Trends and Key Drivers of Income
Inequality
Marshall Economic Research Group
University of Cambridge
May 2014
Conrad Allison
Erlend Fleisje
Will Glevey
Wouter Leenders
Johannes Prochazka
Garima Singhal
Abstract
This paper analyses key past and future trends of income inequality on a global, national
and city level as well as key drivers of within-country income inequality based on existing
literature. While income inequality between countries based on per capita income likely has
decreased recently, income inequality within countries has risen in most OECD and several
developing countries over the last decades. Overall, global income inequality seems to have
decreased for the first time since centuries. While the main winners of this development
have been the emerging global middle class as well as the top one per cent of the global
income distribution, the poorest five per cent and, apart from Africa, mainly people in Latin
America and post-communist countries have lost. For the future, we may expect a race
between per capita income and within-country income inequality growth, global income
inequality possibly decreasing if economic growth excels and increasing if within-country
income distributions worsen further. Major determinants of growing income inequality within
countries appear to be skilled-biased technological change and the growth of incomes of
workers in the financial industry, particularly among executives. At a city level, the same two
factors may increase income inequality in future. In addition, institutional factors such as
rent-seeking behaviour, labour market-related drivers or taxation have received increased
attention lately. While the effect of unionisation and trade and globalisation on within-country
income inequality is ambiguous, the government’s power to even income inequality through
redistribution measures is evident.
Trends and Key Drivers of income inequality
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Table of Contents
1.
Introduction.................................................................................................................................... 5
2.
Objective and Scope of the Paper ............................................................................................. 7
3.
Definition of Inequality ................................................................................................................. 7
4.
Income Inequality at a National and Global Level ................................................................... 9
4.1.
4.1.1.
Origins of Income Inequality Theories – the “Kuznets Curve” ............................... 9
4.1.2.
Income Inequality Within Countries ......................................................................... 10
4.1.3.
Income Inequality Across Countries ........................................................................ 22
4.1.4.
Global Income Inequality........................................................................................... 24
4.1.5.
Conclusion ................................................................................................................... 27
4.2.
5.
6.
Drivers of Income Inequality ............................................................................................. 28
4.2.1.
Skill-Biased Technological Change ......................................................................... 28
4.2.2.
Political Economy ....................................................................................................... 31
4.2.3.
Labour Market ............................................................................................................. 35
4.2.4.
Financial Sector .......................................................................................................... 37
4.2.5.
Trade and Globalisation ............................................................................................ 39
Inequality in Cities compared to the National Level .............................................................. 43
5.1.
General Trends of Inequality in Cities ............................................................................. 43
5.2.
Drivers of Urban Inequality ............................................................................................... 45
Empirical Evidence on Drivers of Income Inequality............................................................. 49
6.1.
At a Global or National Level ............................................................................................ 49
6.1.1.
Skill-Biased Technological Change ......................................................................... 49
6.1.2.
Taxation ....................................................................................................................... 55
6.1.3.
Sectoral Shifts ............................................................................................................. 59
6.1.4.
Financial Sector .......................................................................................................... 60
6.1.5.
Trade and Globalisation ............................................................................................ 67
6.2.
7.
General Trends in the Past ................................................................................................. 9
In Cities compared to the National Level ........................................................................ 75
Future Trends of Income Inequality ......................................................................................... 80
7.1.
Future Trends of Income Inequality at a Global or National Level ............................. 80
7.1.1.
General Future Trends of Income Inequality ......................................................... 80
7.1.2.
Future Trends of Drivers of Income Inequality....................................................... 82
Trends and Key Drivers of income inequality
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7.1.2.1.
Skill-Biased Technological Change ..................................................................... 82
7.1.2.2.
Sectoral Shifts ......................................................................................................... 84
7.1.2.3.
Financial Sector ...................................................................................................... 84
7.1.2.4.
Trade and Globalisation ........................................................................................ 85
7.2.
Future Trends of Income Inequality in Cities ................................................................. 86
8.
Discussion ................................................................................................................................... 87
9.
Conclusion ................................................................................................................................... 93
10. Bibliography ................................................................................................................................ 94
11. Appendix.................................................................................................................................... 100
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1.
Introduction
Just two days before the opening day of the 2014 World Economic Forum (WEF) in Davos,
the international organization Oxfam released a report titled “Working For The Few”,
catching vast media attention (The Guardian, 2014; USA Today, 2014). In their report Oxfam
warns of a trend towards rapidly increasing economic inequality, which “threatens to exclude
hundreds of millions of people from realizing the benefits of their talents and hard work.”
(Oxfam, 2014, p. 2). The richest one per cent of the world population owns almost half of the
world’s wealth and 85 people in the world own as much as the bottom half of the world
population, according to the report. Oxfam further argues that in 24 out of 26 countries the
richest one per cent increased their share of income from 1980 to 2012, highlighting not only
an alarming state of income inequality, but also a startling trend. The report retrieves a lot of
data from Piketty and Saez (2003) and Alvaredo, Atkinson, Piketty, and Saez (2011). They
found out that the income share of the top 10% of earners in the US, accounting for over
50% in 2012, has hit an all-time high since the US government started collecting income
data in 1913. Additionally, they found that a fall in the incomes of the top 1% US earners due
to the recent financial crisis was only temporary, their incomes having increased by 31%
from 2009 to 2012 while incomes of the other 99% stagnated with a growth rate of 0.4%
(Saez, 2013). Their findings have initiated a broad discussion about income inequality in the
US, reflected also by the recent correspondence between Robert Solow and Greg Mankiw
(Mankiw, 2013; Solow, Mankiw, Burkhauser, & Larrimore, 2014).
However, the importance of income inequality is not new. The number of available studies
dealing with the effects of inequality is enormous. While some argue that inequality promotes
economic growth through the creation of economic incentives (Edin & Topel, 1997;
Chaudhuri & Ravallion, 2007); and fostering equality could cause inefficiency (Okun, 1975),
others claim that there is a negative relationship between income inequality and economic
growth (Rodrik & Alesina, 1994; Benabou, 1996; Perotti, 1996). Berg & Ostry (2011), for
instance, studied the relationship between inequality and sustainable economic growth and
identified income distribution as the factor with the greatest impact on the duration of growth
spells, opposing Okun’s view that fighting inequality would cause inefficiency (see figure 1).
Trends and Key Drivers of income inequality
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Figure 1: Impact of different factors on growth spells (Berg & Ostry, 2011)
Barro (2000) summarizes the relationship between income inequality and economic growth
in four main categories, three of the four categories implying a negative, and the last one a
positive relationship. Although Barro himself finds evidence for a negative relationship
between inequality and growth for poor and a positive relationship for rich countries, he
concludes that there is little overall relation between income inequality and growth.
Still today, therefore, there is no general consensus on the effects of inequality on economic
growth. However, the volume of available literature implies the relevance of income
inequality not only for moral reasons. Instead of focusing on the potential effects of income
inequality and its pure interaction with economic growth, this paper presents an analysis of
potential key drivers of income inequality and a discussion of how, based on the identified
variables, future trends around income inequality may evolve.
Trends and Key Drivers of income inequality
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2.
Objective and Scope of the Paper
Oxford Economics produces historical estimates and forecasts of households by income
band for countries and cities. Because their method relies on data of the spread of income
across households represented by the Gini coefficient, Oxford Economics’ forecasts
implicitly include information on income inequality too. Oxford Economics approached the
Marshall Economic Research Group of the University of Cambridge in order to conduct
research on the topic of income inequality and improve their forecasts on household income
distribution and to strengthen the evidence base and theoretical underpinnings of their
forecasts. The report aims at giving an overview of past trends of and the key drivers behind
income inequality, and at analysing potential future trends of income inequality across as
well as within-countries. Focus will be on drivers of “within-country” income inequality (see
section 3.).
The report is structured along five main parts. After a short section on the definition of
inequality, the first part will outline the key trends of income inequality and theories that
determine income inequality based on existing literature. In the second part empirical
evidence will be presented in order to contrast real data with theories. Based on the
theoretical foundation and historical data, the third part will deal with projections of future
trends of inequality. The fourth part will discuss the findings, comparing theories with
empirical evidence and drawing implications for the future. Finally, the last part will
summarize findings and emphasize the main conclusions of the study.
3.
Definition of Inequality
This study deals with primarily the drivers and trends of income inequality. Income inequality
can certainly differ from inequality in wealth or other forms of inequality, such as gender or
social inequality. Income inequality can be analysed within parts of a country, i.e. at city or
regional levels, at a national level or even at a supranational and global level. At a global
level, a common distinction is made between two components of global income inequality,
i.e. between “within-country” and “across-country” income inequality. Sala-i-Martin (2006)
defines the “within-country” component of income inequality as “the amount of inequality that
would exist in the world if all countries had the same income per capita (that is, the same
distribution mean) but the actual within-country differences across individuals. This measure
is a weighted average of within-country inequalities.” (p. 388). The “across-country”
component, on the other hand, is defined as “the amount of inequality that would exist in the
world if all citizens within each country had the same level of income, but there were
differences in per capita incomes across countries.” (p. 388). Global income inequality, i.e.
income inequality across all individuals in the world, can refer to either of the two
Trends and Key Drivers of income inequality
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components on a global scale or to combine both of them. Because the “across-country”
component of global income inequality is based on the per capita income of different
countries, factors determining this component are highly correlated with those causing
economic growth, i.e. GDP per capita. Because this paper aims at analysing the direct key
drivers behind income inequality, focus will be on the “within-country” component, although
the other component as well as global income inequality will be touched upon too.
As a measure of income inequality, generally the Gini coefficient will be used, because it is
still the most widely used index of income inequality and the one Oxford Economics is using
in their projections. The Gini coefficient is calculated by contrasting the cumulative share of
the population with the cumulative share of the income earned of the total population of the
underlying geography. However, because the main focus of this paper is on analysing
existing literature and evidence, most data will be presented in the way it was originally
calculated or measured. This might include other indices, such as the Theil index or the
mean logarithmic deviation (MLD), than the Gini coefficient or different variants of the Gini
coefficient (e.g. pre-tax vs. post-tax values). Both the Theil index and the mean log deviation
belong to the class of generalised entropy inequality measures. Their main property is the
additive decomposability characteristic, which means that an aggregate inequality measure
can be decomposed into two components (Rohde, 2007). In the area of income inequality
this means that these indices are used to decompose global income inequality into a “withincountry” and an “across-country” component, whereby the sum of the two components
defines global income inequality. 1 Other income inequality measures used in this paper
include the income share of a certain population group of total income in geography, such as
the top 1%, the top decile (“D10”) or the first four deciles (“D1-D4”).
1
The mathematical definition is: Theil index
∑
( )
, MLD
∑
, where n is the population or the
number of subgroups, yi is the income of the ith person or subgroup and ya is the mean income of population or subgroup.
Trends and Key Drivers of income inequality
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4.
Income Inequality at a National and Global Level
4.1.
General Trends in the Past
4.1.1. Origins of Income Inequality Theories – the “Kuznets Curve”
One of the first modern academic papers dealing with income inequality was Simon Kuznets’
“Economic Growth and Income Inequality” published in 1955, which is still being frequently
cited today (Kuznets, 1955). Kuznets analysed data on income inequality in England,
Germany and the United States and particularly tried to explain the supposed decline in
income inequality in the first half of the 20th century. He observed a widening trend in income
inequality from about 1780 to 1850 in England, from about 1840 to 1890 in the United States
and from about the 1840 to the 1890s in Germany. Once having reached a peak in income
inequality, he argued that all the three countries experienced a narrowing phase of income
inequality, which he estimates to have started in the last quarter of the 19th century in
England and at approximately the beginning of the First World War in the United States and
Germany.
According to Kuznets, this trend was related to the development stage of the countries. In
the first phase of development a country would first experience increased inequality due to a
sectoral shift of the economy from agriculture to industrialisation and urbanisation. Inequality
would go up in this phase, because the industrial is more unequal than the agricultural sector
and a shift towards the more unequal sector would thus lead to higher inequality in the
overall economy. Also, upper-income groups can save more than lower-income groups. This
increases inequality, because income from saving goes up more for the richer population.
After having reached a peak, eventually, a country would face a decline in income inequality
for three reasons. Firstly, redistribution measures by the state would lead to lower income
inequality levels. Secondly, the dynamics of a capitalist economy would dampen income
inequality, because new entrepreneurs would create new industries. These dynamics over
time quickly reduce the share of assets in old industries (the source of capital income for the
current high income group) in total assets, in favour of assets in new industries (the source
of capital income for the nouveau riche, “the new rich”). Finally, there is the structural shift of
the labour force towards high service incomes. Previously, only the high income group had
benefited from the higher level of service income. According to Kuznets, inequality would fall
if also the rest of the labour force would make the shift to services.
This theory turned out to become known as the “Inverted U-Curve” or “Kuznets Curve”
hypothesis, indicating that a country experiencing economic growth would first undergo a
phase of increasing inequality until inequality subsequently would decline again.
Trends and Key Drivers of income inequality
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Although this relationship became a standard element of higher economics education,
Kuznets himself underlined in his paper that it involves “…perhaps 5 per cent empirical
information and 95 per cent speculation, some of it possibly tainted by wishful thinking.”
(Kuznets, 1955, p. 26). Its empirical validity is therefore controversial.
4.1.2. Income Inequality Within Countries
Overall historical trends
Kuznets (1955) estimated that income inequality increased in England, Germany and the
United States during industrialisation and then declined from the beginning of the 20th
century until approximately the 1950s within the mentioned countries. Bourguignon and
Morrisson (2002) analysed income inequality within and across countries from 1820 to 1992,
using data from 33 countries or country groups for smaller countries, where each country or
country group accounted for at least one per cent of world population or world GDP in 1950.
They came to the conclusion that, measured by the Theil index and averaging country
inequality by total income, inequality within countries or country groups increased slightly
from 1820 to 1910 and then declined sharply until 1950, supporting Kuznets’ estimates.
From 1950 to 1992 again it increased marginally (see table 1).
Table 1: Past trend of inequality within country groups measured by the Theil index (Bourguignon &
Morrisson, 2002)
Sala-i-Martin (2006) estimated world distribution of income by integrating income
distributions for 138 countries from 1970 to 2000. Using the Theil index, results also show a
slight 4.7% increase in world income inequality within countries from 0.255 in 1970 to 0.267
in 1992, which is lower than the 8.6% increase estimated by Bourguignon and Morrisson
(2002) in the same period. From 1992 to 2000 Sala-i-Martin (2006) predicted a further
inequality increase within countries to 0.284, which accounts to an overall 11.4% increase in
Trends and Key Drivers of income inequality
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within-countries income inequality from 1970 to 2000. Using the mean log deviation (MLD),
the increase from 1970 to 2000 is a lot bigger, accounting for 30.0%.
Atkinson, Piketty and Saez (2011) study top share incomes of twenty-two countries over the
last century using income tax statistics, whereby data is mainly pre-tax. The following series
(figures 2 to 4) do not include realized capital gains. Atkinson, Piketty and Saez identify a “Ucurve” rather than an “Inverted U-curve” for Western English speaking countries from 1910
to 2005, i.e. income shares of the top 1% declined from 1910 to about 1975 and then
increased until the beginning of the 21st century (see figure 2).
Figure 2: Top 1% income share of Western English speaking countries from 1910 to 2005 (Atkinson,
Piketty and Saez (2011) referring to Atkinson and Piketty (2007), (2010))
On the other hand, for Middle European countries and Japan, they estimate a rather “Lshaped” pattern during the same period (see figure 3). Except for some disruption during
World War I and an increase in the 1930s, top 1% income shares fell until the 1950s and
then stagnated until the beginning of the 21st century.
Trends and Key Drivers of income inequality
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Figure 3: Top 1% income share of Middle European countries and Japan from 1900 to 2005
(Atkinson, Piketty and Saez (2011) referring to Atkinson and Piketty (2007), (2010))
Finally, for selected current and former developing countries they identify a pattern between
a “U” and “L-shape”, although the variety across the countries is greater (see figure 4). Both,
China and India, which have recently experienced high economic growth, faced an increase
in the top 1% income share between 1980 and 2005.
Figure 4: Top 1% income share of selected developing countries from 1920 to 2005 (Atkinson, Piketty
and Saez (2011) referring to Atkinson and Piketty (2007), (2010))
Trends and Key Drivers of income inequality
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Overall, Atkinson, Piketty and Saez’s estimates show a decline in the income share of the
top 1% in the first half of the 20th century, followed by a stagnation and an increase in the
last three decades for the majority of the countries studied, which is consistent with the
results of Bourguignon and Morrisson (2002) and Sala-i-Martin (2006).
As key underlying factors of the decline of the top 1% income shares during the first half of
the twentieth century, Atkinson, Piketty and Saez (2011) mention the two World Wars and
the Great Depression for the large majority of countries studied. While fighting countries,
such as Japan (where WWII was the defining event for the country’s income concentration
according to Moriguchi and Saez (2008), experienced falls, some non-fighting countries,
such as the Netherlands during WWI, saw an increase in top income shares (see figure 3).
For combatant nations, top income shares fell during the world wars because of two main
forces. Firstly, capital income losses were high due to physical capital destruction and
financial capital losses as a result of hyperinflation or direct redistribution through
confiscation or tax policies. Given the fact that capital income made up an overwhelmingly
large part of total income of the top 1% at that time, these losses had a big impact on the top
1% income shares too. Secondly, the wage structure changed following the world wars. For
instance, in the US low unemployment rates as well as newly imposed wage controls led to
an equalization of earned incomes.
Increases in the top 1% income shares in Australia, New Zealand and Singapore in the
1950s are largely ascribed to the commodity price boom.
The rise of top income shares in the second half of the twentieth century was a result of
mainly an increase in labour income, which led to an increase of the labour income share
among the top 1%. As a potential main factor Atkinson, Piketty and Saez (2011) list a
decline in income tax progressivity, although findings are not only one-sided. Finally, they
state that top income shares of different countries may be correlated with each other, i.e. the
income share of one country may help predict the income share of another country. For
instance, Saez and Veall (2005) use US’s top income share as an explanatory variable for
the Canadian top income share and Leigh and van der Eng (2009) calculate the correlation
between the top income shares of Indonesia and other countries, obtaining the highest
positive correlation with India and a negative correlation with Argentina. Exploring crosscountry top income shares appears to be “a rich seam” for the future according to Atkinson,
Piketty and Saez (2011, p. 65).
Although a lot of the research of Piketty and Saez is based on market income, i.e. based on
pre-tax income and including realized capital gains, a study of Burkhauser, Feng, Jenkins
and Larrimore (2012), using data from the Current Population Survey, largely replicates
inequality trends since the 1960s as estimated by Piketty and Saez (2003). Armour,
Trends and Key Drivers of income inequality
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Burkhauser and Larrimore (2013), using post-tax and post-transfer data and including in-kind
income and accrued capital gains, conclude that inequality did not increase as significantly
as claimed by Piketty and Saez during the 1980s and 1990s, but agree that current
inequality levels are at or near record levels.
