THE CONSTANCY OF THE CORE: TRENDS IN GLOBAL STRATIFICATION
By
Michael Allen Sobocinski

A THESIS
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
Sociology—Master of Arts
2017

ABSTRACT
THE CONSTANCY OF THE CORE: TRENDS IN GLOBAL STRATIFICATION
By
Michael Allen Sobocinski
Theoretical perspectives on international stratification include concepts involving strata
or tiers of countries that have different levels of development, or different relations with
the global economy. The two dominant theoretical traditions are world-systems and
modernization theories. In this paper, I consider both perspectives while examining how
the stratification of countries has changed since 1960. Fifty years of World Bank data on
national GDP were examined and rank-ordered, tracking shifts in the positions of
important countries and assessing these patterns to see whether the data were more
consistent with one of these theories. The results showed a pattern in which the world’s
wealthiest states stably tended to constitute approximately 15% of the world’s population
over time. This pattern supported the idea of a “core” economic area, as defined by
world-systems theory, but the distribution of states with the bottom 85% of the world’s
population provided evidence against other concepts of world-systems theory and was
better accounted for by modernization theory.

The number of states that are

predominantly peripheral in character has been shrinking greatly over time, and the
population within those states now constitutes a minority of the world’s population, while
the majority of the world now lives in what have been characterized as semi-peripheral
areas. Longitudinal economic data therefore strongly suggests that the original worldsystems classification schema must be revised, reconciled with, or replaced by
modernization theory in order to accurately describe the 21st century world economy.

Copyright by
MICHAEL ALLEN SOBOCINSKI
2017

ACKNOWLEDGMENTS

In appreciation of the special support of Dr. Raymond Jussaume and faculty members in
the College of Social Science at Michigan State University.

iv

TABLE OF CONTENTS

LIST OF TABLES ............................................................................................................. vi
INTRODUCTION ...............................................................................................................1
CHAPTER 1: CONCEPTUAL BACKGROUND FOR THIS RESEARCH ......................5
CHAPTER 2: THE INITIAL PHASE OF EXPLORATORY RESEARCH .......................9
CHAPTER 3: THE NEW RESEARCH DATA AND ITS PROCESSING ......................20
CHAPTER 4: RESULTS OF THIS RESEARCH .............................................................27
CHAPTER 5: DISCUSSION OF RESEARCH RESULTS ..............................................40
CHAPTER 6: CONCLUSION ..........................................................................................44
APPENDICES ...................................................................................................................52
APPENDIX I: PARTIALLY PROCESSED DATA
FROM THE WORLD BANK ...................................................................53
APPENDIX II: PROCESSED WORLD BANK DATA FOR 1960-1965 ............63
APPENDIX III: PROCESSED WORLD BANK DATA FOR 1986-1990 ...........68
APPENDIX IV: PROCESSED WORLD BANK DATA FOR 2005-2010...........73
APPENDIX V: PROCESSED CIA FACTBOOK DATA FOR 2011 ...................78
APPENDIX VI: CIA FACTBOOK DATA WITH CHINA SUBDIVISIONS .....83
APPENDIX VII: CIA FACTBOOK DATA IN REGIONAL GROUPINGS .......89
REFERENCES ..................................................................................................................95

v

LIST OF TABLES

Table 1: Initial Exploratory Groupings and Comparisons, by Region ..................17
Table 2: World Bank Data for Three Time Periods, by Region ............................31
Table 3: Partially Processed Data from the World Bank .......................................53
Table 4: Processed World Bank Data for 1960-1965 ............................................63
Table 5: Processed World Bank Data for 1986-1990 ............................................68
Table 6: Processed World Bank Data for 2005-2010 ............................................73
Table 7: Processed CIA Factbook Data for 2011 ..................................................78
Table 8: CIA Factbook Data with China Subdivisions ..........................................83
Table 9: CIA Factbook Data in Regional Groupings.............................................89

vi

INTRODUCTION

The main goal of this paper is to empirically assess whether international
economic data are well-described by the world-system framework that was originated by
Immanuel Wallerstein.

Wallerstein had described modern history in terms of an

expanding capitalist system, in which an economic core commodifies productive activity
and proletarianizes the less-developed areas that it establishes relations with. When first
proposed during the Cold War era of the 1970s, Wallerstein had provided a descriptive
classification system into which various geographic and social areas could be understood
as fitting into an expanding system of global capitalism. It was empirically evident to
Wallerstein that this economic system had, during the course of centuries of exploration,
conquest, trade, and international change, come into contact with and then heavily
reshaped almost all of the inhabited areas of the world. Since that time, his basic
framework has become a staple for many sociology textbooks about international
development and global stratification, even within introductory sociological textbooks,
which treat his classifications of “core,” “semi-peripheral,” and “peripheral” areas of the
world economy as fundamental knowledge. (Anderson 2003: 216, 443; Wallerstein
1974, Wallerstein 1983)
After periodically examining economic data at a national and world regional level
over the past few decades, I started to question whether Wallerstein’s conception of a
world-system is still very descriptive of more recent patterns seen in global stratification.
Plenty of research continues to occur within the world-system theoretical framework, and
this paper takes a systematic look at official economic data from the World Bank, as a

1

test of whether the world-system framework still feels highly relevant even though the
Cold War period, in which it was first conceived, had ended over 25 years ago.
I had published some of my early thoughts in a brief article that had appeared in
the Fall of 2003, bolstered by some quantitative ideas that I had employed in research
about metropolitan areas a few years before. My ideas, examined in this research thesis,
involved a longitudinal comparison of the economic production levels in all major
countries around the world, rank-ordered and weighted by their populations so as to place
the population of each country into a kind of estimated percentile ranking, and to see how
the positions of these countries change over time. Albert J. Bergesen and Michelle Bata
had published a 2002 article that had analyzed global stratification trends in ways that
were considered generally supportive of the world-systems framework, but I felt that
their approach to the subject did not provide a sufficient test of the validity of that
framework. In addition, a decade of additional data seemed to be reaffirming my initial
doubts about the framework’s validity. (Bergesen and Bata 2002, Sobocinski 2003,
Sobocinski 2000)
This thesis begins with an overview of some relevant literature and concepts that
provide the underlying basis for the new research I performed, followed by an
explanation of the research itself, and how I interpret the results. The primary research
task involved the use of World Bank data to re-examine the kind of analysis performed
by Bergesen and Bata, with a more comprehensive set of data and a new organization of
that data into a stratified ranking system.

Specifically, composite data on national

economic trends from 1960 to 2010 were assessed in a manner similar to that performed
by Bergesen and Bata, but refined by my use of a percentile-ranking technique which I

2

had initially employed to model urban stratification trends. I use the data to test my own
idea from 2003 that a global stratification structure does exist, and persists over time, but
that it tends to be limited to the testable hypothesis that “core areas” have maintained a
fairly stable proportion of the world’s population in recent decades, while all of
Wallerstein’s other classification categories have shifted markedly.

Despite the

constancy of the core, the historical decline in “peripheral” areas provides a strong reason
for reassessing the relevance of Wallerstein’s framework for today’s global conditions. I
now consider mainstream texts in economic development and economic geography (e.g.
Perkins 2006, Dicken 2015) and even the classic “modernization” theory of Rostow to be
a more accurate summary of the trends observed in the data. However, conceiving of a
fairly stable portion of the world either as a “core” or as the richest and most
economically central countries, is still a very useful concept consistent with both
perspectives. The weakness of the world-systems framework is the impressive extent and
variety of economic growth that has occurred in all regions of the world, including parts
of Sub-Saharan Africa which had previously been Wallerstein’s strongest argument for
perceiving international exploitation (also described in Radelet 2010). In my research, I
confirmed that certain nations have modernized and joined “the core,” but that this seems
to include only a limited percentage of the world’s population, while other countries have
run into at least temporary limits in their developmental progress and, although no longer
“peripheral,” so appear to be stuck for the foreseeable future in a partially developed, or
“semi-peripheral” status. Simultaneously, many areas that Wallerstein (1983) considered
essentially frozen by exploitation into a permanent peripheral status have turned out not
to be, even though many do still remain ranked near the bottom of an expanding system.

3

One of the largest shortcomings of the type of analysis I (and others) have
performed is that the chosen data deal only with national-level data, even though
Wallerstein himself had asserted that a world-systems analysis should not use such a
crude level of analysis (e.g. Wallerstein 2004), since core areas are more properly
identified as specific production centers that tend to encompass only certain parts of most
countries. National-level data tends to be used as a kind of “sample of convenience,”
even though the amount of information, and the ability to process it, has now reached a
level that should allow researchers to progress beyond it, into greater levels of subnational detail. Therefore, after replicating (with refinements) the kind of national-level
analysis already performed by these researchers, this paper will proceed to explore the
use of other patterns in national-level data that, while still conveniently accessible from
sources such as the World Bank, could help to add nuance and greater explanatory
potential to this type of analysis. The result might lead to improvements in the theoretical
understanding of global inequality and development patterns over time. In addition, since
I and many other researchers had originally been inspired by a world-systems conceptual
framework, some comments will be added about the extent to which the data supports the
continued use of that framework, or might be more successfully described through
competing theories such as modernization theory and mainstream development
economics.

4

CHAPTER 1: CONCEPTUAL BACKGROUND FOR THIS RESEARCH

In 2000, I had authored a paper for my master’s research in urban and regional
planning, which contained several components that greatly illuminate the ideas I later
presented about the structure of global inequality in my 2003 article. Although my
master’s research had been focused upon the analysis of local patterns of residential
stratification in American urban areas, in my 2003 article, I recognized that some of the
techniques I had developed might also be applied to an assessment of global trends. The
following parallels were drawn between the comparative assessment of urban
neighborhoods, in my 2000 research paper, and the comparative assessment of national
economic indicators, as described in my 2003 article:
(1) Many social scientists find themselves having to deal with limited amounts of
time, funding, quality data sources, and technical knowledge with which to analyze that
data, and that it is therefore useful to develop new techniques for processing and
interpreting existing data from readily available sources, such as the U.S. Census Bureau
or the World Bank.
(2) Both local neighborhood data (by census tract, block group, etc.) and national
economic data share in common that they are, in essence, geographic units, and therefore
should be examined in a manner that recognized and makes use of relevant geographic
principles, including spatial variations within the pre-defined areas being used, contiguity
and proximity between areas that may exhibit “spatial autocorrelation,” and the use of
boundaries that may not match well with actual development patterns that would be
observed through field research.

5

(3) Since economic stratification can be assessed spatially, at both a local and a
global level of analysis, some of the techniques that were found to be useful for assessing
local stratification patterns within U.S. cities might also prove useful for assessing
international stratification patterns within a global economic system. Specifically, I
proposed the analytic technique of sorting spatial areas into a hierarchy that is ranked by
the economic indicator for that area, weighted by the area’s population, and then
“stacked” so that each area’s population occupies a range of percentiles within the overall
structure of interest (either a metropolitan area or a world-economy).
(4) My 2000 paper described various advantages as well as shortcomings of the
approach described in (3), but I proposed various ways in which these shortcomings
could be reduced by the consideration of selected additional variables that are known to
be associated with spatial arrangements within the unit of geographic analysis. For the
economic analysis of pre-defined census areas within the United States, these included a
consideration of housing types (group quarters, renter-occupied housing units, and
owner-occupied housing units), such that information that was known to be associated
with different housing types could be used to distinguish portions of the pre-defined
geographic area that have housing of that type. For the analysis of inequality and
development trends at a global level, distinctions could include the consideration of each
country’s urbanized population proportions, the ratio of a country’s government size to
its national production levels, the proportion of a country’s production that occurs in
sectors known to be higher-profit, and the use of available poverty and inequality
indicators (e.g. Gini). These types of indicators are readily available or derivable, and
although they are most convenient (and globally comparable) at a national level, they

6

may be creatively used to make estimates about sub-national characteristics that, in the
current case, could help to more precisely define Wallerstein’s “core areas” in subnational terms and thus provide a more robust test of the relevance of world-systems
theory for the most recent trends in global inequality.
The article by Bergesen and Bata had presented national-level economic
information in a form that allowed global stratification patterns to be assessed and
tracked longitudinally, albeit in a rather simplified form that focuses on a bifurcation of
nations into “core” and “non-core” categories.

This current thesis proposes and

illustrates several means by which that binary classification could be improved upon.
The work of Bergesen and Bata had only assessed trends through 1990, and also hadn’t
included a consideration of structural mobility for countries within the world-system.
Instead, they had simply classified various countries at the outset as either “core” or
“non-core,” then pooled selected data for these two categories and tracked the selected
indicators over time.

Rapidly developing places such as Greece, Hong Kong, and

Portugal were simply classified as “Non-Core” without recognizing that their status may
have changed over the course of the 25 years from 1965 to 1990. Bergesen and Bata also
did not include all of the world’s countries, but only 72 of them for which certain types of
data were available. They also did not weight the national-level data by population size
(national data for tiny countries such as Seychelles was averaged in with national data for
huge countries such as China and India), a choice that would certainly have affected
some of the conclusions that they drew from their research. Certain aspects of their work
were not adequately explained, such as the criteria used to categorize the 72 countries,
did not specify the adjustments (or lack thereof) for purchasing power or the

7

standardization of economic values across time, or how certain smoothing techniques or
interpolations were performed in the construction of one of their key graphs.
These two main sources, my own earlier work and that of Bergeson and Bata,
share in common the use of national level data for a longitudinal analysis of global
stratification, using the framework and categories of world system analysis as a
theoretical guide. We both saw the modern world system as an actual system in which
various forces are exerted by innumerable agents in all parts of the system, which on
balance achieve a kind of structure, temporary equilibria (in terms measurable by
socioeconomic indicators), and a kind of geopolitical momentum that seems to offer a
kind of predictability. One of the main appeals of Wallerstein’s world system theory is
the testable premise that an expanding economic system had become dominant in
northwestern Europe and proceeded over centuries to eventually encompass the entire
human world. By combining and building upon these two basic source materials, this
research paper will consider all of the world system categories (core, semi-periphery,
periphery, and “external arenas”), and their relative proportions at a given moment
according to size of the populations living within each type of area, rather than mere
counts of the number of countries so classified. This perspective will be presented in
terms of the calculated cumulative percentile values of particular countries when they are
ranked in order of their per capita Gross Domestic Products. This research will consider
aspects of geographic proximity and world regional groupings (defined by economic
similarities, historical relationships, and contemporaneous geopolitical relations), and
will include a consideration of ways that the data and its patterns could be interpreted
from different theoretical perspectives.

8

CHAPTER 2: THE INITIAL PHASE OF EXPLORATORY RESEARCH

The enormous complexity of the topic of global stratification trends over the past
fifty years quickly became clear.

There are many ways to potentially measure

developmental trends (or stagnation) throughout the world, and some simplifying
assumptions were necessary to make the current work feasible within the timeframe that
was available to accomplish it. The main simplification of this research paper is its
almost exclusive reliance upon PCGDP (PPP). Admittedly, this involves the acceptance
of the weaknesses, shortcomings, and limitations of this indicator, yet PCGDP is still
correlated with many other indicators that are available (e.g. life expectancy, literacy
rates, etc.). No assumption is here being made about any causal relationships that may or
may not underlie these correlations (Sen 1999: 3-6, Stiglitz et al. 2010).
A key question involves the adequacy of economic adjustments used to
standardize PCGDP figures over time, so that they may reliably be expressed in terms of
constant U.S. currency. Although it would not be valid to say that the same monetary
valuation could gain access to the same amount of goods in 1960 as in 2000, due to
enormous changes in the technology and supplies (or scarcity) of various goods over that
time frame (e.g. Dicken 1992: 110-118), there is nevertheless value for this research in
crude PCGDP indicators because the consideration of changes in the overall distribution
of this measure across the whole world, over time, is meant to be indicative of the social
and political relations of wealth and poverty. Wealth and poverty tend to be defined
differently across time and space, relative to particular historical and geographic frames
of reference. Persons now defined as poor when compared to modern development

9

standards would not necessarily be so labeled if local or historical standards were instead
being used (Escobar 2012, for example, describes the social construction of these
concepts). However, disparities of power tend to be connected with the sheer economic
might that a country (or alliance of countries) can bring to bear in promoting their own
cultural and lifestyle preferences, needs, and desires. Social stratification, whether at a
community level (e.g. the classic “Middletown” study) or at a global level (world systems
analysis) is concerned primarily with the distribution of goods and power across the
population, rather than with trying to define absolute measures to document a sense of
historical progress. (An overview of world stratification concepts can be found in Kerbo
1991: 494-523.)
Still, the general correlation between PCGDP and other indicators, as well as the
significance of weighting this national indicator by population size and taking a global
perspective (Ferreira and Ravallion, 2008: 2-13) means that the measure does have value
as an indicator.

In addition, the idea of the indicator also allowing some level of

comparison across time is not totally without merit.

For example, when various

indicators such as urbanization, fertility rates, life expectancy, infant mortality, and
PCGDP are compared for different countries and regions over time, a general pattern is
discernable in which the average levels of these measures of health and prosperity in the
richest countries of 50 years ago are now being widely observed in “developing”
countries throughout the world. For example, the fertility rate in more developed regions
was 2.8 in 1950 and this rates was reached (on average) in less developed regions in the
early 2000s (Bloom, 2011: 563). Similar patterns are seen for life expectancy, under-five
mortality rates, and urban share of the population. Thus, although even the richest

10

persons and organizations in 1960 could not have used their wealth to buy access to
today’s technological wonders that didn’t yet exist at that time, when it comes to some of
the basic quality of life indicators (such as literacy or mortality), a comparison can be
plausibly made—the fact that the PCGDP of the United States in the early 1960s is
approximately the same as that for South Korea in the last 5 years (see Appendix I for
these figures) does not mean that different conditions and inequalities within these
countries at those times must be overlooked, but does turn out to be strongly suggestive
of the overall health and living conditions in the two countries. It is partially on this basis
a case can be made that countries like South Korea have changed their position in the
world stratification system over time. South Korea today is in many ways comparable to
various rich “core” countries, either as they were in the 1960s (in the case of the United
States, one of the richest of the rich) or of some parts of the European Union today (such
as Spain). But when “core” status is proposed for this country, later in this research, it is
not based purely upon PCGDP. Consideration has also been given to the development of
large global corporations by South Korea (e.g. Hyundai), its expansion of high-level trade
with other core areas, and its established place within a larger geopolitical context (e.g. a
vital ally of the other rich countries in maintaining a highly militarized border with a
highly incompatible and sometimes belligerent nearby areas, for nearly 60 years, as
summarized in Jones 2001: 491). Issues of economic development cannot be separated
from issues of security, power, and force in the international arena (Russett and Starr
1985), nor from geographic differences involving natural resources, climate, wildlife and
pests, and soil productivity, among others (Harrison 1984, Diamond 1999), but these
factors add too many complexities for this research paper, and have been generally

11

handled through a recognition of general regional patterns and connections between
countries throughout the world, which themselves are strongly connoted by Wallerstein’s
core, semi-periphery, and periphery categories of classification. (Appendix VII makes
use of such geographic groupings in this research paper.)
Although the first part of the research, by using PCGDP as a purely national
indicator, simplifies stratification by ignoring differences within countries, later parts of
this research propose and illustrate some ways to potentially correct for that limitation.
The convenient availability of national data is a strong advantage that may facilitate
lower-cost, more timely analysis by an individual researcher, especially with the more
powerful computing devices and software of today, as opposed to the entire teams of
specialists that are necessary for creating specific development policies (Escobar 2012).
The type of research performed here is not offered as a replacement for the extensive and
complex techniques usable by those with sufficient time, resources, technical knowledge,
and quality data, but is instead offered as a convenient, low-cost indicator that may prove
useful in illuminating certain overarching patterns in recent historical conditions.
One of the interesting aspects of considering the geographic aspects of global
wealth and development patterns stems from the nature of the political divisions that have
been in place, and how the organization of human affairs at a national level has shaped
the way that things have changed over time. One aspect of this clearly involves foreign
policy and applications of force (including overt military threats and strikes) over time.
Even though transportation costs have gone down over time, at least when trade and
travel is able to proceed smoothly (Allen 2011: 57-58), military security is one of the
considerations in which enemy locations and proximity has tended to matter a great deal.

12

Part of the focus in this research paper has been to keep the role of geography under
consideration in the regional and international trends that the data reveals—not only the
fact that extractable natural resources may favor certain countries over others, but the
extent to which some countries and areas have had to adapt their policies and activities to
face actual problems of internal or international conflict, or the extent to which historic
proximities may have favored the establishment of early trading, resource, or power
advantages whose effects still have momentum in terms of the inequalities and trends of
today and the near future. Although it is not claimed that such things have a universal or
readily predictable and clear-cut effect, it is strongly suggested that certain patterns
become clearer when viewed from a perspective of world regional geography (as
traditionally presented in such texts as that of Salter, et al, in which certain culture areas
can readily be identified by various features and shared history, and then analyzed).
If a global economic “core” is at first crudely sought and defined in terms of
entire countries, then the problem of defining and locating core areas seems to become
fairly straightforward.

For example, there is widespread agreement that rich North

America, Western Europe, and some countries of the Pacific Rim are “core” areas.
Wallerstein has long proposed that the start of an expanding capitalist world system was
rooted in the Netherlands (Wallerstein 2004: 57), an idea that is defensible in terms of
general economic histories as well, due to their central historical role in investment and
banking innovations (e.g. Gordon 2005, Heilbroner and Milberg 2011).

Although

Wallerstein had defined the core in terms of such things as concentrated banking power
that allowed for the continual reinvestment that is one of capitalism’s defining features,
there are many correlated aspects of capital concentration and nodal connectedness that

13

could similarly be used to indicate an area’s status in terms of a global economic system.
One productive means of observing this, for example, has been to evaluate cities in terms
of a global hierarchy (Knox and Taylor 1995). Today, when the richest countries are
highly urbanized, and metropolitan areas often stretch across each other, there are many
cases in which it does not seem too much of a stretch to classify entire nations in terms of
the defining characteristics of the most productive sectors of their economies.
We would expect the core countries to be the most productive (in terms of the
standard productivity measures preferred by the capitalist system itself, such as Gross
National Product or Gross Domestic Product), to tend to be highly urbanized, contain
clear concentrations of wealth, and to exhibit this wealth in many ways, such as higher
standards of health, higher average incomes, the formation of major new industries or
corporate centers of operation, and a pattern of highly influential research and
technological breakthroughs (which often stem from large-budgeted research universities,
correlated with high levels of literacy and education). Although Human Development
Indicators were eventually developed to more conveniently summarize some of the
aspects of life quality that go beyond (or are imperfectly correlated with) mere production
quantity, nevertheless there is still a significant correlation between all of these indicators
and the country’s level of production. In this research, this connection is used to justify
the simplification of using per capita Gross Domestic Product as a convenient general
indicator of a country’s standing within a world economic system. The fact that GDP
may be considered a purely artificial measure created by that economic system should
probably not be considered a large issue for the purpose of this research, which mainly
asks the question of whether production has indeed expanded over time in the manner

14

that proponents of capitalism (and traditional development paradigms) have claimed that
it would. There is widespread agreement that global production per person has indeed
expanded greatly over time (supported by data to be presented later in this paper), but
there is much disagreement about why certain regions of the world have seen markedly
different rates of economic expansion, or have even seen declines if measured in real per
capita terms.
Therefore, this research actually started with some exploratory research about
general groupings of countries that form major economic regions. If framed in terms of
countries alone, whose borders may sometimes seem to have had arbitrary locations and
who differ radically in size, power, and even autonomy, one might easily miss seeing the
forest through the trees. The starting point of this research, therefore, was rooted in the
recognition of patterns of similarity within certain regions of the world, and profound
differences between these regions. If regions are composed of a collection of countries
that have similarities in their economic status and the types of connections between them
(e.g. trade relations), then there is an immediate plausibility for the classification of
countries or entire regions within different classifications such as those used in world
systems analysis: core, semi-periphery, periphery, and external areas. In my 2003 article,
I stated a generally accepted idea that there really are no substantially populated areas
that are external to the world system anymore. More controversially, however, I also
claimed that the proportion of the world in countries with peripheral status had been
declining greatly over time, while the proportion in core countries had remained
relatively stable.

This was interpreted as lending support to the hypothesis that

15

international class conflict would grow in importance, if inequalities between the world’s
many different states (weighted by population) continued to shrink.
These claims needed to be formalized, replicated, and expanded to include more
data than the few decades that had been referred to in my article. Some exploratory
research therefore involved an examination of the less formally processed information
that had underlain my earlier research, followed by the current main research project that
examines a large-scale set of national indicators over the course of 50 years, from 1960 to
2010.
Several sources of convenient information from Almanacs and the CIA World
Fact Book web site had initially been compared to verify that the ways in which world
regions had traditionally been defined were also matching fairly well with an economic
grouping for the purpose of examining a global hierarchy of nations. Numerous sources
have already presented these basic facts in regional terms (e.g. deSouza and Stutz 1994,
deSouza 1990, Salter et al. 1998). However, economically-defined regions in a dynamic
world system must not be assumed to be fixed and unchanging. Table 1 illustrates the
results of several regional groupings of countries over time, based upon these casual
convenient information sources (World Almanac, CIA World Fact Book), but dates are
approximate because these sources often employed estimates or official statistics from
different years, according to what was available at the time of publication or web-posting.
Moreover, in this exploratory stage, various different regional grouping were used,
according to what best seemed to fit the historical alliances, trading partners, and
production level similarities existed within that region.

