Study: Globalization report 2014 - Who benefits most from globalization?

Globalization report 2014
Who benefits most from globalization?

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Contents
Contents
Executive summary 6
1 Introduction 8
2 Who benefits most from globalization? 9
2.1 Methodology 10
2.1.1 Determining the “globalization champion” 10
2.1.2 Scenarios for future globalization developments 16
2.2 Globalization index: results 19
2.2.1 Descriptive analysis of the globalization index 19
2.2.2 Regression analyses on the correlation between globalization
and economic growth 26
2.3 Growth effects of globalization 29
2.3.1 Determining the globalization champion based on income gains per capita 31
2.3.2 Globalization-induced income gains per capita in relation to the starting level 35
2.3.3 Globalization-induced income gains at the country level 37
2.3.4 Overall globalization gains in comparison to the gross domestic product 39
2.3.5 Income gains per capita in relation to changes in income distribution 39
2.4 Future scenarios for globalization developments 44
2.4.1 “Accelerated globalization” results scenario 44
2.4.2 “Diverging globalization” results scenario 49
3 The most appealing foreign markets 57
3.1 Focus and methodology of the Prognos Free Trade and Investment Index 58
3.2 The most appealing foreign markets 2013 60
4 Bibliography 67
5 Appendix A – Additional tables 68
6 Appendix B – Additional figures 75
Imprint 82

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Executive summary
Executive summary
The “Globalization report 2014: Who benefits most from globalization?” study comprises two
parts. The first part focuses on the question to what extent different countries have benefited
from globalization in the past and to which degree this is possible in the future. The second
part uses the Prognos Free Trade and Investment Index to offer a differentiated measure for the
attractiveness of foreign markets for German companies.
The methodology of the ex-post analysis in the first section of the report is based on scenario
calculations for 42 countries during the period 1990–2011. One scenario assumes that globalization
has not progressed further since the beginning of the study period. The comparison of this scenario
and the actually observed economic development then allows the quantification of globalization-
induced gains in added value and a comparison across nations.
Key findings of the ex-post analysis based on scenario calculations can be summarized as follows:
• If we add up the differences in the gross domestic product per capita between the scenario
and the historically observed development over the entire study period, Finland achieves the
greatest globalization gains among all the countries under review, with an annual average
of €1500 per capita. From this perspective, Germany ranks in the top third along with many
smaller European countries. By contrast, the large developing nations finished exclusively at
the bottom of the ranking.
• The weak positions of developing nations – especially that of China – can be traced back
among other things to the low economic output per capita in the initial year of the study
period. As such, the average annual globalization-induced income gain per capita in relation
to the gross domestic product per capita in 1990 was around 18.5 percent for China, compared
to just under 6 percent for Germany and a mere 2 percent for the United States.
The projections in the first part of the report are based on two additional scenario calculations with
the help of the macroeconomic model VIEW. The “accelerated globalization” scenario assumes
that, in the future, globalization will progress one and a half times as fast as in the past. In the
“diverging globalization” scenario, the economic development is simulated under conditions in
which the level of integration with the rest of the world is assumed to have stagnated in Greece,
Portugal and Spain.

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Executive summary
The essential results of the projections can be summarized as follows:
• The “accelerated globalization” scenario shows that Eastern European countries and major
developing nations in particular can anticipate elevated growth rates of around 0.5 percentage
points until the year 2020, if the pace of globalization were to increase by 50 percent. By
contrast, significantly lower growth could be anticipated for major national economies with a
high per capita income.
• In the “diverging globalization” scenario, declines in growth are, as anticipated, most extreme
in the countries that are directly affected by the modeled stagnation in globalization: Greece,
Portugal and Spain. By the year 2020, these nations would lose up to one percentage point in
yearly economic growth. National economies that would indirectly suffer the heaviest impact,
such as Italy, are key trade partners of the directly affected countries.
The Prognos Free Trade and Investment Index – the main component of the study’s second
part – bundles a broad spectrum of economic, institutional and sociopolitical indicators into a
comprehensive measure of the attractiveness of foreign markets for German companies. While the
presentation as a ranking ensures clarity, the large number of countries under consideration and
a high degree of detail in the set of indicators enable us to recognize the foreign markets whose
appeal for German companies is still underestimated so far.
The key findings of the analysis based on the Prognos Free Trade and Investment Index can be
summarized as follows:
• The Prognos Free Trade and Investment Index shows that despite the current crisis in the
European Union and especially in the euro zone countries, the most attractive conditions for
foreign activities by German decision makers continue to be found in European nations.
• Beyond that, the United States and some Asian countries offer the most appealing foreign
markets for German companies.

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1 Introduction
1 Introduction
The increasing economic, political and social interconnectedness of the world is ubiquitous. It
is evident in the steadily rising sales of German mechanical construction companies beyond the
country’s borders as well as in the fact that more Asians use Facebook than North Americans
and that the United Nations now has almost as many members as there are sovereign states.
As different as they may seem, all of these developments are manifestations of a worldwide
phenomenon – globalization.
No one disputes that the world is becoming more interconnected. But how the consequences
of globalization are evaluated is very different, and often ideologically motivated. Opponents
of globalization, e.g., postulate that it promotes inequality between countries as well as within
societies. Proponents of globalization reply that the international interconnectedness opens up
new markets, enabling growth and wealth.
Numerous scientific studies attempt to provide an objective basis for the discussion. Bergh and
Nilsson (2010) conclude that most notably the social aspects of globalization lead to greater
inequality in net household income. Dreher (2006) finds that globalization has a significantly
positive influence on economic growth. Dollar and Kray (2001), Greenaway et al. (1999) and the
World Bank (2002) come to similar conclusions.
One weakness of the cited studies is that although they note the positive effect of globalization
on growth, they do not quantify it sufficiently – leaving unclear the extent to which different
countries benefit from globalization.
This Prognos globalization report is divided into two sections. The major focus on the topic of
“Who benefits most from globalization?” is intended to close the knowledge gaps sketched out
above. The goal of this study is to determine the extent to which all highly developed national
economies and the key developing nations were able to benefit from the ongoing globalization
between the years 1990 and 2011. The study thus reveals the greater and smaller beneficiaries of
the globalization process which makes it possible to determine the “globalization champion”. In a
second step the future effects of globalization are estimated with the help of scenario calculations.
The second part of the globalization report focuses on the analysis of the economic, institutional
and sociopolitical framework conditions in 100 national economies using the Prognos Investment
and Free Trade Index. The index ranks these 100 economies and shows which foreign markets
offer the greatest opportunities as well as the biggest risks for German exports and investment
activities.

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2 Who benefits most from globalization?
The first part of the globalization report quantifies the growth gains of developed national
economies and leading developing nations.1 To this end two analyses are carried out which differ
with respect to the time periods as well as the study methods utilized.
The first analysis refers to the time period since the year 1990. It quantifies globalization with the
help of a specifically designed index and also includes an econometric analysis of the interrelated
effects between globalization and economic development. In combination, these findings allow for
the conversion of the country-specific gains and losses related to globalization into a ranking and
thereby determine the “globalization champion”.
The second analysis is intended to exemplify the mechanics of globalization and to make them
comparable across countries with the use of scenarios with regard to future developments. The
methodology of this analysis is geared towards the macroeconomic model VIEW. The advantage
of using VIEW lies in having the ability to directly model the most important channels of the
macroeconomic effects of globalization. The following scenarios are studied in this way:
1. “Accelerated globalization” – This projection assumes that globalization continues to accelerate
and that it progresses on average one and a half times as fast as in the past two decades.
2. “Diverging globalization” – This scenario assumes that international integration stagnates in
countries in the southern euro zone while globalization maintains its pace in the remaining
countries. This scenario is motivated by the currently uncertain financial situation in these
countries which hampers foreign trade activity.
Both scenarios are anchored in the baseline projections of the Prognos World Report 2013 which
enables a comparison of the scenario calculation results to a reliable benchmark.
1 The national economies being studied are the 42 countries from the Prognos World Report 2013. This list of countries includes
all highly developed national economies as well as all the major developing nations, and thus around 90 percent of the global
economic output.

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2.1 Methodology
The detailed analysis of the interrelated effects between globalization and economic development
forms the foundation for both parts of the study. The analysis of the ex-post time period uses
the knowledge of the interrelated effects to quantify the economic changes brought about by
globalization and to create a list of globalization beneficiaries. For the scenario calculations, this
same knowledge forms the basis for directly modeling the essential mechanisms of globalization
and for making predictions about future developments. The main steps of the approach for both
analyses are described in detail as follows.
2.1.1 Determining the “globalization champion”
Determining the globalization champion encompasses the following process steps:
• Step 1: Conception of the globalization index
• Step 2: Studying the interrelated effects between globalization and economic development
• Step 3: Determining the “globalization champion”
Step 1: Conception of the globalization index
In order to quantify the economic effects of globalization the complex process that is globalization
has to be made measurable first. This is done with the help of a comprehensive index which
includes differentiated indicators that describe the economic as well as the political and social
aspects of globalization (Table 1).2
The selected economic indicators are divided into two categories. The first category, “Transaction
variables,” includes indicators that refer to actual transactions of goods, services or financial
assets. A larger transaction volume indicates that a country is more strongly interconnected with
the rest of the world. The category, “Transaction restrictions,” includes indicators for restrictions
on the free transfer of goods and financial capital. Restrictions to transaction are a sign of a less
globalized country. Both the social and political aspects of globalization are represented in the
individual sub-indices of the KOF Index of Globalization.3
All in all, the selected indicators depict the process of globalization very well with regard to the
depth and breadth of the sub-aspects under consideration. In order to achieve a comprehensive
picture of globalization, the indicators must be compiled into an index.
2 Indicator selection is based on the KOF Index of Globalization, see Dreher (2006).
3 A similar simplification is not possible for the economic components of globalization, because a high degree of detail regarding
the indicators is needed in the effect analyses for the future scenarios of globalization.

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To this end, the data is first adjusted for outliers and then normalized to a standardized measure
between 0 and 100.4
4 To correct for oultiers, the manifestations of an indicator that lie below the 5 percent quantile and above the 95 percent quantile
for this indicator are revised to the upper or lower limits for this quantile.
Table 1: Utilized globalization indicators
Indicators Description Source
Economic indicators
Transaction variables
Trade in goods
(as a % of gross domestic product)
Total exports and imports of goods as a
percentage of the gross domestic product.
World Bank,World
Development Indicators, 2013
Trade in services
Total exports and imports of services as a
percentage of the gross domestic product.
World Bank,World
Foreign direct investments
Total inward and outward foreign direct
investments (stocks) as a percentage of the gross
domestic product.
United Nations Conference on
Trade and Development, 2012
Portfolio investments
(in % of the gross domestic product)
Portfolio investments stock:Total assets and
liabilities as a percentage of the gross domestic
product.
International Monetary
Fund, Coordinated Portfolio
Investment Survey, 2013
Payments to foreigners
Sum of wage payments to foreign workers and
return on capital as a percentage of the gross
domestic product. Income from intangible assets is
not captured.
World Bank,World
Transaction restrictions
Import barriers This indicator is based on the question in the
Global Competitiveness Report: “In your country,
do tariff and non-tariff barriers to trade reduce the
opportunity of imported goods to compete on the
domestic market?”The phrasing of the question
has changed slightly over the years.
Fraser Institute, 2013
Import tariffs Indicator between 0 and 10. Higher values mean
lower import tariffs.A value of 0 reflects an
average import tariff of 50%.
Taxes on international trade Taxes on international trade include import and
export tariffs, profits from monopolies, capital
gains and taxes on capital gains.
World Bank,World
(as a % of tax revenues) Index consisting of two equally weighted
components. (1) Indicator based on the question
in the Global Competitiveness Report: “How
common is foreign corporate ownership in your
country?” (2) Indicator of the International
Monetary Fund that includes 13 types of capital
controls.
Social indicators
Sub-index “Social Globalization” of
the KOF Index of Globalization
The sub-index includes indicators on personal
contacts, information flows and cultural proximity.
ETH Zurich, KOF Index of
Globalization, 2013
Political indicators
Sub-index “Political Globalization” of
the KOF Index of Globalization
The sub-index includes indicators such as the
number of diplomatic representations and
international agreements, membership in
international organizations and participation in UN
security missions.
ETH Zurich, KOF Index of
Globalization, 2013
Source: Prognos 2014

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Higher values mean “more globalization” in each instance. 5 The correction for outliers is justified
due to technical reasons as well as reasons that relate to the objective of the study: With respect
to the latter, not every extreme event is an expression of globalization6
; and technical, because
outliers lead to distorted values after indicators are normalized.
In the next step, the econometric indicators are first compiled into a sub-index. This is done
separately for the indicators in the two categories, transaction variables and transaction
restrictions. Principal component analysis is applied as a statistical weighting which investigates
the possible linear combinations of the individual indicators and selects the weighting factors
such that the variance of the weighted sum of all indicators is maximized. This way the principal
component analysis maximizes the statistical power of the resulting index. The resulting sub-
indices for both categories are assigned equal weights when forming the sub-index that relates to
the economic facet of globalization.7
Subsequently the three sub-indices are aggregated into a globalization index. Here the economic
sub-index is assigned a weight of 60 percent while the social as well as political sub-indices are
weighted at 20 percent. This intentional specification reflects the idea that the economic facets
of globalization are considered most important when it comes to economic development. Thus,
the disproportionate weighting of the economic components should always be seen as linked
to the objectives of this study and does not represent a general value judgment concerning the
significance of the individual components for globalization.
Some of the time series used exhibit data gaps. Missing values are treated as follows: Gaps in the
midst of a series are linearly interpolated. The most recent available data points substitute for
missing values at the beginning or end of a time series. If an indicator is not available for a country
for the entire period of time, the entire series is imputed using regression analyses. To this end,
an indicator is explained through all other utilized indicators in an auxiliary regression analysis.
Knowledge about the explanatory power and manifestations of the existing indicators enables us
to approximate the indicator that is unavailable.
Step 2: Studying the interrelated effects
The goal of this process step is to quantify the effect of globalization on growth using regression
analysis. This enables us to filter out the effect of individual influencing variables on economic
growth by statistically controlling for the effects of other explanatory variables of economic
development.
5 The following formula is used to normalize indicators for which rising values indicate „more globalization.“ (Xj,t – Min(X))/
(Max(X) – Min(X)) * 100. The variable Xj,t is the individual manifestation of the indicator for the country j at time t. Max(X) and
Min(X) are the maximum and minimum of this indicator for all countries and points in time. The following formula is used to
normalize indicators for which rising values indicate „less globalization.“ (Max(X) – Xj,t)/(Max(X) – Min(X)) * 100.
6 For example, the goods turnover in Antwerp harbor overestimates the actual imports and exports for Belgium.
7 The weighting selected for the categories resembles that of the KOF Index of Globalization.

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In the regressions, economic development is operationalized through the growth of economic
output per capita in percent. The specifically designed globalization index serves as the central
explanatory variable. The regression results for this variable indicate the extent to which economic
development is driven by globalization. In light of the importance of globalization for a domestic
economy’s performance, we anticipate a positive and statistically significant effect for this variable.
To ensure that the influence of globalization is neither overestimated nor underestimated, further
determinants of economic development must be taken into account (Table 2). The anticipated
growth effects of these variables are based on both theoretical considerations and empirical
findings:
• The level of the gross domestic product per capita is considered in light of the theory of
economic convergence.8
This theory states that domestic economies with a low gross domestic
product per capita tend to display a higher rate of economic growth, which indicates a negative
effect of this determinant.
• A higher birth rate has the short-term effect of distributing a given economic growth across
a larger population base. Accordingly, we anticipate that higher birth rates correspond to
smaller growth of economic output per capita.9
• By contrast, a positive on economic growth per capita can be assumed with regard to investment
activities (private and public) because as a determinant of capital stock investments contribute
substantially to the potential of national economies.
• The inflation rate serves as an indicator of macroeconomic stability. A low inflation rate is
believed to stimulate economic activity, while a high inflation rate can counter overheated
economic growth. Based on these considerations, we expect inflation to have a negative impact
on economic growth.10
• Government spending as well as the debt ratio are considered key indicators of fiscal policy.
While in terms of neoclassical theory and empirical findings we can assume that a high debt
ratio is related to a reduction in economic growth, the influence of government spending is
ambiguous a priori.11 On the one hand, high government spending can crowd out private
investment activity. On the other hand, consumptive public spending can generate additional
demand, promoting private investment.
8 The gross domestic product per capita is entered in the regressions with its values delayed by two years to prevent the economic
growth per capita as an independent variable being used partially to explain itself.
9 Over the long term, a high birth rate can have positive effects on economic growth. However, such effects are not the subject of
this study.
10 Theoretically, this is not necessarily the case. Negative inflation rates (deflation) can be expected to exert negative effects on
growth. However, in this analysis, with the exception of Japan, deflation phases are of minor importance.
11 See Reinhard and Rogoff (2010).

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• Additionally, we control for the quality of the legal system with the Rule of Law Index. A highly
developed legal system is considered an important prerequisite for strong economic growth.
• Secondary education as a proxy for human capital should have a positive impact on economic
growth.
• We further control for the global economic crisis of 2008 and 2009 using an indicator variable.
The regression analysis includes all 42 countries contained in the Prognos World Report and
addresses the period between 1992 and 2011.12 Therefore, 20 data points are available for each
country and each variable. This data structure is taken into account by means of specific panel
regression models.13
Bei der genauen Spezifikation des Regressionsmodells müssen zwei potenzielle Problemquellen
berücksichtigt werden: unbeobachtete Heterogenität und die mögliche Endogenität verschiedener
Einflussgrößen.
In the specification of the regression model, two potential problem sources need to be taken into
account: unobserved heterogeneity and possible endogeneity of different explanatory variables.
12 Since the gross domestic product per capita is used in the regressions with its values delayed by two years, the data used for the
regressions refers to the period of time between 1990 and 2011.
13 All analyses were performed with the Stata 12 statistics program.
Table 2:Variables with a potential influence on economic growth as control
variables for the regression analysis
Variables that influence
economic growth
Control variables Source
Level of gross domestic product
per capita
Gross domestic product per capita in the next-to-last
period (in logarithms)
World Bank,World
Birth rate Birth rate per woman (in logarithms) World Bank,World
Investments Gross capital formation
World Bank,World
Inflation Increase in consumer prices
(in %)
World Bank,World
Government spending Government consumer spending
World Bank,World
Public debt Public debt
International Monetary Fund,
2013
Quality of institutions Rule of Law Index (scale from 0 to 10) Fraser Institute, 2013
Secondary education Number of secondary school attendants divided by
the number of people entitled to secondary education
(in %)
World Bank,World
Crisis indicator 2008–2009 Indicator variable with a value of 1 for the years
2008–2009 and a value of 0 for all other years.

