The pace of poverty reduction after the Millennium Development Agenda

UNU‐MERIT Working Paper Series

#2014-006
Poor trends
The pace of poverty reduction after the Millennium Development Agenda
Richard Bluhm, Denis de Crombrugghe, Adam Szirmai

Working Paper Series on Institutions and Economic Growth: IPD WP19

This working paper is part of the research programme on ‘Institutions, Governance and
Long‐term Economic Growth’, a partnership between the French Development Agency
(AFD) and the Maastricht Graduate School of Governance (Maastricht University – UNU‐
Merit). The research builds on the Institutional Profiles Database IPD, jointly developed by
AFD and the French Ministry of the Economy since 2001.

ISSN 1871‐9872

Maastricht Economic and social Research institute on Innovation and Technology (UNU‐MERIT)
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Maastricht Graduate School of Governance (MGSoG)
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Tel: (31) (43) 388 4400, Fax: (31) (43) 388 4499

Poor trends
The pace of poverty reduction after the Millennium Development Agenda∗
Richard Bluhm†

Denis de Crombrugghe‡

Adam Szirmai§

February 2014
Working Paper

Abstract
We review the origins of the dollar-a-day poverty line, discuss historical poverty
and inequality trends, and forecast poverty rates until 2030 using a new fractional
response approach. Three findings stand out. First, global poverty reduction since
1981 has been rapid but regional trends are heterogeneous. Second, the pace of
poverty reduction at 1.25$ a day will slow down. Our optimistic scenarios suggest
a poverty rate of 8-9% in 2030, far short of the World Bank’s new 3% target. Third,
rapid progress can be maintained at 2$ a day, with an additional one billion people
crossing that line by 2030.

Keywords: poverty, inequality, consumption growth
JEL Classification: I32, O10, O15

∗

This paper was prepared for the AfD/IDS/JICA workshop on the “Quality of Growth” February
3 -4th 2014 in Paris, France. We have greatly benefited from discussions with several participants.
In particular, we would like to thank Lawrence Haddad and Nicolas Meisel for useful comments and
suggestions. We gratefully acknowledge financial support from the Agence Fran¸aise de D´veloppement
c
e
(AFD). The findings, interpretations and conclusions expressed in this paper are solely the responsibility
of the authors and do not necessarily represent policies or views of Maastricht University, UNU-MERIT,
AFD and/or other affiliated institutions. All remaining errors are those of the authors.
†
Maastricht University, UNU-MERIT, e-mail: richard.bluhm@maastrichtuniversity.nl
‡
Maastricht University, e-mail:d.decrombrugghe@maastrichtuniversity.nl
§
UNU-MERIT, Maastricht University, e-mail: szirmai@merit.unu.edu
rd

1

1

Introduction

“We are at an auspicious moment in history, when the successes of past decades and an
increasingly favorable economic outlook combine to give developing countries a chance, for
the first time ever, to end extreme poverty within a generation” (Jim Yong Kim, World
Bank President, speaking at Georgetown University, April 3, 2013)
Only 13 years after the Millennium Summit in September 2000 at which world leaders
agreed on halving the 1990 global poverty rate at 1.25$ a day by 2015, the end of
extreme poverty seems to be in sight. Recent estimates suggest that the first Millennium
Development Goal (MDG) was already reached in 2010 and about 700 million people
were lifted out of poverty. In 2013, the World Bank declared a new organizational goal of
ending extreme poverty by 2030, that is, reducing the 1.25$ a day poverty rate to 3% by
2030. The last two decades clearly ushered in unprecedented success, but is 2030 really
likely to mark the end of extreme poverty? We demonstrate that this is unlikely.
In this paper we review the origins of the ‘dollar-a-day’ poverty line, discuss progress
over the last three decades, and forecast 1.25$ and 2$ a day poverty rates until 2030. It
is well-known that regional trends in poverty alleviation are very heterogeneous. In spite
of rising inequality, rapid growth in China was the driving force behind global progress
over the last two decades and accounts for more than three quarters of the reduction in
the number of people living below 1.25$ a day. However, most of the poverty reduction
potential coming from China is now exhausted. Poverty reduction in the developing world
outside China has been considerably slower, although economic growth has accelerated
significantly since 2000. In 2010, three-fourths of the extremely poor lived in Sub-Saharan
Africa and South Asia, as opposed to approximately 40% in 1981. This changing regional
composition of world poverty has important ramifications for future trends in poverty
reduction.
Building on a new method for estimating poverty elasticities developed in Bluhm et al.
(2013), we show that the pace of poverty reduction at 1.25$ a day is likely to slow down
significantly after 2015. Extreme poverty barely falls below 8% in the most optimistic
scenario. Ravallion (2013) first suggested the 3% target relying on the assumption that
consumption in developing countries would continue to grow at the average post-2000
trend, or 4.5% per year. We find this ‘equal-growth’ assumption too optimistic. Poverty
tends to be higher in countries with rapid population growth and lower than average
consumption growth. None of our scenarios predict a poverty rate near 3% once countryspecific trends from 2000 to 2010 are used. However, the 2$ a day poverty rate may fall
below 20% in 2030, while a slowdown happens only late during the forecast period or not
at all. A distinct advantage of our approach is that it is computationally inexpensive.
Hence, it can easily be used for benchmarking progress as new data become available.
The paper is structured as follows. Section 2 discusses some of the controversies
surrounding the setting and updating of international poverty lines. Section 3 is a datadriven review of global poverty and inequality trends with a particular focus on China,
India, Brazil and Nigeria. Section 4 presents projections of global and regional poverty
rates until 2030 at the 1.25$ and 2$ a day poverty lines using different growth and
inequality scenarios. Section 5 concludes and offers some policy recommendations.

2

2

Drawing the line: international poverty lines

The 1$ a day poverty line was first defined in a background paper to the 1990 World
Development Report (in 1985 PPP$), then updated to 1.08$ (in 1993 PPP$) in 2000,
and again updated to 1.25$ (in 2005 PPP$) in 2008. While the first update went by
almost unnoticed, the most recent change has sparked a controversy. Redefining extreme
poverty as living below 1.25$ a day raised the global poverty headcount by about 10
percentage points and reclassified approximately 450 million people as extremely poor
(Chen and Ravallion, 2010). In this section, we briefly review the origins of the 1$ a day
measure and discuss shortcomings of the current updating procedure.
The problem of setting a global poverty line is far from trivial. Even if we could use
a ‘basic needs’ or calorie-intake approach to devise a minimum consumption bundle for
the entire world, it is difficult to apply any such bundle in international comparisons.
Subsistence needs, relative prices, and purchasing power vary across countries and over
time. Faced with these problems, Ravallion et al. (1991, henceforth RDV) suggested an
original solution. Since many national poverty lines are set using a basic needs or calorieintake method, there should be a universal lower bound among the absolute poverty lines
which may be recovered from the data. Converting 33 national poverty lines and the
corresponding consumption levels from the 1970s and 1980s into international dollars,
RDV showed that a poverty line of about 31$ per month (1.02$ a day, in 1985 prices) was
shared by the six poorest countries in their sample while those of two other countries came
close. They argued that a rounded-off poverty line of 1$ a day was a sensible threshold for
measuring global poverty, since any one poverty line is likely to be estimated with error
and the non-food allowance included in the subsistence basket varies across countries.
RDV also estimated a lower line of 23$ per month (about 76 cents a day) for the poorest
country in their sample. This lower line is close to India’s poverty line at the time,
which was widely used as the international poverty line during the 1970s and 1980s (e.g.
Ahluwalia et al., 1979).
Setting the poverty line in international prices has the advantage that domestic
inflation is typically taken into account when average incomes or expenditures from
surveys are converted into (base year) international dollars, so that the line itself does
not have to be explicitly updated annually. However, purchasing power parities (PPPs)
change over time as countries grow richer (due to the Balassa-Samuelson effect). In
addition, the quality of PPP estimates has been improving substantially with each
round of the International Comparison Program (ICP), so that updates are needed
approximately every decade. When the 1993 ICP data became available, Chen and
Ravallion (2001) revised the 1$ a day line to 1.08$ in 1993 prices. Using the same data
as in the original study, they found that 1.08$ a day is the median poverty line of the ten
poorest countries. However, when the 2005 ICP was completed, instead of converting the
old poverty line to 2005 prices, new data were collected and the poverty line was redrawn.
Ravallion et al. (2009, henceforth RCS) compiled a dataset of 74 national poverty lines
to update the original analysis. They found that national poverty lines do not rise with
per capita consumption until a certain turning point (about 60$ per month) but increase
strongly thereafter (left panel, Figure 1). RCS set the global line as the average poverty
line of the 15 countries below this threshold, or 1.25$ a day in 2005 prices.
Deaton (2010), as well as Deaton and Dupriez (2011), take issue with this approach.
They argue that updating the international poverty line based on new data leads to
“graduation effects” when countries move out of the reference group. They illustrate their
3