The OECD (2011a) confirms Atkinson, Piketty and Saez’s (2011) findings, showing
increases in the top 1% income shares across OECD countries from 1990 to 2007 and the
sharpest rises in English speaking countries (see figure 5).
Figure 5: Shares of top 1% incomes in total pre-tax incomes, 1990-2007 (or closest year)
(OECD, 2011a)
As the main reasons behind the recent surge of top income shares, the OECD mentions a
more global market for talent and a growing use of performance-related pay, which
particularly benefited top executives and the finance industry (see section 4.2.4. and 6.1.4.).
Also, reductions of marginal top tax rates contributed to the mentioned trend (see section
6.1.2.).
Within-country income inequality measured by the Gini coefficient shows a similar picture
(OECD, 2011a). From 1985 to 2008 the Gini coefficient increased in 17, and decreased in
only two out of 22 countries, the remaining three countries showing a change within a band
of two percentage points (see figure 6). The largest increases were observed in New
Zealand, Sweden and Finland, highlighting an increase even in the Scandinavian countries,
where inequality rates have been traditionally low. On the other hand, Greece and Turkey,
two countries with high inequality levels as of 1985, could decrease inequality, although the
latter country together with Mexico still exhibits the highest Gini coefficient among the 22
countries studied. Because in the 2000s also the traditionally high-inequality country Chile
reduced its Gini coefficient, the OECD localizes “tentative signs of a possible convergence of
Trends and Key Drivers of income inequality
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inequality levels towards a common and higher average level across OECD countries”
(OECD, 2011a, p. 22).
Figure 6: Gini coefficients of income inequality, 1985 and 2008 (OECD, 2011a)
Such a hypothesis of convergence in income inequality is supported by Dhongde and Miao
(2013). Studying 32 developing and 23 developed countries from 1980 to 2005, they find
that income inequality converged across countries, the speed of convergence in Gini being
higher than the conventional 2% per year speed of convergence of per capita income.
Figure 7: Average change from 1980 to 2005 in inequality (y-axis) in relation to initial 1980 inequality
level (x-axis) (Dhongde & Miao, 2013)
Trends and Key Drivers of income inequality
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While income inequality increased in relatively equal countries as of 1980 such as China, the
UK or Scandinavian countries (Finland and Norway), relatively unequal countries such as
Brazil, Nigeria or Turkey experienced a decline in income inequality. The negative
relationship between the initial Gini level in 1980 and the annual average change in the Gini
coefficient is shown in figure 7. 2 Dhongde and Miao also find that developed countries
converged faster than developing countries and that average income inequality increased in
developing countries relative to developed countries during the period.
Results of the recent international research project “GINI” (“Growing Inequalities’ Impacts”)
show similar findings. Studying income inequality trends in EU and OECD countries over the
past 30 years, Tóth (2013) states that countries may shift between inequality regimes in the
longer run. The Nordic countries, for instance, left the division of the most equal countries,
other transition countries such as the Baltics, Bulgaria or Romania reached a higher
inequality regime over the last 30 years. Within the EU, growing European economic
integration, by increasing labour and wage competition and weakening labour unions, is
mentioned as a potential driver behind within-country income inequality convergence
(Beckfield (2006); supported by preliminary results of Auguste (2012)).
Given the relevance of the USA and of emerging countries for the global economy and
Oxford Economics’ interest in specifically these countries, the following section will briefly
discuss key trends and drivers of the USA, the “BRIC” (Brazil, Russia, India and China)
countries and South Africa.
Income inequality in USA
As shown in figure 2, the US experienced a U-shaped pattern of its top 1% income share
over the last century (Atkinson, Piketty, & Saez, 2011). A more recent update by Saez
(2013) shows real pre-tax income growth, including realized capital gains, of the top 1% and
the bottom 99% of US households from 1993 to 2012, thus including data following the
recent “Great Recession”. Saez concludes that although the top 1% had a larger real income
loss than the bottom 99% in both the 2001 and the 2007-2009 recessions, their real income
growth over the entire period from 1993 to 2012 exceeded the one of the bottom 99% by far,
overall growth accounting for 86.1% vs. 6.6%, respectively. During the recent recovery from
2009 to 2012, the top 1% captured 95% of the real income growth, leaving incomes of the
bottom 99% stagnating (see table 2).
2
The importance of analysing changes in inequality rather than levels when looking at inequality across countries is highlighted
by the IMF (2007), who mention poor reliability, lack of coverage and inconsistent methodology as key problems of crosscountry comparison.
Trends and Key Drivers of income inequality
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Table 2: Real income growth of US households by groups from 1993 to 2012 (Saez (2013) updating
data from Piketty and Saez (2003))
Income inequality in Brazil
Brazil has one of the highest income inequality levels in the world. Inequality increased in the
1980s, the Gini coefficient reaching more than 0.6, due to years of crisis. During the 1990s,
however, inequality did not increase, potentially due to the introduction of market-oriented
reforms and narrowing effects of trade liberalization on wage inequality (Gasparini & Lustig,
2011). Since then inequality, measured by the Gini coefficient, has gradually declined (see
figure 8).
Figure 8: Absolute poverty, relative poverty and Gini coefficient from 1995 to 2012 in Brazil (Arnold &
Jalles, 2014)
Trends and Key Drivers of income inequality
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The key drivers behind the recent success in fighting income inequality have been labour
market policies and social transfers (see figure 9).
Figure 9: Contributions of different factors to declining inequality in Brazil (Arnold & Jalles, 2014)
The leading variable behind differences in labour income has been improvements in access
to education as well as a rise in real minimum wages, the average real wage having risen by
around 25% over the last decade. Social transfers mainly have involved pensions and the
conditional cash transfer programme “Bolsa Familia”, which provides direct transfers to
households whose monthly income is below 70 Brazilian reales (BRL) (Arnold & Jalles,
2014). Although a lot of progress has been made, income inequality still remains high as can
be seen in figure 10, comparing the Gini coefficient of Brazil to other emerging countries.
Figure 10: Gini coefficient of household income of different emerging countries and OECD average in
early 1990s and late 2000s (OECD, 2011c)
Trends and Key Drivers of income inequality
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Income inequality in China
As shown in figure 4 by Atkinson, Piketty and Saez (2011), income inequality, based on the
top 1% income share, has risen gradually in recent years in China. Although the available
time series for China is short and the top 1% income share still relatively low with 5.9% in
2003, the trend seems to go towards the direction of other OECD countries. Figure 10 shows
a similar picture based on the Gini coefficient. China’s Gini increase from 1993 to 2008 was
the largest among the seven emerging countries shown, the value in 2008 exceeding the
OECD average by almost one third. The OECD mentions as potential reasons the increasing
differences in income inequality within provinces and particularly between urban and rural
areas, the former ones benefiting and the latter ones losing, potentially due to the great
disparities in access to basic (social) services. A study by Wei and Wu (2007) who, using the
Theil index, decompose national inequality into a rural, urban and a between rural-urban
component, confirms this trend (see figure 11), although social protection has been
improved in rural areas after the study period (OECD, 2011c).
Figure 11: Components of national inequality in China from 1985 to 2004 (IMF (2007) referring to Wei
and Wu (2007))
Income inequality in India
Based on Atkinson, Piketty and Saez (2011), India’s top 1% income share exhibited a “U”pattern from the 1930s to 2000 (see figure 4), although less pronounced than several
Western English speaking countries (see figure 2). The top 1% income share fell sharply in
1940, increased significantly again around 1950 and then fell until the beginning of the
1980s. Since then it has increased. Figure 10 shows that the Gini coefficient increased
significantly from the early 1990s to the late 2000s. The OECD (2011c) states that there is
growing concern that different states of the country are benefitting to a different extent from
India’s economic growth, the result being a widening of the gap between richer and poorer
states. According to the UN (2008), skill-biased technological change is a main driver behind
this trend. Also, the fact that India, just as China, spends between three and four times less
on social protection than the OECD average may have contributed to the recent trend
(OECD, 2011c).
Trends and Key Drivers of income inequality
19
Income inequality in Russia
Available data on Russia’s development of income inequality is limited. Figure 10 shows that
the Gini coefficient has increased from the early 1990s to the late 2000s, although by a lot
less than China’s or India’s value. Overall, with a value of more than 0.4, based on the Gini
coefficient income inequality was still higher than in both China and India as of the late
2000s. Factors that may have supported this development are low minimum wages and
fairly poor employment protection legislation (OECD, 2011c). IMF (2007) data shows a
decline in the Gini coefficient from the early 1990s to the early 2000s.
Income inequality in South Africa
South Africa is one of the countries with the highest income inequality levels in the world.
Southern Africa as a region, together with Latin America, is uniquely unequal as stated
further below by Palma (2011). Already at an outrageously high level in the early 1990s,
South Africa’s Gini coefficient worsened further until the late 2000s, accounting for almost
0.7 (see figure 10). A particular challenge for South Africa is inequality between races.
Similar to China and India, public social expenditure is very low in South Africa,
unemployment benefits are meagre and employment protection regulations the poorest
among six of the seven countries studied (OECD, 2011c). IMF (2007) data shows a small
decline in the Gini coefficient from the early 1990s to around 2000.
Income inequality between different geographic regions
A wider study dedicated to within-country income inequality between different geographic
regions was conducted by Palma (2011) who performed a cross-sectional analysis of the
income distribution of 135 countries in 2005. He comes to the conclusion that there was a
wide range of within-country income inequality across countries in 2005, ranging from a Gini
coefficient of 23 in Sweden to 71 in Namibia. About 80% of the world population live in a
region in which the median country Gini is approximately 40. Palma also finds that Latin
America (“LA”) and middle-income Southern Africa (“SAf”) are uniquely unequal as of 2005,
while Eastern Europe (“EE”) is following a similar low-inequality path as the Nordic countries
(“No”) (see figure 12), although the latter have recently experienced an increase in their Gini
coefficients (as shown in figure 5). Latin America is of particular concern, the median Gini
(54) being almost 50% higher than the overall Gini of the other 116 countries studied.
Palma also tests Kuznets’ “Inverted-U” hypothesis, indicating at the same time that his
analysis is “simply meant to be a cross-sectional description of cross-country differences,
categorised by income per capita” (p. 12), i.e. no time series data is used. Although the
“Inverted-U” hypothesis statistically works if no “dummy variables” for the different regions
Trends and Key Drivers of income inequality
20
are used, i.e. if no region is analysed individually in the regression; when excluding or
controlling for the two outliers, Latin America and Southern Africa, it does not hold. Running
regressions for the individual regions, Palma does not find a homogenous relationship
between inequality and GDP per capita. Given the large variation of Gini coefficients of
countries with similar GDP per capita (as shown in circle A in figure 12) as well as a large
variation of GDP per capita given a similar Gini coefficient (circle B in figure 12), Palma
concludes that his results do not support the “Kuznets curve” hypothesis. According to him,
there is analytically and statistically no reason that some regions, such as Latin America and
Southern Africa, will follow patterns of other regions and decrease their Gini coefficients if
GDP per capita shoots up. A country does not necessarily have to experience the upwardsloping part of the “Kuznets curve”, before being successful at cutting back on its inequality
rate.
A
B
A
Figure 12: Gini coefficient and Ln of income per capita (proxied by GDP per capita) in 2005 for
different regions and countries (Palma, 2011)
For the full classification please see Appendix 1. Ca = Caribbean; Cn = China, EA1 = East Asia-1 = Korea and Taiwan; EA1* =
East Asia-1* = Singapore and Hong Kong; EA2 = East Asia-2 = Indonesia, Malaysia and Thailand; EE = Eastern Europe; EU =
Continental Europe, including Switzerland (i.e., non-Anglophone European Union, excluding the Nordic countries, which are
reported separately, and Switzerland), EU* = Austria and Germany (Ginis below 30); EU** = EU excluding Austria and
Germany; FSU = Former Soviet Union; In = India, Jp = Japan, LA = Latin America; No = Nordic Countries = Denmark, Finland,
Norway and Sweden; NA = North Africa; OECD-1 = Anglophone OECD and EA1* = Australia, Ireland, New Zealand, United
Kingdom and United States, and Singapore and Hong Kong; Ru = Russia, SS-A = Sub-Saharan Africa (excluding middleincome Southern Africa - SAf); SA = South Asia; SAf = middle-income Southern Africa; US = United States.
Finally, Palma argues that given the trend of increasing within-country income inequality, it is
the top decile (“D10”) as well as the lowest four deciles (“D1-D4”) of the population of a
country that matter when analysing income inequality, their income shares varying
significantly across countries (see figure 13). On the other hand, the remaining five deciles
Trends and Key Drivers of income inequality
21
(“D5-D9”) of the population have a fairly similar income share of total income of a country,
accounting for about 50% across the 132 countries.
Figure 13: Income shares of D1-D4, D5-D9 and of D10 of total income in 132 countries in 2005
(Palma, 2011)
4.1.3. Income Inequality Across Countries
Past trends of income inequality across countries differ from those of income inequality
within countries. According to Bourguignon and Morrisson (2002) income inequality across
countries increased sharply from 1820 up to 1950, then stabilized and only increased
marginally until 1992, based on both the Theil index (see table 3) and the mean logarithmic
deviation.
Table 3: Income inequality across country groups from 1820 to 1992 based on the Theil index
(Bourguignon & Morrisson, 2002)
The mentioned study by Sala-i-Martin (2006), investigating the period from 1970 to 2000,
shows a slight 4.3% decrease in income inequality across countries from 1970 to 1992,
Trends and Key Drivers of income inequality
22
measured by the Theil index – as opposed to an increase of 4.3% according to Bourguignon
and Morrisson (2002). From 1992 to 2000 Sala-i-Martin estimates a further 6.4% decrease,
resulting in an overall decrease of 10.4% in income inequality across countries from 1970 to
2000. Milanovic (2012) studied income inequality across countries in 150 countries from
1952 to 2011, based on two different concepts, in the first concept giving each country equal
weight, in the second, weighting countries’ income according to their population. The trends
are presented in figure 14.
Figure 14: Two concepts of income inequality across countries measured by Gini coefficient from
1952 to 2011 (adapted from Milanovic (2012))
Concept one shows an overall increase in income inequality across countries from 1952 to
2000 with two sharp increases and three more stable phases in between. From 2000 to
2011 income inequality declined by approximately 0.03. Concept two, accounting for
different population sizes of countries, shows a different picture. Income inequality across
countries based on this concept declined gradually from 1952 to 2000, with a short break
from about 1955 to 1960, and then decreased more sharply until 2011, the overall decline
being about 0.12 Gini points, i.e. about 18%. Comparing the concept two-trend with the trend
in the other two studies, we get a marginal (~1-2%) decline from 1970 to 1992 and a decline
of about 17% from 1970 to 2000, which shows a similar trend as Sala-i-Martin (4.3% and
10.4%, respectively), although there is some deviation in the magnitude. Both Milanovic
(2012) and Sala-i-Martin (2006) attribute the high growth rates of China and India in the last
decades to the decrease in income inequality across countries. Although there are
differences between the three studies described in this section, especially due to the
different samples, indices and underlying data used, the overall trend shows into the same
direction. While income inequality across countries, being based on the average income per
Trends and Key Drivers of income inequality
23
country, sharply increased from the beginning of the 19th until the middle of the 20th century,
it since then has stabilised and over the last two decades, in particular due to the growth of
China and India, even decreased.
4.1.4. Global Income Inequality
While by analysing income inequality within countries alone, one would assume that all
countries have the same average income; by solely focusing on income inequality across
countries, one would miss the fact that different countries have different income distributions
across their populations. Global income inequality aims at combining the two components in
order to end up with a measure of income inequality across all individuals on the world.
Considering again the study of Bourguignon and Morrisson (2002), we see that total
inequality, measured by the Theil index, increased from 1820 to 1910 by about 53% and
then stabilised, showing slight movements up and down from decade to decade. From 1970
to 1992 it increased by 5.8% (see table 4).
Table 4: Total inequality from 1820 to 1992 measured by the Theil index
(Bourguignon & Morrisson, 2002)
In contrast, Sala-i-Martin (2006) estimates a 1.5% decrease from 1970 to 1992, using the
Theil index. For the full thirty-year period from 1970 to 2000 he calculates a decline of 3.7%,
the Theil index showing a value of 0.783 in 2000. Sala-i-Martin also reports seven other
measures of global income inequality, six of the seven indices showing a decline (ranging
from 2.4% to 22.7%) and only one, the variance of log income, showing a slight increase in
world income inequality.
Both authors also estimate the share of the two types of income inequality of total inequality.
According to Bourguignon and Morrisson within-country income inequality accounted for
about 80% of total inequality in the first half of the 19th century, the remaining 20% made up
Trends and Key Drivers of income inequality
24
by income inequality across countries, which is due to the fact that at that time most
countries had a similar income level. By 1950, however, the share of within-country
inequality of total income inequality had dropped to 40%.
Sala-i-Martin estimates a similar relationship, assigning only 31% of overall world inequality
to the within-country income inequality in 1970, using the Theil index. However, he estimates
an increase of the share of the within-country component to 36% for 2000. Thus the
importance of the within-country relative to the across-country component decreased
significantly from the beginning of the 19th century to the middle of the 20th century, but
recently has increased again. Milanovic (2012) supports the theory that from the 19th century
until 2000, the across-country component has gained significantly in importance, highlighting
that today “citizenship” matters considerably more.
Figure 15: World income distribution based on the Gini coefficient from 1970 to 2000
(Sala-i-Martin, 2006)
Sala-i-Martin (2006) also calculates a Gini coefficient for the world income distribution during
the investigation period, shown in figure 15. While global inequality increased from 1970 to
1980, it has continually declined from 1980 to 2000 (with the exception of an increase prior
to 1990) “for the first time since centuries” (p. 27). However, given the tight scale (from 0.63
to 0.665) on the y axis in figure 15, the magnitude of the estimated overall decline from 1970
to 2000 might be misleading from looking at the graph, the actual difference in Gini points
amounting to approximately 0.015 or 2.3% over the thirty-year-period.
Milanovic (2012) similarly calculates global Gini coefficients for six individual years between
1988 and 2008, based on household surveys. He estimates a decline in global income
inequality of 0.014 Gini points from 2002 to 2012 and, similar to Sala-i-Martin although at a
later moment in time, describes this decrease as the perhaps first-time decline in global
inequality since the Industrial Revolution and mentions that the world might have passed the
Trends and Key Drivers of income inequality
25
peak of the “Kuznets curve”. However, Milanovic notes that because African coverage of
household surveys is significantly below the rest of the world (75% in Africa vs. 94% world
average in 2008), the estimate may be downward-biased.