As the European Union

expanded, various island nations became wealthy, and other changes were noted, it

16

seemed plausible to classify such nations into the region in which they seemed best
suited, so long as it resulted in contiguous groupings of proximate and comparable
countries. For example, a tourist island nation in the Caribbean could be classified with
the south in a year in which its production level was low, and with wealthy North
American when it was high. The purpose was to get a feel for the kinds of annual
fluctuations that were common, and to work out the most logical regional groupings to
illustrate commonalities within a region and differences between regions—in other
words, regional categories based upon global stratification principles.
Table 1: Initial Exploratory Groupings and Comparisons, by Region
Region
North America
Western Europe
Pacific & E. Asia
(Pacific Rim)
Eastern Europe
Latin America
W. Asia, N. Africa
Southeast Asia
Communist Asia
(Chinese Realm)
South Asia
Sub-Sah. Africa
World

2001 World
Almanac
313 million pop.
$29,000 PCDDP
390 million pop.
$21,000 PCGDP
226 million pop.
$19,000 PCGDP
338 million pop.
$4,000 PCGDP
514 million pop.
$6,000 PCGDP
437 million pop.
$5,000 PCGDP
394 million pop.
$4,000 PCGDP
1.432 billion pop.
$3,000 PCGDP
1.357 billion pop.
$2,000 PCGDP
697 million pop.
$1,000 PCGDP
6.198 billion pop.
$6,000 PCGDP

2002 World
Almanac
312 million pop.
$32,051 PCGDP
391 million pop.
$21,331 PCGDP
227 million pop.
$19,489 PCGDP
484 m (incl. CIS)
$4,658 PCGDP
519 million pop.
$6,487 PCGDP
483 million pop.
$3,446 PCGDP
671 million pop.
$3,008 PCGDP
1.298 billion pop.
$3,731 PCGDP
1.077 billion pop.
$1,751 PCGDP
(calculation
missing)

2003 CIA web site
(World Fact Book)
331 million pop.
$36,115 PCGDP
467 million pop.
$23,055 PCGDP
231 million pop.
$24,011 PCGDP
337 million pop.
$6,652 PCGDP
539 million pop.
$7,074 PCGDP
430 million pop.
$5,447 PCGDP
555 million pop.
$3,861 PCGDP
1.320 billion pop.
$4,463 PCGDP
1.388 billion pop.
$2,402 PCGDP
701 million pop.
$1,661 PCGDP
6.299 billion pop.
$7,811 PCGDP

2008 CIA web site
(World Fact Book)
928 m. (W. Hemis)
$23,953 PCGDP
808 m. (N Eurasia)
$23,933 PCGDP
257 million pop.
$28,785
Included with W.
European region
Included with N.
American region
448 million pop.
$10,036 PCGDP
591 million pop.
$4,751 PCGDP
1.346 billion pop.
$6,208 PCGDP
1.578 billion pop.
$2,668 PCGDP
827 million pop.
$2,148 PCGDP
6.790 billion pop.
$10,500 PCGDP

This exercise provided an idea of the approximate (and changing) boundaries
between regions over time, as certain countries changed status and were more reasonably
classified as part of a different region. This could either involve economic advances or

17

political changes, such as the creation of a new country (e.g East Timor) or geopolitical
changes. For example, the 2002 groupings included Pakistan and Afghanistan with the
Arabic countries located to their west, in view of the active coordination of the United
States with both countries, stemming from the terrorist events of September 11, 2001,
while growing discrepancies between PCGDP in India and Bangladesh led to an
experimental classification of the latter as more akin to poor Myanmar to its east. Again,
the point of this exploration was not yet to make a clear comparison over time, but to
better explore the variability of economic and geopolitical factors that would cause
instability in any attempted classification system.
Only a fraction of the information I studied has been presented in Table 1.
Almanac information was considered (and often mapped out) dating back to 1989. One
of the difficulties was a general trend toward the use of data that was adjusted for
purchasing power parity (PPP), an adjustment that seems to better emphasize the
international differences in production levels. Earlier data has not been presented here
when it did not employ the PPP adjustments, so that the elements provided in Table 1 can
more directly be compared with each other. Moreover, most years’ data was only
informally examined for trends at first, with only a few years further processed to
produce the regional summaries just listed. These sources often official national data
with estimates from different years and thus often did not merit a more precise
calculation effort. These weaknesses will be addressed through the formal new research
described in the next sections. Another main benefit from this informal comparison was
to confirm that various proposed trends were indeed being supported by the data—global
production, as well as the per capita Gross Domestic Product (PCGDP) within various

18

regions were definitely expanding at a rate that tended to exceed inflation (since these
were American sources, all values were presented in terms of American dollars as valued
at their time of publication). The formal new research that follows expresses its values in
terms of constant (real) American dollars, and thus allows for defensible rather than
informal comparisons.
Corresponding to the informal mapping and calculations just described, this
Almanac and web site data from various years was also ranked by PCGDP and examined
with respect to the proportion of the world’s population that fell into various income
levels. Together, this exploratory research formed the basis for my 2003 article and the
hypotheses proposed within it. Two of these hypotheses will now be tested: (1) that a
consistent proportion of the world’s population lives in countries that are plausibly
classified as within “core areas,” and (2) that economic changes over time have resulted
in a reduced number of persons living in peripheral areas, as those in semi-peripheral
areas expands. The following additional topics will also be evaluated: (3) whether
development trends in the past 50 years support the general principle of economic
development, as classically defined in terms of expanded production and wealth, (4) the
extent to which certain geographic regions have been favored with greater or lesser rates
of economic growth or decline, (5) whether any such regions are plausibly explainable in
terms of basic geographic principles, such as the proximity of developing areas to
existing wealthy ones, and finally (6) the extent to which hypothesis number 1 may be
connected with topic number 5.

19

CHAPTER 3: THE NEW RESEARCH DATA AND ITS PROCESSING

Two main sources of data were used for the truly new portion of this research
paper.

One was the World Bank’s World Databank web site, located at

http://databank.worldbank.org/ddp/home.do?Step=1&id=4, which offered data PCGNP
data going back to 1960, conveniently downloadable into spreadsheets for handy
processing. This information was used for a longitudinal analysis of global stratification
and development trends. The other source was the CIA World Factbook, located at
https://www.cia.gov/library/publications/the-world-factbook/geos/xx.html,

which

was

used for a detailed examination of the current state of the world. As part of a more
detailed assessment of the role of China, 2010 information was obtained from The
Economist web site at http://www.economist.com/content/all_parities_china, which
presented basic population and economic information for all of China’s major internal
administrative divisions.
The World Bank data was downloaded for every country and for every individual
year from 1960 to 2010.

The data selected was in a form that had already been

standardized by the World Bank so that all values were expressed in terms of constant
2000 U.S. dollars. Unfortunately, many early years had a great number of missing
values, which were later imputed with the relative values for the respective regions in
which those countries were located.
For ease of analysis and presentation, it was decided not to process all of the data
for every year in its raw form. Instead, a first step involved the combinations of groups
of years so that the mean values for blocks of approximately 5 years were used to

20

represent those periods of time. This was considered to be not only a convenient way of
reducing the amount of data processing necessary to test the two main hypotheses, but
also to be justifiable so that the periodic recessions and fluctuations in commodity prices
(e.g. Moyo 2010: 19) would not be mistaken for a typical or long-term level of
production value in countries which suffer from those challenges. Appendix I, at the end
of this paper, presents the World Bank data after this initial processing.
Although various shortcomings and limitations of the GDP measure are now wellknown (e.g. Stiglitz et al 2010), it was considered acceptable as a starting point since
what is most important from a world system approach is not a precise indicator of relative
living standards for the common people, as alternative national measures such as the
Human Development Index endeavored to come closer to (Payne and Phillips 2010: 123124), but tends to indicate the extent of commodification that has taken place within a
country that is formally recognized by a world-economy. In other words, much of the
exchange in traditional and indigenous economic systems involves informal networks,
barter, and other economic behavior that can escape the monitoring of states and largescale institutions (Wallerstein 1983: 27). Despite any invalidities in the measure itself for
various other purposes, the PCGDP value should be well indicative of the status of a
country within the capitalist world-system that is now in place (Hopkins and Wallerstein
1996: 3).
This World Bank data had a large amount of economic information missing from
the early years for many countries, including very important ones like the Soviet Union
and Germany. However, it did include more general information about larger regions,
such as “Europe and Central Asia,” that would usually be expected to match up quite well

21

with the largest countries in those regions. These regional values were thus used as the
basis for data imputation where important major countries were missing from particular
years (and groupings of years). Some very small countries, territories, and dependencies
were removed from this data entirely, when neither population nor economic data was
provided for a particular year.

Although this missing data certainly weakened the

precision of the analysis, it was not considered so severe a problem as to prevent general
development trends from being evaluated.
Further processing of the World Bank data also took place entirely in
spreadsheets, and involved (1) the separation of regional and composite data from the
listing for individual countries, so as to avoid a double counting problem, and to provide
a means for double checking the separate lists through a comparison of column totals, (2)
removal of a few minor territories for which imputation was not considered worthwhile,
(3) the imputation of missing economic data, using the value from its respective region of
the world (all necessary population data and regional economic data was present in the
data set), (4) sorting the data so that it was in descending rank-ordered by each country’s
PCGDP, and (5) adding columns that tracked the cumulative population of the ranked
countries, and the percentiles that correspond to the cumulative population.
The result of this processing is presented in Appendix II at the end of this paper.
It can be seen that the column labeled “percentile” denotes the approximate position of
the upper case within each country’s population, so that as one peruses Table 4 from the
bottom up, the poorest countries in terms of PCGDP are at the bottom, and the
cumulative percentage of all persons within those countries are denoted as one proceeds
up the table to the richest country, which is denoted as being at the 100th percentile. Due

22

to rounding, many small-sized countries are classified as having the same percentile
value.

It is obvious that this ranking procedure ignores internal differences within

countries, but since a large diverse country like the U.S. is widely regarded as the modern
standard by which all other countries’ production levels are judged, the fact that it
contains non-productive areas within it can at first be ignored, until the methodological
basis for this comparison is understood.

In the discussion section of this paper,

possibilities will be presented for considering sub-national indicators within national
level data, for a more sophisticated analysis. At this point, the basic premise is that the
U.S. is widely considered to be a core area whenever information at the national level is
considered, regardless of the fact that inequality, poverty, and other indicators of noncore areas within the country are widely known to exist (Pieterse 2010: 28).

For

example, a 1975 map showed that infant mortality levels within various sections of
Detroit were comparable to some third world countries of the time (Ley 1983: 336).
At the end of Appendix II, there appear two informational summaries that provide
data by world region and by World Bank income level categories, in the same format as
the data had appeared for the individual country listings. Differences between the totals
for these three parts of the Appendix were the result of small territories and dependencies
being removed from the national data list, but also from the fact that Taiwan was missing
from the World Bank data. The table of world regions was the source from which the
imputed economic data was taken and applied to individual countries with missing data.
The income level classifications were included at the bottom of the listings, not
because they are considered particularly useful, but to demonstrate the problem of
categorizing countries in terms of a static method. The World Bank data had evidently

23

counted China as an “upper middle income” country, based upon its current status in
2012. But when studying 1960 data, this classification is utterly contradicted by the fact
that China at that time was the poorest country for which data was available. Thus,
although the exploratory research described previously may justly be criticized as being
too disorganized and informal, part of its value was to find ways to try to avoid problems
involving ill-fitting classifications of the type illustrated in this instance.
The schedule in which this research had to be conducted did not allow for the
processing of all 10 time periods listed in Appendix I, so it was decided to compare the
earliest period (1960-1965) with the latest period (2005-2010) and one intermediate
period (1986-1990). The choice of intermediate period was decided in part by the greater
availability of information for that time-frame, and thus, a lessened amount of data
imputation. Appendix III provides the final results of the data processing for the period
of 1986-1990, and Appendix IV provides the results for the period of 2005-2010.
Information from the CIA World Factbook web site was copied and entered into a
spreadsheet, and the processed version of this data is provided in Appendix V. Although
the CIA site did not offer a convenient historical database, it offered the benefit of
containing almost a complete set of economic information for all current countries,
including Taiwan. This stems from the source being willing to provide current estimates
for each country, all of which were presented in 2011 U.S. dollars (and reflected
purchasing power parity). A comparison of the two lists not only indicates the kind of
discrepancies that can arise when comparing data from different sources, but also the fact
that some countries’ production value varies widely from year to year. For example,
Liechtenstein was at the top of the CIA listing, with PCGDP of $141,100 in 2011. The

24

five-year average from the World Bank period of 2006-2010 provides a more modest
figure of $81,855. Nevertheless, the correspondence between the percentiles assigned to
the countries in both lists is quite strong, using the ranking technique I had developed.
For example, if all the top countries in the CIA list (those at or above the label stating
“Core?” in the far right column) are compared with their corresponding listings in the
most recent World Bank spreadsheet, the biggest differences stem from the few cases in
which data was imputed. Of the 40 top-ranked countries in the CIA list that also appear
in the World Bank list, only one very minor discrepancy would result from using these
two lists to define the core countries, which is that Antigua and Barbuda appears a bit
lower in the CIA listings and thus didn’t make its top 40. As will be described later, it is
the weight of population, rather than mere ranking of countries, that helps to stabilize
these lists. Small rich countries may have incomes that bounce up and down from year to
year, but they still tend to stay within the top percentiles of the world’s PCGDP. Less
wealthy countries that have similar variation still tend to stay within a semi-peripheral
realm of the global population percentiles—even more so when the 5-year averages are
used to represent the average within such fluctuations.

Such averages are readily

amenable to longitudinal tracking.
Appendix VI displays the CIA information when supplemented with sub-national
data about China, obtained from The Economist website. The Economist data was from
2010, and the CIA data I chose was from almost the same time (2011), so their totals for
China were very close to each other. Some adjustments were then made to scale the
respective populations slightly upward so that they were increased by the same
proportion that the CIA national data had increased over the course of the subsequent

25

year. Thus, the total 2011 population for China was divided by the 2010 population to
produce an adjustment factor of approximately 1.01776, which was then used as a factor
to multiply each Chinese sub-division’s population values, so that their sum would equal
the total national population for 2011. The total of the adjusted 2010 data then matched
the 2011 figures even more precisely, for the adjustment to population was the basis for
estimated GDP values (i.e. total rather than per capita), which are included in an
additional column that had not appeared in previous spreadsheets. The final data table in
Appendix VII displays the same information as Appendix V, but grouped by world
region and with the addition of this new GDP column, which allowed spreadsheet
calculations to be made for the various custom-defined world regions that were formed
by grouping economically and historically related countries. This process had been
referred to informally in the Exploratory Research section of this paper, but Appendix
VII explicitly lists which countries were included in particular regions. It will be argued
later in this paper that these regions shift over time so that certain semi-peripheral
countries can be considered to have joined one of the core regional areas that, although
expanding geographically, was expected in my 2003 hypothesis to include a fairly
constant proportion of the world’s population.

26

CHAPTER 4: RESULTS OF THIS RESEARCH

Page 19 of this paper had listed a number of hypotheses and topics that this
research was meant to address. The relevance of all the collected data (see Appendices)
for each of these topics will be described in this section. First was the hypothesis that a
consistent proportion of the world’s population lives within the core areas. Since it has
already been stated that a flexible definition of a core area is to be preferred to a fixed
definition (because of known changes in national characteristics over time), there is a real
danger of this question being addressed through circular reasoning. It had already been
described that world regions and core classification had been viewed not in terms of
purely objective characteristics completely derivable from this data set, but rather from a
broad historical and comparative consideration of the relative positions in a global
economic and geopolitical system. Appendix VII presents a good suggestion for what
this current classification (by global region) might be. It stemmed from the need to place
all countries within some regional classification, and each region so formed was assessed
in terms of its economic, political, and historical characteristics within the overall worldsystem. This aspect of the research will be described further in a moment, but it helps to
describe the basis by which certain suggested cut-points between the three basic
Wallerstein categories (core, semi-periphery, and periphery) have been noted in the
rightmost columns of the various other information tables (Appendices II through V).
The most economically important players within all of the core regions identified in
Appendix VII fall within approximately the top 15% of the ranked and populationweighting listing of countries, from both the CIA source (Appendix V) and the most

27

recent World Bank listings (Appendix IV). The listings in Appendix II and Appendix III
have similarly been marked with comparable cut-points that were not only consistent
with the top 15% figure, but also with the general geopolitical significance of these
players at the time—at least in the West. Although the comparison of data over time
provides some support for the hypothesis, this support is clearest only from the end of the
Cold War in the late 1980s. Prior to that, the data is not only quite problematic to
interpret, but the most significant economic and geopolitical players (as seen in Appendix
II) would have to include the Soviet Union, which would involve a much larger portion
of the population. It could easily be guessed that this is evidence that the capitalist
world-system was not quite fully global until the end of the Cold War drew near and that
although the proportion of the core within a capitalist world-system may very well have
included the same 15% proportion of that system, that this proportion was of a not-fully
world encompassing system, while a competing system was clearly in place. Thus, this
hypothesis is judged to have received qualified support from the data, but merits further
research to see whether it also held true during the Cold War period, which raises
additional questions about how the capitalist world-system is properly to be defined
during those times when it was not yet fully global. In tracing the history of the World
System, Wallerstein identified “external arenas” that were not yet part of the system (as
well as anti-systemic movements, a concept that can sometimes be used to describe
various states at certain times), and how these categories would be defined in terms of
this particular framework of simple economic data would clearly need to be worked out
(Wallerstein 1974, Wallerstein 1983). Moreover, Wallerstein has recognized the need to
reassess portions of his original framework over time, as additional information and

28

theoretical developments are considered by the social science community (Wallerstein
1999, Wallerstein 2011: xi-xvii).
The second hypothesis was that economic changes over time have resulted in a
reduced number of persons living in peripheral areas, with a simultaneous expansion of
the proportion in semi-peripheral areas. This idea is completely supported by the data for
1960 to 2010. Most prominent is the rise of China and, more recently, India. It is no
longer viable to consider China to be a peripheral country, as its 1960 ranking had clearly
classified it. India’s status transition is somewhat less clear. Large rural parts of both
countries clearly have the potential to still be called peripheral, but this argument could
be applied to many of the large core countries as well, and the distinction between core
and non-core becomes most clear when considering (1) the proportion of a country that
lives in rural areas, and (2) the quality of the lifestyle, connectedness, and income/health
characteristics of the average rural inhabitant when compared to the average urban
inhabitant. The rich countries generally have a smaller proportion of rural inhabitants,
and less urban/rural disparity in socio-economic indicators than is seen in developing
countries. One aspect of this can be seen in the correlation between PCGDP and the Gini
index of inequality, especially for large countries (Dollar 2004, Ferreira and Ravallion
2008; with disagreement from various other researchers who used different measures and
techniques, such as Bergesen and Bata 2002). The fact that China is but a single country
should not be a reason for dismissing the significance of its growth. Not only has it long
been the largest country in the world, but it is not alone. The research data show that
entire regions have seen increases in their status. Although China and India together
accounted for about 37% of global population (in all three of the World Bank data

29

periods shown in Appendices II through IV), the regional data shown in both the World
Bank categories as well as the Appendix VII data demonstrates that these development
trends are not at all limited to just a couple of countries, despite their great importance.
Rather than identifying which countries specifically have grown markedly, it is actually
easier to specify the few specific areas and countries that have not. These exceptions
tend to be found in Sub-Saharan Africa, and selected portions of Asia. Any other regions
of the world have only very small proportions of their populations that live in countries
that have not become markedly wealthier over the past 50 years. The hypothesis is
considered to be solidly confirmed, in that middle-income levels now predominate
globally, and the World’s per capita GDP is quickly approaching levels that were seen
only in the core areas back in 1960.
Additional topics had also been selected for examination, even though they were
not framed narrowly as testable hypotheses. One has already been generally addressed
under the question of the large shift in countries (and entire regions) from peripheral to
semi-peripheral status. This turned out to already provide an answer to the third question
that this research was to evaluate: whether development trends in the past 50 years had
generally supported the principle that economic development, as classically defined in
terms of expanded production and wealth, has indeed taken place very widely.
Greater analysis of the exception areas noted above was, in turn, the fourth topic
of this research, in which regional patterns and trends in development were examined.
The key data for this analysis appears in Appendix VII, although relevant information has
also been presented in Table 1 (the exploratory research) and at the end of the data lists in
Appendices II through IV. The World Bank’s own classifications and summaries are

30

provided in Table 2, which presents data selected from Appendices II through IV and
compares it with the new regional designations used in Appendix VII of this paper. It
must be noted that the World Bank trends in this table are the ones of greatest
importance, since all are presented in terms of constant U.S. dollars for the price levels of
the year 2000. CIA data in the right column are presented only for casual comparison,
since they use prices expressed in terms of 2011 U.S. dollars. Although not strictly
comparable, and using differently defined regions, the CIA data in the table is still
suggestive of continued improvements in most regions of the world. (NOTE: Two
custom-defined African regions were combined for consistent presentation in Table 2.)
Table 2: World Bank Data for Three Time Periods, By Region
Region
North America
Western Europe
Eastern
Europe
and Central Asia
Latin America
Pacific & E. Asia
(Pacific Rim)
Chinese Realm
Southeast Asia
W. Asia, N. Africa
South Asia
Sub-Sah. Africa
World

World Bank Data
1960-1965
207 million pop.
$14,337 PCDDP
688 million pop.
$4,681 PCGDP
(included in W.
Europe, above)
235 million pop.
$2,239 PCGDP
1.082 billion pop.
$1,140 PCGDP
(included in E.
Asia, above)
(included in E.
Asia, above)
104 million pop.
$812 PCGDP
594 million pop.
$173 PCGDP
245 million pop.
$449 PCGDP
3.164 billion pop.
$2,594 PCGDP

World Bank Data
1986-1990
272 million pop.
$26,452 PCGDP
833 million pop.
$8,961 PCGDP
(included in W.
Europe, above)
426 million pop.
$3,585 PCGDP
1.766 billion pop.
$3,004 PCGDP
(included in E.
Asia, above)
(included in E.
Asia, above)
239 million pop.
$2,409 PCGDP
1.075 billion pop.
$304 PCGDP
486 million pop.
$536 PCGDP
5.096 billion pop.
$4,423 PCGDP

World Bank Data
2005-2010
338 million pop.
$36,742 PCGDP
883 million pop.
$12,757 PCGDP
(included in W.
Europe, above)
576 million pop.
$4,828 PCGDP
2.173 billion pop.
$4,938 PCGDP
(included in E.
Asia, above)
(included in E.
Asia, above)
368 million pop.
$3,616 PCGDP
1.536 billion pop.
$683 PCGDP
701 million pop.
$621 PCGDP
6.687 billion pop.
$5,942 PCGDP

2011 CIA data
Custom-defined
351 million
$47,152 PCGDP
517 million pop.
$31,925 PCGDP
301 million pop.
$11,851 PCGDP
596 million pop.
$11,780 PCGDP
236 million pop.
$33,299 PCGDP
1.379 billion pop.
$8,496 PCGDP
567 million pop.
$5,984 PCGDP
470 million pop.
$11,178 PCGDP
1.694 billion pop.
$3,267 PCGDP
903 million pop.
$2,315 PCGDP
6.790 billion pop.
$10,500 PCGDP

Most notable within the table is the huge expansion of the East Asian production
figures in recent years. While the World Bank had combined rich and poor countries

31

within this region, growth is still notable from 1960 to the present, with the 2006-2010
average having growth to $4,938 in 2000 U.S. dollars. Although the 2011 dollars are not
strictly comparable (according to an online inflation calculator, a dollar in 2011 is worth
$1.31 in 2000 dollars: http://www.usinflationcalculator.com/) rough estimates suggest
that although Southeast Asia as a region may not be quite so impressive, the Chinese area
and rich Pacific Rim have boomed substantially. Splitting the World Bank region into
three smaller regions still reveals overall growth in all three.
The final research questions may be addressed together: whether regional
development disparities are plausibly explainable in terms of basic geographic principles,
and the extent to which this may be connected with the seemingly steady proportion of
the world’s population in core areas. For this question, the detailed data in Appendix VII
was compared with various exploratory findings, some of which were presented in Table
1. Recall that my 2003 article had proposed that the core expands geographically while
maintaining a fairly consistent share of the world’s population. This makes sense in
terms of the much lower population growth rates exhibited by the established rich “core”
countries, when compared to the relatively high growth rates in other (poorer) areas of
the world.

Mathematically, the only way that the core could maintain a constant

proportion is to expand at about the same rate as the world’s population, but since the
national growth rates do not achieve, this, new countries must therefore be classified as
part of the core, over time. The precise form this seems to have taken involves (1) the
expansion of rich nation free-trade areas, such as EFTA and the EU, and (2) the eventual
development of selected nations to a level that makes them economically comparable to

32

the established, traditional rich countries. Examples include numerous small nations,
plus a few medium-sized ones such as Taiwan and South Korea.
In demonstrating precisely which countries have been able to take advantage of
this type of “global structural mobility,” World Bank longitudinal data is essential (and
Appendix I provides a convenient format, without imputations, for tracking this and
while recognizing some of the data limitations). Taken region by region, then, the trends
affecting the expansion of the core have been as follows:
(1) In the Western Hemisphere, the two main core countries have long been the
United States and Canada, and these have mainly been joined by the rapid growth in tiny
island nations of the Caribbean, but as Appendix VII shows, this area taken alone is much
richer than other core areas ($47,152 per capita GDP in 2011 dollars, compared to the
low $30K values of Western Europe and the rich Pacific Rim.) If Mexico is included as
part of the North American core area, on the basis of its recent growth trends and the
establishment of NAFTA, then the North American core area average becomes a bit more
modest, closer to the level of the other ones. It may be possible to closely examine other
Western Hemisphere trade agreements and to pick and choose other areas that make the
region’s core PCGDP comparable to other core regions, but there are alternative ways of
looking at these regional relationships, so the main Western Hemisphere transition has
involved the elevation of the vast majority of non-rich countries (in 1960) to middleincome, semi-peripheral countries in 2011.
(2) In Europe, the clearest and most readily definable expansion of a core area has
been in evidence for several decades due to the rise of the European Community concept,
its implementation of a European Union and associated free trade areas and common

33

currency. Moreover, the transformation of the formidable Eastern Bloc and COMECON
relationships has opened up vast new regions for new trade, investment, labor migrations,
and geopolitical coordination. Bit by bit, Eastern Europe has become Western (so that
many newly democratic countries that used to be classified as East Europe are now more
properly considered as Central Europe), and this transition, which has been so important
for global security as the Cold War drew to a close and the Soviet Union was split into
the Commonwealth of Independent States (CIS), has systematically involved preferential
treatment, extended to country after country, causing the original European Community
participants to multiply into several dozen members of an actual union of currency,
migration, and trade (with some exceptions that do not nullify the basic premise). This
process promises to continue for some time, with the addition of Balkan states (such as
Croatia in 2013). Since 1960, when even major states such as Germany and Italy were
still recovering from the destruction of World War II, and still wrestling with internal
divisions (in Italy, a rich north versus a poor south; in Germany, the split until 1989 into a
capitalist West versus a totalitarian East, and a divided capital separated from the West).
Both of those problems were being healed by 1990, and today, although internal wealth
disparities still exist, aid and free trade has successfully been able to elevate some of the
historically poorer areas of the continent (e.g. Spain and Ireland) to a level of PCGDP
comparable to many of the original core nations, while steadily seeing a rise in the
incomes of eastern and even Balkan areas, from Estonia to Greece. The figures by 2010
are quite clear with regard to these countries. The poorest country in Europe is no longer
Albania, but Moldova (formerly the Moldavian S.S.R.), since of the 15 countries of the

34

CIS, only the 3 Baltic states have been readily integrated into the European Union. There
are political as well as economic reasons for this pattern. (Black 2005: 209-210, 235-247)
(3) In Asia and Oceania, the solidification of a rich fringe—first established by
wealthy British Commonwealth members such as Australia in coordination with the
United States’ massive military presence in the area as a result of World War II and its
already-established interests in its Pacific possessions and former colony of the
Philippines (Paterson et al. 1995, Schmitz 2007). Cold War policy soon mandated a
policy of containment that took place in the “far east” just as much as it had along the
“Iron Curtain” in Europe, and the reconstruction of Japan and many of its former
conquered areas allowed its politico-economic might to now be allied with the Western
Core rather than against it (Rourke, 1989). After revolution in China, war in the Korean
peninsula, and conflicts in the Indochina region, the development and strengthening of a
rich Pacific Rim alliance constituted an important part of foreign policy, later
complicated by the ambivalent status of the small but important Asian “tigers” and the
official return of the historic colonial centers of Hong Kong and Macao to Chinese rule,
albeit with special agreements in place to maintain their profitable functions (Roskin and
Berry, 1999). More discussion will be given to the transitional status of China and its
relationship to these two core regional areas as well as Taiwan and Singapore.

