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FEBRUARY 11th
THE RISE AND FALL OF WORLDWIDE INCOME INEQUALITY, 1820-2035
James Gwartney
Florida State University
Hugo Montesinos
Florida State University
250 S. Woodward Ave
Tallahassee, FL 32306
(850) 408-4465
Joseph ConnorsFlorida Southern [email protected]
This draft, 01/30/2019, was prepared for presentation and discussion at the Colloquium on
Market Institutions and Economic Processes at NYU, scheduled for 02/11/2019.
Abstract
The development process and the demographic changes that are a central element of it, explain
both the nearly two centuries of increasing income inequality prior to 2000 and the reversal of
this trend that followed. There are at least four phases of the development process: (1) pre-
development, (2) initial growth, (3) improved productivity, and (4) receding growth. Prior to the
Industrial Revolution, the entire world was in phase 1. During 1820-1950, about 20 countries,
mostly in Western Europe, North America, and Oceania, moved out of phase 1 and grew more
rapidly than the rest of the world, widening income inequality. Between 1960 and 2000, an
increasing share of developing countries moved into phase 2 and achieved growth rates similar to
the high-income countries, slowing the rise in inequality. By 2000-2015 most developing
countries had moved into phases 2 or 3, while the high-income countries slid into phase 4,
leading to a sharp reduction in worldwide income inequality. These recent reductions are likely
to continue in the near term because of the continuation of favorable demographics, lower cost of
transportation and communications, improvements in institutions, increases in human capital,
and progress against malaria in developing countries.
JEL Classification: O1, J11, D31.
2
Introduction
Income inequality continues to be a hot topic among both economists and policymakers. It is
important to recognize that income inequality can be measured (1) across countries, (2) within a
country, and (3) worldwide. Cross-country inequality focuses on the variation in income among
various countries. Within-country income inequality reflects the variation of income among units
(e.g. individuals, households, or families) within a specific country. Finally, worldwide income
inequality is a reflection of how incomes vary among individuals and households throughout the
world.
Considerable recent research has focused on changes in income inequality within countries
[Atkinson, Piketty, and Saez 2011; Alvaredo et al. 2013; Novokmet et al. 2018]. There is
substantial variation in income inequality within countries. Income inequality after taxes and
transfers is relatively low in Canada, Japan and most western European countries and quite high
in Brazil, Egypt, India, Mexico, and South Africa [Solt 2016]. Moreover, within-country income
inequality has increased in recent decades in several large economies, including China, Russia,
and the United States [Assouad, Chancel, and Morgan 2018].
Recent scholarly research has also addressed cross-country and worldwide income
inequality [Bourguignon and Morrisson 2002; Sala-i-Martin 2006; Hellebrandt and Mauro 2015;
Bourguignon 2015; Milanovic 2016]. This article will focus on global income inequality and
make three significant contributions to this literature. First, the development process and
accompanying demographic changes are used to explain the changes in worldwide income
inequality during the past two centuries: the rise from 1820-1970, the relative stability during
1970-2000, and the sharp decline during 2000-2015. Second, Gini coefficient measures are
developed for cross-country income inequality from 1820 to 2015 and for each of the three types
of income inequality for 1960 to 2015. Third, the impact of the development process, the
accompanying demographic trends, and other factors that influence economic growth are used to
project the direction of income inequality in the decades immediately ahead.
Prior to 1970, demographic changes accompanying the development process contributed to
the sizeable increases in cross-country and worldwide income inequality. However, beginning in
the 1970s, changing demographic factors accompanying the development process led to a
leveling off and eventual reversal of the long-term trend. During 2000-2015, most developing
countries were in phases 2 and 3, where growth rates are higher, while the high-income countries
were moving into phase 4, where growth rates are lower. As a result, there was a dramatic
reduction in income inequality during 2000-2015. The recent changes are unprecedented: for the
first time in history, worldwide per capita income is increasing and income inequality is
declining. Further, dramatic reductions in transportation and communication costs have made it
possible for developing countries to achieve historically high growth rates. This transportation-
communication revolution accelerates the catch-up process. Interestingly, while many are
focusing on increases in within-country income inequality, the world is experiencing a dramatic
reduction in cross-country and worldwide income inequality.
This article is structured as follows: Section I examines the four phases of the development
process. Section II describes how a country’s phase of development can be identified and
explains why growth rates will differ across the four phases of development. Section III tracks
the per capita GDP of both modern high-income and developing countries during the past two
centuries and analyzes how changes in their income levels have impacted income inequality.
3
Section IV derives Gini coefficient measures of income inequality for cross-country, within-
country, and the worldwide distribution of income. The Gini measures document both the nearly
two centuries of increasing income inequality prior to 2000 and the dramatic reversal of that
trend since the turn of the century. Section V explains why the recent reductions in worldwide
income inequality are highly likely to continue in the years immediately ahead and section VI
summarizes.
I. Four Phases of the Development Process
Economic development is a process. At least four different phases of this process are
observable.1
Phase 1 is the pre-development or Malthusian phase. It is characterized by low per capita
income and absence of sustainable economic growth. During this phase, the birth rate is high,
children and youthful teenagers comprise a large share of the population, while the share of
prime working-age adults is relatively small. It is a constant struggle to obtain adequate food,
clothing, and shelter, and few people survive to ages beyond 60 or 65 years. Prior to 1800, this
was the state of the world. According to Maddison [2007], the per capita income of the world
increased by only about 50 percent during the 800 years from 1000 to 1800.
Phase 2 is the initial growth phase. During this phase, sustainable growth occurs, and per
capita income rises. As income levels rise above subsistence, initially this will lead to more rapid
population growth. However, if the country is able to break out of the Malthusian phase and
sustain growth of per capita income, the higher income levels will eventually exert a negative
impact on the quantity of children, but a positive effect on investment in child development
[Becker 1960; Becker and Lewis 1973; Barro and Becker 1989; Doepke 2015; Lucas 2017]. As
per capita income increases, the opportunity cost of having children rises, leading to a reduction
in the birth rate, smaller family sizes, and slower growth of the youthful population. As a result,
through time prime working age adults become a larger share of the population, which also
contributes to the growth of per capita income. The growth of income and smaller family sizes
induce families to invest more time and resources on education, training, and other inputs
designed to improve the quality of children. This investment does not directly increase current
productivity, but it will have a payoff in the future.
Phase 3, it might be called the improved productivity phase, involves a gradual increase in
productivity as the generation with more schooling and training moves to adulthood. As the
higher-skill generation becomes a larger and larger share of the adult population, their higher
productivity will enhance the growth of per capita income. In turn, the higher income levels will
lead to a continuation of the falling birth rate, and decline in the young and increase in prime
working-age adults as a share of total population. These trends will also enhance the growth of
per capita income.
Phase 4 is the receding growth phase. During this phase, the share of population in the prime
age category begins to decline as those in the older age groups become a larger and larger share
of the total population. The productivity of those in the oldest age groupings will fall and many
1 Galor and Weil [2000] divide the development process into three periods instead of four—the Malthusian regime,
the post-Malthusian regime, and the modern growth regime. We use demographic changes and growth of per capita
GDP to identify movement through phases of development, while Galor and Weil focus on population growth and
human capital.
4
will choose to retire from work activities. Both of these trends—a decline in prime working age
adults and increase in the elderly as a share of the total population—will reduce the long-term
growth of per capita income.
Our analysis of development as a process is closely related to that of Lucas (2004 & 2017).
Like Lucas, we seek to explain the Industrial Revolution and its impact on economic
development in the two centuries that followed. Lucas integrates two models: a Malthusian
model similar to our phase 1 and a human capital model similar to our phases 2 and 3. Lucas
recognizes that the transition from Malthusian stagnation to sustainable growth is complex,
lengthy, and still ongoing in some countries today. Our analysis is supportive of this view.
However, there are also important differences. First, we integrate the role of demographic
changes (increases in the share of population in the prime age category) as countries move out of
the Malthusian phase, while Lucas focuses on the role of migration from agriculture to urban
productive activities. Second, once started, we explain that favorable demographic changes will
interact with growth of per capita income in a manner that accelerates economic growth and
propels its continuation. Finally, our analysis also integrates another transition. As growth leads
to higher income levels and longer life expectancy, eventually the share of population in older
age groupings will increase and the share in the prime age category will decline, leading to a
slowdown in growth (phase 4).
Both economic theory and empirical research indicate that institutions and policies,
investment in human and physical capital, climate, location, and natural resources exert an
impact on development, per capita income, and growth. Development research has focused on
these factors as determinants of growth. But, research in this area has often ignored the
development process and the demographic changes accompanying it. While our focus is on the
development process, we recognize that several factors will influence the rate of growth at
various phases of development and the pattern and speed at which countries move through the
process. Our emphasis on development as a process is supplementary—it is designed to deepen
our understanding of growth and development issues. As will be shown, tracking the
development process enhances our understanding of historic trends and changes in worldwide
income inequality.
II. Identifying the Development Phase of Countries
How can the development phase of a country be identified? The income levels, growth rates,
and share of population in various age groups can help us do so. Data for real per capita GDP
from Maddison [2018], the Penn World Table [Feenstra et al. 2016] back to 1960, and World
Bank [2018] for 1980-2015 will be utilized to determine the income levels and growth rates of
countries. The population figures from the World Bank [2018] for 1960-2015, including the
share in various age groups, will be used for the demographic calculations. Data on economic
and political institutions are also available for a large number of countries since 1980. For
countries for which the institutional data are available back to 1980, the income and
demographic data are also available for 135 countries continuously since 1970 (and 110 since
1960). These 135 countries will constitute our primary data base. In 2015, the population of these
135 countries was 6.7 billion, 93 percent of the population of the world.2
2 Data for Taiwan are unavailable from the World Bank. Therefore, there is one fewer country in the World Bank
dataset.
5
When a country is in phase 1, it will have a large share of population under age 15, a small
share in the prime working age 25-59, a low per capita income, and little or no economic growth
over a lengthy time period. If 40 percent or more of a country’s population is under age 15 or if
real per capita GDP (derived by the purchasing power parity method and measured in 2011
dollars) is less than $4 per day, the country will be placed in phase 1.
