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Income inequality since 1820

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This chapter focuses on income inequality as measured by gross (i.e. pre-tax) household income across individuals within a country. It builds upon a number of large-scale initiatives to chart income inequality trends over time, supplementing them with data on wages and heights for the earlier period. Income inequality trends follow a U-shape in most Western European countries and the Western Offshoots. It declined between the end of the 19th century until about 1970, followed by a rise. In Eastern Europe, communism resulted in strong declines in income inequality, followed by a sharp increase after its disintegration in the 1980s. In other parts of the world (China in particular) income inequality is on the rise recently. The chapter also provides evidence on the global income distribution, i.e. assuming all people belong to the same community. This distribution was unimodal in the 19th century, became increasingly bi-modal between 1910 and 1970 and suddenly reverted back into a unimodal distribution between 1980 and 2000.
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How Was Life?
Global Well-being since 1820
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http://dx.doi.org/10.1787/9789264214262-en
Income inequality since 1820
Michail Moatsos, Joerg Baten, Peter Foldvari, Bas van Leeuwen, Jan Luiten van
Zanden
Please cite this chapter as:
Moatsos, Michail, et al. (2014), “Income inequality since 1820”, in Jan
Luiten van Zanden, et al. (eds.), How Was Life?: Global Well-being since
1820, OECD Publishing.
http://dx.doi.org/10.1787/9789264214262-15-en
This work is published on the responsibility of the Secretary-General of the OECD. The
opinions expressed and arguments employed herein do not necessarily reflect the official views
of the OECD member countries, or those of the IISH.
This document and any map included herein are without prejudice to the status of or
sovereignty over any territory, to the delimitation of international frontiers and boundaries and to
the name of any territory, city or area.
How Was Life?
Global Well-being Since 1820
© OECD, IISH 2014
199
Chapter 11
Income inequality since 1820
by
Michail Moatsos, Utrecht University,
Joery Baten, Tuebingen University
and
Peter Foldvari, Bas van Leeuwen and Jan Luiten van Zanden, Utrecht University
This chapter focuses on income inequality as measured by gross (i.e. pre-tax)
household income across individuals within a country. It builds upon a number of
large-scale initiatives to chart income inequality trends over time, supplementing
them with data on wages and heights for the earlier period. Income inequality trends
follow a U-shape in most Western European countries and the Western Offshoots.
It declined between the end of the 19th century until about 1970, followed by a
rise. In Eastern Europe, communism resulted in strong declines in income inequality,
followed by a sharp increase after its disintegration in the 1980s. In other parts of
the world (China in particular) income inequality is on the rise recently. The chapter
also provides evidence on the global income distribution, i.e. assuming all people
belong to the same community. This distribution was unimodal in the 19th century,
became increasingly bi-modal between 1910 and 1970 and suddenly reverted back
into a unimodal distribution between 1980 and 2000.
HOW WAS LIFE? GLOBAL WELL-BEING SINCE 1820 © OECD, IISH 2014
200
11. INCOME INEQUALITY SINCE 1820
Introduction
The importance of income inequality at the local, regional and global scale hardly
needs to be stressed: the enormous increase of income inequality on a global scale is one
of the most significant – and worrying – features of the development of the world economy
in the past 200 years (van Zanden, et al., 2013). Several international organisations and
commentators have drawn attention to the increase in income inequality in a number
of developed and emerging countries in the run-up to the recent global financial crisis.
For these reasons, the subject has become one of the most discussed topics in the social
sciences; in particular, the debate on the measurement and interpretation of recent trends
in global inequality – is it still increasing? and why or why not? – has attracted considerable
attention (Anand and Segal, 2008; Bourguignon and Morrisson, 2002; Deininger and Squire,
1996; Jones, 1997; Milanovic, 2002 and 2007).
Levels and trends in income inequality are very relevant for people’s and societies’
well-being. In a sense, the information that income inequality provides is additional and
complementary to that referring to average personal income. Since an increase in GDP per
capita, by itself, gives us information only about average income gains, income inequality
provides more detailed insights about how much the benefits of economic growth in a
society or region are spread. It tells us who is getting the benefits of economic growth,
and in what proportions. Besides this connection with well-being, an extensive literature
investigates the impact of income inequality on a range of social outcomes, such as trust,
crime, social mobility, health and educational achievement (Wilkinson and Pickett, 2007).
In what follows, we address and document the long-run trends in income inequality.
First we present a new long-run dataset on income inequality (van Zanden et al., 2013) that
has the benefit of internal consistency, but also makes it possible to describe, for the first
time, historical developments in income inequality on a global scale spanning about two
hundred years. Second, we use this dataset to describe historical developments in income
inequality both within and between countries.
Description of the concepts used
The analysis presented in this chapter refers to the distribution of gross (i.e. pre-tax)
household income across individuals, with inequality in this distribution described by the
Gini coefficient. Both choices are not uncontroversial.
First, because alternative measures of household economic resources (e.g. post-tax
income, consumption, including or excluding a range of more detailed components such
as imputed rents or capital gains) and alternative units of analysis (e.g. households, or
consumption units based on different “equivalence scales”) are typically used to examine
income inequality. We selected gross household income as the measure in focus due to
the availability of historical data: the further back we go in time, the more data is available
in gross (pre-tax) household income terms, rather than in other forms. As using different
definitions can lead to different conclusions about trends in income inequality, the data
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we assembled in this dataset are either based on gross household income or have been
converted to a gross household income basis using various adjustments (see below for
details).
Second, because other inequality measures also exist, such as the Theil index, which
do not always display similar levels or trends when applied to the same distribution.