case using India and Guinea Bissau as examples. India was part of the initial reference
group in RDV, and both countries appear in RCS’s more recent reference group. India
has a relatively low poverty line (0.90$ a day in 2005 prices) and a population of more
than a billion people, whereas Guinea Bissau has a higher poverty line (1.51$ a day in
2005 prices) and is home to less than 1.5 million people. As average consumption in
India grew considerably until 2000, it crossed the 60$ threshold and is no longer part
of the reference group. Even though the average Indian has become richer, both the
international poverty line and the global poverty headcount increased as a result of India
dropping out of the average. With Guinea Bissau the case is reversed. Its poverty line
is currently part of the average. A move out of the reference group would entail a fall in
the global poverty line and a reduction in global poverty that is many times greater than
the population of Guinea Bissau. The left panel of Figure 1 illustrates this relationship.
The bold horizontal line marks the 60$ per month threshold (labeled RCS’09).
Figure 1 – Poverty lines and consumption levels around 2000

300

(b) population−weighted

300

(a) unweighted

RCS’09

DD’11

Poverty line per capita (monthly)
100
200

DD’11

Poverty line per capita (monthly)
100
200

RCS’09

2.00$ a day
Guinea Bissau

2.00$ a day
Guinea Bissau

1.25$ a day

1.25$ a day
India

China

0

China

0

India

3

4

5
Log per capita consumption (monthly)

6

7

3

4

5
Log per capita consumption (monthly)

6

7

Notes: Author’s calculations using the data reported in Ravallion et al. (2009) and following the
illustration of Deaton (2010). The non-linear trends are estimated using a (weighted) local linear
smoother with bandwidth 0.8.

A related issue is that the ICP data are primarily designed for comparing living standards
of entire populations, not just poor people. The typical consumption basket of the poor,
and the associated price level, may be very different than the reference basket used for
computing PPPs.
To address both the graduation issue and the PPP issue, Deaton and Dupriez (2011)
propose an alternative procedure. Linking consumption surveys to ICP data for the 50
poorest countries, they simultaneously estimate the poverty line and PPPs of those near
the poverty line (PPPs for the Poor, or P4s). This yields lower poverty lines in between
0.92$ and 1.19$ a day. However, the effect of the P4s on the global poverty counts – at
similar poverty lines – is relatively small. The resulting estimates of global poverty are
lower primarily due to the lower poverty lines and not due to differences in relative prices.
To an extent, the Deaton-Dupriez criticism can be addressed within the RCS approach
by (1) weighting the national poverty lines by population sizes, and (2) extending the
4

reference group of “poorest” countries. The Deaton-Dupriez proposal, labeled DD’11
below, is to select the 50 poorest countries to constitute a fixed reference group. Clearly,
the threshold of 50 countries is arbitrary. A possible alternative is to replicate the RCS
approach but estimate the consumption gradient using population weights instead of
equal weights (right panel, Figure 1). This is the approach proposed here. Examining
the plot to find the point where the slope of consumption begins to be positive, we visually
identify a threshold of about 5 log dollars or 148.41$ per month (C ∗ ), which is just under
5$ a day. There are 39 countries below this line, suggesting an alternative reference group
consisting of those 39 countries. A population-weighted average of the national poverty
lines for this group implies a global poverty line of 1.06$ a day whereas the simple average
is 1.46$ a day (both in 2005 prices).
Table 1 – Estimates of the international poverty line (Z ∗ )

Consumption
Estimation
Sample

ln(C ∗ )

C∗

RCS’09
Alternative
DD’11
Full sample

4.09
5.00
5.53
6.50

60.00
148.41
251.59
668.31

International Poverty Lines (in 2005 PPP$)
Equally-weighted
Population-weighted
∗
¯
¯
Z
Z
Q50 (Z) Z
Z∗
N
1.25
1.46
1.76
2.91

1.25
1.46
1.26
1.46

1.27
1.38
1.51
2.00

1.17
1.06
1.12
1.84

1.17
1.06
1.00
1.06

15
39
50
74

¯
Notes: Author’s calculations using the data reported in Ravallion et al. (2009). Z is the (unweighted
or weighted) average poverty line. Q50 (Z) is the (unweighted) median poverty line. The regression
based columns estimate the average level of the poverty line before the consumption gradient turns
positive. Following Ravallion et al. (2009), we obtain Z ∗ from Zi = Z ∗ Ii + (α + βCi ) × (1 − Ii ),
where Zi is the national poverty line expressed in 2005 PPP dollars, Z ∗ is the mean poverty line
for the reference group, Ci is average per capita consumption in 2005 PPP dollars, and Ii is one if
Ci ≤ C ∗ and zero otherwise; that is, it indicates whether the country is in the reference group. This
method imposes that the slope is zero until a monthly consumption level C ∗ .

Table 1 provides alternative estimates of the international poverty line using different
approaches, references groups and weights. We can fully reproduce the main findings of
RCS. The average poverty of the poorest 15 countries is 1.25$ a day and the full sample
median is 2$ a day. Three other results stand out. First, all estimates are above or equal
to 1$ a day in 2005 prices. Second, population weights lead to universally lower estimates
of the poverty line. Third, the estimated poverty line is fairly sensitive to both the choice
of C ∗ and the summary measure (mean, median, weighted or not). While this lends
some support to the notion that 1.25$ is a relatively high upward revision, the choice of
method, cutoff and weights remains subjective. Moreover, updating the old 1.08$ poverty
line in 1993 prices to 1.25$ in 2005 prices implies a (global) inflation rate of about 1.2%
per year. Precisely this point leads Chen and Ravallion (2010) to argue that “as long as
it is agreed that $1 in 1993 international prices is worth more than $1 at 2005 prices,
the qualitative result that the new ICP round implies a higher global poverty count is
robust” (p. 1612). In the following, we will consider the policy consequences of using
both 1.25$ a day and the full-sample median of 2$ a day as absolute poverty lines (which
is common practice in the literature, e.g. see Chen and Ravallion, 2010).
Counting the global poor involves several difficult methodological choices on top of
choosing an international poverty line that can have large effects on the estimated poverty
5

rates.1 The inherent difficulty of convincingly solving these issues led some to suggest
that the current approach should be abandoned (e.g Klasen, 2009; Reddy and Pogge,
2010). One alternative is to set national poverty lines in local currencies using the same
method in each country. While this approach tries to sidestep the issue of purchasing
power parity comparisons altogether, it would certainly raise new problems. Alas, a
considerable degree of indeterminacy regarding the level of extreme poverty in the world
seems unavoidable (but more is known about rates of change which we discuss in the
next section).