The share of population under age 15 in 2015 was 40 percent or more in 31 of the 135
countries for which the income data were available. In most of these 31 countries, this was the
case throughout 1960-2015.3 Moreover, in most of these countries, the percent of population in
the prime working-age 25-59 group was 35 percent or less in 2015 and throughout 1960-2015.
Thus, the age composition of these 31 countries indicates that they were in phase 1, the
Malthusian phase, throughout 1960-2015.
Insert Table 1 about here
Table 1 presents the per capita GDP and growth rates for these 31 countries for various
years and time periods during 1960-2015. As expected, the per capita GDP and growth rates of
these economies were exceedingly low. Measured in 2011 PPP dollars, only one (Zimbabwe) of
these countries had a per capita GDP of more than $2,700 in 1960. In 2015, the per capita GDP
of 24 of the 31 Malthusian economies was less than $3,000. Only three (Angola, Republic of
Congo, and Nigeria) of the 31 had a 2015 per capita GDP of more than $4,000. The 2015 per
capita real GDP was lower than in 1960 for ten of the 30 countries for which the data were
available in both years (Note: the GDP data were unavailable for Angola in 1960, and therefore
the growth data for periods including the 1960s are only available for 30 of the 31 countries).
The story was even more dismal for the 1960-2000 period. Twenty of the 30 Malthusian
economies had a lower per capita GDP in 2000 than 1960.4 During 1960-2000, the simple
average annual growth rate for the 30 phase 1 economies was -0.26 percent. The annual growth
rate of the population weighted per capita GDP for this group was even lower, -0.64 percent.
Only five of the 30 achieved a growth rate of 1 percent or more during this 40-year period. The
low per capita GDP and meager growth rates during 1960-2000 are indicative of countries in
phase 1 of the development process.
Why have the Malthusians failed to grow? There are several reasons for the stagnation of
these economies. The Malthusians are the most geographically disadvantaged countries in the
world [Gallup, Sachs, and Mellinger 1999; Sachs 2003]. Almost without exception, their climate
is hot, humid, and disease-prone. They are distant from the major markets of the world. In
addition, their economic institutions are characterized by poorly defined and insecure property
rights, restrictions on trade, and political intervention and corruption. Put simply, entrepreneurs
3 The only exceptions to this rule were Benin and Cameroon in 1960 and Central African Republic, Gambia, and
Sierra Leone in 1960 and 1965. In these cases, their share of population under age 15 was almost 40 percent and it
was increasing. 4 In contrast, only two (Ghana and Haiti) of the 83 non-Malthusian developing countries had a lower per capita
income in 2000 than 1960. One could argue that these two countries should have been included in the Malthusian
group. However, their population share under age 15 implies that Haiti transitioned out of phase 1 between 2000-
2005 and Ghana during 2010-2015.
6
and investors—both domestic and foreign—will find it more attractive to engage in productive
activity elsewhere.5
How can you tell when a country is moving from phase 1 to phase 2? Both growth of per
capita GDP and changes in the age composition of the population provide the answer. When the
share of population under age 15 falls below 40 percent of the total, this is indicative of
movement from phase 1 to phase 2. As this happens, the prime age group as a share of total
population nearly always rises above 35 percent during the same period or in the near future.
Using the 40 percent under age 15 threshold as the dividing point, 65 developing countries
moved from phase 1 to phase 2 between 1960 and 2015. In 55 of these cases, the prime age
population increased above 35 percent of the total either during the same or following 5-year
period as the under age 15 share receded below 40 percent of the total. In nine of the ten
remaining cases, prime age adults as a share of total population moved above the 35 percent
threshold during the next five years. Thus, while reduction in the under age 15 population below
the 40 percent threshold is the criteria used to identify movement from phase 1 to phase 2, this
change is almost always accompanied by a rise of the prime-age population above 35 percent of
the total, either during the same period or in the near future. Finally, because strong sustained
growth would push per capita income well above subsistence levels, countries are not moved
into phase 2 unless they have a per capita GDP of more than $1,460 (4 dollars a day) in 2011
PPP dollars.
What makes it possible for a country to break out of Malthusian stagnation and begin the
growth process? Historically, several factors including improvements in institutions, price
increases for an important export resource, technological advancements, and population control
measures, have all contributed to the movement of countries from phase 1 to phase 2. Often, the
breakout from phase 1 reflects a combination of these factors.
A country will move from phase 2 to phase 3 as the generation with more schooling and
better training moves to adulthood. With a lag of perhaps two or three decades, the generation
with more schooling and training will begin to enter the labor force, which will increase
productivity as the country moves to phase 3. The movement from phases 2 to 3 is generally an
evolutionary process. The growth of per capita GDP, increases in the schooling of the young, and
length of phase 2 will differ among countries. Thus, the time of movement from phase 2 to phase
3 cannot be identified with a high degree of precision. Therefore, these two phases will be
combined in our empirical analysis.
In contrast with phase 1, countries in phases 2 and 3 will generally experience persistent
economic growth and rising levels of per capita income. Over a period of three, four, or even five
decades, the rising income levels, improvements in nutrition, health, sanitation, and similar
factors will lead to increases in life expectancy. As the elderly population become a larger and
larger share of the total, eventually the prime working-age population will begin to shrink, and
the country will move from phase 3 to phase 4. This will place a drag on the growth of per capita
5 Gallup, Sachs, and Mellinger [1999] provide country data on the share of population at risk of malaria in 1994. In
24 of the 31 Malthusian countries, 100 percent of the population was at risk of malaria. In six others, more than 60
percent of the population was at risk. Mauritania was the only Malthusian country without a high-risk of malaria.
With regard to institutions, none of the 31 Malthusian economies ranked in the top half among countries with an
economic freedom rating in 1995, and only two (Kenya and Uganda) did so in 2000 and 2005 [Gwartney, Lawson,
and Hall 2017].
7
income. Thus, like phase 1, the growth rate of countries in phase 4 will generally be lower than
for phases 2 and 3.
In the early 1980s, the World Bank classified 21 countries as “high-income industrial”6. The
per capita GDP, growth rates, and demographic characteristics indicate that all 21 of these
countries were in either phases 2 or 3 of the development process during 1960-2000. However,
the situation began to change during the first decade of the 21st century. The share of population
in the prime working-age category began to decline, indicating movement into phase 4. When
the prime age population falls by 1.0 percent or more from its peak during a 5-year period, this is
indicative of a move from phase 3 to phase 4. By 2005, the share of population in the prime age
group of seven countries (Austria, Denmark, Finland, Germany, Japan, Netherlands, and
Sweden) in the high-income group had declined by at least one full percentage point. By 2010,
the prime working-age population of another nine countries (Australia, Belgium, Canada,
France, United Kingdom, Italy, Norway, New Zealand, and the United States) had also fallen by
one percentage point or more. By 2015, the prime working-age population of 19 of the 21 high-
income countries (Switzerland and Luxembourg were the exceptions) had fallen by a similar
amount.
Consider the implications of the development process. Once a country moves from phase 1
to phase 2, it enters a virtuous cycle. Growth of per capita income leads to a reduction in the
birth rate, which leads to a decline in the young and increase in prime working-age adults (25-
59) as a share of the population. In turn, this expansion in population share in the prime working-
age category propels additional growth.7 The declining share of children and higher earnings
both increase the incentive to invest in additional schooling and upgrade human capital, which
eventually leads to higher worker productivity during phase 3. Typically, the transition from
phase 1 through phases 2 and 3 will be a lengthy process, perhaps four or five decades.8 In
contrast with phase 1, countries in phases 2 and 3 of development will generally grow rapidly. Of
course, other factors such as counterproductive institutional change, political instability, or
unfavorable changes in the world price of an important resource could dampen growth, but the
potential for strong growth will be high during phases 2 and 3 of development. But, the virtuous
6 The 21 high income industrial countries were Australia, Austria, Belgium, Canada, Switzerland, Germany,
Denmark, Spain, Finland, France, United Kingdom, Ireland, Iceland, Italy, Japan, Luxembourg, Netherlands,
Norway, New Zealand, Sweden, and the United States. In the decades following World War II, these countries were
among the world’s richest. In 1960, the 17 countries with the highest per capita income in the world were all from
this set of 21. The four exceptions (Italy, Ireland, Spain, and Japan) ranked in the top 30.
7 Regression analysis was run with growth of per capita GDP as the dependent variable and lagged prime age adults
as a percent of the total as an independent variable for 1960-2015. Similarly, regressions were run with the prime
age population as a percent of the total as the dependent variable and lagged growth of per capita GDP as an
independent variable. Annual growth rates over both ten and fifteen-year periods were examined and lags of both 5
and 10 years were considered. This analysis was conducted with all 135 countries and with the 21 high-income
countries excluded, with and without per capita GDP at the beginning of the decade, and with level as well as
change in the demographic variable as independent variables. In all regressions the changes in the prime age
population exerted the predicted positive impact on subsequent growth of per capita GDP and the growth of per
capita GDP exerted the predicted positive impact on the subsequent change in prime age adults as a share of the total
population. The variables of interest were significant at the 1 percent level in all regressions. These findings are
highly consistent with our virtuous cycle hypothesis. 8 After countries moved out of phase 1, the increases in the prime age population as a share of the total were
consistent and large. The mean percentage point increases were 4.9 after ten years, 9.7 after twenty years, and 12.7
after thirty years.
8
cycle will not last forever. Eventually, unfavorable demographic changes—an increase in the
elderly and reduction in prime working-age adults as a share of total population—will lead to a
slowdown in growth.
III. Phases of Development and Their Impact on Relative Incomes Across Countries
The 135 countries of our primary data set can be disaggregated into three separate groups:
(1) the 21 high-income industrial countries, (2) the 31 Malthusian economies that have remained
in phase 1 of development throughout 1960-2015, and (3) the 83 other developing economies.