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Unobserved heterogeneity is based on the circumstance that even a careful selection of
determinants cannot ensure that all differences between the countries under consideration
are adequately accounted for. If these unobserved characteristics correlate with neither the
dependent variable nor the control variables under consideration, no complication arises. If this
does not apply, unobserved heterogeneity becomes a problem because the explanatory power
of unobserved characteristics may falsely be assigned to other determinants. Thus, unobserved
heterogeneity can result in distorted estimates for all determinants. For this reason, fixed effects
models were used in the analysis. These control for differences between the countries that can
assumed to be approximately constant over the observed period of time.14
Endogeneity problems can, e.g., occur when interdependencies exist between the dependent
variable and one or more determinants. This type of connection can, e.g., be surmised for
investment activities and economic growth: Strong investment activities encourage economic
growth (and constitutes part of it) while, at the same time, positive economic development leads
to a positive investment climate. In such cases, the difficulty arises in that we cannot differentiate
which changes in the determinant influence the dependent variable and which changes result
from reverse causality. Endogeneity problems also lead to distorted results.
To account for potential endogeneity problems, instrumental variable procedures (short: IV
methods) are used. In this two-step process (also called a two-stage least squares estimation),
each variable for which an endogeneity problem has to be suspected is divided into two parts:
one part that is exogenous with respect to the dependent variable and one endogenous part. In
the second step of the process – the actual regression – only the exogenous part of the original
regressor is taken into account. This ensures that no endogeneity problems exist in the final
regression. In order to apply this method, at least one instrumental variable is needed for each
potential endogenous determinant. It must be highly correlated with the endogenous explanatory
variable while simultaneously holding explanatory power for the dependent variable, but must not
be affected by the same endogeneity problem.
In this study the time series of the potentially endogenous control variables are lagged by one
year and then used as instrumental variables. Under the assumption that the dependent variable
can be affected by current and past growth rates of the gross domestic product, but not by future
realizations, these time series meet all requirements for suitable instrumental variables. Based on
this approach, the assumption of exogeneity was discarded for the variables investment activity
and birth rate. 15
14 We are testing the fixed effects model in a comparison with a simple OLS model (least squares estimation) The unrestricted fixed
effects model contains one constant and 41 country-specific indicator variables. The restricted OLS contains only the constant.
The LR test between the two models examines whether the implicit restriction of the country-specific indicator variables to the
value 0 is justified. However, the test results refute this hypothesis. In this context, the fixed effects model seems to be the more
convincing alternative.
15 The option “endog” of the Stata command “xtivreg2” was used to test for joint exogeneity for different variable combinations.
The endogeneity of birth rate corresponds with empirical findings that were able to determine a correlation between economic
development and fertility. See Barro and Lee (1994).

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The regression results with respect to the effects of globalization can be interpreted as follows:
If the globalization index rises by one point, the growth of the gross domestic product per capita
increases by b percentage points, whereby b equals the level of the estimated growth effect of
globalization. An illustration: The economic growth per capita is 2.5 percent; the estimator for the
effect of globalization is b=0.2. In this case, a rise in the globalization index of one point leads
to an increase in economic growth from 2.5 to 2.7 percent. This relationship is constant for all
observed countries and for the entire study period.
This knowledge of the sensitivity of economic growth per capita with regard to globalization is used
in the next step to quantify the globalization-induced growth gains for the individual countries.
Step 3: Determining the „globalization champion”
The quantification of globalization-induced growth gains involves two steps:
• In the first step, we calculate for each country which growth rates would have resulted from a
stagnation in the level of globalization. To this end, annual changes in the globalization index
are multiplied by the estimator for the globalization-induced growth effect and subtracted
from the historical series of growth rates.
• Starting with the gross domestic product at the beginning of the study period and using the
newly calculated growth rates a counterfactual growth trajectory can be constructed for each
country that depicts the economic course if the globalization had been stagnant.
The comparison of the historical series of the gross domestic product and those that result
from counterfactual growth path enables us to tabulate and compare the globalization-induced
growth gains and losses for the individual countries. The “globalization champion” is determined
according to the highest globalization-induced gains in income per capita between 1990 and 2011.
2.1.2 Scenarios for future globalization developments
The scenario calculations aim to demonstrate the significance that increasing global
interconnectedness may have for future economic developments. To this end, two independent
scenarios were devised.
The “accelerated globalization” scenario assumes that globalization will progress at one and a half
times the pace of the period from 1990 to 2011. The absolute increase in the speed of globalization
should turn out the same for all countries in this scenario. This stipulation has two desirable
characteristics. First, it renders the increase in the pace of globalization comparable for all
countries. Additionally, a relative alignment is achieved for the globalization speeds: Nations that
only managed a comparatively weak expansion of their degree of integration between 1990 and

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2011 increase their pace of globalization more strongly in this scenario than those countries with a
relatively high speed of globalization. The scenario parameter thus implies a realistic catching-up
process for countries that were only able to achieve a rather weak globalization progress over the
last two decades.
In the “diverging globalization” scenario, globalization comes to a stop in the euro countries
Greece, Portugal and Spain. This scenario demonstrates the hidden risks that result for these
countries solely through stagnation of their level of interconnectedness with the rest of the world.
Both scenarios are implemented using the global macroeconomic model VIEW by Prognos (Box 1).
Predictions from the Prognos World Report 2013 serve as the starting point for the scenario
calculations. These baseline projections play a key role by setting an anchor point as the “most
likely scenario” or reference development. It thus constitutes the basis for simulating changes
resulting from the scenario parameters. The implicit assumption that the baseline projection is
compatible with a “normal globalization development” is justified because no breaks are assumed
in the globalization dynamic for this reference development, but rather the most probable courses
for all facets of economic development.
Box 1: VIEW, the global economic model by Prognos
VIEW is a comprehensive macroeconomic model. It includes the origin and use of goods
and services as well as the labor market and public finances, and systematically connects
all participating countries through exports, imports, exchange rates, etc.
This global forecasting and simulation model allows for a consistent and detailed
representation of future developments of the global economy. Interactions and feedback
between individual countries are captured and modeled explicitly in VIEW. For that reason,
its analytical meaningfulness goes far beyond the isolated country models with exogenous
defined parameters for the global economic system. In its current version, VIEW includes
the 42 most important countries in the world based in terms of economic output – and thus
over 90 percent of the global economic output.
On the basis of a set of key exogenous parameters such as demography, the future
development of international oil prices or the consolidation rules for national budgets,
VIEW generates projections for the global economy and individual countries. Furthermore,
VIEW allows for the consideration of a wide range of scenarios. It is for instance possible
to capture the consequences an alternative development in one country has for the
developments in all remaining countries.

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The different globalization developments are simulated through their impacts on foreign trade
dynamics. Here the most important variable is the growth rate of the imports of goods. In each
scenario this variable is specified in terms of its deviation from the baseline projection according
to the conception of the scenario.16 By contrast, the growth rate of the exports of goods in each
scenario results from the changes in imports t and the international trade relationships which are
taken into account for in the model.17 Foreign trade does not just model one of the most significant
channels of the effects of globalization on economic development. Because of the detailed
representation of bilateral trade relationships in VIEW, foreign trade is optimally suited in the
model for representing and analyzing the complex effects of increasing worldwide integration.
The specific implementation of the stipulated development of globalization in each of the scenarios
accounts for the historical development of the level of imports and its interaction with the
development of the globalization index:
• For the “accelerated globalization” scenario, in a first step the average yearly increase of the
globalization index is calculated for all countries between 1990 and 2011. Half of this value
constitutes the scenario parameter regarding the absolute acceleration of globalization. This is
the same for all countries.18 To determine its influence on each country’s foreign trade, ex-post
data is used to determine the change in growth for a country’s goods imports. To this end, the
average growth rate of the imports of in the ex-post time period is set in relation to the average
annual difference of the globalization index and finally multiplied by the scenario parameter
for accelerated pace of globalization.
• Parameters for the “diverging globalization” scenario only affect Greece, Portugal and Spain
for which globalization is assumed to stagnate. To model this development, the annual growth
rates of goods imports for each country are set in relation to the average annual changes in the
globalization index. The results in this calculation indicate the degree to which the increasing
globalization in each individual country is accompanied by a change in goods imports. Since
the scenario aims to model stagnation in the degree of integration and the baseline forecast is
assumed to be reconcilable with normal globalization development in a historical context, the
scenario parameters for the growth rates of goods imports can be determined as the difference
between the growth rates in the baseline forecast and the globalization-related growth rates
calculated as described.
16 At this point, we intentionally refrain from exogenizing imports of both goods and services. Contentwise no major differences
would be expected in the results for the scenario calculations since imports of goods make up more than 90 percent of total imports
for almost every country under consideration. Technically, this approach is imperative because each exogenous implementation
requires a modification of the model’s logic.
17 A simultaneous exogenization of exports and imports of goods would not be compatible with the international trade networks that
are considered in the model. Such an approach would disregard significant features of global economic interconnections and thus
not lead to meaningful results.
18 To prevent outliers, the simulated absolute increase of the globalization index is limited to a maximum 200 percent of the
increase in the period of time between 1990 and 2011.

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2.2 Globalization index: results
This section initially presents the results of the descriptive analysis of the globalization index.
Building on that, the regression results are analyzed with regard to the interrelated effects
between globalization and growth of the gross domestic product. Finally, a globalization champion
is determined based on globalization-induced income gains.
2.2.1 Descriptive analysis of the globalization index
The analysis of the globalization index shows that highly-developed, well-integrated national
economies that tend to be smaller exhibit especially high manifestations of the globalization level
as Ireland, the Netherlands and Belgium place themselves on the top of the rankings (Table 3).
In contrast, larger and highly-developed countries such as Germany, France, Italy and Spain rank
at mid-table. Noteworthy in this context is the comparatively high position of the United Kingdom.
Major developing nations such as China, Brazil and India place themselves mainly toward the
bottom of the globalization index. These results are thus comparable with the results of other
globalization indices (Box 2).
Table 3: Globalization index for the year 2011
Rank Country Globalization index Rank Country Globalization index
1 Ireland 91.00 22 Greece 63.55
2 Netherlands 89.30 23 Slovenia 63.14
3 Belgium 89.00 24 Italy 63.13
4 United Kingdom 82.44 25 Chile 62.37
5 Denmark 80.95 26 Israel 61.87
6 Sweden 79.58 27 Bulgaria 61.69
7 Austria 78.16 28 Poland 60.79
8 Hungary 77.56 29 United States 60.74
9 Switzerland 77.43 30 Latvia 58.47
10 Finland 76.71 31 Romania 56.49
11 Portugal 75.66 32 Lithuania 56.37
12 Estonia 73.89 33 Japan 50.06
13 France 72.98 34 Turkey 48.80
14 Czech Republic 70.78 35 South Africa 48.62
15 Spain 69.70 36 South Korea 47.75
16 Canada 69.29 37 Russia 43.45
17 Germany 69.23 38 Mexico 42.33
18 Slovakia 68.60 39 China 40.19
19 New Zealand 68.56 40 Brazil 40.08
20 Norway 68.03 41 Argentina 34.51
21 Australia 67.13 42 India 32.41

20
Box 2: Comparison of the globalization index with the New
Globalization Index19, the Globalization Index of Ernst Young and the
Economic Intelligence Unit (EIU)20, and the KOF Index of Globalization.21
In the comparison of the different globalization indices, index rankings were limited to the
list of countries considered in this study (Table 4). At first the comparison reveals many
commonalities. Small, highly-developed nations such as the United Kingdom ranked near
the top in all indices under consideration. Large and highly-developed national economies
such as Germany, France, Italy, Canada and Spain place themselves in the middle of the
ranking. In contrast, the United States and Japan are ranked at the bottom end of the
midrange in all indices. The last places in the globalization indices are held consistently by
major developing nations.
But the comparison of the indices also revealed differences. The average absolute deviation
in the ranking positions between the globalization index used in this study and the New
Globalization Index amounts to 4.2 places. The corresponding values for the Globalization
Index of Ernst Young/EIU and the KOF Index of Globalization amount to 3.6 and 2.2
places, respectively. The relatively large deviations in the New Globalization Index are due
to an older data set from 2005 and due to the fact that all trade flows were weighted with
the distance to the individual trade partner for this index. This approach causes global
trade to be weighted more heavily than regional trade which improves the ranking position
for Argentina and South Africa for example but worsens that of Hungary and the Czech
Republic. In regard to the Ernst Young/EIU index, the differences essentially arise as the
result of differences in the indicators considered and the weighting procedure. The Ernst
Young/EIU, e.g., index takes into account the mobility of the labor force which is not the
case in the index used for this study. Conversely, the political aspects of globalization are
not considered in the index from Ernst Young/EIU, which places comparatively well-
integrated countries such as Austria, Portugal and Turkey at a disadvantage. Deviations
from rankings in the KOF Index of Globalization are comparatively small which is not
surprising due to the conceptual similarities to the globalization index utilized in this
study. Different weighting of the sub-indices mainly results in differences for Estonia,
which received a comparatively higher value for the economic sub-index leading to an
improved ranking position in the index used here.
19 See Vujakovic (2010).
20 See Ernst Young (2013). The index from Ernst Young/EIU is based on a survey of business experts from the year 2012,
supplemented by data from government statistics.
21 The ranking from the 2013 KOF Index of Globalization, which was used in this consideration, refers to the year 2010.

21
Table 4: Differences between the globalization index and other indices with
regard to rankings
Rank in the globali-
zation index
Country New Globalization
Index
Ernst Young, EIU KOF Index of
Globalization
1 Ireland 0 0 –1
2 Netherlands 0 –1 0
3 Belgium –2 0 1
4 United Kingdom 0 –3 –5
5 Denmark –2 0 1
6 Sweden –3 –1 –2
7 Austria 3 –8 5
8 Hungary –15 1 0
9 Switzerland 5 4 –2
10 Finland –4 0 –2
11 Portugal –10 –17 3
12 Estonia 1 – –10
13 France 1 0 –2
14 Czech Republic –8 –2 2
15 Spain –2 –3 1
16 Canada 7 2 5
17 Germany 4 9 –1
18 Slovakia 6 9 3
19 New Zealand 4 1 –4
20 Norway 7 –4 0
21 Australia 3 –1 3
22 Greece –5 –8 4
23 Slovenia –3 – 0
24 Italy 3 –4 4
25 Chile –2 –3 –6
26 Israel 4 8 –2
27 Bulgaria 0 7 –4
28 Poland 0 4 5
29 United States 4 6 2
30 Latvia –4 – –2
31 Romania –11 1 0
32 Lithuania – – 3
33 Japan –6 –2 –3
34 Turkey –8 –4 2
35 South Africa 9 –5 0
36 South Korea –2 6 –1
37 Russia 3 –2 3
38 Mexico 2 5 0
39 China 7 2 0
40 Brazil 0 3 0
41 Argentina 8 0 0
42 India 6 0 0
Note:The difference in a country‘s ranking position is calculated as the country‘s ranking position in the globalization index
used in this study minus the ranking position in the respective comparison index. “–“ indicates that the country in question
is not considered in the individual index.

22
A glance at the sub-indices gives an indication of how the ranking in the overall index should be
evaluated (Table 5). For example, the leading positions of Ireland, the Netherlands and Belgium
result from high values in the economic and social sub-indices. But the three front runners also
post high values in the political sub-index, even though other countries hold the top places here.
At first, the low globalization index values for major developing nations may seem surprising, but
they are reflected consistently in the poor positions for these countries in the economic and social
sub-indices.22 One reason for this result is the normalization of total transaction volumes in the
economic sub-index with the size of the respective economy (Box 3).23
Box 3: China’s position in the globalization index
China ranks 39th in the overall index. This result is largely determined by China’s low
position in the economic sub-index. What may seem surprising in light of China’s
importance for the global economy can be explained with a look at China’s values for the
individual indicators:
First, we need to bear in mind that the economic sub-index not only incorporates transaction
volumes, but also other indicators that measure the restrictions on transactions. Due to its
restrictive trade policy, China finishes at the end of the set here for all four indicators.
This is most pronounced for the capital controls indicator. With 3.0 out of 10 points, China
shows the third-lowest value for this indicator among all observed nations. For comparison:
Frontrunners in the globalization index like Ireland or the Netherlands exhibit values
between 8 and 9 points.
Second, China does not show particularly favorable values for indicators in the “transaction
volumes” category in comparison to other national economies. This applies to portfolio
investments (10.5 of the gross domestic product and rank 38) as well as foreign direct
investments (15 percent of the gross domestic product and rank 42) and trade in services
(6 percent of the gross domestic product and rank 39). Even in trade in goods (46 percent
of gross national product) China only achieves 29th place among all countries under
consideration. One important reason for this finding is that, for the globalization index, the
absolute transaction volumes of a country are normalized with the gross domestic product.
With respect to trade in goods, China, e.g., ranks in second place behind the United States
with an absolute value of over €2.4 billion, which is five times as high as that of Belgium. If
we consider these numbers as percentage values in relation to the gross domestic product
of the individual nation, Belgium achieves values nearly three times as high as China.
22 To a minor extent this also applies to the political sub-index.
23 Results of empirical research show that methods that place large national economies at less of a “disadvantage” lead to similar
results; see, e.g., Vujakovic (2010).

23
Table 5: Sub-indices of the globalization index for the year 2011
Rank Country Economy Rank Country Social Rank Country Politics
1 Ireland 88.36 1 Ireland 99.43 1 Italy 99.75
2 Netherlands 84.97 2 Austria 98.27 2 France 99.48
3 Belgium 82.74 3 Belgium 98.21 3 Belgium 98.57
4 United Kingdom 74.17 4 Switzerland 97.01 4 Spain 97.98
5 Estonia 72.93 5 Netherlands 97.00 5 Austria 97.81
6 Denmark 72.12 6 Canada 96.73 6 United Kingdom 97.11
7 Sweden 71.34 7 Denmark 93.34 7 Sweden 95.89
8 Hungary 69.48 8 France 93.29 8 Brazil 95.72
9 Finland 68.38 9 Portugal 92.87 9 Portugal 95.31
10 Switzerland 65.24 10 United Kingdom 92.56 10 Denmark 95.03
11 Austria 64.90 11 Czech Republic 90.13 11 Canada 95.03
12 Portugal 63.37 12 Norway 89.48 12 Netherlands 94.57
13 New Zealand 61.21 13 Germany 88.94 13 Switzerland 94.45
14 Czech Republic 59.04 14 Slovakia 88.72 14 Argentina 94.40
15 France 57.37 15 Sweden 87.99 15 Turkey 94.00
16 Chile 56.81 16 Spain 87.92 16 Germany 93.33
17 Slovakia 56.43 17 Finland 87.57 17 India 92.81
18 Latvia 55.09 18 Hungary 86.94 18 United States 92.81
19 Bulgaria 54.76 19 Greece 86.74 19 Norway 92.76
20 Germany 54.63 20 Australia 85.98 20 Greece 92.63
21 Spain 54.19 21 Poland 82.55 21 Hungary 92.43
22 Australia 52.65 22 United States 81.98 22 Australia 91.74
23 Norway 52.64 23 Italy 80.09 23 Romania 91.44
24 Slovenia 51.74 24 Estonia 79.29 24 Poland 91.20
25 Canada 51.56 25 Slovenia 78.00 25 Finland 90.86
26 Lithuania 51.44 26 New Zealand 77.40 26 Ireland 90.51
27 Israel 50.39 27 Israel 76.66 27 Chile 89.93
28 Greece 46.13 28 Latvia 73.74 28 South Korea 89.78
29 Italy 45.27 29 Romania 70.50 29 Japan 89.64
30 Poland 43.40 30 Russia 70.49 30 South Africa 87.68
31 United States 42.97 31 Lithuania 70.27 31 Czech Republic 86.67
32 Romania 40.18 32 Japan 67.63 32 China 85.46
33 South Africa 36.26 33 Turkey 67.33 33 Russia 85.13
34 South Korea 34.93 34 Bulgaria 60.14 34 Slovakia 84.98
35 Mexico 31.08 35 Chile 51.49 35 Bulgaria 84.04
36 Japan 31.01 36 Mexico 48.70 36 Slovenia 82.48
37 Turkey 27.56 37 China 48.11 37 New Zealand 81.75
38 China 23.65 38 Argentina 47.93 38 Israel 81.39
39 Brazil 22.15 39 South Africa 46.61 39 Estonia 71.36
40 Russia 20.54 40 South Korea 44.20 40 Mexico 69.74
41 India 14.12 41 Brazil 38.24 41 Lithuania 57.23
42 Argentina 10.07 42 India 26.89 42 Latvia 53.34

24
Highly-developed national economies that place themselves at mid-table of the overall index take
on leading positions in the political sub-index. This applies in particular to Italy, France and Spain,
and to a lesser extent to Germany. In the social sub-index and particularly in the economic sub-
index, these countries rank mostly at mid-table. In particular, Germany, despite being a global
export champion for a long time, only achieves middle-of-the-road ranking throughout. Germany’s
distance from the pack leaders, as measured in index points, is especially significant in the
economic sub-index.
To be able to better classify a few sub-aspects of these results, it is illustrative to visualize the
country-specific differences for a few indicators. However, when interpreting the manifestations
of the individual indicators, we must keep in mind that high or low values are not necessarily
associated with an implicit value judgment. However, as a gauge for sub-aspects of the integration
of individual countries with the rest of the world, they hold decisive explanatory power for the
ranking in the overall index or the sub-indices.
For example, Germany‘s relatively low value on the globalization index can at least be partially
explained by scale effects. In 2011, the total of exports and imports of goods was around €2 billion
and therefore four times as high as that of Belgium. This order is reversed when viewed in relation
to the gross domestic product: Belgium imported and exported goods at a value of around 128
percent of its economic output. This degree of openness is around 77 percent for Germany. Similar
relationships exist for other economic indicators as well.
A further reason for the leading positions of Ireland, the Netherlands and Belgium and Germany’s
comparatively poor performance lies in geographic circumstances and financial market structures.
For example, the Netherlands and Belgium have very high foreign trade levels partly because
of their well-developed port infrastructures. By contrast, Ireland exhibits an astronomical 1,300
percent in portfolio investments in relation to its gross domestic product. Ireland also occupies a
leading position in foreign direct investment, which is nearly 261 percent of its gross domestic
product. By comparison, Germany merely displays values of 143 and 60 percent, respectively, in
these areas. In the same context, the United Kingdom’s high figure in the economic sub-index
reveals that it benefits in this ranking from London as a strong financial center.
A look at the development of the globalization index since the year 1990 shows that the rankings
have only experienced minor changes over the last 21 years (Figure 1, Table 28 through Table 32
in Appendix A). While, as expected, an upward trend in the index is observed for most countries,
it is relatively consistent across countries. The countries at the top of the ranking, Ireland, the
Netherlands and Belgium have remained unchanged since the beginning of the study period. The
same applies to the rankings of developing nations, although they are showing signs of catching
up slightly.

