five meters in Southeast Asia and the latter in the centimeter
range.1
China promotes BDS as more reliable—if not more accurate—than
GPS for the Asia region. Furthermore, China’s satellite system offers special-
ized functions such as the ability to use Beidou for SMS, and an enhanced
capacity to overcome the “urban canyon” effect, whereby satellite imagery
is impeded by a density of tall buildings, a problem of particular salience
in highly urbanized Asian society.2
While the current BDS enjoys strong political and financial support,
China’s initial efforts to develop a satellite navigation system experienced a
slow start. The inception of BDS can be traced to 1985: A Chinese military
officer attending a conference on GPS realized that while the free use of
GPS greatly benefited Chi-
na, it was also conditional.
In other words, China was
dependent upon the Unit-
ed States for its satellite
navigation needs—a state of
affairs that was fundamen-
tally unacceptable to the
People’s Liberation Army
(PLA). This marks the first
rationale for an indepen-
dent BDS system and set
in motion a plan to build the first generation of Beidou.3
Yet for the next
ten years, technological and economic obstacles resulted in little headway.
A number of events in the 1990s shifted the terms of support for
Beidou’s development. The 1990 Gulf War demonstrated in spectacular
fashion the force-multiplying effect of U.S. space-based assets.4
Then in
two separate incidents—the Yinhe Incident in 1993 and China’s large-scale
military exercises in the Taiwan Strait in 1996—China’s access to the GPS
signal was allegedly interrupted.5
These events underscored two lessons
for the PLA: First, a satellite navigation system was requisite for a modern
military; and second, as long as China was dependent on GPS, the country
would be vulnerable. Consequently, funding for an experimental regional
navigation system (Beidou-1) was initiated around 1997 to 1998.6
This first generation program was a modest system consisting of two
satellites in geosynchronous orbit (with one spare) and one ground station,
which provided active (vulnerable two-way) transmission with limited accu-
racy (20 to 30 meters) and only regional coverage. While space was a prior-
The inception of BDS can be traced
to 1985: A Chinese military officer
attending a conference on GPS
realized that while the free use of
GPS greatly benefited China, it was
also conditional.

131Implications for the Individual and the State
ity for China’s leaders during the 1990s, the state’s limited fiscal resources
meant that the manned space effort took precedence over China’s satellite
programs, including the navigation system.
The core rationale for Beidou in its early phases—its military signifi-
cance and the vulnerability of relying on GPS—was primarily based in na-
tional defense terms. However, a confluence of changing strategic, political,
social, and economic circumstances over the following decade motivated
China to build a far more robust satellite navigation program.
A Perfect Storm: Beidou Takes Off
On the surface, an important turning point for Beidou-2 was the falling out
between the Chinese and the Europeans over participation in the Galileo
project, Europe’s navigation system. China joined Galileo with a €230 mil-
lion euro investment in 2003, but it was apparently excluded from certain
aspects of the project in 2006. Soon after, China independently developed
its own global system, the Beidou Navigation Satellite System (Beidou-2).7
While this event undoubtedly helped cement the logic of launching
an autonomous navigation capability, other elements in China’s national
development have conspired to drive the program’s development. In short,
Beidou has come to be seen as a strategic asset by China’s leaders. For
the Chinese, a strategic asset incorporates a quality that extends beyond
national security or traditional military utility, as strategy is usually per-
ceived in the West.8
Domestic stability and economic growth are first-order
considerations for China’s leaders: In the official discourse, these areas are
framed as the “core national goals of economic and social development
and national security.”9
Therefore, a strategic asset serves not only external
security needs, but it also maintains social order and furthers economic
development. Beidou and China’s satellite program are viewed as emerging
sectors that are strategically significant for military needs, and for China’s
overall national evolution.10
With regard to the economy, BDS has taken on new importance in
several respects. Global positioning and navigation are indispensable for a
wide range of critical domestic activities, including shipping and air traffic
control. Furthermore, banking transactions, stock exchanges, and other
financial activities would screech to a halt without the precise timing ap-
plications of these systems. Additionally, road transportation, the electricity
sector, the Internet, and weather monitoring all depend heavily on global
position and navigation.11
These services can be accessed free of charge
through GPS, but reliance on GPS would leave China’s vital economic and
financial foundations vulnerable to disruption. An influential figure in
the Beidou program, Major General Yuan Shuyou, noted, “The Americans
would just have to slightly tamper with GPS and our whole system could
be paralyzed.”12
Thus, the supporting functions of BDS in the economy are
now increasingly seen as critical national infrastructure.
Beidou may contribute to the economy in more direct ways, as well.
One objective for building an independent system has always been the lure
of capturing the lucrative regional and global satellite navigation market,

estimated at roughly $250 billion.13
Sun Jiadong, a scholar and chief de-
signer of Beidou, has stated that China hopes to capture 80 percent of the
domestic market and 20 percent of the global market. If these targets are
achieved, annual revenue could reach as high as ¥500 billion.14
In order to promote marketization of its navigation services, China
has gone on an impressive “transparency spree.” In December 2012, Ran
Chengqi, the director of the China Satellite Navigation Office, announced
the publication of two important technical documents—Beidou’s interface
control document and the BDS Open Service Performance Standard—that
assist developers in manufacturing receivers and chips.15
However, the
hurdles to achieving market viability are high. To date, Beidou has captured
less than 10 percent of the domestic market.16
High-level officials have
publicly called into question national policy planning to develop Beidou,
citing industry excess, inefficiency and abuse.17
Therefore, while Beidou has
promise as a financially viable service, this objective remains an aspiration.
Beidou has been recognized as a strategic industry for other reasons,
as well. A central feature of China’s recent industrial and science and tech-
nology (ST) policies has been to foster a National Innovation System
(NIS).18
This policy orientation is being operationalized through a broad
national investment strategy to promote indigenous efforts in science and
technology. For the past thirty years, China’s development strategy has been
dominated by lower-end manufacturing to take advantage of the country’s
cheap and relatively unskilled labor pool. China’s leadership understands
that the next round of development will require far greater inputs of human
talent and ingenuity. The high-end technology demands of a broad space
program are a poster child for such efforts.
To reach this goal, numerous initiatives and plans have been initi-
ated since the late 1980s,19
but the centerpiece was the “National Medium
and Long-Term Plan for the Development of Science and Technology
2006–2020” (MLP), released in 2006. This document represents the Chinese
belief that innovation can be steered by the government. It relies heavily on
supply-side policies for research and education. New targets to strengthen
indigenous innovation have raised concerns about the emergence of techno-
nationalism, in which the technological capabilities of a nation’s firms are
key sources of their competitive prowess, and these capabilities are built
by national action.20
Beidou is not mentioned specifically in the MLP, but
aerospace features prominently in its target areas.21
Chinese leaders more recently detailed their intentions to develop
a comprehensive NIS by 2020 in a circular, “Opinions on Deepening the
Reform of the Scientific and Technological System and Speeding up the
Building of a National Innovation System.”22
According to the document,
the value of emerging industries of strategic importance (a list that includes
the satellite industry) will total about 8 percent of national GDP by 2015
and 15 percent by 2020. Domestic media reports in 2010 suggested that
China would invest up to $1.5 trillion over five years to support and expand
the strategic industries, a figure that represents an annual expenditure of
5 percent of GDP.23

Other important national planning documents like China’s 11th and
12th Five-Year Plans (2006–2010 and 2011–2015) support Beidou indirectly
through increased budget allocation for high technology RD. Further-
more, there are now three white papers—authoritative documents on policy
directions—on China’s space activities that demonstrate the Chinese state’s
concentrated focus on space programs, with Beidou as an increasingly cen-
tral component. Finally, since the Beidou program was specified as one of
the strategic emerging industries in 2010, a rash of policy planning docu-
ments to develop Beidou have been released by the State Council.24
The Beidou program would foster a domestic space industry. Further-
more, as a highly dual-use technology—for civilians and military alike—it
would bolster development in the context of civil-military integration
(CMI), a policy initiative launched in China in 2003. Historically, China’s
defense industry has been sharply segregated from the civilian sector of the
economy. By bridging the most innovative parts of the civilian and military
sectors, China’s industry policymakers hope to reap greater economic effi-
ciencies in manufacturing and marketing, and to promote synergies in sci-
ence and technology, although the initiative has met with limited success.25
Given the vast regional and global market potential for satellite navigation
services, the Chinese government recognizes the potential of the Beidou
program as a critical impetus for CMI policy.26
In summary, the Beidou program has steadily migrated from the pe-
riphery to center stage of China’s strategy to build an NIS. This evolution is
evident in an overall shift in China’s space program, which had been domi-
nated by manned space since the 1990s. Although China continues to make
great strides in manned space (Shenzhou) and the space lab (Tiangong),
the satellite program has received increasing attention since the mid-2000s,
with Beidou a large part of this focus.27
A satellite navigation program is es-
sential for supporting financial and civilian infrastructure, and it promises
great rewards if regional and global satellite navigation markets can be won.
But the implications of such a system for security, including military and
domestic stability, are considerable.
External Security
From the perspective of Beijing, China’s external security is threatened by
several factors.28
First, the status of Taiwan is a core national interest for
China. Cross-strait relations have generally improved since the election of
President Ma Ying-jeou in 2008. However, short of reunification, Taiwan will
remain a potential flash point, particularly for U.S.-China relations. Second,
the Asian regional security environment is growing increasingly complicated.
Threats include island disputes in the South China Sea (principally with the
Philippines, Vietnam, and Malaysia) and the East China Sea (with Japan);
residual border tensions with India; simmering tensions over North Korea’s
nuclear program; and U.S. military activity near China’s northwestern bor-
der in Afghanistan. Finally, in many respects, China’s interests are expand-
ing globally. Its reliance on natural resources around the world (primarily
energy and rare minerals) is growing, and leaders in Beijing are increasingly