3

Taking stock:
three decades

poverty reduction over the past

While the global Millennium Development Goal (MGD) of halving the 1.25$ a day poverty
rate in 1990 by 2015 was reached in 2010 (Chen and Ravallion, 2013), progress has been
very uneven across regions. Most poverty reduction over the last three decades occurred
in East Asia and, to a lesser extent, in South Asia. Poverty in Sub-Saharan Africa, on
the contrary, has hardly budged and the continent as a whole will – most probably – fail
to meet the first MDG by 2015.
Figure 2 – Population-weighted poverty trends by region, 1981 to 2010, 1.25$ a day
Europe and Central Asia

Latin America and Caribbean

Middle East and North Africa

South Asia

Sub−Saharan Africa

0
100
50
0

Poverty Headcount at 1.25$ a day

50

100

East Asia and Pacific

1980

1990

2000

2010 1980

1990

2000

2010 1980

1990

2000

2010

Notes: Author’s calculations based on surveys from PovcalNet.

1

For example, it is not clear that the ICP 2005 provides an adequate picture of the consumption
patterns in the 1980s or 1990s. Another issue is the use of survey means versus national accounts means.
The use of national accounts typically leads to much lower poverty estimates. See Sala-i-Martin (2006),
Pinkovskiy and Sala-i-Martin (2009), and, in particular, Dhongde and Minoiu (2013) for a comparison
of different methods.

6

Figure 2 shows population-weighted time trends estimated for each region using the
nationally-representative household surveys available in the World Bank’s PovcalNet
database (using consumption-based data if available). A pooled OLS regression of the
poverty headcount ratio on time for all developing countries reveals that every year the
poverty headcount at the 1.25$ a day line fell on average by 1.5 percentage points (clusterrobust t = -4.11). There is substantial regional heterogeneity. On average, poverty fell
by 2.21 percentage points annually in East Asia and Pacific (cluster t = -10.08), by about
1 percentage point in South Asia (cluster t = -4.82), but only by 0.02 percentage points
in Sub-Saharan Africa (cluster t = -0.05). In the other three developing regions progress
has been slow but steady. In Europe and Central Asia, Latin America and Caribbean,
and Middle East and North Africa the estimated slopes imply an expected annual change
of 0.05 (cluster t = -1.91), 0.30 (cluster t = -5.76) and 0.14 (cluster t = -2.40) percentage
points, respectively. Using an alternative 2$ a day poverty line, the magnitudes and
differences in speeds across regions remain broadly similar.2
An important question is whether consumption growth or redistribution is driving
the decline in poverty. Estimating the historical contributions of growth and changes in
distribution during the 1980s and 1990s, Kraay (2006) found that most poverty reduction
was due to income or consumption growth. Our analysis broadly corroborates this finding
(although we do not explicitly estimate contributions). The population-weighted growth
rate of the survey means from 1981 to 2010 across all countries is a very robust 4%
per year (cluster t = 3.03). Over the same period, within-country inequality actually
increased slightly by about 0.7% per year (cluster t = 1.64), implying that, on average,
changes in distribution may have in fact moderately slowed the pace of poverty reduction.
Poverty reduction over the last three decades has mainly or entirely been due to income
and consumption growth. However, both the high average growth rate in the survey
means and the apparent rise in within-country inequality are driven by China. Excluding
China, the survey means grew about 1.8% per year (cluster t = 2.45) and inequality
barely moved (increased 0.047% per year, cluster t = 0.13). In other words, poverty
reduction in the developing world outside China has been steady but slow and has (on
average) not been helped by improvements in distribution.3
These findings are also in line with estimates of poverty at the 1.25$ a day poverty
line reported by the World Bank (see Appendix Table A-1). Chen and Ravallion’s (2010)
estimates indicate rapid progress in China, little improvement in Sub-Saharan Africa,
and moderate poverty reduction elsewhere. The poverty headcount ratio in Sub-Saharan
Africa only fell by about three percentage points over the entire period from 1981 to
2010, and actually exceeded its 1981 value for most of the period. Combining these
trends with population growth rates reveals the dire absence of a robust positive trend
in terms of the number of global poor outside of East Asia. While China has lifted an
astonishing 680 million people out of poverty between 1981 and 2010, the rest of the
world has only about 50 million fewer extremely poor people in 2010 than in 1981. This
trend is owed to persistently high poverty rates coupled with strong population growth
in Sub-Saharan Africa and India. This is most evident in Sub-Saharan Africa where the
number of extremely poor has roughly doubled over three decades (in spite of the slight
2

East Asia and Pacific (slope = -2.31, cluster t= -7.33), Europe and Central Asia (slope=-0.26,
cluster t=-2.33), Latin America and Caribbean (slope=-0.55, cluster t = -5.74), South Asia (slope =
-0.72, cluster t= -4.92), and Sub-Saharan Africa (slope = -0.02, cluster t= -0.07). Throughout the text,
“cluster t” denotes a cluster-robust t-statistic, with clusters defined by countries.
3
Excluding India in addition to China from the sample does not qualitatively alter this result.

7

decrease in the headcount ratio). The rise of China from a poor to a middle income
country also implies that the relative composition of world poverty is changing rapidly.
In 1981 about 40% of the world’s extremely poor lived in Sub-Saharan Africa and South
Asia, by 2010 their share has risen to 75%.
A very intuitive approach to illustrating past progress (or lack thereof) is to
approximate the shape of the income or expenditure distribution at various points in
time and examine how the features of the distribution (esp. quantiles) shift over time.
Figures 3 and 4 plot the lower tail (up to 400$) of the monthly income or expenditure
distribution for the most populous country of the four poorest regions – East Asia, South
Asia, Latin America, and Sub-Saharan Africa – in 1985, 1990, 2000 and 2010. After
lining up the survey data in time, we estimate the different density functions using a
log-normal approximation.4 While the assumption of log-normality has its weaknesses5 ,
it usually provides a useful first estimate of the shape of the income distribution.
We can illustrate a few essential concepts with these graphs. The area under the
curve to the left of the poverty line gives the fraction of the population that is poor (the
poverty headcount ratio), while the spread of the distribution reflects inequality. The raw
difference between two such areas under the curve is the absolute change in the poverty
headcount ratio in percentage points and the relative difference gives the percent change
in the poverty headcount ratio. The sensitivity of poverty reduction to changes in income
or inequality is often measured in the form of elasticities or semi-elasticities. The income
elasticity of poverty is the percent change in poverty for a one percent increase in incomes,
and the income semi-elasticity of poverty is the percentage point difference in poverty
for a one percent increase in incomes. The inequality elasticity and semi-elasticity are
defined analogously. An attractive feature of the semi-elasticity is that it first increases
and then decreases again during the development process. It measures the pace of poverty
reduction in terms of the percentage of the population lifted out of poverty. Hence, it
is usually more informative for policy-makers and more useful than reporting relative
changes.6
Figure 3 visualizes the tremendous progress in reducing poverty rates in China over
the last three decades.7 As noted before, poverty in China at 1.25$ a day fell rapidly
over the entire period. The biggest gains occurred early on, between 1985 and 2000,
when the peak of the distribution was close to the 1.25$ and 2$ poverty lines. By 2010,
the peak of the distribution has moved considerably to the right of both poverty lines
and the overall spread has widened. A great many Chinese are now considered part of

4

We interpolate and extrapolate the data as follows. First, we project mean consumption forward and
backward using the corresponding growth rates of personal consumption expenditures from the national
accounts. Second, we linearly interpolate between the available Gini coefficients and extrapolate beyond
the first or last available measure by keeping inequality constant. The same data set (with all countries
from PovcalNet) is later used for computing the inequality indices in the developing world.
5
Log-normality typically works better with consumption surveys than with income surveys (Lopez
and Serven, 2006), tends to underestimate the level of poverty (Dhongde and Minoiu, 2013), and
overstates the pace of poverty reduction (Bresson, 2009).
6
In relatively rich countries with low percentages of people below the poverty line, elasticities can be
very misleading. Small reductions in the poverty headcount rate can manifest themselves as very high
elasticities. For a more detailed discussion of the properties of elasticities and semi-elasticities of poverty
see Bourguignon (2003), Klasen and Misselhorn (2008), and Bluhm et al. (2013).
7
The implied poverty rates for China correspond well with the official World Bank estimates. At the
1.25$ a day poverty line, our estimates imply a poverty rate of 60.56% in 1985, 56.92% in 1990, 31.97%
in 2000 and 9.75% in 2010.