However, even though many other measures have interesting properties (for example, the
Theil coefficient is additive), the use of the Gini coefficient is widespread in the literature
on income inequality. In addition, the Gini coefficient is used in the data sources that we
heavily rely upon as a primal source of income inequality data. Hence, in this chapter we
will focus on this measure.
As it is explicitly developed as a measure of income inequality, the Gini coefficient has
some particular properties that make it appealing. One is that it has a direct relationship
with the so-called Lorenz curve, which is obtained by plotting the cumulative percentage
of income held by the cumulative percentage of the reference population. The Gini is
proportional to the area between the line indicating perfect equality and the Lorenz curve,
and hence is increasing with the degree of inequality. So a Gini of 0 indicates perfect equality,
while a Gini of 1 indicates perfect inequality.1 Another definition of the Gini coefficient is
in terms of income differences between every pair of individuals in a population (Sen,
1973, 1976). An important property of the Gini coefficient is that any income transfer from
the rich to the poor leads to a decline in the measure (i.e. the Gini coefficient moves in the
“right” direction). However, as mentioned above, the Gini coefficient also has some less
desirable properties, one of which is that the same Gini can be derived from very different
income distributions. For example, two very different situations, one where the middle and
upper classes have a much higher income than the lower class, and a second where the
upper class is extremely rich compared to the other two strata, could in principle lead to
the same Gini coefficient. More generally, the Gini coefficient is most sensitive to the part
of the income distribution around the median (Buhmann et al., 1988).
Gini coefficients can be calculated on the basis of different income and population
concepts. For example, they can refer to households or individuals, and be based on either
gross (i.e. pre-tax) or net income, or on either income or consumption, or they can refer to
either urban centres or the whole country. More generally, estimates of the Gini coefficient
obviously depend on the data produced by statistical offices, as they require consistency
over time in the concepts used and the underlying data sources (e.g. household surveys
or administrative tax records), in measurement conventions, and other methodological
choices. These difficulties are obviously compounded when trying to obtain historical
estimates reaching back to 1820.
Beyond providing an historical perspective on income inequality in individual
countries, this chapter has an additional goal: to describe changes in the global distribution
of household income, i.e. the distribution that one would observe when treating all people
in the world as if they were living in a single country. This implies additional challenges
relative to that of reconstructing historical series of within-country income inequality, as
it requires combining information from both micro-sources (e.g. tax records or surveys)
and macro-sources (e.g. national accounts). This is a challenge, and requires additional
assumptions, e.g. that levels and trends in the reference income variable from micro- and
macro-sources are the same, an assumption that in reality may not always hold true. In
the database used in this chapter, the assumption made is that cross-country differences
in average household income can be proxied by differences in GDP per capita.
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Historical sources
There is no single repository of Gini coefficients that contains estimates for every
country and for every year. Hence, we relied on a variety of different sources to construct
our dataset. For the post-1960 period most of our data came from the World Income
Inequality Database (UNU-WIDER, 2008), a large compilation of country estimates coming
from a variety of individual sources. For earlier periods, data were taken from a range of
historical sources2 and from studies on the top-income share that have recently become
more widely available (Atkinson, Piketty and Saez, 2011). A good overview of most of the
historical work on income inequality can be found in Milanovic, Lindert and Williamson
(2007), and at the Global Income and Prices website at UC Davis.3 Additional recent work
has been done, for example, by Bertola et al. (2009) for parts of South America, Rossi et al.
(2001) for Italy, Bergson (1984) for the Soviet Union, and Soltow and van Zanden (1998a) for
the Netherlands.
As stressed by François and Rojas-Romagosa (2005), the Gini values that are available
from the World Income Database refer to various concepts and data sources: both levels
and the trends pertaining to particular series can be very different. They distinguish three
main concepts, due to the differences in trends: gross household income, net household
income and expenditure data.
In the construction of the dataset used in this chapter, we followed the methodology
suggested by François and Rojas-Romagosa (2005), and converted all available estimates
of Gini coefficients into a gross household income basis. To that end, we tested (across
a large sample of countries) the hypothesis that trends in Gini coefficients for gross and
net household income were similar to those for household income and consumption.
These tests suggest that this hypothesis holds true in all countries, with the exception of a
relatively short period after the Second World War. Beyond this, average consumption may
evolve differently from household income through borrowing and lending, and average
expenditures are not a linear function of income since wealthy people tend to save more.
Changes in all these parameters probably account for diverging trends in various types of
Ginis observed in the after-war period. In that sense, the post-Second World War period is
special, since many countries expanded their system of income taxation and made it more
progressive. After 1980, trends between gross and net household income and expenditure
are again quite similar, although this may not hold in specific countries and sub-periods.4
Based on this empirical observation, we converted post-Second World War estimates of the
Gini coefficient into a gross household income basis, by using regression techniques (the
details are described in Van Zanden et al., 2013).
While using the World Income Database as a reference source, a range of other
sources, including SEDLAC5 (2013) and Milanovic (2012), have been used to extend this
information back in time. The first type of information used is related to top-income share
estimates, and in particular to the historical development of the share of the richest 1%
or 5% in total income, which was pioneered by the work of Piketty and Atkinson.6 These
data, which basically refer to a single point on the Lorenz curve, can be converted into Gini
coefficients using the assumption of log-normality in the underlying (and non-observed)
income distribution. In other words, by assuming that the income distribution is log-
normal, we can compute the Gini coefficient of a log-normal distribution that has a given
income share for people at the very top. Like most of the assumptions made in historical
analysis, the assumption of log-normality is not a perfect one, and there is room for error,
11. INCOME INEQUALITY SINCE 1820
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particularly at the extremes of the income distribution. An alternative assumption that
has been previously proposed in the literature is that household income follows a Pareto
distribution. However, Soltow (1998) has demonstrated that when the entire income
distribution is considered, the hypothesis of a log-normal distribution is preferable.