concerned with guaranteeing the country’s supply. Moreover, multilateral
trade in goods, services, and technology is no longer a luxury, but a neces-
sity for China’s economic health.
In the aggregate, these external security conditions call for greater mili-
tary capabilities. Whether it is the means to monitor China’s regional secu-
rity environment or the ability to project power beyond China’s borders, an
independent, robust navigation-positioning system is essential. In addition
to the Beidou-2 system, which boasts a military signal with centimeter-level
accuracy, other space-based platforms such as reconnaissance and commu-
nications satellites significantly expand the military capabilities of the PLA.
In fact, over the past several years, the PLA has acquired a range of assets
that greatly enhance force projection, precision-guidance, target acquisition,
command and control, force deployment, and damage assessment.29
These
assets include twenty-three communication satellites (only ten of which
are thought to have commercial application), and forty-four earth observa-
tion and remote sensing satellites (of which roughly half are operated by
the military). China now has the world’s second largest satellite program.
Although China’s program is
not necessarily equivalent to
the United States in terms of
function and capability, these
gross figures of communica-
tion, observation, and navigation-positioning approach U.S. military figures,
especially in terms of regional context.30
Finally, the applications of BDS and China’s comprehensive satellite
suite are of particular value for China’s near-seas maritime domain. This
is crucial for creating a more complete Command, Control, Communica-
tions, Computers, Intelligence, Surveillance and Reconnaissance (C4ISR)
network in the region. Furthermore, Beidou-2 can be applied to mineral
exploration, fishery resource management, survey and mapping, and other
satellite geodetic functions that are especially useful for asserting China’s
claims and interests in the South and East China Seas.
Internal Security
Domestic order is a growing preoccupation of China’s leaders. Annual re-
ports of mass unrest continue to mount and have roughly averaged 100,000
“mass incidents” per year since 2004.31
While they result from a wide variety
of perceived economic, social, environmental, and legal inequities, the phe-
nomenon is pervasive across regional and social segments of the country.
Viewed from this perspective, China’s domestic stability appears to be a
monumental challenge.
The overarching rubric for this phenomenon is weiwen, or “maintain-
ing stability,” and it has captured the leadership’s attention, as well as a
growing portion of central government resources. The influence of weiwen on
national policymaking was demonstrated in 2011, when the central govern-
ment announced it would devote more funding to weiwen than to the entire
China now has the world’s second
largest satellite program.

national defense budget.32
This pivot to internal social order has profound
implications for the government’s ability to monitor, surveil, and ultimately
control a large and complicated demography in a vast geographic space. It
is probably no accident
that coincident with this
timeframe, China’s satel-
lite program (observation,
communication, and nav-
igation-positioning) dra-
matically increased.33
A
robust satellite navigation
system—along with other
geo-location and imagery
technologies—would pro-
vide the government with a powerful tool for maintaining internal stability.
Perhaps the most salient example of these developments is the advent
of ubiquitous location-aware technologies. China is the largest mobile
phone and Internet user market in the world, with a billion mobile phone
users and more than 600 million Internet users.34
In this sense, China is the
world’s largest networked society. Navigation-chip enhanced mobile phones
are increasingly offering a high degree of detail on individual activities,
movement, and even behavior.35
Available technology can predict with 93
percent accuracy the location of an individual after observing only three
months of phone-usage patterns.36
The ability to automatically generate “personal maps” could offer
society a wide range of beneficial applications in medicine, marketing, and
legitimate law enforcement, to name a few.37
But as with most technologies,
location-aware technology has dual-use implications. These personal maps
can reveal the most salient details of people’s lives—political and religious
beliefs, suspicious associations, and undesirable habits. Vehicles are also
targets for Beidou receivers. China’s Ministry of Transportation has begun
requiring the installation of Beidou receivers in vehicles in many provinces,
particularly in those heavily involved in the car manufacturing industry.38
Geospatial data is being developed in concert with imagery surveil-
lance technology, including CCTV cameras. Analysts estimate there are now
30 million cameras operating in China—one for every forty-three people—
and this figure is expected to grow 20 percent annually over the next five
years.39
License plate recognition systems are also ubiquitous. Integrated
with navigation and positioning technologies, these systems can effectively
place vehicles and individuals under constant surveillance. The potential for
navigational omniscience, and the intense degree of personal information
collection, provides the government with an unprecedented ability to track,
monitor, and ultimately control individuals in society.
In the United States, a legislative and regulatory backlash has begun
against the so-called “surveillance state.” For example, it was recently de-
cided that a warrant was required to install GPS tracking in vehicles. Other
bills are being introduced that limit the state’s free reign to collect geospatial
This pivot to internal social order
has profound implications for the
government’s ability to monitor,
surveil, and ultimately control a
large and complicated demography
in a vast geographic space.

data from cell phones, as well.40
No such law exists in China, although public
opposition to personal data collection is growing.41
Considering the legal
and civil libertarian track record of the Chinese government, restrictions on
such technologies are likely to be weak.
Natural disasters are another phenomenon that threaten the safety
and livelihood of Chinese citizens. China has long suffered from floods,
droughts, and earthquakes, but the populace’s ability to express their
dissatisfaction at the government’s handling of them is something new.
During the Wenchuan Earthquake of 2008, many poorly-constructed
public schools collapsed, causing the deaths of thousands of children, and
sparking national outrage. This event and several following (including the
Yushu Earthquake) demonstrated the need for space-based assets (imagery,
communication, and navigation-positioning) to effectively manage disaster
relief. In fact, the ability of Beidou to relay short messages in the absence of
cell phone availability, in addition to its navigational purposes, was appar-
ently a great boon to the program.42
Whither Beidou
The Beidou program has slowly but steadily progressed from a secondary
consideration in China’s space program to a centerpiece of its National In-
novation System goals and a programmatic priority. Certainly, the easing
of national fiscal constraints
overall has not hurt Beidou’s
prospects. However, its politi-
cal and financial support stem
from other sources, as well. A
robust, indigenous satellite
navigation system contributes
fundamentally to a number of
critical economic, industrial,
and science and technology
policies, as well as China’s ex-
ternal and internal security
imperatives.
In this context, how should Beidou be recognized? Will it be a force
for advancing social and economic development, and serve as an engine for
more efficient industrial, science and technology policies? Or will Beidou
primarily operate in a security capacity, assisting the military in managing
China’s external threats, and serving as a coercive tool for domestic monitor-
ing and control? Naturally, it will serve all of these objectives, though not
necessarily in equal measure.
The so-called business model rationale for building Beidou—that it
will become commercially and financially viable by capturing a significant
portion of the regional and global navigation market—is a strong motiva-
tor and prominent in official statements. But this rationale is relatively
weak, and justifying Beidou in these terms has been an afterthought. The
A robust, indigenous satellite
navigation system contributes
fundamentally to a number of
critical economic, industrial, and
science and technology policies,
as well as China’s external and
internal security imperatives.