8

Figure 3 – Estimates of the expenditure distribution for China and India, 1985 to 2010
China

India
1985
.015

.02

1985
1990

1990
2000

2010

2010

0

0

.005

Density (Share of Population)
.005
.01

.01
.015

2000

0

38 60.83

100

200
300
Income/ Expenditure in PPP$ per month

400

0

38 60.83

100

200
300

400

Notes: Author’s calculations. China’s expenditure distribution is estimated based on a weighted
mean and a rural-urban ln-mixture for the Gini coefficient. China’s surveys in PovcalNet are
consumption-based after 1987 and income-based before.

a developing country ‘middle class’ (if defined between 2$ and 13$ per day).8 However,
this also implies that the poverty reduction potential from China is largely exhausted.
The income semi-elasticity of the poverty headcount is far beyond its peak and steadily
approaching zero. In addition, inequality has increased remarkably over the same period.
In 1985, the Gini coefficient was 0.28 and by 2010 it has risen to 0.44.
In India, on the contrary, there remains much more potential for poverty reduction
in the medium-term future. While the mode of the income distribution was near the
1.25$ line around 1985 and 1990, the peak of the distribution in 2000 and 2010 is located
between the two poverty lines. The process of “bunching up” in front of 2$ a day observed
by Chen and Ravallion (2010) implies that, in the medium-term future, the pace of
poverty reduction in India (defined as the absolute change in the headcount) will be
particularly fast at the 2$ a day line and continue at a fast but decelerating pace at the
1.25$ line. Put differently, India’s income semi-elasticity around 2010 is very high and
a moderate rate of growth will immediately have a large (but decreasing) effect on the
poverty headcount ratio at both thresholds.
Figure 4 illustrates two very different cases. The left panel shows that from 1985 to
1990 poverty reduction in Brazil was very slow, with some progress at the 2$ a day line
but a nearly unchanged poverty rate at the 1.25$ line. Yet, on average, Brazilians were
already considerably better-off in the 1990s than their Chinese or Indian counterparts in
2010. After 1990, the pace of poverty reduction accelerates and by 2010 only 4.92% of the
population were below the 1.25$ a day poverty line.9 Lifting the remaining people out
of poverty will require sustained economic growth, as both the income and distribution
8

Ravallion (2010) defines the size of the ‘middle class’ by developing country standards as the
proportion of the population living on at least 2$ per day but less than 13$ per day, where the upper
bound is the poverty line in the United States. Naturally, this is one of many possible definitions.
9
The World Bank estimates a poverty rate of 5.38% at 1.25$ a day for Brazil in 2010.

9

Figure 4 – Estimates of the expenditure distribution for Brazil and Nigeria, 1985 to 2010
Nigeria
.015

.002

Brazil
1985

1985
1990

2000

2000

2010

2010

0

0

.0005

.001

.005
.01

.0015

1990

0

38 60.83

100

200
300

400

0

38 60.83

100

200
300

400

Notes: Author’s calculations. Brazil’s distribution is based on incomes instead of expenditures.

semi-elasticities of poverty in Latin America as a whole are rather low (Bluhm et al.,
2013). With a Gini of 0.56 in 1985 income inequality is initially very high in Brazil,
peaks at 0.61 in 2000 and then falls again to 0.55 by 2010, thus positively contributing
to poverty reduction after 2000. The right panel illustrates that poverty in Nigeria was
considerably higher in 2000 or 2010 than in 1985. Nigeria’s plight is characteristic for most
of Sub-Saharan Africa in the 1980s and 1990s, as real consumption on the subcontinent
was declining at a pace of about 0.82% per year. Only after 2000, expenditures recover
and the poverty headcount ratio begins to decline. Yet even by 2010, the peak of the
expenditure distribution is still noticeably to the left of the poverty line and the implied
poverty rate at 1.25$ a day is 65.96%.10 In addition, inequality in Nigeria increases over
the observed period, starting from a Gini of 0.39 in 1985 to 0.49 in 2010.
Taken together, these four distributions exemplify the changing composition of global
poverty and broadly represent the trends in their respective regions. Over the last
three decades, most poverty reduction occurred in East Asia where consumption growth
was fastest, some poverty reduction occurred in India where real consumption growth
was steady, and little poverty reduction occurred in Sub-Saharan Africa where real
consumption growth was slow and volatile. This suggests that without signiﬁcantly faster
growth in Sub-Saharan Africa than in the past, possibly coupled with improvements in the
income or expenditure distribution, the global pace of poverty reduction will inevitably
slow down in the near future.
Another essential aspect of poverty analysis is studying the evolution of inequality.
In this part, we focus only on inequality among citizens of developing countries, as
our interest is the changing relative position of people in the developing world rather
than their position vis-`-vis rich countries. Interestingly, many of the global trends are
a
also evident even when we restrict our attention to this truncated distribution. We
compute three measures of inequality by applying Young’s (2011) mixture of log-normal
10

The World Bank estimates a poverty rate of 67.98% at 1.25$ a day for Nigeria in 2010.

10

distributions approach to the PovcalNet data. ‘Overall inequality’ is the Gini coefficient
for citizens of developing countries regardless of their country of residency. ‘Within
inequality’ is a population-weighted summary measure of inequality within each country.
Last, ‘between inequality’ is the population-weighted Gini coefficient of average incomes
among all developing countries. In other words, the first measure encompasses both
the within-country and between-country components that make up overall inequality in
the developing world. Naturally, global inequality – including the citizens of developed
countries – is typically estimated to be considerably higher. Recent estimates of the
global Gini suggest that it is around 0.65-0.70 and even higher if underreporting of topincomes is taken into account (e.g. see Pinkovskiy and Sala-i-Martin, 2009; Milanovic,
2012; Chotikapanich et al., 2012; Lakner and Milanovic, 2013).
Table 2 – Inequality in the developing world, 1980 to 2010

Gini coefficient
Year
1980
1985
1990
1995
2000
2005
2010
∆ 1980-2010 (in %)
∆ 1990-2010 (in %)
∆ 2000-2010 (in %)

Overall

Within

Between

Mean
Consumption

Population

N

0.596
0.555
0.578
0.559
0.537
0.535
0.554
-7.19
-4.17
3.07

0.356
0.353
0.367
0.385
0.395
0.399
0.404
13.63
10.10
2.20

0.486
0.421
0.449
0.411
0.374
0.372
0.399
-17.82
-11.08
6.64

73.29
79.05
95.98
98.41
102.45
120.34
150.72
–
–
–

2907.8
3223.2
4049.3
4555.2
4931.4
5285.6
5625.1
–
–
–

83
86
104
114
121
123
123
–
–
–

Notes: Author’s calculations. The sample size varies over the years. A total of 124 countries
are recorded in PovcalNet but we lack PCE data for West Bank and Gaza. The results are very
similar if we constrain the developing world to consist out of the 104 countries from which we have
(interpolated) data from 1990 onwards. Due to the lower coverage, the results for the 1980s should
be interpreted with caution. For details on the ln-mixture calculations refer to Young (2011).