For the period before the Second World War, there are only a few direct estimates
of income inequality, and these are available only for a small number of countries.7 For
other countries in this period, we relied on a method inspired by the “extraction rate”
concept (Milanovic et al., 2007) to derive additional estimates. According to this method,
changes in the Gini coefficient are linked to the development of the Williamson index, i.e.
the ratio between the average family income (measured by GDP per capita) and the real
wage of unskilled labour. When this ratio goes up, income inequality may also be expected
to rise, and vice versa. The link has been tested empirically and used to extrapolate and
interpolate Gini coefficients (details are supplied in van Zanden et al., 2013). The sources
used for the real wage of unskilled labour were Williamson (1999, 2000a, 2000b), Mitchell
(1998a, 1998b, 1998c), Allen (2001), Mironov (2004) and Allen et al. (2010), while estimates
of the average family income were based on estimates of GDP per capita from Maddison
(2003).8
Another source of information on income inequality in the 19th century comes from a
method based on evidence of the footprint of income inequality on the human body. Baten
(2000, 1999), Pradhan et al. (2003), Moradi and Baten (2005), Sunder (2003) and Guntupalli
and Baten (2006) have argued that the variance in height across individuals within a country
(as measured by the coefficient of variation) can be used as a proxy for income distribution.
As the studies included here use large samples, individual genetic differences average out.
As higher-income people have access to better nutrition and shelter and suffer less from
disease, they also tend to be taller, while the opposite applies to the lower-income strata.
This fact can be used to link the variation in height of a certain cohort and the income
distribution during the decade of their birth.9 Historical data on height are available from
hundreds of previously published articles, as summarised in Chapter 7 of this report, and
provided the basis for income inequality estimates for around one-third of our sample.
Naturally, we excluded studies that referred to very small samples of height measurements,
or to a special group within a given country. We were also cautious to avoid the distortion of
our estimates by factors such as mixed-aged samples, military truncation, gender, prison or
other sample selectivity issues.10 Finally, for cases where these methodological approaches
to the estimation of income inequality could not be applied, some of the remaining missing
data on income inequality were estimated using multiple imputation methods. Besides
the direct and indirect sources for income inequality information, estimates of average
household income per capita are also necessary for our analysis of global inequality. As
mentioned above, the proxy that we used for this was GDP per capita expressed in 1990
international dollars (the same series that is used in Chapter 3).
Table 11.1 gives an overview of the various sources used in this chapter by type of
method used. Out of the 869 estimates used here, the WIID database supplied 43% of the
data-points, various historical studies provided another 8%, changes in the Williamson
index (the GDP/wage ratio) made it possible to estimate 6% of all estimates, and height
data helped to make 33% of the country estimates. When both height data and GDP/wage
ratio were available, Gini coefficients were estimated as the unweighted average of the
two (8%).
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Data quality
Table 11.2 presents our assessment of the quality of the data used. Most data concerning
income inequality in the 19th century are based on indirect sources and subject to large
margins of error, and hence are classified as “estimates”. Only recently does the quality
improve a lot, reaching level one for many world regions in the most recent period. Income
inequality at the regional level also requires the aggregation of income levels of individual
countries, which greatly increases the problems involved.
Providing a historical perspective on income inequality at the global level is an even
more demanding task. Although the United Nations (UNU-WIDER) now provides extensive
data on within-country income inequality, they do not cover all countries for all years,
Table 11.1. Estimates of income inequality by source and year, 1820-2000
Number of countries
Year All WIID ‘New’ ginis GDP/wage ratio Heights Both 4&5 (50/50)
1820 39 0 6 6 18 6
1850 40 0 1 8 20 8
1870 54 0 11 5 27 11
1890 60 0 8 5 34 13
1910 71 1 10 7 43 10
1929 74 2 15 9 39 9
1950 81 13 10 8 41 9
1960 88 54 4 2 27 1
1970 94 60 2 2 29 1
1980 83 71 0 0 12 0
1990 99 98 1 0 0 0
2000 86 71 1 0 0 0
Total 869 370 69 52 290 68
Note: For an assessment of data quality, see Table 11.2.
Source: Clio-Infra, www.clio-infra.eu.
12 http://dx.doi.org/10.1787/888933097433
Table 11.2. Quality of data on income inequality by region
and benchmark year, 1820-2000
Western
Europe
(WE)
Eastern
Europe (EE)
Western
Offshoots
(WO)
Latin
America and
Caribbean
(LA)
Sub-Saharan
Africa
(SSA)
Middle
East and
North Africa
(MENA)
East Asia
(EA)
South and
South-
East Asia
(SSEA)
182044344..44
187044344444
191033334333
1950 2/3 3 2/3 3 3 3 3 3
197012122222
200011111111
Note: 1. High quality; 2. Moderate quality; 3. Low quality; and 4. Estimates. See the section on «Data Quality» in
Chapter 1 for a description of the quality criteria.
Source: Clio-Infra, www.clio-infra.eu.