According to Milanovic (2012) the winners of the decrease in global income inequality are
the top 1% of the world income distribution, supporting the findings of Atkinson, Piketty and
Saez (2011) and the OECD (2011c) (see figure 5), as well as the middle class of emerging
market economies. On the other hand, as main losers Milanovic identifies the very poorest,
the lowest 5% of the world income distribution, as well as the people between the 75th and
the 90th percentile. The two latter groups had basically the same real income in 2008 as in
1988. The two winner groups, on the contrary, increased their real income by equal to or
more than 60% over the twenty-year period (see figure 16).
Figure 16: Percentage change in real income of different percentiles of the global income distribution
measured in 2005 constant international dollars from 1988 to 2008 (Milanovic, 2012)
Geographically, Milanovic regards 200 million Chinese, 90 million Indian and about 30
million people each from Indonesia, Brazil and Egypt who all belong to the “emerging global
middle class”, as the main winners, the top 1% being more spread out among various
countries, although US citizens with approximately 50% make up the largest share. On the
other hand, mostly people in Africa, some in Latin America and post-Communist countries
make up the loser group.
Trends and Key Drivers of income inequality
26
4.1.5. Conclusion
Overall, the main past and current trends of income inequality at a national and global level
can be summarized as follows:

“Within-country” income inequality increased throughout the 19th century, then
declined from the beginning to the middle of the 20th century and since then has
increased in most OECD and several developing countries.

Over the recent decades several studies have shown a convergence of withincountry income inequality in developed and developing countries, although in the
latter to a lesser extent. Across OECD countries this convergence is towards a higher
average level of within-country income inequality.

Global income inequality increased from the industrialization to the early 20th century,
has stabilized since then and might have even slightly declined in the last two to
three decades.

The recent decline in global income inequality was mainly due to a decrease in
“across-country” income inequality and high growth rates in emerging countries such
as China and India, implying a convergence also of across-country income inequality.

While in the 19th century the “within-country” component accounted for the vast part
of global income inequality, today the “across-country” component is dominating,
accounting for about 64% in 2000.

The main winners from the potential decline in global income inequality in the last
decades were the top 1% of the population as well as the “emerging global middle
class”, including in particular many Chinese and Indian citizens. These two groups
were able to increase their real incomes by more than 60% from 1988 to 2008.

The main losers, whose real income stayed about the same from 1988 to 2008, were
the poorest 5% of the world income distribution as well as people between the 75th
and 90th percentile, including mainly Africa and Latin America.

Middle-income Southern Africa and Latin America can be regarded as two main
outliers among country groups worldwide, having uniquely high levels of income
inequality.

The deciles 5 to 9 approximately have a 50% share of total income in most countries,
while the top decile and deciles 1 to 4 differ significantly between different countries.

The “Kuznets Curve” remains controversial, some arguing that even just now the
world has passed the peak of the curve, while others state that countries do not have
to experience the upward-sloping part of the curve, before closing the gap on high
income countries.
Trends and Key Drivers of income inequality
27
4.2.
Drivers of Income Inequality
The last part has shown that income inequality has followed certain trends in the past and
that different factors may have contributed to these trends. This section will summarize the
key drivers that may determine income inequality.
4.2.1. Skill-Biased Technological Change
Skill-biased technological change (SBTC) is thought to be one of the main drivers of the
increase in inequality in large parts of the developed world since the 1970s (Acemoglu,
2002; Autor, Katz, & Kearney, 2008). Traditionally, technological progress was assumed to
be Hicks-neutral (Hicks, 1932); the ratio of the marginal productivities of the factors of
production should remain unaffected by technological progress. SBTC, however, favours
skilled workers by increasing their productivity, and thus their wages, relative to that of
unskilled workers. In this way, skill-biased technological change contributes to rising
inequality. According to Greenwood and Yorukoglu (1997), the adaptation to, and adoption
of new technologies require skill. Technological progress should therefore lead to an
increase in the returns to skills. Goldin and Katz (1998), however, stress that technological
progress is not inherently skill-biased – during the Industrial Revolution, skilled artisans were
replaced by unskilled workers in factories – but that since the early twentieth century,
technological change has indeed been predominantly skill-biased.
An important assumption we are making is that education is a good approximation of the
more vague term “skills”. Given the enormous increase in the supply of college graduates on
the labour market, we would expect a fall in the return to education. In 1940, only 6.4% of
the employed population in the US was a college graduate, compared with 26.1% in 1990.
The share of high school dropouts decreased over the same period from 67.9% to 12.7%
(Autor, Katz, & Krueger, 1998). The fact that there was in fact an increase in the college
wage premium (Juhn, Murphy, & Pierce, 1993; Acemoglu, 2002) implies that the demand for
skills has even grown faster. To use Jan Tinbergen’s metaphor: the race between schooling
[the supply-side of skills] and technology [the demand-side of skills] is being won by
technology (Tinbergen (1975); Goldin & Katz (2008)). Acemoglu (2002) concludes that this
skill-bias has been present in technological change for the last seventy years. In addition,
Goldin and Katz (1998) give evidence for skill-biased technological change in the 1910s and
1920s.
Acemoglu (1998; 2002) introduces a model to explain the skill-bias of technological change.
Technological change is then endogenous and depends on profit-incentives. The argument
is that profit depends to a great extent on market size. The increased supply of collegegraduates, and thus skills, provided the profit opportunities for firms to invent skillcomplementing technologies. Using this framework, the initial increase in the supply of skills
Trends and Key Drivers of income inequality
28
leads to an increase in demand (as a result of new technology) that can and in this case did
‘overshoot’ the initial increase in supply.
Computerisation has led to a reinterpretation of the skill-biased technological change
narrative. Early papers on the effect of computers showed, in line with the theory of skillbiased technological change, that the ‘computer revolution’ favoured college graduates
(Krueger, 1993; Autor, Katz, & Krueger, 1998). Computerisation complements skilledworkers, while it substitutes for unskilled workers. At the same time, and this is the
reinterpreted part of the theory, it leaves non-routine manual tasks largely unaffected
(Michaels, Natraj, & Van Reenen, 2014; Goos & Manning, 2007; Autor, Levy, & Murnane,
2003). The result is a polarisation of the labour market, ‘with employment polarizing into
high-wage and low-wage jobs at the expense of middle-skill jobs’ (Autor, Katz, & Kearney,
2006).
Another example of skill-biased technological change can be found in the phenomenon of
“superstars”, as analysed by Rosen (1981). Here, developments in communication and
transport technology give a small number of the higher-skilled the opportunity to expand their
market and take home a larger share. Although the original “superstars” – in the sports, arts,
media industries – do not seem very relevant statistically for the recent rise in inequality,
Rosen’s analysis can be extended to other sectors in the economy, such as the financial
sector. This extension of Rosen’s analysis will be discussed in section 4.2.4.
It must also be noted that skill-biased technological change interacts with trade unions
(Acemoglu, Aghion, & Violante, 2001; Acemoglu, 2002). SBTC leads to deunionisation,
which in turn has an effect on inequality. This link will be further explained in the section
4.2.3..
Finally, we should mention the deficiencies of the theory of skill-biased technological change
as the main driver of the rise in inequality. The first deficiency is visible when comparing
figure 2 to figure 3. Countries experiencing the same technological changes show different
patterns in inequality: English-speaking countries have seen inequality rising rapidly since
the 1980s, whereas inequality has remained stable in several continental European
countries and Japan (Atkinson & Piketty, 2007; Atkinson, Piketty, & Saez, 2011). Since
technology cannot give an explanation for these differences, the focus is shifted to labour
market institutions, taxation, and rent-seeking behaviour in bargaining over one’s pay
(Piketty & Saez, 2003; Piketty, Saez, & Stantcheva, 2014; Piketty, 2014). This new line of
research will be discussed in section 4.2.3. Also, country-specific reasons are given.
Canada’s rise in inequality, for example, can be explained with what is called the “brain drain
Trends and Key Drivers of income inequality
29
threat”. Canadian workers can easily move to the US to work and so the higher incomes at
the top in the US cause a similar surge in Canadian top incomes (Saez & Veall, 2005).
The second deficiency is the rise of the top 1% relative to the top 5-1% or the 10-5%, as
presented in figure 17. All these groups have essentially the same education, and thus skillbiased technological change cannot explain this divergence. Again, institutional factors are
sought to explain this rise (Atkinson, Piketty, & Saez, 2011).
The final deficiency is the inability of the skill-biased technological change hypothesis to
explain the trends in the gender and racial wage gaps (Card & DiNardo, 2002). Let’s start
with the gender wage gap. Skill-biased technological change leads to a higher premium on
skill. Card and DiNardo (2002) find that although college-educated women use computers on
the job less, the gender wage gap for this group of women actually fell during the 1980s. The
fall is attributed to “gender-specific factors” (Card & DiNardo, 2002). Similarly, blacks use
computers on the job less than whites, but the racial wage gap closed – instead of increased
as the skill-biased technological change hypothesis predicts – in the 1970s and remained
stable afterwards (Card & DiNardo, 2002).
Figure 17: Decomposing the Top Decile US Income Share into three Groups, 1913–2007
(Atkinson, Piketty, & Saez, 2011)
Trends and Key Drivers of income inequality
30
4.2.2. Political Economy
Inequality as a whole can be seen as a fundamentally social phenomenon, and so too can
inequality of income. A political system effects income distribution through its laws,
institutions and policies. Democracy and the politics of redistribution can directly impact the
post-tax incomes of various social groups. Trying to examine the extent these social factors
can effect inequality is a difficult and wide ranging one, and it has been noted that there is a
lack of firm evidence compared to other factors when it comes to how political institutions
effect income distribution (Inter-American Development Bank (IDB), 1998).
The Equalising Effects of Democracy
A democratic government is one that allows all eligible citizens to participate equally;
similarly, a society in which institutions concentrate political power with only a subset of the
population would be considered non-democratic. Initially, there would seem to be reasonably
strong theoretical reasons to suggest that an increase in democracy, as well as the length of
a country’s democratic experience should reduce inequality (Rueschemeyer, Stephens, &
Stephens, 1992). The intuitive explanation of this is that greater political equality will make
policy decisions that effect income distribution more representative of society as a whole.
It has been emphasised that the more democratic a society, the more redistribution there
should be (Meltzer & Richard, 1981). A widening of the voting franchise, for example, will
extend power further down the income distribution, making the median voter poorer. The
median voter will therefore be keener (and there will be greater pressure on the government)
to redistribute wealth away from the rich towards themselves. In other words, taxation should
increase and perhaps become more progressive.
Another mechanism in which democracy should help to reduce income inequality is to
promote structural reform (Acemoglu, Naidu, Restrepo, & Robinson, 2013). It is suggested
that non-democratic societies often impose limits on migration out of rural areas, as well as
repress labour, in order to keep agricultural wages low and redistribute towards the landed
elite. An increase in democracy should then lead to a structural shift in which there is greater
urbanisation. The impact of urbanisation on inequality will be explored further later (see
section 5.).
An increase in democracy gives those with less power a chance to organise and use their
organisations as a power base for entry into the political decision making process. As the
poor tend to be much less able to influence decision makers than the rich due to a lack of
connections and funds, political parties become the most effective channel to do this (Huber,
Nielson, Pribble, & Stephens, 2005). Therefore, we would expect the impact of differing
Trends and Key Drivers of income inequality
31
political parties to be a significant aspect of any effect democracy as a whole has. It can be
shown that parties on the left of the political spectrum tend to increase income redistribution
through the welfare state, and so if these are the strong parties we would expect democracy
to have a greater impact.
Issues with the Transmission Mechanisms
However, more modern literature has suggested that the impact of democracy on income
inequality may be more ambiguous. If the richer segments of a society are able to disrupt the
transmission mechanism, or if they break down for other reasons, then an increase in
democracy may fail to bring about any significant change in income inequality.
Captured Democracy
An increase in a democracy transfers more de jure power to the poor sections of an
economy; however, the rich can counter this through methods of boosting their de facto
power (allocated as a result of wealth, weapons or the ability to organise, rather than through
the political institutions). This enables the rich elite to continue to use their privileged position
and power to ensure government policy benefits them. There is a risk of democracy –
especially in newly democratised states - being highly dysfunctional or effectively captured.
This is because its architecture is often chosen by the previous elite (Acemoglu & Robinson,
2008). For example, the constitution imposed by the Pinochet government in Chile prior to its
democratisation was used as a way to restrict future redistribution. Another way de facto
power can be maintained is through the threat of a future coup. This is likely what happened
recently in Liberia, where highly redistributional policy was prevented and Charles Taylor
elected in 1997.
Bonica, McCarthy, Poole, and Rosenthal (2013) also attempt to define factors that may
prevent democracy working effectively to reduce inequality in the specific case of the US
One significant point is that voting participation is skewed towards the top end of income
distribution. While fewer than half of households earning less than $15,000 voted in recent
elections, more than 80% of those earning over $150,000 did. It is also true that a higher
fraction of the poor tends to be non-citizens who would therefore be unable to exert any
political power through elections. This gives a link between inequality and immigration.
A second point is that the political institutions in America, along with the growing trend of
partisanship, provide many hurdles before any income redistributing legislation can come
into force. For legislation to be passed, it must have among other things a majority in both
the Senate and Congress (and a two-thirds majority to override a Presidential veto), more
Trends and Key Drivers of income inequality
32
than the 60 vote minimum to prevent filibusters as well as be able to stand up to court
challenges and foot-dragging from the individual states. There is also the risk of
gerrymandering, the manipulation of the boundaries of electoral constituencies, and
population distribution across the states that can lead to geographical inequality of power.
This all leads to a high chance of political gridlock preventing progress being made, and any
reform passed is inevitably limited and very moderate. In theory, this could work in both
ways, but it tends to favour the rich. They have often used their previous political influence to
ensure inflation, which will maintain the income distribution. For example, although minimum
wages must be increased manually through the political process, tax brackets have been
indexed to inflation to make sure there is little tax bracket ‘drift’ or ‘creep’.
Finally, Bonica et al. (2013) emphasise the fact that policy is much more responsive to rich
opinion than poor, precisely because this is where the majority of political power is
concentrated. If 80% of the richest segment of society supports a policy change, it has a
50% chance of being passed. If it is instead support from 80% of the poor, a policy change
would appear to only have a 32% chance of being successful. This undermines the
fundamental argument that an increase in democracy makes government policy regarding
income redistribution (or anything else for that matter) more representative of the whole
society. This issue is closely linked to the huge growth in the income of the very rich, often
associated with the financial sector (see section 4.2.4.). The richest 0.01% earns 5% of US
income, but they contribute over 40% of all campaign donations. In the decades since 1980,
there has also been a growing reliance on the generosity of big individual donors rather than
sources such as trade unions. Even more money is spent on lobbying, the act of directly
pressuring policy makers to try and shape legislation positively for yourself – i.e. companies
and individuals trying to earn higher profits. This is further helped by the ‘Revolving Door’
phenomenon. This entails people moving from high level positions in the private sector, into
public office and then back again. Even with the best of intentions, this will likely cloud
decision making to the benefit of friends and connections.
Offsetting Mechanisms
Although we have seen how an increase in democracy should cause lower inequality, there
are various forces that shape the political equilibrium. Acemoglu et al. (2013) note that some
of these may lead to situations in which greater political enfranchisement can actually resist
the drive towards equality.
For example, the preferences of the poorer subsections of society may not be as would
traditionally be expected. If social mobility is high, they may find themselves with a higher
income in a short space of time. However, if this is coupled with a sticky tax policy regime
Trends and Key Drivers of income inequality
33
(so that tax rates are unlikely to change in the short run), this could have a negative impact
on income distribution. The poor may have an incentive to oppose greater redistribution as it
may lead to them lose out later on if they become rich. Piketty (1995) among others has
suggested that these kinds of differing beliefs about tax effects can be self-fulfilling and may
lead to multiple equilibria. This would mean a democratic society may result in high income
inequality and little redistribution if the right expectations hold.
A second factor that may prevent the poor from using any political influence to redistribute
income away from the rich would be the effect of social institutions (Acemoglu, Naidu,
Restrepo, & Robinson, 2013). A political party may not be in favour of policies to reduce
income inequality, but whether or not they receive votes depends on a variety of other policy
stances. It must also be considered that religious or ethnic heterogeneity may also reduce
demand for redistribution as members of given factions may prefer for wealth to remain
within their social group. In this way a larger franchise may not shift median voter opinion
towards favouring greater redistribution and so lower inequality.
A similar point is to do with middle class bias caused by a concept called Director’s Law
(Stigler, 1970). This states that in a situation of limited enfranchisement of a society, it is the
middle class that typically becomes the most politically significant – those with very high
incomes are small in number and the poor may yet to be given significant political power.
This means that they are able to fashion the bulk of government activity to benefit
themselves while the majority of the tax cost is borne by both the rich and poor (much like in
a captured democracy). An example commonly given of this is from 19th century Britain. As
the franchise was extended down the income distribution, there was a fall in public good
spending as the middle class were paying for most of it through property taxation, which was
duly cut.
Conclusion
Therefore, although integral to the talking of income inequality, the impact of democracy and
the political process on inequality is ambiguous. It would seem that an increase in
democracy should reduce the incidence of inequality by opening up redistribution policy to
the influence of the poorer sections of the population. However, its impact can be severely
limited by the fact the previously enfranchised elite can capture the system in a cyclical
process that gains them more influence and greater wealth. This higher wealth can then be
used to further buy political influence.
Trends and Key Drivers of income inequality
34
4.2.3. Labour Market
Unionisation
Unionisation has declined rapidly in both the UK and the US since the 1980s, and many
argue that this is a significant driver of inequality.
Acemoglu, Aghion and Violante (2001) argue that the increase in wage inequality in the last
quarter of the 20th century in the UK and US was partly due to rapid deunionisation. They
argue that unions act to maintain consistent wage differentials between skilled and unskilled
workers. A decline in the proportion of high-skilled workers joining unions increases the
wages of the high-skilled, leading to increased inequality.
Card, Lemieux and Riddell (2004) find that unions significantly reduce wage inequality
amongst men and that a significant part of the increase in male wage inequality in the US
and the UK since the 1980s can be explained by the fall in union density. However, they find
no similar effect for women. This is because male union members tend to be mostly those of
middling skill and unions compress the wages of these groups, reducing overall inequality. In
contrast, female unionisation is more common at the top of the skill distribution because
lower end of the women at the bottom of the pay distribution are often part-time workers,
who are less likely to be unionised. This means that unions do not reduce inequality for
women.
Acemoglu, Aghion and Violante (2001) also consider the causes of deunionisation. Tougher
regulation limited union power, reducing the incentive to join unions. The nature of sectoral
shifts (see also sub-section further below) in the UK and the US, from manufacturing to
services, reduced union density as these sectors typically employ more women and parttime workers, who are less likely to be unionised. The biggest reason given by Acemoglu,
Aghion and Violante (2001), however, is skill-biased technological change. As outlined in
section 4.2.1., this is technological change that affects the marginal product of high- and low
skilled workers to different extents. Skill-biased technological change that increases the
marginal product of high-skilled workers more than low skilled workers will increase the
potential wages of the highly skilled compared to the less skilled. As unions act to maintain
consistent wage differentials over time, this reduces the incentive for high-skilled workers to
join unions.