Further, the 31 Malthusians and the 83 other developing countries might be combined, which
would result in a fourth group: 114 developing economies.
Table 2 presents data on the per capita GDP for each of the four groups and shows the per
capita GDP ratio of the high-income countries divided by the developing economies for various
years. Panel A uses the classic data set of Maddison [2018] to calculate per capita GDP in 2011
dollars back to 1820. The per capita GDP data were available for 16 of the high-income
countries in 1820, 1870, 1913, and 1950.9
The Maddison data prior to 1950 were also incomplete for developing countries. The per
capita GDP figures were available during 1820-1950 for only 36 of the 83 non-Malthusian
developing countries. Fortunately, the most populous developing countries (China, India,
Indonesia, Brazil, Mexico, Philippines, and Turkey) are included. Thus, the 36 developing
countries of the Maddison set comprise the bulk of the population (more than 90 percent) of the
non-Malthusian developing countries. Moreover, the 1950 per capita GDP figures indicate that
the 36 countries are highly representative of the entire group. The 1950 per capita GDP of the
group of 36 was $1,450 compared with $1,426 for the entire set of 83 countries. The data for
virtually all of the Malthusian countries are unavailable from Maddison prior to 1950. Thus, the
per capita GDP for the Malthusian group and all developing countries and the parallel income
ratios for years prior to 1950 are not included in panel A.
Insert Table 2 about here
In 1820, people throughout the world were poor. The high-income countries of the
Maddison dataset had a per capita GDP of $1,461 in 1820. By way of comparison, the per capita
GDP of the non-Malthusian developing countries was $821. As column 6 shows, the per capita
income of the high-income group was only 1.8 times that of the developing countries in 1820.
When most everyone is poor, the per capita income differences between the high and low-
income countries are relatively small.
During 1820-1950, the per capita GDP of the high-income countries of Western Europe,
North America, and Oceania grew more rapidly than the rest of the world. The per capita GDP of
the high-income group rose from $1,461 in 1820 to $2,506 in 1870 and $5,413 in 1913. By 1913,
the per capita income of the high-income group was 4.4 times that of the developing economies.
Moreover, the gap continued to widen. By 1950 the per capita GDP of the high-income
9 The five countries for which the data were unavailable are Iceland, Luxembourg, Belgium, New Zealand, and
Switzerland. Because these countries are small, their omission exerts only a minimal impact on the per capita GDP
of the high-income group. The data for 1950 illustrate this point. As Table 2, panel A shows, the per capita GDP for
the 16 high-income countries with data throughout 1820-1950 was $8,469 in 1950, compared to $8,464 for all of the
21 countries of the group.
9
economies was 5.8 times ($8,469 compared to $1,450) that of the 36 developing countries with
data back to 1820. The 1950 per capita GDP figures for the broader groups of 21 high-income
and 83 developing countries were similar. The 1950 per capita GDP of the 21 high-income
countries was $8,464, 5.9 times the $1,426 figure for the 83 non-Malthusian developing
economies.
Exhibit II, panel A also presents the 1950-2015 Maddison data for the 21 high-income
countries and the developing groups. The per capita GDP of the 31 Malthusian and 83
developing countries were similar during 1950-1970. In 1970, the per capita GDP of the
Malthusians was $2,310 compared to $2,291 for the 83 developing countries. After 1970,
however, the income levels of the two groups followed a dramatically different path. By 1990,
the per capita GDP of the non-Malthusian group had risen to $3,676 while the parallel figure for
the Malthusians had declined to $2023. Further, by 2000 the per capita GDP of the non-
Malthusians had risen to $5,159 while the figure for the Malthusians had fallen to $1,483.
Panel A (columns 5, 6 and 7) use the Maddison data to derive the per capita GDP ratio of
the high-income to the three developing groups for 1950-2015. The per capita GDP ratio of the
high-income group divided by the 31 Malthusian countries (column 5) rose from 7.0 in 1950 to
8.6 in 1980, and then soared to a peak of 25.5 in 2000, before receding to 15.9 in 2015. The path
of the non-Malthusian developing group was similar during 1950-1970, but dramatically
different after 1970. The ratio of the high-income to the non-Malthusian developing group
(column 6) rose from 5.9 in 1950 to 7.7 in 1970 and 7.8 in 1990. However, since 1990, the ratio
has fallen sharply receding to 4.0 by 2015. Interestingly, the per capita GDP ratio of the high-
income to non-Malthusian developing economies was actually lower in 2015 than a century
earlier in 1913.
Exhibit 2 also presents the per capita GDP figures (in 2011 PPP dollars) for the Penn World
Table (panel b) for 1960-2015 and the World Bank (panel c) for 1980-2015. The Pattern is
similar to that for the Maddison data. Like the Maddison data, the Penn World Table and World
Bank figures indicate that the per capita GDP of the high-income countries divided by the
Malthusian group rose sharply during the final decades of the 20th century, but declined
substantially during 2000-2015. This ratio was approximately 16 in 2015. As Panels B and C
show, the ratio of the high-income to the other developing countries rose more slowly and it
peaked in 1990 and has declined sharply since, receding to approximately 4 in 2015.
The increasing ratio of the high-income group compared to the Malthusian economies
during 1960-2000 is not surprising. During these four decades, the 21 high-income countries
were all in phases 2 and 3 of development, while the Malthusians were all stuck in phase 1. As
our analysis of the development process indicates, growth rates are systematically higher in
phases 2 and 3 than phase 1.
All three of the data sets indicate there has been a dramatic change in the ratio of the per
capita GDP of the high-income countries relative to their developing counterparts since 2000.
What accounts for the reversal of the trend toward greater inequality, a trend that had persisted
for at least two centuries? Since 2000, most of the 21 high-income countries have moved from
phase 3 to phase 4, where growth rates are lower. In contrast, all of the 83 non-Malthusian
developing countries had moved out of the Malthusian phase by 2000 into phases 2 and 3, where
growth rates are higher. At the same time, some of the 31 Malthusian economies are showing
signs of breaking out of phase 1 and moving into phase 2 (see Table 1). Thus, their growth rates
10
have been stronger during 2000-2015. This combination of factors accounts for the declining
ratio of the per capita GDP of the high-income to Malthusian countries during 2000-2015.
When all of the 114 developing countries are included (Table 2, column 7), the ratio of the
high-income to developing economies is slightly higher, but the pattern is the same as for the 83
non-Malthusian developing countries. In both cases (and for all three of the data sets), the per
capita GDP ratio of the high-income countries relative to developing economies declined during
1990-2015. Moreover, the 2015 ratio is lower than in 1960 and about the same as the ratio for the
Maddison data in 1913, illustrating that the degree of inequality between the high and low-
income countries in 2015 is not much different than the situation a century ago.
Most people believe that beginning around 1800, the Industrial Revolution started to
transform the world. This was certainly true for the approximately 15 percent of the world’s
population living in Western Europe, North America, and Oceania. As the Maddison data
(Exhibit 2, panel A) show, the per capita GDP of the high-income countries rose by a whopping
480 percent during the 130 years following 1820. But the change was much less transformative
in the rest of the world. The per capita GDP of the developing countries rose by only 76
percent—less than half a percent annually—during this 130-year period. The $1,450 per capita
GDP of the non-Malthusian developing countries in 1950 was virtually the same as the $1,461
per capita income of the high-income countries in 1820. Further, the stagnation of the 31
Malthusian economies continued for another 50 years. Measured in 2011 dollars, the per capita
GDP of the Malthusian countries was approximately $1500 in 2000 (see panels A, B, and C),
virtually the same as the 1820 figure of the high-income countries nearly two centuries earlier at
the beginning of the Industrial Revolution. In summary, the lives of the 85 percent of the world’s
population living in developing countries were only marginally affected during the century and a
half following the Industrial Revolution. They did a little better during this period than prior to
1800, but not much.
Thus, the transformative growth of the developing economies is a relatively recent
phenomenon. For the non-Malthusian developing economies, it started during the last three
decades of the 20th century, as more and more of these economies moved from phase 1 to phase
2 of development. In 1960 and 1970, three-fourths of the world’s population still lived in
countries that were in the Malthusian phase and only one-fourth in countries that had moved to
phases 2 and 3. By 2000, however, 83 percent of the world’s population resided in countries that
had moved to phases 2 and 3 of development and the figure soared to 88 percent by 2015.
Meanwhile, after the turn of the century, the high-income countries were moving into phase 4,
where growth rates are slower. This combination—most of the developing world in phases 2 and
3 and the high-income countries in phase 4—is closing the income gap between the high and
low-income countries. We now turn to the measurement of how these changes have impacted
worldwide income inequality.
IV. Gini Coefficient Measurement and Worldwide Income Inequality, 1820-2015
It is important to distinguish between income inequality (1) among countries and (2) within
a country. When most people discuss income inequality, they generally focus on the latter. This
is understandable because political decision-making typically does not extend beyond country
borders. Thus, worldwide income inequality—inequality in the distribution of worldwide
income—is often ignored by the news media, policy-makers, and political commentators.
11
However, the world is increasingly interrelated and there is more interaction of people
across national boundaries. This elevates the importance of worldwide income inequality.
Further, income differences across countries are substantially greater than those within any
specific country. Thus, worldwide inequality may be an even more important welfare
consideration than inequality within countries. This section will develop three measures of
income inequality. The first is a measure of cross-country inequality, assuming that individual
incomes within each country are equal to the country’s per capita GDP. The second is a measure
of within-country inequality, assuming that all countries have the same per capita GDP as that of
the World. The third is a measure of worldwide inequality that reflects both cross-country
income differences and income differences within each country. All measures will be developed
across time, making it possible to compare inequality over a period of nearly two centuries.
The Gini coefficient is a measure of the degree of income inequality among individuals or
groups. When income is arrayed from low to high, the Gini curve outlines how the cumulative
share of received income changes as the cumulative share of the population increases. The Gini
coefficient ranges from zero to one, where zero represents perfect income equality and one
indicates that a single individual (or entity) receives all the income. Thus, increases in the size of
the Gini coefficient indicate more inequality—a larger share of the total income is received by a
smaller percentile of the population.