12 http://dx.doi.org/10.1787/888933097452
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and they are not necessarily from comparable sources (Milanovic, 2006). This data source
provides inequality data gathered from various national sources and methodologies that
vary across countries, and across periods for a given country.11 This implies that both cross-
country and inter-temporal comparability are an issue. Alternative sources, such as the
estimates compiled by the OECD Income Distribution Database, are based on consistent
definitions (e.g. in terms of the components included in the basic income concept) and
treatments (e.g. in terms of treatment of negative income, or choice of equivalent scales),
and are adjusted for breaks in statistical methodology. However, they are not used in this
chapter, first, because the estimates are limited to OECD countries and, second, because
the Gini coefficients available from the OECD refer to disposable income (i.e. net of taxes)
and market income (i.e. net of taxes and public transfers), rather than to the gross income
concept used in this chapter. As a result, the estimates shown here for individual countries
since the 1970s and 1980s may differ significantly from those reported by the OECD in its
own reports on the subject (e.g. Japan). The various methods we used to provide estimates
for the missing values of our income inequality series, although quite elaborate, are also
imperfect. A more straightforward approach to constructing a similar long-run dataset
on income inequality is found in Bourguignon and Morrisson (2002). One feature of the
methodology they applied for estimating income inequality values before 1950 was the
assumption that within-country income inequality remained stable over time. Also, for
large parts of the world, estimates from the post-1914 or post-1945 period were used
to extrapolate the country-data available for the various time periods back to the 19th
century. Despite these differences in methodology, the findings reported by Bourguignon
and Morrisson are remarkably similar to those shown here.
Main highlights of trends in income inequality
In this section we highlight two main sets of results: the development of within-
country income inequality and the evolution of global income inequality.
Within-country trends in inequality
We begin by describing the long-run trends in income inequality in individual countries.
A selection of the countries with data available for the long-run period from 1820 until 2000
is shown in Table 11.3. Values of the Gini coefficient on income inequality in 1820 ranged
from the modest values of 33 for India, 35 for Poland and 38 for Spain, all the way up to 59
for the United Kingdom and France, 58 for Egypt and Turkey, and 57 for the United States.
China (45), Canada (45), Germany (51), Japan (51) and Brazil (47) were among the countries
in the middle ground. By 1850, all the countries shown in Table 11.3 experienced a decline
in income inequality, followed by a renewed increase in the period up to 1870. However, the
ups and downs of the 19th century are probably less informative than the broad trends.
In the 20th century, the trends are more pronounced. In the period between the two
world wars, income inequality in most countries in Western and Eastern Europe as well
as in the Western Offshoot countries rose and then dropped again, considerably so after
the Second World War. Egypt, China, South Africa, Brazil, Thailand and Mexico also follow
this pattern closely. A notable departure from the pattern is Sweden, which experienced a
decline in income inequality from 1890 until 1980. Among the Eastern European countries,
Poland also defied this trend by recording a rather slowly declining level of inequality
throughout the late 19th century and first half of the 20th century. India also joined the
group of outliers by maintaining a very low level of slightly increasing income inequality
until around the Second World War. Finally, Kenya followed the trend in the first half of the
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Table 11.3. Income inequality in selected countries, 1820-2000
Gini coefficient
Western Europe
(WE)
Eastern Europe
(EE)
Western Offshoots
(WO)
Latin America and
Caribbean
(LA)
Middle East
and North
Africa (MENA)
Sub-Saharan Africa
(SSA)
East Asia
(EA)
South and
South-East Asia
(SSEA)
GBR NLD FRA DEU I TA ESP SWE POL RUS AUS CAN USA MEX BRA ARG EGY TUR KEN NGA ZAF CHN JPN IND IDN THA
1820 [59] [56] [59] [51] [54] [38] [55] [35] [58] .. [45] [57] [40] [47] [47] [58] [58] .. [55] .. [45] [53] [33] [52] [47]
1830 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..
1840 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..
1850 [43] [48] [54] [40] [51] [32] [46] [36] [54] [41] [27] [44] [32] [37] [34] [63] [37] .. .. .. [33] [46] [39] [42] [42]
1860 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..
1870 [49] [57] [58] [48] [51] [34] [52] [38] [50] [48] [44] [51] [51] [39] [52] [45] [56] [46] .. .. [41] [46] [40] [39] [36]
1880 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..
1890 [37] [42] [48] [39] [46] [31] [59] [30] [38] [39] [41] [46] [44] [36] [45] [33] [..] [33] [37] [36] [31] [47] [32] [39] [34]
1900 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..
1910 42 47 55 44 49 35 57 28 40 41 41 51 51 38 51 42 .. [49] .. [45] 39 52 31 42 42
1920 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..
1930 43 42 62 46 51 36 51 26 43 36 42 54 55 60 45 46 54 [55] .. [59] 44 52 31 50 47
1940 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..
1950 30 36 58 47 43 35 40 23 36 38 36 39 52 49 41 39 49 56 .. 52 32 36 35 54 39
1960 29 43 52 39 44 28 40 26 28 35 35 38 53 55 42 43 55 68 51 69 31 38 37 40 43
1970 29 36 45 40 39 35 37 29 23 32 34 36 56 58 35 43 52 50 38 70 28 35 40 44 44
1980 34 30 35 38 39 41 29 30 25 39 34 37 51 57 42 50 50 57 35 67 30 37 31 40 46
1990 39 32 37 49 33 34 31 31 26 42 32 40 48 59 43 54 44 49 44 63 34 36 40 39 50
2000 40 32 37 51 37 33 35 35 40 .. 41 44 47 61 47 54 46 51 51 55 44 33 47 50 47
Notes: For an assessment of data quality, see Table 11.2. RUS 1930-1970: refers to the Soviet Union.
Values in brackets [ ] indicate very tentative data.
Source: Clio-Infra, www.clio-infra.eu.
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20th century, by experiencing an increasing income inequality, with a more rapid increase
in the second half of the 20th century.