Table 2 reveals some interesting trends. First, between 1990 and 2005, overall inequality
in the developing world has been falling from 2005 to 2010, but it exhibits an increase
in 2010. At the same time, within-country inequality has been rising steadily since the
1980s. Last, between-country inequality fell over most of the period but also shows
a slight increase between 2005 and 2010. If we exclude China from the computations
given that its weight is very high, then these trends are considerably muted or even
non-existent.11 Hence, two developments drive the overall change. First, inequality of
incomes within China has been increasing significantly and, second, its relative position
among developing countries has been changing rapidly. Rising mean incomes in China
from the 1980s onwards initially implied a reduction of between-country inequality as
11

Overall inequality is estimated as 58.33 in 1990 and 58.36 in 2010, within-country inequality is
estimated as 38.36 in 1990 and 39.12 in 2010, and between-country inequality is estimated as 45.04 in
1990 and 45.44 in 2010. Removing India in addition to China has little effect on the trends in the
inequality measures.

11

the average citizen in China was moving from the bottom towards the middle of the
developing country ranks, but they now put upward pressure on overall inequality as
incomes in China continue to grow and the distance to incomes in Sub-Saharan Africa
increases.12

4

Going forward: poverty projections until 2030

As the expiration date of the MDGs is approaching quickly, new goals will have to
be selected. Picking among a wide range of possible benchmarks invariably involves
formulating expectations towards a fundamentally uncertain future. Thus, it becomes
important to ask: what can the current data and methods tell us about the prospects
for poverty alleviation over the next two decades? The list of policy-relevant questions is
long. What level of poverty do we expect to prevail in 2030? Will it be feasible to truly
eradicate extreme poverty by 2030? Or, how quickly do we expect poverty rates under
the 2$ a day poverty line to decrease? Here, we provide both a glimpse into several likely
futures and some potential answers to these questions.
This section draws heavily on Bluhm et al. (2013), where we develop a ‘fractional
response approach’ for estimating income and inequality (semi-)elasticities of poverty.
Among other things, the paper shows that this new method can be used to easily forecast
global poverty rates using only two variables (the survey mean and the Gini coefficient).
A key advantage of this approach over, say, linear trend extrapolations is that it builds
in the non-linearity of the poverty-income-inequality relationship. Neither the income or
inequality elasticity nor the income or inequality semi-elasticity is assumed to be constant.
In other words, we can account for the fact that income growth will have less and less
of an effect in very rich countries, where the mass of the distribution is far to the right
of the poverty line, and an increasing effect in very poor countries, where the mass of
the distribution is to the left of the poverty line.13 Similarly, the effect of changes in
distribution will indirectly depend on the prevailing levels of both income and inequality.
We are of course not the first to present poverty projections over the next two decades.
Ravallion (2013), for example, outlines a very optimistic scenario where an additional
billion people are lifted out of extreme poverty by 2025-2030. Karver et al. (2012) discuss
the future of the MDGs more generally and simulate poverty rates at the 1.25$ and 2$ a
day poverty lines for 2030.14 Yet there are some important conceptual and methodological
differences between our approach and these studies. First, we find little reason to assume
that the developing world will continue growing at the accelerated 2000 to 2010 pace for
12

This trend is corroborated by the literature on global inequality. According to Lakner and Milanovic
(2013), average incomes in Sub-Saharan Africa were $742 in 1988 and still just $762 per year in 2008 (in
2005 PPP$), while Chinese incomes increased by 228.9% and no longer make up a large part of the lower
tail of the global income distribution. They also show that inequality within China has risen between
1988 and 2008.
13
The inability to account for countries that have relatively high incomes and zero poverty at some
point in time (typically the beginning or end of a spell) is a key weakness of studies investigating poverty
elasticities.
14
Karver et al. (2012) allow for country-specific growth rates but use older data (their PovcalNet
reference year is 2008) and disregard the difference between GDP per capita growth and growth of the
survey mean. This leads them to overestimate the speed of poverty reduction relative to our forecasts.
A recent study by Chandy et al. (2013) echoes some of our results. They use GDP per capita rather
than consumption expenditure data for most of the period, but apply a conversion factor, and report
lower poverty estimates.

12

another twenty years. The well-known instability of growth rates across decades should
not be ignored, especially since the high average growth rates in the developing world were
driven by rapid growth in China. It is more realistic to expect countries to grow at rates
much closer to their individual long-run growth path. Second, the changing composition
of the countries contributing to global poverty matters a lot for the expected speed of
global poverty reduction. Unless there is a persistent acceleration of consumption growth
in Sub-Saharan Africa and sustained consumption growth in India, we can show that the
pace of poverty reduction at the 1.25$ line is likely to experience a pronounced slowdown
in all of our forecast scenarios (defined below). Third, pro-poor growth can potentially
make a sizable difference in the expected poverty rates, while a rise in within-country
inequality will hasten the arrival of the slowdown. Fourth, our method approximates the
‘official’ PovcalNet results at a fraction of the computational cost, so that a variety of
scenarios can be easily estimated (and frequently updated with the arrival of new data).
We define three different constant growth scenarios on the basis of the historical
personal consumption expenditure (PCE) growth rates from the national accounts.15 An
‘optimistic’ scenario uses the average PCE growth rate of each country from 2000-2010,
during which period growth rates were significantly higher than before 2000. A ‘moderate’
growth scenario uses the average PCE growth rate of each country from 1980 to 2010
– the long run average over the entire dataset. Finally, a ‘pessimistic’ growth scenario
uses the 1980 to 2000 average PCE growth rates. The latter scenario assumes that mean
consumption in Sub-Saharan Africa is shrinking at a rate of about 0.82% per year.16
Table A-2 in the Appendix reports the population-weighted average regional growth rates
over several different periods to illustrate the implied regional income dynamics.
For each growth scenario, we also simulate three different inequality patterns. ‘Propoor growth’ implies an annual decline in the Gini coefficient of approximately -0.5%,
‘distribution-neutral growth’ keeps inequality constant at the level prevailing in 2010,
and ‘pro-rich growth’ implies an increase in the Gini coefficient of approximately 0.5%
per year.17 As an illustration, if a country’s Gini coefficient is 0.40 in 2010 and we apply
the pro-poor pattern, then by 2030 we project a Gini coefficient of about 0.36. If we
apply the pro-rich pattern, then the Gini coefficient is about .44 in 2030. Changes of
this magnitude are in line with the population-weighted regional trends obtained from
the surveys.
We forecast the poverty rates until 2030 as follows. First, we estimate the model
outlined in Bluhm et al. (2013) for the 1.25$ a day poverty line using all nationallyrepresentative surveys recorded in PovcalNet over the period from 1981 to 2010. Next,
after lining up all surveys in 2010, we apply each of the nine growth and distribution
scenarios to project the income and inequality data forward to 2030, country per
country.18 Then, we predict the poverty headcount ratios in five-year intervals over
15

The term ‘national accounts’ refers to data from the World Development Indicators or the Penn
World Table 7.1, whichever has more data over the 30 year horizon.
16
Owing to the post-communist transition, consumption and incomes in Europe and Central Asia
were shrinking over the same period. However, given the small number of poor in 2010, the influence of
that region on the global poverty headcount in 2030 is minimal.
17
All reported growth rates (in percent) are computed as log differences if not otherwise noted.
18
To line up all surveys in 2010, we use the actually observed PCE growth rates from the national
accounts to extrapolate the survey means from the latest available survey. In doing so, we keep inequality
constant at the last observed Gini coefficient. In 2010, the average year when the last survey was
conducted is 2006.7, so about 3 years prior to 2010. More than 40% of the last surveys were conducted
in 2009 or 2010.