Lindbeck and Snower’s (1986) theory suggests that higher unionisation may lead to higher
inequality by creating unemployment. They argue that hiring and firing costs such as training
and severance pay allow ‘insiders’ (fully-trained workers) to push up wages above
Trends and Key Drivers of income inequality
35
equilibrium levels. Firms will respond by employing fewer workers. Even though ‘outsiders’
(the unemployed) would be prepared to work for less than ‘insiders’, costs associated with
hiring and firing means they will not be employed. Unions increase workers’ ability to push
up wages through the threat of strikes and work-to-rule for example. They also increase the
costs of firing by increasing severance packages, and of hiring by campaigning for higher
levels of training. Therefore, one would expect higher levels of unionisation to lead to higher
unemployment and higher wages through the creation of economic rents. Higher
unemployment would increase inequality although higher wages for lower-paid unionised
insiders may counterbalance this somewhat.
Therefore, whilst union decline is a factor in causing inequality and one that plays a
significant role in the UK and the US, it seems that the causes of unionisation, rather than
unionisation itself, are the real causes of inequality.
Taxation and wage bargaining
Alvaredo, Atkinson, Piketty and Saez (2013) argue that most of the increases in inequality
are due to increasing wages at the top of the income distribution, which cannot be explained
by productivity alone. They suggest that there is a negative causal relationship between top
marginal tax rates and top income shares. Three explanations are explored.
The first explanation is given by Slemrod (1996), who argues that lower tax rates reduce the
incentive for tax avoidance, increasing declared income at the top, but not actual income.
This hypothesis is severely weakened by Alvaredo, Atkinson, Piketty and Saez (2013) who
show that the pattern of the rise of the top income shares stays the same, even if we include
a broader measure of income (including capital gains), thereby including the most important
of the avoidance channels.
The second is that lower top marginal tax rates stimulate economic activity among top
income earners (Feldstein, 1995). However no correlation between top marginal tax rates
and economic growth was found to support this hypothesis.
The final, and favoured, explanation is the ‘compensation bargaining effect’ (Alvaredo,
Atkinson, Piketty, & Saez, 2013). Because measuring the productivity of workers is a difficult
task, workers might be able to partly set their own wages. If top marginal tax rates are lower,
the incentive for high earners to bargain harder or use their power to influence pay
committees is stronger, thereby increasing the income share of the top 1%. This case of
rent-seeking, the effort the higher paid workers make to further increase their own pay,
comes then at the expense of the other 99%.
Trends and Key Drivers of income inequality
36
Sectoral Shifts
One key factor that cannot be ignored is sectoral shifts. In 1955 Kuznets suggested that the
shift from agriculture to industry led to an increase in inequality. This was because the
agricultural sector is often more equal than the industrial sector, and as the size of the latter
sector increases inequality will rise. The shift from industry to services in the western world
may be a driver of inequality for the same reason. As mentioned earlier (sub-section
unionisation), the service sector generally has a lower union density than industry and
manufacturing, which may explain the higher inequality in the service sector.
Sectoral shifts are not necessarily a driver of inequality themselves, as they are driven by
other factors that are ultimately the drivers of inequality. Dunn (2012) identifies the level of
corporate taxation and competition from overseas due to globalisation as key causes of the
manufacturing decline in the US.
4.2.4. Financial Sector
Since the financial crisis of 2007-2008, the financial sector, especially in the US and the UK,
has come under increased scrutiny from the economics profession. In the context of income
inequality, the focus is on the relative rise of the compensation (including variable
compensation; bonuses) of financial sector employees, as documented by Bakija, Cole and
Heim (2012), Kaplan and Rauh (2010), and Philippon and Reshef (2012) for the US, Bell
and Van Reenen (2010; 2013; 2014) for the UK, and Godechot (2012) for France.
The first and traditional explanation is found in the phenomenon of “superstars”, formulated
by Rosen (1981), “wherein relatively small numbers of people earn enormous amounts of
money and dominate the activities in which they engage” (Rosen, 1981, p. 1). Rosen
identified two reasons for this phenomenon and the resulting skewed income distribution.
First, lesser-talented are imperfect substitutes for the greater-talented. Rosen illustrates this
with a surgeon who is 10% better at saving lives than his colleagues, but whom most people
would be willing to pay more than a 10% premium for his work. The second reason is a
technological one, so called ‘joint consumption technology’. This kind of technology has fixed
costs, but no or almost no variable costs. In this sense it is similar to a public good in being
non-rival, i.e. the consumption of person X does not impact person Y’s consumption. The
difference is that the property rights are defined and so the seller can exclude people
unwilling to pay a certain price.
Trends and Key Drivers of income inequality
37
Recent developments in communication and transport technologies have enabled the
greater-talented to reach an even larger market, without incurring much extra cost, thereby
increasing their income share even further. The joint consumption technology effect has thus
become larger and should lead to more income inequality.
The second part of Rosen’s theory, the joint consumption technology argument, can be
applied to investment banking. As it does not matter much in terms of the amount of effort to
put out whether one has to invest £10 million or £100 million, the “superstars” in the financial
sector can increase the scale of their services without increasing high extra costs. Bell and
Van Reenen (2014) further illustrate this application; as markets have become more global
and liquid, the greater-talented financial employees can reach an even larger market for their
services.
The second explanation, favoured by Bivens and Mishel (2013), focuses on rents – the
income received in excess of the competitive compensation – generated in the financial
sector. These rents may be the result of a lack of product market competition or implicit and
explicit government subsidies (as a result of banks being too-big-to-fail, for example) to the
financial sector (Bell & Van Reenen, 2014; Bivens & Mishel, 2013). This argument is
supported by Philippon and Reshef (2012), who find that there is a 30-50% educationadjusted wage premium for financial sector employees and an even higher premium for
financial sector executives (200-250%). Philippon and Reshef see financial deregulation as
one of the main drivers of this wage premium.
Linked closely to this is the process of wage setting, as described by Bebchuk, Fried and
Walker (2002). In optimal conditions, the wages of executives and managers at the top of
their firms should be controlled by a board of directors. The wages are expected to be set
with the aim of maximum shareholder value in mind. However, this often fails to happen, as
the top managers can naturally use their power to influence their own pay, giving them the
opportunity to extract further rents. This process of executive compensation is clearly
relevant to firms in sectors throughout the economy, but the financial sector is probably the
strongest example of the mechanism in action, as is clearly visible in the analysis of the
compensation practices at the failed banks Lehman Brothers and Bear Stearns (Bebchuk,
Cohen and Spamann 2010). Bebchuk, Fried and Walker further note that the more relative
power a manager has within a firm, the greater the wage premium he can extract (Bebchuk,
Fried, & Walker, 2002). This can help explain differences in pay for CEOs at different firms,
but can also perhaps go some way into accounting for differences between countries. For
example, in US firms’ CEOs tend to have greater influence compared to their non-US
equivalents due to more dispersed ownership and so fewer large stockholders being able to
Trends and Key Drivers of income inequality
38
constrain rent seeking. This leads to US CEOs typically seeing much higher wage premia
than their counterparts elsewhere.
The final explanation for surging compensation of financial sector employees is given by
Piketty and Saez (2003) and Alvaredo, Atkinson, Piketty and Saez (2013). Although these
papers do not explicitly focus on financial sector employees, the theory can be applied to the
financial sector. This line of reasoning stresses the importance of social norms on pay
inequality. When high-income earners bargain over their pay, the result of the bargain is
influenced by social norms, notions of fairness and the fear for public outrage over what are
perceived to be excessive incomes. In addition, Levy and Temin (2011) argue that
redistributive measures and policies constraining high pay are endogenous and at least
partly caused by social norms.
4.2.5. Trade and Globalisation
Trade and the increasing integration between nations have been seen by some as a driver
of inequality. The basic line of argument concerning trade has revolved around
the Heckscher–Ohlin model; this builds on Ricardo’s work of comparative advantage and
places emphasis on a country’s factor endowments being crucial to trade. The Stolper and
Samuelson effect is a subsequent extension of this, hypothesising that inequality should
increase in developed and fall in the developing countries.
The mechanism is as follows: a country has two factors of production, skilled and unskilled
labour, with the former being relatively abundant in developed countries and the latter in
developing. Focusing first just on the developed country: their comparative advantage lies in
skill-intensive goods which should in theory then be relatively cheap. However, as a country
exports what it has a comparative advantage in, the relative price of skill intensive goods is
raised in the domestic market. This in turn raises the return to the factor used more
intensively in production and lowers it for the other factor of production. The opposite is
applicable for developing countries that see a rise in the price of less-skill intensive goods.
Moreover, for the developed country this precipitates a fall in the ratio of skilled to unskilled
workers in both industries due to the higher wages of skilled workers; hence there is a fall in
the marginal productivity of unskilled workers in terms of both goods. In short, there is a
lowering of wages for less skilled workers in developed countries, while wages for skilled
workers rise. The opposite argument applies for developing countries where inequality
should in theory decrease.
Trends and Key Drivers of income inequality
39
Figure 18 below illustrates this relationship between factor price and good price (left hand
panel) as well the similar relationship between factor price and labour ratio in both skilled (X)
and unskilled intensive goods (Y).
Figure 18: The relationship between real wages and relative prices and skilled-unskilled labour ratio
respectively (Krugman, 1995)
This theory in the literature has usually been dominant in describing the rising inequality in
America. Interestingly, Krugman and Lawrence (1993) at first argue that in the case of
America, domestic as opposed to international reasons are to blame for the rising inequality.
Deindustrialisation, rather than being caused by imports, was attributed to a shift away from
domestic spending on manufactured goods, with the economy spending 59.3 per cent of its
income on services and construction in 1991. This shift in spending is ascribed to the
lowering of prices of manufactured goods due to technological and hence productivity
improvements which have been passed down to consumers. However, recently Krugman
reconsiders this conclusion due to developments after the former paper was written, claiming
that “consequences can closely resemble the textbook effect.” (Krugman, 2008, p. 103).
Some of these developments include large rises in exports from developing countries to the
US in recent years and the substantially lower average wages of the newly emerging
developing countries in contrast to the ones studied previously. He highlights that China’s
average hourly compensation in manufacturing is only 3 per cent of the US level while in
1990, average hourly compensation in manufacturing in the four tigers was 25 per cent of
the US level, and 39 per cent by 1995. Hence, the distributional effects of trade in the US are
predicted to be greater due to the greater disparity in wages.
Trends and Key Drivers of income inequality
40
Considering now not just the classic case of the US, but world inequality, inter-nation as
opposed to between nation disparity and expanding from the narrow focus of trade to
globalisation we turn to Lindert and Williamson’s theory (2001). From a historical point of
view, they reach the conclusion that globalisation moderated rising inequality between
nations and that the net impact of globalisation is too small to explain observed rise in world
inequality between 1820 to 2000, where world inequality is measured in terms of income
gaps between nations. Indeed they assert that rising inequality due to globalisation is a
result of incomplete openness as opposed to increasing integration. They do subscribe to
the Stolper and Samuelson effect as a determinant of within nation inequality, but stress
between nation inequality as being based on determinants of per capita income.
In examining between nation inequality a useful theory may be the Prebisch–Singer thesis.
This hypothesises deteriorating terms of trade between primary and manufacturing goods,
thus leading to growing inequality between developing and developed economies as the
former is known to export primary products, the latter manufactured goods. In the long run
the prices of primary products have been observed to decline; hence those with a high
export dependency on primary products will have to export larger quantities to afford the
same quantity of manufactured goods as before.
Moreover, Lindert and Williamson flag up migration as an important factor in income
convergence; migration affects output and wages by influencing the aggregate supply of
labour. Generally a rise in emigration should increase wages by shifting the upwards sloping
supply curve of labour up (indicating a fall in labour supply) with labour and wages on the x
and y axis respectively, while the opposite is true for the destination country. Hence the
supply curve crosses the downward sloping demand curve at a higher wage level and lower
employment level. This in turn is offset by capital flows globally, which contrary to economic
theory, are believed to flow into rich countries hence increasing inequality. This is known as
the Lucas paradox.
Additionally, globalisation involves the rise of multinationals which can be seen to widen
inequality thus: through the use of sweatshops and child labour in developing countries and
through the negative policy effects they impose. Government policy may be adversely
selected to include lower tax rates and benefits to attract multinationals which subsequently
could increase between nation inequality (see section 4.2.2.). Hence, contrary to the StolperSamuelson effect which hypothesises a fall in inequality in developing countries, this would
suggest a rise.
Trends and Key Drivers of income inequality
41
The global nature of the production process is another important aspect of globalisation,
which can be analysed using a model by Kremer and Maskin (2003) examining workers of
different skill levels. Cross matching of different skill levels between countries in a production
process can lead to a rise in inequality in a developing country. For example, assume 4
different skill levels in decreasing order: A,B,C,D, with A and B being skill levels in a
developed country, C and D in a developing country. Then assuming that only workers B
and C can cross match together to produce a good, wages of workers C rise while those of
D fall as D workers are “marginalized by globalisation” (p.16) due to being forced to selfmatch in the production process which yields a worse outcome.
Hence, we see rising
inequality in the developing country as opposed to falling as stipulated by the StolperSamuelson effect.
In conclusion, there are opposing theories to whether or not trade and globalisation would
affect inequality and in what direction: the Stolper-Samuelson effect predicts a fall in within
country income-inequality in developing countries while the Kremer and Maskin model
hypothesises the opposite. Similarly, the Prebisch-Singer thesis predicts rise in betweencountry income inequality while Lindert and Williamson point to globalisation as helping to
moderate between-country inequality due to immigration causing wage convergence (though
the effect being partially offset by capital flows). This lack of clear conclusion is illustrated in
Krugman’s change in opinion itself which is based on the increasing wage differential
between trading countries. However, it is important to note that the actual effect of rising
trade on wages cannot be quantified easily, as stressed by Krugman, due to the global
nature of the current production process and its intricacy.
Trends and Key Drivers of income inequality
42
5.
5.1.
Inequality in Cities compared to the National Level
General Trends of Inequality in Cities
Understanding inequality in cities is important. As measured by the within-city Gini index, it
has been found to be strongly correlated with crime levels (positively) and reported
happiness (negatively) (Glaeser, Resseger, & Tobio, 2009). The destabilising effect of
inequality is also likely to be stronger in dense populations. And while urbanisation has
plateaued (or increases very slowly) in most developed countries, much of the developing
world will still see considerable urbanisation: according to a UN report, “Harmonious Cities”
(2008), “the population in urban areas in less developed countries will grow from 1.9 billion in
2000 to 3.9 billion in 2030”, a rate of 1.8% p.a. compared to ca. 1.0% growth in the overall
population of these countries. Thus understanding urban inequality will become increasingly
important. Despite this, it has perhaps been somewhat ignored as a research area,
especially in theory: while data, at least in the developed countries, is relatively abundant,
and the empirical correlations between urban inequality and a number of variables have
been estimated, only a limited amount of work has been done on the theoretic side, and
what has been done tends to be US-centric. On the other hand, other countries sharing
many characteristics with the US (Western Europe) and countries at other stages of
development (East, Asia, Latin and South America, sub-Saharan Africa) may offer
opportunities to test these theories more generally.
Urban and national or international inequality: fundamental differences
Glaeser, Resseger & Tobio (2009) have pointed out that “paradoxically, local inequality is
actually the inverse of area-level income segregation. Holding national inequality constant,
local inequality falls as people are stratified across space so that rich live with rich and poor
live with poor. A perfectly integrated society, where rich and poor were evenly distributed
across space, would have highly unequal metropolitan areas”. To illustrate, imagine a
country divided in a number of local areas. Initially, each worker within an area earns the
same wage – but this wage differs greatly between each area. In this case, local inequality is
zero, income within each area being evenly distributed. Next, the government, in an attempt
to reduce stratification, decides to rearrange every worker in the country at random. There is
now zero income stratification – but income inequality within each area is no longer zero.
Thus whereas national level inequality is often considered undesirable, with much modern
literature linking inequality with poor growth, local level inequality may not be as undesirable
(although the destabilising effect of inequality may be even greater in densely populated
areas). Local policymakers must also consider the exit opportunities of wealthy, high-skill
individuals; compared to national governments, local governments “face a far more mobile
Trends and Key Drivers of income inequality
43
tax base” (Glaeser, Resseger, & Tobio, 2009, p. 643), the barriers to exit being much smaller
within countries than across them. Therefore, local attempts at redistribution may actually be
counterproductive, by attracting low-skilled and repelling high-skilled workers. However,
national level policy will likely have a similar effect on urban as on national inequality.
One would expect many of the factors driving national level inequality, discussed in section
4.2., to have similar effects on the city level as well. For instance, it seems natural to assume
that a higher financial sector employment rate will be associated with higher inequality on a
city, not just national, level. However, in some ways, local level inequality differs
fundamentally from national level inequality, and in the following section, some of the factors
of special importance in an urban context will be examined. But as most of the literature
takes the peculiarities of urban inequality as their starting points, and to some extent base
their theories around this, a short overview of the differences between urban and overall
inequality in the world today will be provided.
Urban inequality in the regions of the world
In the US, the Gini coefficient has increased in urban areas since at least 1950, both
absolutely and compared to rural inequality (Glaeser, Resseger, & Tobio, 2009). Given a
relatively high level of overall inequality, US cities are among the most unequal in the
developed world. The 50 largest US metropolitan areas have an average Gini of 0.46,
compared to a country average of 0.45, while New York is highest with 0.51 (retrieved from
Oxford Economics database, 13.02.2014). In comparison, while Western Europe is similar to
the US in level of development, its cities are among the least unequal in the world (UNHABITAT, 2008).
In Latin and South America, urban inequality tends to be high and roughly equal to, or
slightly higher than, overall inequality. The same is true for most African countries for which
data are available. In China, urban inequality is in fact lower than rural, 0.34 and 0.37
respectively, while urban incomes are over three times rural incomes (UN-HABITAT, 2008).
Development over time
In the traditional inequality models developed by Kuznets (1955, see section 4.1.1.), Lewis
(1954) and others, the strong growth witnessed by many developing countries should have
led to increased inequality. However, this has not, generally been the case on the urban
level. According to a UN-HABITAT (2008) analysis, positive economic growth was
accompanied by reduced urban inequality as often as increased in a sample of 28
developing countries. Some cities have gone through increased inequality, such as Beijing,
Trends and Key Drivers of income inequality
44
dar es Salaam and Accra, in others it has remained constant, Phnom Penh and Kigali
among them. Additionally, we would expect to find the world’s most unequal cities within
developing countries, yet as it stands, urban inequality is also very high in the US. As the
starting point of much of the literature, US trends deserve some further discussion. In
America, in 1950, Gini coefficients were lower in urban areas than rural ones. However, by
1970, the gap was eliminated and today, the urban Gini coefficient is now significantly higher
in urban areas (retrieved from Oxford Economics database, 13.02.2014). Glaeser et al.