In

general, the core area shows signs of expanding from the mere fringe and into the heart of
the region’s inland territories itself, through substantial economic growth and wealth in
China’s coastal zones, the Malaysian-Singapore-Brunei area, and Thailand. Although the
various Pacific States have not seen growth to the extent that the Caribbean micro-states

35

have, they have been treated here (due to their small size and political history) as part of
the core Pacific area dominated by the United States and its current allies.
(4) The West Asian and North African region has also see a substantial boom in
development. Although this growth has long been dismissed as “merely” oil driven
prosperity, and long-standing claims and assumptions have held that this commodity
would merely be a temporary boon that could rapidly be depleted and thus restore the
region to poverty, this does not seem to be the case, as the most recent PCGDP figures
should demonstrate (Simon 1995: 22-26, 287-293). It is quite true that inequalities in this
region are starkly defined by the presence of small countries that appear (only at first
glance) as if they were carved out explicitly to make easier the exporting of their products
to the Western powers that temporarily had dominion there after World War I. Although
the actual history does not support such an ex-post facto interpretation, the current
geopolitical function of these states lends support to these relationships as a latent though
not a manifest function of the European subdivision of the Ottoman Empire and
subsequent maneuverings during and after World War II. (Hahn 2005, Axelrod 2009)
Moreover, it must be noted that many countries in this region are proximate to the
European Core, and event those who do not enjoy substantial oil riches often have
economic and migration networks that relate them economically to the European Union,
as well as some colonial connections maintained from the past.
(5)

The South Asian area remains predominantly peripheral, although large

sections of India have been able to achieve semi-peripheral status. A breakdown by the
sub-national states within India was considered for this research, but the internal diversity
between its states was not nearly as striking as that measured in China. Despite well-

36

heralded recent development efforts in provinces like Kerala, the traditional PCGDP
measurement finds that area to be quite undistinguished, with the richest (most globally
integrated) parts of the subcontinent’s economy instead seen in its north (e.g.
Chandigarh). (Economist n.d.)
(6) Sub-Saharan Africa had at first appeared almost as gloomy as had been
described by authors like Wallerstein (1983) and Moyo (2010), but an unexpected bright
spot seemed to appear in the most recent trends. In Appendix VII, this was identified as a
proposed new region that runs from the South African realm up the southwest coast of
the continent to include Equatorial Guinea. With the exception of small segment of
coastline held by Congo-Kinshasa (surrounded by lands under Angolan control), the
nations in this area are significantly above the averages for the traditional Sub-Saharan
region, and thus might eventually receive wider recognition as one of the region’s success
stories. Before this can occur, however, extreme levels of internal inequality would have
to be addressed. Namibia’s PCGDP looks far less impressive when it is revealed that the
country ranks first as having the highest Gini index in the entire world (CIA 2011).
Nevertheless, this is the most prominent advance in the continent in recent records,
although it is also noted that gradual increases in the West African area just north and
northwest of this area (e.g. Cameroon, Nigeria, Ghana) have also exhibited a quieter level
of growth that, due to the greater population and less unequal distributions of its
production wealth, may eventually become the more influential area of economic growth
advancement, and semi-peripheral production, trade relations, and investments.
Regarding the unique role of China as the most populous country, now grown to
have the second-largest economy in the world, an additional assessment was included in

37

which various subdivisions of the country were placed into the global analysis as if they
were separate countries. The sheer scale of China’s richest cities alone amounts to the
size of a major European county, and the richer coastal areas of China contain total
populations as large as the entire European Union! This information has been provided
in Appendix VI. Despite some fairly wealthy areas, and a few very rich cities such as
Hong Kong and Macao (which technically are Chinese, but previously would have best
been considered part of the rich “Pacific Rim” of capitalism, and “communist
containment”), the country’s richer areas do not yet quite reach the level of wealth that
normally designates core status. Some of them do seem comparable, however, to the
Central European countries that are currently being integrated into the European Union
core. Repeating this type of analysis in another ten years, if development patterns for that
area persist, could be very instructive. It is too early yet to tell whether much beyond the
richest of Chinese port cities could properly be considered part of the “core,” or whether
the country is generally too large to be incorporated, except perhaps for a few selected
provinces, into the core, proper (following my principle of a core that expands only
enough to make up the difference in growth rates between rich and poorer areas of the
world). Geopolitically, it seemed to make great sense for development and economic ties
to occur with China (since for example, the West had agreed to return the areas of Macao
and Hong Kong, and followed through peacefully on their promise). At the moment, an
expanding core appears more likely to select the most proximate areas accessible to the
historical core, for further expansion, such the Balkans, Mexico, Malaysia, etc. as these
regions show signs of continuing to develop. (Mexico, however, has become very large;

38

its incorporation into the core would likely mean substantial delays for other areas, if my
hypothesis holds true in the future.)
It is possible that the whole history of development since World War II could be
interpreted in terms of a juggling act, with some balls being kept in the air while others
are allowed to drop, in accordance with various geopolitical priorities as they change.
Working out the details of this ideas would require an extensive amount of additional
research, however, and it is possible that the idea is too simple and impractical to yield
much practical value, except perhaps to explain why some areas failed to develop, in
terms of global political patterns, rather than because of a compulsion toward pure greed
or hegemony on the part of the rich. (Levine, 1983: 31-46, 115-151, 341-352)

39

CHAPTER 5: DISCUSSION OF RESEARCH RESULTS

This research, although perhaps helpful as a general guide, should nevertheless be
interpreted as reinforcing the complexity of global trends in development, wealth, and
inequality. Many of the initial research questions had been inspired by critical theoretical
frameworks and commentators, as well as ambitious new historical interpretations, while
attempting to reconcile these views with more traditional (and specialized) paradigms
that have come out of fields such as developmental economics, international relations,
military history, economic geography, economic history, and foreign policy (e.g. Allen
2011, Arrighi 1994, Dicken 2007, Gillis et al 1996, Grandin 2010, Levine 1983: 379-393,
McMichael 2012). These initial intentions were far too ambitious, and it became clear
that the most that could be hoped for was an unsteady effort to balance the most critical
perspectives with the most traditional ones, through a comparison with actual historical
data sets. In general, a strong case can be made that traditional modernization theory has
proven correct within the terms that it established for itself, and when the geopolitical
imperatives of the last 50 or more years are taken into account. An examination of
specific cases, however, reveals that much of the critical ideas are also factually
supportable. In both cases, the overall context and perspective in which the facts exist
and are tied together become the means by which these subjects must be evaluated by
researchers, activists, and theorists, and yet the sheer number of variables and disciplines
that tie into the subjects can be quite overwhelming. It would probably take many years
for even teams of specialists to piece together all the available data and try to reconcile
disparate theoretical frameworks into a coherent whole, but more credit should probably

40

be given to the traditional approaches of development economics (as in Gillis et al. 1996),
which do show an honest (and highly sophisticated) effort to tie together environmental,
health, and equity issues into a coherent whole, and to make things actually happen in the
field. While such integration may be seen by some as mere cooption, critics should be
heartened to some extent by the effect they have actually had on policy. Although
environmental economics (e.g. Goodstein 1995) presents a series of plausible (on their
face) mechanisms by which pollution problems might be capped and ultimately reduced,
it does appear that these mechanisms would be too few and too late to prevent large-scale
changes over the next century (UNHDI Report 2011). On the other hand, critics should
not be too quick to downplay the substantial role that intra-national and international
conflict has played in disrupting actual development programs, through no intentions of
the assisting countries and agencies.
The challenge for each individual contemplating these matters may ultimately rest
in one’s choice of which level to focus upon at a given time and for a given purpose. It is
probably unfair, from the perspective of an overall understanding, for critics from the left
to consistently seek and publicize all flaws and inequalities within each country, and the
system as a whole, but a progressive philosophy really demands no less—in order to
make progress, one has to identify flaws and injustices and figure out how they arose. On
the other hand, the actual threats from nuclear proliferation, hostile states, and disruptive
terrorist attacks are very real, and should not be lightly dismissed. Enough failings have
been seen in recent U.S. history and policy that it cannot be assumed that Western
hegemony is all-powerful, or even capable of the kind of consistent domination that it is
sometimes accused of. On the other hand, an interesting idea worth considering is that,

41

regardless of their original origins or intentions, certain forms of alternative development
may end up being promoted (or tacitly accepted) for its possible latent functions of
slowing economic growth in areas of the world that the world-system is not yet able to
structurally accommodate. Each person and agency must struggle with the possibility
that certain outcomes may be beyond their control, or that in a complex system, there is
no “good” that cannot also be viewed as having a “bad” side to it, for someone,
somewhere. Nevertheless, it should be very clear that warfare has often been a great
inhibitor of economic growth, except perhaps in cases involving semi-peripheral
countries that throw their weight around and end up becoming accommodated into the
core in the end, as with Germany, Italy, Japan, and perhaps Russia in the future
(Sobocinski 2003). The sheer extent of warfare as a disruptor of development around the
world must be taken seriously, however (Kidron and Smith 1991: 12-15).

In an

unpublished research paper from 2013, I compared the effects of several types of crisis
upon national GDP growth, and found that civil war hindered growth more severely, on
average, than natural disasters, political coups, or humanitarian crises (Sobocinski 2014).
A final note of interest, with regard to the problems seen in Africa’s relative lack
of development success, may be to refer to the possibility of some form of racist effect,
even if only of an aversive (unintentional or passively neglectful) form. If the world is
truly becoming a gigantic system, in which international social classes and stratification
continue to develop, then the world must necessarily be viewed as a multicultural system.
Sociological research on race and ethnicity has shown that wherever substantial
inequalities of wealth and power exist between different racial-ethnic groups, recognized
and socially constructed as such, an ethnic hierarchy tends to develop. Such a hierarchy

42

might be perceived in the relatively low status of Sub-Saharan Africa, and the great
inequalities within it. (Marger 2012: 457-488)
On the other hand, purely economic and geographic explanations can also be
offered for disparities in different regions, with great plausibility (Allen 2011, Diamond
1999, Harrison 1984).

It is probable that the questions under consideration here

ultimately encompass the entirety of the social sciences.

43

CHAPTER 6: CONCLUSION

Wallerstein’s world-system framework has often been cited for its conceptually
appealing characterization of recent economic history in terms of an economic core
(which actually includes multiple geographic areas), expanding its wealth through its role
at the head of an expanding global capitalist system, in which semi-peripheral areas serve
a set of mediating functions with respect to less-developed peripheral areas, all of which
have particular functions within a system that has been characterized as fairly stable with
respect to the presence of these three fundamental types of areas.

My research

demonstrates a huge decline in the populations that live in peripheral areas during the
past half-century. Although this huge portion of the world’s population now lives in
countries with predominantly semi-peripheral characteristics, rather than reaching a state
of the highest economic development, the economic trends have nevertheless been most
consistent with the classic ideas of modernization theory—economic development—
rather than the fixed and exploitative relationship that has tended to characterize the
critical world-systems framework.

Moreover, as an increasing number of the most

critical regions within developing states, such as China, become similar in character to
core areas through the expansion of their middle classes and their most advanced cities,
the increasingly international nature of production suggests that stratification within these
countries may be gradually headed toward patterns observed historically in the core. The
most advanced areas in developing states have more recently been characterized within a
tiered world-city system rather than in the vaguer terms which Wallerstein had used
(Sassen 2006, Chen 2013: 288).

44

A truly surprising phenomenon has been the growth in China from the very
bottom of the 1960 rankings, to instead take up the bulk of the middle of the world’s
economic strata. Although modernization theory can offer explanations for this, starting
with the beneficial effect of Chinese reforms that began in the late 1970s, Wallerstein had
not foreseen such a shift, any more than Marx had foreseen that “communist” revolutions
would occur in poor countries rather than the most advanced ones. The current trends
suggest that the world-systems “periphery” might similarly disappear entirely, within a
matter of decades, unless the term comes to refer, equivocally, only to the poorest areas
within otherwise industrialized and even rich states. Such a shift would belie the fact
that, characteristic of industrial and post-industrial economies, the extractive economic
sectors which had served as a key, defining characteristic of peripheral areas, would
provide the livelihood of ever-smaller percentages of the world’s population. Moreover,
political and social advances have indeed accompanied many economic improvements.
Although problems such as corruption and poverty remain widespread, nevertheless the
World Bank has noted large declines in poverty and its correlated problems, as described
by the United Nations’ Millenium Development Goals (e.g. see Beaudet 527). The new
importance of ecological problems, terrorist actions, and budgeting problems stemming
from aging populations, all suggest a new phase in an admittedly global system, but not
one that is specifically the kind of stratified world economy that Wallerstein had
described as if it would be an enduring structure expected to persist as long as capitalism
does.

Economists increasingly emphasize multiple “capitalisms,” in the plural (e.g.

Heilbroner and Milberg 2011), to the extent that the broad and vaguely disparaging

45

concept of “capitalism” is even still considered to be appropriate for formal use outside of
a critical-conflict context.
A superior theoretical framework seems to exist by drawing upon fields outside of
sociology, however. Wallerstein himself (1996) had advocated working toward more
cross-disciplinary and even trans-disciplinary theory. One form of unification has been
offered by critical, Marx-derived frameworks, which are not limited to sociology, but
which are certainly vulnerable to many types of critique, and have certainly had their
faults analyzed by sociologists (e.g. Sanderson 2012, Elwell 2013, Elwell 2016). A more
complex form of advanced theory can certainly come from sociologists who are able to
develop a strong working knowledge of multiple interconnected fields such as
international relations, international business, global economics, economic geography,
and of course, history itself, as a traditional unifier of facts that have stemmed from
diverse forms of research. The prominent sociologist Peter Berger, as far back as 1993,
decried the trends he saw in which many sociologists gave up the very idea of Weber’s
“value free” research ideal, in favor of “partisan advocacy” in which “large numbers of
sociologists now proudly announce their non-objectivity” (Berger 1993: 12). After long
thought, I find that I agree wholeheartedly with Berger’s concerns with this trend.
Perhaps the world-systems framework has now come to resemble the sort of “Grand
Theory” that C. Wright Mills (1959) had criticized, in which abstracted ideas accumulate
faster than is empirically defensible. Could it be that sociology’s original set of grand
questions are currently being more productively researched by persons in the fields of
political science, economics, and history?

46

I believe that the current era, in which critical theory has dominated sociology,
must come to an end in favor of newer, less politically motivated, and more promising
research techniques and questions. The evidence that I have examined in this paper,
which contradicts not only the world-systems framework but also the dependency theory
framework which so many sociologists have also held dear, as a distinctive product of
sociology, is not the only evidence that calls for a dramatic shift toward a less critical,
more mainstream and multidisciplinary approach. Even among sociological theorists,
there are promising alternatives to critical Marx-derived theories, and the extent to which
sociologists such as Theda Skocpol have been able to receive recognition within other
fields such as political science could be taken as a useful indicator of quality. Science is
not just about how well one’s research fits into one’s own particular field, but how well it
stands up to the empirical scrutiny of anyone who sees fit to question it, including other
fields of science (e.g. Jared Diamond, who was trained as a biologist but did productive
work in what should have been the realm of macrosociology), or even non-scientist lay
persons.
Although I feel that sociological theorists such as Gerhard Lenski offer
respectable frameworks which could accommodate additional data and research questions
that advance the field, I fear that advances in demonstrably valid sociological theory have
been slowed because the current sociological curriculum does not yet mandate sufficient
study of the other social sciences, especially those dealing with those most fundamental
societal institutions involving the economy and politics (i.e. property and power). The
fundamental frameworks of modern power deserve to be understood from their own
perspectives, even though these are classified as the distinctive realms of economics and

47

political science, and not merely by obsessing over inequalities and a critical-conflict
paradigm.
Marx had keenly perceived historical revolutions as predominantly just the
replacement of one ruling class by another, yet he presumed that a new socialist system
might somehow become an exception. Social scientists today should realize the necessity
of incremental progressivism, and the impossibility of a revolution that somehow would
require an instantaneous mass re-education of society’s members and mass reorganization
of its institutions and the habits and worldviews of their constituent individuals. The
history of such efforts has been extremely clear. Sudden massive social change results in
oppression, death, injustice, and usually the change of the initiative social movement into
new forms that are often unrecognizable and inconsistent with the ideals originally
intended by the most active reformers. Despite this, a full generation after the end of the
Cold War, the predominant visible approach that sociologists often take as a theoretical
framework (and therefore teach to students) is that the field demands a Marxian sense of
praxis, that “capitalism” and other aspects of social systems are so strongly entrenched
that radical activism is not only warranted but even to be actively encouraged (especially
in poorer areas of the world that have the greatest instability, conflict, and poverty). I
consider such overconfidence to be dangerous and unflattering to the original ideas of
sociology as a science, which does not originate with Karl Marx despite his rote
characterization as one of the founding figures of sociology. In addressing one of the
foundational questions of sociology, what makes society possible, Emile Durkheim
proposed that the answer lies in shared norms and values. As we see new kinds of
political change which threaten to reverse progressivism in so many ways, this

48

foundational insight of Durkheim’s must receive new and urgent emphasis within a new
generation of sociologists, and not ignored by presumptuous and reckless assumptions
that current systems are simply evil and readily replaceable. History has shown the evils
that arise from large-scale breakdowns and changes in social systems, some of which are
readily characterizable as regressive and therefore should serve as a warning for those
who may not realize the paucity and fragility of sociology’s actual, demonstrable
accomplishments, compared with the ease with which reactionary political rhetoric and
policies can arise when social changes threaten the understanding and livelihoods of large
portions of society.
I earnestly hope that my research forms part of a growing movement to question
politicized and critical theoretical frameworks that have outgrown their empirical basis,
and to strive to establish sociology in more Weberian terms as a social science that seeks
understanding, as an objectively (now intersubjectively) defensible product of mostly
dispassionate research in which personal motives and political ideologies, while
obviously present in researchers as individuals, are merely informed by the results of
research rather than seen as necessarily the main driving force behind it, as the critiques
of postmodern deconstructionism seem to have concluded. Instead of a sociology that
specializes in applied topics that appear to be geared toward political activism (especially
forms of activism that have noticeably partisan implications), sociology must instead
return to its earlier aspirations to advance scientific theories that can transcend
contemporary politics through their empirical verifiability and primary goals of
understanding rather than changing social systems—endeavoring to understand all of

49

society and societies both in the ways in which they have worked well and of course the
ways in which they arguably haven’t.
One analogy might be made with medicine, in which various treatments might be
painful but over time have mandated a growing set of principles regarding ethics, pain
alleviation, and the ideal of “doing no harm.” In this era in which trust in western social
institutions has declined while the academic and political tone of sociology has been
predominantly critical and in many ways seemingly eager to assist that decline, perhaps
because of an overconfidence that the “decline of capitalism” is natural and inevitable,
and would necessarily lead to a better system. Such a view must be strongly challenged
on the basis of historical evidence, the insights found in other social sciences, and of
course the current political trends which appear to be connected with a polarization that
echoes that found within the social sciences—between those (in whatever field) who are
comfortable with accepting and studying social systems as they currently exist and have
existed, and those who feel that mere study is insufficient and impossible to divorce from
immediate application through political advocacy, whether in classrooms, in the media,
or in the field. Sociological ethics must today remain keenly aware of the real harms that
arise from a breakdown in social systems, and to many of those actors who had advocated
too radical of reforms for their society or its government to tolerate. Activism has met
with severe failure as well as progressive reforms. Their success is neither certain nor in
all cases demonstrably worth the costs. The potentially severe costs of either success or
failure must not be discounted through an undue emphasis upon the mere prospects for
possible success in achieving some sort of reform, nor a too-hasty presumption that the
attempt is mandated wherever suffering exists. It is vital to recognize the actual harms

50

and pain that are sometimes caused by a disproportionately critical approach to our
subject matter. Theories that are too politically loaded and no longer well-substantiated
by current arrays of empirical data must be replaced or greatly revised. We should also
learn eagerly from other fields such as history, economics, and political science, and how
they have dealt with essentially sociological topics through their use of markedly
different theories.

51

APPENDICES

52

APPENDIX I: PARTIALLY PROCESSED DATA FROM THE WORLD BANK
Table 3: Partially Processed Data from the World Bank
Part One of Table (1960 to 1985 averages)
GDP per capita (constant 2000 US$)
Country Name
Afghanistan (all data missing)
Albania (from 1980)
Algeria
American Samoa (all data missing)
Andorra (from 1970)
Angola (from 1985)
Antigua and Barbuda (from 1977)
Arab World
Argentina
Armenia (from 1990)
Aruba (from 1987)
Australia
Austria
Azerbaijan (from 1990)
Bahamas, The
Bahrain (from 1980)
Bangladesh
Barbados
Belarus (from 1990)
Belgium
Belize
Benin
Bermuda
Bhutan (from 1981)
Bolivia
Bosnia and Herzegovina (from 1994)
Botswana
Brazil
Brunei Darussalam (from 1974)
Bulgaria (from 1980)
Burkina Faso
Burundi
Cambodia (from 1993)
Cameroon
Canada
Cape Verde (from 1981)
Cayman Islands (all data missing)
Central African Republic
Chad
Channel Islands (from1998)
Chile
China
Colombia
Comoros (from 1980)

1960-65avg 1966-70avg 1971-75avg 1976-80avg 1981-85avg
--1,123
-----5,367
--9,788
8,174
-11,649
-268
3,835
-8,345
983
263
22,190
-945
-261
1,540
--127
93
-513
10,195
--347
248
-1,924
87
1,243
--

53

--1,283
-18,256
---6,144
--11,604
10,130
-14,746
-274
5,341
-10,279
1,117
288
29,733
-998
-338
1,761
--137
111
-491
12,385
--343
224
-2,157
105
1,388
--

--1,507
-18,767
--1,974
6,993
--13,119
12,779
-12,739
-241
6,386
-12,862
1,414
291
34,695
-1,023
-672
2,567
23,188
-139
125
-529
14,256
--351
205
-2,171
135
1,667
--

-1,061
1,822
-17,867
-4,168
2,308
7,136
--14,005
14,984
-12,587
11,128
251
7,072
-14,643
1,718
285
42,909
-1,113
-991
3,255
29,456
1,294
153
138
-657
16,117
--354
187
-2,205
163
1,907
412

-1,087
1,957
-15,114
796
5,465
2,307
6,633
--14,936
16,398
-15,032
9,429
260
7,322
-15,795
1,849
316
45,143
286
949
-1,408
3,220
23,136
1,431
161
143
-863
17,195
684
-308
165
-2,343
236
2,004
428

Table 3 (cont’d)
Congo, Dem. Rep.
Congo, Rep.
Costa Rica
Cote d'Ivoire
Croatia (from 1990)
Cuba (from 1970)
Curacao (all data missing)
Cyprus (from 1975)
Czech Republic (from 1990)
Denmark
Djibouti (from 1990)
Dominica (from 1977)
Dominican Republic
East Asia & Pacific (all income levels)
East Asia & Pacific (developing only)
Ecuador
Egypt, Arab Rep.
El Salvador
Equatorial Guinea (from 1985)
Eritrea (from 1992)
Estonia (from 1980)
Ethiopia (from 1981)
Euro area
Europe & Central Asia (all income levels)
Europe & Central Asia (developing only)
European Union
Faeroe Islands (2000 only)
Fiji
Finland
France
French Polynesia (from 1965)
Gabon
Gambia, The (from 1966)
Georgia (from 1965)
Germany (from 1970)
Ghana
Gibraltar (all data missing)
Greece
Greenland (from 1970)
Grenada (from 1977)
Guam (all data missing)
Guatemala
Guinea (from 1986)
Guinea-Bissau (from 1970)
Guyana
Haiti (from 1991)
Heavily indebted poor countries (HIPC)
High income
High income: non-OECD
High income: OECD
Honduras

321
639
1,818
615
-----12,743
--947
1,140
134
836
477
1,581
----7,092
4,681
-6,705
-1,123
8,142
8,391
8,676
2,257
-860
-285
-4,056
---1,018
--667
-295
9,119
-9,348
767

54

329
658
2,192
768
-1,778
---15,429
--1,017
1,621
159
880
540
1,839
----9,120
5,716
-8,274
-1,227
9,881
10,547
8,872
2,933
275
1,019
11,895
273
-5,807
9,242
--1,179
-173
725
-312
11,634
4,539
11,953
876

340
813
2,699
904
-1,991
-3,846
-17,430
--1,450
1,996
199
1,082
574
1,985
----11,217
6,777
-9,907
-1,592
12,527
13,057
9,360
4,716
291
1,285
12,989
284
-7,938
11,137
--1,381
-168
782
-327
13,736
6,490
14,088
930

276
852
3,124
1,021
-2,426
-5,783
-19,156
-2,210
1,714
2,269
247
1,314
768
2,129
--3,428
-12,818
7,576
-11,162
-1,773
14,079
14,949
10,271
6,373
337
1,712
14,999
245
-9,234
14,402
2,180
-1,609
-162
821
-339
15,599
8,930
15,968
1,087