In the 1950s, most countries in Latin America, Asia and Africa also experienced
increased income inequality, but to varying degrees. China seems to be the sole exception
to this pattern, with a small decrease in income inequality from an already low level. In
Europe and the Western Offshoots, the situation is more diversified, as income inequality
declined in most countries but increased in others. Income inequality declined in Canada
and the United States, and even more so in France, Germany, the former USSR, Spain and
Australia. Conversely, income inequality increased in the Netherlands, Italy, Poland and, to
a lesser degree, Sweden. The United Kingdom stands out in this period with a rather stable
level of income inequality. In the 1960s, most countries experienced rather stable income
inequality, although this declined in France and Sweden and increased sharply in Kenya
and South Africa.
France is notable for a continuous decline in income inequality in the period from the
interwar years until 1980. South Africa, Brazil and Mexico kept a high level in the 1970s, but
inequality dropped sharply in Mexico, from a coefficient of 59 in 1970 to 47 by 1980. The
decline was smaller in the case of Brazil, but still substantial. In South Africa, the decline
in income inequality was mostly recorded during the 1990s. Starting from the 1980s, most
countries experienced a rise in their inequality levels, although, based on our series, Japan
maintained low inequality levels from the 1950s onwards. In the group of countries with
rising inequality in the period since the 1980s, one of the most striking increases was in
China, whose Gini coefficient rose by about half between 1980 and 2000.
The country with the highest income inequality over the entire period is South Africa,
with a peak of 70 in the 1970s. Among the other countries, only Kenya in 1960 came close
to that level, with a Gini coefficient of 68. For a long period from the beginning of the
20th century up until the 1960s, Poland achieved the lowest income inequality, with values
of around 25. In the period as a whole, Spain and Thailand had the most stable level of
inequality, with values staying within a relatively small range. In contrast, the former Soviet
Union experienced the largest changes in inequality, followed by South Africa and Sweden.
It is hard not to notice the sharp increase in income inequality experienced by the
vast majority of countries from the 1980s. There are very few exceptions to this, with Japan
being the most prominent one (i.e. a decline starting from a rather low level of income
inequality). Another exception is South Africa, which started-off from a staggering Gini
coefficient of 70 in 1970.
Looking beyond trends in individual countries and regions, we obtain a global perspective
by considering income inequality as if the world were one country. This is shown in the second
column of Table 11.4 (the World Gini). Although global income inequality rises throughout the
period, the third column (within-country inequality) clearly shows the “egalitarian revolution”
in the mid-20th century, which translated into significant declines in this measure. However,
this trend reversed strongly in the last decade, as within-country inequality levels returned to
the values recorded in 1820. Overall, the increase in global inequality experienced from 1820
to 2000 was largely caused by an increase in between-country inequality (fourth column)
rather than within-country inequality (third column). The exceptions to this pattern are the
years leading to 2000, when the increase in within-country inequality just offset the decrease
in between-country income inequality. Throughout the period as a whole from 1820 to 2000,
global interpersonal income inequality increased by 30% (column 2), while between-country
11. INCOME INEQUALITY SINCE 1820
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208
inequality increased from a very low level of 16 in 1820 to 56 by 1970. However, over the last
50 years, between-country inequality has remained broadly stable, dropping only in the last
two decades of the 20th century, the second decline in the dataset since 1820.
Figure 11.1 presents the same information about the evolution of global inequality
in a different way. Changes in the shape of this distribution in different periods reflect
the combined effects of the increase in average income levels in individual countries, the
Table 11.4. Gini coefficients of within-country
and between-country inequality, 1820-2000
Year World Gini Within country inequality Between country inequality
1820 49 45 16
1850 46 38 23
1870 55 45 32
1890 52 36 38
1910 58 40 44
1929 63 44 49
1950 65 38 55
1960 64 38 54
1970 65 37 56
1980 65 36 56
1990 66 39 56
2000 66 45 54
Note: For an assessment of data quality, see Table 11.2.
Source: Clio-Infra, www.clio-infra.eu.
12 http://dx.doi.org/10.1787/888933097490
Figure 11.1. Global income distributions in selected years, 1820-2000
Thousands of people at given level of income in US dollars at 1990 PPP
300 000
250 000
200 000
150 000
100 000
50 000
1001 00010 000 100 000
2000 1980 1970 1960
1950 1929 1820
Thousands of people
Gross income in 1990 GK$
350 000
Note: For an assessment of data quality, see Table 11.2.
Source: Clio-Infra, www.clio-infra.eu.
12 http://dx.doi.org/10.1787/888933096198
11. INCOME INEQUALITY SINCE 1820
HOW WAS LIFE? GLOBAL WELL-BEING SINCE 1820 © OECD, IISH 2014 209
changes in its distribution within countries, and the growth of countries’ population (all
income levels are expressed in 1990 Geary–Khamis dollars). What is particularly striking is
the change in the shape of the income distribution through time (for similar analyses of the
more recent period, see Milanovic, 2002, and Sala-i-Martin, 2006). Between 1820 and 1950,
the world income distribution is unimodal and basically log-normal, although, looking at
the 1950 distribution, a thickening of its right “tail” can already be noticed. Over the next
few decades, a different distribution starts to emerge, with two separate peaks; while this
pattern is already distinguishable in 1950, it becomes more pronounced in the 1960s, 1970s
and 1980s, when a big gap between the rich and poor “peaks” appears. However, in the
1990s the two peaks begin to get closer, and by 2000 the distribution has become unimodal
again.