(2009) found a 44% correlation between inequality and city size, suggesting that large cities
tend to be more unequal. Wheeler (2005) and Baum-Snow and Pavan (2013) have found
that the urban-rural wage premium has increased, and that the increase has been greatest
for high-skilled workers.
China has seen increasing urban inequality and a growing rural-urban income divide (see
also section 4.1.2.) accompany its great economic growth, whereas nearby Malaysia has
seen falling urban inequality despite high levels of both growth and overall inequality (UNHABITAT, 2008). India has seen increasing inequality (see also section 4.1.2.), and the 2008
UN report “Harmonious Cities” claims that “strong evidence also exists that increasing wage
and income inequality in India is attributed to skill-biased technological change and greater
wage differentials within key urban economic sectors” (p. 60).
Evidently, urban inequality is diverse and does not seem to follow any single path. In the
following section, some theories which attempt to make sense of these trends will be
discussed: skill-biased technological change, workplace segregation and the effect of city
size.
5.2.
Drivers of Urban Inequality
Skill-biased technological change on the local level
Individuals’ incomes reflect their human capital, as well as returns to that capital. Skill-biased
technological change (SBTC) (see section 4.2.1) predicts that income inequality will, in the
long term, increase as the supply of skilled workers increases (as search costs fall and the
expected payoff to skill-complementing technology increases), and if educational (and thus
skill) attainment is concentrated in cities, this implies that income inequality will keep
increasing in urban areas, both in terms of absolute Gini coefficient and relative to non-urban
and national inequality. According to Wheeler (2005), educational attainment has indeed
been concentrated in urban areas: overall, the fraction with college education among white
Trends and Key Drivers of income inequality
45
males rose from 15.4% to 54.2% from 1950 to 1990, from 17% (urban) and 12% (rural) in
1950 to 57% (urban) and 42% (rural) in 1990. Based on UNESCO data (FAO and UNESCO,
2003), educational attainment is higher in urban areas in most, but not all, developing
countries as well. Thus, SBTC predicts that income inequality should be higher in the
countries with large urban-rural differences in educational attainment.
Segregation by skill
Kremer and Maskin’s (1996) theory on segregation by skill and the effects of this on (wage)
inequality (see section 4.2.5.) can also be applied on the local level. Wheeler (2005) has
suggested that segregation may be higher in cities: search costs in the urban labour market
will tend to be lower (shorter distances, better flow of information, “thicker” market allowing
faster replacements), and housing prices high in areas close to high-skilled labour
employers. In turn, this increases the returns to human capital in these areas further,
attracting new high-skilled workers and possibly increasing the urban-rural wage differential
for skilled workers. With higher returns to human capital, SBTC predicts increased inequality
compared to the national level. Internationally, barriers in the global labour market have
waned, possibly magnifying this effect. Thus, this theory predicts that urban inequality will
have increased most where skill or human capital dispersion has increased the most, and
where within-firm wages are highly correlated.
Rational location choice: comparative advantage, amenities and density
Glaeser et al. (2009), looking at a sample of over 220 US urban areas, find that returns to
education are higher in urban areas. They also find that this urban education premium has
increased. However, returns to education also vary considerably between cities; from a 22%
return on college education in Wyoming to 52% in Texas (Wheeler, 2005). In standard
theory, these differences present an “arbitrage” opportunity, and in a mobile labour market,
as exists in the US, we would expect the returns to education to even out. Dahl (2002) and
Glaeser et al. (2009) consider “rational location choice” as a possible explanation for why
this has not happened. Firstly, areas with industry offering comparatively good returns to
low-skilled or high-skilled workers will tend to attract these. Secondly, the nature of amenities
in cities may attract workers of different skill. Some cities have amenities relatively more
attractive to high-skilled (generally, wealthy) workers (Glaeser et al. (2009) suggest the
French Riviera and Paris). Other amenities, such as public transport, tend to attract lowskilled (generally, less wealthy) workers. These two effects may explain the persistent
difference Brueckner, Thisse and Zenou (1999) use this model to explain why “central Paris
[is] rich and downtown Detroit poor”. If the centre of a city has an amenity advantage, that is
where the rich will concentrate; and vice versa if the suburbs have the advantage. This could
Trends and Key Drivers of income inequality
46
help explain the observed, persistent differences in inequality patterns between different
cities worldwide; some (most US metropolises and some European) displaying the Detroit
pattern of a poor centre and wealthy suburbs, while the Parisian pattern is found in some
European and most Latin American cities (Brueckner, Thisse, & Zenou, 1999, p. 92).
Cities rich in “patrician” amenities will tend to attract high earners, pushing up inequality.
More “plebeian” amenities will mainly attract lower-income earners, not contributing to
inequality in the same way. Glaeser et al. (2001) divide cities into three categories. First,
dense, high amenity cities, including New York, Boston, San Francisco in the US, and
historically important cities in Europe such as London, Paris or Barcelona, will benefit from
their amenity endowments as incomes rise, attracting increasing shares of high-skill workers,
which might contribute to increased inequality in these cities. The second category consists
of dense cities with traditionally high industrial employment, such as Detroit and Philadelphia
in the US, or Northern English towns such as Manchester and Liverpool. Lacking amenities,
many will struggle to attract the high-skilled. Both growth and inequality may remain
relatively low. The third category is referred to as the “edge city”: sprawling and low-density,
inhabitants relying on cars for transport. Examples are Los Angeles and several Australian
and New Zealand cities. In Europe, taxes on cars and fuel combined with public transport
subsidies mean that these are mostly non-existent. Being so reliant on transport,
technological innovation and government policy on this front may determine how these cities
develop. Thus the density-inequality relationship may be complicated, as both very high and
low-skill cities may have a high population density.
City size and inequality
Historically, the relationship between city size and inequality has been disputed. Looking at
US metropolitan areas, Long, Rasmussen and Haworth (1977) found a significant, positive
relationship after controlling for manufacturing employment, ethnic mix and income, whereas
Danziger (1976) found no such relationship. In the Kuznets framework, orthodoxy in
development economics at this time, inequality would be expected to be greatest in medium
income cities. Conversely, Nord (1980) argued that the inequality as a function of city size
relationship would display a U-shape: compared to small cities, medium size ones allow
more specialisation, giving more employment opportunities for low-income workers and
allowing them a larger share of income. The explanation for the later increase in inequality
as population rises further is a bit shaky (large cities are alleged to attract poor workers
under “money illusions”, living costs may increase, large companies tend to place their
headquarters in large cities) but could be consistent with the SBTC argument laid out above.
Trends and Key Drivers of income inequality
47
There seems to be wide empirical agreement on inequality increasing with city size today.
This may be due to large cities displaying the same traits as urban areas in general, but
more strongly. For instance, large cities tend to have a higher ratio of skilled to unskilled
workers than small cities, which would increase the potency of skill-biased technological
change (Baum-Snow & Pavan, 2013, p. 1536). Skill segregation would similarly be stronger
in a larger labour market. Behrens and Robert-Nicoud (2013) use three keywords: i) natural
advantage, ii) agglomeration and iii) selection leading to higher inequality in large cities.
Normally, some sort of natural advantage attracts people to a city, especially in its early
phase. This, through SBTC, worker segregation and so on, yields agglomeration economies.
As a large city will tend to have a greater number of firms, there will be greater competition.
The most productive firms will be able to generate substantial profit (analogously to Rosen’s
“superstar” theory, as explained in 4.2.1. and 4.2.4.), capture market share and pay high
wages, unlike the remaining, less profitable firms, increasing inequality. This might be tested
by examining the earnings variance of urban firms; according to this theory, it should be
higher in large cities.
Conclusion
The increasing urban returns to skills/education and rising urban-rural wage differential
(stronger for higher-skilled workers) are both consistent with SBTC (Wheeler, 2005). In this
case, we would expect high levels of education to increase inequality in a city. While
consistent with US data, the development is not seen in Europe (Rodríguez-Pose & Tselios,
2011, p. 367), despite the existence of a significant urban-rural educational divide in Europe
as a whole. The Kremer-Maskin segregation thesis should also tend to increase urban
(compared to rural and thus national) inequality in Europe. However, it must be taken into
account that the higher European tax level will tend to dampen any inequality effects.
The very different levels of inequality in developing country cities at similar stages of
development may reflect the factors which determine national level inequality having a
similar effect on the city level. In the US, city size seems to be a good predictor of inequality,
but examining whether this holds in developing countries could prove interesting.
Trends and Key Drivers of income inequality
48
6.
Empirical Evidence on Drivers of Income Inequality
Having outlined empirical data on past trends of income inequality and theories of drivers of
income inequality, this section will analyse empirical data of drivers of income inequality at a
global or national and at a city level.
6.1.
At a Global or National Level
6.1.1. Skill-Biased Technological Change
Although we have discussed skill-biased technological change, it is important to note that
technological change is not necessarily skill-biased. In the industrial revolution, skilled
artisans were replaced by physical capital, raw materials, and unskilled labour through the
emergence of the factory system (Goldin & Katz, 1998). Since the transition to assembly
lines and the adoption of electronic motors, however, technological change has been
predominantly been skill-biased (Goldin & Katz, 1998).
An often used measure indicating the bias in technological change is the return to a year of
education. The assumption made is that education is a good proxy of skills. This neglects
skills learned on-the-job, but is the best measure available. Another approach is to look at
occupations (blue-collar vs. white-white-collar, production vs. non-production (Goldin & Katz,
1999)) or tasks (routine vs. non-routine (Autor, Levy, & Murnane, 2003)) and to measure
how earnings across these categories change.
Goldin and Katz (1999) argue that a simple supply and demand framework is enough to
understand US inequality in the twentieth century. The supply concerned is the relative
supply of skilled workers, as measured by their level of education. Increases in the demand
for these skilled workers are the result of skill-biased technological change. The interaction
between supply and demand leads to a premium on a high school or college degree, which
consequently results in inequality of wages. Goldin and Katz (1999; 2009) find that, first, this
wage premium for education decreases from the 1910s onwards and only starts increasing
again in the 1950s. This education premium has continuously risen from the 1950s onwards,
with a short fall-back in the 1970s, before rising to pre-1920s heights again.
The data on the relative supply of college and high school workers from Goldin and Katz
(2009) is presented in the second column of table 5.
Trends and Key Drivers of income inequality
49
Table 5: Changes in the College Wage Premium and the Supply and Demand for College
Educated Workers: 1915 to 2005 (100 x Annual Log Changes) (Goldin & Katz, 2009)
The decrease of the wage premium from the 1910s onwards is the result of the so-called
“High School Movement” in the US, the period 1910-1940. The share of the American youth
entering (and graduating from) secondary schools increased enormously during these years,
as can be seen in figure 19. Thus, the supply of skilled workers increased considerably.
Figure 19: High School Enrolment and Graduate Rates, United States and West North Central
(Goldin & Katz, 1999)
Trends and Key Drivers of income inequality
50
The 1970s drop in the wage premium can be explained by the entry of the baby boom cohort
to the labour market in the 1970s. The enormous increase in the wage premium from the
1980s onwards is the result of the slowdown in the growth of the relative supply of college
workers, starting in the 1980s and continuing since then (Autor, Katz, & Kearney, 2008;
Goldin & Katz, 2009).
Using the data on college and high school wage premiums presented in figure 20, the
magnitude of the increase in relative demand for skilled workers, the result of skill-biased
technological change, is deducted. Three different numbers for the elasticity of substitution,
, between the skilled and unskilled workers result in three estimates for the relative
demand for college workers. Using their preferred estimates (
presented in the
fourth column of table 5, Goldin and Katz
(2009)
Figure
1 find that relative demand for college
College Graduate
and High
Graduate
Wage1.79%
Premiums:
1915 to3.73%
2005 in 1960workers increased
at an annual
rate School
of 2.27%
in 1915-40,
in 1940-60,
80, and 3.48% in 1980-2005. Their estimates for high school workers are presented in table
6.
College graduate wage premium
High school graduate wage premium
College graduate wage premium
0.6
0.4
0.5
0.35
0.3
0.4
0.25
High school graduate wage premium
0.45
0.3
0.2
1910
1920
1930
1940
1950
1960
1970
1980
1990
2000
2010
Figure 20: College Graduate and High School Graduate Wage Premiums: 1915 to 2005
Sources and Notes:
(Goldin & Katz, 2009)
College Graduate Wage Premium: The plotted series is based on the log college/high school
wage differential series in Appendix Table A8.1. We use the 1915 Iowa estimate and the 1940
to 1980 census estimates for the United States. We extend the series to 1990, 2000, and 2005 by
adding the changes in the log (college/high school) wage differentials for 1980 to 1990 for the
CPS, 1990 to 2000 from the census, and 2000 to 2005 from the CPS to maintain consistency in
the coding of education across pairs of samples used for changes in the college wage premium.
High School Graduate Wage Premium: The plotted series is based on the log (high school/eighth
grade) wage differential series in Appendix Table A8.1. We use the 1940 to 1980 Census
Trends andfor
Keythe
Drivers
of income
estimates
United
States.inequality
To maintain data consistency, we then extend this series
51
backwards to 1915 using the1915 to 1940 change for Iowa and forward to 2005 using the 1980
to 1990 change from the CPS, the 1990 to 2000 change from the February 1990 CPS to the 2000
Table 6: Changes in the High School Wage Premium and the Supply and Demand for High
School Educated Workers: 1915 to 2005 (100 x Annual Log Changes) (Goldin & Katz, 2009)
Overseeing these data, Goldin and Katz (2009) conclude that the changes in the wage
premium for skilled workers are explained mainly by supply changes. This conclusion is
supported by the fact that the wages of British white-collar (i.e. skilled) workers remained
high in the 1910s and 1920s, when their American counterparts lost ground to workers in
manual trades. Britain did not have something like the High School Movement until the
Butler’s Education Act in 1944 and the British white-collar workers did therefore not have to
face new competition of an increased number of educated workers like the Americans.
Finally, Bell (1996) notes the large increase in wage premium in the UK during the 1980s
and equates this to the increased use of computers. In fact, he finds that 41% of the rise in
return to a university degree in the UK over the decade can be attributed to the use of
computer technology. Once further measures of skill (mathematical skills, reading skills,
organisational skills) are introduced into the regression, Bell finds 57% of the rise in return to
a degree can be explained, with similarly large figures at other education levels. This implies
that skill-biased change had a significant impact on the wage premium of education, and so
through this is an important factor when it comes to income inequality.
Trends and Key Drivers of income inequality
52
Table 7: Predictions of Task Model for the Impact of Computerization on Four Categories of
Workplace Tasks (Autor, Levy, & Murnane, 2003)
It would seem that the introduction of computers into the workplace had its effect by
changing the structure of labour demand (Autor, Levy, & Murnane, 2003). Table 7 describes
some examples of what Autor et al define as routine and non-routine tasks, while figure 21
presents the trends of the amount of labour doing these various types of task.
Trends and Key Drivers of income inequality
53
Figure 21: Trends in Routine and Non-routine Task Input, 1960-1998
(Autor, Levy, & Murnane, 2003)
What they clearly show is that the changing composition of labour has been biased towards
the more complex non-routine tasks, especially since the 1980s, with labour demand for
more routine tasks falling away. This is consistent with the theory that computerisation
enabled more capital intensive production methods, and that this increased demand for
skilled labour while reducing that for non-skilled. A simple anecdotal example of this would
be the replacement of manual production lines with the use of robotic arms, and the
increased demand for skilled labour to design and maintain them consequently.
A more macro-based approach is pursued by the IMF (2007) and the OECD (2011b),
plotting inequality measures against the share of ICT capital in the total capital stock and the
share of ICT investment in total gross fixed capital formation respectively. Both reports find a
positive, but weak correlation, as shown for the OECD report (2011b) in figure 22.
Trends and Key Drivers of income inequality
54
Figure 22: Association Between Trends in Wage Dispersion and ICT Intensity, 1985-2007
(OECD, 2011b)
6.1.2. Taxation
Taxation naturally has an equalising effect on the post-tax income distribution. Progressive
taxes and redistributive transfers result in the distribution of disposable income to be more
equal than that of market income, as shown in figure 23 (OECD, 2011b).
Trends and Key Drivers of income inequality
55
Figure 23: Gini Coefficients of inequality of market and disposable incomes, persons of working age,
late 2000s (OECD, 2011b)
The theoretical section (4.2.3.) reported three narratives about how taxation, especially the
top tax rate, affects the income distribution. The empirical relationship that needs to be
explained is presented in figure 24.
Figure 24: Changes in Top Income Shares and Top Marginal Tax Rates
(Piketty, Saez, & Stantcheva, 2014)
Trends and Key Drivers of income inequality
56
The first explanation is that lower top marginal tax rates reduce the incentive of tax
avoidance and thus increase reported taxable income. This would mean that the rise in top
income shares is merely a statistical construct. We can refute this thesis because of the data
given by Alvaredo, Atkinson, Piketty and Saez (2011; 2013), as shown in figure 25. It is
shown that if we use a broader measure of income, including capital gains, the main
avoidance channel, the trend of an increasing top income share remains the same.
Figure 25: Top 1 Percent Income Share in the United States
(Alvaredo, Atkinson, Piketty, & Saez, 2013)
The second is the supply-side story (Feldstein, 1995; Lindsey, 1987), stressing that lower
top marginal tax rates would increase economic activity among the top earners, thereby
increasing their income. This relationship between top marginal tax rates and economic
activity, however, is not discernible from the data in Piketty, Saez and Stantcheva (2014),
presented in figure 26. We thus need another explanation.
Trends and Key Drivers of income inequality
57
Figure 26: Top Marginal Tax Rates and Growth from 1960–1964 to 2006–2010
(Piketty, Saez, & Stantcheva, 2014)
This required ‘other explanation’ is the third explanation, given by Alvaredo, Atkinson, Piketty
and Saez (2013) and Piketty, Saez and Stantcheva (2014). In this rent-seeking scenario, as
explained in the theoretical section (4.2.3.), the top 1% benefits at the expense of the bottom
99%. Piketty, Saez and Stantcheva (2014) support this thesis by using evidence on CEO
compensation, controlling for firm performance. In the supply-side story, top marginal tax
rates should not have an effect on CEO compensation when controlled for firm performance.
This is because the supposed increased productive effort on behalf of the CEOs following a
marginal tax rate cut would be fully captured by firm performance. The evidence is presented
in figure 27. In addition to firm performance, CEO compensation in figure 27 is also
controlled for CEO characteristics that would influence CEO compensation (age, tenure,
education). Contradicting the supply-side story, figure 27 shows a strong negative
relationship between top marginal tax rates and CEO compensation, controlled for firm
performance and CEO characteristics.