251
1,319
2,820
810
-3,261
-7,430
-20,969
-2,723
1,847
2,556
324
1,306
968
1,546
621
-3,685
137
13,814
8,023
-11,971
-1,727
16,303
16,127
11,858
5,224
334
2,094
16,284
203
-9,199
15,292
2,334
-1,505
-161
700
-319
16,934
9,519
17,400
1,053

Table 3 (cont’d)
Hong Kong SAR, China
Hungary (from 1965)
Iceland
India
Indonesia
Iran, Islamic Rep. (from 1965)
Iraq (from 1997)
Ireland (from 1970)
Isle of Man (from 1984)
Israel
Italy
Jamaica (from 1966)
Japan (from 1960)
Jordan (from 1975)
Kazakhstan (from 1990)
Kenya
Kiribati (from 1970)
Korea, Dem. Rep. (all data missing)
Korea, Rep.
Kosovo (from 2000)
Kuwait (from 1995)
Kyrgyz Republic (from 1986)
Lao PDR (from 1984)
Latin America & Caribbean
(all income levels)
Latin America & Caribbean (developing only)
Latvia (from 1965)
Least developed countries: UN classification
Lebanon (from 1988)
Lesotho
Liberia
Libya (from 1999)
Liechtenstein (from 1970)
Lithuania (from 1990)
Low & middle income
Low income
Lower middle income
Luxembourg
Macao SAR, China (from 1982)
Macedonia, FYR (from 1990)
Madagascar
Malawi
Malaysia
Maldives (from 1995)
Mali (from 1967)
Malta (from 1970)
Marshall Islands (from 1981)
Mauritania
Mauritius (from 1976)
Mayotte (all data missing)
Mexico
Micronesia, Fed. Sts. (from 1986)

3,829
1,809
11,214
158
201
965
---6,778
6,676
-9,545
--255
--1,236
-----

5,440
2,179
13,285
202
211
1,232
-7,335
-8,443
8,585
2,967
15,180
--298
1,218
-1,721
-----

7,405
2,870
16,647
211
272
1,840
-8,089
-11,433
10,394
3,570
19,676
1,119
-382
1,692
-2,349
-----

10,432
3,606
20,637
227
349
1,948
-9,518
-12,111
12,240
2,958
22,621
1,588
-411
1,350
-3,199
-----

13,621
4,034
23,375
250
433
1,489
-10,575
8,219
13,403
13,632
2,703
25,699
2,006
-417
712
-4,033
---209

2,239
2,200
1,557
--154
647
---492
232
269
14,494
--401
102
888
----368
--2,665
--

2,573
2,518
1,777
--178
751
-32,622
-581
245
315
16,174
--409
122
1,071
-167
1,827
-476
--3,289
--

3,097
3,035
2,446
--207
802
-32,475
-695
244
355
19,235
--405
142
1,334
-176
2,268
-485
--3,820
--

3,542
3,476
2,959
--276
763
-35,649
-805
243
407
20,305
--359
158
1,724
-214
3,857
-461
1,685
-4,485
--

3,566
3,496
3,477
259
-282
620
-38,771
-859
243
439
22,023
10,562
-301
150
2,113
-188
4,793
1,882
432
1,741
-5,079
--

55

Table 3 (cont’d)
Middle East & North Africa (all income levels)
Middle East & North Africa (developing only)
Middle income
Moldova (from 1980)
Monaco (from 1970)
Mongolia (from 1981)
Montenegro (from 1997)
Morocco
Mozambique (from 1980)
Myanmar (all data missing)
Namibia (from 1980)
Nepal
Netherlands
New Caledonia (from 1965)
New Zealand (from 1977)
Nicaragua
Niger
Nigeria
North America
Northern Mariana Islands (all missing)
Norway
Not classified
OECD members
Oman
Pakistan
Palau (from 1991)
Panama
Papua New Guinea
Paraguay
Peru
Philippines
Poland (from 1990)
Portugal
Puerto Rico
Qatar (from 2000)
Romania (from 1980)
Russian Federation (from 1989)
Rwanda
Samoa (from 1982)
San Marino (from 1970)
Sao Tome and Principe (all missing)
Saudi Arabia (from 1968)
Senegal
Serbia (from 1990)
Seychelles
Sierra Leone
Singapore
Sint Maarten (Dutch part) (all missing)
Slovak Republic (from 1984)
Slovenia (from 1990)
Solomon Islands (from 1990)

-812
525
----660
---141
9,216
7,554
-1,074
343
291
14,337
-11,761
-8,646
1,065
208
-1,965
476
690
1,802
726
-2,726
4,449
---200
----603
-2,410
234
2,500
-----

56

1,581
905
624
-50,458
--723
---145
11,645
8,528
-1,296
329
279
17,314
-14,213
-10,973
2,857
263
-2,519
602
747
2,033
798
-3,907
6,162
---198
-13,112
-7,233
563
-2,518
262
3,758
-----

2,167
1,162
750
-56,044
--833
---141
13,874
10,749
-1,369
264
401
19,156
-16,917
-12,839
4,138
288
-2,995
713
873
2,192
895
-5,518
7,659
---204
-14,327
-12,297
526
-3,217
290
6,023
-----

2,754
1,368
874
826
63,261
--993
203
-2,263
145
15,415
10,673
10,258
1,270
252
416
21,458
-20,702
-14,455
4,791
316
-3,090
665
1,182
2,244
1,047
-6,083
8,484
-1,844
-238
-16,281
-15,728
525
-4,171
285
8,140
-----

2,662
1,352
937
895
65,378
463
-1,011
168
-2,102
152
15,929
9,143
11,017
976
224
324
23,009
-23,801
-15,642
5,943
375
-3,353
612
1,365
2,142
1,042
-6,587
8,908
-2,005
-264
1,145
16,998
-11,745
503
-4,123
292
10,878
-5,023
---

Table 3 (cont’d)
Somalia (all data missing)
South Africa
South Asia
South Sudan (all data missing)
Spain
Sri Lanka
St. Kitts and Nevis (from 1977)
St. Lucia (from 1980)
St. Martin (French part) (all missing)
St. Vincent and the Grenadines
Sub-Saharan Africa (all income levels)
Sub-Saharan Africa (developing only)
Sudan
Suriname (from 1975)
Swaziland (from 1970)
Sweden
Switzerland (from 1980)
Syrian Arab Republic
Tajikistan (from 1985)
Tanzania (from 1988)
Thailand
Timor-Leste (from 1999)
Togo
Tonga (from 1981)
Trinidad and Tobago
Tunisia (from 1961)
Turkey
Turkmenistan (from 1987)
Turks and Caicos Islands (all missing)
Tuvalu (from 1990)
Uganda (from 1982)
Ukraine (from 1987)
United Arab Emirates (from 1975)
United Kingdom
United States
Upper middle income
Uruguay
Uzbekistan (from 1987)
Vanuatu (from 1979)
Venezuela, RB
Vietnam (from 1984)
Virgin Islands (U.S.) (all data missing)
West Bank and Gaza (1994-2005 only)
World
Yemen, Rep. (from 1990)
Zambia
Zimbabwe

-2,387
173
-4,602
279
---1,339
449
449
281
--12,764
-556
--352
-220
-3,886
702
1,664
------10,600
14,749
721
4,140
--5,722
---2,594
-546
399

57

-2,938
213
-6,225
316
---1,274
502
503
261
-577
15,292
-559
--466
-295
-4,461
858
2,035
------12,018
17,819
862
4,170
--6,104
---3,139
-581
433

-3,234
219
-7,930
344
---1,472
570
570
257
2,490
695
17,544
-741
--565
-311
-4,906
1,144
2,348
-----56,038
13,518
19,669
1,057
4,365
--6,164
---3,559
-567
568

-3,326
236
-8,719
399
3,049
2,316
-1,516
584
583
304
2,633
736
18,806
28,554
987
--722
-333
-6,146
1,390
2,684
-----56,023
14,786
22,030
1,247
5,134
-1,157
6,311
---3,896
-507
492

-3,397
263
-8,933
479
3,574
2,308
-1,853
558
558
276
2,381
856
20,209
28,940
1,074
457
-872
-307
1,383
6,533
1,547
2,726
---185
-50,065
15,683
23,637
1,351
4,844
-1,272
5,135
200
--4,051
-441
527

Table 3 (cont’d)
Part Two of Table (1986 to 2010 averages)
GDP per capita (constant 2000 US$)
Country Name
Afghanistan (all data missing)
Albania (from 1980)
Algeria
American Samoa (all data missing)
Andorra (from 1970)
Angola (from 1985)
Antigua and Barbuda (from 1977)
Arab World
Argentina
Armenia (from 1990)
Aruba (from 1987)
Australia
Austria
Azerbaijan (from 1990)
Bahamas, The
Bahrain (from 1980)
Bangladesh
Barbados
Belarus (from 1990)
Belgium
Belize
Benin
Bermuda
Bhutan (from 1981)
Bolivia
Bosnia and Herzegovina (from 1994)
Botswana
Brazil
Brunei Darussalam (from 1974)
Bulgaria (from 1980)
Burkina Faso
Burundi
Cambodia (from 1993)
Cameroon
Canada
Cape Verde (from 1981)
Cayman Islands (all data missing)
Central African Republic
Chad
Channel Islands (from1998)
Chile
China
Colombia
Comoros (from 1980)
Congo, Dem. Rep.
Congo, Rep.

1986-90avg 1991-95avg 96-2000avg 2001-05avg 2006-10avg
-1,046
1,880
-15,393
832
8,557
2,097
6,180
795
15,912
16,698
18,195
1,251
17,020
9,265
273
8,403
1,410
17,626
2,127
308
47,075
400
846
-2,012
3,509
19,979
1,712
171
153
-849
19,290
834
-293
187
-2,796
360
2,205
420
238
1,199

58

-746
1,699
-14,709
616
9,685
2,281
6,976
481
18,243
17,749
20,056
782
15,420
11,086
297
8,019
1,150
19,331
2,876
303
45,989
543
914
437
2,443
3,424
19,213
1,484
174
145
221
611
19,346
913
-252
183
-3,755
538
2,451
390
144
1,107

-1,044
1,743
-16,508
638
10,177
2,449
7,844
557
19,864
20,437
22,480
562
17,132
11,961
340
9,054
1,128
21,392
2,974
331
53,005
693
1,001
1,292
2,917
3,650
18,574
1,460
204
113
260
616
21,783
1,101
-252
175
42,386
4,733
830
2,566
365
101
1,025

-1,391
1,963
-19,868
756
10,956
2,635
7,249
885
19,282
23,001
24,649
899
18,721
14,149
403
9,057
1,568
23,337
3,588
357
61,446
868
1,030
1,684
3,643
3,808
18,602
1,919
233
111
360
676
24,551
1,318
-237
232
41,830
5,257
1,225
2,633
362
86
1,064

-1,771
2,172
-21,678
1,275
13,241
3,032
9,745
1,365
-24,954
26,673
2,047
17,188
13,320
509
9,642
2,422
24,662
3,649
371
70,361
1,189
1,170
2,128
4,110
4,395
17,994
2,512
261
113
521
705
25,784
1,751
-236
285
44,110
6,120
2,034
3,108
344
98
1,163

Table 3 (cont’d)
Costa Rica
Cote d'Ivoire
Croatia (from 1990)
Cuba (from 1970)
Curacao (all data missing)
Cyprus (from 1975)
Czech Republic (from 1990)
Denmark
Djibouti (from 1990)
Dominica (from 1977)
Dominican Republic
East Asia & Pacific (all income levels)
East Asia & Pacific (developing only)
Ecuador
Egypt, Arab Rep.
El Salvador
Equatorial Guinea (from 1985)
Eritrea (from 1992)
Estonia (from 1980)
Ethiopia (from 1981)
Euro area
Europe & Central Asia (all income levels)
Europe & Central Asia (developing only)
European Union
Faeroe Islands (2000 only)
Fiji
Finland
France
French Polynesia (from 1965)
Gabon
Gambia, The (from 1966)
Georgia (from 1965)
Germany (from 1970)
Ghana
Gibraltar (all data missing)
Greece
Greenland (from 1970)
Grenada (from 1977)
Guam (all data missing)
Guatemala
Guinea (from 1986)
Guinea-Bissau (from 1970)
Guyana
Haiti (from 1991)
Heavily indebted poor countries (HIPC)
High income
High income: non-OECD
High income: OECD
Honduras
Hong Kong SAR, China
Hungary (from 1965)

3,008
702
5,261
3,441
-9,531
5,351
23,761
1,174
3,621
1,913
3,004
438
1,290
1,093
1,525
570
-3,937
131
15,559
8,961
2,161
13,567
-1,709
18,907
17,726
13,813
4,471
316
1,990
18,332
214
-9,491
16,888
3,172
-1,410
357
171
646
-304
19,498
10,191
20,138
1,054
18,559
4,362

59

3,422
610
3,872
2,441
-11,179
4,816
25,245
996
4,147
2,034
3,465
623
1,328
1,193
1,787
586
166
2,964
111
17,146
9,447
1,747
14,744
-1,874
18,242
19,102
14,002
4,563
310
703
20,664
232
-9,812
15,472
3,591
-1,523
340
189
755
445
273
21,378
12,501
22,017
1,089
22,962
3,796

3,848
652
4,572
2,541
-12,538
5,337
28,511
790
4,632
2,550
3,794
866
1,335
1,379
2,107
1,676
202
3,662
123
18,774
10,443
1,665
16,486
23,224
2,030
21,519
20,651
13,870
4,521
310
639
21,962
251
-10,668
17,342
4,289
-1,662
359
185
964
424
282
23,795
14,103
24,538
1,129
24,305
4,203

4,239
590
5,530
3,050
-14,062
6,085
30,517
767
4,948
2,905
4,185
1,173
1,438
1,539
2,330
5,269
170
5,321
135
20,397
11,730
2,067
18,351
-2,216
25,088
22,339
-4,026
327
858
23,309
277
-12,799
19,157
5,204
-1,740
395
157
995
396
300
26,046
15,674
26,888
1,212
27,274
5,176

5,082
579
6,501
4,132
-15,226
7,355
31,658
853
5,933
3,708
4,938
1,804
1,672
1,838
2,571
8,167
140
6,722
192
21,396
12,757
2,708
19,551
-2,280
27,699
23,153
-4,102
337
1,201
25,011
332
-14,285
21,255
5,578
-1,859
414
158
1,127
387
343
27,563
17,883
28,448
1,396
33,999
5,777

Table 3 (cont’d)
Iceland
India
Indonesia
Iran, Islamic Rep. (from 1965)
Iraq (from 1997)
Ireland (from 1970)
Isle of Man (from 1984)
Israel
Italy
Jamaica (from 1966)
Japan (from 1960)
Jordan (from 1975)
Kazakhstan (from 1990)
Kenya
Kiribati (from 1970)
Korea, Dem. Rep. (all data missing)
Korea, Rep.
Kosovo (from 2000)
Kuwait (from 1995)
Kyrgyz Republic (from 1986)
Lao PDR (from 1984)
Latin America & Caribbean (all income levels)
Latin America & Caribbean (developing only)
Latvia (from 1965)
Least developed countries: UN classification
Lebanon (from 1988)
Lesotho
Liberia
Libya (from 1999)
Liechtenstein (from 1970)
Lithuania (from 1990)
Low & middle income
Low income
Lower middle income
Luxembourg
Macao SAR, China (from 1982)
Macedonia, FYR (from 1990)
Madagascar
Malawi
Malaysia
Maldives (from 1995)
Mali (from 1967)
Malta (from 1970)
Marshall Islands (from 1981)
Mauritania
Mauritius (from 1976)
Mayotte (all data missing)
Mexico
Micronesia, Fed. Sts. (from 1986)
Middle East & North Africa (all income levels)
Middle East & North Africa (developing only)

26,421
294
526
1,222
-12,310
11,155
14,768
15,697
2,886
30,554
1,898
1,612
438
680
-5,971
--431
213
3,585
3,505
3,950
259
3,278
287
456
-46,221
4,291
930
246
480
28,982
12,108
2,059
288
136
2,324
-184
5,841
2,176
421
2,319
-4,746
1,859
2,409
1,300

60

25,510
340
712
1,405
-15,273
12,844
16,429
17,098
3,515
34,923
1,639
1,226
421
649
-8,397
-21,085
311
247
3,722
3,633
2,552
248
4,345
331
99
-52,527
2,975
981
235
512
35,886
14,774
1,705
254
137
3,153
1,690
190
7,477
2,313
405
2,889
-5,059
2,074
2,655
1,369

28,939
422
799
1,499
969
21,940
17,493
18,659
18,522
3,524
36,335
1,725
1,109
414
750
-10,441
1,088
20,384
261
302
4,010
3,907
2,895
268
4,658
365
137
6,423
68,325
3,028
1,110
249
573
41,192
14,470
1,669
246
156
3,855
2,074
205
9,187
2,128
410
3,511
-5,401
2,081
2,882
1,499

33,116
519
848
1,758
748
28,183
23,493
19,285
19,726
3,636
37,600
1,946
1,680
411
807
-12,812
1,406
21,444
306
372
4,141
4,019
4,236
306
4,931
402
161
6,476
72,695
4,159
1,288
274
656
49,247
17,812
1,777
239
147
4,219
2,660
242
9,908
2,307
415
4,100
-5,799
2,204
3,129
1,645

36,469
725
1,049
2,107
728
29,550
28,537
21,658
19,613
3,774
39,562
2,437
2,346
456
783
-15,384
1,726
25,067
365
493
4,828
4,686
5,609
379
5,906
458
151
7,737
81,855
5,525
1,686
326
839
54,016
28,252
2,144
251
170
4,962
3,753
262
10,834
2,410
468
4,823
-6,168
2,127
3,616
1,895

Table 3 (cont’d)
Middle income
Moldova (from 1980)
Monaco (from 1970)
Mongolia (from 1981)
Montenegro (from 1997)
Morocco
Mozambique (from 1980)
Myanmar (all data missing)
Namibia (from 1980)
Nepal
Netherlands
New Caledonia (from 1965)
New Zealand (from 1977)
Nicaragua
Niger
Nigeria
North America
Northern Mariana Islands (all missing)
Norway
Not classified
OECD members
Oman
Pakistan
Palau (from 1991)
Panama
Papua New Guinea
Paraguay
Peru
Philippines
Poland (from 1990)
Portugal
Puerto Rico
Qatar (from 2000)
Romania (from 1980)
Russian Federation (from 1989)
Rwanda
Samoa (from 1982)
San Marino (from 1970)
Sao Tome and Principe (all missing)
Saudi Arabia (from 1968)
Senegal
Serbia (from 1990)
Seychelles
Sierra Leone
Singapore
Sint Maarten (Dutch part) (all missing)
Slovak Republic (from 1984)
Slovenia (from 1990)
Solomon Islands (from 1990)
Somalia (all data missing)
South Africa

1,020
972
67,876
532
-1,128
169
-1,911
169
17,719
11,560
11,472
767
201
328
26,452
-27,147
-17,893
6,737
430
-3,037
605
1,346
2,008
950
3,101
7,866
10,761
-2,076
2,648
245
1,230
18,492
-8,804
494
1,444
4,970
261
13,818
-5,379
8,362
1,101
-3,190

61

1,083
554
69,652
426
-1,175
184
-1,950
192
19,661
13,523
11,526
652
176
364
27,981
-30,098
-19,442
7,248
479
6,349
3,372
722
1,433
1,760
957
3,090
9,227
12,665
-1,621
1,960
220
1,165
20,774
-9,287
462
852
6,081
220
18,372
-4,331
7,641
1,274
-2,956

1,234
363
71,982
458
1,567
1,254
219
-2,029
215
22,596
12,913
12,828
730
171
366
31,822
-35,812
-21,532
8,250
503
6,616
3,758
714
1,421
2,045
1,030
4,052
10,665
14,926
30,053
1,685
1,611
213
1,310
26,554
-9,275
474
808
7,081
171
22,124
-5,143
9,232
1,275
-3,003

1,441
438
78,868
539
1,677
1,443
281
-2,211
232
24,603
-14,506
806
169
403
35,100
-39,074
-23,463
9,512
544
6,134
4,079
624
1,330
2,187
1,114
4,835
11,564
16,794
31,883
2,015
2,137
246
1,609
29,613
-9,169
515
944
7,106
214
25,430
-6,098
11,093
943
-3,194

1,901
564
99,400
727
2,166
1,733
358
-2,606
254
26,625
-15,071
915
175
496
36,742
-40,944
-24,771
10,744
646
6,402
5,446
683
1,485
2,871
1,303
6,126
11,793
-33,876
2,625
2,864
314
1,804
31,464
-9,409
552
1,172
8,161
257
30,535
-8,110
13,029
1,098
-3,697

Table 3 (cont’d)
South Asia
South Sudan (all data missing)
Spain
Sri Lanka
St. Kitts and Nevis (from 1977)
St. Lucia (from 1980)
St. Martin (French part) (all missing)
St. Vincent and the Grenadines
Sub-Saharan Africa (all income levels)
Sub-Saharan Africa (developing only)
Sudan
Suriname (from 1975)
Swaziland (from 1970)
Sweden
Switzerland (from 1980)
Syrian Arab Republic
Tajikistan (from 1985)
Tanzania (from 1988)
Thailand
Timor-Leste (from 1999)
Togo
Tonga (from 1981)
Trinidad and Tobago
Tunisia (from 1961)
Turkey
Turkmenistan (from 1987)
Turks and Caicos Islands (all missing)
Tuvalu (from 1990)
Uganda (from 1982)
Ukraine (from 1987)
United Arab Emirates (from 1975)
United Kingdom
United States
Upper middle income
Uruguay
Uzbekistan (from 1987)
Vanuatu (from 1979)
Venezuela, RB
Vietnam (from 1984)
Virgin Islands (U.S.) (all data missing)
West Bank and Gaza (1994-2005 only)
World
Yemen, Rep. (from 1990)
Zambia
Zimbabwe

304
-10,440
539
5,387
3,132
-2,449
536
536
271
2,000
1,063
22,827
31,760
959
449
297
1,161
-294
1,526
5,069
1,591
3,235
1,074
-1,144
176
1,428
33,712
18,563
27,237
1,480
5,167
676
1,282
4,934
213
--4,423
461
400
516

62

349
-11,722
640
6,771
4,214
-2,855
501
501
291
1,958
1,205
22,829
32,298
1,126
240
289
1,736
-257
1,667
4,931
1,783
3,577
765
-1,260
201
963
33,903
19,663
28,933
1,585
5,903
562
1,338
5,191
271
-1,237
4,655
458
352
518

420
-13,327
797
8,533
4,572
-3,393
510
509
338
1,942
1,294
25,718
33,386
1,235
128
296
1,961
359
288
1,832
5,724
2,088
4,087
530
-1,412
246
605
33,815
23,261
32,918
1,837
6,936
529
1,418
4,982
368
-1,369
5,049
516
322
555

504
-15,208
918
8,628
4,548
-4,060
541
537
397
2,156
1,455
29,583
35,075
1,272
182
346
2,161
313
273
2,055
7,873
2,471
4,333
1,035
-1,663
282
831
34,033
27,088
36,246
2,186
6,478
621
1,331
4,569
480
-1,039
5,456
548
339
431

683
-15,942
1,175
9,760
5,148
-4,898
621
615
497
2,556
1,560
32,324
37,565
1,455
255
424
2,575
328
281
2,040
10,580
2,994
5,217
1,773
-1,730
353
1,070
25,905
28,647
37,936
2,952
8,257
839
1,500
5,678
654
--5,942
566
398
319

APPENDIX II: PROCESSED WORLD BANK DATA FOR 1960-1965
Table 4: Processed World Bank Data for 1960-1965
World Bank Data
Country Name
Bermuda
United States
Luxembourg
Greenland
Sweden
Denmark
Norway
Bahamas, The
Iceland
United Kingdom
Canada
Australia
Japan
Netherlands
French Polynesia
France
Belgium
Austria
Finland
New Caledonia
Israel
Italy
Venezuela, RB
Argentina
Albania*
Andorra*
Armenia*
Azerbaijan*
Belarus*
Bosnia and Herzegovina*
Bulgaria*
Channel Islands*
Croatia*
Cyprus*
Czech Republic*
Estonia
Faeroe Islands
Germany
Gibraltar
Ireland
Isle of Man
Kazakhstan
Kosovo
Kyrgyz Republic
Liechtenstein
Lithuania

1960-1965
average
PCGDP
22,190
14,749
14,494
14,337
12,764
12,743
11,761
11,649
11,214
10,600
10,195
9,788
9,545
9,216
8,676
8,391
8,345
8,174
8,142
7,554
6,778
6,676
5,722
5,367
4,681
4,681
4,681
4,681
4,681
4,681
4,681
4,681
4,681
4,681
4,681
4,681
4,681
4,681
4,681
4,681
4,681
4,681
4,681
4,681
4,681
4,681

1960-1965
average
Population Cumulative pop
47,200 3,145,855,340
187,722,333 3,145,808,140
324,150 2,958,085,807
35,733 2,957,761,657
7,593,500 2,957,725,924
4,666,667 2,950,132,423
3,652,333 2,945,465,757
124,474 2,941,813,424
184,333 2,941,688,950
53,406,333 2,941,504,617
18,793,502 2,888,098,284
10,834,413 2,869,304,782
96,132,762 2,858,470,370
11,886,605 2,762,337,608
85,792 2,750,451,003
48,218,594 2,750,365,211
9,266,833 2,702,146,617
7,151,167 2,692,879,784
4,503,000 2,685,728,617
86,132 2,681,225,617
2,334,837 2,681,139,485
51,109,117 2,678,804,648
8,307,748 2,627,695,532
21,463,333 2,619,387,784
1,739,320 2,597,924,451
15,899 2,596,185,131
2,036,169 2,596,169,232
4,233,705 2,594,133,063
8,398,767 2,589,899,358
3,290,705 2,581,500,591
8,041,001 2,578,209,886
112,091 2,570,168,885
4,215,311 2,570,056,794
577,499 2,565,841,484
9,687,667 2,565,263,985
1,252,728 2,555,576,318
35,385 2,554,323,590
74,175,000 2,554,288,205
22,343 2,480,113,205
2,845,333 2,480,090,862
49,170 2,477,245,529
10,987,503 2,477,196,359
1,009,500 2,466,208,856
2,368,534 2,465,199,356
17,603 2,462,830,822
2,874,783 2,462,813,219