program’s initial considerations were primarily strategic in nature, with the
business element only arriving much later—not unlike what happened in the
United States with the development of GPS. The difference is that GPS is an
established program, free of charge, and one that dominates the global and
even Asian regional navigation market. The threshold for breaking into this
market dominance is formidable, and China’s chances of overcoming this
huge disadvantage is highly uncertain. Indeed, few dividends have been seen
so far for Beidou. Therefore, the business model rationale should be seen as
aspirational in nature, and not a firm justification on which to embark on
such an expensive program.
Despite these odds, the program seems to be gaining momentum,
strongly suggesting that other motivations are at work, and additional fac-
tors suggest the security dimension of Beidou is prominent. The Beidou
program, like the majority of China’s space activities, is effectively owned
and operated by the military. The General Armaments Department (GAD),
a powerful central body in the PLA, is tasked with implementing the mod-
ernization of equipment and weapons, of which an important portfolio
is the space program. There is a civilian, NASA-like agency in China, the
National Space Administration, but it has little say over policy.43
In light of
China’s efforts to commercialize Beidou, there are many stakeholders in the
Chinese government that participate in designing policy for the program.44
Yet the PLA appears to maintain a nearly exclusive purview over the program
through the Beidou Office under GAD.45
A large and strongly-supported—
both politically and financially—space program, including Beidou, is not
something the PLA is willing to give up.
The military control of the Beidou program does not preclude civilian
use. However, given the uncertain prospects for market viability, it seems
that security applications are a priority of the program. While Beidou ap-
pears to play an increasingly central role in China’s NIS policies, its impact
is partial and indirect, while its security applications are direct and criti-
cal. Moreover, if the primary purpose of Beidou is commercialization and
national innovation, a military-operated management system—particularly
in an opaque organization like the PLA—is hardly the best option. To date,
policies to develop Beidou have been confusing,46
and the complicated com-
mand and control arrangement within the military has severely hampered
commercial application in the past.47
As with many technologies, Beidou has the potential for both civilian
and military use: It could foster socioeconomic and scientific development
in China, or serve as a coercive tool for domestic and international security
threats. This combination of potential applications has resulted in strong
political and program-
matic support. However,
given the aggravated state
of China’s external and
internal security environ-
ment, the nature of the
program’s governance un-
It could foster socioeconomic and
scientific development in China, or
serve as a coercive tool for domestic
and international security threats.

der China’s military, as well as the state of civil legal protections in China,
the development of Beidou has disquieting implications for both individu-
als within China’s borders and for China’s strategic interaction with other
countries.
Notes
1
For civilian signal within region, see “Chinese Beidou Positioning Accuracy Up to Five
Meters in the ASEAN Region, Exceeds that of Beijing,” Science and Technology Daily, Dec 29,
2013. For military signal, see Shi Chuang et al, “Precise Orbit Determination of Beidou
Satellites with precise positioning,” Science China Earth Sciences, 2012, 55(7): 1079–1086.
2
Kevin Pollpeter, Patrick Besha, Alanna Krowlikowski, “The Research, Development, and
Acquisition Process for the Beidou Navigation Satellite Programs,” Study of Innovation and
Technology in China, Policy Brief, January, 2014.
3
Bo Qingjun, the PLA officer that attended the conference, would later become head of
GSD’s Bureau of Surveying and Mapping, one of the principle agencies managing the Bei-
dou system. See “探秘中国北斗卫星导航定位系统 [The Quest for China’s Beidou Satellite
Navigation and Positioning System],” Newsweek, Jan 1, 2011.
4
Liu Huaqing, 刘华清回忆录 [Memoires of Liu Huaqing],” (Beijing: PLA Publishing House,
2004).
5
For the Yinhe Incident see, “中國研發“北斗”的真正動力 [The Real Power in China’s Devel-
opment of Beidou],” Global Times Network, Dec 31, 2012. For the Taiwan Strait Incident, see,
Li Yun, Wu Xu and Gao Chong, “仰望北斗星为梦想导航 [Looking up at Beidou, Dreaming
of Navigation],” Xinhua, Nov 9, 2013.
6
“中国北斗导航卫星诞生记：研发时曾受经费不足困扰 [Birth of Chinese Beidou Naviga-
tion Satellite: Plagued by Lack of Funding in RD],” China Newsweek Net, Dec 30, 2010.
7
Robert B. de Selding, “European Officials Poised to Remove Chinese Payloads from Galileo
Stats,” Spacenews.com, March 12, 2010.
8
For example, this is articulated by Wang Jisi in “China’s Search for a Grand Strategy,”
Foreign Affairs (March/April 2001), pp 68–79.
9
For example, see: “国务院办公厅关于印发国家卫星导航产业中长期发展规划的通知
[State Council General Office Notification on the Issuance of the Medium and Long term
National Satellite Navigation Industry Development Plan],” October 9 2013, No 97. http://
www.gov.cn/zwgk/2013-10/09/content_2502356.htm
10
Ibid.
11
“美国有GPS，我们有北斗! [The United States has GPS, We have Beidou!],” Guangming
Daily, Dec 28, 2011.
12
“北斗”加速织网明年覆盖亚太 [Beidou Network Accelerated—Will Covering the Asia-
Pacific by Next Year],” Beijing Daily, April 11, 2011.
13
“GNSS Market Report: 2013” by European GNSS Agency, see: http://www.gnss.asia/sites/
gnss.asia/files/GNSS_Market%20Report_2013.pdf
14
“北斗中国人自己的GPS [Beidou, The Chinese People’s Own GPS],” China Youth Daily,
Oct 10, 2012.
15
“The News Release at the Press Conference of the State Council Information Office,”
Beidou Satellite Navigation System Website (accessed March 25, 2014) www.beidou.gov.cn
16
“北斗发展面临’战国纷争’ [Beidou Development is Facing the ‘Warring States Disputes’],”
Dianzi Xinxi Chanyewang, March 20, 2014.
17
“北斗产业找不着北？杨元喜：北斗产业园区遍地开花 [Is the Beidou Industry Adrift?
Yang Yuanxi: Beidou Industrial Parks Sprouting Everywhere],” People.net, March 7, 2014.
18
Patrick Besha, “National, Regional and Sectoral Innovation Systems in China: General
Overview and Case Studies of Renewable Energy and Space Technology Sectors,” PhD dis-
sertation, George Washington University, submitted May 19, 2013.
19
The 863 Program was established in 1986 to stimulate the development of advanced
technologies, one of which was space technologies.

20
Sylvia Schwaag Serger and Magnus Breidne, “China’s Fifteen-Year Plan for Science and
Technology: An Assessment,” Asia Policy, No 4 (July 2007), 135–164.
21
Including high-resolution earth observation systems, manned space flights and the moon
probe.
22
“国务院关于加快培育和发展战略性新兴产业决定 [Decision to Accelerate the Develop-
ment of Strategic Emerging Industries],” 2010, No. 32.
23
“China eyes new strategic industries to spur economy.” July 23, 2012. http://www.reuters.
com/article/2010/12/03/us-china-economy-investment- idUSTRE6B16U920101203.
24
“国务院办公厅关于印发国家卫星导航产业中长期发展规划的通知 [Medium and Long
term National Satellite Navigation Industry Development Plan],” issued by the State
Council, 2013, No 97; “北斗卫星导航系统推广应用的若干意见今发布 [Some Opinions
on Promoting the Development of Beidou Satellite Navigation Industry],” March 11, 2014,
No 1214; and”国务院办公厅关于印发国家卫星导航产业中长期发展规划的通知 [Some
Opinions of the State Council on Promoting the Information Consumption to Expand
Domestic Demand].”
25
Besha, Patrick (2011).”Civil-Military Integration in China: A Techno-Nationalist Approach
to National Development.” American Journal of Chinese Studies, 3
26
“北斗卫星导航系统推广应用的若干意见今发布 [Some Opinions on Promoting the De-
velopment of Beidou Satellite Navigation Industry]” March 11, 2014. No 1214.
27
As a rough comparison, in the 1990s, China launched about sixteen satellites. In the
next five years, that number increased to 27. Since then, China has launched well over one
hundred.
28
Andrew Nathan and Andrew Scobell, China’s Search for Security (Columbia University Press:
New York, 2012).
29
Eric Hagt and Matt Durnin, “Space: China’s Tactical Frontier,” Journal of Strategic Studies,
Vol 34 Issue 5, 2011.
30
Details on satellite launches come from UCS database, updated January 2014. http://
www.ucsusa.org.
31
Jonathan Walton, “Intensifying Contradictions: Chinese Policing Enters the 21st Century,”
NBR Analysis, Feburary 2013.
32
Willy Lam, “Beijing ‘Weiwen’ Imperatie Steals Thunder at NPC,” China Brief (Jamestown
Foundation), Vol 11, Issue 4, March 10, 2011.
33
See footnote 27.
34
“China mobile subscribers up 1.1 percent in June to 1.05 billion,” Reuters, July 20, 2012,
and “At D11, It’s Clear: China Beats U.S. in Mobile Internet, Forbes, May 30, 2013.
35
The state and implications of this era of navigational omniscience for individuals is de-
scribed in the new book by Hiawatha Bray, You Are Here: From the Compass to GPS, the History
and Future of How We Find Ourselves (Basic Books: New York, 2014).
36
Chaoming Song et al., “Limits of Predictability in Human Mobility,” Science, February 19,
2010, 1018–1021.
37
38
“交通部：九省区市车辆必须安装北斗导航 [Ministry of Transportation: Nine Provinces,
Regions and Municipalities Required to Install Beidou Navigation],” Southern Weekend, Jan
16, 2013.
39
Frank Langfitt, “In China Beware: A Camera May be Watching You,” NPR, Jan 29, 2013.
40
For example, the Location Privacy Protection Act, introduced by Senator Al Franken in
2014.
41
“傅蔚冈：安装北斗导航何成’政治任务’？[Fu Weigang: How is Installing [on vehicles]
Beidou Navigation a ‘Political Task’?]” Caixin, Jan 16, 2013.
42
Authors interviews with space program officials, Beijing, August, 2013.
43
Mark A. Stokes with Dean Cheng, “China’s Evolving Space Capabilities: Implications for
U.S. Interests,” prepared for the U.S.-China Economic and Security Review Commission,
April 26, 2012
44
“中国北斗导航卫星诞生记：研发时曾受经费不足困扰 [Birth of Chinese Beidou Navi-
gation Satellite: Plagued by Lack of Funding in RD],” China Newsweek Net, Dec 30, 2010.
45
The full name is the China Satellite Navigation Management Office.