Trends and Key Drivers of income inequality
58
Figure 27: International CEO Pay and Top Tax Rates - Average CEO compensation with controls
(Piketty, Saez, & Stantcheva, 2014)
6.1.3. Sectoral Shifts
Silver and Bures (1997) provide the most comprehensive analysis of sectoral shifts, which is
discussed in section 6.2. There is further literature outlined below.
Dunn (2012) looks at the changes in manufacturing as a percentage of GDP and
employment and the Gini coefficient since World War Two in the US. Manufacturing output
was a quarter of GDP in 1950, compared to 12 per cent in 2010 and as a percentage of total
employment it has fallen from 25 per cent to just 8 per cent. In this time the Gini coefficient of
income inequality has risen from 0.379 to 0.440. This basic correlation is used to claim that
there is a causal relationship between manufacturing decline and inequality.
Kohn and Antonczyk (2012) that wage inequality was low in East Germany after
reunification, but it grew for both men and women in the 1990s to reach the level of West
Germany. They used regression analysis to find the extent to which this was due to changes
in industry (Char. I in table 8), as opposed to changes in people’s personal circumstances
(Char. P in table 8). They looked specifically at the difference in log wages between the top
80 and the top 20 per cent of the wage distribution, between the top 80 and the top 50 per
cent, as well as between the top 50 and the top 20 per cent.
Trends and Key Drivers of income inequality
59
Table 8: Decomposition of changes in wage differentials, female and male workers
(Kohn & Antonczyk, 2012)
For women, changes in industry increased wage differentials for all three measures. They
suggested that this is due to an increase in the size of the service sector and a decline in the
size of the manufacturing sector. The negative effect in relative wages was particularly
noticeable for workers at the bottom of the distribution, possibly reflecting the range in wages
of jobs in the service sector. In contrast, the increase in wage dispersion for males due to
industry changes is less pronounced, as wages for men at the bottom fell to a lesser extent.
Overall, there is limited literature on the empirical impact of sectoral shifts on inequality, but
what does exist seems to suggest that sectoral shifts, particularly the decline in
manufacturing, are correlated with an increase in inequality.
6.1.4. Financial Sector
In this section, some empirical evidence on trends on financial sector-driven inequality will
be presented, focusing on the US, UK and France. The possible mechanisms behind the
growth will also be discussed, especially skill-biased technological change (SBTC) and rentseeking.
Bakija, Cole and Heim (2012), using US tax return data, find that financial professionals
have accounted for 25% of the increased earnings of the top 0.1% income earners between
1979 and 2005, because the share of financial professionals among the top 1% and 0.1%
Trends and Key Drivers of income inequality
60
gradually increased until 2000 as can be seen in figure 28. They also state that the
headcount share of financial professionals tends to fluctuate strongly with the stock market
as can be seen in figure 28 after 2000 when the “dot-com bubble” happened. It is suggested
that globalisation and technological change may have enabled greater “superstar” effects.
Figure 28: Headcount share of financial professionals in the top 1% and 0.1% in the US (Own
analysis based on data from (Bakija, Cole, & Heim, 2012))
Using US Census and Current Population Survey (CPS) data, Philippon and Reshef (2012)
construct an index of education, wages and “task complexity”; The latter measures
complexity along five measures including analytical thinking, finger dexterity and eye-handfoot coordination.
By this measure, they find that the sector profile of finance in the US has changed over time:
The US finance sector developed from a high-skill, wage sector in 1909 to the 1930s, to an
average skills and wage sector from ca. 1950 to 1980, then again towards a high-skill, highwage sector from then on. From the 1980s onwards, financiers also started to earn more
than engineers of similar skill levels. Until 1990, wages were similar in finance and the rest of
the private sector; however, 16 years later, the premium was 70% (50% when educationadjusted). In the top income decile, the premium was 80%, and for executives, even greater
at 200% to 250%. Philippon and Reshef (2012) evaluate several possible finance-related
variables. They conclude that “from a statistical perspective, we believe that we have tried
the most plausible explanatory variables and that regulation, IPOs (“Initial Public Offerings”),
credit risks, and IT are the best predictors of skill demand in the financial sector” (p. 20). In
Trends and Key Drivers of income inequality
61
regressions, IT, evaluated on a measure of its use in finance relative to the remaining
economy, is found to be positively correlated with skill demand in finance, being a
complement to non-routine tasks and a substitute to routine tasks (a form of SBTC). IPOs
and credit risk (measured by the US corporate default rate), both requiring advanced
analysis, are also associated with demand for skill. On the other hand, the ratio of
international trade to GDP, finance patents and total stock market value to GDP are not
found to be significant predictors.
Lastly, the authors conclude that deregulation is a major determinant of skill and wages in
finance as can be seen in figure 29. In the regression analysis, regulation is split up into
restrictions on bank branching (the % of US population living in a state with deregulated
branching restrictions within-state), the Glass-Steagall Act of 1933 (legislated 1933,
weakened from 1987 until its 1999 repeal), interest rate ceilings and separation of banks and
insurance companies. They find a strong association between these measures (especially
the GS-Act) and skills and wages in finance.
Figure 29: Relative finance wages and financial deregulation in the US from the beginning of the 20th
st
to the beginning of the 21 century (Philippon & Reshef, 2012)
In total, regulation indicators are found to be “the most robust determinant of both relative
wages and education” (p. 17). However, this presents endogeneity problems, if regulations
are not exogenous, but respond to economic forces. They argue that this may be the case to
an extent, but that “regulations still matter” (p. 19). The persistent finance lobbying of
policymakers is not consistent with regulations responding solely to economic developments.
Regulation also interacts with other factors such as SBTC and “superstar” effects, which
may have larger effects in a deregulated environment.
Trends and Key Drivers of income inequality
62
“Superstar” effects (as per Rosen, 1981) are evaluated in Philippon and Reshef (2012) by
“excess compensation”. This is defined as the difference between actual finance executive
compensation and an “equilibrium benchmark” based on Gabaix and Landier (2008), where
an executive’s compensation is determined by the size of his/her own firm as well as median
firm size (as the best executives will have the largest impact in a large firm). “Superstar”
effects are found to account for some of the increases in finance executive compensation,
but leave “much of the excess wage unexplained” (Philippon & Reshef, 2012, p. 21).
Bakija, Cole and Heim (2012) also raise some caution against SBTC or “superstar” effects
as explanations for increasing wage inequality. The pattern of widening income inequality in
the US and UK is mostly absent in Continental Europe and Japan, which presumably have
access to the same technology, and should if anything see greater effects, as globalisation
has increased market sizes more there than in the US (a very large market by itself). On the
other hand, it may be that English-speakers have a significant advantage on the world
market, allowing English speaking “superstars” to reap the advantages of joint consumption
and market expansion on the expense of other language speakers. An uncontroversial
example would be from pop music, where Anglophone artists have a large worldwide market
share.
The United Kingdom has also gone through increasing inequality. About half of this has been
increased earnings to the top 0.1% earners (Bell & Van Reenen, 2014). The authors also
analyse how much of this rise in upper tail inequality can be attributed to the financial sector.
By analysing data from the Annual Survey of Hours and Earnings, they find that since 1999
“between two-thirds and three-quarters of the total income gain for those in the top 1% has
gone to bankers, even though they account for only one-third of the top percentile of
workers” (p. 1), and they account most of this gain to bonuses. They note the divergence of
weekly wages with annual wages increase due to the absence of bonuses in the former.
This can be seen in figure 30 where, although the share of weekly earnings increased by
0.39%-points for the top one per cent, it was a larger 1.8%-point increase in annual wages.
Trends and Key Drivers of income inequality
63
Figure 30: Percentage Point Change in Wage Share of Weekly and Annual Wages in the UK from
1999 to 2008 (Bell & Van Reenen, 2014)
The structure of payment in the financial sector makes it unique: in 2008 while overall 40 per
cent of workers received bonus, in the financial sector this figure jumps to 84 per cent, with
the top percentile workers in finance receiving 44% of their total pay in bonuses. They further
comment that “of the 7.4% of the wage bill accruing to the top percentile in 2002, 5.5% was
paid as salary and 1.9% was bonuses. By 2008, the figures were 4.9% and 4.0%
respectively” (p. 10).
Bell and Van Reenen (2014) also analyse the “superstar” effects and rent-seeking theories
as explanations for the finance-driven inequality described above. If the wages of top
earners are due to the first, there is no reason for intervention on efficiency grounds, as they
are simply rewarded for productivity. However, if there is rent-seeking involved, it may be
efficient to increase the number of competitors in the market or stricter regulation. While Bell
& Van Reenen do not analyse empirically the mentioned explanations, Philippon and Reshef
(2012) estimate rents to account for a 30% to 50% wage difference between finance and the
rest of the private sector, and for a 200% to 250% wage difference for executives in finance.
Godechot (2012), using data from the DADS, a French wage database covering the years
from 1976 to 2007, shows that “wage inequality started to increase in France in the mid1990s”, but that different inequality measures show widely differing trends. Using P90/P10
(the ratio of the 90th percentile to the 10th percentile incomes), inequality has been stable
since the 1970s. But at the top of the wage distribution, inequality has been increasing
Trends and Key Drivers of income inequality
64
sharply, with half the total income increase in the top 0.1% accruing to the top 0.01% (i.e. the
top 10% within this group).
Figure 31: Change in share of the top 0.1% of overall wages from 1994 to 2007, by industry
(Godechot, 2012)
Finance has been central to this development: Godechot (2012) finds that finance has
captured almost half (48%) of the rise of the wage share of the top 0.1%, their share
increasing by 0.85%-points (from 1.10% to 1.95%) from 1996 to 2007 as is shown in figure
31. In comparison, technical professionals’ (e.g. engineers’) share in the top 0.1% has
“stagnated” at around 10%, which seems inconsistent with SBTC explaining the increased
inequality. Over the same period, the manufacturing sector’s representation has sunk from
38% to 14%. Looking at (literal) “superstars” in entertainment (that is, artists and
sportspeople), Godechot finds that these have increased their share of the top 0.1% from
6% to 10-12% in total because of a rising share of the sportspeople (presumably football
players), but that the effect on inequality is nevertheless limited. Non-finance elites have
therefore not been responsible for much of the increase in inequality. However, mobility from
non-finance to finance has been low in France despite the increasing compensation level in
finance, suggesting that talent is sector-specific. This can be thought of as a form of rent, as
the financial employees’ compensation is higher in finance than it would be elsewhere,
causing “excess in earnings over the amount necessary to keep the factor in its current
occupation” (p. 18).
Trends and Key Drivers of income inequality
65
The contribution of finance to the increase of the wage share of the top factiles over the last
years in France, the UK and the US is summarized in figure 32.
Figure 32: Contribution of finance to the increase in the wage share of the top income fractiles in
France, the UK and the US (Godechot, 2012)
The effects of social norms relating to compensation practices have not seen the same
amount of empirical evaluation, probably due to the difficulty of quantifying these factors.
Conclusion
Deregulation emerges as one of the primary predictors of financial sector wages and
therefore inequality (Philippon & Reshef, 2012). The question of whether this is due to fewer
constraints on productivity, or enhanced rent-seeking ability is controversial. The rent
estimate of a 30% to 50% finance wage differential given in Philippon and Reshef (2012) is
calculated somewhat crudely, as it simply refers to the residual (i.e. adjusted for a number of
observed factors) wage of financial workers. They do not consider sorting effects or other
sources of unobserved skill. As shown in Bell and Van Reenen (2014) incentive pay has
come to make up an increasing share of total compensation in finance. This increases the
expected payoffs of the hardest-working individuals, so that these may be increasingly
attracted to finance.
Skill-biased technological change, through the “superstar” effects, is found to have a definite,
but limited effect on financial sector wages (Philippon & Reshef, 2012; Godechot, 2012). On
the other hand, Bakija, Cole and Heim (2012) show that apart from the US and UK, most
Trends and Key Drivers of income inequality
66
advanced economies have not gone through similar top income inequality developments,
despite similar access to technology and globalisation. However, seen in conjunction with
the importance of regulations (Philippon & Reshef, 2012), it may be that stricter regulations
elsewhere have limited the scope of SBTC and the “superstar” effects.
6.1.5. Trade and Globalisation
There is conflicting evidence and empirical work regarding the effects of globalisation and
trade on inequality: some have concluded that trade is a contributing source of the rising
inequality (Borjas & Ramey, 1994; Wood, 1995; Freeman, 1995; Richardson, 1995); others
have observed no significant relationship (Fieleke, 1994; Edwards, 1997), while still others
have evidence implying that greater participation in international trade reduces income
inequality significantly (Chakrabarti, 2000).
More recently, Zhou et al. (2011) found robust evidence that globalisation reduces inequality
when examining 60 developed, developing and transitional countries. They used the
following regression equation:
where Gini is the Gini coefficient of country i; Globalization index represents the globalization
index of country i (This database contains derivations on all four aspects of globalization:
economic integration, personal contact, technological connections, and political engagement
to form the Kearney index and PC index); Education is the education level of country I
(measured by adult literacy rate and combined primary, secondary, and tertiary gross
enrolment); Urbanization is the urbanization level of country i (measured by the ratio of
urban population to total population); the β’s are regression coefficients; and ε is the error
term, representing all unobserved variables affecting the Gini coefficient.
Gini coefficients were regressed on all determinant variables with and without the education
and urbanization variables, and in both cases they find the coefficient on the globalization
variable to be negative and highly significant. This result provides evidence that increasing
levels of globalization decrease income inequality in that country. (Note: a negative sign
means the Gini coefficient is decreasing hence, within-country income inequality is
decreasing).
Trends and Key Drivers of income inequality
67
The first thing to bear in mind is the difficulty in obtaining data for these empirical studies as
there is no definition of globalisation or how to measure it. This makes comparing different
studies increasingly difficult.
In relation to the trade aspect of globalisation, Calderon and Chong (2001), by using a panel
of countries for 1960–1995, show that the type of exports and the volume of trade appear to
affect the long run distribution of income. They find a 5% increase in the volume of trade
leads to a long-run decline of 1.26 points in income inequality, as reflected by a decrease in
the Gini index. Moreover, while the Prebisch-Singer hypothesis stipulates increasing
between income inequality due to primary exports, they find that export orientation towards
primary activities may be associated with higher within-country inequality as well.
Conversely, export orientation towards manufacturing goods may be linked with lower
inequality. They hypothesize this relationship to be consistent with the fact that primary
activities do not produce extensive linkages to the local economy so that inequality between
the elite and the rest of the society will persist and may increase. Nevertheless, an IMF
report (2007) finds evidence for the opposite case, namely that agriculture exports have a
positive effect on income inequality (see figure 33 below).
Interestingly, when examining industrial and developing countries separately, Calderon and
Chong (2001) find, while the impact of volume of trade (‘openness’) is positive and barely
statistically significant for industrial countries, it is negative and statistically significant for
developing countries. This is somewhat consistent with the Heckscher–Ohlin theorem. An
increase of 5% in the volume of trade is linked with a long-run decline of 3.5 points in income
inequality in developing countries.
Trends and Key Drivers of income inequality
68
Figure 33: Inequality versus exports in agriculture (change in log of indicators over last available 10
years) (International Monetary Fund (IMF), 2007)
Related to volume of trade and inequality, one can analyse the volume of trade and the
associated poverty levels in a country, as shown in figure 34 below.
Trends and Key Drivers of income inequality
69
Figure 34: Poverty rates and trade volumes in developing countries, 1980-2007
(Santos-Paulino, 2012)
We can see that rises in trade volume in Africa had little impact on poverty rates, which
returned to their 1980 rate in 2007. The same can be said about the least developed
countries, while other developing countries seem to show a large drop in poverty rates when
trade volume started increasing in 1990. This may seem to suggest that the level of income
of a country may influence how trade and globalisation affects within-country inequality. The
least developed countries in figure 34 have real GDP per capita of $315.7 and $450.5 in
Trends and Key Drivers of income inequality
70
1980 and 2007, compared to developing Africa with $869.4 and $930.3, and other
developing countries with $974.8 and $2,672.4 respectively.
This is exactly what Barro (2000) finds. He discovers a positive and long-term association
between the levels of openness and inequality, where the variable used to measure
openness was the ratio of exports plus imports to GDP, and inequality was the Gini
coefficient. He states that, contrary to what is depicted in the Heckscher–Ohlin theorem, it is
believed that an expansion of international openness will benefit most the domestic residents
who are already relatively well off; hence, rich groups will be most able to take advantage of
the opportunities offered by global commerce. Since this would be especially important when
the average level of income is low, the implication is that increased openness would be most
likely to raise inequality in poor countries.
Column five of table 9 shows a positive and significant effect of the openness ratio on
inequality. Column six adds an interaction term between the openness ratio and the log of
per capita GDP.
Table 9: Determinants of inequality (Barro, 2000), Note: The openness variable is the ratio of exports
plus imports to GDP, filtered for the estimated effects on this ratio from the logs of population and land
area
The result is that the openness ratio is again significantly positive, whereas the interaction
term is significantly negative. This implies the positive relation between openness and
Trends and Key Drivers of income inequality
71
inequality is most pronounced in poor countries. The estimated relation weakens as
countries get richer, and reaches zero at a level of per capita GDP of around $13,000 (1985
US dollars). Hence, for the United States and the other major OECD countries, the implied
relation between inequality and openness is negative.
However, once again, the IMF report (2007) has contradictory evidence. It, along with the
OECD (2011a), looks at both trade and FDI when examining globalisation. They find that the
former decreases within-country income inequality while the latter increases it as FDI
(foreign direct investment) flows increase demand for skilled labour in developing countries
and decrease demand for relatively unskilled labour in developed countries where FDI is
flowing out. Hence, the IMF report finds that globalisation (measured using trade plus FDI
flows) as more of a contributory factor than technology to rises in the Gini coefficient in
developed countries as financial globalisation has expanded much more rapidly here than in
developing countries. One must bear in mind, however, that the majority of FDI is between
industrial countries.
Alternatively, we can once again take a look at factor endowments. However, instead of
looking at skilled vs. unskilled labour, which determines wage inequality, we shall focus on
all the factors of production and hence income distribution, i.e. income that can be gained
from a number of sources.
This is indeed what Spilimbergo, Londoño and Székely (1999) investigate in their paper, with
findings that suggest the effect of openness and globalisation on within-country inequality is
determined by factor abundance relative to world levels. They differentiate between three
factors: land, skilled labour and capital. Their broad conclusions can be summarised thus:
that land and capital intensive countries have a less equal income distribution, while skilledlabour intensive countries have a more equal income distribution. Moreover, Calderon and
Chong (2001) obtain results inconsistent with the Heckscher-Ohlin model.
The former result is due to land and capital having the property of “no natural upward limit to
their accumulation” (Spilimbergo, Londoño, & Székely, 1999, p. 81), whereas labour skill
cannot be accumulated forever. Hence, for a country endowed mostly with land and capital,
wealth can be easily concentrated in the hands of few.