63

Percentile
99%
99%
93%
93%
93%
93%
93%
93%
93%
93%
91%
91%
90%
87%
87%
87%
85% Core?
85%
85%
85%
85%
85%
83%
83%
82%
82%
82%
82%
82%
82%
81%
81%
81%
81%
81%
81%
81%
81%
78%
78%
78%
78%
78%
78%
78%
78%

Table 4 (cont’d)
Macedonia, FYR
Malta
Moldova
Monaco
Montenegro
Poland
Romania
Russian Federation
San Marino
Slovak Republic
Slovenia
Switzerland
Tajikistan
Turkmenistan
Ukraine
Uzbekistan
Spain
Puerto Rico
Uruguay
Greece
Trinidad and Tobago
Barbados
Hong Kong SAR, China
Portugal
Mexico
Singapore
Seychelles
South Africa
Gabon
St. Kitts and Nevis
St. Lucia
St. Martin (French part)
Suriname
Turks and Caicos Islands
Virgin Islands (U.S.)
Antigua and Barbuda*
Aruba*
Cayman Islands*
Cuba*
Dominica*
Grenada
Haiti
Jamaica
Panama
Chile
Costa Rica
Hungary
Peru
Turkey
El Salvador
Latvia

4,681
1,434,390
4,681
321,666
4,681
2,688,833
4,681
22,485
4,681
487,832
4,681 30,533,000
4,681 18,736,333
4,681 123,295,644
4,681
16,410
4,681
4,260,710
4,681
1,613,753
4,681
5,618,166
4,681
2,293,143
4,681
1,740,139
4,681 44,087,291
4,681
9,382,640
4,602 31,239,859
4,449
2,476,773
4,140
2,617,628
4,056
8,452,167
3,886
869,481
3,835
233,020
3,829
3,345,384
2,726
9,038,917
2,665 41,634,501
2,500
1,770,333
2,410
44,605
2,387 18,660,682
2,257
493,369
2,239
49,843
2,239
89,907
2,239
4,591
2,239
310,831
2,239
5,727
2,239
37,733
2,239
57,066
2,239
56,161
2,239
8,437
2,239
7,531,018
2,239
62,485
2,239
92,574
2,239
4,067,129
2,239
1,693,528
1,965
1,215,176
1,924
8,152,886
1,818
1,457,124
1,809 10,073,834
1,802 10,682,843
1,664 30,001,716
1,581
3,003,725
1,557
2,197,422

64

2,459,938,437
2,458,504,047
2,458,182,381
2,455,493,548
2,455,471,063
2,454,983,231
2,424,450,231
2,405,713,898
2,282,418,254
2,282,401,844
2,278,141,135
2,276,527,382
2,270,909,216
2,268,616,073
2,266,875,935
2,222,788,643
2,213,406,004
2,182,166,144
2,179,689,371
2,177,071,744
2,168,619,577
2,167,750,097
2,167,517,077
2,164,171,693
2,155,132,776
2,113,498,275
2,111,727,942
2,111,683,337
2,093,022,655
2,092,529,285
2,092,479,443
2,092,389,536
2,092,384,945
2,092,074,114
2,092,068,387
2,092,030,654
2,091,973,588
2,091,917,427
2,091,908,990
2,084,377,972
2,084,315,488
2,084,222,914
2,080,155,785
2,078,462,257
2,077,247,081
2,069,094,195
2,067,637,072
2,057,563,238
2,046,880,395
2,016,878,679
2,013,874,954

78%
78%
78%
78%
78%
78%
77%
76%
72%
72%
72%
72%
72%
72%
72%
70%
70%
69%
69%
69%
69%
69%
69%
68%
68%
67%
67%
67%
66%
66%
66%
66%
66%
66%
66%
66%
66%
66%
66%
66%
66%
66%
66%
66%
66%
65%
65%
65%
65%
64%
64%

Table 4 (cont’d)
Brazil
St. Vincent and the Grenadines
Colombia
Korea, Rep.
American Samoa*
Brunei Darussalam*
Cambodia*
Korea, Dem. Rep.
Lao PDR
Macao SAR, China
Marshall Islands
Micronesia, Fed. Sts.
Mongolia
Myanmar
New Zealand
Northern Mariana Islands
Palau
Samoa
Solomon Islands
Timor-Leste
Tonga
Tuvalu
Vanuatu
Vietnam
Algeria
Fiji
Nicaragua
Oman
Guatemala
Belize
Iran, Islamic Rep.
Dominican Republic
Bolivia
Malaysia
Georgia
Ecuador
Bahrain*
Iraq
Jordan
Kuwait
Lebanon
Libya
Qatar
Saudi Arabia
United Arab Emirates
Yemen, Rep.
Honduras
Philippines
Tunisia
Paraguay
Guyana

1,540
1,339
1,243
1,236
1,140
1,140
1,140
1,140
1,140
1,140
1,140
1,140
1,140
1,140
1,140
1,140
1,140
1,140
1,140
1,140
1,140
1,140
1,140
1,140
1,123
1,123
1,074
1,065
1,018
983
965
947
945
888
860
836
812
812
812
812
812
812
812
812
812
812
767
726
702
690
667

78,511,227
83,586
17,258,035
26,790,592
21,615
89,385
5,776,040
11,618,991
2,258,311
186,787
15,891
48,244
1,030,691
22,120,322
2,503,167
10,620
10,241
118,416
127,338
523,019
67,787
6,464
68,933
36,507,485
11,335,873
428,766
1,915,343
592,746
4,455,826
99,414
23,497,229
3,597,838
3,550,121
8,858,945
3,786,250
4,784,233
174,442
7,904,279
977,822
366,362
2,057,103
1,481,551
59,252
4,399,522
116,546
5,371,696
2,173,120
28,283,575
4,407,727
2,038,336
599,861

65

2,011,677,531
1,933,166,304
1,933,082,718
1,915,824,683
1,889,034,091
1,889,012,477
1,888,923,092
1,883,147,052
1,871,528,061
1,869,269,751
1,869,082,964
1,869,067,073
1,869,018,829
1,867,988,138
1,845,867,816
1,843,364,650
1,843,354,030
1,843,343,789
1,843,225,373
1,843,098,035
1,842,575,016
1,842,507,229
1,842,500,765
1,842,431,832
1,805,924,347
1,794,588,475
1,794,159,708
1,792,244,365
1,791,651,619
1,787,195,793
1,787,096,380
1,763,599,150
1,760,001,312
1,756,451,191
1,747,592,246
1,743,805,996
1,739,021,763
1,738,847,321
1,730,943,042
1,729,965,220
1,729,598,858
1,727,541,756
1,726,060,205
1,726,000,953
1,721,601,431
1,721,484,885
1,716,113,189
1,713,940,069
1,685,656,495
1,681,248,768
1,679,210,432

64%
61%
61%
61%
60%
60%
60%
60%
59%
59%
59%
59%
59%
59%
58%
58%
58%
58%
58%
58%
58%
58%
58%
58%
57% Semi-periph?
57%
57%
57%
57%
56%
56%
56%
56%
56%
55%
55%
55%
55%
55%
55%
55%
55%
55%
55%
54%
54%
54%
54%
53%
53%
53%

Table 4 (cont’d)
Morocco
Liberia
Congo, Rep.
Cote d'Ivoire
Senegal
Syrian Arab Republic
Zambia
Cameroon
Egypt, Arab Rep.
Papua New Guinea
Angola*
Cape Verde*
Comoros*
Djibouti*
Equatorial Guinea
Eritrea
Ethiopia
Gambia, The
Guam
Guinea
Guinea-Bissau
Kiribati
Mali
Mauritius
Mayotte
Mozambique
Namibia
Sao Tome and Principe
Somalia
Swaziland
Tanzania
Uganda
Madagascar
Zimbabwe
Mauritania
Thailand
Central African Republic
Niger
Congo, Dem. Rep.
Nigeria
Ghana
Sudan
Sri Lanka
Bangladesh
Benin
Botswana
Kenya
Chad
Sierra Leone
Togo
Pakistan

660
647
639
615
603
556
546
513
477
476
449
449
449
449
449
449
449
449
449
449
449
449
449
449
449
449
449
449
449
449
449
449
401
399
368
352
347
343
321
291
285
281
279
268
263
261
255
248
234
220
208

12,457,013
1,186,756
1,083,685
4,017,221
3,268,504
4,961,881
3,284,161
5,719,369
29,814,056
2,059,784
5,195,688
224,935
202,288
99,746
260,054
1,519,244
23,974,084
392,100
71,478
3,677,090
596,760
42,741
5,418,923
708,116
27,225
8,050,598
641,501
64,434
2,990,651
370,037
10,859,703
7,380,919
5,426,533
4,079,602
917,729
29,504,133
1,573,425
3,501,047
16,422,869
48,507,241
7,283,054
12,278,361
10,607,420
53,824,631
2,506,908
558,330
8,784,824
3,118,820
2,278,182
1,626,431
48,873,003

66

1,678,610,572
1,666,153,559
1,664,966,803
1,663,883,118
1,659,865,897
1,656,597,393
1,651,635,512
1,648,351,351
1,642,631,982
1,612,817,926
1,610,758,142
1,605,562,454
1,605,337,519
1,605,135,232
1,605,035,486
1,604,775,433
1,603,256,189
1,579,282,105
1,578,890,005
1,578,818,527
1,575,141,437
1,574,544,677
1,574,501,937
1,569,083,014
1,568,374,898
1,568,347,673
1,560,297,075
1,559,655,574
1,559,591,140
1,556,600,489
1,556,230,452
1,545,370,749
1,537,989,831
1,532,563,298
1,528,483,696
1,527,565,967
1,498,061,834
1,496,488,409
1,492,987,363
1,476,564,494
1,428,057,253
1,420,774,199
1,408,495,839
1,397,888,419
1,344,063,788
1,341,556,880
1,340,998,550
1,332,213,726
1,329,094,906
1,326,816,724
1,325,190,293

53%
53%
53%
53%
52%
52%
52%
52%
52%
51%
51%
51%
51%
51%
51%
51%
51%
50%
50%
50%
50%
50%
50%
50%
50%
50%
49%
49%
49%
49%
49%
49%
49%
48%
48%
48%
47%
47%
47%
47%
45%
45%
45%
44%
42%
42%
42%
42%
42%
42%
42%

Table 4 (cont’d)
Indonesia
201 97,909,725 1,276,317,290
40%
Rwanda
200
2,991,408 1,178,407,566
37%
Afghanistan*
173 10,170,266 1,175,416,158
37%
Bhutan*
173
241,804 1,165,245,892
37%
Maldives
173
96,455 1,165,004,088
37%
India
158 460,403,646 1,164,907,633
37%
Lesotho
154
891,391
704,503,987
22%
Nepal
141 10,209,662
703,612,596
22%
Burkina Faso
127
5,077,339
693,402,934
22%
Malawi
102
3,743,933
688,325,595
22%
Burundi
93
3,074,162
684,581,662
22%
China
87 681,507,500
681,507,500
22% Periphery/ext.
Serbia**
-South Sudan**
-West Bank and Gaza**
-* Data was missing; estimates were used from the regional
categories below:
** Data not added when no population information was given (e.g. South Sudan was part of
Sudan)
North America
Europe & Central Asia
(all income levels)
Latin America & Caribbean
(all income levels)
East Asia & Pacific
(all income levels)
Middle East & North Africa
(developing only)
Sub-Saharan Africa
(all income levels)
South Asia
World
High income
Upper middle income
Lower middle income
Low income

14,337 206,563,035

3,155,800,244

100%

4,681 687,671,301

2,949,237,209

93%

2,239 235,366,951

2,261,565,909

71%

1,140 1,082,451,811

2,026,198,958

64%

812 104,365,974

943,747,146

30%

449 244,954,286
173 594,426,886
2,594 3,164,165,616

839,381,172
594,426,886

27%
19%

9,119 768,393,799
721 1,216,970,883
269 925,897,624
232 252,903,311
3,164,165,616

3,164,165,616
2,395,771,817
1,178,800,935
252,903,311

100%
76%
37%
8%

67

APPENDIX III: PROCESSED WORLD BANK DATA FOR 1986-1990
Table 5: Processed World Bank Data for 1986-1990
World Bank Data
Country Name
Monaco
Bermuda
Liechtenstein
United Arab Emirates
Switzerland
Japan
Luxembourg
United States
Norway
Iceland
Denmark
Sweden
Brunei Darussalam
Canada
Finland
United Kingdom
Hong Kong SAR, China
San Marino
Germany
Austria
France
Netherlands
Belgium
Bahamas, The
Greenland
Australia
Aruba
Italy
Andorra
Israel
Singapore
French Polynesia
Ireland
Macao SAR, China
New Caledonia
New Zealand
Isle of Man
Puerto Rico
Spain
Cyprus
Greece
Bahrain
Bosnia and Herzegovina*
Channel Islands*
Faeroe Islands*
Gibraltar*

1986-1990
average
PCGDP
67,876
47,075
46,221
33,712
31,760
30,554
28,982
27,237
27,147
26,421
23,761
22,827
19,979
19,290
18,907
18,563
18,559
18,492
18,332
18,195
17,726
17,719
17,626
17,020
16,888
16,698
15,912
15,697
15,393
14,768
13,818
13,813
12,310
12,108
11,560
11,472
11,155
10,761
10,440
9,531
9,491
9,265
8,961
8,961
8,961
8,961

1986-1990
average
population
29,978
58,700
28,246
1,614,940
6,595,400
122,569,800
374,790
244,672,600
4,206,700
249,280
5,130,200
8,451,400
238,506
26,963,800
4,950,000
56,947,215
5,626,680
23,579
78,379,400
7,615,056
57,574,858
14,759,600
9,907,880
247,487
54,660
16,538,800
61,743
56,643,600
50,481
4,457,600
2,866,400
186,980
3,526,800
335,186
160,532
3,355,600
67,832
3,473,590
38,684,600
738,025
10,050,600
462,011
4,299,790
137,684
47,104
26,711

68

cumulative
5,077,877,628
5,077,847,650
5,077,788,950
5,077,760,704
5,076,145,764
5,069,550,364
4,946,980,564
4,946,605,774
4,701,933,174
4,697,726,474
4,697,477,194
4,692,346,994
4,683,895,594
4,683,657,088
4,656,693,288
4,651,743,288
4,594,796,073
4,589,169,393
4,589,145,814
4,510,766,414
4,503,151,358
4,445,576,500
4,430,816,900
4,420,909,020
4,420,661,533
4,420,606,873
4,404,068,073
4,404,006,330
4,347,362,730
4,347,312,248
4,342,854,648
4,339,988,248
4,339,801,268
4,336,274,468
4,335,939,281
4,335,778,749
4,332,423,149
4,332,355,318
4,328,881,728
4,290,197,128
4,289,459,103
4,279,408,503
4,278,946,491
4,274,646,701
4,274,509,017
4,274,461,913

percentile
100%
100%
100%
100%
100%
99%
97%
97%
92%
92%
92%
92%
92%
92%
91%
91%
90%
90%
90%
89%
88%
87%
87%
87%
87%
87%
86%
86%
85% Core?
85%
85%
85%
85%
85%
85%
85%
85%
85%
85%
84%
84%
84%
84%
84%
84%
84%

Table 5 (cont’d)
Kosovo*
Montenegro*
Saudi Arabia
Antigua and Barbuda
Barbados
Slovenia
Portugal
Oman
Argentina
Korea, Rep.
Malta
St. Kitts and Nevis
Slovak Republic
Czech Republic
Croatia
Uruguay
Trinidad and Tobago
Seychelles
Venezuela, RB
Mexico
Gabon
Hungary
Lithuania
Latvia
Estonia
Dominica
Cayman Islands*
Haiti*
St. Martin (French part)*
Turks and Caicos Islands*
Virgin Islands (U.S.)*
Brazil
Cuba
Lebanon
Turkey
South Africa
Grenada
St. Lucia
Poland
Panama
Costa Rica
American Samoa*
Cambodia*
Guam*
Korea, Dem. Rep.*
Myanmar*
Northern Mariana Islands*
Palau*
Timor-Leste*
Jamaica
Chile

8,961
8,961
8,804
8,557
8,403
8,362
7,866
6,737
6,180
5,971
5,841
5,387
5,379
5,351
5,261
5,167
5,069
4,970
4,934
4,746
4,471
4,362
4,291
3,950
3,937
3,621
3,585
3,585
3,585
3,585
3,585
3,509
3,441
3,278
3,235
3,190
3,172
3,132
3,101
3,037
3,008
3,004
3,004
3,004
3,004
3,004
3,004
3,004
3,004
2,886
2,796

1,790,000
593,524
15,023,317
63,022
257,569
1,993,540
9,961,140
1,734,049
31,727,906
41,996,600
353,900
42,313
5,268,180
10,354,000
4,753,200
3,068,860
1,200,762
68,893
18,837,868
81,016,608
872,819
10,447,103
3,647,600
2,662,140
1,558,200
71,859
23,534
6,832,152
24,510
10,650
104,793
144,410,005
10,364,467
2,901,868
52,270,452
33,694,684
98,064
130,380
37,812,760
2,319,911
2,917,252
43,995
8,888,626
128,190
19,565,961
38,030,130
38,574
14,511
708,593
2,361,540
12,741,383

69

4,274,435,202
4,272,645,202
4,272,051,678
4,257,028,361
4,256,965,338
4,256,707,769
4,254,714,229
4,244,753,089
4,243,019,041
4,211,291,135
4,169,294,535
4,168,940,635
4,168,898,322
4,163,630,142
4,153,276,142
4,148,522,942
4,145,454,082
4,144,253,320
4,144,184,427
4,125,346,559
4,044,329,951
4,043,457,132
4,033,010,029
4,029,362,429
4,026,700,289
4,025,142,089
4,025,070,230
4,025,046,696
4,018,214,545
4,018,190,035
4,018,179,385
4,018,074,592
3,873,664,587
3,863,300,121
3,860,398,253
3,808,127,801
3,774,433,117
3,774,335,053
3,774,204,673
3,736,391,913
3,734,072,002
3,731,154,750
3,731,110,755
3,722,222,129
3,722,093,938
3,702,527,977
3,664,497,848
3,664,459,274
3,664,444,763
3,663,736,170
3,661,374,630

84%
84%
84%
84%
84%
84%
83%
83%
83%
83%
82%
82%
82%
82%
82%
81%
81%
81%
81%
81%
79%
79%
79%
79%
79%
79%
79%
79%
79%
79%
79%
79%
76%
76%
76%
75%
74%
74%
74%
73%
73%
73%
73%
73%
73%
73%
72%
72%
72%
72%
72%

Table 5 (cont’d)
Russian Federation
St. Vincent and the Grenadines
Iraq*
Kuwait*
Libya*
Qatar*
West Bank and Gaza*
Malaysia
Mauritius
Colombia
Marshall Islands
Belize
Romania
Macedonia, FYR
Botswana
Peru
Suriname
Georgia
Dominican Republic
Namibia
Jordan
Algeria
Micronesia, Fed. Sts.
Bulgaria
Fiji
Kazakhstan
Tunisia
Tonga
El Salvador
Serbia
Ukraine
Guatemala
Belarus
Paraguay
Ecuador
Vanuatu
Azerbaijan
Samoa
Iran, Islamic Rep.
Congo, Rep.
Djibouti
Thailand
Tuvalu
Morocco
Solomon Islands
Egypt, Arab Rep.
Turkmenistan
Swaziland
Honduras
Albania
Moldova

2,648
2,449
2,409
2,409
2,409
2,409
2,409
2,324
2,319
2,205
2,176
2,127
2,076
2,059
2,012
2,008
2,000
1,990
1,913
1,911
1,898
1,880
1,859
1,712
1,709
1,612
1,591
1,526
1,525
1,444
1,428
1,410
1,410
1,346
1,290
1,282
1,251
1,230
1,222
1,199
1,174
1,161
1,144
1,128
1,101
1,093
1,074
1,063
1,054
1,046
972

146,734,400
106,374
17,161,447
1,994,508
4,158,506
438,161
1,978,248
17,207,076
1,040,840
31,915,137
43,896
179,520
23,035,200
1,875,190
1,301,616
20,796,302
393,771
4,768,800
6,908,032
1,304,490
2,952,800
24,030,900
92,090
8,900,800
723,144
16,127,490
7,829,619
94,659
5,193,122
7,586,000
51,515,200
8,529,252
10,130,600
4,024,231
9,792,415
139,605
6,984,000
159,303
51,685,814
2,263,642
498,519
55,287,180
8,881
23,813,718
292,980
54,402,067
3,484,106
800,197
4,620,012
3,177,920
3,654,000

70

3,648,633,248
3,501,898,848
3,501,792,473
3,484,631,026
3,482,636,519
3,478,478,013
3,478,039,852
3,476,061,603
3,458,854,527
3,457,813,686
3,425,898,549
3,425,854,653
3,425,675,133
3,402,639,933
3,400,764,743
3,399,463,127
3,378,666,825
3,378,273,053
3,373,504,253
3,366,596,221
3,365,291,731
3,362,338,931
3,338,308,031
3,338,215,941
3,329,315,141
3,328,591,997
3,312,464,507
3,304,634,888
3,304,540,229
3,299,347,107
3,291,761,107
3,240,245,907
3,231,716,655
3,221,586,055
3,217,561,825
3,207,769,410
3,207,629,805
3,200,645,805
3,200,486,502
3,148,800,688
3,146,537,046
3,146,038,526
3,090,751,346
3,090,742,465
3,066,928,747
3,066,635,766
3,012,233,700
3,008,749,594
3,007,949,397
3,003,329,385
3,000,151,465

72%
69%
69%
68%
68%
68%
68%
68%
68%
68%
67%
67%
67%
67%
67%
67%
66%
66%
66%
66%
66%
66%
66%
66%
65%
65%
65%
65%
65%
65%
65%
64%
63%
63%
63%
63%
63%
63%
63%
62%
62%
62%
61%
61%
60%
60%
59%
59%
59%
59%
59%

Table 5 (cont’d)
Syrian Arab Republic
Philippines
Cameroon
Bolivia
Cape Verde
Angola
Armenia
Nicaragua
Cote d'Ivoire
Kiribati
Uzbekistan
Guyana
Papua New Guinea
Equatorial Guinea
Sri Lanka
Eritrea*
Mayotte*
Sao Tome and Principe*
Somalia*
Mongolia
Indonesia
Zimbabwe
Senegal
Yemen, Rep.
Liberia
Tajikistan
Kenya
Kyrgyz Republic
Pakistan
Mauritania
Comoros
Bhutan
Zambia
China
Guinea
Nigeria
Gambia, The
Benin
Afghanistan*
Maldives*
Tanzania
India
Togo
Central African Republic
Madagascar
Lesotho
Bangladesh
Sudan
Sierra Leone
Rwanda
Congo, Dem. Rep.