IV.A. Methodology
The main ingredient to calculate cross-country income inequality (income inequality among
countries) is their mean income per capita. Comparable measures of cross-country per capita
GDP are available from 1820 to 2015. This allows us to compute the first form of inequality,
among countries, for nearly two centuries. To calculate the second type of inequality, within
countries, the main ingredient is a comparable measure of dispersion for the incomes of
individuals or households living in each country. A comprehensive effort to collect, combine,
and standardize these data has recently been completed by Solt [2016] who constructed the
Standardized World Income Inequality Database (SWIID). The SWIID provides comparable
Gini indices of income inequality for 192 countries, for as many years as possible, from 1960 to
2015. The SWIID “offers coverage double that of the next largest income inequality data set, and
its record of comparability is three to eight times better than those of alternate data sets [Solt
2016, 1].” The SWIID allows us to compute the second form of inequality, within-country, for
nearly six decades.
The next challenge is to combine the cross-country and within-country inequalities to derive
a worldwide income inequality. The main ingredient for this step is to assume a distributional
form to gradually allocate the total income of each country (the per capita GDP multiplied by its
population) among cumulative population shares in a way that is consistent with both the per
capita GDP and the Gini coefficient. The usual assumption is that the income per capita is log-
normally distributed within a country.10 In other words, the natural logarithm of the per capita
10 See for example Pinkovskiy and Sala-i-Martin [2009], Sala-i-Martin and Pinkovskiy [2010], and Hellebrandt and
Mauro [2015]. Some have questioned the appropriateness of the log normal income distribution assumption [Lopez
and Servén 2006; Chen and Ravallion 2010]. These authors have suggested that the assumption of log normality
may be too strong when the income data is generated from surveys based on consumption expenditures. The SWIID
uses survey data of this type. However, our analysis focuses on trends in income inequality and any inaccuracy
introduced by this assumption will not introduce a systematic bias. The same trend of increased global inequality
12
GDP of individuals within a country follows a normal distribution. Aitchison and Brown [1957]
show that, under this assumption, the Gini coefficient (𝐺𝑖) and the within-country standard
deviation (𝜎𝑖) for the log-income are related by the formula 𝜎𝑖 = √2Φ−1 (1+𝐺𝑖
2), where Φ is the
cumulative standard normal distribution. Furthermore, Young [2011] provides a way to calculate
the Gini coefficient for the mixture of log-normal distributions for countries, even if the mixture,
the worldwide distribution of income, is not log-normal. Young’s formula is not only accurate
(as it replicates almost identically the global inequality estimates of Sala-i-Martin [2006]) but it
is also fast, and it provides an intuitive way to decompose global inequality into its cross-country
and within-country components. The three aggregate measures of inequality are calculated,
mathematically, as follows:
𝐺𝑖𝑛𝑖𝐶𝑟𝑜𝑠𝑠−𝐶𝑜𝑢𝑛𝑡𝑟𝑦 = ∑∑𝑤𝑖𝑤𝑗𝑌𝑖
𝑌
𝑁
𝑗=1
𝑠𝑖𝑔𝑛(𝑌𝑖 − 𝑌𝑗)
𝑁
𝑖=1
,
(1);
𝐺𝑖𝑛𝑖𝑊𝑖𝑡ℎ𝑖𝑛−𝐶𝑜𝑢𝑛𝑡𝑟𝑦 = ∑∑𝑤𝑖𝑤𝑗
𝑁
𝑗=1
(2Φ [1
2√𝜎𝑖
2 + 𝜎𝑗2] − 1) ,
𝑁
𝑖=1
(2);
𝐺𝑖𝑛𝑖𝐺𝑙𝑜𝑏𝑎𝑙 = ∑∑𝑤𝑖𝑤𝑗𝑌𝑖
𝑌
𝑁
𝑗=1(
2Φ
[ ln(𝑌𝑖) − ln(𝑌𝑗) + 0.5𝜎𝑖
2 + 0.5𝜎𝑗2
√𝜎𝑖2 + 𝜎𝑗
2
]
− 1
)
,
𝑁
𝑖=1
(3);
where 𝑤𝑖 is the share of population of country 𝑖, 𝑌𝑖 is the per capita GDP of country 𝑖, and 𝑌 is
the per capita GDP for the world. Note that one can think of the cross-country inequality as a
case in which all individuals within each country have the same per capita GDP as that of the
country, that is 𝜎𝑖2 = 0. Similarly, the within-country inequality is a special case in which there
are no per capita GDP differences among countries (that is, 𝑌𝑖 = 𝑌 for all i) and therefore all the
inequality stems from differences in individual incomes within the countries.
Table 3 (column 1, panel A) presents the cross-country Gini coefficient for various years
from 1820 to 2015. Note, the Gini coefficient in 1820 was very low, 0.235. This is not
surprising, as people throughout the world were poor in 1820 and the mean income differences
between the high and low-income countries were relatively small. During the 93 years that
followed, the per capita GDP of the high-income countries grew at an annual rate of 1.4 percent,
compared to 0.4 percent for the rest of the world. Even though the growth of per capita income
was slow, and the differential looks small, growth substantially increased cross-country income
differences over the 93 years. Thus, the cross-country Gini coefficient increased to 0.394 in 1870
and 0.495 in 1913. As the high-income countries continued to grow more rapidly than those with
before 2000 and decreasing inequality thereafter is present in other analysis of global income inequality [Milonovic
2016; Bourguignon 2015]. Moreover, Young [2011] compared the inequality estimates using the log normality
assumption with several other measures of inequality and found the estimates to be quite accurate.
13
lower per capita income, the cross-country Gini coefficient increased to 0.585 in 1950 and 0.586
in 1960.
Insert Table 3 about here
Between 1960 and 1990, the cross-country Gini coefficient fluctuated in a narrow range
between 0.586 and 0.604. After 1990 however, the cross-country Gini coefficient declined
sharply from 0.594 in 1990 to 0.575 in 2000 and 0.466 in 2015. Thus, the Gini coefficient
indicates that cross-country income differences increased substantially over the 140 years from
1820 to 1960, stabilized around 0.600 during 1960-1990 before declining sharply after 1990,
particularly during the first 15 years of this century. The 2015 cross-country Gini coefficient of
0.466 was lower than the parallel figure for 1913, indicating that income inequality among
countries today is lower than it was a century ago.
The SWIID data providing the within-country Gini coefficients are unavailable prior to
1960. Therefore, the within-country and worldwide Gini can only be derived for years since
1960. In addition, the SWIID data provide the within-country Gini coefficient for both market
income (before taxes and transfers) and disposable income (after taxes and transfers). Thus, it is
possible to calculate the within-country and worldwide Gini coefficients for both market and
disposable income. Taxes and transfers generally reduce income inequality. As a result, the Gini
coefficients for disposable income are smaller (indicating less inequality) than the parallel Gini
for market income.
Table 3, panel A, presents the within-country Gini coefficients for both market income
(column 2) and disposable income (column 3) at five-year intervals for 1960-2015. The within-
country Gini coefficient for both market and disposable income rose during 1960-2015. For
market income, the Gini coefficient rose slightly from 0.405 in 1960 to 0.411 in 1980, but more
substantially to 0.468 in 2015. While the Gini for disposable income was smaller, it followed a
similar path, rising from 0.354 in 1960 to 0.361 in 1980 and 0.410 in 2015. Thus, the within-
country Gini coefficient for both market income and disposable income rose by approximately
six hundredths of a point over the 55 years. This increase indicates that, on average, inequality of
both market and disposable income within countries has been rising during the past several
decades.
Panel A of Table 3 also provides the worldwide Gini coefficient of market income (column
4) and disposable income (column 5) at five-year intervals during 1960-2015. The worldwide
Gini coefficient reflects both cross-country and within-country income inequality. Again, the
Gini coefficient is smaller for disposable income than market income, indicating that taxes and
transfers reduce income inequality.
The worldwide Gini coefficient for both market and disposable income rose between 1960
and 1980, was relatively stable between 1980-2000, but declined sharply thereafter. In the case
of market income, the Gini coefficient increased from 0.685 in 1960 to 0.700 in 1980 and 0.690
in 2000. The worldwide Gini for disposable income rose by a similar amount from 0.657 in 1960
to 0.672 in 1980 and 0.661 in 2000. These figures indicate that the 160-year upward trend in
worldwide inequality continued through 1980 before stabilizing during the last two decades of
the 20th century.
14
Since 2000, however, there has been a dramatic reversal. The worldwide Gini coefficients
for both market and disposable income have declined sharply, indicating a substantial reduction
in worldwide income inequality. The worldwide Gini coefficient for market income declined
from 0.690 in 2000 to 0.672 in 2005, 0.644 in 2010 and 0.627 in 2015. Thus, while the
worldwide Gini coefficient for market income rose by 0.014 during 1960-1990, it fell by an even
larger amount, 0.072 during the quarter of a century that followed. Moreover, the Gini
coefficient of disposable income followed a similar pattern. In 1990, the worldwide disposable
income Gini was 0.671, but it declined to 0.641 in 2005 and 0.586 in 2015.11 Thus, after
increasing by 1.4 hundredths of a point during 1960-1990, the worldwide Gini fell by 8.5
hundredths of a point during 1990-2015.
The Gini data of panel A are for the 135 countries of our primary database. This set of
countries covers approximately 93 percent of the world’s population. Following the collapse of
communism, a number of new countries were formed. Because the GDP data for these countries
were not available continuously back to 1970, they are not included in our primary database.
Both the SWIID and Maddison data are available for 157 countries back to 1990. These
countries constitute 96 percent of the world’s population. In order to verify that the Gini
measures are not biased by the omission of countries, we also calculated the Gini coefficients for
the larger set of 157 countries for years ending in zero or five during 1990-2015.