Without taking into account trade first, their first regression supports this claim, the results of
which are shown in table 10 below:
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Table 10: where l stands for arable land per capita, k for capital per worker, s for
percentage of population with higher education, and Gdppc is the PPP adjusted
GDP per capita taken from the World Penn Tables 1995 (Spilimbergo, Londoño, & Székely, 1999)
The sign for the coefficient related to the abundance of skilled labour (variable “Aist”) is
negative, meaning a fall in inequality (as measured by the Gini coefficient), and is statistically
significant at the 95 per cent level.
Next, adding in openness (which they test using seven different indices: including Trade
Flows, measured by (Exports+Imports)/GDP; more information in Appendix B of Spilimbergo
et al), they find their signs for the factor endowment coefficients are unchanged, while the
coefficient on openness is significant and positive, suggesting, keeping factor endowments
constant, openness is associated with higher inequality.
Finally, they find that openness seems to undo the effect of factor endowments on inequality
because the coefficients on the interaction between a specific endowment and openness
have opposite signs than the coefficient on the endowment itself. Hence, the second broad
conclusion is demonstrated, as this result is opposite to what the Heckscher–Ohlin
framework would predict. The latter would predict for example, a country well-endowed with
land would worsen its income distribution as it opens up to globalisation and trade as the
price of land increases (other wages remaining unchanged), however the above results find
a coefficient of negative 1.71 (variable “AiltXOpenit”), indicating a fall in inequality (see table
11 below).
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73
Table 11: Income distribution and trade openness (Spilimbergo, Londoño, & Székely, 1999)
Their results do confirm the Stolper-Samuelson theory as they find that inequality increases
in countries that are relatively well-endowed with skills when the economy opens; hence,
trade openness increases the premium for skilled workers as predicted.
Next, similar to Barro (2000), they conduct separate regressions for developed and
developing countries and find that trade has practically no impact on the personal income
distribution in industrial countries (see table 12). The reason is that although openness
worsens the distribution through its effect on skills, this is totally offset by the progressive
impact over capital. In the special case of Latin America they find trade openness has a
negligible effect over income distribution which is in line with the argument that when factor
endowments are very similar to the world average, only small changes in relative prices will
take place with openness, due to the absence of a comparative advantage.
Table 12: Trade and income distribution for different regions (Spilimbergo, Londoño, & Székely, 1999)
Trends and Key Drivers of income inequality
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The overall effect of trade and globalisation is still disputed with papers still being presented
and finding contradictory evidence. The IMF and OECD reports both conclude that
globalisation has a small effect on within-country income inequality and that “technological
progress had a greater impact than globalisation on inequality within countries.”
(International Monetary Fund (IMF), 2007, p. 31; OECD, 2011b). The Heckscher-Ohlin
model is both supported and disproved, with Calderon and Chong (2001) showing evidence
in favour of the model in developing countries while Barro (2000) and Spilimbergo, Londoño,
and Székely (1999) show evidence to the contrary when adding an interaction term of GDP
and factor endowments respectively.
6.2.
In Cities compared to the National Level
Skill-biased technological change
The explanatory power of the entire SBTC hypothesis has been called into question, for
instance by Card (2002). Some empirical evidence on city levels may be helpful in
evaluating any local level effects.
Wheeler (2005), using data from US census samples, found that in 1950, 17% of
metropolitan (meaning residential in one of ca. 220 US cities) and 12% of non-metropolitan
area workers had “some college” education or above. In 1990, the numbers were 57% and
42% respectively, a 15% gap compared to 5% in 1950. As mentioned in section 5.1., urban
inequality has overtaken rural over the same period. The urban-rural wage premium for all
workers has remained roughly constant at 12-14% (p.15) between 1950 and 1990. Returns
to observable skills, i.e. education, have increased overall; the difference in earnings
between high school dropouts and college graduates having widened considerably; for
otherwise identical workers3, having no high school (i.e. 0-8 years of schooling) had a -17%
coefficient with wages in 1950 and -30% in 1990; for college graduates the coefficient rose
from 25% to 42%. Results for experience were similar: someone with 26-30 years of work
experience earning 40% more than one with 0-5 years in 1950, compared to 61% in 1990.
Most interesting in this context is the development in urban-rural wage differences for
different skill levels, however. The returns to education in urban areas have grown much
faster than that in rural areas. This, combined with a roughly constant overall urban-rural
wage difference, means that the urban wage premium “has not been steady within education
3
This means that returns are adjusted for “marital status, weeks worked, and dummies for nine occupations, nine industries,
and three Census regions” to isolate the effect of the relevant variables (p. 30).
Trends and Key Drivers of income inequality
75
groups” (p. 17) which can be seen from table 13 below, presenting the urban wage premia
of different education levels in 1950 and 1990, again for otherwise “identical” workers:
Year
No high school i.e. 0- High school graduate
College
8 years of education
more
1950
6.5
5.5
9.5
1990
-4.6
3.4
16.4
grad.
or
Table 13: Urban-rural wage premium, by education level (Wheeler, 2005)
Furthermore, “residual” inequality, i.e. inequality due to variables not observed in the
regression model, have increased with time. Acemoglu (2002) has suggested that this is due
to higher returns to unobserved skill, that is, productivity differences not due to “observed”
measures such as education or experience, but individual ability. For any level of education
and experience, SBTC would tend to improve the productivity of those with high unobserved
skill more than those with low unobserved skill. Wheeler (2005, p. 19) indeed finds the
residuals to have been increasing in urban areas at a higher rate than in rural areas.
Glaeser, Resseger and Tobio (2009) note the large differences in returns to skill between
different urban areas, there being a 73% correlation between Gini and estimated returns to
college education. There is also a strong, positive relationship between returns to skill, and
education in an area – which is consistent with SBTC. Glaeser et al. conclude that why these
different returns persist despite the arbitrage opportunity is not well understood, but lists
some factors which can be thought of as exogenous in this context such as “historical
tendencies toward having more skilled people” (Glaeser, Resseger, & Tobio, 2009, p. 645)
(college enrolment in 1850 being a good predictor of modern-day inequality, which can
hardly be linked to SBTC) and immigration, especially from Latin America (which is
concentrated in states geographically close to Mexico, especially California, Texas and
Florida), which is associated with “a concentration of low skilled workers” (Glaeser,
Resseger, & Tobio, 2009, p. 633)
Workplace segregation
In the first paper proposing workplace segregation as a driver of inequality, the authors
provided evidence of this by using within-firm wage differences (Kremer & Maskin, 1996).
Other things equal, a firm having a diverse workforce in terms of skill will have a low
correlation of wages within the firm: the larger the skill variance within the firm, the larger the
wage difference will be. Therefore, McDonalds employees, a large number of low-skilled
workers, will have a high wage correlation at a low level of wages (despite a small fraction of
workers, in management and similar, being well-paid). Microsoft employees, being relatively
Trends and Key Drivers of income inequality
76
homogenous, but at a high skill-level, will have a high correlation as well, but at a high level
of wages. In contrast, the authors pointed to General Motors, which employed workers of all
skill levels. Statistically, the development has been significant, the within-firm logarithmic
wage correlation increasing from 0.36 to 0.44 from 1986-1992 in France, and 0.76 to 0.80
from 1975-1986 in the US. This seems to contradict SBTC, which, within each firm, would
tend to disperse earnings across the skill distribution as the effect of skill differences had an
increasing effect on earnings. Segregation by worker classification (blue-collar, foreman,
clerk, manager etc.) had also increased in France where the best data was available.
City Size and inequality
Most modern studies on developed world countries support a positive relationship between
city size and inequality. Baum-Snow and Pavan (2013) conclude that while there was no
such relationship as late as the 1970s, one exists today. Baum-Snow and Pavan found the
relationship to be explained by increased wage inequality in industries heavily located in
larger cities. These industries move to cities to exploit agglomeration economies (see
section 5.2.) from economies of scale and networking effects. The higher wage inequalities
were found to be primarily due to higher residual inequality. They argue that this represents
skill demand having increased more in large cities, and imply that SBTC has been stronger
in larger cities. Agglomeration, making workers more productive, may also explain why
urban wages are so much higher overall, when adjusting for education levels being higher in
cities.
Amenities and density
The empirical research on city amenities and inequality patterns is limited; this may be
related to the difficulty of measuring amenities in a meaningful way. Glaeser, Kolko and Saiz
(2001) constructed a basic amenity index, assuming that places with high housing prices
relative to income are explained by amenities. Using this ranking, a significant correlation
between this index and population growth was found between 1980 and 2000 (Glaeser,
Kolko, & Saiz, 2001, p. 29), as well as between the index and worker-skill levels. In other
words, amenity rich cities are growing, and attracting relatively more high-skilled workers.
Combined with the effect of SBTC, this suggests that amenity levels may be positively
correlated with inequality. However, it should be noted that the index is only a very
rudimentary measure and that eight out of ten top cities are located in California, meaning
that other factors affecting California could bias the estimation.
Some authors have attempted to estimate whether the net utility of amenities and other
urban externalities is positive, regressing wages on city size and density. Most studies (for
Trends and Key Drivers of income inequality
77
an overview, see Cropper (1981)) find that it tends to be negative; in other words people
generally need wage compensation to settle in cities. If the negative externalities (such as
pollution, congestion, crime) tend to increase more than the positive ones as cities grow in
size, this may explain part of the increasing urban-rural wage differences as urbanisation
has increased.
Sectoral shifts
Silver and Bures (1997) looked at the impact of sectoral shifts on inequality in the US in the
1980s. They looked at data for 79 MSAs (metropolitan statistical areas), all with populations
of over 500,000 people. They ran a regression using the Gini coefficient of family income
inequality as the dependent variable and the independent variables were the share of local
employment in the manufacturing, distributive services, producer services, social services
and personal services sectors. They looked both at the absolute values of Gini in 1980 and
1990 as well as the change between these ten years to find if there was a correlation
between the independent and dependent variables.
Table 14: Ordinary Least Squares Regression on Family Income Inequality, 1980 and 1990
(Silver & Bures, 1997)
Table 14 shows that the proportion of workers in the manufacturing (-0.07) and producer
service sectors (-0.04) were negatively associated with inequality in 1980, whilst the
proportion working in the distributive (+0.08), social (+0.06) and personal services (+0.11)
were positively associated with inequality. Values in brackets are the coefficient on the
Trends and Key Drivers of income inequality
78
independent variable, in this case the proportion of workers in a given sector, for each
regression and can be found in columns two (for the manufacturing sector) and four (for the
other sectors). In 1990, the proportion of workers in producer services (+0.079, see column
six) was positively associated with inequality and the proportion of workers in social services
(-0.058, column six) was negatively associated with inequality, with no change for the other
variables.
Table 15: OLS Regression on Rates of Change in Family Income Inequality, 1980-1990
(Silver & Bures, 1997)
They also looked at the changes in these proportions, or sectoral shifts. Table 15 shows that
the proportion working in the manufacturing and the producer services are the only sectors
that were negatively associated with changes in Gini from 1980 and 1990. The coefficient on
the change in the proportion working in manufacturing was -0.186 and producer services
+0.266 (see column two), suggesting that a shift from manufacturing to services, the so
called manufacturing decline, had a very significant effect in worsening inequality in US
urban areas. It is also notable that increases in the proportion of self-employed workers were
associated positively with increases in equality within this period.
Trends and Key Drivers of income inequality
79
There is some support to the idea that sectoral shifts have a significant impact on inequality,
but it is not conclusive, as there is no clear causal link.
7.
Future Trends of Income Inequality
Having identified key trends and drivers of income inequality in the past, this section will
outline how future trends of income inequality and its drivers may evolve.
7.1.
Future Trends of Income Inequality at a Global or National Level
7.1.1. General Future Trends of Income Inequality
As mentioned in section 4.1.4., Milanovic (2012) states that given the recent decline of
global income inequality, the world might have passed the upward-sloping part of the
“Kuznets curve”. If that is indeed the case, according to Milanovic, “in perhaps some fifty
years … we might be back to the state of affairs that existed around the time of the Industrial
Revolution” (p. 18). However, this cautious estimate is controversial. Sala-i-Martin (2006),
highlighting that Africa is the main concern for the future development of global income
inequality, which is supported by the World Bank (2007), predicts that, unless incomes of
African citizens are increasing fast, the world is going to experience a rise in world income
inequality in the near future.
Hillebrand (2008) projects the global income distribution for 2015 and 2050, using the simple
accounting procedure (SAP) based on two scenarios. Using the “Market-First” scenario,
implying the high-growth, high-globalization, world peace scenario, which is based on
assumptions such as world growth being higher than in the last 20 or 50 years, he comes to
the conclusion that the world Gini is going to fall from 0.634 in 2005 to 0.610 by 2050. In this
scenario, he assumes that within-country income distributions are going to remain constant.
Figure 35: Global Gini estimates based on two scenarios for 2015 and 2050 (Hillebrand, 2008)
Trends and Key Drivers of income inequality
80
Using the “Trend Growth” scenario, on the other hand, he assumes that most countries are
going to experience the same growth as in the last 25 years (i.e. from 1981 to 2005). In this
scenario, the world Gini, after a short marginal decline until 2015, is predicted to surge to
0.708 in 2050. The two scenarios are shown in figure 35. Absolute per capita income gaps
between OECD countries and the rest of the world are predicted to grow in both scenarios.
As far as within-country income inequality goes, projections are not one-sided either. Higgins
and Williamson (2002) predict large decreases in within-country inequality, measured by the
Gini coefficient, over the next 50 years in Africa, Latin America and the Pacific Rim region as
the higher-earning middle-age cohorts grow in proportion to the rest of the population. The
World Bank (2007), on the contrary, reaches a different conclusion, creating a hypothetical
income distribution for all countries for 2030 by applying three main exogenous changes
(demographic changes, shifts in the sectoral composition of employment and economic
growth and relative wages changes) to the initial (2000) global income distribution. Although
the World Bank estimates average per capita incomes to converge and a global middle class
to emerge, implying a trend towards decreasing across-country inequality, they estimate
within-country income inequality to rise. In fact, they suggest that within-country income
inequality may grow in two-thirds of all countries, the main driver being the widening
difference in earning potential between skilled and unskilled workers (see also section
7.1.2.1.) and demographic changes, i.e. aging societies becoming more unequal.
Piketty supports this future trend of rising within-country income inequalities. In his book
Capital in the Twenty-First Century (2014), he describes the importance of capital income on
increasing income inequality within countries (see also Piketty and Zucman (2014)). His
theory states that if the rate of return on capital (r) is greater than the rate of economic
growth (g), income from capital is growing at a higher rate than labour income. Given that
capital income is more concentrated among the upper part of the income distribution than
labour income, the capital owners, and so the upper part of the income distribution, are
becoming richer relative to workers, i.e. the lower part of the income distribution. Piketty
estimates the rate of return on capital to be greater than the growth rate of world output over
the remaining part of the 21st century as it was the case prior to the 20th century (see figure
36).
Trends and Key Drivers of income inequality
81
Figure 36: After-tax rate of return vs. growth rate at the world level, from antiquity until 2100
(Piketty, 2014)
From this estimate follows a prediction of increasing income inequality and a growing key
role of wealth accumulation through inheritance. Piketty (2011) estimates the annual flows of
inheritance to be about 20-25% of national income by 2050 in France, back to the 19th
century level from less than 5% in 1950.
7.1.2. Future Trends of Drivers of Income Inequality
7.1.2.1.
Skill-Biased Technological Change
Technology is the most important black box of economics. Pretending to be able to predict
its future trend and bias can easily be regarded as hubris. The other side determining the
wage premium of educated workers, the supply of educated workers, can be explored more
easily.
In the last few decades, from the 1970s onwards, we have seen a slowdown in the growth of
the relative supply of college workers. On the one hand, tuition fees are rising in the US and
in some countries in Europe (BBC News, 2011; Bloomberg, 2013). In addition to that, state
subsidies to students are cut in several European countries as a result of austerity measures
(New York Times, 2013; European Commission/EACEA/Eurydice, 2013). With the private
cost of education rising, we should expect a further slowdown in the growth of the relative
supply of college workers.
On the other hand, the enormous increase of the college wage premium in the last few
decades, as documented by Goldin and Katz (2009) should be an incentive for people to
invest in education. A World Bank Report (2007) estimates that the wage differential
Trends and Key Drivers of income inequality
82
between skilled and unskilled workers is going to rise in the global economy until 2030, as
shown in figure 37.
Figure 37: Ratio of skilled wages relative to unskilled wages in different regions of the world in 2001
and estimated for 2030 (World Bank, 2007)
Given that we believe people to respond to monetary incentives, this would lead us to
believe that the relative supply of college workers will increase again.
We now have identified two antagonistic forces. Which one will dominate is unclear and will
surely depend on education policies that are implemented in the coming years.
There is one final note we can make about technology. Acemoglu (1998; 2002) suggests a
model in which the bias of technological change is endogenous and depends on the market
size. The markets concerned are the markets for the application of innovations. According to
Acemoglu, the skill-bias of recent technologies is the result of the large increase in the
relative supply of college workers when the baby boom cohort entered the labour market.
The increase in skilled workers meant that the market for skill-complementary innovations
had increased as well and thus that skill-complementary innovations became more
profitable.
If we assume that these innovations occur with some lag, future innovations will respond to
the slowdown of growth of the relative supply of college workers from the 1980s onwards.
What will happen after that will depend on the outcome of the first discussion of this part;
what will happen to the supply of college workers?
Trends and Key Drivers of income inequality
83
7.1.2.2.
Sectoral Shifts
With a broad measure such as sectoral shifts, with many causal factors, it is difficult to
predict future trends. Nonetheless, “Manufacturing Europe’s Future” (Bruegel, 2013), a
paper published by the think tank Bruegel, looks at some of the potential challenges facing
manufacturers in Europe that may lead to a further manufacturing decline in Europe (p. 40).
These challenges include climate change and greater consumer expectations for
personalised and sustainable products. They also list potential opportunities, which may
reinvigorate manufacturing in Europe. The most significant ones are growing demand for
manufactured goods in emerging markets and new technology.
Overall, they predict that in Europe manufacturing as a share of GDP, employment and
value added will continue to decline. This will be because of faster growth in demand for
services than for manufactured goods. Fast productivity growth in manufacturing will lead to
a further decline in employment, as will the continued shift in manufacturing to economies
with lower unit labour costs. They also predict that manufacturing in Europe will be
dominated by high value added activities, such as research and development, design,
financial and aftersales services. The decline in manufacturing in Europe, as well as a shift
to the mentioned high wage, high value sectors may lead to a further increase in inequality.
7.1.2.3.
Financial Sector
Financial regulation has received considerable public interest over the last few years, and
there is strong evidence that regulation indeed affects wages, firm size and task complexity
in the financial sector (Philippon & Reshef, 2012). In response to new regulation including
Basel 3 (worldwide) and the Dodd-Frank Act (US), it is expected for “wages and skill
intensity to converge, and excess wages to disappear” (Philippon & Reshef, 2012, p. 1605).