959
11,618,786 2,996,497,465
950
58,577,303 2,984,878,679
849
11,500,290 2,926,301,375
846
6,368,425 2,914,801,085
834
339,682 2,908,432,660
832
9,814,529 2,908,092,978
795
3,490,284 2,898,278,449
767
3,947,681 2,894,788,165
702
11,700,770 2,890,840,485
680
69,286 2,879,139,715
676
19,566,961 2,879,070,429
646
733,898 2,859,503,468
605
3,962,317 2,858,769,571
570
351,370 2,854,807,254
539
16,893,720 2,854,455,884
536
3,033,812 2,837,562,164
536
83,192 2,834,528,353
536
111,069 2,834,445,161
536
6,494,594 2,834,334,091
532
2,091,686 2,827,839,497
526 177,958,068 2,825,747,811
516
9,840,636 2,647,789,743
494
6,824,721 2,637,949,107
461
11,006,046 2,631,124,386
456
2,182,047 2,620,118,340
449
5,015,533 2,617,936,293
438
21,905,751 2,612,920,760
431
4,251,600 2,591,015,009
430 105,304,286 2,586,763,409
421
1,893,894 2,481,459,123
420
417,107 2,479,565,229
400
536,626 2,479,148,122
400
7,430,412 2,478,611,496
360 1,101,258,000 2,471,181,084
357
5,359,379 1,369,923,084
328
92,767,653 1,364,563,705
316
886,489 1,271,796,052
308
4,504,113 1,270,909,563
304
18,263,135 1,266,405,450
304
206,447 1,248,142,315
297
23,965,650 1,247,935,868
294 815,642,600 1,223,970,218
294
3,471,005 408,327,618
293
2,810,202 404,856,613
288
10,653,675 402,046,410
287
1,579,595 391,392,736
273 100,050,953 389,813,140
271
25,312,188 289,762,188
261
3,829,995 264,450,000
245
6,844,472 260,620,005
238
34,076,119 253,775,533

71

59%
59%
57% Semi-periph?
57%
57%
57%
57%
57%
57%
56%
56%
56%
56%
56%
56%
56%
56%
56%
56%
55%
55%
52%
52%
52%
51%
51%
51%
51%
51%
49%
49%
49%
49%
48%
27%
27%
25%
25%
25%
24%
24%
24%
8%
8%
8%
8%
8%
6%
5%
5%
5%

Table 5 (cont’d)
Ghana
214
14,019,524 219,699,414
4%
Lao PDR
213
3,966,604 205,679,889
4%
Vietnam
213
63,210,540 201,713,285
4%
Niger
201
7,348,927 138,502,745
3%
Chad
187
5,651,721 131,153,818
3%
Mali
184
8,392,502 125,502,096
2%
Uganda
176
16,497,874 117,109,595
2%
Guinea-Bissau
171
977,415 100,611,721
2%
Burkina Faso
171
8,845,928
99,634,306
2%
Nepal
169
18,197,285
90,788,378
2%
Mozambique
169
13,402,201
72,591,092
1%
Burundi
153
5,312,969
59,188,892
1%
Malawi
136
8,567,613
53,875,923
1%
Ethiopia
131
45,308,309
45,308,309
1% Periphery/ext.
South Sudan**
-* Data was missing; estimates were used from the regional categories
below:
** Data not added when no population information was given (e.g. South Sudan was part of Sudan)
North America
Europe & Central Asia
(all income levels)
Latin America & Caribbean
(all income levels)
East Asia & Pacific
(all income levels)
Middle East & North Africa
(all income levels)
Sub-Saharan Africa
(all income levels)
South Asia
World

26,452

271,695,100 5,096,020,488

100%

8,961

832,915,281 4,824,325,388

95%

3,585

425,905,749 3,991,410,107

78%

3,004 1,766,218,503 3,565,504,358

70%

2,409

238,534,227 1,799,285,856

35%

536 485,656,577 1,560,751,629
304 1,075,095,052 1,075,095,052
4,423 5,096,020,488

31%
21%

High income
Upper middle income
Lower middle income
Low income

19,498 964,724,984 5,096,020,488
1,480 1,974,032,427 4,131,295,504
480 1,677,587,196 2,157,263,076
246 479,675,881 479,675,881
5,096,020,488

100%
81%
42%
9%

72

APPENDIX IV: PROCESSED WORLD BANK DATA FOR 2005-2010
Table 6: Processed World Bank Data for 2005-2010
World Bank Data
Country Name
Monaco
Liechtenstein
Bermuda
Luxembourg
Channel Islands
Norway
Japan
United States
Switzerland
Iceland
Hong Kong SAR, China
Qatar
Sweden
Denmark
San Marino
Singapore
Ireland
United Kingdom
Isle of Man
Macao SAR, China
Finland
Austria
Netherlands
United Arab Emirates
Canada
Kuwait
Germany
Australia
Belgium
France
Andorra
Israel
Greenland
Italy
Brunei Darussalam
Bahamas, The
Spain
Korea, Rep.
Cyprus
New Zealand
Greece
Bahrain
Antigua and Barbuda
Slovenia
Faeroe Islands*
Gibraltar*
Portugal
Malta
Oman

2006-2010 avg. 2006-2010 avg.
PCGDP
population
99,400
35,336
81,855
35,517
70,361
64,200
54,016
488,979
44,110
151,609
40,944
4,770,402
39,562
127,647,841
37,936
304,121,163
37,565
7,650,360
36,469
313,732
33,999
6,966,440
33,876
1,381,829
32,324
9,225,173
31,658
5,491,913
31,464
31,164
30,535
4,778,740
29,550
4,396,665
28,647
61,399,227
28,537
81,765
28,252
518,458
27,699
5,314,176
26,673
8,334,032
26,625
16,463,198
25,905
6,145,079
25,784
33,357,010
25,067
2,546,126
25,011
82,071,511
24,954
21,509,880
24,662
10,711,857
23,153
64,174,219
21,678
82,476
21,658
7,330,560
21,255
56,503
19,613
59,765,037
17,994
384,656
17,188
333,565
15,942
45,308,254
15,384
48,596,400
15,226
1,076,484
15,071
4,273,080
14,285
11,236,025
13,320
1,044,183
13,241
86,856
13,029
2,027,759
12,757
48,595
12,757
29,264
11,793
10,618,083
10,834
410,732
10,744
2,636,669

73

cumulative
6,664,076,450
6,664,041,114
6,664,005,597
6,663,941,397
6,663,452,418
6,663,300,809
6,658,530,407
6,530,882,566
6,226,761,402
6,219,111,042
6,218,797,311
6,211,830,871
6,210,449,041
6,201,223,868
6,195,731,955
6,195,700,792
6,190,922,052
6,186,525,386
6,125,126,160
6,125,044,394
6,124,525,936
6,119,211,760
6,110,877,728
6,094,414,530
6,088,269,451
6,054,912,440
6,052,366,315
5,970,294,804
5,948,784,924
5,938,073,067
5,873,898,848
5,873,816,372
5,866,485,812
5,866,429,309
5,806,664,272
5,806,279,615
5,805,946,051
5,760,637,797
5,712,041,397
5,710,964,913
5,706,691,833
5,695,455,808
5,694,411,625
5,694,324,769
5,692,297,009
5,692,248,414
5,692,219,150
5,681,601,067
5,681,190,335

percentile
100%
100%
100%
100%
100%
100%
100%
98%
93%
93%
93%
93%
93%
93%
93%
93%
93%
93%
92%
92%
92%
92%
91%
91%
91%
91%
91%
89%
89%
89%
88%
88%
88%
88%
87%
87%
87%
86%
85%
85%
85% Core?
85%
85%
85%
85%
85%
85%
85%
85%

Table 6 (cont’d)
Trinidad and Tobago
St. Kitts and Nevis
Argentina
Barbados
Saudi Arabia
Uruguay
Equatorial Guinea
Seychelles
Slovak Republic
Libya
Czech Republic
Estonia
Croatia
Palau
Mexico
Poland
Chile
Dominica
Lebanon
Hungary
Venezuela, RB
Latvia
Grenada
Lithuania
Panama
Turkey
St. Lucia
Costa Rica
Malaysia
American Samoa*
French Polynesia*
Guam*
Korea, Dem. Rep.*
New Caledonia*
Northern Mariana Islands*
St. Vincent and the Grenadines
Aruba*
Cayman Islands*
Curacao*
Puerto Rico*
Sint Maarten (Dutch part)*
St. Martin (French part)*
Turks and Caicos Islands*
Virgin Islands (U.S.)*
Mauritius
Brazil
Cuba
Botswana
Gabon
Jamaica
Maldives
Dominican Republic
South Africa
Belize

10,580
9,760
9,745
9,642
9,409
8,257
8,167
8,161
8,110
7,737
7,355
6,722
6,501
6,402
6,168
6,126
6,120
5,933
5,906
5,777
5,678
5,609
5,578
5,525
5,446
5,217
5,148
5,082
4,962
4,938
4,938
4,938
4,938
4,938
4,938
4,898
4,828
4,828
4,828
4,828
4,828
4,828
4,828
4,828
4,823
4,395
4,132
4,110
4,102
3,774
3,753
3,708
3,697
3,649

1,330,989
51,104
39,716,212
272,188
26,145,488
3,334,789
662,711
86,082
5,409,480
6,136,838
10,407,980
1,341,162
4,432,648
20,235
110,627,917
38,145,335
16,793,390
68,194
4,164,766
10,039,338
27,933,400
2,265,578
103,742
3,357,585
3,406,093
70,915,712
170,294
4,521,246
27,497,970
66,225
264,491
175,500
24,120,362
246,378
63,090
109,152
105,312
55,136
140,650
3,953,704
39,036
29,350
35,716
109,805
1,267,814
191,498,520
11,264,265
1,954,358
1,450,823
2,684,800
307,643
9,663,872
48,818,540
322,577

74

5,678,553,666
5,677,222,677
5,677,171,573
5,637,455,361
5,637,183,172
5,611,037,684
5,607,702,895
5,607,040,184
5,606,954,101
5,601,544,621
5,595,407,783
5,584,999,804
5,583,658,642
5,579,225,994
5,579,205,759
5,468,577,842
5,430,432,506
5,413,639,116
5,413,570,922
5,409,406,156
5,399,366,818
5,371,433,418
5,369,167,839
5,369,064,097
5,365,706,511
5,362,300,419
5,291,384,707
5,291,214,412
5,286,693,167
5,259,195,197
5,259,128,972
5,258,864,481
5,258,688,981
5,234,568,620
5,234,322,242
5,234,259,151
5,234,150,000
5,234,044,687
5,233,989,552
5,233,848,901
5,229,895,197
5,229,856,161
5,229,826,811
5,229,791,096
5,229,681,291
5,228,413,477
5,036,914,957
5,025,650,692
5,023,696,335
5,022,245,512
5,019,560,712
5,019,253,069
5,009,589,197
4,960,770,657

85%
85%
85%
84%
84%
84%
84%
84%
84%
84%
84%
84%
84%
83%
83%
82%
81%
81%
81%
81%
81%
80%
80%
80%
80%
80%
79%
79%
79%
79%
79%
79%
79%
78%
78%
78%
78%
78%
78%
78%
78%
78%
78%
78%
78%
78%
75%
75%
75%
75%
75%
75%
75%
74%

Table 6 (cont’d)
West Bank and Gaza*
Colombia
Tunisia
Peru
Russian Federation
Romania
Namibia
Thailand
El Salvador
Suriname
Bulgaria
Jordan
Belarus
Marshall Islands
Kazakhstan
Fiji
Algeria
Montenegro
Macedonia, FYR
Bosnia and Herzegovina
Micronesia, Fed. Sts.
Iran, Islamic Rep.
Azerbaijan
Tonga
China
Guatemala
Egypt, Arab Rep.
Samoa
Turkmenistan
Albania
Cape Verde
Morocco
Tuvalu
Kosovo
Ecuador
Swaziland
Vanuatu
Paraguay
Syrian Arab Republic
Honduras
Armenia
Philippines
Angola
Georgia
Bhutan
Sri Lanka
Serbia
Bolivia
Congo, Rep.
Guyana
Solomon Islands
Ukraine
Indonesia
Nicaragua

3,616
3,108
2,994
2,871
2,864
2,625
2,606
2,575
2,571
2,556
2,512
2,437
2,422
2,410
2,346
2,280
2,172
2,166
2,144
2,128
2,127
2,107
2,047
2,040
2,034
1,859
1,838
1,804
1,773
1,771
1,751
1,733
1,730
1,726
1,672
1,560
1,500
1,485
1,455
1,396
1,365
1,303
1,275
1,201
1,189
1,175
1,172
1,170
1,163
1,127
1,098
1,070
1,049
915

3,933,203
45,000,707
10,334,120
28,467,495
142,030,000
21,514,115
2,200,685
68,233,834
6,131,680
514,922
7,622,127
5,789,600
9,606,800
53,002
15,741,465
843,982
34,428,993
629,229
2,052,174
3,772,524
110,398
72,283,531
8,764,866
102,865
1,324,647,902
13,701,429
78,334,121
181,867
4,920,151
3,180,935
487,256
31,324,134
9,783
1,795,400
14,054,495
1,151,071
228,124
6,230,915
19,645,775
7,305,955
3,080,033
90,181,187
18,042,145
4,406,740
700,846
20,467,446
7,351,350
9,618,383
3,835,973
751,495
510,397
46,295,860
234,923,376
5,638,230

75

4,960,448,080
4,956,514,876
4,911,514,170
4,901,180,050
4,872,712,554
4,730,682,554
4,709,168,439
4,706,967,754
4,638,733,920
4,632,602,240
4,632,087,318
4,624,465,191
4,618,675,591
4,609,068,791
4,609,015,788
4,593,274,323
4,592,430,341
4,558,001,349
4,557,372,119
4,555,319,946
4,551,547,422
4,551,437,024
4,479,153,494
4,470,388,627
4,470,285,762
3,145,637,860
3,131,936,431
3,053,602,311
3,053,420,444
3,048,500,293
3,045,319,358
3,044,832,102
3,013,507,967
3,013,498,185
3,011,702,785
2,997,648,290
2,996,497,219
2,996,269,095
2,990,038,180
2,970,392,405
2,963,086,450
2,960,006,417
2,869,825,231
2,851,783,086
2,847,376,346
2,846,675,500
2,826,208,054
2,818,856,704
2,809,238,322
2,805,402,349
2,804,650,854
2,804,140,457
2,757,844,597
2,522,921,221

74%
74%
73%
73%
73%
71%
70%
70%
69%
69%
69%
69%
69%
69%
69%
69%
69%
68%
68%
68%
68%
68%
67%
67%
67%
47%
47%
46%
46%
46%
46%
46%
45%
45%
45%
45%
45%
45%
45%
44%
44%
44%
43%
43%
43%
43%
42%
42%
42%
42%
42%
42%
41%
38%

Table 6 (cont’d)
Djibouti
Uzbekistan
Kiribati
Iraq
Mongolia
India
Cameroon
Papua New Guinea
Afghanistan*
Myanmar*
Vietnam
Pakistan
Mayotte*
Sao Tome and Principe*
Somalia*
Cote d'Ivoire
Yemen, Rep.
Moldova
Senegal
Cambodia
Bangladesh
Sudan
Nigeria
Lao PDR
Mauritania
Lesotho
Kenya
Tanzania
Guinea
Zambia
Haiti
Benin
Kyrgyz Republic
Mozambique
Uganda
Comoros
Gambia, The
Ghana
Timor-Leste
Zimbabwe
Rwanda
Chad
Togo
Mali
Burkina Faso
Sierra Leone
Tajikistan
Nepal
Madagascar
Central African Republic
Ethiopia
Niger
Malawi
Guinea-Bissau

853
839
783
728
727
725
705
683
683
683
654
646
621
621
621
579
566
564
552
521
509
497
496
493
468
458
456
424
414
398
387
371
365
358
353
344
337
332
328
319
314
285
281
262
261
257
255
254
251
236
192
175
170
158

855,915
27,318,952
96,536
30,205,077
2,668,757
1,140,169,751
18,766,222
6,550,424
32,543,010
47,267,161
85,123,493
167,497,786
191,986
160,019
8,930,794
19,009,513
22,648,477
3,572,039
11,794,385
13,824,909
145,502,176
41,437,552
150,773,480
6,021,599
3,295,058
2,127,924
38,491,296
42,325,085
9,575,495
12,367,051
9,736,282
8,358,385
5,278,264
22,336,910
31,368,426
697,344
1,636,970
23,272,672
1,080,667
12,501,578
10,018,262
10,654,797
5,777,843
14,470,639
15,530,356
5,604,922
6,696,501
28,900,994
19,558,302
4,241,123
79,459,110
14,468,003
14,026,595
1,454,557

76

2,517,282,991
2,516,427,076
2,489,108,124
2,489,011,587
2,458,806,511
2,456,137,753
1,315,968,003
1,297,201,780
1,290,651,357
1,258,108,347
1,210,841,186
1,125,717,693
958,219,907
958,027,922
957,867,903
948,937,109
929,927,596
907,279,119
903,707,080
891,912,695
878,087,786
732,585,610
691,148,057
540,374,577
534,352,978
531,057,921
528,929,997
490,438,700
448,113,615
438,538,120
426,171,069
416,434,787
408,076,402
402,798,138
380,461,228
349,092,802
348,395,458
346,758,488
323,485,816
322,405,149
309,903,571
299,885,310
289,230,513
283,452,670
268,982,031
253,451,675
247,846,753
241,150,252
212,249,259
192,690,956
188,449,834
108,990,724
94,522,720
80,496,125

38%
38%
37%
37%
37%
37%
20% Semi-periph?
19%
19%
19%
18%
17%
14%
14%
14%
14%
14%
14%
14%
13%
13%
11%
10%
8%
8%
8%
8%
7%
7%
7%
6%
6%
6%
6%
6%
5%
5%
5%
5%
5%
5%
4%
4%
4%
4%
4%
4%
4%
3%
3%
3%
2%
1%
1%

Table 6 (cont’d)
Liberia
151
3,655,885
79,041,568
Eritrea
140
4,948,719
75,385,683
Burundi
113
7,935,846
70,436,964
Congo, Dem. Rep.
98
62,501,118
62,501,118
South Sudan**
-* Data was missing; estimates were used from the regional
categories below:
** Data not added when no population information was given (e.g. South Sudan was part of
Sudan)

1%
1%
1%
1% Periphery/ext.

North America
Europe & Central Asia
(all income levels)
East Asia & Pacific
(all income levels)
Latin America & Caribbean
(all income levels)
Middle East & North Africa
(all income levels)
South Asia
Sub-Saharan Africa
(all income levels)
World

36,742

337,542,374

6,686,992,707

100%

12,757

883,304,396

6,349,450,333

95%

4,938

2,172,771,244

5,466,145,937

82%

4,828

575,878,146

3,293,374,694

49%

3,616
683

367,725,216
1,536,089,651

2,717,496,548
2,349,771,332

41%
35%

621
5,942

813,681,682
6,686,992,707

813,681,682

12%

High income
Upper middle income
Lower middle income
Low income

27,563
2,952
839
326

1,111,767,455
2,418,757,871
2,392,069,363
764,398,018
6,686,992,707

6,686,992,707
5,575,225,251
3,156,467,381
764,398,018

100%
83%
47%
11%

77

APPENDIX V: PROCESSED CIA FACTBOOK DATA FOR 2011
Table 7: Processed CIA Factbook Data for 2011
PCGDP PPP in 2011 U.S. dollars: Ranked World Listings
(Source: CIA World Fact Book web site, April 2012)
PCGDP
Population
Liechtenstein
$141,100
36,713
Qatar
$102,700
1,951,591
Luxembourg
$84,700
509,074
Bermuda
$69,900
69,080
Singapore
$59,900
5,353,494
Norway
$53,300
4,707,270
Brunei
$49,400
408,786
(Hong Kong)
$49,300
7,153,519
United Arab Emirates
$48,500
5,314,317
United States
$48,100
313,847,465
Switzerland
$43,400
7,655,628
Netherlands
$42,300
16,730,632
Austria
$41,700
8,219,743
Australia
$40,800
22,015,576
Kuwait
$40,700
2,646,314
Sweden
$40,600
9,103,788
Canada
$40,300
34,300,083
Denmark
$40,200
5,543,453
Ireland
$39,500
4,722,028
Finland
$38,300
5,262,930
Iceland
$38,000
313,183
Germany
$37,900
81,305,856
Taiwan
$37,900
23,113,901
Belgium
$37,600
10,438,353
Andorra
$37,200
85,082
San Marino
$36,200
32,140
United Kingdom
$35,900
63,047,162
France
$35,000
65,630,692
Monaco*
$35,000
30,510
Japan
$34,300
127,368,088
(Macau)
$33,000
578,025
South Korea
$31,700
48,860,500
Israel
$31,000
7,590,758
Bahamas
$30,900
316,182
Spain
$30,600
47,042,984
Italy
$30,100
61,261,254
Vatican City*
$30,100
836
Cyprus
$29,100
1,138,071
Slovenia
$29,100
1,996,617
New Zealand
$27,900
4,327,944
Greece
$27,600
10,767,827
Bahrain
$27,300
1,248,348

78

Cumulative pop
7,015,992,197
7,015,955,484
7,014,003,893
7,013,494,819
7,013,425,739
7,008,072,245
7,003,364,975
7,002,956,189
6,995,802,670
6,990,488,353
6,676,640,888
6,668,985,260
6,652,254,628
6,644,034,885
6,622,019,309
6,619,372,995
6,610,269,207
6,575,969,124
6,570,425,671
6,565,703,643
6,560,440,713
6,560,127,530
6,478,821,674
6,455,707,773
6,445,269,420
6,445,184,338
6,445,152,198
6,382,105,036
6,316,474,344
6,316,443,834
6,189,075,746
6,188,497,721
6,139,637,221
6,132,046,463
6,131,730,281
6,084,687,297
6,023,426,043
6,023,425,207
6,022,287,136
6,020,290,519
6,015,962,575
6,005,194,748

Percentile
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
95%
95%
95%
95%
94%
94%
94%
94%
94%
94%
94%
94%
92%
92%
92%
92%
92%
91%
90%
90%
88%
88%
88%
87%
87%
87%
86%
86%
86%
86%
86%
86% Core?

Table 7 (cont’d)
Oman
Czech Republic
Malta
Seychelles
Saudi Arabia
Barbados
Slovakia
Portugal
Antigua and Barbuda
Trinidad and Tobago
Estonia
Poland
Hungary
Equatorial Guinea
Lithuania
Croatia (EU/core status in
2013)
Argentina
Russia
St. Kitts and Nevis
Botswana
Chile
Gabon
Lebanon
Malaysia
Latvia
Uruguay
Mexico
Mauritius
Belarus
Turkey
Libya
Dominica
Panama
Bulgaria
Grenada
Kazakhstan
St. Lucia
Venezuela
Romania
Iran
St. Vincent and the
Grenadines
Brazil
Costa Rica
Montenegro
South Africa
Serbia

$26,200
$25,900
$25,700
$24,700
$24,000
$23,600
$23,400
$23,200
$22,100
$20,300
$20,200
$20,100
$19,600
$19,300
$18,700

3,090,150
10,177,300
409,836
90,024
26,534,504
287,733
5,483,088
10,781,459
89,018
1,226,383
1,274,709
38,415,284
9,958,453
685,991
3,525,761

6,003,946,400
6,000,856,250
5,990,678,950
5,990,269,114
5,990,179,090
5,963,644,586
5,963,356,853
5,957,873,765
5,947,092,306
5,947,003,288
5,945,776,905
5,944,502,196
5,906,086,912
5,896,128,459
5,895,442,468

86%
86%
85%
85%
85%
85%
85%
85%
85%
85%
85%
85%
84%
84%
84%

$18,300
$17,400
$16,700
$16,400
$16,300
$16,100
$16,000
$15,600
$15,600
$15,400
$15,400
$15,100
$15,000
$14,900
$14,600
$14,100
$13,600
$13,600
$13,500
$13,300
$13,000
$12,900
$12,400
$12,300
$12,200

4,480,043
42,192,494
138,082,178
50,726
2,098,018
17,067,369
1,608,321
4,140,289
29,179,952
2,191,580
3,316,328
114,975,406
1,313,095
9,542,883
79,749,461
6,733,620
73,126
3,510,045
7,037,935
109,011
17,522,010
162,178
28,047,938
21,848,504
78,868,711

5,891,916,707
5,887,436,664
5,845,244,170
5,707,161,992
5,707,111,266
5,705,013,248
5,687,945,879
5,686,337,558
5,682,197,269
5,653,017,317
5,650,825,737
5,647,509,409
5,532,534,003
5,531,220,908
5,521,678,025
5,441,928,564
5,435,194,944
5,435,121,818
5,431,611,773
5,424,573,838
5,424,464,827
5,406,942,817
5,406,780,639
5,378,732,701
5,356,884,197

84%
84%
83%
81%
81%
81%
81%
81%
81%
81%
81%
80%
79%
79%
79%
78%
77%
77%
77%
77%
77%
77%
77%
77%
76%

$11,700
$11,600
$11,500
$11,200
$11,000
$10,700

103,537
205,716,890
4,636,348
657,394
48,810,427
7,276,604

5,278,015,486
5,277,911,949
5,072,195,059
5,067,558,711
5,066,901,317
5,018,090,890

75%
75%
72%
72%
72%
72%

79

Table 7 (cont’d)
Macedonia
Azerbaijan
Colombia
Peru
Cuba
Thailand
Tunisia
Suriname
Dominican Republic
Jamaica
Maldives
China
Belize
Ecuador
Bosnia-Herzegovina
Palau
Albania
El Salvador
Turkmenistan
Tonga
Guyana
Namibia
Ukraine
Algeria
Kosovo
Egypt
Kiribati
Bhutan
Samoa
Jordan
Angola
Sri Lanka
Paraguay
Armenia
Georgia
Swaziland
Syria
Morocco
Nauru
Guatemala
Vanuatu
Bolivia
Indonesia
Fiji
Congo - Brazzaville
Mongolia
Honduras

$10,400
$10,200
$10,100
$10,000
$9,900
$9,700
$9,500
$9,500
$9,300
$9,000
$8,400
$8,400
$8,300
$8,300
$8,200
$8,100
$7,800
$7,600
$7,500
$7,500
$7,500
$7,300
$7,200
$7,200
$6,500
$6,500
$6,200
$6,000
$6,000
$5,900
$5,900
$5,600
$5,500
$5,400
$5,400
$5,200
$5,100
$5,100
$5,000
$5,000
$4,900
$4,800
$4,700
$4,600
$4,600
$4,500
$4,300

2,082,370
9,493,600
45,239,079
29,549,517
11,075,244
67,091,089
10,732,900
560,157
10,088,598
2,889,187
394,451
1,343,239,923
327,719
15,223,680
4,622,292
21,032
3,002,859
6,090,646
5,054,828
106,146
741,908
2,165,828
44,854,065
35,406,303
1,836,529
83,688,164
101,998
716,896
194,320
6,508,887
18,056,072
21,481,334
6,541,591
2,970,495
4,570,934
1,386,914
22,530,746
32,309,239
9,378
14,099,032
227,574
10,290,003
248,216,193
890,057
4,366,266
3,179,997
8,296,693

80

5,010,814,286
5,008,731,916
4,999,238,316
4,953,999,237
4,924,449,720
4,913,374,476
4,846,283,387
4,835,550,487
4,834,990,330
4,824,901,732
4,822,012,545
4,821,618,094
3,478,378,171
3,478,050,452
3,462,826,772
3,458,204,480
3,458,183,448
3,455,180,589
3,449,089,943
3,444,035,115
3,443,928,969
3,443,187,061
3,441,021,233
3,396,167,168
3,360,760,865
3,358,924,336
3,275,236,172
3,275,134,174
3,274,417,278
3,274,222,958
3,267,714,071
3,249,657,999
3,228,176,665
3,221,635,074
3,218,664,579
3,214,093,645
3,212,706,731
3,190,175,985
3,157,866,746
3,157,857,368
3,143,758,336
3,143,530,762
3,133,240,759
2,885,024,566
2,884,134,509
2,879,768,243
2,876,588,246

71%
71%
71%
71%
70%
70%
69%
69%
69%
69%
69%
69%
50%
50%
49%
49%
49%
49%
49%
49%
49%
49%
49%
48%
48%
48%
47%
47%
47%
47%
47%
46%
46%
46%
46%
46%
46%
45%
45%
45%
45%
45%
45%
41%
41%
41%
41%

Table 7 (cont’d)
Philippines
Cape Verde
Iraq
India
Moldova
Tuvalu
Uzbekistan
Vietnam
Solomon Islands
Nicaragua
East Timor
Ghana
Sudan
(West Bank and Gaza Strip)
Pakistan
Laos
Nigeria
Djibouti
Yemen
(Western Sahara)
Papua New Guinea
Marshall Islands
Kyrgyzstan
Cambodia
Cameroon
Micronesia
Mauritania
The Gambia
Tajikistan
Sao Tome and Principe
Senegal
Chad
North Korea
Bangladesh
Kenya
Cote D'Ivoire
Zambia
Benin
Burkina Faso
Tanzania
Lesotho
Nepal
Myanmar
Mali
Uganda
Rwanda
Haiti

$4,100
$4,000
$3,900
$3,700
$3,400
$3,400
$3,300
$3,300
$3,300
$3,200
$3,100
$3,100
$3,000
$2,900
$2,800
$2,700
$2,600
$2,600
$2,500
$2,500
$2,500
$2,500
$2,400
$2,300
$2,300
$2,200
$2,200
$2,100
$2,000
$2,000
$1,900
$1,900
$1,800
$1,700
$1,700
$1,600
$1,600
$1,500
$1,500
$1,500
$1,400
$1,300
$1,300
$1,300
$1,300
$1,300
$1,200

103,775,002
523,568
31,129,225
1,205,073,612
3,656,843
10,619
28,394,180
91,519,289
584,578
5,727,707
1,201,255
25,241,998
34,206,710
4,332,801
190,291,129
6,586,266
170,123,740
774,389
24,771,809
522,928
6,310,129
68,480
5,496,737
14,952,665
20,129,878
106,487
3,359,185
1,840,454
7,768,385
183,176
12,969,606
10,975,648
24,589,122
161,083,804
43,013,341
21,952,093
14,309,466
9,598,787
17,275,115
43,601,796
1,930,493
29,890,686
54,584,650
14,533,511
35,873,253
11,689,696
9,801,664

81

2,868,291,553
2,764,516,551
2,763,992,983
2,732,863,758
1,527,790,146
1,524,133,303
1,524,122,684
1,495,728,504
1,404,209,215
1,403,624,637
1,397,896,930
1,396,695,675
1,371,453,677
1,337,246,967
1,332,914,166
1,142,623,037
1,136,036,771
965,913,031
965,138,642
940,366,833
939,843,905
933,533,776
933,465,296
927,968,559
913,015,894
892,886,016
892,779,529
889,420,344
887,579,890
879,811,505
879,628,329
866,658,723
855,683,075
831,093,953
670,010,149
626,996,808
605,044,715
590,735,249
581,136,462
563,861,347
520,259,551
518,329,058
488,438,372
433,853,722
419,320,211
383,446,958
371,757,262

41%
39%
39%
39%
22%
22%
22%
21% Semi-periph?
20%
20%
20%
20%
20%
19%
19%
16%
16%
14%
14%
13%
13%
13%
13%
13%
13%
13%
13%
13%
13%
13%
13%
12%
12%
12%
10%
9%
9%
8%
8%
8%
7%
7%
7%
6%
6%
5%
5%

Table 7 (cont’d)
Comoros
$1,200
737,284
361,955,598
5%
Guinea
$1,100
10,884,958
361,218,314
5%
Guinea-Bissau
$1,100
1,628,603
350,333,356
5%
Ethiopia
$1,100
93,815,992
348,704,753
5%
Mozambique
$1,100
23,515,934
254,888,761
4%
Afghanistan
$1,000
30,419,928
231,372,827
3%
Togo
$900
6,961,049
200,952,899
3%
Malawi
$900
16,323,044
193,991,850
3%
Madagascar
$900
22,585,517
177,668,806
3%
Sierra Leone
$800
5,485,998
155,083,289
2%
Niger
$800
17,078,839
149,597,291
2%
South Sudan*
$800
10,625,176
132,518,452
2%
Central African Republic
$800
5,057,208
121,893,276
2%
Eritrea
$700
6,086,495
116,836,068
2%
Somalia
$600
10,085,638
110,749,573
2%
Zimbabwe
$500
12,619,600
100,663,935
1%
Liberia
$400
3,887,886
88,044,335
1%
Burundi
$400
10,557,259
84,156,449
1%
Congo - Kinshasa
$300
73,599,190
73,599,190
1% Periphery
WORLD TOTAL:
$11,324 7,015,992,197
* Economic data was not provided for Monaco or Vatican City, so the figures for surrounding France and Italy
were substituted.
South Sudan economic data was not available, so info for the adjacent Central African Republic was substituted.
Various small territories and dependencies were not included. The effect of their exclusion is considered trivial
here.