Table 3, panel B, presents these results. Unsurprisingly, given the large overlap of the two
populations, the results for the 157 countries (panel B) are very similar to the figures for our
primary dataset (panel A). The cross-country Gini coefficient for the larger set of countries were
slightly lower for all years, while the within country Gini coefficients were virtually identical for
the two datasets. As a result, the worldwide Gini coefficient for the larger set of countries were a
little lower. These patterns were present for both the market and disposable income Gini
coefficients. Most importantly, the Gini coefficients of panel B, like those of panel A, indicate
that there was little change in worldwide income inequality during the 1990s, but this was
followed by a sharp reduction in worldwide income inequality during 2000-2015. This was true
for both the market and disposable income Gini coefficients. While the worldwide Gini
coefficients are slightly lower for the larger set of countries, their pattern is identical to the
figures of panel A. Both illustrate that there has been a substantial reduction in worldwide
income inequality during the first 15 years of this century.
Milanovic [2016] and Bourguignon [2015] also described the rise of global income
inequality prior to 2000 and the subsequent decline. They argue that the recent reduction in
worldwide inequality was the result of globalization and express uncertainty about the
11 Some might believe that an increase in within-country inequality and, at the same time, a reduction in worldwide
income inequality is paradoxical. This is not the case because cross-country income differences also exert an impact
on overall income inequality. Consider the example of the United States and China. Within-country income
inequality increased substantially in both countries between 1980 and 2015. Using the disposable income figures,
the Gini coefficient for within-country inequality of the United States increased from 0.316 in 1980 to 0.380 in
2015. Similarly, the within-country Gini coefficient for China rose from 0.290 in 1980 to 0.401 in 2015. During this
period, however, the per capita income of China grew rapidly relative to the United States. The cross-country Gini
coefficient declined sharply from 0.618 in 1980 to 0.296 in 2015. As a result, the overall income differences among
households declined in the unified U.S.-China group. The unified Gini coefficient for the two countries combined
fell from 0.712 in 1980 to 0.512 in 2015. In recent decades, the per capita income of developing countries,
particularly the non-Malthusians, has risen relative to high-income countries. This cross-country reduction in income
inequality has more than offset the increases in within-country inequality in some countries.
15
continuation of this trend. Our analysis indicates that it is not globalization per se, but rather the
demographic changes accompanying the growth process that underlie the rise and fall of income
inequality. A pattern similar to ours was also found by Hellebrandt and Mauro [2015]. Based on
growth rate projections, they forecast that the recent reductions in income inequality will
continue. Our research indicates that demographic changes underlie the rise and fall of
worldwide income inequality. We now turn to the explanation of why the recent trend is likely to
continue.
V. Five Reasons Why the Recent Reduction in Income Inequality Will Continue
Both the per capita GDP of high-income countries compared to developing economies and
the Gini measure of income inequality indicate that the gap between those with high and low
incomes has narrowed at least since 2000. Will the trend toward less income inequality continue?
There are at least five reasons to believe that it will.
Reason 1: In the years immediately ahead, the overwhelming share of the developing
economies will be in phases 2 and 3 of the development process where growth rates are higher,
while high-income countries will be in phase 4 where growth rates are lower.
Table 4 shows the number of the 114 developing and 21 high-income countries by phase of
development, 1960-2035. The population share for each of the groups is shown in parentheses.
The 1960-2015 figures are based on the actual population estimates from the World Bank
[2018], while the 2015-2035 figures are based on the medium fertility variant population
projections of the United Nations Population Division [2017].
Since 1970, there has been steady movement of the 114 developing economies from phase 1
to phase 2. There were 94 of the developing economies (93.8 percent of their population) in
phase 1 in 1970, and only 20 (6.2 percent of their population) in either phases 2 or 3. By 2000,
however, the number of developing economies in phases 2 or 3 had risen to 65 (79.8 percent of
their population) while the number in phase 1 had fallen to 49 (20.2 percent of their population).
In 2015, 75 developing economies (84.0 percent of their population) were in either phases 2 or 3,
eight others (1.8 percent of their population) had moved to phase 4, and only the 31 Malthusians
(14.2 percent of their population) remained in phase 1.12 As our analysis indicates, when
countries move from phase 1 to phase 2, they begin a virtuous cycle of declining birth rates,
falling share of population under age 15, and increasing share of population in the prime
working-age category, which leads to an acceleration in growth. This was a major contributing
factor to the acceleration in the growth rate of the non-Malthusian developing economies during
1960-2015.
Insert Table 4 about here
Table 4 also shows the share of population of the high-income countries by phase of
development during 1960-2015. All of the high-income countries were in either phases 2 or 3
prior to 2000. But, there was a dramatic change during the first 15 years of this century. By 2015,
12 The eight developing economies that moved into phase 4 by 2015 were Barbados, Hong Kong, Hungary, Malta,
Portugal, Qatar, Singapore, and Thailand. These were among the earliest developing countries to move from phase 1
to phase 2.
16
19 of the 21 high-income countries (99 percent of their population) had moved to phase 4 and the
other 2 (Switzerland and Luxembourg) are expected to do so by 2020.
As the share of population in phases 2 and 3 of the developing countries increased during
1960-2015, the growth of their per capita income rose. All of the high-income countries were in
phases 2 or 3 throughout 1960-2000 and the growth rates of these economies were also strong.
Since 2000, however, the share of population of the high-income group in phase 4 of
development has soared and, as expected, the growth of their per capita income has declined
substantially. Therefore, the developing countries have grown more rapidly and narrowed the
income gap relative to the high-income countries since 2000. The population projections for
2015-2035 indicate that the population share living in developing countries in phases 2 and 3 of
development will remain large, while all of the population of the high-income countries will be
in phase 4 by 2020. Because growth rates are higher in phases 2 and 3 than phase 4, the pattern
of more rapid growth of the developing countries compared to their high-income counterparts
can be expected to continue. In turn, this will result in the continuation of declining world-wide
income inequality.
Figure 1 illustrates how the movement of countries through the phases of development,
depicted by Table 4, impacts the share of population in the prime working-age 25-59 category
for the 21 high-income, 83 developing, and 31 Malthusian countries. The prime age ratio is
important because an increase in the prime age population as a share of the total will tend to
enhance growth, while a decline will retard it.
Insert Figure 1 about here
For the high-income group, the share of population in the prime age category was relatively
constant during the 1960s, but it rose substantially from 42.4 percent in 1970 to 49.0 in 2000.
However, as more and more of the high-income countries moved to phase 4 during 2000-2015,
the prime age population share has steadily fallen from its peak of 49 percent in 2000 to 46.9
percent in 2015 and it is expected to decline to 44.1 percent in 2035. Thus, it rose by 6.6
percentage points between 1970 and 2000, but it is projected to fall by 4.8 percentage points
between 2000 and 2035.
Turning to the figures for the 83 non-Malthusian developing countries, their prime working-
age population declined from 36.6 percent in 1960 to 33.9 percent in 1975. However, during the
four decades following 1975, the prime age population of the non-Malthusian developing
countries rose steadily to 47.5 percent in 2015, a whopping 13.6 percentage points. By 2015, the
prime-age population as a share of the total of these developing economies was greater than the
parallel figure for the high-income countries. During 2015-2035, the prime age share is projected
to rise briefly, but by 2035 it is expected to recede to the level of 2015.
As Figure 1 shows, the prime age share of the 31 Malthusian economies is substantially
lower than for either of the other two groups. The ratio of prime age to total population of the
Malthusian economies fell from 33.5 percent in 1960 to 30.1 percent in 2000. In contrast, it rose
from 30.1 percent in 2000 to 31.5 percent in 2015 and it is projected to rise another 4.6
percentage points by 2035.
Figure 2 reflects how the demographic changes of Figure 1 impact the growth rates of the
three groups. The fifteen-year moving average growth of per capita GDP is shown for the high-
17
income, developing, and Malthusian country groups for the period 1965-2015.13 From 1965 to
1975 the high-income countries have a higher growth rate than the other two groups. The
average annual growth rate over the period for the high-income group was just above 4 percent
while the growth rate for the developing countries trended between 2 and 3 percent. As more and
more of the non-Malthusian developing countries moved to phases 2 and 3 of development and
the share of their population in the prime working age increased between 1975 and 2000, the
growth of their per capita GDP began to outpace the high-income countries.
Insert Figure 2 about here
The prime age population as a share of the total increased substantially for both developing
country groups during 2000-2015, while the parallel share of the high-income countries was
declining. These shifts contributed to the stronger growth of developing countries relative to their
high-income counterparts. Moreover, the prime age population of both groups of developing
economies will continue to rise relative to the high-income countries for at least two more
decades.14 This pattern of demographic changes will lead to a continuation of the more rapid
growth of the developing economies relative to high-income countries in the years immediately
ahead.
Reason 2: Because low-income countries can emulate advanced technologies and successful
business practices of high-income countries, they are able to grow more rapidly than their higher
income counterparts. The lower transportation and communication costs of recent decades have
enhanced the gains from this source.
Discovery of new, improved technologies and better ways of doing things are an important
source of growth and development. Advanced economies invest billions in research and
technological development designed to improve products and reduce production cost. The
advanced technologies and successful business practices of high-income countries provide an
important secondary benefit for developing countries. Businesses and entrepreneurs in
developing countries can merely copy (or adopt at a low cost) the successful technologies and
practices of the more advanced economies. Moreover, the reductions in transportation and
communication costs of recent decades accelerates the transmission and enlarges the gains from
this source.
As the result of lower transportation costs and improved communications, there is now more
exchange of both goods and ideas between people living in high and low-income countries than
at any time in history. Consider how just one change, albeit an important one, the development
of the standardized steel shipping container, has impacted international trade and the well-being
of people living in countries distant from major markets. Because these containers are
standardized in size and design, thousands of them can be stacked on large ships, and transported
at a low cost to ports throughout the world. Upon arrival, machines can lift them on to rail cars
and trucks for transport to inland distribution centers and manufacturing facilities. As a result,
13 The data cover the period 1950-2015, but the graph begins in 1965 due to the fifteen-year moving average. 14 The working-age population as a share of the total presented in Figure 1 is based on the population weighted
means. Because the population weighted means might be driven by a few large countries such as China and India,
we also derived parallel graphics using the simple means. The pattern based on the simple mean values was similar
to the pattern for the population weighted means, indicating that the demographic changes were widespread among
the countries in the three groups.