One might argue that the switch from a unimodal to a bimodal distribution in the
1960s was caused by the long wave of de-globalisation that set in after 1914, i.e. a decline
in external trade caused by two world wars, a depression and a bi-polar world system. This,
however, is a topic for further research – here we can observe only that this change from
a unimodal world distribution towards a bimodal one was accompanied by the decline of
within-country inequality: the “egalitarian revolution” of the 20th century seems to have
been a phenomenon linked to the development of strong nation states, with more freedom
to steer domestic policies in the de-globalised world of 1914-1960. However, almost
simultaneously, these processes also gave rise to a bimodal income distribution globally.
After 1980, globalisation contributed to higher income inequality within countries, while
at the same time leading to a decline of income inequality between countries, again in a
closely interrelated process.
When looking more closely at the different world regions (Table 11.5), Latin America
and the Caribbean is one of the regions with the highest average within-country
inequality for the 20th century, as many would expect. The levels of its Gini coefficients
are matched by those recorded in Sub-Saharan Africa from 1950 onwards. Furthermore,
there seems to be one major reversal: in the 19th century, both Asia and Latin America
and the Caribbean showed the lowest levels of inequality; this completely changed
by the end of the 20th century, which clearly suggests that economic growth has led
to a widening of between-country inequality in both regions. The decline in income
inequality is also very strong in Eastern Europe and the former Soviet Union during
the period from 1950-1990. After the dissolution of the Soviet Union and the fall of the
“iron curtain”, this trend reversed and in the last two decades inequality has increased
dramatically. Regional inequality in Western Europe and the Western Offshoots showed
a major decrease in the period until 1980. Western Europe started off from a Gini of 55
in 1820 and went down to a more modest Gini of 37 in 1980. Since the 1980s, a small
increase in the Gini coefficient has been observed. In the Western Offshoots, the pattern
is very similar, but the rise in recent decades is much stronger. In Asia, the story is quite
the opposite, at least in its beginning: starting from a low value in 1820 of 45 and 35, for
East Asia and South and Southeast Asia respectively, both regions experienced a strong
increase, which is most prominent in the 1960s for East Asia and in the 1980s for South
and Southeast Asia. This rising trend also extended to the last three decades as well.
11. INCOME INEQUALITY SINCE 1820
HOW WAS LIFE? GLOBAL WELL-BEING SINCE 1820 © OECD, IISH 2014
210
Correlation with GDP per capita
Figure 11.2 shows the correlation of GDP per capita with income inequality for all
countries, with data being divided into three main periods and shown in a semi-logarithmic
form. The first panel in the figure refers to the period before the 1930s, the next panel to the
period from 1950 to 1970, and the last panel to the period from the 1980s onwards. In the
first period, a negative correlation between GDP per capita and income inequality appears
for countries with the lowest annual incomes; that correlation turns positive among
countries with incomes from USD 800 up to about USD 2 000; from that point onward, the
Figure 11.2. Correlation between Gini coefficients and GDP per capita
in three time periods, 1820-2000
Gini coefficients and US dollars at 1990 PPP (semi-logarithmic scale)
2001 000 5 000 20 000
20
40
60
80
2001 000 5 000 20 000
20
40
60
80
2001 000 5 000 20 000
20
40
60
80
1820−19291950−1970 1980−2000
Inequality (Gini coef.)Inequality (Gini coef.)Inequality (Gini coef.)
GDP/c (1990 GK$) GDP/c (1990 GK$) GDP/c (1990 GK$)
Loess fit95% confidence interval
Note: For an assessment of data quality, see Table 11.2.
Source: Clio-Infra, www.clio-infra.eu
12 http://dx.doi.org/10.1787/888933096217
Table 11.5. Regional averages of income inequality, 1820-2000
Gini coefficients, unweighted averages
Western
Europe
(WE)
Eastern
Europe
(EE)
Western
Offshoots
(WO)
Latin
America
and
Caribbean
(LA)
East Asia
(EA)
South and
South-East
Asia
(SSEA)
Middle East
and
North Africa
(MENA)
Sub-Saharan
Africa
(SSA) World
1820 54 51 56 45 45 35 .. 53 45
1850 45 49 42 37 34 38 46 46 38
1870 50 48 51 48 41 42 52 50 45
1890 41 36 45 41 32 34 35 36 36
1910 46 39 50 45 40 35 40 42 40
1929 48 40 52 55 44 36 48 48 44
1950 42 35 39 47 33 39 43 43 38
1960 40 30 37 54 32 39 49 53 38
1970 38 26 36 53 29 40 47 49 37
1980 36 27 37 52 31 35 47 46 36
1990 38 27 39 52 34 41 46 47 39
2000 40 36 44 54 43 48 49 49 45
Note: For an assessment of data quality, see Table 11.2.
Source: Clio-Infra, www.clio-infra.eu.
12 http://dx.doi.org/10.1787/888933097509
11. INCOME INEQUALITY SINCE 1820
HOW WAS LIFE? GLOBAL WELL-BEING SINCE 1820 © OECD, IISH 2014 211
relation is negative again. For the two post-Second World War periods, the relationship is
positive until about USD 3 000, then turns strongly negative, and finally the relationship
turns positive again among the countries in the highest income layers. However in both
these periods, there are very few observations in the top income region. It is important to
notice that for a large income span ranging from a bit below USD 10 000 up until USD 20 000
in the 1980-2000 period, the relation dissolves completely.
This demonstrates the real complexity of the link between income inequality and GDP
per capita. Figure 11.3 shows the correlation of GDP per capita and the Gini coefficient
across all the available countries over time. From 1820 until 1910, income inequality
appears generally positively correlated with GDP per capita: the wealthiest countries are
also relatively more unequal. This relationship reverses at the turn of the century, and after
the Second World War the relation turns mostly negative, remaining negative for the entire
period until the most recent available data.