25
With this we can state that, particularly smaller but highly-developed national economies are
among the most globalized countries in the world. These countries owe their ranking in part to
their economic indicator figures, which are high in relation to economic output. With the exception
of the United Kingdom, the European core states occupy places in the middle range, an outcome
essentially caused by the moderate values of the economic indicators and further strengthened by
the heavy weighting of this sub-index. The major developing nations form the group at the bottom
of the globalization index, but exhibit greater dynamics over time.
Figure 1: Manifestations of the globalization index for selected countries from
1990 to 2011
10
20
30
40
50
60
70
80
90
100
United Kingdom
United States
Russia
Netherlands
Japan
Italy
Ireland
India
France
Germany
China
Belgium
2011201020092008200720062005200420032002200120001999199819971996199519941993199219911990

26
2.2.2 Regression analyses on the relationship between globalization and
economic growth
The following discussion of the regression results for the growth effects of globalization focuses on
our baseline specification (Table 6, Column 2). In addition to the globalization index as the main
explanatory variable, this specification includes the gross domestic product per capita, the birth
rate, investments and a crisis indicator for the years 2008 and 2009 as control variables.24
The results verify that globalization has a significantly positive influence on gross domestic
product per capita growth. The estimated coefficient of 0.35 indicates that an increase of the
globalization index by one point on average leads to an increase in growth rate of the gross
domestic product per capita by 0.35 percentage points. This, e.g., suggests that with an average
rise in the globalization index of 0.76 points per year between 1990 and 2011, Germany owes 0.27
24 The selection of variables for the baseline specification is based largely on the significance of the effects on growth of these
determinants as indicated by the results. Additionally, the two endogenous control variables – investments and fertility – are
included to enable comparable results across all specifications..
Table 6: Regression results regarding the determinants of economic growth per
capita
Dependent variable: Growth of the gross domestic product per
capita as a percent
IV method with FE IV method with FE and
country groups
Total globalization 0.35*** –
(0.07)
Globalization for
Large national economies with a high per capita income – 0.26***
(0.05)
Small national economies with a high per capita income – 0.26***
(0.06)
Large national economies with a low per capita income – 0.29
(0.16)
Small national economies with a low per capita income – 0.40***
(0.10)
Gross domestic product per capita in the next-to-last period
(logarithmized)
–10.48*** –10.02***
(1.60) (1.70)
Birth rate (logarithmized) –10.44*** –10.19**
(2.42) (3.26)
Investments (as a % of the gross domestic product) 0.15 0.12
(0.10) (0.10)
Crisis indicator 2008-–009 –3.55*** –3.59***
(0.43) (0.43)
Number of observations
R² (centered)
840 840
0.40 0.40
Notes:The symbols *, **, *** indicate the significance of the estimation results for the 10%, 5% and 1% levels. Standard
errors are clustered by country and displayed in parentheses.All regressions contain a constant. FE is the abbreviation for
country-specific fixed effects.

27
percentage points of its annual per capita growth to its increasing interconnectedness with the
rest of the world.
This figure equals almost 20 percent of the average growth of the gross domestic product per
capita in the same period of time, which signals the decisive importance that can be attributed to
globalization alongside other drivers of growth such as technological progress.
The other estimated results of the baseline specification show the expected signs. The gross
domestic product per capita, the birth rate and the indicator for the most recent global economic
crisis are included with minus signs in the estimation equation. Also, all of them are statistically
significant. A coefficient of -10.48 for the influence of economic output means that a 1 percent
increase in the gross domestic product per capita leads to a reduction of per capita growth of
0.105 percentage points two years later. Similarly for fertility, a 1 percent increase corresponds
to a 0.105 percentage point decline in growth per capita. The estimated 3.55 coefficient for the
crisis years 2008 and 2009 signifies that the economic growth per capita during this period was
approximately 3.5 percentage points lower than in the rest of the observation period. At 0.15,
the estimated value for investments in relation to the gross domestic product also exhibits the
expected sign, but is not statistically significant.25
Thereliabilityof estimatedresultsischeckedusingavariety ofalternativeregressionspecifications.
As the first alternative, we consider a specification in which the growth effect of globalization is
estimated separately for different country groups, but in which the same explanatory variables are
taken into account. To this end, the countries under consideration are separated into four groups
of approximately equal size based on the gross domestic product per capita in the year 1990 and
the size of the economy, as measured by the gross domestic product of the same year (Table 7).26
The results demonstrate that all four country groups exhibit similar sensitivities in per capita
growth with regard to globalization (Table 6, Column 3). At 0,40, small national economies with
a low gross domestic product per capita show a slightly greater sensitivity in economic growth
with regard to globalization, while all other country groups display a slightly lower sensitivity.
Differences between the estimators are too small to allow meaningful interpretations, as none of
the estimators differ significantly from 0.35.27
25 Estimators that do not signal a statistically significant effect of investments on the gross domestic product are not uncommon in
the empirical literature. See Dreher (2006) and Borys, Polgár, Zlate (2008).
26 The division was performed as follows: First, all countries being studied were separated into two groups according to a median
split with respect to the gross domestic product per capita in the year 1990. This figure amounted to €10,050. Next, the country
groups formed in this way were each divided into two sub-groups based on the median split according to the gross domestic
product in the year 1990. This figure amounted to €250 billion for the group of countries with a high gross domestic product per
capita and €95 billion for the group of countries with a low gross domestic product per capita.
27 The lowest p-value for the two-sided t-test resulted with a value of 0.06 for the group of large national economies with a high
gross domestic product per capita. The next lowest value, 0.15, resulted for the group of small national economies with a low
gross domestic product. Therefore, the null hypothesis that that the estimated sensitivity in per capita growth with respect to
globalization corresponds to a value of 0.35 can only be rejected for the group of large national economies with a high gross
domestic product per capita and also only at a significance level of 10 percent.

28
This result signals that alternative specifications with country-group-specific estimators for the
growth effects of globalization do not come to meaningfully different conclusions. Furthermore,
the estimated coefficients of the remaining explanatory variables hardly differ from those of the
baseline specification.
As alternative specifications, additional regressions with different combinations of explanatory
variables were run using both the baseline specification as well as the specification with
country-group-specific sensitivities as starting points.28
Results of these regressions corroborate
the finding that both the estimated effects of globalization on growth as well as those of the
remaining explanatory variables are robust and can be considered reliable (Table 33 and Table 34
in Appendix A).29
The overall result of the regression analyses documents the stable and significant positive
influence of globalization on per capita growth. In particular, the high reliability of the estimations
strengthens the confidence in the regression results. For that reason, the estimated sensitivity
of per capita growth in the baseline specification of 0.35 percentage points for each point of
the globalization index can be considered a key interim result of this section. The „globalization
champion” will be determined in the next section based on this sensitivity.
28 Furthermore, the terms of trade were taken into account in additional regressions as a control variable for the relation of export to
import prices. The results across all specifications exhibit a positive, but insignificant influence of the terms of trade on economic
growth and no change of the estimated effect of globalization on growth.
29 Moreover, all explanatory variables are included in the estimation equation with the expected signs. The only exception is
secondary education, for which the estimated effect turns out to be negative although the estimator fails to reach statistical
significance at a conventional level.
Table 7: Classification of the national economies under consideration based on
the gross domestic product per capita and the size of the economy
Large national economies
with a high per capita
income
Small national economies
with a high per capita
income
Large national economies
with a low per capita
income
Small national economies
with a low per capita
income
Australia Belgium Argentina Bulgaria
Germany Denmark Brazil Chile
France Finland China Estonia
Italy Greece India Latvia
Japan Ireland Mexico Lithuania
Canada Israel Poland Romania
Netherlands New Zealand Portugal Slovakia
Switzerland Norway Russia Slovenia
Spain Austria South Africa Czech Republic
United States Sweden South Korea Hungary
United Kingdom Turkey

29
2.3 Growth effects of globalization
This section aims to answer the question regarding the extent to which the countries under
consideration have benefited from the ongoing globalization in the time period from 1990 to 2011.
This analysis is based on the comparison of the historical development of the gross domestic
product with a counterfactual scenario for which globalization is assumed to have stagnated at
the level prevailing at the outset of the observation period. In other words: We assume in the
scenario that the globalization index in all the years from 1991 to 2011 remained fixed at the
1990 level for the each country.30 We use the differences in the development of the gross domestic
product per capita, summed up over the entire observation period, as the basis for measuring
globalization gains. When interpreting the results, we must distinguish between economic growth
and cumulative income gains (Box 4).
The country whose residents have benefited the most from increasing globalization will be
crowned the „globalization champion.” In accordance with the economics focus of the study, both
the absolute income gains per capita and the per capita income gains weighted according to
purchasing power are used as two alternative indicators to determine the “globalization champion.”
For a differentiated representation of the results with regard to the different starting positions
and proportions of the national economies, we utilized the globalization-induced income gains
per capita in relation to the value of the gross domestic product in the year 1990 as well as the
aggregated income gains of the entire national economy. In order to also convey an impression of
the extent to which global integration tendencies are associated with changes in the distribution
of net household income, we subsequently compare the globalization-induced income gains with
the changes in the Gini coefficients for the individual countries.
Box 4: Interpreting the globalization-induced income gains as an
indicator for determining the „globalization champion”
The assumed stagnation of globalization causes low economic growth and thus an
unfavorable growth path. The yearly difference between the level of the gross domestic
product per capita according to this alternative path and the actual development shows the
absolute economic gains (Figure 2).
30 The development of the gross domestic product per capita is calculated with the following formula for the counterfactual scenario:

!#!
!!!
=
!#!#
!!!#
∗ 1 +
!! − 0,35 ∗ (!! − !!!!)
100
!
!!!!

In this formula, gt represents the given historical growth rate of the gross domestic product per capita in percent, POPt the
population in year t and GIt the value of the globalization index in year t. Subsequently the gross domestic product itself is
determined through multiplication of the gross domestic product per capita with the given historical population figures.

30
These gains for each country under consideration are summed up for the entire time period
of 1990 to 2011 as a measure for the cumulative effects of globalization. In this study, the
variable calculated in this way will be designated as the “cumulative income gain induced
by progressing globalization.” This variable should not be confused with variables that are
used in the system of national accounts, such as the available income.
Furthermore, we must distinguish between cumulative income gains and changed growth
rates. For example, even a one-time higher growth rate of the gross domestic product
induces income gains that accumulate over the remaining study period, even when growth
rates in the remaining time frame remain unchanged. By contrast, a one-time globalization-
related income gain has no implications for the growth rate in the following years.
Figure 2: Schematic representation of the development of the gross
domestic product and globalization-induced income gains
actual development stagnation of globalization
95
100
105
110
115
120
125
130
135
109876543210
income gain

31
2.3.1 Determining the „globalization champion” based on income gains per
capita
Considering the absolute income gains per capita resulting from increasing globalization, we see
that two Scandinavian nations occupy the first two places in the ranking (Table 8).31 According to
this approach, Finland is the “globalization champion”, followed by Denmark. With Switzerland,
Austria, Greece, Ireland and Sweden, five additional small European countries rank among the
top ten. But some large national economies including Germany and Japan report large income
gains per capita as well and, thus, can count themselves among the stronger beneficiaries of the
globalization process.
Places 11 through 24 are occupied primarily by Central European countries or national economies
with a gross domestic product per capita that is high in comparison with the rest of the world.
Slovenia, South Korea and Estonia are exceptions here. It is noteworthy that residents of large
industrial nations do not benefit equally from the increasing interconnectedness in the world.
Globalization gains per capita in the United Kingdom and United States are less than half as
high as those for Germany, for example. Countries like Italy, Canada and Spain fall under this
category as well. Reasons for this finding can be mainly found in the different developments of
the globalization index. Germany benefits on the one hand in that it was able to post the greatest
growth in the globalization index between 1990 and 2011 among the mentioned countries under
(Table 28 through Table 32 in Appendix A).
Equally important is the fact that Germany’s progress with regard to integration with the rest of
the world can be primarily attributed to the first half of the observation period. In comparison to
many other national economies, globalization-related income gains in Germany were therefore
able to accumulate over a longer period of time.
The lower mid-range of globalization winners is completed primarily by nations from Eastern
Europe and the Baltic states. While these national economies were only able to achieve from 20
to 30 percent of the frontrunners’ globalization gains per capita, this can still be considered an
impressive success especially in light of the economic turmoil after the fall of the Soviet Union.
The large developing nations rank last in the comparison of absolute globalization gains per
capita. Therefore, in terms of absolute cumulative income gains per capita, they do not count to
the strong beneficiaries of globalization, despite their significance for the world economy e which
is due to their large domestic markets and highly dynamic economies.
31 To correctly classify the results, it is important to note that this study does not allow any statements with regard to the income
distribution within a country. The stated income gains induced by progressing globalization refer exclusively to the average of
the population.

32
Table 8:Absolute income gains per capita as the result of increasing
globalization in the period of time from 1990 to 2011
Rank Country Average annual per capita income gain
since 1990 in euros*
Cumulative per capita income gain since
1990 in euros*
1 Finland 1,500 31,400
2 Denmark 1,420 29,800
3 Japan 1,400 29,500
4 Germany 1,240 26,100
5 Switzerland 1,220 25,600
6 Israel 1,080 22,600
7 Austria 1,010 21,300
8 Greece 980 20,500
9 Ireland 970 20,400
10 Sweden 970 20,300
11 Slovenia 900 18,900
12 Netherlands 890 18,700
13 France 800 16,900
14 Portugal 800 16,800
15 South Korea 790 16,500
16 Australia 750 15,800
17 Italy 710 15,000
18 Canada 660 13,800
19 New Zealand 650 13,700
20 Belgium 630 13,200
21 United Kingdom 580 12,100
22 Spain 570 11,900
23 Estonia 560 11,700
24 United States 540 11,300
25 Hungary 410 8,600
26 Latvia 350 7,300
27 Lithuania 330 7,000
28 Chile 300 6,400
29 Norway 300 6,300
30 Czech Republic 300 6,300
31 Slovakia 270 5,700
32 Poland 260 5,500
33 Argentina 230 4,900
34 Turkey 190 4,000
35 Romania 170 3,600
36 South Africa 160 3,400
37 Bulgaria 160 3,400
38 Brazil 120 2,600
39 Russia 120 2,500
40 Mexico 100 2,200
41 China 80 1,700
42 India 20 400
* real prices from the year 2000; rounded values

33
Another important finding emerges for those countries that exhibit the highest values for the
globalization index: Neither Belgium, the Netherlands nor Ireland are among the top-ranked
countries in terms of globalization gains per capita. The reason for this result is that, although
these national economies have a high degree of integration with the rest of the world, they
exhibited low momentum during the study period. This result clearly shows the importance of
ongoing efforts to integrate national economies with the rest of the world, even for – or perhaps
especially for – very globalized nations.
Observing the development over time sheds additional light on how the globalization-induced
income gains per capita should be assessed (Figure 4 through Figure 7 in Appendix B). It shows
that the strongest gains in terms of growth should be attributed to the period from the mid-1990s
to the middle of the first decade of the 21st century. “Globalization champion” Finland and the
other main beneficiaries from globalization were able to increase their gross domestic product per
capita through globalization at the beginning of the study period. This makes clear how important
the developments in the early years of the observation period are for the overall results of this
study: The earlier a country was able to benefit from globalization, the longer the period of time
during which the income gains per capita could be accumulated. The boom in technology and the
important role of the Finnish telecommunications industry in the 1990s may therefore be decisive
factors in the final ranking of the beneficiaries from globalization. By contrast, the selection of
the observation period puts countries such as Chile or Slovakia, which were only able to achieve
significant increases in the globalization index in the later on, at a disadvantage.
To what extend the situation has improved due to the ongoing globalization depends not only
on the absolute income gains, but most notably on the level of consumption that individuals can
afford as a result. For this reason, we analyzed income gains per capita that have been weighted
according to purchasing power as an alternative way to determine the „globalization champion”
(Table 9).
According to this approach, Finland, again, takes the leading position. However, the rankings after
Finland show some changes in comparison to the previous approach. Slovenia, which came in
12th place in the absolute globalization-induced income gains, and 11th-place holder Greece take
second and third place in this ranking. Japan, on the other hand, finds itself ranked 16th here,
due to its high price levels, and Sweden only takes 17th place. Similar logic applies for the United
Kingdom and the United States, which drop eight and nine places, respectively.