46
“北斗产业找不着北？杨元喜：北斗产业园区遍地开花 [Is the Beidou Industry Adrift?
Yang Yuanxi: Beidou Industrial Parks Sprouting Everywhere],” People.net, March 7, 2014.
47
While GAD directs the program’s launch and on-orbit servicing, the General Staff De-
partment manages Beidou application (as well as develop operational requirements for the
military) with oversight of much of the ground infrastructure to execute that. The potential
for friction between these two departments increases and adds layers of security that will
hamper an open-based system necessary for broader use. 席志刚 [Xi Zhigang], “北斗导航
系统商业应用面临窘境 [Beidou System Faces Dilemma in Commercial Application],” “凤
凰周刊[ Ifeng Weekly],” Issue 13, 2011; and 杨时[Yang Shi], “北斗应用：千亿元的大蛋
糕 [Application of Beidou: Cake worth a Hundred Billion Yuan],” 中国新闻周刊 [China
Newsweek, Issue 1, 2011.

141Implications for International Cyber SecuritySAIS Review vol. XXXIV no. 1 (Winter–Spring 2014)
Understanding Interconnectivity
of the Global Undersea Cable
Communications Infrastructure
and its Implications for
International Cyber Security
Margaret Ross
This paper analyzes the interdependent nature of the global undersea cable communications
infrastructure and its implications for international security. The paper concludes that the
number of landing points in a country is not a function of miles of coastline, the number
of other countries to which the country connects via cable links, nor the number of years
the country has been a member of the international undersea cable network. However, the
number of landing points correlates with the country’s structural position in the network,
as well as variations in hard power and socio-political cohesion (as articulated by Barry
Buzan’s framework). Both political and economic motivations discourage diversifying the
number of landing points. Finally, this paper discusses two potential implications. First,
countries that might seek to control citizens’ access to information by limiting the number
of landing points increase the risk to the global undersea communications infrastructure.
Second, countries with fewer landing points and high betweenness put the cable infrastruc-
ture at higher risk than similar scoring countries with more landing points.
Introduction
Underwater sea cables form the physical backbone of intercontinental
communications connectivity. The importance of this aspect of the
global telecommunications infrastructure to the international economy well
predates the coining of the term cyberspace and the United States govern-
ment’s conception of the global information infrastructure as part of a new
cyber “domain” to be secured. Cyberspace refers to the places where digital
information is created and transmitted that fuel the global economy.1
Soon
after the first trans-Atlantic telegraphic cable was laid in 1858, the technol-
ogy enabled European imperialism to reach all corners of the globe. The
three forms of submarine communications (telegraph, telephone, and most
recently fiber-optic) have come to underpin the global economy.2
Margaret Ross is a second-year M.A. candidate at the Johns Hopkins Paul H. Nitze
School of Advanced International Studies (SAIS) concentrating in Strategic Studies.

As of 2006, the president of the United States’ National Security Tele-
communications Advisory Committee (NSTAC) estimated that undersea
cables carried 95 percent of international communications traffic, including
internet traffic. The rest transited over satellite. By 2010, the Institute of
Electrical and Electronics Engineers (IEEE) estimated that percentage had
increased to over 99 percent. Governments, along with emergency services
and law enforcement, rely heavily on the smooth functioning of this com-
munications infrastructure, especially in the event of a terrorist attack or
major natural disaster. The financial services sector’s reliance on this infra-
structure has also grown. The Society for Worldwide Interbank Financial
Telecommunication (SWIFT) reported that, in 2004, it traded 7.4 trillion
U.S. dollars and processed 9 million messages per day between 208 differ-
ent countries across undersea fiber-optic cables. By 2010, that number had
reached 15 million messages per day.3
For a number of reasons fiber-optic cables have supplanted satellites as
the international communications mechanism of choice. The first modern
fiber-optic cables were installed in 1988. The development of these cables was
spurred by the inadequacies of satellites, which include longer latency time
(the amount of time it takes for a message to traverse from source to desti-
nation), which interferes with the timing of voice conversations, as well as
more erratic transmission quality, and a lack of security.4
More importantly,
satellites make a poor back-up to today’s cable infrastructure, because in the
event of catastrophic failure, total satellite capacity would be two orders of
magnitude smaller than the amount of traffic currently traversing cables.5
Threats to the Cable Network
Landing points, the locations where undersea cables make landfall, provide
a gateway between the underwater cable infrastructure and land-over-land
communication networks. Undersea cables tend to converge at relatively
few landing points, which increase the cables’ subjectivity to environmental
threats, such as earthquakes, and man-made threats, both accidental and
deliberate. These manmade threats include shipping activity (such as the
dragging of fishing nets or anchors along the ocean floor where cables lie),
theft of precious metals (criminals often mistake fiber-optic cables for the
older copper cables), piracy targeted at cable technicians and repair boats,
and deliberate sabotage or cutting of cables, whether by common criminals
or terrorists.
The worst outages to date include cables deliberately cut in the Medi-
terranean in 2008 and earthquake damage in the East China Sea in 2006
and 2009. While some of these outages affected hundreds of millions of
customers, in most cases, Internet traffic was impaired rather than made
completely unavailable, although full restoration of services took months
for some customers. However, this does not rule out a catastrophic scenario
caused by a more orchestrated and targeted attack that could strain the
system and cause severe economic damage, especially to smaller countries.
For example, in 2009 vandals severed terrestrial (not undersea) cables in San
Francisco and managed to cut off phone and Internet service to thousands

143Implications for International Cyber Security
of customers in the Bay area for hours. The severity of the outage indicated
that the particular cables cut were likely deliberately chosen.6
A particular vulnerability of the undersea infrastructure is the lack of
physical diversity in the network due to the propensity to re-use the same
geographic corridors to lay cable and for cables to converge at relatively few
landing sites. Because most of the undersea cable infrastructure is owned by
the private sector, there are economic disincentives for companies to invest
in the exploration, stand up, and maintenance of new routes and landing
stations, to file new permits, and to scout for suitable beach and shallow
water topography. Most importantly, companies prefer to re-use existing
sites and routes because the cost of such sites is already known.7
From the public sector perspective, there is an inherent policy trade-
off with respect to physical diversity. Re-use of existing paths facilitates
national and international efforts to designate certain areas as cable protec-
tion zones; however, these areas then risk becoming single points of failure.8
For example, a major earthquake off the coast of Taiwan in 2006 triggered
an underwater mudslide that caused an unprecedented nine simultaneous
cable failures, impairing Internet access in seven East Asian countries that
took seven weeks to repair.9
Securitizing an Interdependent Network
The global undersea cable communications infrastructure exhibits interde-
pendent characteristics because no single actor has ownership over the entire
network; cable routes are shared between consortiums of companies; its
jurisdiction falls under both national and international laws; and coordina-
tion between companies and countries is necessary to ensure speedy repairs
when outages occur. The global network can be considered interdependent
because it indirectly connects all actors with all others and because there
are externalities associated with security or lack of security.10
The undersea cable infrastructure demonstrates some of the chal-
lenges concealed within the concept of international security. Barry Buzan
begins his book People, States, and Fear with the observation that “the logic
of security almost always involves high levels of interdependence among
the actors trying to make themselves secure.”11
Security is about the pursuit
of freedom from threat, but to successfully “securitize” an issue requires
two components.12
First, a case must be made that some threat actor (such
as terrorists, criminals, pirates, and negligent actors) poses an existential
threat to the referent object that requires security, in this case undersea
cable communications infrastructure. Second, a relevant audience, in this
case the stakeholders of that infrastructure (such as governments and their
constituents), must be convinced that protection of that infrastructure war-
rants special action outside the bounds of existing political procedure (such
as special treaties and subsidizing of private industry). However, the idea
of “securitizing” undersea cable infrastructure, which is both international
and interdependent in nature, is fraught with immediate contradictions
because the problem, according to Buzan, is that security is both a contested
and inter-subjective concept about which nation states differ greatly. In