18
international transport costs have been reduced by an estimated 75 percent during the past four
decades.15
Of course, the reductions in transport and communication costs increase the gains from trade
and specialization for people throughout the world. But, they are particularly advantageous to
developing countries because they facilitate their integration into world markets. This integration
makes it possible for previously isolated regions to attract investment, obtain productive inputs,
adopt advanced technologies, emulate successful business practices, and use their unique
resources to produce goods and services for consumers throughout the world.
During the past 50 years, the world has experienced something like the Industrial
Revolution: the transportation-communication revolution. Like the Industrial Revolution, the
more recent revolution has expanded opportunities and enhanced economic growth. But the two
revolutions differ in their impact on inequality. As we previously discussed, the Industrial
Revolution resulted in substantial income gains for about 15 percent of the World’s population,
but the affect was minimal in the rest of the world. In contrast, the impact of the transportation-
communication revolution is exerting a broader impact—it is changing the developing world
even more than the developed economies.16 Today, more than at any time in history, people
living in developing countries have an opportunity to trade with and benefit from interaction with
those living in the high-income developed world. As a result, developing countries are able to
both grow more rapidly than their high-income counterparts and achieve highly impressive
growth rates. Thus, the transportation-communication revolution is reducing rather than
increasing worldwide income inequality.
In recent decades, almost all of the high-growth economies have been developing countries.
Consider the number of countries that have achieved per capita GDP growth greater than 5
percent in recent decades. Using the Maddison [2018] data, 5 countries achieved this threshold
during the 1980s, and all were developing countries. In the 1990s, the per capita GDP of 7
countries grew at an annual rate greater than 5 percent, and all but one were developing
countries. During 2000-2010, a whopping 18 countries achieved annual growth of per capita
GDP greater than 5 percent and all were developing countries. Finally, thirteen countries had
average annual growth rates of 4 percent of more during the quarter of a century, 1990-2015. All
13 were developing countries.
Moreover, developing countries today are able to achieve growth rates beyond what was
thought to be possible only a few decades ago. Growth rates above 2 percent for lengthy time
periods were largely absent prior to 1950. The annual real growth rates of per capita GDP of the
United Kingdom and United States, the most prosperous of the high-income economies during
the 1800s, were 1.0 percent and 1.4 percent, respectively. During the 12 decades from 1820 to
1940, the growth of per capita GDP during a decade never exceeded 3 percent in either the
United Kingdom or United States.
15 Glaeser and Kohlhase [2004] analyze the impact of reductions in transportation costs on the allocation of
populations within regions. They estimate transportation costs have declined by more than 90 percent during the 20th
century. 16 The breadth of the transportation-communication revolution is illustrated by the experience of the 83 non-
Malthusian developing countries. These countries which comprise approximately three-fourths of the world’s
population, generated 27.8 percent of world GDP in 1960, 34.9 percent in 1980, 40.4 percent in 2000, and 56.6
percent in 2015. This increase in the relative contribution of these economies to world GDP has been remarkable.
19
It is informative to compare the high growth rates achieved by many developing countries
since 1960 with the modest growth rates of the earlier era. The per capita GDP of Hong Kong
grew at an annual rate of 4.9 percent during the 40 years from 1960 to 2000. The growth rate of
Singapore was even more impressive, 5.7 percent during the same 40-year period. During the 50
years of 1965-2015, the per capita GDP of South Korea and Botswana grew at annual rates of
5.8 percent and 5.4 percent, respectively. Still more recently, the per capita GDP of China rose at
an annual rate of 5.9 percent during 1980-2015 and India at a 4.8 percent annual rate during
1990-2015.
The high-growth rates of a substantial number of developing economies during recent
decades stand in stark contrast with both those of today’s high-income countries and the growth
rates of the past. These high-growth rates are transforming the world. They are being driven by
low-cost transport of goods and transmission of information and technology. Further, these gains
will continue for the foreseeable future.
Reason 3: The developing economies are adopting institutions and policies more consistent
with growth and prosperity
In the past, counterproductive policies and institutions have undermined the potential of
many developing economies. However, there has been substantial improvement in the
institutional quality of developing countries in recent decades.
Table 5 presents data on several measures of institutional quality for our 114 developing
countries at five-year intervals for 1980-2015. The mean summary economic freedom of the
world rating is presented in column 1 [Gwartney, Lawson, and Hall 2017]. This comprehensive
measure of economic institutions is based on ratings ranging from zero to ten for 42 different
components. Higher ratings indicate institutions and policies more consistent with economic
freedom. The mean summary rating of developing countries rose steadily from 4.84 in 1980 to
6.30 in 2000, and 6.62 in 2015.
Insert Table 5 about here
In addition to the country summary ratings, the Economic Freedom of the World data
provide ratings for five major areas, including legal structure and protection of property rights,
access to sound money, and international exchange. Both economic theory and prior research
indicate that a legal system that provides for the protection of property rights and unbiased
enforcement of contracts is a highly important ingredient for economic growth and development.
The Economic Freedom of the World legal structure data provide a measure of this factor. As
Table 5, column 2 shows, the mean legal structure rating of the developing countries rose from
3.72 in 1980 to 4.58 in 2000, and 4.82 in 2015. Table 5 also shows the mean ratings for access to
sound money (column 3) and international exchange (column 4). The monetary rating rose
sharply from 5.59 in 1980 to 8.04 in 2015. The mean rating for international exchange followed
a similar path, increasing from 4.09 in 1980 to 6.77 in 2015. Like the summary ratings, the
ratings for these institutional categories of the developing countries show substantial
improvement throughout 1980-2015, particularly in the monetary and international trade areas.
The Freedom House [2017] political rights (column 5) and Polity IV [Marshall, Gurr, and
Jaggers 2016] democracy ratings (column 6) are also presented in Table 5. Like the economic
freedom measures, these political measures also improved substantially. The mean political
20
rights measure, in a scale ranging from 1 (least free) to 7 (most free), rose from 3.1 in 1980 to
4.23 in 2015. The polity IV mean rating, in a scale ranging from -10 (strongly autocratic) to +10
(strongly democratic) rose from -3.43 in 1980 to +3.69 in 2015. Thus, the mean rating for this
variable indicates a substantial shift from authoritarian regimes toward democracy during 1980-
2015.
Clearly, the developing countries now have both freer economies and more democratic
political institutions than was the case in 1980. These institutional improvements have
accelerated the growth of developing economies in recent decades.17 Moreover, incentives
impact the development of institutions. The classic article of Acemoglu, Johnson, and Robinson
[2001] highlights this point. As the authors stressed, institutions protecting private property
rights and restraining the powers of the executive were more likely to emerge when colonizing
settlers planned for permanent settlement. Similar forces are currently at work. The
transportation-communication revolution enhances the incentive for developing countries to
adopt sound economic institutions. When lower transport and communication costs increase the
potential gains derived from trade openness, a sound legal system, monetary stability, and
integration into the world market network, businesses, investors, and political decision-makers
will have a stronger incentive to support such policies. The recent institutional improvements of
developing countries shown in Table 5 may already be reflective of this point. The payoff for
developing countries, particularly those distant from major markets, derived from sound
institutions is currently greater than was the case three or four decades ago. This positive change
in incentives increases the probability developing countries will maintain and improve the
quality of their institutions in the future.
Reason 4: The secondary school enrollment and completion rates of young people in
developing economics have increased sharply in recent decades. As these young people move
into the prime working age category, the productivity of the labor force in developing countries
will improve, and thereby contribute to more rapid growth.
Table 6 presents data on the Barro and Lee [2013] secondary schooling enrollment rates for
those age 15 to 19 years and the secondary schooling completion rates for persons age 20 to 24
for the developing economies and the Malthusians for 1960-2010. The secondary schooling
enrollment rate of 15 to 19-year-olds rose from 17.0 percent in 1960 to 37.0 percent in 1980, and
59.5 percent in 2010. The completion rates followed a similar path, rising from 5.9 percent in
1960 to 16.4 percent in 1980, and 34.2 percent in 2010. Of course, the parallel figures for the
Malthusian countries were lower, but they have also increased steadily and substantially over the
50-year period. As these young people move into the work force in the decades immediately
ahead, their increases in schooling will enhance productivity and contribute to the future
economic growth of developing countries.
Insert Table 6 about here
Hanushek and Woessmann [2008; 2010; 2012] emphasize the role of educational quality, as
opposed to quantity, for economic growth. The transportation-communication revolution
provides low-cost access to the highest quality education available in the world. Initiatives such
as MIT OpenCourseWare, Coursera, Edx, Codecademy, Moz, and Youtube illustrate this point.
17 For evidence that institutions exert a positive impact on economic growth see: Barro [1991], Barro [1997],
Gwartney, Holcombe, and Lawson [2006], Shleifer [2009], and Acemoglu et al. [forthcoming].
21
The availability of these technologies is a recent phenomenon bringing a broad, worldwide,
access to high-quality education, a faster diffusion of ideas, and a reduction of disparities in the
level and quality of human capital that is attainable. These opportunities will likely accelerate the
catch-up growth for developing countries entering in phases 2 and 3 of the development process
in the years immediately ahead.
Furthermore, the Malthusian escape process can also be thought of as a transition from
physical capital to human capital [Tamura 1996; Galor and Weil 2000; Galor 2011]. First, the
human capital accumulation is both a cause and a consequence of the transition, enhancing
technological progress. This will push the country into a virtuous cycle of phases 2 and 3 of the
development process. As both the quantity and quality of educational opportunities increase, the
Malthusian escape will accelerate. Second, as technological progress continues to rise and the
costs of high-quality educational opportunities continue to decline, a larger set of countries and a
broader share of their population will have greater access and opportunity to develop their human
capital. While the Industrial Revolution increased inequality by concentrating physical capital in
the hands of a few in a selected group of countries, the transportation-communication revolution
is reducing inequality by providing broader, and more disperse, access to high-quality education
across the globe.