82

APPENDIX VI: CIA FACTBOOK DATA WITH CHINA SUBDIVISIONS
Table 8: CIA Factbook Data with China Subdivisions
PCGDP PPP in 2011 U.S. dollars: Ranked world listings, including a breakdown of Chinese subdivisions
(Source: CIA World Fact Book web site, April 2012, except for Chinese subdivisions - see bottom)
PCGDP
Population
product GDP x pop
Cum. Pop. Percentile
Liechtenstein
$141,100
36,713
$5,180,204,300 7,008,260,653
100%
Qatar
$102,700
1,951,591
$200,428,395,700 7,008,223,940
100%
Luxembourg
$84,700
509,074
$43,118,567,800 7,006,272,349
100%
Macau
$72,110
610,656
$44,034,413,175 7,005,763,275
100%
Bermuda
$69,900
69,080
$4,828,692,000 7,005,152,619
100%
Singapore
$59,900
5,353,494
$320,674,290,600 7,005,083,539
100%
Norway
$53,300
4,707,270
$250,897,491,000 6,999,730,045
100%
Brunei
$49,400
408,786
$20,194,028,400 6,995,022,775
100%
United Arab Emirates
$48,500
5,314,317
$257,744,374,500 6,994,613,989
100%
United States
$48,100
313,847,465
$15,096,063,066,500 6,989,299,672
100%
Hong Kong
$45,580
7,226,097
$329,365,523,111 6,675,452,207
95%
Switzerland
$43,400
7,655,628
$332,254,255,200 6,668,226,109
95%
Netherlands
$42,300
16,730,632
$707,705,733,600 6,660,570,481
95%
Austria
$41,700
8,219,743
$342,763,283,100 6,643,839,849
95%
Australia
$40,800
22,015,576
$898,235,500,800 6,635,620,106
95%
Kuwait
$40,700
2,646,314
$107,704,979,800 6,613,604,530
94%
Sweden
$40,600
9,103,788
$369,613,792,800 6,610,958,216
94%
Canada
$40,300
34,300,083
$1,382,293,344,900 6,601,854,428
94%
Denmark
$40,200
5,543,453
$222,846,810,600 6,567,554,345
94%
Ireland
$39,500
4,722,028
$186,520,106,000 6,562,010,892
94%
Finland
$38,300
5,262,930
$201,570,219,000 6,557,288,864
94%
Iceland
$38,000
313,183
$11,900,954,000 6,552,025,934
93%
Germany
$37,900
81,305,856
$3,081,491,942,400 6,551,712,751
93%
Taiwan
$37,900
23,113,901
$876,016,847,900 6,470,406,895
92%
Belgium
$37,600
10,438,353
$392,482,072,800 6,447,292,994
92%
Andorra
$37,200
85,082
$3,165,050,400 6,436,854,641
92%
San Marino
$36,200
32,140
$1,163,468,000 6,436,769,559
92%
United Kingdom
$35,900
63,047,162
$2,263,393,115,800 6,436,737,419
92%
France
$35,000
65,630,692
$2,297,074,220,000 6,373,690,257
91%
Monaco*
$35,000
30,510
$1,067,850,000 6,308,059,565
90%
Japan
$34,300
127,368,088
$4,368,725,418,400 6,308,029,055
90%
South Korea
$31,700
48,860,500
$1,548,877,850,000 6,180,660,967
88%
Israel
$31,000
7,590,758
$235,313,498,000 6,131,800,467
87%
Bahamas
$30,900
316,182
$9,770,023,800 6,124,209,709
87%
Spain
$30,600
47,042,984
$1,439,515,310,400 6,123,893,527
87%
Italy
$30,100
61,261,254
$1,843,963,745,400 6,076,850,543
87%
Vatican City*
$30,100
836
$25,163,600 6,015,589,289
86%
Cyprus
$29,100
1,138,071
$33,117,866,100 6,015,588,453
86%
Slovenia
$29,100
1,996,617
$58,101,554,700 6,014,450,382
86%
New Zealand
$27,900
4,327,944
$120,749,637,600 6,012,453,765
86%
Greece
$27,600
10,767,827
$297,192,025,200 6,008,125,821
86%
Bahrain
$27,300
1,248,348
$34,079,900,400 5,997,357,994
86%
Oman
$26,200
3,090,150
$80,961,930,000 5,996,109,646
86%
Czech Republic
$25,900
10,177,300
$263,592,070,000 5,993,019,496
86%
Malta
$25,700
409,836
$10,532,785,200 5,982,842,196
85%
Seychelles
$24,700
90,024
$2,223,592,800 5,982,432,360
85%
Saudi Arabia
$24,000
26,534,504
$636,828,096,000 5,982,342,336
85%
Barbados
$23,600
287,733
$6,790,498,800 5,955,807,832
85%

83

Table 8 (cont’d)
Slovakia
Portugal
Shanghai
Antigua and Barbuda
Beijing
Trinidad and Tobago
Estonia
Poland
Hungary
Equatorial Guinea
Tianjin
Lithuania
Croatia (EU/core status
2013)
Argentina
Russia
St. Kitts and Nevis
Botswana
Chile
Gabon
Lebanon
Malaysia
Latvia
Uruguay
Mexico
Mauritius
Belarus
Turkey
Libya
Jiangsu
Dominica
Panama
Bulgaria
Grenada
Nei Monggol (inland
territory)
Kazakhstan
St. Lucia
Zhejiang
Venezuela
Romania
Iran
Guangdong
St. Vincent and the
Grenadines
Brazil
Costa Rica
Montenegro
South Africa
Shandong
Liaoning
Serbia
Macedonia
Azerbaijan

$23,400
$23,200
$22,983
$22,100
$20,841
$20,300
$20,200
$20,100
$19,600
$19,300
$19,284
$18,700

5,483,088
10,781,459
19,744,548
89,018
17,505,476
1,226,383
1,274,709
38,415,284
9,958,453
685,991
12,518,451
3,525,761

$128,304,259,200
$250,129,848,800
$453,788,947,656
$1,967,297,800
$364,831,616,644
$24,895,574,900
$25,749,121,800
$772,147,208,400
$195,185,678,800
$13,239,626,300
$241,405,800,655
$65,931,730,700

5,955,520,099
5,950,037,011
5,939,255,552
5,919,511,004
5,919,421,986
5,901,916,511
5,900,690,128
5,899,415,419
5,861,000,135
5,851,041,682
5,850,355,691
5,837,837,240

85%
85%
85%
84%
84%
84%
84%
84%
84%
83%
83%
83%

$18,300
$17,400
$16,700
$16,400
$16,300
$16,100
$16,000
$15,600
$15,600
$15,400
$15,400
$15,100
$15,000
$14,900
$14,600
$14,100
$13,714
$13,600
$13,600
$13,500
$13,300

4,480,043
42,192,494
138,082,178
50,726
2,098,018
17,067,369
1,608,321
4,140,289
29,179,952
2,191,580
3,316,328
114,975,406
1,313,095
9,542,883
79,749,461
6,733,620
78,876,416
73,126
3,510,045
7,037,935
109,011

$81,984,786,900
$734,149,395,600
$2,305,972,372,600
$831,906,400
$34,197,693,400
$274,784,640,900
$25,733,136,000
$64,588,508,400
$455,207,251,200
$33,750,332,000
$51,071,451,200
$1,736,128,630,600
$19,696,425,000
$142,188,956,700
$1,164,342,130,600
$94,944,042,000
$1,081,711,171,058
$994,513,600
$47,736,612,000
$95,012,122,500
$1,449,846,300

5,834,311,479
5,829,831,436
5,787,638,942
5,649,556,764
5,649,506,038
5,647,408,020
5,630,340,651
5,628,732,330
5,624,592,041
5,595,412,089
5,593,220,509
5,589,904,181
5,474,928,775
5,473,615,680
5,464,072,797
5,384,323,336
5,377,589,716
5,298,713,300
5,298,640,174
5,295,130,129
5,288,092,194

83%
83%
83%
81%
81%
81%
80%
80%
80%
80%
80%
80%
78%
78%
78%
77%
77%
76%
76%
76%
75%

$13,108
$13,000
$12,900
$12,876
$12,400
$12,300
$12,200
$12,074

24,833,349
17,522,010
162,178
53,432,411
28,047,938
21,848,504
78,868,711
100,045,828

$325,515,539,794
$227,786,130,000
$2,092,096,200
$687,995,723,253
$347,794,431,200
$268,736,599,200
$962,198,274,200
$1,207,953,333,095

5,287,983,183
5,263,149,834
5,245,627,824
5,245,465,646
5,192,033,235
5,163,985,297
5,142,136,793
5,063,268,082

75%
75%
75%
75%
74%
74%
73%
72%

$11,700
$11,600
$11,500
$11,200
$11,000
$10,914
$10,772
$10,700
$10,400
$10,200

103,537
205,716,890
4,636,348
657,394
48,810,427
95,364,132
43,865,465
7,276,604
2,082,370
9,493,600

$1,211,382,900
$2,386,315,924,000
$53,318,002,000
$7,362,812,800
$536,914,697,000
$1,040,804,131,451
$472,518,788,770
$77,859,662,800
$21,656,648,000
$96,834,720,000

4,963,222,254
4,963,118,717
4,757,401,827
4,752,765,479
4,752,108,085
4,703,297,658
4,607,933,526
4,564,068,061
4,556,791,457
4,554,709,087

71%
71%
68%
68%
68%
67%
66%
65%
65%
65%

84

Table 8 (cont’d)
Colombia
Peru
Fujian
Cuba
Thailand
Tunisia
Suriname
Dominican Republic
Jamaica
Maldives
Jilin
Belize
Ecuador
Bosnia-Herzegovina
Palau
Albania
El Salvador
Turkmenistan
Tonga
Guyana
Namibia
Hebei
Ningxia Huizu
Ukraine
Algeria
Shaanxi
Chongqing
Hubei
Heilongjiang
Shanxi
Kosovo
Egypt
Hunan
Henan
Kiribati
Hainan
Qinghai
Xinjiang Uygur
Bhutan
Samoa
Jordan
Angola
Jianxi
Sri Lanka
Paraguay
Armenia
Georgia
Sichuan
Anhui
Swaziland
Syria
Morocco
Guanxi Zhuangzu
Nauru

$10,100
$10,000
$9,969
$9,900
$9,700
$9,500
$9,500
$9,300
$9,000
$8,400
$8,346
$8,300
$8,300
$8,200
$8,100
$7,800
$7,600
$7,500
$7,500
$7,500
$7,300
$7,276
$7,205
$7,200
$7,200
$7,187
$7,171
$7,009
$6,777
$6,581
$6,500
$6,500
$6,474
$6,402
$6,200
$6,117
$6,117
$6,046
$6,000
$6,000
$5,900
$5,900
$5,671
$5,600
$5,500
$5,400
$5,400
$5,350
$5,261
$5,200
$5,100
$5,100
$5,011
$5,000

45,239,079
29,549,517
37,453,576
11,075,244
67,091,089
10,732,900
560,157
10,088,598
2,889,187
394,451
27,581,302
327,719
15,223,680
4,622,292
21,032
3,002,859
6,090,646
5,054,828
106,146
741,908
2,165,828
71,752,095
6,411,889
44,854,065
35,406,303
37,351,800
29,616,822
58,215,884
37,962,456
35,418,055
1,836,529
83,688,164
64,933,101
94,549,923
101,998
8,956,290
5,699,457
21,881,844
716,896
194,320
6,508,887
18,056,072
43,763,689
21,481,334
6,541,591
2,970,495
4,570,934
82,642,129
60,454,956
1,386,914
22,530,746
32,309,239
48,954,266
9,378

85

$456,914,697,900
$295,495,170,000
$373,374,695,833
$109,644,915,600
$650,783,563,300
$101,962,550,000
$5,321,491,500
$93,823,961,400
$26,002,683,000
$3,313,388,400
$230,193,543,543
$2,720,067,700
$126,356,544,000
$37,902,794,400
$170,359,200
$23,422,300,200
$46,288,909,600
$37,911,210,000
$796,095,000
$5,564,310,000
$15,810,544,400
$522,068,240,962
$46,197,662,498
$322,949,268,000
$254,925,381,600
$268,447,384,063
$212,382,231,017
$408,035,130,385
$257,271,562,767
$233,086,221,607
$11,937,438,500
$543,973,066,000
$420,376,897,775
$605,308,609,332
$632,387,600
$54,785,624,912
$34,863,579,490
$132,297,631,725
$4,301,376,000
$1,165,920,000
$38,402,433,300
$106,530,824,800
$248,183,880,090
$120,295,470,400
$35,978,750,500
$16,040,673,000
$24,683,043,600
$442,135,389,718
$318,053,525,499
$7,211,952,800
$114,906,804,600
$164,777,118,900
$245,309,827,038
$46,890,000

4,545,215,487
4,499,976,408
4,470,426,891
4,432,973,315
4,421,898,071
4,354,806,982
4,344,074,082
4,343,513,925
4,333,425,327
4,330,536,140
4,330,141,689
4,302,560,388
4,302,232,669
4,287,008,989
4,282,386,697
4,282,365,665
4,279,362,806
4,273,272,160
4,268,217,332
4,268,111,186
4,267,369,278
4,265,203,450
4,193,451,355
4,187,039,466
4,142,185,401
4,106,779,098
4,069,427,298
4,039,810,476
3,981,594,592
3,943,632,136
3,908,214,081
3,906,377,552
3,822,689,388
3,757,756,287
3,663,206,363
3,663,104,365
3,654,148,076
3,648,448,618
3,626,566,774
3,625,849,878
3,625,655,558
3,619,146,671
3,601,090,599
3,557,326,910
3,535,845,576
3,529,303,985
3,526,333,490
3,521,762,556
3,439,120,427
3,378,665,471
3,377,278,557
3,354,747,811
3,322,438,572
3,273,484,306

65%
64%
64%
63%
63%
62%
62%
62%
62%
62%
62%
61%
61%
61%
61%
61%
61%
61%
61%
61%
61%
61%
60%
60%
59%
59%
58%
58%
57%
56%
56%
56%
55%
54%
52%
52%
52%
52%
52%
52%
52%
52%
51%
51%
50%
50%
50%
50%
49%
48%
48%
48%
47%
47%

Table 8 (cont’d)
Guatemala
Vanuatu
Bolivia
Indonesia
Fiji
Congo - Brazzaville
Tibet (Zizang)
Mongolia
Honduras
Yunnan
Philippines
Gansu
Cape Verde
Iraq
India
Moldova
Tuvalu
Guizhou
Uzbekistan
Vietnam
Solomon Islands
Nicaragua
East Timor
Ghana
Sudan
(West Bank and Gaza
Strip)
Pakistan
Laos
Nigeria
Djibouti
Yemen
(Western Sahara)
Papua New Guinea
Marshall Islands
Kyrgyzstan
Cambodia
Cameroon
Micronesia
Mauritania
The Gambia
Tajikistan
Sao Tome and Principe
Senegal
Chad
North Korea
Bangladesh
Kenya
Cote D'Ivoire
Zambia
Benin
Burkina Faso
Tanzania
Lesotho

$5,000
$4,900
$4,800
$4,700
$4,600
$4,600
$4,583
$4,500
$4,300
$4,280
$4,100
$4,031
$4,000
$3,900
$3,700
$3,400
$3,400
$3,335
$3,300
$3,300
$3,300
$3,200
$3,100
$3,100
$3,000

14,099,032
227,574
10,290,003
248,216,193
890,057
4,366,266
3,053,281
3,179,997
8,296,693
46,613,418
103,775,002
26,868,870
523,568
31,129,225
1,205,073,612
3,656,843
10,619
39,081,992
28,394,180
91,519,289
584,578
5,727,707
1,201,255
25,241,998
34,206,710

$70,495,160,000
$1,115,112,600
$49,392,014,400
$1,166,616,107,100
$4,094,262,200
$20,084,823,600
$13,993,185,105
$14,309,986,500
$35,675,779,900
$199,505,427,085
$425,477,508,200
$108,308,412,958
$2,094,272,000
$121,403,977,500
$4,458,772,364,400
$12,433,266,200
$36,104,600
$130,338,443,324
$93,700,794,000
$302,013,653,700
$1,929,107,400
$18,328,662,400
$3,723,890,500
$78,250,193,800
$102,620,130,000

3,273,474,928
3,259,375,896
3,259,148,322
3,248,858,319
3,000,642,126
2,999,752,069
2,995,385,803
2,992,332,522
2,989,152,525
2,980,855,832
2,934,242,415
2,830,467,413
2,803,598,543
2,803,074,975
2,771,945,750
1,566,872,138
1,563,215,295
1,563,204,676
1,524,122,684
1,495,728,504
1,404,209,215
1,403,624,637
1,397,896,930
1,396,695,675
1,371,453,677

47%
47%
47%
46%
43%
43%
43%
43%
43%
43%
42%
40%
40%
40%
40%
22%
22%
22%
22%
21%
20%
20%
20%
20%
20%

$2,900
$2,800
$2,700
$2,600
$2,600
$2,500
$2,500
$2,500
$2,500
$2,400
$2,300
$2,300
$2,200
$2,200
$2,100
$2,000
$2,000
$1,900
$1,900
$1,800
$1,700
$1,700
$1,600
$1,600
$1,500
$1,500
$1,500
$1,400

4,332,801
190,291,129
6,586,266
170,123,740
774,389
24,771,809
522,928
6,310,129
68,480
5,496,737
14,952,665
20,129,878
106,487
3,359,185
1,840,454
7,768,385
183,176
12,969,606
10,975,648
24,589,122
161,083,804
43,013,341
21,952,093
14,309,466
9,598,787
17,275,115
43,601,796
1,930,493

$12,565,122,900
$532,815,161,200
$17,782,918,200
$442,321,724,000
$2,013,411,400
$61,929,522,500
$1,307,320,000
$15,775,322,500
$171,200,000
$13,192,168,800
$34,391,129,500
$46,298,719,400
$234,271,400
$7,390,207,000
$3,864,953,400
$15,536,770,000
$366,352,000
$24,642,251,400
$20,853,731,200
$44,260,419,600
$273,842,466,800
$73,122,679,700
$35,123,348,800
$22,895,145,600
$14,398,180,500
$25,912,672,500
$65,402,694,000
$2,702,690,200

1,337,246,967
1,332,914,166
1,142,623,037
1,136,036,771
965,913,031
965,138,642
940,366,833
939,843,905
933,533,776
933,465,296
927,968,559
913,015,894
892,886,016
892,779,529
889,420,344
887,579,890
879,811,505
879,628,329
866,658,723
855,683,075
831,093,953
670,010,149
626,996,808
605,044,715
590,735,249
581,136,462
563,861,347
520,259,551

19%
19%
16%
16%
14%
14%
13%
13%
13%
13%
13%
13%
13%
13%
13%
13%
13%
13%
12%
12%
12%
10%
9%
9%
8%
8%
8%
7%

86

Table 8 (cont’d)
Nepal
$1,300
29,890,686
$38,857,891,800
518,329,058
7%
Myanmar
$1,300
54,584,650
$70,960,045,000
488,438,372
7%
Mali
$1,300
14,533,511
$18,893,564,300
433,853,722
6%
Uganda
$1,300
35,873,253
$46,635,228,900
419,320,211
6%
Rwanda
$1,300
11,689,696
$15,196,604,800
383,446,958
5%
Haiti
$1,200
9,801,664
$11,761,996,800
371,757,262
5%
Comoros
$1,200
737,284
$884,740,800
361,955,598
5%
Guinea
$1,100
10,884,958
$11,973,453,800
361,218,314
5%
Guinea-Bissau
$1,100
1,628,603
$1,791,463,300
350,333,356
5%
Ethiopia
$1,100
93,815,992
$103,197,591,200
348,704,753
5%
Mozambique
$1,100
23,515,934
$25,867,527,400
254,888,761
4%
Afghanistan
$1,000
30,419,928
$30,419,928,000
231,372,827
3%
Togo
$900
6,961,049
$6,264,944,100
200,952,899
3%
Malawi
$900
16,323,044
$14,690,739,600
193,991,850
3%
Madagascar
$900
22,585,517
$20,326,965,300
177,668,806
3%
Sierra Leone
$800
5,485,998
$4,388,798,400
155,083,289
2%
Niger
$800
17,078,839
$13,663,071,200
149,597,291
2%
South Sudan*
$800
10,625,176
$8,500,140,800
132,518,452
2%
Central African Republic
$800
5,057,208
$4,045,766,400
121,893,276
2%
Eritrea
$700
6,086,495
$4,260,546,500
116,836,068
2%
Somalia
$600
10,085,638
$6,051,382,800
110,749,573
2%
Zimbabwe
$500
12,619,600
$6,309,800,000
100,663,935
1%
Liberia
$400
3,887,886
$1,555,154,400
88,044,335
1%
Burundi
$400
10,557,259
$4,222,903,600
84,156,449
1%
Congo - Kinshasa
$300
73,599,190
$22,079,757,000
73,599,190
1%
World total (adjusted
mixed data):
$11,351 7,008,260,653
$79,551,036,287,486
World total from CIA data
only:
$11,324 7,015,992,197
$79,451,552,857,000
* Economic data was not provided for Monaco or Vatican City; figures for surrounding France and Italy were
substituted.
South Sudan economic data was not available so info for the adjacent Central African Republic was substituted.
BELOW: Additional information for China's political subdivisions (2010 data)
Source for data on China: The Economist web site: http://www.economist.com/content/all_parities_china
(see note)
Richer Chinese provinces and special cities (those that are above the national average):
Macau
$72,110
600,000
$43,266,000,000
South-C
Hong Kong
$45,580
7,100,000
$323,618,000,000
South-C
Shanghai
$22,983
19,400,000
$445,870,200,000
East
Beijing
$20,841
17,200,000
$358,465,200,000
North
Tianjin
$19,284
12,300,000
$237,193,200,000
North 56.6m core?
Jiangsu
$13,714
77,500,000
$1,062,835,000,000
East
Nei Monggol
(inland territory)
$13,108
24,400,000
$319,835,200,000
North
Zhejiang
$12,876
52,500,000
$675,990,000,000
East
Guangdong
$12,074
98,300,000
$1,186,874,200,000
South-C
Shandong
$10,914
93,700,000
$1,022,641,800,000
East
Liaoning
$10,772
43,100,000
$464,273,200,000
Northeast
Fujian
$9,969
36,800,000
$366,859,200,000
East
Richer area subtotal:
$13,476
482,900,000
$6,507,721,200,000 EU sized total!
NOTE: Although grouped together here and mostly composing China's coastal areas, these subdivisions are
distributed across most (4 out of 6) of China's traditional internal regions, as marked in one of the columns above.