Priorities for future research
As the discussion of data limitations has suggested, more work on improving the
comparability of the data sources and their findings would provide a more solid basis to draw
conclusions about income inequality in a country or region or on a global scale. Historical
estimates could be much improved by focusing more research on these issues. Whereas
for most Western European countries and the Western Offshoots, we have relatively
detailed studies that make use of the available historical sources, much more work can be
done in this field for many Asian, African and Latin American countries (for examples of
recent research see the website of the Global Price and Income History Group at UC Davis:
http://gpih.ucdavis.edu/). The more recent work in this field also has its problems. Such work
requires mobilising organisational resources on a world scale, orchestrated by international
organisations. Inspiration for this type of work could be drawn from initiatives like the
International Comparison Program that aims to collect comparative price data and to
estimate purchasing power parity globally.
Figure 11.3. Correlation between Gini coefficients and GDP per capita, 1820-2000
Pearson correlation coefficient and upper/lower bounds of 95% confidence interval
-1.0
-0.5
0
0.5
1.0
1820 1870 19 20 1970
Correlation coefficient 95% confidence interval
Note: For an assessment of data quality, see Table 11.2.
Source: Clio-Infra, www.clio-infra.eu.
12 http://dx.doi.org/10.1787/888933096236
11. INCOME INEQUALITY SINCE 1820
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212
Beyond this effort, further inter-temporal investigation of the relationship between income
inequality and social outcomes will help improve our understanding of the mechanisms
through which higher levels of income inequality tend to make us all worse-off (Wilkinson
and Pickett, 2007). With the increased availability of data, the links between income inequality
and other social indicators could be further researched, and extended to other dimensions of
well-being. To that end, historical global datasets would need to be constructed and utilised.
Notes
1. So a Gini coefficient equal to zero implies perfect absolute equality – i.e. all individuals have the
same income – and a Gini equal to one implies absolute inequality – i.e. one individual has all
the income while the rest have none. The actual impossibility of having a Gini equal to one fits
well with the idea of an inequality-possibility frontier, which takes into account the subsistence
income as a frontier for minimum income for survival, and of maximum possible inequality if one
individual were to receive all the remaining income (Milanovic, Lindert and Williamson, 2007).
2. Studies are available for Australia (1921-2003, A.B. Atkinson and Leigh, 2007); Canada (1920-2000,
Saez and Veall, 2005); France (1905-1998, Piketty, 2007); Germany (1925-1998, Dell, 2007); India (1922-
1999, Banerjee and Piketty, 2005); Indonesia (1920-2004, Leigh and der Eng, 2010); Ireland (1922-2000,
Nolan, 2007); Japan (1886-2002, Moriguchi and Saez, 2006); Korea (1998, Cheong, 2001); Netherlands
(1914-1999, Salverda and Atkinson, 2007); New Zealand (1921-2002, Atkinson and Leigh, 2005);
Spain (1981-2002, Alvaredo and Saez, 2009); Sweden (1903-2004, Roine and Waldenström, 2006);
Switzerland (1933-1996, Dell, Piketty and Saez, 2007); the United Kingdom (1908-2000, Atkinson,
2007); and the United States (1913-2004, Piketty and Saez, 2003).
3. Global Price and Income History Group
4. For example, income inequality increased significantly in the United States since the 1980s, while
consumption inequality was rather stable.
5. Although SEDLAC sources in All The Ginis dataset are treated as “gross”, the data exclude wage
taxes and include direct taxes. This may introduce some additional bias.
6. The data available on income shares of the top 1% and top 5% can be found for a collection of
countries at “The World Top Incomes Database” created by Facundo Alvaredo, Tony Atkinson,
Thomas Piketty and Emmanuel Saez. We make use of these data, but we do not present them
separately.
7. For China from Brandt and Sands (1992);for Japan, several estimates by Soltow and Van Zanden
(1998b); for Indonesia, Van Leeuwen and Földvári (2012).
8. Both series are also used in Chapters 3 and 4 of this report.
9. The decade of birth is used, because the strongest environmental influence on the body growth
process takes place after birth during the first three to five years.
10. This measure could be affected by survivor bias, since measures of inequality in height refer only
to survivors. However, this is consistent with other measures of inequality, as the income earners
who are the base for Gini coefficients of household income only refer to people who survived to the
age of earning an income. For further discussion, see Moradi and Baten (2005).
11. Beyond various between-country differences, there are a number of concerns embedded in the
survey’s methodology per se, particularly the under-representation or under-reporting of the poorer
and richer groups of the population within a country.
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Chapter
This chapter provides an overview of the expected development for a number of macroeconomic figures up to 2030. For this purpose, the relevant developments for two scenarios are described: The Normal Scenario (business as usual) and the Optimal Scenario (pro-growth scenario). In addition, the aim of the chapter is to highlight the role of structural reforms in the implementation of a pro-growth scenario. Besides, the expected developments in the Greek economy until 2030 for the two scenarios are presented for variables such as GDP and other basic macroeconomic variables, potential output and output gap, public debt, competitiveness, investments and inclusive growth.
Chapter
The purpose of the chapter is to find out the meaning of the sustainable development in the post-industrial society in the first half of the 21st century. The financial crisis that started in 2008 is an indicator of how short-term profitability mindsets and related strategies, policies and actions of individuals and individual organizations can cause global economic, ecological and ethical crises. These events have contributed to the judgement that most organizations operate on business models that are not sustainable. The conceptual content contributes to the ongoing discussion about the increasingly important role of sustainable development as a major concern for the profit and non-profit sector that wish to develop the policies that will enable low but sustainable growth of society.