particular, states vary by two major qualities: their overt power, such as a
state’s military and economic capabilities, and their socio-political cohesion,
which can be roughly viewed as a state’s domestic stability.13
Buzan lists six
factors that might affect a state’s degree of socio-political cohesion: political
violence, intrusion of state’s police into the political lives of its citizenry,
ideological conflicts over organization of the state, presence of contend-
ing national identities, lack of a clear hierarchy of political authority, and
state control of media. The primary distinction between states with weak
and strong socio-political cohesion is that in addition to perceived external
threats, weakly cohesive states feel threatened by potential internal interfer-
ence from contending ideas and domestic groups, while strongly cohesive
states’ conceptions of national security are mostly concerned with external
interference.14
Globalized telecommunications infrastructure places weakly cohesive
states in a dilemma. While these states require this infrastructure for their
economic growth, they may simultaneously view this infrastructure as a po-
tential threat to
government au-
thority and a ref-
erent object to be
secured against
the free expres-
sion of ideas and
i n f o r m a t i o n .
Weakly cohesive
states might also feel vulnerable to the potential influence that can be ex-
ercised over contending domestic groups by external political actors and
thus may seek to censor their citizens’ access to outside information.15
For
example, countries such as China impose a strict filtering regime on content
flowing in and out of the country.16
The hypothesis of this paper is that weakly cohesive states will seek to
regulate their population’s use of the Internet and access to external infor-
mation. Therefore, these states will be more likely to possess fewer numbers
of cable landing points than strongly cohesive states, which would facilitate
such regulation and censorship.
Research Design and Data
The remainder of this paper explores the properties of the global undersea
cable communications infrastructure and which factors—geography, eco-
nomic resources, military capability, or level of political freedoms—corre-
late most closely with the number of cable landing points countries might
choose to have. The data shows that diversification of landing points is
not likely a function of the length of a country’s coastline, the number of
connections to other countries, the number of years a country has been a
member of the undersea cable network, nor a function of a government’s
political stability. However, a country’s economic resources and some of the
measures for levels of political rights and civil liberties do correlate signifi-
While these states require this infrastructure
for their economic growth, they may
simultaneously view this infrastructure as
a potential threat to government authority.

cantly with number of landing points. Additionally, an important network
property known as betweenness centrality, which measures the extent to
which an actor lies on the shortest path between other pairs of actors in the
network, also significantly correlates with the number of landing points.
The data for this analysis was drawn from publicly available infor-
mation on 166 countries, territories, and some sub-state entities, such as
Alaska and Hawaii, that are geographically distinct from their country’s
mainland; 265 international cable links that were ready-for-service between
1989 and 2013; and the 686 landing points where these links make land-
fall.17
Attributes were also gathered for 115 countries, including the number
of landing points per country (TeleGeography 2014), the number of years
of membership in the network (calculated using TeleGeography’s data),18
2012 military spending (Stockholm International Peace Research Institute
2013),19
2011 GDP per capita (World Bank 2014), kilometers of coastline
(CIA World Factbook 2013),20
perceptions of political rights and civil liber-
ties (Freedom House Index 2013 and World Governance Indicator for Voice
and Accountability Indicator 2013), 21
perceptions of political stability of
government (WGI for Political Stability and Absence of Violence 2013), 22
and the degree and betweenness centrality of each country (UCInet Soft-
ware 2002). An additional attribute for level of internet and digital media
freedom was obtained for forty-six countries in the dataset (Freedom House
Freedom of the Net 2013).23
Military spending and GDP per capita were used to approximate Bu-
zan’s concept of overt power. The Freedom House and World Governance
Indicators were used to approximate Buzan’s concepts of socio-political
cohesion. Length of coastline, number of years in network, and number of
connections to other countries serve as control variables in the regression
for other potential factors that might determine numbers of landing points.
Network Data
In addition to the internal attributes of each country, the TeleGeography
data was converted into network data in which countries are connected by
their cable links. Cables that make landfall at several points are broken into
separate segments in the dataset. Countries share a link if each has a land-
ing point connecting to an uninterrupted segment of cable. This network
is both a physical representation of cable infrastructure shaped by limita-
tions of geography and environment, as well as a social representation of the
configuration of countries and the political and economic considerations
that have connected them together.
Figure 1 displays the global network with entities sized by their num-
ber of landing points. By visual inspection it is apparent that the number
of landing points for each entity is unrelated to the number of connections
it has to other entities in the network (its degree centrality). Globally, the
median number of landing points of those countries with at least one in-
ternational cable is two. An entity’s position in this network carries certain
implications. For example, the more diverse an entity’s connections, the
more insulated it is from the threat of being cut off from global communi-
cations, whether accidentally or deliberately.

It is important to note that these networks represent snapshots
in time in what is a very dynamic system. For example, the number of
countries linked by undersea cables more than tripled between 1979 and
2005, including the addition of
twenty-eight countries newly
added to the network since 1999.
In 1979, countries across several
continents occupied the top ten
spots in bandwidth. Now, eight
of those top ten countries are in
Asia.24
Structural positions in the network can change significantly with the
addition of a few new cables. For example, the addition of a cable from an
East Asian country to a Latin American country would greatly reduce the
betweenness score of the United States. As of January 2014, TeleGeography
listed twenty cables that would be ready for service sometime in 2014 and
2015. These cables were not included in this analysis since they have not
yet been completed.
Research Findings
A multiple regression analysis (see Appendix) evaluated whether the inde-
pendent variables—kilometers of coastline, years of network membership,
degree centrality (number of connections to other countries), between-
ness centrality, log of military spending, GDP per capita, and stability and
political rights measures—had a significant relationship to the dependent
variable, number of landing points. All three approximations for aspects
of Buzan’s concepts of socio-political cohesion, the FH Index, WGI’s Voice
and Accountability Indicator, and WGI’s Political Stability Indicator, indi-
vidually exhibit a significant relationship with number of landings points
at the .05 alpha threshold. However, when combined in a multiple regres-
sion with degree and betweenness centrality, military spending, GDP per
capita, kilometers of coastline, and years of network membership, they are
no longer significant. Military spending is also only significant when run in
a simple regression on number of landing points, but loses its significance
when other variables are added. Coastline, years of network membership,
and degree centrality are never significant. The FH Index and the two WGI
indicators were substituted individually in three separate multiple regres-
sions, since they are highly correlated to each other. In all three regressions,
the t-statistics for betweenness are significant at the .001 level, while in two
of the regressions t-statistics for GDP per capita are significant at the .05
level. Degree centrality, military spending, kilometers of coastline, and age
of membership were not significant. Betweenness and GDP per capita both
have a positive correlation with numbers of landing points, leading to the
interpretation that those countries that are structurally more central to
the network and which have greater economic resources also tend to have
greater numbers of landing points. It is important to note that this relation-
ship is merely a correlation, not an indication of causation.
. . . these networks represent
snapshots in time in what is a
very dynamic system.

Figure1.CompleteNetworkDiagramSizedbyLandingPoints

For the forty-six countries in the dataset that also had a Freedom of
Net index, a separate simple linear regression was run with number of land-
ing points as the dependent variable. The test produced a significant p-value
at the .05 threshold, indicating a negatively significant relationship between
levels of internet freedoms and number of landing points. The lower the
Freedom of Net score, the higher the level of internet freedom, so a negative
relationship here implies that high levels of internet freedom are associated
with high numbers of landing points.
Interpretation
For the relationship between the stability and freedom indicators and
landing points, the regression analyses yield inconclusive results. There
is a possible simultaneity issue, which occurs when two variables—in this
case, number of landing points and the various stability and freedom in-
dices—influence each other simultaneously. Under this logic, the greater
the number of landing points, the greater a citizen’s potential ability to
access external information, which may correspond to a greater propensity
to openness towards external information. In the reverse direction, greater
levels of political freedoms and civil liberties and greater government sta-
bility may intrinsically be linked with openness and greater demand for a
robust information infrastructure which would tend towards higher num-
bers of landing points. The other issue is that these indices have relatively
strong correlations to GDP per capita and GDP per capita seems to bear
the stronger relationship to number of landing points. Thus, the stability
and freedom indices’ relationship to landing points is dampened when the
model controls for GDP per capita.
A set of potentially more problematic methodological issues pertains
to the indices themselves. First, the FH Index, its Freedom of the Net In-
dex, and the WGI Indicators are perception-based measures, not measures
of actual levels of stability and political and civil liberties. This gives these
indices a subjective quality that leaves them potentially subject to issues of
groupthink and false consensus. Second, these variables serve as proxies for
concepts that are not easily observable and measurable. The WGI Indica-
tors are aggregated from many sources and then categorized into one of six
governance groupings, two of which are used in this analysis. The connec-
tions between WGI’s sources and the constructs they purport to measure
have yet to be validated.25
An important network property known as betweenness centrality
significantly correlates with the number of landing points in the multiple
regression analyses. An actor has high betweenness to the extent that it
lies on the shortest path between other pairs of actors in the network. As-
suming the shortest cable paths between any two countries represent the
most efficient transit path of information, high betweenness countries are
structurally important to the functioning of the entire network. A severe
cable outage in a high betweenness country would affect other countries
in the network more than the same outage in a low betweenness country.26