13

the period 2015 to 2030, country per country. Finally, we calculate population-weighted
regional poverty rates and apply these to the projected total population in each region.
For consistency with PovcalNet, the population projections are also taken from the World
Bank and the ‘developing world’ is defined as in 1990 – the countries targeted by the
MDGs – no matter how high we forecast the average level of consumption to be in 2030.

Headcount ratio at 1.25$ a day (in %)
20
30
40

50

Figure 5 – Actual and projected poverty headcount ratios at 1.25$ a day, 1981 to 2030

Observed Data

10

Projections

0

Linear trend

1980

1990

2000

2010

2020

2030

Notes: Author’s calculations based on Bluhm et al. (2013) and survey data from PovcalNet. The
solid black line beyond 2010 refers to the moderate (distribution-neutral) growth scenario in Table 3,
while the solid grey lines represent the distribution-neutral variants of the optimistic and pessimistic
scenarios. The pro-poor and pro-rich variants are shown as grey dotted lines and are located above
or below a solid line.

Figure 5 plots the historical evolution of the poverty headcount from 1981 to 2010, a
linear trend fitted through the observed data and then extrapolated until 2030, and our
different scenarios. The linear trend serves as a reference for the non-linear projections.
Several points are noteworthy. First, only the linear extrapolation predicts a poverty
rate in the vicinity of zero by 2030. Regressing the global poverty rate at 1.25$ a day
on time one obtains a slope of about one percentage point per year (see also Ravallion,
2013).19 As the global poverty rate is about 20.6% in 2010, the linear trend predicts that
extreme poverty has vanished by 2030. Second, all our projections show a decelerating
rate of poverty reduction. Even in the most optimistic scenario, the pace of poverty
reduction slows down. Most forecasts show a decelerating trend early on. In the optimistic
scenario the slowdown only becomes noticeable by about 2020. Third, all scenarios but
the optimistic pro-poor growth or optimistic distribution-neutral growth scenarios imply
19

This differs from the 1.5 percentage points estimated in the previous section as the global poverty
rate is measured by lining up and weighting all surveys at reference years (three year intervals from 1981
onwards), whereas in the previous section we were using an unbalanced panel of unequally-spaced but
population-weighted survey data.

14

a poverty rate higher than 10% in 2030 at the 1.25$ a day line. The optimistic pro-poor
growth and distribution-neutral scenarios suggest a poverty rate in 2030 of 7.9% and
9.1%, respectively. In a nutshell, 2030 is not likely to mark the end of extreme poverty,
even under very optimistic assumptions. The World Bank’s goal of 3% extreme poverty
in 2030 is not likely to be reached.
Table 3 provides the corresponding regional and total poverty rates in 2030 including
the expected number of poor in the various scenarios. Our moderate growth estimate
suggests a global poverty rate of 13.2% in 2030, implying about 950 million poor versus 1.2
billion poor people in 2010. The pace of poverty reduction will have slowed significantly
both in terms of relative changes and in terms of numbers of poor people. In this scenario,
about 70% of the world’s poor live in Sub-Saharan Africa and about 23% in South Asia
by 2030. In contrast, the (distribution-neutral) optimistic result suggests a poverty rate
of 9.11%, with about 655 million people remaining extremely poor. About 76% percent
live in Sub-Saharan Africa and about 17% in South Asia. The pessimistic case suggests
next to no progress at all. Given an unchanged distribution, the poverty headcount ratio
is estimated at 16.82% and the world is still home to 1.2 billion extremely poor people.
Even if growth rates in Sub-Saharan Africa were to double relative to the post-2000 trend,
the global poverty rate in 2030 is still projected to be 6.50% with pro-poor growth, 7.67%
with distribution-neutral growth, and 8.81% with pro-rich growth.
All of these estimates imply that it will take considerably longer than 2030 to lift
the remaining 1.2 billion people out of poverty. The good news is that by 2030 extreme
poverty in Europe and Central Asia, East Asia, Latin America, and Middle East and
North Africa may virtually disappear (projected to be less than 5% in most forecasts).
However, we predict a strong increase in the (relative) share of global poverty located in
Sub-Saharan Africa, which suggests that a non-trivial fraction of extreme poverty may
be concentrated in ‘fragile states’ (and the rest in more stable countries like Nigeria).
Whether these countries will overcome civil strife, political instability and corruption will
ultimately decide whether there is a lower bound at which extreme poverty will continue
to exist.
Gradual changes in inequality raise or lower the overall headcount in between 1.2
and 2.1 percentage points and account for about 100 million poor people more or less.
Contrary to suggesting that inequality does not matter (we only assume slow changes),
this finding hints at two crucial points. First, if the developing world as a whole is to truly
maintain the impressive record in poverty reduction of the last decades, then this requires
both sustained high growth at the level experienced since 2000 and improvements in
distribution. Second, any systematic worsening of within-country inequality, particularly
in large and largely poor countries like India or Nigeria, will reinforce the slowdown and
thus more strongly decelerate the global rate of poverty reduction.
Readers may wonder why these results are so different from the projections reported
by Ravallion (2013). Our results differ mainly because Ravallion (2013) uses average
growth rates to project poverty in different regions, while we use country-specific growth
rates. There are only minor differences in the data used. Ravallion (2013) uses PovcalNet
to calculate that a PCE growth rate of 4.5% per year may bring the global poverty rate
down to 3% by 2027. However, he makes the (in our view implausible) assumption that
all developing countries will continue to grow equally fast at this common rate of 4.5%.
Likewise, the linear projection of the global poverty rate on time ignores all issues of
aggregation and provides an overly optimistic picture of the medium-term future.20 Yet
20

We do not mean to imply that Ravallion (2013) is not aware of the aggregation issues. In fact, he uses

15

16
567.20

655.36

754.95

836.34

949.49

13.20

1076.37

1076.35

20.44
24.47
32.61
16.47
254.40
725.03

11.63

Panel (b) – Poor population at 1.25$ a day in 2030 (in millions)
14.05
20.23
28.56
16.59
23.29
32.22
0.59
0.76
0.97
5.72
6.86
8.11
16.15
19.44
23.32
24.61
29.30
34.86
2.12
2.94
4.04
6.84
9.18
12.18
83.47
110.38 144.35
169.13
217.08 275.00
449.54
499.97 551.61
611.15
660.85 710.32

10.49

14.96

9.11

14.96

7.88

0.94
5.17
4.59
3.72
12.76
51.75

pro-poor

1209.95

28.00
27.16
38.77
21.15
318.89
772.26

16.82

1.29
5.74
5.46
4.77
15.99
55.12

neutral

1359.23

37.87
30.45
46.05
26.83
394.16
819.22

18.89

1.74
6.44
6.48
6.05
19.77
58.47

pro-rich

Pessimistic (1980-2000)

Notes: Author’s calculations. Population projections are from the World Bank’s Health, Nutrition and Population Statistics database. The diﬀerent scenarios
are estimated using the fractional response approach outlined in Bluhm et al. (2013) and the survey data reported in the World Bank’s PovcalNet database.