34
Table 9: Per capita income gains induced by increasing globalization in the
period of time from 1990 to 2011, adjusted for purchasing power
Rank Country Average annual per capita income gain in
euros,APP*
Cumulative per capita income gain in
euros,APP*
1 Finland 1,630 34,300
2 Slovenia 1,570 33,000
3 Greece 1,570 32,900
4 Germany 1,400 29,300
5 Denmark 1,360 28,600
6 Estonia 1,330 27,900
7 Israel 1,280 26,800
8 Portugal 1,240 26,100
9 Austria 1,220 25,700
10 South Korea 1,190 25,000
11 Switzerland 1,110 23,300
12 Ireland 1,100 23,000
13 Netherlands 1,080 22,700
14 Hungary 1,080 22,600
15 New Zealand 1,030 21,600
16 Japan 980 20,500
17 Sweden 970 20,300
18 Italy 950 19,900
19 France 930 19,500
20 Australia 910 19,100
21 Lithuania 870 18,300
22 Latvia 840 17,700
23 Spain 830 17,500
24 Czech Republic 810 17,100
25 Canada 800 16,700
26 Belgium 770 16,100
27 Bulgaria 630 13,300
28 Poland 620 13,100
29 United Kingdom 600 12,600
30 Romania 590 12,400
31 Chile 580 12,200
32 Slovakia 560 11,700
33 United States 540 11,300
34 Russia 450 9,500
35 Turkey 410 8,700
36 South Africa 370 7,700
37 Norway 290 6,100
38 Argentina 280 5,800
39 Brazil 230 4,900
40 China 200 4,100
41 Mexico 170 3,500
42 India 60 1,200
* adjusted for purchasing power in relation to the United States; real prices from the year 2000; rounded values

35
These shifts in rankings are partially due to considerable changes in the reported income gains.
While Finland’s purchasing power parity adjusted income gains per capita amount to about €3,000
and are therefore 10 percent higher than the absolute unadjusted income gains per capita, the
changes for Greece amount to about €12,500 (or 60 percent), for Slovenia to about €14,000 (or 75
percent), and for Estonia to about €16,000 (or 140 percent). The major developing countries are
also better off in the approach in which the purchasing power is taken into account. Among the
latter, Russia exhibits the largest jump with an increase of €7,000 or 280 percent.
Despite major shifts in the rankings for individual countries, the overall picture described
previously remains largely unchanged. The first positions are primarily occupied by smaller
countries with a high gross domestic product per capita. With the exception of Germany, the large
industrialized nations find themselves exclusively at mid-table. The positions in the middle of the
ranking are completed by Eastern European nations. Without exception, the major developing
countries occupy places at the bottom of the ranking.
2.3.2 Globalization-induced income gains per capita in relation to the starting
level
If we analyze the per capita income gains in relation to value of the gross domestic product per
capita in 1990, we see strong shifts in the country rankings (Table 10). National economies that
exhibited a low to medium level for the gross domestic product per capita in 1990 take on a top
position in this approach – with China leading the way.
Smaller and midsized Eastern European economies as well as the Baltic states, especially Estonia
and Slovenia, earn places in the upper third of the ranking. The majority of smaller national
economies with a high gross domestic product per capita occupy ranks in the middle of the field.
By contrast, Sweden, Switzerland, Belgium and particularly Norway find themselves at the lower
end of the ranking. At just about one percent, Norway’s cumulative globalization-induced income
gain per capita in relation to the value of the gross domestic product per capita in 1990 turns out
around 18 times smaller than China’s, and about seven times smaller than that of its neighbor
Finland.
Germany takes the highest rank in the ranking among the highly developed industrial nations with
17th place. Spain, France and Italy rank in the lower midrange. Results for the United Kingdom
and United States are especially noteworthy. Their positions at the lower end of the ranking are
the result of relatively low absolute income gains, which were further put into perspective by the
high baseline level of the gross domestic product per capita.

36
Table 10: Per capita income gains resulting from globalization from 1990 to 2011
in relation to the gross domestic product per capita in the year 1990
Rank Country Average annual per capita income gain
in relation to the gross domestic product
per capita in the year 1990 as a percent
relation to the gross domestic product
per capita in the year 1990 as a percent
1 China 18.5 388
2 Estonia 11.3 238
3 South Korea 10.5 220
4 Slovenia 9.9 208
5 Greece 9.3 194
6 Bulgaria 8.8 186
7 Chile 8.7 184
8 Hungary 8.7 182
9 Portugal 8.4 176
10 Romania 8.3 175
11 Latvia 8.1 171
12 Poland 7.8 164
13 Lithuania 7.1 149
14 Finland 6.9 145
15 Israel 6.5 136
16 Ireland 6.3 133
17 Germany 5.8 122
18 Denmark 5.4 114
19 New Zealand 5.2 108
20 Czech Republic 5.1 107
21 Turkey 5.0 105
22 India 5.0 105
23 Austria 4.9 102
24 South Africa 4.8 100
25 Slovakia 4.7 98
26 Spain 4.6 96
27 Netherlands 4.3 91
28 Russia 4.2 87
29 France 4.0 83
30 Italy 3.9 83
31 Australia 3.9 83
32 Argentina 3.8 80
33 Sweden 3.8 79
34 Japan 3.8 79
35 Brazil 3.4 70
36 Switzerland 3.3 69
37 Canada 3.1 65
38 Belgium 3.1 65
39 United Kingdom 2.7 58
40 Mexico 1.7 37
41 United States 1.7 36
42 Norway 1.0 21

37
Among the major developing nations, only China occupies a position at the top of the ranking.
In contrast to China, the larger absolute per capita income gains of other developing nations are
overcompensated by higher values of the gross domestic product per capita in 1990. For this
reason, Russia finds itself in the lower midrange while Argentina, Brazil and Mexico occupy
places toward the bottom of the ranking. India’s position in the middle of the field is the result
of having the lowest absolute per capita income gain among all countries under consideration,
combined with the likewise lowest level of gross domestic product per capita in the first year of
the observation period.
2.3.3 Globalization-induced income gains at the country level
If we consider the globalization-induced income gains at the country level, we note, not
surprisingly, that exclusively large national economies are represented at the top of the ranking
(Table 11). Japan takes first place with an average yearly income gain of about €180 billion induced
by increasing globalization. Therefore, Japan’s gains over the entire study period add up to
considerably more than €3 trillion, or around 68 percent of the gross domestic product of the year
2011. The globalization gains of the United States, Germany and China also represent impressive
sums. Germany’s income gain over the period of time between 1990 and 2011 equals around 92
percent of its gross domestic product in the year 2011 – which is more than the Federal Republic’s
national debt at this point in time.
But some smaller countries also report considerable globalization gains in relation to their
individual economic output. Estonia’s cumulative globalization-induced income gain amounts to
more than 160 percent of its gross domestic product in 2011. Likewise, countries such as Latvia,
Lithuania, Bulgaria, Slovenia, Romania, Hungary, Finland, Portugal and Greece were able to achieve
globalization gains which correspond to more than 100 percent of their gross domestic product.
The sequence of globalization gains at the country level largely coincides with public perception
since, at an aggregated level, the large national economies are the strongest beneficiaries from
increasing global integration. The fact that, contrary to common belief, the major developing
nations, China and India, do not rank in first and second on this list might be due to two reasons:
First, the specification of the observation period puts both countries at a disadvantage since the
calculations of the absolute income gains are based on the low starting values of the gross domestic
product in 1990. Thus, China and India’s process of catching up based on double-digit growth rates
for the gross domestic product in each in the subsequent years is not taken into account. Second, it
cannot be ruled out that the estimation procedure which estimates a single globalization-induced
growth effect for all countries under consideration cannot do justice to all of China and India’s
unique characteristics.

38
Table 11:Average and cumulative globalization-induced income gains at the country level in
the time period between 1990 and 2011
Rank Country Average annual income gain
since 1990 in billions of
euros*
Cumulative income gain since
1990 in billions of euros*
Cumulative income gain since 1990
in relation to the real gross domestic
product of 2011**
1 Japan 178.7 3,752 68
2 United States 154.8 3,251 24
3 China 103.0 2,163 56
4 Germany 102.0 2,142 92
5 France 50.3 1,057 64
6 Italy 41.4 870 70
7 South Korea 37.7 792 88
8 United Kingdom 34.8 730 38
9 Spain 24.1 506 65
10 Brazil 22.3 468 46
11 Canada 20.9 438 45
12 India 19.6 412 36
13 Russia 17.0 356 75
14 Australia 15.2 319 51
15 Netherlands 14.4 303 63
16 Turkey 12.5 262 57
17 Mexico 11.4 240 26
18 Greece 10.8 226 147
19 Poland 10.1 212 74
20 Switzerland 9.0 188 56
21 Sweden 8.8 184 54
22 Argentina 8.7 182 35
23 Portugal 8.3 175 130
24 Austria 8.3 174 70
25 Finland 7.8 164 102
26 South Africa 7.8 163 77
27 Denmark 7.7 161 86
28 Israel 7.4 156 80
29 Belgium 6.6 138 47
30 Chile 5.0 105 79
31 Hungary 4.1 87 140
32 Ireland 4.0 84 60
33 Romania 3.8 79 126
34 Czech Republic 3.1 65 72
35 New Zealand 2.6 55 77
36 Slovenia 1.8 38 133
37 Slovakia 1.5 31 60
38 Norway 1.4 29 13
39 Bulgaria 1.2 26 123
40 Lithuania 1.1 24 117
41 Latvia 0.8 17 130
42 Estonia 0.8 16 166
* real prices from the year 2000; rounded values; ** in percent

39
2.3.4 Overall globalization gains in comparison to the overall gross domestic
product
In light of the previously discussed findings, it is apparent that there are other sources for growth
in the observed countries aside from globalization. A comparison of the globalization-induced
income gains with the overall growth of the gross domestic product between 1990 and 2011 makes
this even clearer (Table 35 and 36 in Appendix A).
While, for some countries, more than half the income gains are associated with developments
related to globalization, the percentage of globalization-induced income gains comprise less
than 5 percent of the total growth of economic output for other countries.32 The reasons for these
discrepancies must be sought in the specific situation of the individual countries.
For example, for many European countries the creation of an integrated domestic market was
of great importance. The large developing countries presumably benefit from dynamic domestic
markets and the diffusion of technology from industrial nations. Natural resources play an
important role for countries like Norway. A comprehensive discussion of country-specific sources
for growth would require detailed country analyses, which are not part of this study.
2.3.5 Income gains per capita in relation to changes in income distribution
The results of the previous analyses express predominantly positive overall economic effects from
globalization. They do not answer to what extent globalization-induced growth at the country level
is also reflected in an improved economic situation for individuals. Some critics of globalization
hold the view that ongoing global integration is associated with increasing income inequality. The
following analyses aim to reveal the degree to which this prediction aligns with the results of this
study.
We use the Gini coefficient for net income as an indicator for the inequality of income distribution
within a country.33 The coefficient can take on values between 0 and 100. A score of 0 means that
all households in a country have the same net income. Higher values signal greater inequality.
The top value of 100 would result if one single household were to possess the entire net income
of the country.
32 At this place we must point out that the design of scenario calculations is based on an estimator that is uniform for all countries
with regard to the effects of globalization on growth. This approach brings advantages regarding the comparability of globalization
gains; however, country-specific characteristics cannot be fully taken into consideration. To that extent, the resulting globalization
gains in relation to the total development of the gross domestic product should be seen as estimates. They do not satisfy the
complex challenges of a country-specific growth accounting.
33 The Gini coefficient of the SWIID Version 4.0 (Standardized World Income Inequality Database) data set is used in this study, see
Solt (2009). This data set is characterized by good data availability regarding both the number of available countries as well as
the observed time periods.

40
An initial indication that globalization does not lead to greater income inequality arises from
the correlation between the Gini coefficient and a country’s value in the globalization index.
The correlation amounts to 0.52 (p=0.00)34
across all countries and years under consideration,
demonstrating that more heavily globalized countries tend to exhibit a more even income
distribution. This finding is strengthened further if we consider the correlation between the
globalization-induced income gains per capita and the development of the Gini coefficient between
the years 1990 and 2011 (Figure 3).
34 A p-value of below 0.05 (0.01) means that the statistics are distinct from zero with a probability of more than 95 percent (99
percent).
Figure 3: Scatterplot of absolute globalization-induced income gains per capita in
relation to the difference of the Gini coefﬁcients between 1990 and 2011
–10 –5 0 5 10 15 20 25
0
5.000
10.000
15.000
20.000
25.000
30.000
35.000
Switzerland
Denmark
Spain Estonia
Finland
France
Greece
South Africa
United States
Turkey
Sweden
Slovakia
Slovenia
Russia
Romania
Portugal
Poland
New Zealand
Norway
Mexico
Netherlands
LatviaLithuania
South Korea
United Kingdom
Hungary
India
Ireland
Japan
Italy
Israel
Belgium
Bulgaria
Austria
Argentina
Australia
Brazil
Canada
Chile
Germany
Czech Republic
China

41
First it is noticeable that in a number of countries the distribution of net household income
became more even while at the same time the countries enjoyed large gains from globalization.
This applies both to developing nations such as Brazil and Mexico and to European economies
such as Ireland or Austria. By contrast, globalization-induced income gains were associated with
greater inequality in net household income especially in Eastern European countries. However,
despite large income gains due to increasing globalization, the majority of countries being studied
exhibited only a small increase of the Gini coefficient.
Overall, Figure 3 gives the impression that larger globalization-induced income gains per capita
tend to be associated with smaller Gini coefficient increases. This finding is confirmed if we
calculate the correlation between the annual globalization gains and the annual difference in the
Gini coefficients. The correlation amounts to –0.13 (p=0.00).35
With regard to the correlation analyses, it can be summarized that that, based on the approach of
this study, no negative relationship between progressing globalization and the inequality of the
distribution of net household income within individual countries could be ascertained.
Regression analysis can provide a more in-depth look at the connection between globalization
and the distribution of income, with the Gini coefficient serving as a dependent variable. The key
explanatory variable is the degree of economic, political and social integration with the world,
operationalized by the globalization index. In the regressions, we additionally control for other
variables that influence the distribution of income (Table 12). These are:
• Technological progress: Technological progress leads to greater demand for skilled labor.
At the same time, demand for low-skilled workers declines. Such a development can lead
to higher wages for skilled labor and lower wages for unskilled labor, which in turn leads to
broader disparity in household income.
• Access to education: If access to education improves, a greater percentage of the labor force
can be employed in more highly qualified areas as a result. Therefore, better education tends
to promote equality in net household incomes.
• Composition of the employment structure: Wage levels often depend on the sector of the job.
For example, a higher percentage of employees in the agricultural sector – where wages are
comparatively low – can be associated with greater distribution inequality.
By contrast, a higher percentage of women in the workforce may lead to smaller income disparities
because the net income is more equally distributed across all households.
35 This result confirms itself as robust for other indicators as well. The correlation coefficients between income gains that have been
adjusted for purchasing power (or alternatively the income gains in relation to the starting level) and the difference of the Gini
coefficients between 1990 to 2011 amount to -0.16 (p=0.00) and -0.15 (p=0.00), respectively.

42
A panel approach with fixed effects for countries and years is employed as a regression model. It
is estimated using ordinary least squares. The country-specific effects control for time-constant,
unobserved heterogeneity among countries. The year-specific effects serve to statistically capture
the effects of global macroeconomic shocks.36
The regression results show two specifications of the estimation equation, each with different
operationalization of technological progress (Table 13). According to both regressions, globalization
has a positive, but insignificant effect on the Gini coefficient across all countries. The positive
coefficient suggests that globalization tends to increase inequality. However, the coefficient’s lack
of statistical significance indicates that the result could be coincidental. Therefore, the results
cannot be interpreted as evidence for such a correlation.
Technological progress exhibits the expected positive coefficients when measured by the Solow
residual as well by the number of patent applications. A lack of statistical significance reveals,
however, that results regarding this determinant should be interpreted with caution.
36 For similar empirical approaches, see International Monetary Fund (2007), Chapter 4 or OECD (2011), Chapter 2.
Table 12:Variables that potentially influence the distribution of income as control
variables for the regression analyses
Variables affecting
income distribution
Control variables Source
Globalization Globalization index Prognos
Technological progress Solow residual. (Calculation: Change rate of the gross
domestic product minus the sum of the change rates for
the production factors Work and Capital, weighted with the
income percentages
Prognos; basic data: OECD and
EU-Ameco
Technological progress Number of patents (at the European or US patent office) OECD Science and Technology
Indicators
Education Percentage of the population that completed secondary
education or higher
Barro and Lee (2013); Version 1.3
Education Average number of school years Barro and Lee (2013); Version 1.3
Composition of labor
force
Percentage of employees in the agriculture sector (as a %
of the entire labor force)
World Bank,World Development
Indicators, 2013
force
Percentage of employees in the industrial sector (as a % of
the entire labor force)
Indicators, 2013
force
Percentage of employed women (as a % of the entire labor
force)
Indicators, 2013

43
The remaining control variables predominantly exhibit the expected signs, but the estimated
coefficients do not always prove to be significant. Therefore, better education – measured by the
average length of school enrollment – is associated with decreasing inequality of net household
income. The percentage of people employed in agriculture tends to have an inequality-increasing
effect, although the estimated coefficient is not statistically significant. As expected, a higher
percentage of employed women is associated with a smaller disparity in net household income.
Thus, while the regression analysis does suggest that a higher worldwide degree of integration
measured through the globalization index tends to be linked with a larger disparity in net
household income, this finding is limited by the fact that the associated regression results do
not exhibit a conventionally required level of significance. For this reason, an interpretation of
the estimation results should be interpreted with caution: This study was not able to confirm a
connection between globalization and the development of the distribution of income.
Table 13: Regression results with respect to the determinants of the distribution
of income
Dependent variable: Logarithm of the Gini coefficient (1) (2)
Globalization index 0.0018 0.0021
(0.0013) (0.0014)
Technological progress measured via
Solow residual 0.0033
(0.0025)
Number of patents (at the European or US patent
office)
0.0012
(0.0099)
Percentage of the population that completed secondary
education or higher
0.0035* 0.0014
(0.0017) (0.0020)
Average number of school years –0.4454*** –0.2858*
(0.1345) (0.1402)
Percentage of employees in the agriculture sector (as a %
of the entire labor force)
0.0501 0.0447
(0.0352) (0.0350)
Percentage of employees in the industrial sector (as a % of
the entire labor force)
–0.0053 –0.0180
(0.0579) (0.0620)
Percentage of employed women (as a % of the entire labor
force)
–0.8276*** –0.5273**
(0.2190) (0.1995)
Fixed effects for countries
Dummy variables for years
Ja Ja
Ja Ja
R² (centered)
620 872
0.37 0.32
errors are clustered by country and displayed in parentheses.All regressions contain a constant.With the exception of the
percentage of the population that completed secondary education or higher, all other variables use logarithmized values.The
different numbers of observations trace back to data availability.