However, the network disruption potential that a high betweenness country
might exhibit also depends on how easily communications traffic could be
re-routed or how easily new ties could be created. Countries can have low
betweenness scores for two reasons. Either the country does not lie on the
shortest paths of any other country pairs or the country is connected to
other countries that are themselves directly connected.27
Figure 2 is a network diagram showing entities sized by betweenness
with cable ties to ten or more other entities in the global network. The
betweenness scores range from 0 to 6,693 and the United States, Italy,
South Africa, Egypt, Portugal, United Kingdom, India, and France—listed
in descending order—each have betweenness scores greater than 1,000 in
the complete network. The United States has the highest betweenness in
the network because it connects two major groupings of countries in Latin
America and East Asia that are themselves not directly connected.
The tendency indicated by the regression analyses is for the higher
scoring betweenness countries to have higher numbers of landing points,
a characteristic that should
strengthen the network. How-
ever, there are a few countries
in key structural positions that
possess fewer numbers of land-
ing points. Countries with lower
numbers of landing points but
that have high betweenness put
the network at higher risk than
countries with higher numbers
of landing points and the same level of betweenness. These high-between-
ness, fewer-landing point countries create choke points that pose a potential
risk to the entire network.
Figure 3 is a scatter plot showing betweenness on the x-axis and
number of landing points on the y-axis. Countries with betweenness scores
greater than 1,000 are labeled. Four countries, Egypt, India, Portugal, and
South Africa, have relatively high betweenness, but fewer than ten landing
points each. Of this group of four, India had the highest total bandwidth
in service as of 2005, meaning the largest amount of information transiting
its cables.28
Conclusion
This paper proposed several measures by which the security of the global
undersea cable communications infrastructure could be evaluated. It found
that amount of coastline, number of years in the network, and number
of connections to other countries did not correlate significantly with the
dependent variable, number of landing points. The relationship was incon-
clusive for some levels of overt power, such as military spending, and certain
measures of stability and political freedom. Betweenness, GDP per capita,
and level of internet freedom did correlate significantly with the dependent
variable. However, because, the Freedom of the Net index is available for just
These high-betweenness,
fewer-landing point countries
create choke points that pose
a potential risk to the entire
network.

Figure2.EntitieswithTenorCableMoreTiesSizedbyBetweenness

Figure3.BetweennessofInternationalLandingConnectionsbyNumberofLandingPoints

forty-six countries evaluated in the global cable network, this is an area for
future evaluation.
While Buzan’s framework signals that weak and strong states (whether
measured by overt power or socio-political cohesion) will likely differ over
what constitutes “securitizing” this interdependent network, this paper does
not demonstrate definitively that weak and strong states approach the issue
of landing points differently. The increasing interdependency of the global
telecommunications system means that it would be difficult for a country
in the modern age to deliberately disrupt another’s access to global com-
munication without impairing its own. However, this analysis shows that
structural properties of the network mean that not all countries are equally
insulated against this threat.

Appendix:RegressionResults
(1) (2) (3) (4) (5) (6) (7)
landings landings landings landings landings landings landings
WGIvoice 2.665*** 0.823
(4.19) (1.19)
WGIstability 1.572* -0.113
(2.41) (-0.16)
FHindex -1.196*** -0.340
(-3.44) (-1.01)
degree 0.0823 0.0671 0.0835
(0.82) (0.67) (0.83)
betweenness 0.00393*** 0.00420*** 0.00393***
(4.40) (4.80) *
4.34
log10milit~y 1.003 0.679 0.958
(1.36) (0.87) (1.30)
gdppercapita .0000637 0.0000853* 0.0000723*
(1.98) (2.30) *
(2.41)
coastline 0.0000281 0.0000330 0.0000291
(1.16) (1.36) (1.20)
age 0.0704 0.126 0.0819
(0.60) (1.11) (0.70)
FHnet -0.136*
(-2.19)
_cons 4.820*** 5.066*** 8.660*** -2.418 -2.613 -1.521 12.83***
(8.06) (7.95) (6.77) (-1.37) (-1.47) (-0.73) (4.24)
N 113 113 113 110 110 110 46
adj.R-sq 0.129 0.041 0.088 0.499 0.492 0.497 0.077

Notes
1
Daniel T. Kuehl, “From Cyberspace to Cyberpower: Defining the Problem,” in Cyberpower
and National Security, ed. Franklin D. Kramer, Stuart H. Starr and Larry K. Wentz, First Edi-
tion ed. (Dulles, Virginia: Potomac Books, Inc., 2009).
2
Peter J. Hugill, “The Geopolitical Implications of Communications Under the Seas,” in
Communications Under the Seas: The Evolving Cable Network and its Implications, ed. Bernard S.
Finn and Daqing Yang, (Cambridge, Mass.: MIT Press, 2009), 257.
3
Karl Frederick Rauscher, Proceedings of the Reliability of Global Undersea Cable Communications
Infrastructure Study and Global Summit: The ROGUCCI Study and Global Summit Report: IEEE
Communications Society (2010), 179.
4
Jeff Hecht, “Fiber-Optic Submarine Cables: Covering the Ocean Floor with Glass,” in Com-
munications Under the Seas: The Evolving Cable Network and its Implications, ed. Bernard S. Finn
and Daqing Yang, (Cambridge, Mass.: MIT Press, 2009), 46.
5
6
Marguerite Reardon, “How Secure is the US Communications Network?” Cnet, April 13,
2009.
7
Communications Society (2010), 102. and Jim Bishop and John Walker, “There is no Safety
in Numbers: The Security Issues of Multiple Cable Landings,” Submarine Telecoms Forum,
(2006): 16-18.
8
9
Ibid, 172.
10
Stephen P. Borgatti and Xun Li, “On Social Network Analysis in a Supply Chain Context,”
Journal of Supply Chain Management 45 no.2 (2009): 5. and Howard Kunreuther and Geoffrey
Heal, “Interdependent Security,” Journal of Risk and Uncertainty 26 no.2-3 (2003): 231-249.
11
Barry Buzan, People, States, and Fear: An Agenda for International Security Studies in the Post-Cold
War Era, 2nd ed., (Boulder, Colorado: L. Rienner, 1991).
12
Ibid, 18.
13
Ibid, 100.
14
Forrest Hare, “The Cyber Threat to National Security: Why Can’t We Agree?,” (paper
presented at the Conference on Cyber Conflict, Tallinn, Estonia, 2010).
15
Ibid, 106.
16
Edward J. Malecki and Hu Wei, “A Wired World: The Evolving Geography of Submarine
Cables and the Shift to Asia,” Annals of the Association of American Geographers 99 no.2 (2009):
360-382.
17
Cable data was obtained from TeleGeography, a telecommunications market research and
consulting firm. Its submarine cable map is free and available at http://www.telegeography.
com/telecom-resources/submarine-cable-map/index.html.
18
While some countries, such as the U.K. and U.S., have had underwater cable connections
for far longer than since 1989, because of incomplete data, the “age” of country membership
in this network only extends to twenty-five years and corresponds with the oldest cables in
the data which date back only to 1989.
19
SIPRI provided 2012 military spending in terms of 2011 U.S. dollars. It was then logged
for the purpose of this analysis. http://www.sipri.org/research/armaments/milex/milex_da-
tabase
20
Central Intelligence Agency, CIA World Factbook 2013, https://www.cia.gov/library/
publications/the-world-factbook/fields/2060.html
21
Freedom House describes itself as independent watchdog organization dedicated to the
expansion of freedom around the world. It produces an annual comparative assessment