Total

East Asia and Paciﬁc
South Asia
Sub-Saharan Africa

Total

South Asia
Sub-Saharan Africa

neutral pro-rich

Change in Inequality (Gini)
pro-poor neutral pro-rich

Panel (a) – Headcount at 1.25$ a day in 2030 (in percent)
0.65
0.93
1.31
0.76
1.07
1.48
0.12
0.16
0.21
1.21
1.45
1.71
2.27
2.74
3.28
3.46
4.12
4.91
0.48
0.66
0.91
1.54
2.07
2.75
4.19
5.54
7.24
8.48
10.89
13.79
32.09
35.69
39.37
43.62
47.17
50.70

pro-poor

Optimistic (2000-2010)

Average PCE Growth
Moderate (1980-2010)

Table 3 – Projected poverty headcount ratios and poor population at 1.25$ a day in 2030, by region

composition matters, even if we incorporate the optimistic assumption that the post-2000
trend will continue. The average hides that rapid growth is less likely in some countries
than in others. As we have shown, this has direct consequences for when a slowdown
will be observed and how strong the deceleration will be. Even if we assume a uniform
growth rate for all developing countries, a deceleration appears sooner or later within
the next two decades (although it may actually be preceded by a brief acceleration if we
assume growth rates in excess of 5% p.a.). Nevertheless it is comforting that, in line with
Ravallion (2013), our method implies that if consumption in the entire developing world
would grow at a distributional-neutral pace of 7.6% per year, then extreme poverty would
indeed virtually disappear by 2030 (fall to 1.1%). The 3% target can be reached with a
uniform distribution-neutral growth rate of approximately 5.5% per year.21
We repeat this exercise at the 2$ a day poverty line. The results are reported in
Table 4 and Figure A-1 in the Appendix. Interestingly, the linear projection is a much
better approximation of progress at the 2$ poverty line than at the 1.25$ poverty line.
This is not due to a slower historical poverty reduction record: a regression of the global
poverty rate at 2$ a day on time also yields a slope of approximately one percentage point
per year. However, the composition of countries (or people) near the 2$ a day poverty
line in 2010 is more reminiscent of its 1.25$ counterpart in the early 2000s. At the start
of the decade in 2010, the total 2$ poverty rate is 40.67% – roughly double of the 1.25$
poverty rate. Fast growing East Asia and moderately fast growing South Asia still make
up more than half of global poverty, implying that progress in these two regions will have
a large effect on the overall poverty headcount.
Our moderate growth scenario predicts that about 1.87 billion people (26%) live
on less than 2$ a day in 2030 versus about 2.4 billion people in 2010. Considerably
greater gains are possible. Global poverty at the 2$ line falls below 20% in the optimistic
distribution-neutral and pro-poor scenarios. If this occurs in 2030, then more than one
billion people will have left poverty at the 2$ a day line – undeniably a remarkable
achievement. In most scenarios we also observe a slowdown at the 2$ a day line but this
slowdown tends to occur later and is less pronounced than at the lower threshold. In the
most optimistic scenario, the rate of poverty reduction actually accelerates somewhat to
about 1.16 percentage points per year, while the moderate growth scenario gives a trend
of 0.73 percentage points per year over the projection period.
Examining the regional distribution, we find that poverty in East Asia is likely to fall
to around 5% by 2030, down from 29.7% in 2010. Nearly everyone in East Asia will have
entered the middle class (by developing country standards), but this forecast partially
hinges on fast growth in China. In fact, some observers suggest that there is reason
to believe that China runs a non-negligible risk of falling into a ‘middle-income trap’
(Eichengreen et al., 2013) which might make it harder to achieve less than 10% poverty
at 2$ a day by 2030.22 Progress in South Asia is also likely to be rapid. According to our
moderate growth estimate the expected poverty rate is 35.9% in 2030, implying about
716 million poor, down from 66.7% and about 1.1 billion poor in 2010. In the optimistic
PovcalNet precisely to confirm that his ‘optimistic scenario’ is possible once the intrinsic non-linearity of
the poverty-income-inequality relationship is accounted for. Our point is rather that he envisions “the
best possible world” to be used as a benchmark for future progress while we also focus on other, more
likely, scenarios.
21
After completion of the paper, we became aware of a January 2014 working paper by Yoshida et al.
(2014) which, independently and using different methods, comes to very similar conclusions.
22
However, our estimates suggest that this would require an exceptionally large slowdown. For poverty
in East Asia to remain above 10% at 2$ a day, growth needs to be less than half of the 2000-2010 trend.

17

18

pro-rich

1249.19

Panel (b) – Poor
82.42
2.12
28.39
12.64
395.33
723.19

17.36

1383.61

population
100.20
2.63
33.63
15.74
461.03
764.47

19.23

23.73

1528.08

1707.20

at 2$ a day in 2030
120.61
87.49
3.26
13.24
39.73
44.71
19.47
31.94
533.21
629.97
805.00
892.01

21.24

28.26

1866.01

2033.35

(in millions)
106.39
128.23
14.79
16.49
52.52
61.50
38.22
45.46
715.46
805.25
929.76
966.48

25.94

2065.12

95.44
44.25
60.90
57.02
804.56
993.71

28.71

4.39
9.35
8.57
12.86
40.35
70.93

pro-poor

2235.43

115.26
48.63
70.99
65.95
897.12
1027.19

31.07

5.31
10.28
9.99
14.88
45.00
73.32

neutral

2412.30

138.11
53.50
82.42
75.91
991.46
1059.59

33.53

6.36
11.31
11.60
17.12
49.73
75.63

pro-rich

Pessimistic (1980-2000)

Notes: Author’s calculations. Population projections are from the World Bank’s Health, Nutrition and Population Statistics database. The diﬀerent scenarios
are estimated using the fractional response approach outlined in Bluhm et al. (2013) and the survey data reported in the World Bank’s PovcalNet database.

Total

South Asia
Sub-Saharan Africa

Total

South Asia
Sub-Saharan Africa

neutral

Change in Inequality (Gini)
pro-poor neutral pro-rich

Panel (a) – Headcount at 2$ a day in 2030 (in percent)
3.79
4.61
5.55
4.03
4.90
5.90
0.45
0.56
0.69
2.80
3.13
3.49
4.00
4.73
5.59
6.29
7.39
8.66
2.85
3.55
4.39
7.20
8.62
10.25
19.83
23.12
26.74
31.60
35.88
40.39
51.62
54.56
57.46
63.67
66.36
68.98

pro-poor

Optimistic (2000-2010)

Average PCE Growth
Moderate (1980-2010)

Table 4 – Projected poverty headcount ratios and poor population at 2$ a day in 2030, by region

pro-poor growth case, the headcount ratio falls by about one third to less than 20% and
the number of poor decreases to less than 400 million. As a stark contrast, the 2$ a day
poverty rate in Sub-Saharan Africa is expected to remain very high. Our moderate growth
scenario predicts a poverty rate of about 66%, down from 69.9% in 2010, which at current
population projections implies almost one billion poor in Sub-Saharan Africa alone. Even
in the optimistic distribution-neutral growth scenario, we project a poverty rate of about
55% and more than 750 million poor. This is underlined by the analysis in the preceding
section where we suggest that the mass of the consumption distribution is far to the left
of the 2$ a day poverty line in 2010 for most of the subcontinent. Sub-Saharan Africa
remains the development challenge of the first half of the 21st century.