44
2.4 Future globalization development scenarios
2.4.1 Results of the “Accelerated globalization” scenario
Compared to the baseline scenario, the model specification for the “accelerated globalization”
scenario yields differences for the growth rates of imports and exports that vary widely between
nations (Table 14). Here neither the growth rates of foreign trade components nor the course of
development in the globalization index for the ex-post time period is decisive on its own; what is
key, instead, is their interaction. For example, the Baltic states exhibited import growth rates of at
least 6.7 percent per year between 1990 and 2011. At the same time, the 1.4 point average annual
increase of the globalization index came out relatively high compared to other world countries.
As a difference to the baseline forecast, how strong the import growth turns out in relation to
the increase in the globalization index is crucial for the scenario parameters, since imports are
exogenized through a specification in the model.
The hereby exemplified Baltic States exhibit an import growth between 4.7 to 6.1 percent for
each globalization point. Within the scenario and accounting for a uniform yearly increase of the
globalization index amounting to 0.40 points37, this corresponds, in comparison to the baseline
projection, to an additional import growth that falls in the middle range of all countries. Nations
like Argentina, Brazil and India, by contrast, demonstrate high growth rates for imports combined
with a low globalization dynamic in the ex-post period of time, which leads to high growth
parameters for imports in this scenario in comparison to the baseline forecast. The lowest growth
specifications for imports and consequently the low growth rates for exports in this scenario occur
for countries like Belgium, Portugal and a few Eastern European countries that exhibit low growth
rates for imports in relation to the annual increase of the globalization index.
If we consider growth rates of the gross domestic product that result for different periods of time
based on the simulation calculations, we initially notice that all the countries being studied could
benefit from accelerated globalization (Table 15). In light of the consideration that higher trade
volumes are conducive to greater specialization of individual economies and thus further promote
their comparative advantages, this should not come as a surprise.
Based on the fact that the parameters change for all the countries in this scenario, the following
must be taken into consideration when interpreting results for individual countries:
37 This corresponds to 50 percent of the average annual increase of the globalization index for all countries in the period of time
from 1990 to 2011; see Section 2.1.2. The restriction that the simulated additional increase of the globalization index may amount
to a maximum of 100 percent in relation to the average annual difference of the index for the individual country between 1990
and 2011 affects Argentina, Belgium, Mexico, Norway and the United States. The simulated increase of globalization for these
countries amounts to 0.18 points, 0.36 points, 0.33 points, 0.02 points and 0.11 points.

45
Table 14:Average export and import growth: differences between the
“accelerated globalization” scenario and the baseline forecast
Country Imports Exports
2020 2025 2020 2025
Argentina 8.77 8.88 9.36 9.49
Australia 2.58 2.62 2.76 2.51
Belgium 0.98 1.00 0.97 0.98
Brazil 4.53 4.55 5.55 5.37
Bulgaria 0.97 1.01 1.12 1.15
Chile 2.31 2.34 2.49 2.52
China 4.58 4.62 4.27 4.29
Denmark 1.70 1.78 1.64 1.75
Germany 2.01 2.04 1.89 1.92
Estonia 2.33 2.34 2.36 2.39
Finland 1.53 1.57 1.61 1.64
France 2.05 2.07 2.13 2.17
Greece 1.11 1.16 1.39 1.49
India 6.99 7.03 7.45 6.92
Ireland 2.35 2.66 2.18 2.33
Israel 1.57 1.60 1.73 1.84
Italy 2.20 2.24 1.95 2.00
Japan 2.13 2.17 2.29 2.23
Canada 3.81 3.91 3.89 3.94
Latvia 2.10 2.18 2.37 2.45
Lithuania 1.88 1.89 2.00 2.04
Mexico 4.91 4.94 5.07 5.12
New Zealand 1.59 1.65 1.66 1.72
Netherlands 2.08 2.12 1.95 2.01
Norway 1.34 1.38 1.04 1.09
Austria 2.16 2.20 2.10 2.17
Poland 2.71 2.73 2.66 2.68
Portugal 0.69 0.72 0.87 0.92
Romania 0.84 0.88 1.01 1.06
Russia 1.29 1.32 1.33 1.47
Sweden 3.05 3.12 2.80 2.90
Switzerland 3.72 3.80 3.22 3.34
Slovakia 1.49 1.48 1.37 1.38
Slovenia 1.24 1.26 1.23 1.27
Spain 2.66 2.73 2.53 2.63
South Africa 1.86 1.89 2.17 2.31
South Korea 2.75 2.75 2.52 2.50
Czech Republic 1.74 1.75 1.67 1.71
Turkey 4.55 4.58 5.65 5.60
Hungary 1.33 1.34 1.37 1.41
United States 4.40 4.46 5.11 4.91
United Kingdom 2.65 2.75 2.90 3.10

46
Table 15: Economic growth: differences between the “accelerated globalization”
scenario and the baseline forecast
Country Difference between the average economic growth and the baseline forecast in
percentage points
2014 to 2020 2014 to 2025
Argentina 0,11 0,22
Australia 0,12 0,24
Belgium 0,08 0,13
Brazil 0,06 0,13
Bulgaria 0,46 0,76
Chile 0,27 0,69
China 0,42 0,54
Denmark 0,10 0,32
Germany 0,31 0,40
Estonia 0,72 0,94
Finland 0,33 0,48
France 0,05 0,16
Greece 0,19 0,56
India 0,45 0,42
Ireland 0,62 0,35
Israel 0,23 0,57
Italy 0,05 0,14
Japan 0,08 0,32
Canada 0,09 0,29
Latvia 0,12 0,73
Lithuania 0,54 0,80
Mexico 0,20 0,14
New Zealand 0,21 0,46
Netherlands 0,22 0,30
Norway 0,02 0,11
Austria 0,28 0,46
Poland 0,41 0,54
Portugal 0,40 0,61
Romania 0,19 0,60
Russia 0,24 0,56
Sweden 0,19 0,33
Switzerland 0,18 0,35
Slovakia 0,35 0,44
Slovenia 0,35 0,61
Spain 0,14 0,30
South Africa 0,09 0,53
South Korea 0,47 0,42
Czech Republic 0,40 0,59
Turkey 0,10 0,16
Hungary 0,44 0,58
United States 0,04 0,12
United Kingdom 0,21 0,56

47
Precise statements regarding the causes of differences in the gross domestic product between the
scenario and the reference development are only possible to a certain extent, because it is very
difficult to distinguish whether country-specific deviations between the scenario and the baseline
forecast are directly attributable to certain parameters or whether they are caused by complex
interactions between various countries.
It can be generally observed that the growth advantages resulting from the simulation differ
by country. The greatest differences relative to the baseline projection are found primarily in
Eastern European countries. Presumably this is largely due to the high degree of openness of
these countries in the starting year of the simulation calculations and due to the importance of
the contribution of foreign trade to their economic development. For example, Estonia’s relatively
moderate increase in imports and exports, when combined with a degree of openness that exceeds
200 percent, leads to trade growth that reaches significant magnitudes in relation to the gross
domestic product.
Even with a degree of openness below 100 percent, nations such as China and India also achieve
growth increases of around a half a percentage point over the baseline projection. This relatively
strong increase in comparison to other countries results among other things from the high
parameter value for additional import growth in the scenario. As nations that post the lowest
degree of openness of all the countries under consideration – a maximum of 52 percent –
Argentina, Brazil, Turkey and the United States show that strong growth rate increases for imports
and exports do not necessarily lead to high additional growth rates for the gross domestic product.
When accounting for a more favorable economic development relative to the baseline projection,
all countries yield income gains.(Table 16). If we first take a first look at income gains at the
country level, we observe that China would see a cumulative benefit from accelerated globalization
of around €2.7 billion by the year 2025. Other major national economies such as Germany, India,
Japan, the United States and the United Kingdom would also enjoy cumulative income gains of
over €500 billion according to the simulation results. Gains for Australia, France, Italy and Spain
come in rather low by comparison.
In order to mask out pure scale effects, we additionally consider the per capita income gains. This
approach reveals that highly-developed nations tend to have particularly strong gains. Accordingly,
eight European nations are represented among the top ten places. A look at Asia with the per
capita approach also yields a different picture. Here, accelerated globalization benefits South
Korea significantly more than China. However, the example of China is not the only one showing
that developing nations fall toward the end of the ranking in the per capita consideration. In Brazil
and India the cumulative per capita income gains from accelerated globalization are even lower.

48
Table 16: Cumulative income gains in a comparison between the “accelerated globalization” scenario and
the baseline forecast in the time period from 2014 to 2025
Country Cumulative income gain
in billions of euros*
Cumulative per capita in-
come gain in the scenario
in euros*
Cumulative per capita income gain
in euros*, weighted according to
purchasing power in the scenario
in the year 2013 as a percent
Argentina 96 2,058 2,439 15
Australia 98 3,843 4,651 14
Belgium 25 2,127 2,591 8
Brazil 120 550 1,045 10
Bulgaria 12 1,844 7,267 60
Chile 69 3,685 6,973 43
China 2,700 1,932 4,817 58
Denmark 31 5,296 5,091 16
Germany 795 9,793 10,993 34
Estonia 9 7,155 17,062 92
Finland 57 10,252 11,184 35
France 129 1,871 2,163 8
Greece 40 3,505 5,611 28
India 791 581 1,969 56
Ireland 75 15,535 17,530 49
Israel 84 9,621 11,394 36
Italy 91 1,438 1,910 7
Japan 726 5,822 4,045 13
Canada 129 3,413 4,115 12
Latvia 5 2,714 6,610 38
Lithuania 16 5,469 14,420 74
Mexico 160 1,266 1,963 16
New Zealand 23 4,801 7,584 27
Netherlands 115 6,644 8,080 23
Norway 12 2,171 2,093 5
Austria 81 9,292 11,203 31
Poland 129 3,388 7,997 45
Portugal 58 5,477 8,497 47
Romania 18 874 2,998 30
Russia 181 1,282 4,934 36
Sweden 82 8,051 8,075 22
Switzerland 75 8,703 7,938 20
Slovakia 14 2,582 5,331 38
Slovenia 12 5,793 10,125 44
Spain 132 2,741 4,053 17
South Africa 72 1,484 3,331 31
South Korea 510 10,113 15,327 53
Czech Republic 40 3,766 10,226 45
Turkey 67 759 1,678 12
Hungary 30 3,114 8,145 51
United States 861 2,485 2,485 6
United Kingdom 582 8,705 9,046 29
* real prices from the year 2000; rounded values; ** weighted according to purchasing power in relation to the United States

49
However, due to differing wealth levels of the countries being studied, absolute income gains are
only somewhat suitable for assessing wealth developments. Cumulative per capita income gains
in relation to the per capita gross domestic product (in the starting year of scenario calculations)
would be a more informative alternative. This demonstrates that developing nations like China
and India benefit significantly more from accelerated globalization relatively speaking than the
absolute per capita income gain would lead us to believe. Conversely, we see that income gains
of highly-developed nations such as Norway are lower when placed in relation to the respective
country’s level of the per capita gross domestic product.
An intensification of global trade would also yield positive effects on the individual labor markets
(Table 17). The general trend shows that greater economic growth brought about by accelerated
globalization leads to stronger reductions in the unemployment rate. This applies for instance to
the Baltic states as well as Bulgaria, Chile and the United Kingdom. By contrast, national economies
such as Argentina, Brazil and Mexico exhibit only mildly positive effects in their labor markets,
which is not surprising in light of the small differences in economic growth between the scenario
and baseline projection.
For some countries, the input ratio between the production factors labor and capital also plays a role
in the effects on the unemployment rate. Thus, labor markets in highly-developed countries such
as Germany, Italy, France, Austria and the United Kingdom can scarcely benefit from accelerated
globalization. Conversely, India demonstrates a strong reduction in the unemployment rate despite
relatively moderate effects of trade intensification on economic growth. That this correlation does
not apply to all countries is emphasized by the example of China.
In summary, we can determine that accelerated globalization could promote a more favorable
worldwide economic development in the future. However, due to the number and complexity
of scenario parameters, the simulation only allows limited conclusions with regard to country-
specific differences.
2.4.2 Results of the “Diverging globalization” scenario
The “diverging globalization” scenario analyzes the effects of a stagnating globalization in Greece,
Portugal and Spain within the context of the problems these countries are currently facing. First,
the scenario parametrization yields a modified growth rates for imports and exports (Table 18).38In
this approach, we see that global trade volumes in the scenario result overall lower than in the
baseline forecast. Not surprisingly, the three directly affected countries have to accept the greatest
declines in the growth of foreign trade components compared to the baseline projection.
38 The scenario parameters are based on changes in the globalization index of 1.2 points for Greece, 1.3 points for Portugal and 0.5
points for Spain.

50
Table 17: Difference in the unemployment rate in a comparison between the
“accelerated globalization” scenario and the baseline forecast in the years 2020
and 2025
Country Difference in the unemployment rate between the scenario
and the baseline forecast in percentage points
2020 2025
Argentina –0.14 –0.29
Australia –0.31 –0.45
Belgium –0.09 –0.21
Brazil –0.03 –0.06
Bulgaria –0.46 –1.05
Chile –0.21 –0.98
China –0.32 –0.21
Denmark –0.10 –0.23
Germany –0.17 –0.13
Estonia –0.52 –0.71
Finland –0.61 –1.17
France –0.10 –0.37
Greece –0.19 –0.77
India –0.62 –1.10
Ireland –0.28 –0.04
Israel –0.12 –0.36
Italy –0.04 –0.14
Japan 0.00 –0.07
Canada –0.10 –0.51
Latvia –0.12 –1.17
Lithuania –0.46 –0.98
Mexico –0.06 –0.02
New Zealand –0.45 –1.55
Netherlands –0.10 –0.11
Norway –0.11 –0.42
Austria –0.11 –0.15
Poland –0.34 –0.39
Portugal –0.26 –0.56
Romania –0.19 –0.74
Russia –0.11 –0.37
Sweden –0.26 –0.53
Switzerland –0.19 –0.34
Slovakia –0.24 –0.37
Slovenia –0.41 –0.68
Spain –0.28 –1.05
South Africa –0.05 –0.89
South Korea –0.44 –0.55
Czech Republic –0.27 –0.43
Turkey –0.06 –0.09
Hungary –0.27 –0.48
United States –0.06 –0.27
United Kingdom –0.47 –1.58

51
But all the other countries also exhibit lower export and import growth rates in the scenario
relative to the reference development. Italy and Bulgaria are particularly affected since they suffer
from the weakness of key export customers. For example, the majority of Italian exports flow to
Europe: Germany and France alone buy around one quarter of Italy’s exports. In turn, this scenario
depicts Bulgaria as suffering most notably from the weakness of Italy and Greece. Around one
quarter of Bulgaria’s exports go to these two countries.
The relative reduction of global trade in the scenario results in lower economic growth for all the
economies being analyzed (Table 19). In the countries directly affected by stagnating globalization,
the difference between their economic growth and their development in the baseline projection
emerges as particularly high, at around 1 percentage point. It is striking that the declines in
growth for Portugal are similar in value to those in Spain and Greece, even though the reduction
in foreign trade growth in the latter two countries is almost twice as high as for Portugal.
One reason for this observation lies in Portugal’s heavy dependence on foreign trade with its
neighbor, Spain. Around one quarter of all Portuguese exports flow to Spain, and nearly one
third of its imports come from its Spanish neighbor. Therefore, Spain’s weakness represents a
significant risk and an additional burden for Portugal’s economic development.
Though declines in growth are less observable in countries not directly affected, they are still
considerable in absolute terms. In addition to the countries addressed at the beginning of this
section, Italy and Bulgaria, significant reductions in economic growth occur for other central
European nations such as France and Germany as well. By comparison, major developing nations
and countries that are located at great geographical distance from the directly affected countries
have far fewer adverse effects to worry about.
In some cases considerable cumulative income losses result from the change in growth rates (Table
20). From the country level perspective, we see that aside from the directly affected countries, the
greatest losses primarily impact the large national economies. In a negative sense, the United
States and Germany are the frontrunners here. However, we must keep in mind that the results
are primarily driven by size effects, whereby even small changes in economic growth lead to large
gains or losses in absolute income.
Instead, if we consider the cumulative income differences per capita, the greatest income losses
emerge in countries directly affected by stagnating globalization, followed by European countries
with a high gross domestic product per capita.
Lower cumulative per capita income losses result for countries with a comparatively low per capita
gross domestic product– this applies essentially to the major developing nations. This result is put
into perspective quantitatively (but not qualitatively) if we consider income gains that have been
weighted according to purchasing power.

52
Table 18:Average export and import growth: differences between the “diverging
globalization” scenario and the baseline forecast
Country Imports Exports
2020 2025 2020 2025
Argentina –0.06 –0.06 –0.15 –0.11
Australia –0.07 –0.05 –0.18 –0.07
Belgium –0.16 –0.10 –0.17 –0.10
Brazil –0.05 –0.05 –0.14 –0.09
Bulgaria –0.27 –0.24 –0.33 –0.26
Chile –0.12 –0.11 –0.16 –0.12
China –0.04 –0.04 –0.07 –0.07
Denmark –0.19 –0.12 –0.22 –0.12
Germany –0.25 –0.17 –0.30 –0.18
Estonia –0.27 –0.15 –0.27 –0.15
Finland –0.17 –0.11 –0.20 –0.11
France –0.26 –0.21 –0.38 –0.26
Greece –3.22 –3.26 –3.99 –3.91
India –0.02 –0.01 –0.03 –0.01
Ireland –0.15 –0.08 –0.17 –0.09
Israel –0.05 –0.04 –0.06 –0.04
Italy –0.31 –0.25 –0.36 –0.25
Japan –0.07 –0.06 –0.10 –0.01
Canada –0.13 –0.05 –0.15 –0.03
Latvia –0.22 –0.11 –0.22 –0.11
Lithuania –0.19 –0.11 –0.19 –0.10
Mexico –0.02 –0.02 –0.03 –0.02
New Zealand –0.05 –0.03 –0.07 –0.02
Netherlands –0.21 –0.14 –0.23 –0.14
Norway –0.15 –0.09 –0.15 –0.08
Austria –0.21 –0.12 –0.22 –0.10
Poland –0.25 –0.17 –0.25 –0.16
Portugal –2.19 –2.20 –2.51 –2.37
Romania –0.24 –0.20 –0.27 –0.20
Russia –0.09 –0.09 –0.15 –0.11
Sweden –0.12 –0.08 –0.12 –0.07
Switzerland –0.14 –0.10 –0.15 –0.09
Slovakia –0.25 –0.14 –0.25 –0.14
Slovenia –0.18 –0.12 –0.18 –0.11
Spain –3.10 –3.16 –3.37 –3.42
South Africa –0.02 –0.01 –0.02 0.00
South Korea –0.09 –0.04 –0.09 –0.04
Czech Republic –0.27 –0.14 –0.27 –0.13
Turkey –0.07 –0.06 –0.11 –0.08
Hungary –0.22 –0.12 –0.23 –0.12
United States –0.06 –0.07 –0.15 –0.12
United Kingdom –0.13 –0.12 –0.18 –0.13

53
Table 19: Economic growth: differences between the “diverging globalization”
scenario and the baseline forecast
Country Difference between the average economic growth
and the baseline forecast, in percentage points
2014 to 2020 2014 to 2025
Argentina –0.08 –0.08
Australia –0.08 –0.06
Belgium –0.13 –0.09
Brazil –0.06 –0.05
Bulgaria –0.33 –0.30
Chile –0.12 –0.11
China –0.05 –0.05
Denmark –0.18 –0.13
Germany –0.21 –0.17
Estonia –0.22 –0.15
Finland –0.16 –0.12
France –0.20 –0.18
Greece –0.63 –0.96
India –0.02 –0.02
Ireland –0.10 –0.07
Israel –0.04 –0.04
Italy –0.26 –0.22
Japan –0.07 –0.07
Canada –0.11 –0.06
Latvia –0.19 –0.11
Lithuania –0.14 –0.10
Mexico –0.02 –0.02
New Zealand –0.05 –0.03
Netherlands –0.16 –0.13
Norway –0.13 –0.08
Austria –0.20 –0.13
Poland –0.23 –0.18
Portugal –1.07 –1.14
Romania –0.23 –0.22
Russia –0.10 –0.10
Sweden –0.09 –0.07
Switzerland –0.10 –0.08
Slovakia –0.23 –0.15
Slovenia –0.15 –0.12
Spain –0.78 –1.10
South Africa –0.02 –0.01
South Korea –0.07 –0.04
Czech Republic –0.25 –0.15
Turkey –0.06 –0.06
Hungary –0.15 –0.11
United States –0.06 –0.07
United Kingdom –0.11 –0.11

54
The result that European countries are particularly affected by a stagnating globalization is
reinforced when we use the cumulative per capita income gains in relation to the starting level of
the gross domestic product per capita in the year 2013 as a measure for the effects of stagnating
globalization in Europe’s crisis-stricken countries. The three directly affected countries register
cumulative income losses between 63 and around 103 percent of the gross domestic product per
capita in the starting year of the scenario calculation. With the exception of Bulgaria, this indicator
is no more than 24 percent in all the other national economies being studied. Losses are especially
low in this approach for the major developing nations and countries that have only minor trade
relationships with Greece, Portugal and Spain, such as South Africa, Israel and New Zealand.
The decrease in economic growth resulting from the simulated stagnating globalization in Greece,
Portugal and Spain also has an impact on labor markets (Table 21). The unemployment rate attains
especially high levels in the directly affected countries. Spain would feel the heaviest impact,
with its already high unemployment rate in the year 2025 increasing a further 3.6 percentage
points from the almost 19 percent in the baseline projection. At 1.2 and 0.9 percentage points
respectively, the increasing unemployment rate results lower for Greece and Portugal; however,
this still reflects an increase of around 10 percent in relation to the projected unemployment
rate of the baseline forecast in 2025. Moderate increases in the unemployment rate emerge for
countries not directly affected, but tend to be greater for nations with strong trade relationships
with Greece, Portugal or Spain.
Overall, the simulation of stagnating globalization in Greece, Portugal and Spain and the resulting
effects on foreign trade demonstrate that the entire global economy would have to cope with
an array of negative consequences. The adverse economic effects are distributed differently
worldwide in that context. Strong negative effects emerge for the significant trade partners of the
directly affected countries. Conversely, large and geographically distant economies that depend
little on trade with Greece, Portugal or Spain are affected slightly, if scarcely at all.