of political rights and civil liberties that covers 195 countries and fourteen related and
disputed territories. www.freedomhouse.org/report/freedom-world/freedom-world-2013#.
U1A3vhZM9UQ
22
The World Governance Indicators are produced by The World Bank Development Research
Group. They are aggregate indicators that measure six dimensions of governance for over
200 countries and territories. The Voice and Accountability Indicator measures the percep-
tions of the extent to which a country’s citizens are able to participate in selecting their
government, as well as freedom of expression, freedom of association, and a free media.
The Political Stability Indicator measures perceptions of the likelihood that the govern-
ment will be destabilized or overthrown by unconstitutional or violent means, including
politically motivated violence and terrorism. http://info.worldbank.org/governance/wgi/
index.aspx#home
23
Since 2009, Freedom House has produced Freedom of the Net, which is an assessment
of the degree of Internet and digital freedom around the world, now covering sixty coun-
tries. Data is accessible at www.freedomhouse.org/sites/default/files/resources/FOTN%20
2013_Charts%20and%20Graphs_Global%20Scores.pdf
24
360-382.
25
Melissa A. Thomas, “What Do the Worldwide Governance Indicators Measure?” European
Journal of Development Research 22 (2010): 31-54.
26
Stephen P. Borgatti and Xun Li, “On Social Network Analysis in a Supply Chain Context,”
Journal of Supply Chain Management 45 no.2 (2009): 5, 11.
27
Stephen P. Borgatti, M. G. Everett, and Jeffrey C. Johnson, Analyzing Social Networks (Los
Angeles: SAGE Publications Ltd., 2013).
28
360-382.

157Book Review Policymaking in the Era of Big Data
A Dangerous Trade-off :
Policymaking in the Era of
Big Data
Ilaria Mazzocco
Viktor Mayer-Schönberger and Kenneth Cukier, Big Data: A Revolution That Will Transform
How We Live, Work, and Think (Eamon Dolan/Houghton Mifflin Harcourt, 2013), 256 pp.
One of the most astonishing aspects of Edward Snowden’s revelations and
the consequent congressional hearings regarding the NSA’s data collec-
tion activities was the sheer size of the program. While the notion of living in
an increasingly connected and digitalized world is widely recognized, it is easy
to overlook how this state of constant communication is shaping business
and government and what this means for our society as a whole. Viktor Mayer-
Schönberger, professor of internet governance and regulation at the Oxford
Internet Institute at the University of Oxford, and Kenneth Cukier, Data Editor
for TheEconomist, are certain that we live on the cusp of the next paradigm shift.
With this in mind, the authors
of BigData:ARevolutionThatWill
TransformHowWeLive,Work,and
Think set out to build a frame-
work for understanding how
technology allows us to process
increasingly large amounts of
information, how we can use
this information, and why big
data may be the biggest thing
since the printing press.
At the heart of the book
is the idea that we are seeing a
qualitative shift in knowledge as we increase the size of the information we
process. Sampling was a revolutionary instrument, but in many fields it may
outlive its usefulness as we now have the opportunity to work with samples
so large they come close to including the entire statistical population. The
authors make a strong case for building a new methodology for the big data
Ilaria Mazzocco is a first-year M.A. candidate at the Johns Hopkins University Paul H.
Nitze School of Advanced International Studies (SAIS) concentrating in China Studies.
She is an Assistant Editor for The SAIS Review.
Sampling was a revolutionary
instrument, but in many fields
it may outlive its usefulness as
we now have the opportunity to
work with samples so large they
come close to including the entire
statistical population.
SAIS Review vol. XXIII, no. 2 (Summer–Fall 2003)
157
SAIS Review vol. XXXIV no. 1 (Winter–Spring 2014)
© 2014 by The Johns Hopkins University Press

universe. Thanks to several convincing examples, largely from the business
sector, Mayer-Schönberger and Cukier argue that, in the future, knowledge
must rely less on causal links and more on imprecise data collected from an
increasingly large number of sources.
It is counterintuitive that one of the greatest achievements of modern
science—precision—is what we must give up in order to achieve more accurate
results in the future. Big data, the authors argue, is inherently messy. The crux
of their argument holds that approximation on such a massive scale produces
reasonably reliable results, and often does so without the need to create models
as complex as those required for smaller samples. Examples ranging from gram-
mar correction software to credit analysis indicate that the ability to prioritize
quantity over quality can make a big difference. Companies that embrace using
data of dubious quality in order to achieve massive scale are more nimble and
quick—crucial elements in today’s business world.
Logic may be one of the most powerful tools to be developed by the hu-
man mind, yet to use big data we may need to leave logic behind. One of the
mainconsequencesofpracticinganalysisofbig
data is that we can learn relatively little about
causation even as we are able to make predic-
tions more accurately than ever before. Com-
panies like Walmart, Target, and Amazon have
successfullyfoundpatternsintheirconsumers’
behavior that enable them to make smarter
marketingchoices.Thesepatternsdonotmean
these companies need, or even have the ability,
tounderstandwhywomenpurchaseunscented
lotion during their third month of pregnancy,
butitallowsthemtotargettherightcustomers.Mayer-SchönbergerandCukier
believe that in a world where correlation can predict the next flu outbreak by
tracking Google searches, causal links
are no longer as important—and may
even be misleading. The idea that causal
links are becoming obsolete is one of
the more significant and controversial
points in the authors’ argument.
While companies may be able to
part with causality as they build more
effective marketing strategies, it is
problematic to encourage this approach
amongpolicymakers.Ifsocietydoesnotdeveloptheinstrumentstounderstand
whyachildperformspoorlyinschool,thereislittletogainfromknowingwhich
student will start falling behind first. Moreover, as policymakers rely increas-
ingly on correlation in social matters, it will become harder to avoid profiling.
The authors argue that big data will allow policymakers to conduct more
accurate predictions that will avoid profiling such broad—and stereotyped—
categories. Nonetheless, it is hard to forget how the Bloomberg administration
in New York City, lauded by the authors for its forward-looking use of data,
Logic may be one of
the most powerful tools
to be developed by the
human mind, yet to use
big data we may need to
leave logic behind.
The idea that causal links
are becoming obsolete is one
of the more significant and
controversial points in the
authors’ argument.

159Book Review Policymaking in the Era of Big Data
was widely criticized by the public for its heavy-handed crime fighting meth-
ods. In a world where the reason why one commits a crime matters less then
when the crime was committed, there is a real risk that the structural causes
of issues addressed through big data might remain unaddressed. While the
authors recognize the risks of relying exclusively on data, they are able to offer
only limited solutions.
The third consequence of relying on big data is that data begets data.
The growing data-related industry is increasingly divided among those who
collect the data, those who analyze it, and those who know how to use it. This
is changing the playing field in business and providing exciting new opportu-
nities that forward-looking companies like Google are especially well-poised
to take advantage of. With every action we take, whether visiting a hospital,
transcribing digital text on re-captcha, or texting a friend, we are constantly
producing more data and allowing for more correlations, and thus predictions,
to be made. The ownership of this data and how it will be used may give rise
to the next great monopoly or dictatorship.
The word “data” derives from the plural form of the Latin word “datum,”
which loosely translates as “something that is given.” There is some irony to be
found in the etymology of the word as users are finding it increasingly difficult
to monitor where and what is given and for what purpose. Mayer-Schönberger
and Cukier propose moving toward an auditing-style system to monitor data
usage and shifting the responsibility to companies to evaluate whether they
are managing user information responsibly. The authors are also careful to
warn against using big data in ways that may affect individuals’ rights. You
might have a pre-disposition to commit a crime, but this information should
never infringe on your right for a trial based on facts. These are good ideas,
but are they enough?
You could make the argument that some of the United States’ legal
frameworks are outdated when it comes to the risks posed by technology.
From identity thefts associated with the use of social security numbers online
to privacy statements, these instruments must be reviewed. Yet as the authors
themselves recognize, it is difficult to predict where technology will lead us. In
many ways, it is easier to evaluate the positive and negative outcomes of relying
on data for companies, where profits are the clear objective. In government in-
stitutions, where citizens’ lives and their environment are at stake, this clear-cut
goal is harder to identify, let alone achieve. As the NSA data collection program
illustrates, big data is changing more than just our book-purchasing behavior.
In the evolving context of how big data can be used, Mayer-Schönberger and
Cukier’s overview provides a highly accessible and useful framework that can
and should be used to discuss the implications of big data for society.