5

Conclusion and policy recommendations

The main contribution of this paper is to forecast global poverty rates until 2030. To
set the stage, we first highlight that there is a fundamental uncertainty about the precise
levels of extreme poverty. For the sake of comparison, we select the standard poverty
lines of 1.25$ and 2$ a day (in 2005 PPP$). We then discuss a robust set of global poverty
and inequality trends. The global MDG of halving the 1990 extreme poverty level was
reached in 2010 but this apparent progress hides substantial regional heterogeneity. Most
of the global success was driven by rapid growth in China. Inequality among the citizens
of developing countries and between developing countries was declining until 2005, while
average within-country inequality was rising steadily until 2010.
The changing composition of global poverty has profound implications for the mediumterm future. After 2010, fast growing East Asia will contribute less and less to global
poverty reduction, while the share of the global poor residing in Sub-Saharan Africa and
South Asia will continue to rise. All of our projections show that the global rate of
poverty reduction at 1.25$ a day will slow down markedly between 2020 and 2025. None
of our nine scenarios predicts a poverty rate near zero by 2030. This stands in stark
contrast to earlier studies and the ‘3% by 2030’ target recently announced by the World
Bank. The Bank’s target can only be reached if we make the unrealistic assumption of
equally rapid growth in all developing countries. Once country-specific growth rates are
used, even our most optimistic scenarios suggest a poverty rate between 7.9% and 10.5%,
depending on the evolution of inequality. At 2$ a day, the slowdown will occur much later
and remarkable gains are possible if the post-2000 growth trends continue. Optimistic
scenarios suggest that the 2$ a day poverty rate may fall below 20% by 2030, implying
one billion fewer poor people than in 2010.
We propose two new ‘twin targets’ on the basis of these findings. A still ambitious
but realistic benchmark for progress would be to “reduce the proportion of the population
living below 1.25$ to 8% by 2030 and reduce the proportion of the population living below
2$ a day poverty rate below 18% by 2030.” Both of these targets are firmly anchored
in our optimistic pro-poor growth scenarios. The 2$ a day poverty line should receive
more attention in the future to better track continued progress in East Asia and, later
on, South Asia. Partly for the same reason, China has recently raised its own national
poverty line to about 1.80$ a day.
These targets can be reached in a variety of ways but not only through a continuation
of the current path. They will require either an additional acceleration of growth in poorer
countries, or improvements in distribution. Reversing the trend of rising within-country

19

inequalities would speed up the pace of poverty reduction and still ensure progress at
more moderate growth rates. The returns to redistribution are increasingly high in East
and South Asia, and remain relatively large in Latin America. However, in some regions
growth takes precedent. Faster poverty alleviation in Sub-Saharan Africa will require a
signiﬁcant and sustained acceleration in growth.

20

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22

Appendix A
Table A-1 – World Bank poverty estimates by region, 1981 to 2010 (selected years)

Year
1981

1990

1999

2005

2010

Panel (a) – Headcount ratio at 1.25$ a day (in percent)
77.18
56.24
35.58
17.11
China
84.02
60.18
35.63
16.25
1.91
1.91
3.79
1.33
Latin America and Caribbean 11.89
12.24
11.86
8.66
Middle East and North Africa 9.56
5.75
5.01
3.45
South Asia
61.14
53.81
45.11
39.43
India
59.83
51.31
45.62
40.82
Sub-Saharan Africa
51.45
56.53
57.89
52.31

12.48
11.62
0.66
5.53
2.41
31.03
32.67
48.47

Total

52.16

43.05

20.63

Panel (b) – Poor
China
South Asia
India
Sub-Saharan Africa

population
1096.5
835.07
8.21
43.33
16.48
568.38
428.68
204.93

at 1.25$ a day (in millions)
926.42
655.59
332.08
683.15
446.35
211.85
8.87
17.83
6.26
53.43
60.10
47.60
12.96
13.64
10.47
617.26
619.46
598.26
448.34
472.74
466.30
289.68
375.97
394.78

250.90
155.51
3.15
32.29
7.98
506.77
400.08
413.73

Total

1937.83

1908.45

1742.53

1389.2

1214.98

Total excl. China

1102.76

1225.30

1296.18

1177.35

1059.31

34.07

25.09

Notes: Based on PovcalNet and Chen and Ravallion (2010, 2013).

23

Table A-2 – Population-weighted regional PCE growth rates over various periods

Period
2000
2010
South Asia
Sub-Saharan Africa
Overall average
N
¯
T
¯
N ×T

− 1990
2010

5.906
(0.813)
6.085
(0.989)
2.444
(0.239)
3.495
(0.443)
4.448
(0.489)
2.382
(0.689)
4.544
(0.152)
123
10.99
1352

−

5.772
(0.653)
2.755
(0.412)
2.219
(0.140)
2.532
(0.440)
3.612
(0.388)
1.419
(0.470)
3.809
(0.132)
123
20.64
2539

1980
2010

−

5.598
(0.725)
2.558
(0.411)
1.445
(0.098)
1.851
(0.293)
3.179
(0.351)
0.698
(0.472)
3.437
(0.114)
123
27.16
3341

1980
2000

− 1990
2000

5.377
(0.677)
-0.769
(0.916)
0.677
(0.171)
0.495
(0.545)
2.173
(0.284)
-0.818
(0.540)
2.565
(0.161)
122
16.30
1989

−

5.608
(0.508)
-1.225
(1.027)
1.931
(0.337)
1.253
(0.648)
2.511
(0.294)
0.016
(0.688)
2.862
(0.225)
122
9.730
1187

Notes: Author’s calculations. We use growth in per capita consumption from World Development
Indicators or Penn World Table 7.1, depending on which series is longer. Cluster-robust standard
errors are reported in parentheses.

24

70

Figure A-1 – Actual and projected poverty headcount ratios at 2$ a day, 1981 to 2030

Headcount ratio at 2.00$ a day (in %)
30
40
50
60

Observed Data

Linear trend

20

Projections

1980

1990

2000

2010

2020

2030

Notes: Author’s calculations based on Bluhm et al. (2013) and survey data from PovcalNet. The
solid black line beyond 2010 refers to the moderate (distribution-neutral) growth scenario in Table 4,
while the solid grey lines represent the distribution-neutral variants of the optimistic and pessimistic
scenarios. The pro-poor and pro-rich variants are shown as grey dotted lines and are located above
or below a solid line.

25

AFD‐MGSoG/UNU‐Merit Working Paper Series on
« Institutions, Governance and Long term Growth »

In 2010, the French Development Agency (AFD) initiated a partnership with the Maastricht
Graduate School of Governance (Maastricht University ‐ UNU‐Merit) with a view to
exploring the conceptual and econometric relationships between institutions and long‐term
growth. As a development bank with a long‐term lending horizon, AFD is particularly
interested in better understanding the determinants of countries’ long term economic,
social, and political trajectory.

AFD has thus developed a programme on “Institutions, Governance, and Long‐term Growth”
dealing with the five following dimensions:
(i)
Measuring institutions and discussing the meaning of such measures, notably
through the Institutional Profiles Database;
(ii)
Testing the econometric relationship between institutional measures and long
term growth;
(iii)
Exploring through a series of country case studies the historical relationship
between processes of economic accumulation, forms of political organisation,
and social cohesion;
(iv)
Discussing conceptual frameworks for making sense of the interaction between
political, social and economic forces in the process of development;
(v)
Developing methodologies for political economy analyses.

The MGSoG/UNU‐Merit team is involved in the five dimensions with a particular focus on
the first two. Its primary objective is to explore the Institutional Profiles Database jointly
developed by AFD and the French Ministry of the Economy since 2001. Institutional Profiles
Database is unique by its scope (about 350 elementary questions pertaining to all
institutional dimensions covering 148 countries in 2012), its entirely free access, and its
ambition to incorporate the most recent theoretical advances in the field of political
economy.

The present series intends to convey the results of our ongoing research, and in so doing to
reflect the wealth of issues that can be fruitfully addressed from an “institutionalist”
perspective. We hope that readers will find these papers stimulating and useful to develop
their own understanding and research.

Nicolas Meisel (AFD)
Adam Szirmai (MGSoG/UNU‐Merit)

For more information on the programme, please visit our websites:
http://www.merit.unu.edu/ipd/
http://www.afd.fr/lang/en/home/recherche/themes‐recherches/institutions‐gouvernance‐
croissance

The UNU‐MERIT WORKING Paper Series

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and Bart Verspagen
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case of diffusion of solar PV technology in China Michiko Iizuka
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