55
Table 20: Cumulative income gains in a comparison between the “diverging globalization” scenario and the
baseline forecast in the time period from 2014 to 2025
Country Cumulative income gain
in billions of euros*
Cumulative per capita in-
come gain in the scenario,
in euros*
Cumulative per capita income gain
weighted according to purchasing
power in the scenario, in euros*
in the year 2013, as a percent
Argentina –54 –1,178 –1,396 –9
Australia –41 –1,624 –1,965 –6
Belgium –37 –3,185 –3,880 –12
Brazil –82 –380 –722 –7
Bulgaria –7 –1,071 –4,222 –35
Chile –20 –1,066 –2,017 –12
China –275 –197 –491 –6
Denmark –32 –5,544 –5,329 –17
Germany –495 –6,090 –6,836 –21
Estonia –2 –1,883 –4,490 –24
Finland –23 –4,177 –4,557 –14
France –317 –4,641 –5,364 –19
Greece –90 –7,784 –12,463 –63
India –31 –22 –76 –2
Ireland –14 –2,932 –3,309 –9
Israel –8 –985 –1,166 –4
Italy –282 –4,473 –5,941 –23
Japan –346 –2,765 –1,921 –6
Canada –94 –2,535 –3,056 –9
Latvia –3 –1,440 –3,507 –20
Lithuania –3 –1,178 –3,106 –16
Mexico –18 –144 –222 –2
New Zealand –3 –688 –1,087 –4
Netherlands –77 –4,471 –5,437 –16
Norway –29 –5,305 –5,115 –12
Austria –47 –5,479 –6,606 –18
Poland –65 –1,700 –4,014 –22
Portugal –128 –12,020 –18,647 –103
Romania –13 –616 –2,112 –21
Russia –52 –367 –1,413 –10
Sweden –32 –3,197 –3,206 –9
Switzerland –31 –3,671 –3,349 –9
Slovakia –8 –1,472 –3,039 –22
Slovenia –4 –1,894 –3,310 –14
Spain –592 –12,298 –18,184 –77
South Africa –4 –91 –204 –2
South Korea –72 –1,420 –2,152 –7
Czech Republic –21 –2,000 –5,430 –24
Turkey –35 –394 –871 –6
Hungary –9 –956 –2,501 –16
United States –834 –2,439 –2,439 –6
United Kingdom –204 –3,079 –3,199 –10
* real prices from the year 2000; rounded values; ** weighted according to purchasing power in relation to the United States

56
Table 21: Unemployment rate in a comparison between the “diverging
globalization” scenario and the baseline forecast
Country Difference in the unemployment rate between the scenario
and the baseline forecast in percentage points
2020 2025
Argentina 0.08 0.08
Australia 0.07 0.00
Belgium 0.14 0.13
Brazil 0.02 0.02
Bulgaria 0.26 0.26
Chile 0.13 0.18
China 0.15 0.14
Denmark 0.17 0.07
Germany 0.06 0.02
Estonia 0.05 0.01
Finland 0.27 0.19
France 0.26 0.26
Greece 0.63 1.17
India 0.03 0.04
Ireland 0.05 0.01
Israel 0.02 0.02
Italy 0.34 0.24
Japan 0.01 0.01
Canada 0.12 0.05
Latvia 0.12 0.04
Lithuania 0.09 0.06
Mexico 0.01 0.00
New Zealand 0.10 0.08
Netherlands 0.10 0.07
Norway 0.07 0.02
Austria 0.21 0.17
Poland 0.11 0.05
Portugal 0.70 0.89
Romania 0.24 0.26
Russia 0.06 0.07
Sweden 0.09 0.07
Switzerland 0.12 0.08
Slovakia 0.13 0.06
Slovenia 0.15 0.06
Spain 1.78 3.57
South Africa 0.02 0.04
South Korea 0.07 0.07
Czech Republic 0.08 0.01
Turkey 0.03 0.03
Hungary 0.06 0.04
United States 0.13 0.17
United Kingdom 0.19 0.20

57
3 The most appealing foreign markets
The above study showed which countries have benefited most from globalization over the last
time period of approximately 20 years. Scenario calculations based on the globalization-champion
approach have equally highlighted the opportunities as well as risks that arise from deviating
from the previous globalization path. The study demonstrated the extent to which each country is
affected with regards to changes in growth rates and income variables.
For the German economy, the study revealed significant gains. However, this finding is, in a certain
sense, relatively abstract: The measured globalization gains are a statistical result of the interplay
of countless individual decisions taken by companies, private households and the government.
Therefore, an appeal to utilize the potential of globalization can only be addressed at individual
actors and not at an economy as a whole.
In order to seize the available opportunities, it is important for businesses to focus on the right
foreign markets. The term “right” refers to being able to achieve consistent income gains at
company level with foreign activities – and thereby aggregated gains at an overall economic level.
Precisely in this sense, we use the Prognos Free Trade and Investment Index to measure and rank
the attractiveness of foreign markets from a German perspective.
As such, the recorded indicators for measuring market attractiveness go beyond the aspects of
market size or market dynamics and emphasize aspects such as the reliability of framework
conditions. The ranking determined here may deviate significantly from that in the globalization
index: Not all countries that are particularly well globalized offer equally attractive markets from
a German perspective. However, the correlation between the two indices is high.39
39 The correlation of the rankings in the Free Trade and Investment Index and the globalization index amounts to 0.69. In index
values, the correlation even reaches a value of 0.75. This demonstrates the high degree of compatibility/agreement between
the two measures. Total correlation would be achieved at a value of 1. The deviation from a perfect correlation results from the
differing thematic orientation of both indices. For example, the Free Trade and Investment Index evaluates the market size in
the sense of the gross domestic product as positive for the attractiveness of the individual country. In the globalization index,
economic indicators are normalized with the market size of the individual country to prevent distortions of a country’s measured
degree of integration with the rest of the world through variable effects.

58
3.1 Focus and methodology of the Prognos Free Trade and
Investment Index
The Prognos Free Trade and Investment Index comprehensively covers the relevant success
factors for foreign activities with a broad spectrum of economic, institutional and sociopolitical
indicators. Its presentation as a ranking also enables a clear comparison of countries with each
other. Statements about which countries and regions are better suited for German foreign activities
than others are central findings of the Prognos Free Trade and Investment Index. On the one hand,
it identifies the market appeal of key trade partners for Germany and traces this appeal and its
key determinants; on the other, it facilitates the identification of foreign markets whose appeal for
German entities is still largely underestimated.
In order to take a variety of issues into account, the Prognos Free Trade and Investment Index
encompasses four individual rankings according to current state, dynamic trends, exports and
foreign direct investments as well as an overall ranking:
• The “current state” ranking portrays the current appeal of foreign markets.
• The ranking “dynamic trends” demonstrates, in a comparison to the “current state”, which
markets have gained the most appeal in previous years and how dynamically these markets
will develop in the future, independent of their starting level.
• The two rankings “exports” and “foreign direct investments” take different types of foreign
activities into consideration. The first ranking shows which countries have the greatest export
potential, while the second ranking highlights which countries would lend themselves well for
German companies to initiate or expand direct investments in.
• The overall ranking consolidates the current state and dynamic trends. This, on the one hand,
takes into account that a market – even a potential one – is especially attractive only if it
already exhibits an appreciable level of appeal in the present. On the other hand, it ensures that
the attractiveness measurement is not backward -looking: It should emphasize markets that
have strong attractiveness prospects for the future. Under these circumstances, the “current
state” ranking is clearly more heavily weighted in the overall ranking than the “dynamic
trend” ranking. Thus the overall ranking measures the appeal of foreign markets from the
perspective of German businesses in the most comprehensive sense.
Comprehensive knowledge about country-specific circumstances is necessary in order to perform a
suitable assessment of the opportunities and risks of foreign activities. Detailed information about
the individual markets as well as overall economic features and institutional and sociopolitical
characteristics are particularly relevant here.

59
The Prognos Free Trade and Investment Index takes this into account because it is based on a
variety of key location factors of foreign markets and therefore provides crucial starting points
for estimating the success of foreign activities. 33 individual indicators are compiled into nine
sub-indices, thereby enabling an extensive description of export and investment conditions in the
national economies being analyzed (Table 22).40
The individual indicators are partly obtained from internationally recognized indices. These
include the Human Development Index, the Corruption Perception Index, the Ease of Doing
Business Index and selected indices from the World Economic Forum. The remaining indicators
are calculated primarily with the most recent available data from the International Monetary
Fund, the United Nations and the World Trade Organization.
Due to the different scalability of the individual indicators, data must be normalized to a
standardized value range. We use a scale from 0 to 10, with 0 being the worst manifestation of
individual indicators and 10 being the best. Dampening factors are applied to extreme outliers in
order to prevent distortions.
The normalized individual indicators are compiled into nine sub-indices. The subsequent
weighting of the nine sub-indices takes into consideration the different objectives of foreign
activities by German companies by varying according to the orientation of the sub-ranking. For
instance, market size measured by the gross domestic product is weighted more heavily in the
exports sub-ranking than in the foreign direct investment sub-ranking.
The Prognos Free Trade and Investment Index is compiled for 100 economies including the
European Union as an aggregate (without Germany).41
Market size, as measured by the gross
domestic product from the year 2007, the first year in which the globalization report was published,
is decisive for the country selection. The spectrum of national economies ranges from the United
States, with a gross domestic product of US$15.1 trillion, to Zimbabwe with an economic output
of US$9.5 billion in 2011.
40 The availability of the data we used up to this point from the The Global Competitiveness Report series of the World Economic
Forum worsened with the latest publication 2012–2013, and required some adjustments this year. The Market Efficiency
sub-index is affected, as three individual indicators will no longer be available for this area. However, in the future adequate
alternatives from the Fraser Institute can be used for the indicators capital market completeness and capital market controls,
for describing foreign financial markets. On the other hand, we found no suitable substitute for the individual indicator, non-
wage labor costs, so this is no longer used to represent market efficiency. Although the use of two new indicators as well as
the reduced number of explanatory variables makes it more difficult to compare this year‘s rankings with its predecessors, the
results nevertheless remain the same in regard to general trends.
41 In deviation from the calculations and simulations in Chapter 2, the number of countries being analyzed is not restricted by the
country selection in VIEW, which results in a larger set.

60
3.2 The most appealing foreign markets 2013
The ranking of the most attractive foreign markets for German businesses is illustrated through
the overall ranking (Table 23) and the four focus-specific sub-rankings (Table 24 to Table 27).
The overall ranking shows that despite the current crisis in the European Union and above all in
the euro zone countries, the most attractive general conditions for Germany’s foreign activities
continue to exist in European nations. Beyond that, the United States and some Asian countries,
in particular, offer appealing foreign markets for German companies.
Despite a common domestic market and common currency within currently 17 countries, the
European Union still represents a very heterogeneous economic area. This distinctive characteristic
is also emphasized by the broad spectrum of rankings among European Union member states in
the overall ranking: from 5th place (Finland) to 57th place (Greece).
Table 22:The sub-indices and individual indicators of the Prognos Free Trade and
Investment Index
1. Market size 6. Stability
1.1 Gross domestic product 6.1 Sovereign Credit Ranking
6.2 Inflation
2. Openness 6.3 Current account balance
2.1 Degree of openness 6.4 Political stability
2.2 Direct investments 6.5 Exchange rate movements compared to the euro
2.3 Integration status
2.4 Tariffs compared to EU 7. Education, RD, innovations
2.5 Non-tariff barriers to trade 7.1 Secondary education
2.6 Trade disputes 7.2 Higher education
7.3 Availability of natural scientists and engineers
3. Development level 7.4 RD expenditures of private companies
3.1 Per capita income
3.2 Human Development Index 8. Market efficiency
3.3 Intra-industrial trade 8.1 Capital transaction restrictions
3.4 Degree of urbanization 8.2 Capital market completeness
8.3 Wages and productivity
4. Institutions/ Infrastructure 8.4 Local competition
4.1 Property rights 8.5 Anti-monopoly laws
4.2 Level of regulation 8.6 Prevalence of foreign ownership
4.3 Infrastructure
4.4 Corruption 9. Distance from Germany
9.1 Distance of capitals
5. Practical business activities 9.2 Sea route
5.1 Ease of Doing Business Index

61
In this ranking, older member states generally place significantly above the newer members.
The exports sub-ranking particularly makes clear that primarily European countries – especially
Germany’s neighbors – offer the best conditions for German exporters.
In Europe, the northern countries of Finland, Norway, Sweden and Denmark have long been highly
appealing both as an export market and for investment activities by German businesses. Thanks
to the high level of development, a well-established infrastructure, optimal institutional framework
conditions and a high degree of economic and political stability, they currently belong among the
top 15 most appealing foreign markets in four out of five rankings.
By contrast, the northern countries as well as many other European economies fall in the middle
range or the lower half of the “dynamic trends” ranking. In general we observe that countries that
represent very attractive foreign markets for German businesses today and place very well in the
“current state” ranking display comparatively low dynamic trends. This is due primarily to the
already advanced state of development of these economies, which partly limits the potential for
additional growth.
North America represents an exceptionally appealing market for German exporters and investors
overall, with the United States in 2nd place and Canada in 19th place. Both countries exhibit
similar strengths and weaknesses in framework conditions and display a balanced picture in the
individual rankings, with the exception of dynamic trends. The main weak point of both economies
is their lack of openness. Tariffs and non-tariff barriers to trade hamper German exports in this
region to a greater degree than in other countries. A free trade agreement between the European
Union and the United States, which is currently being discussed, would mitigate this problem.
Nonetheless, the strong purchasing power and market size – especially in the United States –
currently ensures demand security for German products. Other strengths of North America lie in
the high quality of the infrastructure, advantageous institutional framework conditions and good
prerequisites for education, research and development and innovation.
Three Asian countries placed among the top 15 in the overall ranking with Singapore (1st place),
Hong Kong (4th place) and Japan (12th place). Although China placed in the top third in 30th
place, it still ranks far behind the top-placed Asian countries. Singapore and Hong Kong are
appealing despite their small market size and are very attractive destinations especially with
respect to direct investment.
By contrast, China takes a leading position in the dynamic trends ranking thanks to its high
growth rates. But China still needs to make substantial adjustments before it can achieve the high
level of the most appealing foreign markets with regard to economic and institutional framework
conditions. All in all, China is on a good path: Since the index was compiled for the first time
in 2008, the country has managed great progress in key institutional prerequisites such as the
protection of property rights.

62
Table 23: Overall ranking of the Free Trade and Investment Index 2013
Rank National economy Value Rank National economy Value
1 Singapore 7.00 51 Mexico 4.68
2 United States 6.98 52 South Africa 4.68
3 EU 6.98 53 Thailand 4.64
4 Hong Kong 6.86 54 Peru 4.64
5 Finland 6.76 55 Croatia 4.61
6 Sweden 6.72 56 Jordan 4.59
7 United Kingdom 6.69 57 Greece 4.57
8 Switzerland 6.66 58 Morocco 4.46
9 Denmark 6.64 59 Colombia 4.46
10 Luxembourg 6.62 60 Kazakhstan 4.39
11 France 6.62 61 Brazil 4.34
12 Japan 6.55 62 Uruguay 4.33
13 Belgium 6.54 63 Trinidad and Tobago 4.23
14 Netherlands 6.49 64 Ghana 4.16
15 Norway 6.33 65 Russia 4.13
16 Ireland 6.31 66 Azerbaijan 4.05
17 Austria 6.25 67 Costa Rica 4.04
18 Estonia 6.11 68 Indonesia 3.94
19 Canada 6.04 69 India 3.91
20 Spain 5.90 70 Lebanon 3.88
21 Qatar 5.89 71 Sri Lanka 3.87
22 Iceland 5.87 72 Vietnam 3.81
23 South Korea 5.85 73 El Salvador 3.81
24 United Arab Emirates 5.77 74 Guatemala 3.79
25 Czech Republic 5.69 75 Libya 3.77
26 Saudi Arabia 5.68 76 Ukraine 3.74
27 Slovenia 5.64 77 Ecuador 3.74
28 Lithuania 5.63 78 Serbia 3.72
29 Australia 5.60 79 Dominican Republic 3.70
30 China 5.60 80 Philippines 3.69
31 Portugal 5.59 81 Egypt 3.67
32 Israel 5.57 82 Nigeria 3.64
33 New Zealand 5.56 83 Argentina 3.56
34 Slovak Republic 5.55 84 Algeria 3.54
35 Poland 5.54 85 Belarus 3.49
36 Taiwan 5.53 86 Camaroon 3.45
37 Latvia 5.46 87 Cote d'Ivoire 3.44
38 Cyprus 5.45 88 Kenya 3.38
39 Italy 5.44 89 Turkmenistan 3.35
40 Hungary 5.43 90 Bangladesh 3.31
41 Malaysia 5.41 91 Pakistan 3.28
42 Bahrain 5.25 92 Syria 3.15
43 Chile 5.23 93 Iran 3.03
44 Bulgaria 5.17 94 Yemen 2.81
45 Oman 5.10 95 Zimbabwe 2.77
46 Kuwait 4.85 96 Angola 2.74
47 Panama 4.76 97 Venezuela 2.69
48 Turkey 4.73 98 Ethiopia 2.54
49 Tunisia 4.71 99 Sudan 2.47
50 Romania 4.70 100 Uzbekistan 2.46