87

Table 8 (cont’d)
Other Chinese provinces and territories (all below the national average):
Hebei
$7,276
70,500,000
$512,958,000,000
Jilin
$8,346
27,100,000
$226,176,600,000
Heilongjiang
$6,777
37,300,000
$252,782,100,000
Shanxi
$6,581
34,800,000
$229,018,800,000
Shaanxi
$7,187
36,700,000
$263,762,900,000
Ningxia Huizu
$7,205
6,300,000
$45,391,500,000
Henan
$6,402
92,900,000
$594,745,800,000
Anhui
$5,261
59,400,000
$312,503,400,000
Hubei
$7,009
57,200,000
$400,914,800,000
Chongqing
$7,171
29,100,000
$208,676,100,000
Hunan
$6,474
63,800,000
$413,041,200,000
Jianxi
$5,671
43,000,000
$243,853,000,000
Hainan
$6,117
8,800,000
$53,829,600,000
Guanxi Zhuangzu
$5,011
48,100,000
$241,029,100,000
Guizhou
$3,335
38,400,000
$128,064,000,000
Sichuan
$5,350
81,200,000
$434,420,000,000
Yunnan
$4,280
45,800,000
$196,024,000,000
Tibet (Zizang)
$4,583
3,000,000
$13,749,000,000
Gansu
$4,031
26,400,000
$106,418,400,000
Qinghai
$6,117
5,600,000
$34,255,200,000
Xinjiang Uygur
$6,046
21,500,000
$129,989,000,000
Less rich area subtotal:
$6,024
836,900,000
$5,041,602,500,000
CHINA Grand Total
(Economist):
$8,751 1,319,800,000
$11,549,323,700,000
CHINA Grand Total
(CIA):
$8,400 1,343,239,923
$11,283,215,353,200
Ratio of CIA 2011 total to Econ 2010
total:
1.017760 (adjustment factor for population data)

88

APPENDIX VII: CIA FACTBOOK DATA IN REGIONAL GROUPINGS
Table 9: CIA Factbook Data in Regional Groupings
PCGDP PPP in 2011 U.S. dollars: Regional groupings and summaries
Source: CIA World Fact Book web site, April 2012
PCGDP
Population
product GDP x pop
Core region: Rich Europe (EU, EFTA, etc.)
Finland
$38,300
5,262,930
$201,570,219,000
Sweden
$40,600
9,103,788
$369,613,792,800
Norway
$53,300
4,707,270
$250,897,491,000
Iceland
$38,000
313,183
$11,900,954,000
Ireland
$39,500
4,722,028
$186,520,106,000
United Kingdom
$35,900
63,047,162
$2,263,393,115,800
Denmark
$40,200
5,543,453
$222,846,810,600
Germany
$37,900
81,305,856
$3,081,491,942,400
Netherlands
$42,300
16,730,632
$707,705,733,600
Belgium
$37,600
10,438,353
$392,482,072,800
Luxembourg
$84,700
509,074
$43,118,567,800
Austria
$41,700
8,219,743
$342,763,283,100
Liechtenstein
$141,100
36,713
$5,180,204,300
Switzerland
$43,400
7,655,628
$332,254,255,200
France
$35,000
65,630,692
$2,297,074,220,000
Andorra
$37,200
85,082
$3,165,050,400
Spain
$30,600
47,042,984
$1,439,515,310,400
Portugal
$23,200
10,781,459
$250,129,848,800
Monaco*
$35,000
30,510
$1,067,850,000
Italy
$30,100
61,261,254
$1,843,963,745,400
San Marino
$36,200
32,140
$1,163,468,000
Vatican City*
$30,100
836
$25,163,600
Malta
$25,700
409,836
$10,532,785,200
Greece
$27,600
10,767,827
$297,192,025,200
Cyprus
$29,100
1,138,071
$33,117,866,100
Slovenia
$29,100
1,996,617
$58,101,554,700
Czech Republic
$25,900
10,177,300
$263,592,070,000
Hungary
$19,600
9,958,453
$195,185,678,800
Slovakia
$23,400
5,483,088
$128,304,259,200
Poland
$20,100
38,415,284
$772,147,208,400
Estonia
$20,200
1,274,709
$25,749,121,800
Latvia
$15,400
2,191,580
$33,750,332,000
Lithuania
$18,700
3,525,761
$65,931,730,700
Bulgaria
$13,500
7,037,935
$95,012,122,500
Romania
$12,300
21,848,504
$268,736,599,200
* Economic data were not provided for Monaco or Vatican City, so the figures for surrounding France and Italy
were substituted.
TOTAL REGION:
$31,925
516,685,735
$16,495,196,558,800
Semi-peripheral region: Central & East Europe, Central Asia (CIS, former communist):
Croatia (EU/core status in 2013)
$18,300
4,480,043

89

$81,984,786,900

Table 9 (cont’d)
Bosnia-Herzegovina
Serbia
Montenegro
Kosovo
Macedonia
Albania
Moldova
Ukraine
Belarus
Russia
Azerbaijan
Armenia
Georgia
Kazakhstan
Uzbekistan
Turkmenistan
Kyrgyzstan
Tajikistan
TOTAL REGION:

$8,200
$10,700
$11,200
$6,500
$10,400
$7,800
$3,400
$7,200
$14,900
$16,700
$10,200
$5,400
$5,400
$13,000
$3,300
$7,500
$2,400
$2,000
$11,851

4,622,292
7,276,604
657,394
1,836,529
2,082,370
3,002,859
3,656,843
44,854,065
9,542,883
138,082,178
9,493,600
2,970,495
4,570,934
17,522,010
28,394,180
5,054,828
5,496,737
7,768,385
301,365,229

$37,902,794,400
$77,859,662,800
$7,362,812,800
$11,937,438,500
$21,656,648,000
$23,422,300,200
$12,433,266,200
$322,949,268,000
$142,188,956,700
$2,305,972,372,600
$96,834,720,000
$16,040,673,000
$24,683,043,600
$227,786,130,000
$93,700,794,000
$37,911,210,000
$13,192,168,800
$15,536,770,000
$3,571,355,816,500

Semi-peripheral region: West Asia and North Africa
Iran
$12,200
Turkey
$14,600
Syria
$5,100
Lebanon
$15,600
Israel
$31,000
(West Bank and Gaza Strip)
$2,900
Jordan
$5,900
Iraq
$3,900
Kuwait
$40,700
Yemen
$2,500
Saudi Arabia
$24,000
Bahrain
$27,300
Qatar
$102,700
United Arab Emirates
$48,500
Oman
$26,200
Egypt
$6,500
Libya
$14,100
Tunisia
$9,500
Algeria
$7,200
Morocco
$5,100
(Western Sahara)
$2,500
Cape Verde
$4,000
TOTAL REGION:
$11,178

78,868,711
79,749,461
22,530,746
4,140,289
7,590,758
4,332,801
6,508,887
31,129,225
2,646,314
24,771,809
26,534,504
1,248,348
1,951,591
5,314,317
3,090,150
83,688,164
6,733,620
10,732,900
35,406,303
32,309,239
522,928
523,568
470,324,633

$962,198,274,200
$1,164,342,130,600
$114,906,804,600
$64,588,508,400
$235,313,498,000
$12,565,122,900
$38,402,433,300
$121,403,977,500
$107,704,979,800
$61,929,522,500
$636,828,096,000
$34,079,900,400
$200,428,395,700
$257,744,374,500
$80,961,930,000
$543,973,066,000
$94,944,042,000
$101,962,550,000
$254,925,381,600
$164,777,118,900
$1,307,320,000
$2,094,272,000
$5,257,381,698,900

Peripheral and semi-peripheral region: South Asia:
Maldives
$8,400

394,451

$3,313,388,400

90

Table 9 (cont’d)
Sri Lanka
Pakistan
Afghanistan
India
Nepal
Bhutan
Bangladesh
Myanmar
TOTAL REGION:

$5,600
$2,800
$1,000
$3,700
$1,300
$6,000
$1,700
$1,300
$3,267

21,481,334
190,291,129
30,419,928
1,205,073,612
29,890,686
716,896
161,083,804
54,584,650
1,693,936,490

$120,295,470,400
$532,815,161,200
$30,419,928,000
$4,458,772,364,400
$38,857,891,800
$4,301,376,000
$273,842,466,800
$70,960,045,000
$5,533,578,092,000

Semi-peripheral region: Southeast Asia:
Indonesia
Philippines
Malaysia
Brunei
Singapore
Thailand
Cambodia
Laos
Vietnam
TOTAL REGION:

$4,700
$4,100
$15,600
$49,400
$59,900
$9,700
$2,300
$2,700
$3,300
$5,984

248,216,193
103,775,002
29,179,952
408,786
5,353,494
67,091,089
14,952,665
6,586,266
91,519,289
567,082,736

$1,166,616,107,100
$425,477,508,200
$455,207,251,200
$20,194,028,400
$320,674,290,600
$650,783,563,300
$34,391,129,500
$17,782,918,200
$302,013,653,700
$3,393,140,450,200

Semi-peripheral region: East Asia:
Mongolia
North Korea
China
(Macau)
(Hong Kong)
TOTAL REGION:

$4,500
$1,800
$8,400
$33,000
$49,300
$8,496

3,179,997
24,589,122
1,343,239,923
578,025
7,153,519
1,378,740,586

$14,309,986,500
$44,260,419,600
$11,283,215,353,200
$19,074,825,000
$352,668,486,700
$11,713,529,071,000

Core region: Pacific Rim including Oceania:
Taiwan
$37,900
South Korea
$31,700
Japan
$34,300
Australia
$40,800
New Zealand
$27,900
East Timor
$3,100
Papua New Guinea
$2,500
Palau
$8,100
Micronesia
$2,200
Marshall Islands
$2,500
Nauru
$5,000
Solomon Islands
$3,300
Vanuatu
$4,900
Kiribati
$6,200
Tuvalu
$3,400
Fiji
$4,600

23,113,901
48,860,500
127,368,088
22,015,576
4,327,944
1,201,255
6,310,129
21,032
106,487
68,480
9,378
584,578
227,574
101,998
10,619
890,057

$876,016,847,900
$1,548,877,850,000
$4,368,725,418,400
$898,235,500,800
$120,749,637,600
$3,723,890,500
$15,775,322,500
$170,359,200
$234,271,400
$171,200,000
$46,890,000
$1,929,107,400
$1,115,112,600
$632,387,600
$36,104,600
$4,094,262,200

91

Table 9 (cont’d)
Samoa
Tonga
TOTAL REGION:

$6,000
$7,500
$33,299

Core region: Rich North America and small island countries:
United States
$48,100
Canada
$40,300
Bahamas
$30,900
Bermuda
$69,900
Antigua and Barbuda
$22,100
St. Kitts and Nevis
$16,400
Dominica
$13,600
St. Lucia
$12,900
St. Vincent and the Grenadines
$11,700
Barbados
$23,600
Grenada
$13,300
Trinidad and Tobago
$20,300
TOTAL REGION:
$47,152

194,320
106,146
235,518,062

$1,165,920,000
$796,095,000
$7,842,496,177,700

313,847,465
34,300,083
316,182
69,080
89,018
50,726
73,126
162,178
103,537
287,733
109,011
1,226,383
350,634,522

$15,096,063,066,500
$1,382,293,344,900
$9,770,023,800
$4,828,692,000
$1,967,297,800
$831,906,400
$994,513,600
$2,092,096,200
$1,211,382,900
$6,790,498,800
$1,449,846,300
$24,895,574,900
$16,533,188,244,100

Semi-peripheral region: Central and South America and larger Caribbean countries
Mexico
$15,100
114,975,406
Cuba
$9,900
11,075,244
Jamaica
$9,000
2,889,187
Haiti
$1,200
9,801,664
Dominican Republic
$9,300
10,088,598
Belize
$8,300
327,719
Guatemala
$5,000
14,099,032
El Salvador
$7,600
6,090,646
Honduras
$4,300
8,296,693
Nicaragua
$3,200
5,727,707
Costa Rica
$11,500
4,636,348
Panama
$13,600
3,510,045
Colombia
$10,100
45,239,079
Venezuela
$12,400
28,047,938
Ecuador
$8,300
15,223,680
Peru
$10,000
29,549,517
Bolivia
$4,800
10,290,003
Paraguay
$5,500
6,541,591
Chile
$16,100
17,067,369
Argentina
$17,400
42,192,494
Uruguay
$15,400
3,316,328
Brazil
$11,600
205,716,890
Suriname
$9,500
560,157
Guyana
$7,500
741,908
TOTAL REGION:
$11,780
596,005,243

92

$1,736,128,630,600
$109,644,915,600
$26,002,683,000
$11,761,996,800
$93,823,961,400
$2,720,067,700
$70,495,160,000
$46,288,909,600
$35,675,779,900
$18,328,662,400
$53,318,002,000
$47,736,612,000
$456,914,697,900
$347,794,431,200
$126,356,544,000
$295,495,170,000
$49,392,014,400
$35,978,750,500
$274,784,640,900
$734,149,395,600
$51,071,451,200
$2,386,315,924,000
$5,321,491,500
$5,564,310,000
$7,021,064,202,200

Table 9 (cont’d)
Peripheral and Semi-peripheral region: South Africa and coastal western countries:
South Africa
$11,000
48,810,427
Swaziland
$5,200
1,386,914
Lesotho
$1,400
1,930,493
Botswana
$16,300
2,098,018
Namibia
$7,300
2,165,828
Angola
$5,900
18,056,072
Congo - Brazzaville
$4,600
4,366,266
Gabon
$16,000
1,608,321
Equatorial Guinea
$19,300
685,991
TOTAL REGION:
$9,400
81,108,330

$536,914,697,000
$7,211,952,800
$2,702,690,200
$34,197,693,400
$15,810,544,400
$106,530,824,800
$20,084,823,600
$25,733,136,000
$13,239,626,300
$762,425,988,500

Peripheral region: Sub-Saharan Africa and nearby microstates:
Sao Tome and Principe
$2,000
Cameroon
$2,300
Nigeria
$2,600
Benin
$1,500
Togo
$900
Ghana
$3,100
Cote D'Ivoire
$1,600
Liberia
$400
Sierra Leone
$800
Guinea
$1,100
Guinea-Bissau
$1,100
The Gambia
$2,100
Senegal
$1,900
Mauritania
$2,200
Mali
$1,300
Burkina Faso
$1,500
Niger
$800
Chad
$1,900
Sudan
$3,000
Eritrea
$700
Djibouti
$2,600
Somalia
$600
Ethiopia
$1,100
Kenya
$1,700
Tanzania
$1,500
South Sudan*
$800
Central African Republic
$800
Congo - Kinshasa
$300
Uganda
$1,300
Rwanda
$1,300
Burundi
$400
Zambia
$1,600
Zimbabwe
$500
Malawi
$900

$366,352,000
$46,298,719,400
$442,321,724,000
$14,398,180,500
$6,264,944,100
$78,250,193,800
$35,123,348,800
$1,555,154,400
$4,388,798,400
$11,973,453,800
$1,791,463,300
$3,864,953,400
$24,642,251,400
$7,390,207,000
$18,893,564,300
$25,912,672,500
$13,663,071,200
$20,853,731,200
$102,620,130,000
$4,260,546,500
$2,013,411,400
$6,051,382,800
$103,197,591,200
$73,122,679,700
$65,402,694,000
$8,500,140,800
$4,045,766,400
$22,079,757,000
$46,635,228,900
$15,196,604,800
$4,222,903,600
$22,895,145,600
$6,309,800,000
$14,690,739,600

93

183,176
20,129,878
170,123,740
9,598,787
6,961,049
25,241,998
21,952,093
3,887,886
5,485,998
10,884,958
1,628,603
1,840,454
12,969,606
3,359,185
14,533,511
17,275,115
17,078,839
10,975,648
34,206,710
6,086,495
774,389
10,085,638
93,815,992
43,013,341
43,601,796
10,625,176
5,057,208
73,599,190
35,873,253
11,689,696
10,557,259
14,309,466
12,619,600
16,323,044

Table 9 (cont’d)
Mozambique
$1,100
23,515,934
$25,867,527,400
Comoros
$1,200
737,284
$884,740,800
Madagascar
$900
22,585,517
$20,326,965,300
Mauritius
$15,000
1,313,095
$19,696,425,000
Seychelles
$24,700
90,024
$2,223,592,800
* South Sudan economic data not available; info for adjacent Central African Republic was substituted.
TOTAL REGION:
$1,611
824,590,631
$1,328,196,557,100
WORLD TOTAL:

$11,324

7,015,992,197

94

$79,451,552,857,000

REFERENCES

95

REFERENCES

Allen, Robert C. 2011. Global Economic History: A Very Short Introduction. Oxford,
New York, et al.: Oxford University Press.
Anderson, Margaret L., and Howard F. Taylor. 2003. Sociology: the Essentials, 2nd ed.
Belmont, CA, et al: Wadsworth/Thomson Learning.
Arrighi, Giovanni. 1994. The Long Twentieth Century. London, New York: Verso.
Axelrod, Alan. 2009. The Real History of the Cold War: A New Look At The Past. New
York: Sterling Publishing.
Beaudet, Pierre, Paul Alexander Haslam, and Jessica Schafer. 2012. The Future of
Development? In Introduction to Economic Development: Approaches, Actors,
and Issues, 2nd ed., eds. Paul A. Haslam, Jessica Schafer, and Pierre Beaudet, 526537. Don Mills, Ontario: Oxford University Press Canada.
Berger, Peter L. 1993. “Sociology: A Disinvitation?” in Society 30 (1): 12-18.
Bergesen, Albert J., and Michelle Bata. 2002. “Global and National Inequality: Are
They Connected?” Journal of World-Systems Research 8 (1): 130-144.
Black, Jeremy. 2005. Introduction to Global Military History: 1775 to the Present Day.
London, New York: Routledge.
Bloom, David E. 2011. “7 Billion and Counting.” Science 333: 562-569.
Central Intelligence Agency.
Skyhorse Publishing.

2011.

The CIA World Factbook 2012.

New York:

Chen, Xiangming, Anthony M. Orum, and Krista E. Paulsen. 2013. Introduction to
Cities: How Place and Space Shape Human Experience. Malden, MA, Oxford,
UK, West Sussex, UK: Wiley-Blackwell.
CIA World Factbook. Database on-line. Information retrieved April 22, 2012
(https://www.cia.gov/library/publications/the-world-factbook/geos/xx.html).
De Souza, Anthony R. 1990. A Geography of World Economy. Columbus, Toronto,
London, Melbourne: Merrill Publishing Company.
De Souza, Anthony R., and Frederick P. Stutz. 1994. The World Economy: Resources,
Location, Trade, and Development, 2nd ed. New York: Macmillan College
Publishing Company, Inc.

96

Diamond, Jared. 1999. Guns, Germs and Steel: The Fates of Human Societies. New
York, London: W.W. Norton and Company, Inc.
Dicken, Peter. 1992. Global Shift: The Internationalization of Economic Activity, 2nd ed.
New York: The Guilford Press.
Dicken, Peter. 2015. Global Shift: Mapping the Changing Contours of the World
Economy, 7th ed. New York: The Guilford Press.
Dollar, David. 2004. “Globalization, Poverty, and Inequality Since 1980.” World Bank
Policy Research Working Paper 3333. Retrieved February 4, 2012 (http://wwwwds.worldbank.org/servlet/WDSContentServer/WDSP/IB/2004/09/28/000112742
_20040928090739/Original/wps3333.doc).
Economist. N.d. “An Indian Summary” interactive maps. Retrieved April 25, 2011
(http://www.economist.com/content/indian-summary).
Economist. 2010. “All the Parities in China” interactive maps. Retrieved April 21, 2012
(http://www.economist.com/content/all_parities_china).
Elwell, Frank W. 2016. Macrosociology: Four Modern Theorists.
Routledge edition (2006 Taylor & Francis).

New York:

Elwell, Frank W. 2013. Sociocultural Systems: Principles of Structure and Change.
Edmonton, AB: AU Press.
Escobar, Arturo. 2012 reissue (1995). Encountering Development: The Making and
Unmaking of the Third World. Princeton, NJ, Oxfordshire, UK: Princeton
University Press.
Ferreira, Francisco H. G., and Martin Ravallion. 2008. “Global Poverty and Inequality:
A Review of the Evidence.” Policy Research Working Paper 4623. The World
Bank Development Research Group Poverty Team.
Gillis, Malcolm, Dwight H. Perkins, Michael Roemer, and Donald R. Snodgrass. 1996.
Economics of Development, 4th ed. New York, London: W.W. Norton and
Company.
Goodstein, Eban S. 1995. Economics and the Environment. Englewood Cliffs, NJ:
Prentice Hall.
Gordon, John Steele. 2005. Empire of Wealth: The Epic History of American Economic
Power, paperback ed. New York, London, Toronto, Sydney: Harper Perennial.

97

Grandin, Greg. 2010. Empire’s Workshop: Latin America, The United States, and the
Rise of the New Imperialism. New York: Holt.
Hahn, Peter L. 2005. Crisis and Crossfire: The United States and the Middle East Since
1945. Washington D.C.: Potomac Books, Inc.
Harrison, Paul. 1984. Inside the Third World. Harmondsworth, UK; New York;
Victoria, Australia; Markham, Canada; Auckland, New Zealand: Penguin Books,
Ltd.
Heilbroner, Robert L., and William Milberg. 2011. The Making of Economic Society,
13th ed. Pearson.
Hopkins, Terence K., Immanuel Wallerstein et al. 1996. The Age of Transition:
Trajectory of the World-System, 1945-2025. London; Atlantic Highlands, NJ;
Leichhart, Australia: Zed Books and Pluto Press.
Jones, Howard. 2001. Crucible of Power: A History of U.S. Foreign Relations Since
1897. Lanham, MD; Oxford, UK: SR Books.
Kerbo, Harold R. 1991. Social Stratification and Inequality: Class Conflict in Historical
and Comparative Perspective, 2nd ed. New York, et al: McGraw-Hill, Inc.
Kidron, Michael, and Dan Smith. 1991. The New State of War and Peace: An
International Atlas. London: Grafton Books.
Knox, Paul L., and Peter J. Taylor, eds. 1995. World Cities in a World-System.
Cambridge, New York, Melbourne: Cambridge University Press.
Levine, Herbert M. 1983. World Politics Debated: A Reader in Contemporary Issues.
New York, et al.: McGraw-Hill.
Ley, David. 1983. A Social Geography of the City.
Publishers, Inc.

New York: HarperCollins

Marger, Martin M. 2012. Race & Ethnic Relations: American and Global Perspectives,
9th ed. Belmont, CA, et al.: Wadsworth.
McMichael, Philip. 2012. Development and Social Change: A Global Perspective, 5th
ed. Thousand Oaks, CA; London; New Delhi; Singapore: Sage Publications.
Mills, C. Wright. 1959. The Sociological Imagination. Oxford: Oxford University
Press.
Moyo, Dambisa. 2009. Dead Aid: Why Aid Is Not Working and How There Is A Better
Way For Africa. New York: Farrar, Straus and Giroux.

98

Patterson, Thomas G., J. Garry Clifford, and Kenneth J. Hagan. 1995. American
Foreign Relations: A History - Since 1895, 4th ed. Lexington, MA; Toronto: D.C
Heath and Company.
Payne, Anthony, and Nicola Phillips. 2010. Development. Cambridge, UK; Malden,
MA: Polity Press.
Perkins, Dwight H., Steven Radelet, and David L. Lindauer. 2006. Economics of
Development, 6th ed. New York and London: W. W. Norton & Company, Inc.
Pieterse, Jan Nederveen. 2010. Development Theory, 2nd ed. London; Thousand Oaks,
CA; New Delhi; Singapore: Sage Publications Ltd.
Radelet, Steven. 2010. Emerging Africa: How 17 Countries Are Leading the Way.
Washington, D.C.: Center for Global Development.
Roskin, Michael G., and Nicholas O. Berry. 1999. IR: The New World of International
Relations, 4th ed. London, et al: Prentice-Hall, Inc.
Rourke, John T. 1989. International Politics on the World Stage, 2nd ed. Guilford, CT:
The Dushkin Publishing Group, Inc.
Russett, Bruce, and Harvey Starr. 1985. World Politics: The Menu For Choice, 2nd ed.
New York, Oxford: W.H. Freeman and Company.
Salter, Christopher L., Joseph J. Hobbs, Jesse H. Wheeler, Jr., J. Trenton Kostbade.
1998. Essentials of World Regional Geography, 2nd ed. Orlando, FL: Harcourt
Brace & Company.
Sanderson, Stephen K. 2016. Rethinking Sociology: Introducing and Explaining a
Scientific Theoretical Sociology. New York: Routledge edition (2012 Taylor &
Francis).
Sassen, Saskia. 2006. Cities in a World Economy, 3rd ed. Thousand Oaks, CA, London,
UK, New Delhi, India: Pine Forge Press (Sage Publications).
Schmitz, David F. 2007. The Triumph of Internationalism. Washington D.C.: Potomac
Books, Inc.
Sen, Amartya. 1999. Development as Freedom. New York: Anchor Books.
Simon, Julian L. 1995. The State of Humanity. Malden, MA; Oxford, UK: Blackwell
Publishers, Inc.

99

Sobocinski, Mike. 2000. “Residential Class Indicators: Census-Based Techniques for
Modeling Neighborhood Quality and Social Class in Urban Areas.” Master’s
research, Department of Urban and Regional Planning, Michigan State
University. Available in MSU Main Library, call number 140 771 THS.
Sobocinski, Mike. 2003. “Future Trends in the World System?” Comparative &
Historical Sociology (ASA section newsletter) 15(3): 8-9. Retrieved September
6, 2017 (http://asa-comparative-historical.org/newsletter/chs03fall.pdf).
Sobocinski, Mike. 2014. “Exploring Hypothetical Relationships between Crisis and
Economic Growth.” Unpublished paper submitted for PLS 826, under Dr. John
Kerr, Michigan State University.
Stiglitz, Joseph E., Amartya Sen, and Jean-Paul Fitoussi. 2010. Mismeasuring Our
Lives: Why GDP Doesn’t Add Up. New York: The New Press.
United Nations Human Development Index Report. 2011. United Nations publication,
retrieved April 18, 2012 (http://hdr.undp.org/en/reports/global/hdr2011/).
Wallerstein, Immanuel. 1974. The Modern World-System I: Capitalist Agriculture and
the Origins of the European World-Economy in the Sixteenth Century. London:
Academic Press.
Wallerstein, Immanuel. 1983. Historical Capitalism. London, New York: Verso.
Wallerstein, Immanuel et al. 1996. Open the Social Sciences: Report of the Gulbenkian
Commission on the Restructuring of the Social Sciences. Stanford, CA: Stanford
University Press.
Wallerstein, Immanuel. 1999. “The West, capitalism, and the modern world-system.”
Pp. 10-56 in China and Historical Capitalism. Cambridge, New York,
Melbourne, Madrid, Cape Town: Cambridge University Press.
Wallerstein, Immanuel. 2004. World-Systems Analysis: An Introduction. Durham NC:
Duke University Press.
World Bank Databank. Database on-line. Information retrieved April 21, 2012
(http://databank.worldbank.org/ddp/home.do?Step=1&id=4).

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