Reason 5: Improvements in health, protective measures, and prevention have reduced the
adverse impact of malaria in recent years.
Malaria has substantially reduced the productivity and climate for investment of many poor
countries, particularly those in Africa. But, progress has been made. Table 7 presents data on
reported cases of malaria as a percentage of the total population for the 31 Malthusian, the non-
Malthusian African, the 83 other developing countries, and for Asia and Latin America. These
data indicate that the incidence of malaria is most severe in Africa, and particularly for the
Malthusian African countries. The incidence of malaria in the 83 developing economies has been
around 1 percent throughout 1990-2015, and it has been in a declining pattern since 2000.
Further, the figures indicate that malaria is no longer a major problem in either Asia or Latin
America. In contrast, the incidence of malaria is substantially higher in Africa. The malaria cases
as a share of the population for the 31 Malthusian economies rose from 10.7 percent in 1990 to
13.5 percent in 2005, before receding to 9.4 percent in 2015. Data are available for eight non-
Malthusian African economies (Botswana, Cabo Verde, Gabon, Ghana, Mauritius, Namibia,
Swaziland, and South Africa). While the problem of these countries is less severe than for the
Malthusian countries, it is nonetheless substantial, particularly in Gabon, Ghana, and Namibia.
Insert Table 7 about here
The recent decline in malaria cases, along with continuing improvements in prevention,
health care, and treatment, provide reason for future optimism.18 As previously indicated, a
substantial share of the 31 Malthusian economies have grown more rapidly and shown signs of
moving from phase 1 to phase 2 during the last 15 years. With continuing progress against
18 Since 2000, substantial progress has been made against malaria. The prevalence of infection in children ages 2-10
has been cut in half in Africa during 2000-2015 [Bhatt et al. 2015]. Based on Gapminder data [Rosling 2008] and
our own calculations, malaria fatalities per 10,000 reported cases have fallen from 41.9 in 1990 to 23.0 in 2010.
Otten et al. [2009], in an examination of malaria in Rwanda and Ethiopia, document substantial reductions in the
number of malaria cases and deaths resulting from malaria since 2001.
22
malaria, many of these countries are likely to break out of phase 1 and begin the virtuous cycle in
the years immediately ahead.
VI. Summary and Conclusion
Prior to the Industrial Revolution, the world was Malthusian. Growth of per capita income
was absent and most everyone was poor—phase 1 of development. The Industrial Revolution
brought change, but mostly for people living in a small set of countries in Western Europe, North
America, and Oceania, representing about 15 percent of the world’s population. Even though
growth of per capita income in these locations was slow by today’s standards, it was more rapid
than the rest of the world. Therefore, income inequality continually expanded during the 150
years following the Industrial Revolution.
This slow-motion world began to speed up in the last half of the 20th century. Developing
countries began to escape Malthusian stagnation and move to phases 2 and 3 of development. As
they did so, they benefitted from the virtuous cycle of development—growth of per capita
income, reductions in the birth rate, and increases in the prime age population as a share of the
total, which propelled additional growth. Further, huge reductions in transportation and
communication costs driven by innovation and technology facilitated production far from the
world’s major markets, integrating many developing countries into the world market network.
This second economic revolution, the transportation-communication revolution, both increased
the gains from trade and the transmission speed of successful business ideas and practices
throughout the developing world. This combination of factors made it possible for more and
more developing countries to move out of the Malthusian phase of development, benefit from the
accompanying virtuous demographic changes, and achieve historically high growth rates that
were unattainable only a few decades earlier. Further, the growth of the high-income countries
decelerated as they moved into phase 4 of development. Thus, the two-century trend toward
increasing income inequality slowed during the latter part of the 20th century and eventually
reversed.
The Gini coefficients confirm the substantial increase in income inequality from 1820 to late
in the 20th century, slowdown in the trend during the latter part of the 20th century, and sharp
reversal of trend during 2000-2015. For the first time in history, the world experienced both
substantial increases in per capita income and reductions in income inequality. Further, it is
highly likely that this situation will continue for another decade or so because most of the
developing countries are in phases 2 and 3 of the development process while the high-income
countries are in phase 4. In addition, the low transport and communication costs, improvements
in economic and political institutions, increases in human capital of the working-age population,
and declining incidence of malaria will elevate the growth of developing countries in the decades
immediately ahead.
The development process and accompanying demographic changes as countries move
through different phases of development enhance our understanding of the factors underlying the
historic pattern of worldwide income inequality. Others have noted the pattern of inequality
described here. However, because the development process was not incorporated into their
analysis, they were unable to explain why the two-century trend of increasing income inequality
reversed and use the forces underlying the reversal to make a persuasive case about the future
direction of global inequality.
23
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27
Tables and Figures
Table 1: Per Capita GDP and Annual Growth Rates for 31 Malthusian Economies, 1960-2015.
Country 1960 1980 2000 2015 1960-2015 1960-2000 2000-2015
Angolaa
3,120 1,647 8,631 11.04
Benin 1,281 1,842 1,489 2,141 0.93 0.38 2.42
Burkina Faso 1,557 1,608 1,355 1,527 -0.04 -0.35 0.80
Burundi 973 1,157 962 723 -0.54 -0.03 -1.90
Cameroon 1,067 1,694 2,252 2,754 1.72 1.87 1.34
Central African Rep. 1,395 1,044 911 605 -1.52 -1.07 -2.73
Chad 1,482 888 989 2,387 0.87 -1.01 5.87
Congo, Rep. 1,396 2,888 2,727 4,526 2.14 1.67 3.38
Congo, Dem. Rep. 2,119 1,782 443 836 -1.69 -3.91 4.23
Côte d'Ivoire 2,070 3,136 2,127 3,473 0.94 0.07 3.27
Ethiopia 750 1,105 608 1,576 1.35 -0.52 6.35
Gambia 1,994 2,241 1,812 1,948 -0.04 -0.24 0.48
Guinea 1,419 1,427 1,059 1,606 0.23 -0.73 2.78
Guinea-Bissau 841 1,267 1,357 1,313 0.81 1.20 -0.22
Kenya 1,396 2,033 2,086 3,090 1.44 1.00 2.62
Liberia 887 1,118 524 798 -0.19 -1.32 2.80
Madagascar 1,121 1,092 975 1,288 0.25 -0.35 1.86
Malawi 1,092 945 873 960 -0.23 -0.56 0.63
Mali 1,164 638 885 1,563 0.54 -0.69 3.79
Mauritania 1,037 1,961 1,945 3,323 2.12 1.57 3.57
Mozambique 546 459 606 1,277 1.54 0.26 4.97
Niger 1,361 1,497 669 895 -0.76 -1.78 1.94
Nigeria 2,179 6,414 2,657 5,540 1.70 0.50 4.90
Rwanda 728 1,058 705 1,676 1.52 -0.08 5.77
Senegal 2,337 1,515 1,850 2,446 0.08 -0.58 1.86
Sierra Leone 1,962 1,754 998 1,033 -1.17 -1.69 0.23
Tanzania 1,360 2,159 1,137 2,429 1.05 -0.45 5.06
Togo 1,102 1,847 1,160 1,482 0.54 0.13 1.63
Uganda 1,210 940 1,129 1,883 0.80 -0.17 3.41
Zambia 1,706 1,462 1,153 3,537 1.33 -0.98 7.47
Zimbabwe 2,710 4,003 2,696 1,759 -0.79 -0.01 -2.85
Simple Average 1,408 1,809 1,348 2,227 0.50 -0.26 2.80
Population Weighted Average 1,913 2,743 1,483 2,791 0.69 -0.64 4.22
Minimum 546 459 443 605 -1.69 -3.91 -2.85
Maximum 2,710 6,414 2,727 8,631 2.14 1.87 11.04
Source: Maddison [2018] and own calculations. Maddison [2018] presents alternative per capita GDP
figures that are recommended for calculation of growth rates. This series was also used to calculate the
growth rates for columns 5, 6, and 7. The pattern of the results was similar to those shown above.
Note: a The 1960 per capita GDP for Angola was unavailable.
28
Table 2: Per capita GDP of the 31 Malthusian, 114 Developing, and 83 non-Malthusian
Developing Economies relative to the 21 High-Income Industrial Countries
(1) (2) (3) (4) (5) (6) (7)
Year
High Income
21
Malthusian
31
83 non-
Malthusian
Developing
114
Developing
Ratio
(1) / (2)
Ratio
(1) / (3)
Ratio
(1) / (4)
1820 1,461 821 1.8
1870 2,506 845 3.0
1913 5,413 1,219 4.4
1950 8,469 1,450 5.8
1950 8,464 1,216 1,426 1,408 7.0 5.9 6.0
1960 11,782 1,525 1,698 1,683 7.7 6.9 7.0
1970 17,633 2,310 2,291 2,293 7.6 7.7 7.7
1980 23,590 2,743 3,186 3,144 8.6 7.4 7.5
1990 28,832 2,023 3,676 3,505 14.3 7.8 8.2
2000 37,876 1,483 5,159 4,734 25.5 7.3 8.0
2010 41,839 2,417 9,173 8,279 17.3 4.6 5.1
2015 44,514 2,791 11,015 9,845 15.9 4.0 4.5
1960 11,712 1,913 1,629 1,654 6.1 7.2 7.1
1970 17,493 2,297 2,279 2,281 7.6 7.7 7.7
1980 23,075 2,498 3,152 3,090 9.2 7.3 7.5
1990 29,171 1,291 3,490 3,265 22.6 8.4 8.9
2000 38,396 1,087 5,037 4,585 35.3 7.6 8.4
2010 41,990 2,408 9,003 8,133 17.4 4.7 5.2
2015 45,230 2,804 11,143 9,963 16.1 4.1 4.5
1980 20,226 2 ,635 3 ,608 3 ,536 7.7 5.6 5.7
1990 32,272 1,877 4,378 4,120 17.2 7.4 7.8
2000 39,082 1,700 6,073 5,571 23.0 6.4 7.0
2010 42,100 2,500 9,506 8,578 16.8 4.4 4.9
2015 44,538 2,813 11,406 10,165 15.8 3.9 4.4
Panel A: Maddison (2018)
Panel B: Penn World Table
Panel C: World Bank
Notes: 1. The mean per capita GDP figures for each group were derived by first calculating the total
GDP for each group and then dividing by the total population of the group. The figures in panel A are
from Maddison [2018] and are expressed in 2011 PPP dollars.