63
Table 24: “Current status” sub-ranking of the Free Trade and Investment Index
2013
1 United States 7.20 51 South Africa 4.62
2 EU 7.19 52 Tunisia 4.62
3 Singapore 7.16 53 Croatia 4.60
4 Finland 6.94 54 Jordan 4.59
5 United Kingdom 6.90 55 Panama 4.57
6 Sweden 6.88 56 Thailand 4.55
7 Denmark 6.84 57 Peru 4.45
8 Hong Kong 6.80 58 Colombia 4.32
9 France 6.80 59 Kazakhstan 4.30
10 Switzerland 6.79 60 Morocco 4.29
11 Luxembourg 6.73 61 Brazil 4.22
12 Japan 6.69 62 Trinidad and Tobago 4.16
13 Netherlands 6.62 63 Uruguay 4.15
14 Belgium 6.60 64 Russia 4.04
15 Norway 6.46 65 Azerbaijan 3.98
16 Ireland 6.42 66 Costa Rica 3.93
17 Austria 6.37 67 Ghana 3.91
18 Estonia 6.26 68 Lebanon 3.75
19 Canada 6.15 69 Indonesia 3.74
20 Spain 6.02 70 Libya 3.69
21 Iceland 5.89 71 Sri Lanka 3.68
22 South Korea 5.84 72 India 3.67
23 United Arab Emirates 5.80 73 El Salvador 3.67
24 Qatar 5.78 74 Guatemala 3.64
25 Czech Republic 5.72 75 Ukraine 3.63
26 Slovenia 5.69 76 Vietnam 3.56
27 Lithuania 5.68 77 Philippines 3.56
28 Saudi Arabia 5.67 78 Serbia 3.54
29 Portugal 5.66 79 Egypt 3.54
30 Australia 5.63 80 Ecuador 3.52
31 New Zealand 5.63 81 Dominican Republic 3.48
33 Slovak Republic 5.55 83 Argentina 3.33
34 Latvia 5.54 84 Algeria 3.33
35 Cyprus 5.53 85 Cote d'Ivoire 3.27
36 Italy 5.52 86 Camaroon 3.26
37 Poland 5.50 87 Belarus 3.24
38 China 5.49 88 Kenya 3.17
39 Taiwan 5.48 89 Pakistan 3.12
40 Hungary 5.45 90 Bangladesh 3.00
41 Malaysia 5.42 91 Syria 2.91
42 Bahrain 5.31 92 Iran 2.89
43 Chile 5.19 93 Turkmenistan 2.89
45 Oman 4.99 95 Zimbabwe 2.60
46 Kuwait 4.80 96 Venezuela 2.58
47 Romania 4.67 97 Angola 2.55
48 Turkey 4.65 98 Sudan 2.31
49 Mexico 4.63 99 Ethiopia 2.24
50 Greece 4.62 100 Uzbekistan 2.07

64
Table 25: “Dynamic trends” sub-ranking of the Free Trade and Investment Index
2013
1 Turkmenistan 7.54 51 Ireland 5.30
2 Hong Kong 7.37 52 Ethiopia 5.29
3 Qatar 6.80 53 Hungary 5.25
4 China 6.65 54 Kenya 5.23
5 Panama 6.49 55 Austria 5.23
6 Ghana 6.40 56 Kazakhstan 5.22
7 Peru 6.33 57 Finland 5.21
8 India 6.12 58 Mexico 5.19
9 Oman 6.06 59 Camaroon 5.18
10 Bangladesh 6.04 60 Lithuania 5.18
11 Belgium 6.02 61 South Africa 5.17
12 Vietnam 6.01 62 Guatemala 5.16
13 Morocco 5.97 63 Slovenia 5.16
14 South Korea 5.95 64 Lebanon 5.10
15 Taiwan 5.94 65 Norway 5.09
16 Uruguay 5.93 66 El Salvador 5.08
17 Uzbekistan 5.92 67 United States 5.06
18 Nigeria 5.89 68 EU 5.06
19 Poland 5.87 69 Costa Rica 5.05
20 Ecuador 5.77 70 Romania 5.04
21 Saudi Arabia 5.76 71 Canada 5.01
22 Indonesia 5.70 72 France 5.00
23 Luxembourg 5.69 73 New Zealand 4.97
24 Dominican Republic 5.68 74 Russia 4.96
25 Belarus 5.66 75 Portugal 4.96
26 Colombia 5.66 76 Cote d'Ivoire 4.95
27 Iceland 5.66 77 Egypt 4.91
28 Slovak Republic 5.59 78 Trinidad and Tobago 4.88
29 Chile 5.58 79 Philippines 4.85
30 Tunisia 5.57 80 Spain 4.85
31 Sri Lanka 5.56 81 Denmark 4.85
32 Argentina 5.54 82 United Kingdom 4.82
33 Singapore 5.53 83 Pakistan 4.76
34 Thailand 5.52 84 Ukraine 4.76
35 Bulgaria 5.51 85 Estonia 4.74
36 Switzerland 5.49 86 Italy 4.71
37 United Arab Emirates 5.48 87 Croatia 4.70
38 Brazil 5.44 88 Bahrain 4.68
39 Czech Republic 5.39 89 Latvia 4.67
40 Algeria 5.38 90 Azerbaijan 4.67
41 Turkey 5.38 91 Cyprus 4.66
42 Syria 5.36 92 Jordan 4.58
43 Kuwait 5.35 93 Libya 4.47
44 Malaysia 5.34 94 Angola 4.44
45 Israel 5.34 95 Zimbabwe 4.38
46 Australia 5.33 96 Iran 4.27
47 Netherlands 5.32 97 Yemen 4.25
48 Sweden 5.32 98 Greece 4.06
49 Serbia 5.32 99 Sudan 3.83
50 Japan 5.31 100 Venezuela 3.67

65
Table 26: “Exports” sub-ranking of the Free Trade and Investment Index 2013
1 EU 7.83 51 South Africa 4.43
2 United States 7.38 52 Croatia 4.39
3 United Kingdom 6.93 53 Morocco 4.36
4 France 6.81 54 Jordan 4.35
5 Sweden 6.61 55 Kuwait 4.34
6 Netherlands 6.57 56 Panama 4.26
7 Denmark 6.55 57 Peru 4.25
8 Luxembourg 6.45 58 Colombia 4.22
9 Japan 6.44 59 Brazil 4.21
10 Finland 6.44 60 Thailand 4.12
11 Switzerland 6.37 61 Russia 4.08
12 Belgium 6.36 62 Ghana 3.94
13 Singapore 6.30 63 Belarus 3.93
14 Norway 6.19 64 Kazakhstan 3.93
15 Austria 6.19 65 Libya 3.91
16 Ireland 6.12 66 Serbia 3.76
17 Hong Kong 6.05 67 Uruguay 3.72
18 Spain 6.04 68 Lebanon 3.71
19 Estonia 6.02 69 Trinidad and Tobago 3.67
20 Czech Republic 5.73 70 Costa Rica 3.66
21 Canada 5.66 71 Ukraine 3.61
22 Italy 5.64 72 Egypt 3.60
23 Lithuania 5.64 73 Azerbaijan 3.59
24 Iceland 5.62 74 India 3.57
25 Latvia 5.57 75 El Salvador 3.48
26 Portugal 5.57 76 Dominican Republic 3.46
27 Slovenia 5.54 77 Algeria 3.42
28 Hungary 5.54 78 Sri Lanka 3.42
29 Slovak Republic 5.54 79 Indonesia 3.42
30 China 5.53 80 Guatemala 3.37
31 Poland 5.51 81 Ecuador 3.34
32 United Arab Emirates 5.48 82 Argentina 3.33
34 South Korea 5.34 84 Philippines 3.25
35 Cyprus 5.31 85 Vietnam 3.24
36 Qatar 5.31 86 Syria 3.22
37 Saudi Arabia 5.18 87 Camaroon 3.17
38 Bulgaria 4.97 88 Pakistan 3.13
39 Bahrain 4.96 89 Cote d'Ivoire 3.10
40 Malaysia 4.92 90 Kenya 3.01
41 Australia 4.90 91 Turkmenistan 2.97
42 Turkey 4.80 92 Bangladesh 2.88
43 New Zealand 4.77 93 Venezuela 2.80
44 Greece 4.72 94 Yemen 2.75
45 Tunisia 4.69 95 Iran 2.68
46 Romania 4.65 96 Zimbabwe 2.64
47 Mexico 4.64 97 Angola 2.57
48 Taiwan 4.58 98 Sudan 2.31
49 Chile 4.58 99 Uzbekistan 2.15
50 Oman 4.57 100 Ethiopia 2.13

66
Table 27: “Foreign direct investment” sub-ranking of the Free Trade and
Investment Index 2013
1 Singapore 7.93 51 Panama 4.84
2 Finland 7.50 52 Tunisia 4.82
3 Hong Kong 7.37 53 Mexico 4.81
4 Sweden 7.25 54 Romania 4.71
5 Denmark 7.25 55 Kuwait 4.67
6 United Kingdom 7.23 56 Colombia 4.63
7 Switzerland 7.22 57 Greece 4.52
8 Netherlands 7.01 58 Uruguay 4.46
9 United States 6.99 59 Kazakhstan 4.46
10 EU 6.97 60 Morocco 4.45
11 Japan 6.96 61 Croatia 4.36
12 France 6.91 62 Ghana 4.35
13 Norway 6.84 63 Trinidad and Tobago 4.30
14 Ireland 6.84 64 Sri Lanka 4.19
15 New Zealand 6.80 65 Brazil 4.14
16 Luxembourg 6.77 66 Costa Rica 4.13
17 Belgium 6.73 67 Indonesia 4.01
18 Estonia 6.61 68 Vietnam 3.90
19 South Korea 6.61 69 Guatemala 3.88
20 Canada 6.53 70 Azerbaijan 3.84
21 Austria 6.51 71 India 3.79
22 Iceland 6.32 72 Philippines 3.76
23 Taiwan 6.30 73 Lebanon 3.73
24 Australia 6.23 74 Egypt 3.69
25 Saudi Arabia 6.15 75 El Salvador 3.69
26 United Arab Emirates 6.14 76 Russia 3.66
27 Qatar 6.13 77 Nigeria 3.58
28 Spain 6.13 78 Ecuador 3.57
29 Malaysia 6.07 79 Dominican Republic 3.54
30 Israel 6.02 80 Pakistan 3.52
31 Cyprus 5.91 81 Kenya 3.49
32 Portugal 5.85 82 Ukraine 3.39
33 Bahrain 5.85 83 Camaroon 3.36
34 Lithuania 5.77 84 Argentina 3.25
35 Chile 5.77 85 Bangladesh 3.24
36 Latvia 5.76 86 Serbia 3.20
37 Czech Republic 5.72 87 Libya 3.16
38 Slovenia 5.70 88 Cote d'Ivoire 3.12
39 Slovak Republic 5.57 89 Iran 2.90
40 Poland 5.44 90 Algeria 2.71
41 Hungary 5.44 91 Zimbabwe 2.59
42 Oman 5.36 92 Syria 2.55
43 Italy 5.23 93 Belarus 2.50
44 China 5.21 94 Venezuela 2.25
45 Thailand 5.18 95 Angola 2.22
47 South Africa 5.02 97 Sudan 2.09
48 Jordan 5.00 98 Ethiopia 2.05
49 Peru 4.92 99 Turkmenistan 1.75
50 Turkey 4.88 100 Uzbekistan 1.19

67
4 Bibliography
4 Bibliography
International Monetary Fund. (2007). World Economic Outlook. Globalization and Inequality.
Bergh, A., Nilsson, T. (2010). Do liberalization and globalization increase income inequality?
European Journal of Political Economy: 488–505.
Borys, M. M., Polgár, É. K., Zlate, A. (2008). Real convergence and the determinants of growth
in EU candidate and potential candidate countries: A panel approach. European Central
Bank, Occational Paper No 86.
Dollar, D., Kray, A. (2001). Trade, growth, and poverty. Washington DC: World Bank Discussion
Paper.
Dreher, A. (2006). Does globalization affect growth? Evidence from a new index of globalization.
Applied Economics, 1091–1110.
Ernst Young. (2013). Looking beyond the obvious. Globalization and new opportunities
forgrowth. EYGM Limited.
Greenaway, D., Morgan, W., Wright, P. (1999). Exports, export composition and growth. Journal
of International Trade Economic Development: 41–51.
Kearney, A. T., Magazine, F. P. (2002). Globalization Index. www.foreignpolicy.com.
Lee, R. J., Barro, J. W. (1994). Sources of economic growth. Carnegie-Rochester Conference
Series on Public Policy: 40: 1–46.
OECD. (2011). Devided we stand. Why inequality keeps rising.
Reinhard, C. M., Rogoff, K. S. (2010). Growth in a time of debt. American Economic Review:
Papers Proceedings 100: 573–578.
Solt, F. (2009). Standardizing the World Income Inequality Database. Social Science Quarterly, 90
(2): 231–242.
Vujakovic, P. (2010). How to measure globalization? A new globalization index (NGI). Atlantic
Economic Journal, 38 (2): 237.
Weltbank. (2001). Globalization, Growth and Poverty. New York: Oxford University Press.

68
5 Appendix A – Additional tables
Table 28: Globalization index over time:Argentina to Germany
Argentina Australia Belgium Brazil Bulgaria Chile China Denmark Germany
1990 30.8 54.8 81.5 29.4 28.8 41.4 17.3 66.0 53.3
1991 32.2 55.5 82.9 31.4 27.3 43.7 17.4 69.3 58.7
1992 33.9 57.2 84.1 32.7 34.8 43.8 17.6 73.1 58.9
1993 37.3 58.7 84.7 33.2 34.2 44.5 18.0 75.2 59.9
1994 40.1 60.7 85.9 33.7 34.9 45.9 22.0 72.6 60.1
1995 43.1 61.1 82.7 35.7 38.8 46.7 23.7 75.3 61.4
1996 42.6 60.9 85.0 36.4 45.7 47.0 23.9 76.2 63.4
1997 42.4 61.3 87.3 36.1 43.8 48.4 24.7 76.0 65.7
1998 42.3 62.7 87.7 35.3 42.9 49.3 28.8 75.5 67.7
1999 41.8 64.0 89.7 36.1 45.8 51.6 27.8 77.2 69.9
2000 41.2 65.3 93.5 35.6 49.9 53.0 28.4 83.2 73.3
2001 39.1 66.2 93.2 39.8 50.1 58.2 33.7 82.4 71.9
2002 42.0 65.1 91.6 40.4 47.3 58.0 36.6 81.6 73.3
2003 39.7 66.1 90.6 38.7 50.6 61.7 37.2 82.1 74.1
2004 40.2 66.1 91.0 40.5 56.5 63.8 41.5 81.9 73.1
2005 38.2 65.0 90.7 41.7 54.7 64.5 43.3 82.6 72.1
2006 37.9 67.2 91.8 41.3 60.9 66.4 40.5 83.0 72.8
2007 37.5 68.9 92.6 41.6 69.2 69.3 42.8 85.3 73.2
2008 37.4 65.3 91.8 39.7 66.1 68.3 41.5 82.2 70.5
2009 35.7 67.4 91.5 40.0 63.2 66.8 42.0 80.8 70.2
2010 35.5 68.0 90.1 40.8 62.2 65.7 42.2 81.5 69.7
2011 34.5 67.1 89.0 40.1 61.7 62.4 40.9 80.9 69.2

69
Table 30: Globalization index over time: Canada to Austria
Canada Latvia Lithuania Mexico New Zealand Netherlands Norway Austria
1990 62.6 27.3 27.0 35.4 54.1 76.2 67.6 65.0
1991 63.7 28.3 27.7 35.8 57.3 77.9 69.4 68.4
1992 64.3 29.9 28.7 39.5 59.4 78.4 68.6 68.4
1993 65.6 33.6 30.9 39.4 60.7 78.5 69.4 69.5
1994 66.5 38.5 37.6 40.3 62.6 80.1 70.1 70.2
1995 67.8 42.1 40.6 44.2 64.1 79.0 69.7 70.0
1996 68.9 46.5 45.5 39.9 64.8 80.3 70.3 71.4
1997 70.4 47.8 47.6 39.0 65.6 81.9 70.7 73.0
1998 71.9 49.5 47.4 38.5 66.2 84.6 71.4 74.5
1999 73.5 48.8 46.9 37.7 69.0 88.1 70.9 76.6
2000 74.8 49.4 47.2 37.6 71.4 93.9 71.6 79.2
2001 74.2 51.4 50.5 36.3 69.4 91.3 70.7 78.6
2002 72.4 52.0 51.4 37.1 69.0 88.8 68.2 78.4
2003 72.9 52.6 52.1 36.8 67.9 90.7 72.0 79.9
2004 73.2 56.6 54.3 37.1 68.9 88.7 68.5 80.3
2005 70.9 57.4 54.2 41.7 68.4 89.6 65.2 80.4
2006 70.5 59.1 54.5 39.4 70.2 90.2 68.6 81.7
2007 71.5 60.8 56.1 40.4 69.7 91.5 71.1 84.5
2008 69.8 59.5 57.8 39.6 70.1 90.5 68.7 81.0
2009 71.0 56.1 53.4 41.3 68.9 87.9 72.1 80.7
2010 70.8 57.7 56.0 42.1 68.7 89.0 70.1 79.5
2011 69.3 58.5 56.4 42.3 68.6 89.3 68.0 78.2
Table 29: Globalization index over time: Estonia to Japan
Estonia Finland France Greece India Ireland Israel Italy Japan
1990 34.9 55.8 61.7 39.0 18.2 76.6 40.6 53.3 37.8
1991 35.7 59.1 64.1 48.1 18.7 78.6 40.2 55.2 39.1
1992 38.1 61.4 65.4 49.0 19.7 79.7 40.6 56.7 43.5
1993 42.9 64.0 66.5 51.4 20.5 81.4 43.5 58.9 44.1
1994 49.7 65.0 64.2 52.0 20.9 83.1 44.1 58.4 44.1
1995 60.9 65.8 65.3 52.3 21.6 82.5 43.6 59.2 40.9
1996 61.9 69.7 66.3 54.0 23.7 83.2 45.7 60.5 44.7
1997 65.5 70.7 68.7 55.8 23.8 83.6 48.3 62.5 45.6
1998 65.6 72.1 71.5 58.9 23.8 87.7 51.1 64.9 46.8
1999 66.1 73.4 73.7 62.1 24.0 88.8 54.9 66.2 47.5
2000 68.5 77.5 76.3 65.5 24.6 91.3 58.4 68.6 48.6
2001 69.5 77.4 72.3 65.7 25.1 91.1 60.4 67.2 48.1
2002 69.1 76.9 73.5 65.5 25.4 90.0 61.5 66.1 47.6
2003 71.1 78.2 74.2 67.8 26.9 89.4 62.5 65.2 50.6
2004 74.7 78.6 76.8 69.0 27.5 89.9 61.0 67.9 50.9
2005 72.7 76.1 75.3 66.3 30.2 90.4 63.6 66.7 51.4
2006 74.4 76.2 76.6 66.8 30.5 87.5 63.1 65.8 52.6
2007 76.7 78.1 77.5 68.4 32.0 89.0 64.1 66.1 52.8
2008 75.7 75.4 73.6 67.8 33.1 87.6 65.5 64.4 51.2
2009 73.7 74.9 75.6 66.3 33.3 91.3 64.2 65.1 51.4
2010 75.8 76.6 75.3 64.9 32.7 92.1 64.9 64.8 51.3
2011 73.9 76.7 73.0 63.6 32.4 91.0 61.8 63.1 50.1