161Book Review A Prophet for the Digital Heretics
A Prophet for the Digital Heretics:
Evgeny Morozov’s Quest to
Debunk Silicon Valley Solutionism
Bartholomew Thanhauser
Evgeny Morozov, To Save Everything, Click Here: The Folly of Technological Solutionism (Public
Affairs, 2013), 432 pp.
Is it possible to be addicted to the Internet? Evgeny Morozov seems to think
so. His laptop has an easily removable Wi-Fi card and his home has a safe
with a timed combination lock into which he can throw all Internet enablers:
phone, Wi-Fi card, router cable. But even that is not enough. Necessity breeds
innovation, and Morozov has found a way to use screwdrivers to pry the lock
apart. Now, the screwdrivers go into the safe, as well.
On the surface, these probably seem like the actions of an eccentric, albeit
highly determined, man. And to those who believe the Internet is an inherently
positive force, Morozov probably is exactly that. However, to those who take a
more critical view of the Internet, and more broadly, our society’s discourses
on technology, Evgeny Morozov and his book, To Save Everything, Click Here,
offer a powerful, paradigm-shifting rebuttal. Although heavy-handed at times,
Morozov provides an intelligent shot of cynicism into the way we view, use,
and discuss technology.
Morozov, a self-described “digital her-
etic,”1
offers two main critiques to articulate
his worldview. The first critique takes aim at
what he calls Silicon Valley’s “solutionism,”
which is the idea that technology, or more
specifically, the Internet, can be the solution
to all the world’s problems. Global-warming?
Ruthless autocrats? Endangered naked mole
rats? Don’t worry, there’s an app for all of these
problems, and Facebook, Google, and others
are building it. Morozov’s second critique is of solutionism’s spawn—“Internet-
centrism”—which is the inescapable (and often detrimental) centrality that
Silicon Valley solutionists attach to the Internet. The Internet is the square-
Bartholomew Thanhauser is a first-year M.A. candidate at the John Hopkins University
School of Advanced International Studies (SAIS) concentrating in Southeast Asian stud-
ies. He is an Assistant Editor for The SAIS Review.
TheInternetisthesquare-
peg-in-the-round-hole
thatSiliconValleyusesin
its endeavors to explain
and improve everything.
SAIS Review vol. XXIII, no. 2 (Summer–Fall 2003)
161
SAIS Review vol. XXXIV no. 1 (Winter–Spring 2014)
© 2014 by The Johns Hopkins University Press

peg-in-the-round-hole that Silicon Valley uses in its endeavors to explain and
improve everything.
To Morozov, solutionism and Internet-centrism’s “promise of eternal
amelioration” is a “digital straightjacket.”2
Not only does he reject the belief
that the two are capable of solving the world’s problems, but he sees this
very mindset as dangerous. It warps the role of technology in our lives and
re-distributes power in potentially negative ways. It is a mindset that has al-
lowed Silicon Valley to co-opt morality and provide solutions to problems that
don’t necessarily exist. Its goal of a “frictionless,” perfectly efficient society is
as quixotic as it is harmful; as Morozov writes, “sometimes, imperfect is good
enough; sometimes, it’s much better than perfect.”3
Morozov is no Luddite; he does not advocate readers lock their electron-
ics in a safe like he does, much less throw them away. His qualms are not with
technology, but with solutionism. He recognizes that technology is often a
positive force. It is only in casting it as a panacea that our intellectual curiosity
deadens, our agency weakens, and our understanding of the human condition
becomes tenuous. To Morozov, the Internet is not a solution, but a series of
tubes and wires.
This is a powerful statement, and Morozov gives it further potency by
grounding it in history. He argues that, although the Internet has changed
our lives in numerous ways, the belief that it will transform us into different
humans living in different
societies is a kind of “geek
creation myth.” He com-
pares the Internet to past
inventions,notingthat“al-
most every new invention
is met with great expecta-
tions that it will promote
human understanding.”4
Paved roads, telegraphs,
radios, and televisions
were all predicted to erase
cultural differences, end
conflict, and eliminate human misunderstandings—and none lived up to these
monumental predictions. He uses the term “epochalism” (one of the seemingly
hundreds of neologisms in the book) to describe the radical proclamations
that follow each new invention, and he views Silicon Valley’s idolization of the
Internet as simply the most recent incarnation of epochalism.
Furthermore, he charges that this current epochalism fuels misconcep-
tions about the Internet. One by one, he works to discredit each misconcep-
tion. He argues that the Internet is neither natural nor neutral. Twitter’s
“trending” and Google’s autocomplete functions are not mirrors that reflect
reality as their creators claim; they are manipulable algorithms that actively
shape reality. Morozov even takes on popular ideas like “transparency” and
“openness,” arguing that despite the hagiographic public support they enjoy,
both can be detrimental to society. Calling himself an “Internet realist,”5
he
He uses the term “epochalism” (one of
the seemingly hundreds of neologisms
in the book) to describe the radical
proclamations that follow each new
invention, and he views Silicon Valley’s
idolization of the Internet as simply the
most recent incarnation of epochalism.

163Book Review A Prophet for the Digital Heretics
describes some of the downsides of transparency (it can weaken trust, discour-
age communication, and punish compromise) and argues that it is a mistake
to worship it uncritically.
Taking on these popular beliefs about the Internet is no easy task, but
Morozov does so by using his impressive knowledge of the contemporary
technological landscape. At times, Click Here reads like an encyclopedia of
little-known, but undeniably cool technological developments. Some of these
are straw men propped up for Morozov to excoriate, while others are used as
examples worthy of his praise. However, all the products he describes—rang-
ing from “Forget Me Not” lamps that open upon touch to “the Eye Tribe,”
which tracks eye movement to gauge how much content a reader absorbs on
a page—leave the reader shaking his or her head in amazement. There is seem-
ingly no limit to the diversity of bizarre yet fascinating technologies Morozov
can critique.
He evaluates these inventions and the solutionism and Internet-centrism
that fuel many of them with a caustic, entertaining wit. He calls the twelfth
librarian of the U.S. Congress, Daniel Boorstin, “America’s most overrated
historian.”6
He notes that seventy-nine year-old Microsoft researcher and re-
nowned engineer, Gordon Bell, “writes like an inexperienced teenager who’s
not had his fair share of diverse social interactions.”7
Finally, he claims that
New York Times best-selling author Jane McGonigal is “utterly confused about
human experience.”8
These are not ad hominem attacks: Morozov is nothing if
not thorough in his critiques. Nevertheless, it is little wonder that Morozov
is a “feared reviewer of other technology pundits’ books.”9
His critiques are as
entertaining as they are unrelenting.
Yet at times, Morozov seems so focused on tearing down the ideas of oth-
ers that he leaves little room to build up his alternate vision of the role technol-
ogy should play in the world. Mo-
rozov offers some suggestions:
a world where Google, Twitter,
Facebook, and their various rela-
tives must “acknowledge [their]
own immense role in shaping the
public sphere, and start playing
that role in a more responsible
manner.”10
Furthermore, a world
in which technological inven-
tions do not solve our problems,
but empower us to think critically and solve them ourselves. Lastly, a world
that recognizes that just as the Internet is not inherently solution-producing,
neither is Internet regulation inherently creativity-staunching.
Nevertheless, in building this alternate paradigm, Morozov occasionally
falls victim to his own critiques. For someone who derides Internet-centrism,
he struggles to imagine societal advances without the Internet. Furthermore,
he uses the same epochalistic terms that he criticizes his contemporaries for
using. Most notably, in defending his arguments, Morozov uses a few clunker
examples that seem fueled more by paranoia than logic (for example, he give
Yet at times, Morozov seems so
focused on tearing down the ideas
of others that he leaves little room
to build up his alternate vision of
the role technology should play in
the world.

serious consideration to the possibility that Amazon will replace novelists with
robots), and uses techno-jargon excessively (ranging from “technostructural-
ists” to “datasexuals”).
Morozov seems unusually aware of Click Here’s shortcomings. In his
postscript, he offers, “on the odd chance that this book succeeds, its greatest
contribution to the public debate might lie in redrawing the front lines of the
intellectual battles about digital technologies.”11
This is Click Here’s biggest
achievement. In challenging solutionism and Internet-centrism, Morozov has
provided a crucial counterweight to the dominant views on technology that
threaten to “impoverish and infantilize our public debate.”12
Rather than offer
a rigid alternate to these views, Morozov shifts them, pushing contemporary
discourses on technology past the misguided yet intoxicating feeling that we
“are living through a revolution.”13
Notes
1
Evgeny Morozov, To Save Everything, Click Here: The Folly of Technological Determinism (Public
Affairs, 2013), xiii.
2
Ibid, xiii.
3
Ibid, xv.
4
Ibid, 292.
5
Ibid, 93.
6
Ibid, 51.
7
Ibid, 272.
8
Ibid, 308.
9
Ian Tucker, “Evgeny Morozov: ‘We are abandoning all the checks and balances,’” The Observer,
March 9, 2013, http://www.theguardian.com/technology/2013/mar/09/evgeny-morozov-
technology-solutionism-interview (accessed March 21, 2014).
10
Morozov, Click Here, 146.
11
Ibid, 355.
12
Ibid, 43.
13
Ibid, 357.

Sais.34.1

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