2. The high-income group of panel A from 1820-1950 includes 16 of the 21 high-income countries.
The five countries for which the per capita GDP figures were unavailable are: Belgium, Iceland,
Luxembourg, New Zealand, and Switzerland. Three high-income countries (Germany, Spain, and
Japan) had data in 1800 and 1850, but not 1820. The value for 1820 was interpolated by adding 2/5 of
the change over the period to the 1800 real per capita GDP. Over the period 1820-1950, only 36 of the
83 developing countries (column 3) had continuous data. These 36 countries constituted over 90
percent of the developing world population in 1950. During 1820-1950, the per capita income data
were unavailable for all of the Malthusian countries. Finally, the second 1950 row begins the data that
includes the 21 high-income countries, the 31 Malthusian countries, and 77 of the 83 non-Malthusian
developing countries. The six developing countries omitted from the Maddison data were: The
Bahamas, Belize, Bhutan, Brunei Darussalam, Fiji, and Suriname.
29
Table 3: Gini Coefficients Across-Country, Within-Country, and Worldwide. Pre and Post Taxes and Transfers.
Panel A: 135 countries of primary dataset
(1) (2) (3) (4) (5)
Cross-Country
Year
Market Income
(Pre-Tax, Pre-Transfer)
Disposable Income
(Post-Tax, Post-Transfer)
Market Income
(Pre-Tax, Pre-Transfer)
Disposable Income
(Post-Tax, Post-Transfer)
1820 0.235
1870 0.394
1913 0.495
1950 0.585
1960 0.586 0.405 0.354 0.685 0.657
1965 0.594 0.406 0.356 0.692 0.665
1970 0.598 0.406 0.357 0.697 0.670
1975 0.602 0.407 0.358 0.699 0.672
1980 0.604 0.411 0.361 0.700 0.672
1985 0.595 0.417 0.366 0.696 0.667
1990 0.594 0.428 0.379 0.699 0.671
1995 0.578 0.442 0.392 0.690 0.659
2000 0.575 0.450 0.402 0.690 0.661
2005 0.544 0.462 0.413 0.672 0.641
2010 0.493 0.469 0.413 0.644 0.606
2015 0.466 0.468 0.410 0.627 0.586
Panel B: Extended set of 157 countries
1990 0.585 0.425 0.375 0.689 0.660
1995 0.572 0.439 0.388 0.685 0.655
2000 0.570 0.447 0.398 0.686 0.657
2005 0.539 0.458 0.409 0.667 0.636
2010 0.488 0.464 0.409 0.639 0.601
2015 0.461 0.464 0.405 0.624 0.584
Source: Maddison [2018], World Bank [2018], Solt [2016], Young [2011], and own calculations
Notes: The 135 countries of panel comprise 92 percent of the world's population while the 157 countries of panel B comprise 96 percent of
the World's population.
Within-Country Worldwide
30
Table 4: Number of developing and high-income countries by phase of development, 1960-2035
(1) (2) (3) (4) (5) (6)
Year phase 1 phases 2 or 3 phase 4 phase 1 phases 2 or 3 phase 4
1960 96 (95.0%) 18 (5.0%) 0 (0%) 0 (0%) 21 (100%) 0 (0%)
1970 94 (93.8%) 20 (6.2%) 0 (0%) 0 (0%) 21 (100%) 0 (0%)
1980 75 (55.8%) 39 (44.2%) 0 (0%) 0 (0%) 21 (100%) 0 (0%)
1990 64 (47.3%) 50 (52.7%) 0 (0%) 0 (0%) 21 (100%) 0 (0%)
2000 49 (20.2%) 65 (79.8%) 0 (0%) 0 (0%) 20 (90.2%) 1 (9.8%)
2010 32 (13.3%) 82 (86.7%) 0 (0%) 0 (0%) 5 (6.8%) 16 (93.2%)
2015 31 (14.2%) 75 (84.0%) 8 (1.8%) 0 (0%) 2 (1.0%) 19 (99.0%)
2020 27 (12.5%) 74 (84.6%) 13 (2.9%) 0 (0%) 0 (0%) 21 (100%)
2025 21 (12.4%) 68 (59.5%) 25 (28.1%) 0 (0%) 0 (0%) 21 (100%)
2030 14 (11.7%) 68 (58.7%) 32 (29.6%) 0 (0%) 0 (0%) 21 (100%)
2035 9 (7.0%) 71 (65.8%) 34 (27.2%) 0 (0%) 0 (0%) 21 (100%)
Sources: World Bank [2018], Penn World Table [Feenstra et al. 2015], United Nations [2017], and own calculations
Note: Figures for 2020-2035 are based on the United Nations Demographic Projections. A country is classified as Malthusian if it was in
phase 1 of development continuously since 196-2015. A country is in phase 1 of development in any given year, if its share of population
under age 15 is larger than 40 percent or if its per capita GDP is lower than $1,460 (4 dollars a day). A country is considered in phase 4 if
its prime working age 25-59 share of total population has declined by 1 percent or more from its peak.
Number in each phase (and population share)
for 114 developing economies
Number in each phase (and population share)
for 21 High-income economies
31
Table 5: Economic and Political Institutions for the 114 Developing Economies, 1980-2015
(1) (2) (3) (4) (5) (6)
Year
Economic
Freedom
(Summary Legal System Sound Money
International
Exchange
Freedom House
(Political Rights)
Polity IV
(Democracy)
1980 4.84 (81) 3.72 (66) 5.59 (88) 4.09 (73) 3.10 (104) -3.43 (104)
1985 4.91 (87) 3.80 (84) 5.90 (91) 4.19 (77) 3.29 (104) -2.91 (104)
1990 5.20 (91) 3.97 (85) 5.80 (91) 4.96 (83) 3.51 (105) -0.49 (104)
1995 5.79 (91) 4.66 (87) 6.06 (91) 6.29 (84) 3.95 (106) 1.99 (106)
2000 6.30 (91) 4.58 (91) 7.23 (91) 6.83 (91) 4.16 (106) 2.49 (106)
2005 6.45 (100) 4.69 (100) 7.49 (100) 6.73 (100) 4.28 (106) 3.07 (107)
2010 6.59 (109) 4.80 (109) 7.74 (109) 6.87 (109) 4.16 (106) 3.26 (107)
2015 6.62 (114) 4.82 (114) 8.04 (114) 6.77 (114) 4.23 (106) 3.69 (107)
Source: Gwartney et al. [2017], Freedom House [2017], and Marshall et al. [2016].
Note: The number of countries for which data were available is indicated in parentheses. The Economic Freedom of the World index
(columns 1-4) ranges theoretically from 0 (least economically free) to 10 (most economically free). The Freedom House Political
Rights index (column 5) has been inverted so that the scale ranges from 1 (least free) to 7 (mostly free). The Polity IV democracy
index (column 6) ranges from -10 (least democratic) to +10 (most democratic).
32
Table 6: Secondary Enrollment Rates (ages 15-19) and Secondary Completion Rates (ages 20-
24) for Developing and Malthusian Economies, 1960-2010
Table 7: Malaria Reported Cases as a Percentage of the Total Population
Year
Enrollment rate
(age 15-19)
Completion rate
(age 20-24)
Enrollment rate
(age 15-19)
Completion rate
(age 20-24)
1960 17.0 5.9 4.8 1.0
1965 20.3 7.4 5.9 1.4
1970 25.4 10.7 7.8 1.8
1975 31.3 12.9 11.8 3.6
1980 37.0 16.4 15.8 5.5
1985 40.4 19.1 17.9 7.3
1990 42.3 23.6 18.3 9.3
1995 44.5 24.3 17.8 8.5
2000 48.3 26.4 19.5 8.3
2005 53.9 30.0 23.8 9.9
2010 59.5 34.2 31.5 11.6
Source: Barro and Lee [2013]
Note: Columns 1 and 2 are for the 98 (of 114) developing economies for which the
data were available. Columns 3 and 4 are for 23 (of 31) Malthusian Economies for
which data were availalable.
Developing Economies Malthusian Economies
Year 31 Malthusian
Non
Malthusian
Africa
Other
Developing
83 Asia Latin America
1990 10.7 2.7 0.7 0.6 0.4
1995 9.0 4.7 1.2 1.0 0.6
2000 11.6 7.6 1.4 0.4 0.4
2005 13.5 5.9 1.0 0.2 0.4
2010 11.1 3.7 0.7 0.1 0.1
2015 9.4 4.0 0.7 0.0 0.1
Source: World Health Organization [2015], Gapminder [Rosling 2008], and World Bank [2017]
Note: The original source for these data is the World Health Organization. Reported Malaria cases
per 100,000 people from 1990 to 2006 comes from Rosling [2008]. The World Bank [2017]
provides the number of cases per 1,000 people at risk from 2005 to 2015. These sources were
combined to derive a comparable series of malaria cases as a share of total population for the
period 1990-2015.
33
Figure 1: Share of Total Population in the Prime Working Age 25-59 Group for the 21 High-
Income, 31 Malthusians, and 83 Other Developing Economies, 1960-2015 (Actual); 2020-2035
(Projected)
Source: World Bank [2018] and United Nations Population Division [2017]
Figure 2: Annual Growth Rates for the High-Income, Developing, and Malthusian Countries
1965-2015 (15-Year Moving Average)
Source: Maddison [2018]
25
30
35
40
45
50
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035
Per
cen
t
21 High-Income 83 Non-Malthusian Developing 31 Malthusian
-2
-1
0
1
2
3
4
5
6
1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015
Gro
wth
rat
e
21 High-Income Non-Malthusian Developing Malthusian