70
Table 31: Globalization index over time: Poland to Slovenia
Poland Portugal Romania Russia Sweden Switzerland Slovakia Slovenia
1990 39.6 48.8 22.5 24.4 69.7 71.0 47.3 31.5
1991 41.1 52.7 26.8 24.5 71.2 73.3 46.0 34.9
1992 44.4 57.0 26.9 25.5 71.3 73.3 45.1 37.2
1993 46.1 60.6 28.7 29.1 73.3 74.8 43.9 39.3
1994 46.1 61.2 31.4 30.2 74.0 74.9 44.4 42.2
1995 47.4 62.3 35.5 32.1 74.4 74.5 47.2 41.7
1996 47.4 63.4 37.1 32.9 74.2 76.3 48.9 43.4
1997 48.7 64.5 38.7 33.7 75.8 80.0 51.0 49.5
1998 50.6 65.8 38.7 36.5 76.7 83.2 52.2 51.0
1999 51.3 66.2 40.3 37.6 77.6 85.3 52.9 51.1
2000 53.2 69.4 41.9 40.0 80.6 90.6 56.2 53.5
2001 51.0 71.5 43.1 41.4 79.9 87.8 58.0 54.7
2002 52.8 68.9 43.7 42.7 80.4 85.7 54.6 55.0
2003 55.6 71.0 44.4 42.9 80.9 84.7 54.8 59.0
2004 62.7 74.9 47.4 41.8 81.5 80.7 69.6 64.5
2005 59.6 72.3 52.0 42.6 81.2 82.6 69.8 64.1
2006 61.0 76.0 48.9 42.5 83.2 80.9 70.3 64.4
2007 63.4 77.8 61.2 44.1 84.9 81.5 72.1 66.8
2008 62.3 76.4 61.2 40.9 82.6 78.3 71.8 67.3
2009 63.0 77.3 60.2 43.5 83.9 78.8 70.3 63.8
2010 61.7 77.6 58.5 44.4 83.2 79.5 69.5 63.8
2011 60.8 75.7 56.5 43.4 79.6 77.4 68.6 63.1
Table 32: Globalization index over time: Spain to the United Kingdom
Spain South Africa South Korea Czech
Republic
Turkey Hungary United States United
Kingdom
1990 57.1 27.7 23.9 53.7 36.3 44.7 58.4 73.3
1991 58.4 25.7 25.6 57.3 38.1 45.5 59.7 72.7
1992 60.5 24.7 27.9 56.0 38.9 48.1 59.6 72.5
1993 61.7 24.3 34.1 54.9 41.8 49.8 60.8 74.8
1994 63.0 24.6 34.7 56.2 46.6 51.6 60.9 73.0
1995 63.6 29.3 35.1 58.2 48.4 55.4 62.0 74.8
1996 64.2 31.3 36.4 59.0 47.8 58.6 62.5 75.8
1997 65.8 34.3 37.8 60.8 49.0 63.3 63.2 76.2
1998 67.1 36.9 41.2 62.3 47.0 65.3 64.0 78.1
1999 68.6 44.5 40.3 64.2 46.3 66.4 64.9 80.3
2000 71.2 46.3 41.6 66.4 47.0 68.1 65.5 83.5
2001 70.8 47.7 44.8 67.0 47.2 71.6 63.9 81.5
2002 70.6 48.2 43.7 68.0 45.7 68.5 61.2 80.8
2003 72.3 48.1 43.0 66.9 47.8 68.0 62.5 82.2
2004 71.6 46.6 45.4 72.6 49.6 77.0 63.8 79.4
2005 69.6 47.8 44.3 71.3 53.5 73.9 63.0 81.5
2006 70.2 49.1 46.1 71.8 50.2 78.2 64.5 83.7
2007 71.7 50.9 48.4 75.2 50.4 79.2 65.7 82.6
2008 70.2 49.7 48.1 72.8 50.3 78.2 62.4 81.0
2009 70.5 49.9 47.5 72.4 51.7 81.1 60.4 82.6
2010 70.7 49.9 47.4 72.4 50.5 80.1 60.9 82.9
2011 69.7 48.6 47.8 70.8 48.8 77.6 60.7 82.4

71
Table 33: Regression results on the determinants of per capita growth –
robustness checks
Dependent variable: Growth of
the per capita gross domestic
product as a percent
IV method
with FE
IV method
with FE
IV method
with FE
IV method
with FE
IV method
with FE
IV method
with FE
Total globalization 0.35*** 0.34*** 0.32*** 0.32*** 0.32*** 0.33***
(0.08) (0.08) (0.08) (0.08) (0.08) (0.08)
Gross domestic product per
capita in the next-to-last period
(logarithmized)
–10.48*** –10.25*** –10.20*** –10.06*** –10.01*** –9.93***
(1.60) (1.59) (1.69) (1.77) (1.78) (1.76)
Birth rate (logarithmized) –10.44*** –9.89*** –9.88*** –10.86*** –10.76*** –10.97***
(2.42) (2.38) (2.46) (2.61) (2.64) (2.83)
Investments (as a % of the gross
domestic product)
0.15 0.15 0.14 0.05 0.05 0.06
(0.10) (0.10) (0.11) (0.14) (0.14) (0.14)
Crisis indicator 2008–2009 –3.55*** –3.60*** –3.39*** –3.40*** –3.35*** –3.35***
(0.43) (0.43) (0.48) (0.50) (0.48) (0.48)
Inflation (as a %) –0.003 –0.003 –0.003 –0.003 –0.003
(0.003) (0.004) (0.003) (0.003) (0.003)
Government consumer spending –0.18 –0.13 –0.11 –0.11
(0.16) (0.16) (0.16) (0.16)
Public debt (as a % of the gross
domestic product)
–0.04 –0.04 –0.03
(0.02) (0.02) (0.02)
Rule of Law Index 0.45 0.47
(0.40) (0.40)
Continuing education –0.01
(0.02)
R² (centered)
840 840 840 840 840 840
0,40 0,41 0,41 0,40 0,41 0,41

72
Table 34: Regression results on the determinants of per capita growth with
country-specific estimators for the effects of globalization on growth –
robustness checks
Dependent variable: Growth of
the per capita gross domestic
product as a percent
IV method
with FE and
country
groups
IV method
with FE and
country
groups
IV method
with FE and
country
groups
IV method
with FE and
country
groups
IV method
with FE and
country
groups
IV method
with FE and
country
groups
Globalization for
Large national economies with
a high per capita income
0.26*** 0.26*** 0.26*** 0.29*** 0.30*** 0.29***
(0.05) (0.04) (0.05) (0.06) (0.06) (0.06)
Small national economies with
a high per capita income
0.26*** 0.25*** 0.25*** 0.21** 0.22** 0.22**
(0.06) (0.06) (0.07) (0.07) (0.08) (0.08)
Large national economies with
a low per capita income
0.29 0.26 0.26 0.25* 0.23 0.23
(0.16) (0.14) (0.14) (0.12) (0.12) (0.12)
Small national economies with
a low per capita income
0.40*** 0.39*** 0.37*** 0.37*** 0.38*** 0.38***
(0.10) (0.10) (0.10) (0.10) (0.10) (0.10)
Gross domestic product per
capita in the next-to-last period
(logarithmized)
–10.02*** –9.74*** –9.75*** –9.51*** –9.33*** –9.27***
(1.70) (1.62) (1.67) (1.71) (1.75) (1.73)
Birth rate (logarithmized) –10.19** –9.81** –9.77** –10.96** –11.12** –11.29**
(3.26) (3.21) (3.30) (3.38) (3.40) (3.60)
Investments (as a % of the gross
domestic product)
0.12 0.12 0.12 0.02 0.02 0.02
(0.10) (0.10) (0.10) (0.14) (0.14) (0.14)
Crisis indicator 2008– 2009 –3.59*** –3.65*** –3.46*** –3.47*** –3.40*** –3.41***
(0.43) (0.44) (0.50) (0.52) (0.50) (0.50)
Inflation (as a %) –0.00 –0.00 –0.00 –0.00 –0.00
(0.00) (0.00) (0.00) (0.00) (0.00)
Government consumer spending –0.15 –0.10 –0.07 –0.07
(0.16) (0.16) (0.16) (0.16)
Public debt (as a % of the gross
domestic product)
–0.04* –0.04 –0.04
(0.02) (0.02) (0.02)
Rule of Law Index 0.56 0.58
(0.38) (0.38)
Continuing education –0.01
(0.02)
R² (centered)
840 840 840 840 840 840
0.40 0.41 0.41 0.40 0.41 0.41

73
Table 35: Globalization-induced absolute increase in the gross domestic product per capita between 1990
and 2011 in relation to the total increase in the gross domestic product per capita
Rank Country Absolute increase in the gross domestic
product per capita caused by increasing
globalization, in euros*
Total absolute increase in the per capita
gross domestic product, in euros*
Portion of the increase in the gross
domestic product per capita caused by
increasing globalization, as a percent
1 Finland 2,070 8,310 25.0
2 Japan 1,780 5,950 30.0
3 Israel 1,750 8,400 20.9
4 Denmark 1,670 7,370 22.7
5 Germany 1,510 7,190 21.0
6 Ireland 1,450 15,260 9.5
7 Slovenia 1,410 4,820 29.2
8 South Korea 1,410 10,650 13.2
9 Austria 1,290 8,710 14.8
10 Netherlands 1,240 8,420 14.7
11 Sweden 1,210 10,700 11.3
12 Australia 1,160 8,930 13.0
13 Portugal 1,130 3,240 34.8
14 Greece 1,080 3,040 35.6
15 France 960 4,830 19.9
16 United Kingdom 940 9,790 9.6
17 Switzerland 940 5,270 17.8
18 Estonia 910 2,220 41.0
19 New Zealand 800 3,720 21.5
20 Spain 700 4,410 15.8
21 Italy 680 2,550 26.8
22 Hungary 670 1,500 44.7
23 Belgium 670 6,380 10.5
24 Canada 640 6,940 9.2
25 Slovakia 640 3,710 17.2
26 Lithuania 640 2,000 31.8
27 Latvia 610 1,990 30.9
28 Chile 530 4,180 12.6
29 Poland 510 4,040 12.6
30 Czech Republic 500 2,780 17.9
31 Romania 320 840 37.9
32 Bulgaria 310 1,080 28.9
33 United States 310 11,100 2.8
34 South Africa 290 750 38.8
35 Turkey 270 2,550 10.6
36 Russia 220 470 46.8
37 China 210 2,440 8.5
38 Mexico 200 1,670 12.2
39 Brazil 190 1,570 12.1
40 Argentina 180 6,550 2.7
41 Norway 100 13,310 0.7
42 India 40 590 7.3
* real prices from the year 2000; rounded values; ** in percent

74
Table 36: Globalization-induced relative increase in the gross domestic product per capita between 1990 and
2011 in relation to the total increase in the gross domestic product per capita
Rank Country Increase in the gross domestic product
per capita caused by increasing globa-
lization in relation to the baseline level,
as a percent
Total increase in the gross domestic
product per capita in relation to the
baseline level, as a percent
Portion of the increase in the gross
domestic product per capita caused by
increasing globalization in relation to
the baseline level, as a percent
1 China 49.1 573.9 9.3
2 South Korea 18.8 142.1 13.3
3 Estonia 18.6 45.2 52.0
4 Bulgaria 17.2 59.6 33.0
5 Romania 15.5 41.0 15.2
6 Slovenia 15.5 53.0 42.5
7 Poland 15.1 119.8 14.3
8 Chile 15.0 119.1 30.5
9 Latvia 14.4 46.7 51.9
10 Hungary 14.2 31.7 37.3
11 Lithuania 13.6 42.9 44.3
12 India 12.4 169.4 26.3
13 Portugal 11.8 34.0 7.8
14 Slovakia 11.1 64.2 30.7
15 Israel 10.6 50.6 47.0
16 Greece 10.2 28.8 19.2
17 Finland 9.6 38.4 10.8
18 Ireland 9.5 99.7 27.3
19 South Africa 8.5 22.0 21.0
20 Czech Republic 8.5 47.3 46.1
21 Russia 7.8 16.7 63.5
22 Turkey 7.2 67.8 13.4
23 Germany 7.1 33.8 24.0
24 Denmark 6.4 28.1 17.5
25 New Zealand 6.3 29.5 24.7
26 Austria 6.2 41.7 16.8
27 Australia 6.1 46.9 15.0
28 Netherlands 6.0 41.1 17.0
29 Spain 5.7 35.8 23.6
30 Brazil 5.2 43.1 14.4
31 Japan 4.8 16.0 26.3
32 Sweden 4.8 41.9 13.1
33 France 4.7 23.8 31.6
34 United Kingdom 4.5 46.6 11.8
35 Italy 3.8 14.1 32.0
36 Mexico 3.3 27.2 12.4
37 Belgium 3.3 31.3 27.5
38 Canada 3.0 32.7 11.7
39 Argentina 2.9 107.9 13.4
40 Switzerland 2.5 14.2 3.3
41 United States 1.0 34.8 3.0
42 Norway 0.3 44.4 2.9

75
6 Appendix B – Additional figures
Figure 4: Gross domestic product per capita with and without globalization from
1990 to 2011; Argentina to France; real GDP in euros, at prices of 2000
Germany
Finland FranceEstonia
DenmarkChina
ChileBulgaria
Belgium
Brazil
AustraliaArgentina
GDP per capita GDP per capita without globalization
20.000
22.000
24.000
26.000
28.000
20112005200019951990
18.000
20.000
22.000
24.000
26.000
28.000
20112005200019951990
1.400
1.800
2.200
2.600
3.000
20112005200019951990
3.000
4.000
5.000
6.000
7.000
8.000
20112005200019951990
26.000
28.000
30.000
32.000
34.000
36.000
20112005200019951990
20.000
22.000
24.000
26.000
28.000
20112005200019951990
20.000
21.000
22.000
23.000
24.000
25.000
26.000
20112005200019951990
16.000
20.000
24.000
28.000
32.000
20112005200019951990
5.000
7.000
9.000
11.000
13.000
20112005200019951990
3.500
4.000
4.500
5.000
5.500
20112005200019951990
400
1.000
1.600
2.200
2.800
20112005200019951990
2.000
3.000
4.000
5.000
6.000
7.000
8.000
20112005200019951990

76
1990 to 2011; Greece to Netherlands; real GDP in euros, at prices of 2000
Latvia
New Zealand NetherlandsMexico
LithuaniaCanada
JapanItaly
Ireland
Israel
IndiaGreece
20.000
22.000
24.000
26.000
28.000
30.000
20112005200019951990
10.000
11.000
12.000
13.000
14.000
15.000
16.000
20112005200019951990
16.000
18.000
20.000
22.000
24.000
26.000
20112005200019951990
6.000
6.500
7.000
7.500
8.000
20112005200019951990
15.000
20.000
25.000
30.000
35.000
20112005200019951990
18.000
19.000
20.000
21.000
22.000
20112005200019951990
37.000
39.000
41.000
43.000
45.000
20112005200019951990
2.000
3.000
4.000
5.000
6.000
7.000
20112005200019951990
11.000
12.000
13.000
14.000
15.000
16.000
17.000
20112005200019951990
2.000
3.000
4.000
5.000
6.000
7.000
20112005200019951990
20.000
22.000
24.000
26.000
28.000
30.000
20112005200019951990
300
400
500
600
700
800
900
1.000
20112005200019951990

77
Slovakia
Spain South AfricaSlovenia
SwitzerlandSweden
RussiaRomania
Poland
Portugal
AustriaNorway
1990 to 2011; Norway to South Africa; real GDP in euros, at prices of 2000
3.000
4.000
5.000
6.000
7.000
8.000
20112005200019951990
24.000
26.000
28.000
30.000
32.000
34.000
36.000
20112005200019951990
30.000
33.000
36.000
39.000
42.000
45.000
20112005200019951990
20.000
22.000
24.000
26.000
28.000
30.000
20112005200019951990
9.000
10.000
11.000
12.000
13.000
20112005200019951990
1.600
2.000
2.400
2.800
3.200
20112005200019951990
1.500
2.000
2.500
3.000
3.500
20112005200019951990
35.000
37.000
39.000
41.000
43.000
20112005200019951990
4.000
5.000
6.000
7.000
8.000
9.000
10.000
20112005200019951990
3.000
3.300
3.600
3.900
4.200
20112005200019951990
7.000
9.000
11.000
13.000
15.000
20112005200019951990
12.000
13.000
14.000
15.000
16.000
17.000
18.000
20112005200019951990

78
1990 to 2011; South Korea to the United Kingdom; real GDP in euros, at prices
of 2000
United KingdomUnited States
Turkey
Hungary
Czech RepublicSouth Korea
7.000
9.000
11.000
13.000
15.000
17.000
19.000
20112005200019951990
5.000
6.000
7.000
8.000
9.000
20112005200019951990
3.500
4.000
4.500
5.000
5.500
6.000
6.500
20112005200019951990
3.500
4.000
4.500
5.000
5.500
6.000
65.00
20112005200019951990
30.000
33.000
36.000
39.000
42.000
45.000
20112005200019951990
20.000
22.000
24.000
26.000
28.000
30.000
32.000
34.000
20112005200019951990

80
About the authors
Project team
Dr. Michael Böhmer (Project Leader)
Dr. Johannes Weisser
Markus Hoch
Tina Schneidenbach
work in the Economy, Society, State unit at Prognos AG at its Munich, Germany and Basel,
Switzerland locations.
About the Global Economic Dynamics (GED) project
The Global Economic Dynamics (GED) project of the Bertelsmann Foundation contributes to
improving the understanding of the growing complexity of global economic developments. By
using the most up-to-date tools and methods for measuring, forecasting and modeling global
economic dynamics, the project seeks to make globalization, its economic effects and its political
consequences more transparent and understandable.
Contact
Bertelsmann Stiftung
GED Team
Program Shaping Sustainable Economies
Carl-Bertelsmann-Straße 256
33311 Gütersloh | Germany
Phone +49 5241 81-81353
Fax +49 5241 81-681353
ged@bertelsmann-stiftung.de
GED Team
Director
Andreas Esche
Director, Program Shaping Sustainable Economies
Phone +49 5241 81-81333
Fax +49 5241 81-681333
andreas.esche@bertelsmann-stiftung.de
Global Economic Dynamics (GED)

81
Project collaborators
Dr. Jan Arpe
Project Manager
Phone +49 5241 81-81157
Fax +49 5241 81-681157
jan.arpe@bertelsmann-stiftung.de
Samuel George
Project Manager
Phone +49 5241 81-81661
Fax +1 202 384-1984
samuel.george@bfna.org
Dr. Thieß Petersen
Senior Expert
Phone +49 5241 81-81218
Fax +49 5241 81-681218
thiess.petersen@bertelsmann-stiftung.de
Dr. Ulrich Schoof
Project Manager
Phone +49 5241 81-81384
Fax +49 5241 81-681384
ulrich.schoof@bertelsmann-stiftung.de
Cooperation partner
Prognos AG
Henric Petri-Straße 9
4010 Basel | Switzerland
Contact person
Dr. Michael Böhmer
Chief Economist, Prognos AG
Phone +49 89 9541586-701
Fax +49 89 9541586 288-701
michael.boehmer@prognos.com
Global Economic Dynamics (GED)

82
Imprint
Imprint
© 2014 Bertelsmann Stiftung
33311 Gütersloh
www.bertelsmann-stiftung.de
Responsible for content
Dr. Thieß Petersen
Editor
Sibylle Reiter
Translation
Peritus Precision Translations, Inc.
San Carlos, California, USA
Design
Nicole Meyerholz, Bielefeld
Photo credits
© Sergey Nivens – Fotolia.com

www.bertelsmann-stiftung.de
Address | Contact
33311 Gütersloh
GED Team
Program Shaping Sustainable Economies
Phone +49 5241 81-81353
ged@bertelsmann-stiftung.de
www.ged-project.de

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