Thesis
There is a lack of cliometric literature addressing the characteristics of Mexican migration during the Age of Mass migration (1850–1914). To fill this void, I analyze an original data set—the Mexican Border Crossing Records (MBCRs) publication N° A3365—to disentangle the initial mechanics of Mexican migration in the early twentieth century. I first offer a historical overview on Mexican migration to the United States in Chapter 1. In Chapter 2, I introduce these novel micro data that record individual characteristics of migrants that crossed the Mexico-US border from 1906 to 1908. In Chapter 3, I address the initial determinants of the Mexican-American migration stream. I use the migrant’s location of last residence and final destination to identify migration corridors at the local level (migration streams between Mexican municipalities and US counties). In addition, I provide a quantitative assessment of the push and pull factors that may explain differences in migration intensity across corridors. These factors include the US-Mexico wage gap, market potentials, living standards and access to railways. In Chapter 4, I use the migrant’s height—a proxy for physical productivity of labor—to quantify the selectivity of Mexican migration. In addition, I exploit the Panic of 1907 as a natural experiment of history to study the speed that migrant self-selection adjust and change to both environmental and economic factors. This financial crisis provides me with exogenous variation in height to evaluate if unexpected shocks affecting the demand of immigrant workers can induce short-run changes in migrant self-selection. To explain shifts in selection patterns, I focus on labor institutions as mechanism of adjustment. Specifically, I study the enganche, a system of labor recruiting that neutralized mobility and job-search costs. In Chapter 5, I exploit the reported locations of birth, last residence and destination to classify migrants based on their chosen migration method: direct or stage migration. The micro data reveal that forty percent of the migrants moved within Mexico before crossing the border. I estimate correlations between stage migration and potential wage at the destination controlling for the immigrants’ age, literacy, sex, marital status and birthplace. In Chapter 6, I offer some concluding remarks. My findings expand our knowledge about the initial patterns of Mexican migration using micro data not analyzed previously. They show that in the early twentieth century, the decision to migrate was a function of diverse forces, which effects and magnitudes varied across Mexican regions. Also, Mexican migration was characterized by an intermediate or positive selection, and labor institutions involved in the migration process shaped migrant self-selection. Finally, Mexicans used stage migration to reach the US border, and it was associated with a significant wage premium at the destination.
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This chapter introduces the theme of our study: how colonial connections impacted women’s work and household living standards in two parts of the Dutch Empire—the Netherlands and Java—in the nineteenth and early twentieth centuries. It provides an overview of the relevant debates in the international economic history literature and introduces several important concepts, along with the sources and methods this study has employed. This chapter also sets the stage for the rest of this book by illustrating how the metropole and the colony were in many ways similar at the beginning of the nineteenth century, but diverged notably in the course of the colonial period.
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* This paper is part of the NSF grant funded project "Global Prices and Income 1350-1950" headed by Peter Lindert and the Spinoza-premium project on Global Economic History funded by NWO (The Netherlands). We want to thank Peter Lindert for suggestions and encouragements at every stage of this paper. We also want to thank Kishimoto Mio and Lillian Li for pointing to us useful sources of price data and Kariin Sundback for collecting the VOC data. Our paper also benefited from the lively discussion at the 43, Cliometrics Conference held at Lake Tahoe in June 2005.We also want to express our appreciation to the works of the so-called "California School," particularly to Kenneth Pomeranz, for giving the initial inspiration to this important "Great Divergence" debate.
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A new dataset for estimating the development of global inequality between 1820 and 2000 is presented, based on a large variety of sources and methods for estimating (gross household) income inequality. On this basis, and two sets of benchmarks for estimating between-country inequality (the Maddison 1990 benchmark and the recent 2005 ICP round), we estimate the evolution of global income inequality and of the number of people below various poverty lines over the past two centuries. We find that between 1820 and 1950 increasing per capita income is combined with increasing global inequality, and with an increase in the absolute number of people below the poverty line. After 1950 global inequality as measured by the Gini coefficient remains more or less constant, and also the number of poor starts to decline in absolute terms. It also appears that the global income distribution was uni-modal in the 19th century, became increasingly bi-modal between 1910 and 1970 with two world wars, a depression and de-globalization, and was suddenly transformed back into a uni-modal distribution between 1980 and 2000.
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Soweit Entwicklungsländer durch außenhandelspolitische Maßnahmen das gesamtwirtschaftliche Wachstum zu fördern versuchen, ziehen sie häufig auch ergänzende währungspolitische Maßnahmen in Betracht. Handeln kann es sich dabei um verschiedene Formen der Devisenbewirtschaftung, die offene oder indirekte Differenzierung im Wechselkurssystem (multiple Wechselkurse), Systeme gleitender Paritätsanpassungen in Abwertungsrichtung, eine gezielte Unterbewertung der heimischen Währung.
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This paper presents new homogeneous series on top income shares in Germany from 1891 to 1995 using tax returns data. The general pattern is consistent with recents results for France, i.e. the secular decline in income inequality is for the most part a capital income phenomenon. Very top incomes were badly hurt by the majors shocks of the 1914-1945 period and never recovered afterwards possibly because of the rise in progressive taxation. Since 1945, top income shares have been relatively stable, with no rise during the recent years (unlike in the U.S.). The striking episode before WWII is how nazi power brought top income shares to almost double within five years. The striking result after WWII is that German top incomes are more concentrated within the top decile than in other industrialized countries. Thus the German super rich were richer than their American counterparts until the late 1980's.