It is also noted that whether the resulting outflow of skills and human capital is desirable is a
complex question.
The impact of “superstars”, mainly evaluated by firm size, appears to be significant, but
leaves a lot unexplained (Godechot, 2012; Philippon & Reshef, 2012). Interestingly, Bell and
Van Reenen (2014, p. 179) find that “the financial crisis seems to have been so far little
more than a blip for the pay of bankers”. Whether this will remain to be the case will depend
on the regulations mentioned above and policies like the implementation of higher top tax
rates or bonus caps.
Trends and Key Drivers of income inequality
84
7.1.2.4.
Trade and Globalisation
The effects of globalisation and trade on inequality are as of yet undecided. Whether we
accept a negative or positive view of its effect, the future trend will be decided by the
intensification or retreat of globalisation. Historically, there have indeed been waves of
globalisation, with the period after the Great Depression marking a retreat from trade. Also,
political crises, such as the recent Crimea crisis between Ukraine and Russia, may dampen
global economic integration. Therefore, it would be foolish to believe that globalisation will
progress unhindered.
Nevertheless, given the extent of technology, internet, social networks, and the extent of
interconnectedness present in the world today, it does seem unlikely that globalisation will
be regress. Moreover, given the negative effects on GDP per capita after barriers to trade
were erected post the Great Depression, most countries have learnt their lesson: hence why,
following the recent Great Recession, barriers to trade were relatively unaffected. The further
progress of trade relies on the success of the Doha round and whether differences between
developing and developed countries, especially concerning agricultural subsidies, can be
resolved.
While there are concerns about the effect of globalisation regarding the environment, jobs
and inequality, figure 38 below indicates that impact overall, as perceived by the global
population, will be positive of furthering globalisation. This may be an indicator that
globalisation will indeed progress.
Figure 38: Global Agenda Outlook 2013 (World Economic Forum, 2013)
Trends and Key Drivers of income inequality
85
7.2.
Future Trends of Income Inequality in Cities
The effects of trade, globalisation, skill-biased technological change and sectoral shifts will
presumably be similar on the urban level, to the national level, as discussed in section 7.1.2..
With regards to SBTC, cities have shown a tendency to become increasingly skilled over the
last decades (Glaeser, Resseger, & Tobio, 2009), and if this trend is not reversed or halted,
urban inequality may keep rising above national level inequality. Again, this might be
measured by the development of educational attainment in cities. As discussed in section
7.1.2.1., the long-run development will depend on whether supply or demand for high-skilled
workers dominates. As an indicator for workplace segregation, measuring within-firm wage
and education correlation over time can be used.
Most modern studies (e.g. (Baum-Snow & Pavan, 2013; Glaeser, Resseger, & Tobio, 2009))
show a positive relationship between income inequality and city size, although the
mechanism is not quite clear (see section 6.2.). This relationship suggests that many
developing country cities will go through increasing inequality levels as urbanisation and
population increase. A UN report (2008) states that by 2050 the developing world will have
an urban population of 5.3 billion, more than double the current population of 2.5 billion. Of
the world total, almost two thirds of urban dwellers will be living in Asia, and one quarter will
be African. Nevertheless, this still represents a far lower rate of urban population growth (ca.
2% p.a. on average) than the 4% p.a. developing country average seen in the 1950s, 1960s
and 1980s. Africa currently has the highest urbanisation rates in the world (3.3% p.a.
between 2000 and 2005). In addition, Asia still has high rates (2.5% p.a.) despite a slowing
total population growth; a number of Chinese cities have extremely high rates of population
growth, above 10% p.a., partly as a result of liberalisation of urban-rural migration.
In the developed world in contrast, urban population growth is limited and this should not be
a large source of widening inequality. UN-HABITAT (2008) estimates an increase from 900
million to 1,100 million by 2050. In sum, this means that 95% of the urbanisation growth until
2050 will be in developing countries.
Trends and Key Drivers of income inequality
86
8.
Discussion
When analysing global income inequality, it is common to differentiate between income
inequality between countries, reflected by GDP per capita, and income inequality within
countries, i.e. the income dispersion around the average income of a country. While there is
broad consensus that income inequality within countries has increased over the last three
decades, recent trends regarding income inequality between countries are more
controversial. When using the population-weighted Gini coefficient as estimated by Milanovic
(2012), it seems that income inequality has fallen. Global income inequality, as a result of
the two, appears to have at least decelerated when compared to the 19th century and over
the last two to three decades even declined.
Given the recent convergence of not only per capita income, but also within-country income
inequality across countries, if recent trends continue to hold on for the future, one may
deduce that income inequality between countries will decline, while income inequality within
countries will rise, especially if returns to capital remain higher than economic growth rates
as estimated by Piketty and Zucman (2014). As the World Bank (2007) predicts, due to the
economic growth in emerging economies such as Brazil, China or India, a global middle
class will be growing until 2030, which would imply decreasing global income inequality. At
the same time, available literature highlights the importance of Africa for future global income
inequality. If the continent does not pick up similar growth rates as China or India soon, the
result may be a further divergence between the poorest and the richest on a global scale.
Whether global income inequality is going to decline or rise in future, will depend on which of
the two counteracting factors, potentially decreasing inequality between countries and
increasing inequality within countries, is going to have the greater impact. Also, different
indicators of income inequality may provide different results. While the Gini coefficient is
more responsive to changes in the middle of the income distribution (Palma, 2011), other
indicators such as the top 1% of income earners look at the edge of the income distributions.
The effects of a growing global middle class and a further divergence between the richest
and the poorest of the world population may be captured distinctly by the two indicators.
Given that within-country income inequality appears to be the main concern for the future
development of global income inequality, having an understanding of the variables driving it
seems to be essential for policy-makers.
It cannot be disputed that skill-biased technological change is one of the major explanations
for rising inequality in much of the Western world, especially Anglo-Saxon countries. As
indicated in section 4.2.1., however, it suffers from a few shortcomings.
Trends and Key Drivers of income inequality
87
The biggest problem is the cross-country variation; why have some countries experienced a
large increase in inequality (UK, US, Canada, Australia, see figure 2) and others have not
(France, Germany, Japan, The Netherlands, see figure 3). This puzzle has led economists to
shift their focus to institutional factors and differences in those factors across countries, such
as tax policies (Piketty & Saez, 2003; Alvaredo, Atkinson, Piketty, & Saez, 2013; Piketty,
2014). Also, the departure of the top 1% from the other highly-educated income earners in
the US, as visible in figure 17 (section 4.2.1.), cannot be explained by returns to education
and skill-biased technological change, since the education of this group is similar (Atkinson,
Piketty, & Saez, 2011). Finally, trends in wage differentials – gender and racial wage gaps –
are left unexplained by skill-biased technological change. Card and DiNardo (2002) propose
a more institutional approach to explain these trends.
Despite these shortcomings, skill-biased technological change theory should not be
disregarded; it remains one of the main explanations of the rise in income inequality. Rather
than examining it individually, however, it should be complemented with the more recent
institutionalist approach outlined above and pursued by Alvaredo, Atkinson, Piketty and
Saez (Alvaredo, Atkinson, Piketty, & Saez, 2013; Atkinson & Piketty, 2007; Piketty & Saez,
2003; Piketty, 2014).
Sectoral shifts have been cited as another potential driver of income inequality. Overall,
there is limited literature on the empirical impact of sectoral shifts on inequality. Most
empirical papers include correlations or regressions that suggest a relationship between
sectoral shifts and inequality. The most common finding is that a manufacturing decline is
associated with an increase in inequality. However, sectoral shifts are not necessarily a
cause in themselves, but often driven by other factors such as skill-biased technological
change or globalisation. There is definitely a need for more research on this driver, to
ascertain its precise role. Research on the effect of sectoral shifts in developing countries is
also limited.
Within sectors, due to its growth over the last decades, the financial sector has been cited as
a key driver of rising income inequality. Three main theories that have been proposed are
the “superstar” theory, rent-seeking and social norms affecting compensation of financial
employees and mainly executives.
Overall, empirical data supports the “superstar” theory, mentioning technological change and
globalisation as potential drivers. However, other high income economies such as
continental Europe and Japan, which have experienced similar technological advances, do
not show the same inequality trend. Financial deregulation is also cited as a key variable
explaining growing income of high earners in the financial industry.
Trends and Key Drivers of income inequality
88
The second theory, rent-seeking is similarly supported by empirical data. Rent-seeking is
found to account for a 30 to 50% wage premium between employees in the financial sector
and in other private sector industries. The growing use of bonuses may have supported rent
seeking. Given that bonuses are performance-contingent, i.e. only paid out if certain
performance (e.g. share prices) targets have been met, companies and shareholders may
be more willing to agree to higher overall compensation packages of managers. This is
because if a substantial part of total compensation comes as a bonus, shareholders
themselves will potentially gain even more if the targets are met.
Finally, given its difficulty to measure, no empirical data has been found examining the final
theory, which explains social norms as a factor of wage determination. The observed rise of
incomes of financial sector employees compared to other industries may indicate that
people’s aversion towards unfairly high compensation has declined, provided that the public
knows about the trend. However, because of the recent financial crisis and the fact that
following the crisis at least in the US the top 1% has captured 95% of real income growth
(see table 2, section 4.1.2.), one would assume that this has changed again. To what extent
this theory can explain, if we at all can measure it, growing income inequality particularly
related to the financial sector, will depend on the frequency and magnitude of similar events
in the future and how media communicates them to the public.
Related to sectoral shifts is unionisation. Deunionisation was argued by Acemoglu, Aghion
and Violante (2001) to lead to increased inequality. Lindbeck and Snower’s (1986) insideroutsider theory, on the other hand, provides an explanation for the opposite, i.e. that unions
can increase inequality. Deunionisation in the West is often linked to the decline in
manufacturing, as the manufacturing industry normally has a higher union density. However,
it is not clear that these trends in the developed world will be repeated in developing
countries.
Another potentially significant driver of income inequality is the impact of political institutions
and democracy. One of the fundamental functions of a government is to try and ensure
wealth is distributed across the enfranchised population in a way society deems ‘fair’. The
most obvious example of this is in the use of taxation. The direct effect, that higher marginal
tax rates for high earners reduce the skew of the earnings distribution, is clearest. Alvaredo,
Atkinson, Piketty and Saez (2013) put forward a more complicated argument that lower top
marginal tax rates increase the incentive for employees to bargain for higher wages.
Regardless of the explanation, it is clear that top marginal tax rates are negatively correlated
with increases in inequality. This driver highlights the role that needs to be played by
governments if inequality is to be reduced. In a perfect world, therefore, it would seem theory
Trends and Key Drivers of income inequality
89
would strongly support the hypothesis that an increase in democracy will lead to lower
income inequality (Rueschemeyer, Stephens, & Stephens, 1992). The basic argument is
that greater political equality means that the median voter’s opinions come to more closely
represent the overall population’s; this will then lead to greater redistribution of income from
the previously enfranchised elites towards the rest of the population (Meltzer & Richard,
1981).
However, as previously noted, the real-world impact that democracy has is much more
ambiguous. Various issues exist that can firstly prevent the advancement of democracy, and
secondly prevent any increase in democracy from reducing income inequality. One such
offsetting mechanism is the middle-class of redistribution, as seen in Director’s Law (Stigler,
1970).
The impact of political institutions (and further socio-economic factors) is a very large topic
within the overarching theme of inequality, and one that will continue to be the subject of
much research in a variety of disciplines. This is perhaps the most important reason as to
why its relative importance compared to other drivers of inequality will always be difficult to
quantify. Even a firm definition of what may constitute a political institution for example can
be difficult to settle upon; perhaps this has caused the notable lack of empirical evidence of
its impact on inequality when compared to other drivers (Inter-American Development Bank
(IDB), 1998). Having said that, however, it would seem that we would be safe in asserting
political institutions have the potential to greatly impact income inequality, under the correct
circumstances. If we wish to engage in the most basic of empirical studies, casting a simple
gaze over the various political regimes, democratic or less so, across the world will surely
attest to the latent power of social institutions over inequality.
The main theory linking trade and wage inequality is the Heckscher-Ohlin theory, and the
extension of this model is the Stolper-Samuelson model. Both theories are based on the
idea of comparative advantage and in short, they predict a rise in wage inequality in
developed countries and a fall in developing countries. Empirically, there has been evidence
both for and against the theory; Calderon & Chong (2001) show evidence in favour of the
model in developing countries, while Barro (2000) and Spilimbergo, Londoño, & Székely,
(1999) show evidence to the contrary when adding an interaction term of GDP and factor
endowments respectively.
Moreover, the effect of globalisation in general, away from this model, is still nebulous, with
there being opposing theories as well as empirical evidence to support or refute them.
Theories that have been postulated include: the Prebisch-Singer hypothesis which predicts a
rise in across country inequality due to developing countries’ reliance on primary exports
Trends and Key Drivers of income inequality
90
with decreasing terms of trade (the opposite effect was found to be true by an IMF (2007)
report regarding within-country income inequality); and capital flows globally (in the form of
FDI) which, contrary to economic theory, are believed to flow into rich countries hence
increasing across country inequality (supported by the IMF (2007) report).
As highlighted by Krugman (2008), the effect of globalisation on inequality will produce
different results depending on what year the data was taken due to the increase in
globalisation in the last decade and the extent of the wage differentials between a country
such as the US and China now as opposed to the differential two decades ago between the
US and the four tigers. This makes analysing the effects of the globalisation and trade more
exciting as well as difficult. Additionally, the task is further complicated by the sheer
complexity of globalisation. The main focus of this paper was on trade, with financial flows
and labour mobility being touched upon, but to grasp a better understanding of globalisation
as a whole, all three factors need to be thoroughly assessed and analysed.
On a city level, the amenity based theory suggests that how the urban-rural wage
differences develop depends on the perceived quality of life disadvantage of living in cities.
Development on this front is difficult to predict. Firstly, it is dependent on “exogenous”,
political choices, such as spending on culture, security, public transport and so on. Glaeser,
Kolko and Saez (2001) found that housing costs had risen faster than incomes in cities from
ca. 1980-2001. Together with “the rise of reverse commuting” (p. 33), that is, commuting
from city to suburbs, this suggests that cities offer a better quality of life than in the past. As
seen in section 5.2., the “three types of city” analysis suggests that inequality will, in the
future, increase in high amenity cities and be stagnant in low-amenity ones. But also here,
policy choices are important, especially for low-density “edge cities”. It should be noted that if
workers perceive the net value of urban amenities as negative, high housing prices in
relation to incomes would not be expected. However, extremely high housing prices are
found in many cities. Thus it may appear that negative net amenity value contradicts rational
location choice. But firstly, even though workers may require higher wages to be satisfied
with settling in some cities, or the average city, other cities may have substantial positive
amenity values. Secondly, productivity must be taken into account, which through SBTC,
worker segregation or other factors discussed previously, may be higher in cities. Workers
may be more concerned about the net value of income after housing costs, than the fraction
of their income used on housing. As a numerical example, living in a high-productivity city,
earning £100,000 and paying £25,000 in rent gives a higher net value than living in a lower
productivity area, earning £75,000 and paying £10,000 in rents. Also, rational location choice
is primarily about one city versus another. If both cities have higher productivity than rural
areas, housing prices may be high although inhabitants see the net value of amenities as
Trends and Key Drivers of income inequality
91
negative. In other words, for any given level of income, they would rather live in the
countryside – but to live in the countryside, they would have to accept lower income, as
productivity is lower.
As the UN report “Harmonious Cities” (2008) points out, urban inequality development has
differed greatly within developing countries, even with structurally similar economies. They
attribute this to active inequality reduction policies, while also noting that democratic
developments which have “created more opportunities for lower-income groups to influence
the distribution of income – have had little or no impact on income inequalities; in fact,
inequalities have grown” (p. 70). However, with less democratic institutions, inequality might
have grown even faster.
Poor countries’ inequality development may also be more affected by calamities than highincome countries (UN-HABITAT, 2008), both natural and human-made. Wars are more
prevalent, epidemics (due to demise some households lose an income earner, increasing
household inequality) more lethal. This suggests that income inequality in poor countries
may be more volatile, making predictions even more difficult than in high-income countries.
Trends and Key Drivers of income inequality
92
9.
Conclusion
This paper has analysed recent and potential future trends as well as key drivers of income
inequality based on existing literature. It shall be mentioned that no original analysis was
conducted and therefore no existing studies verified. Also, because the scope of this paper
was rather wide and the purpose to give a broad overview of the topic of income inequality at
a global, national and city level with some focus on the US, it may be interesting for future
research to more thoroughly analyse individual drivers in individual countries, such as
emerging and developing economies.
The paper has shown that while between-country income inequality seems to have declined
recently, within-country income inequality has converged to a higher average level. Because
future predictions tend to foresee a further increase in the upcoming decades, understanding
the key drivers behind this trend appears to be fundamental. Skill-biased technological
change, the growth of incomes of the top 1% of the income distribution in particularly the
financial sector and the link between the two factors have been identified as key drivers.
Effects of trade and globalisation and unionisation on income inequality, on the contrary,
seem to be less clear. At a city level, the amenity-based theory, according to which higher
incomes are paid to compensate for greater disutility in cities, has been proposed to explain
income inequality. A growing financial sector, which usually is located in cities, and skillbiased technological change may enforce rising income inequality in cities.
Finally, a negative relationship has been observed between top marginal tax rates and
income inequality and a positive one between financial deregulation and income inequality,
which draws the attention to the function of governments and the role of democracy. Given a
government’s intrinsic ability to affect income inequality, how income inequality ultimately
evolves in future may depend on the extent to which people want the on-going trend to
revert, i.e. how social norms develop, and to what extent people actually can effect a
change, which will depend on whether more countries adopt democratic political systems
and whether transmission mechanisms within existing democracies work.
Trends and Key Drivers of income inequality
93
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11. Appendix
Appendix 1
Ad Figure 12, section 4.1.2. (Palma, 2011): Regional figures are median values. However, in
three regions where one country dominates, their data is used instead of the median; this is
the case for Brazil in Latin America (Ecuador is the actual median country, Gini=53.7); South
Africa in Southern Africa; and India in South Asia. Also, in the ‘Former Soviet Union’ the
value for Russia and that for the median country (excluding Russia) are highlighted; as it is
in the Anglophone OECD vis-à-vis the US
Ca = Caribbean = Guyana, Jamaica, St. Lucia, and Trinidad and Tobago; Cn = China, EA1
= East Asia-1 = Korea and Taiwan; EA1* = East Asia-1* = Singapore and Hong Kong; EA2
= East Asia-2 = Indonesia, Malaysia and Thailand; EE = Eastern Europe = Albania, Belarus,
Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, Hungary, Macedonia, Poland,
Romania, Slovak Republic and Slovenia; EU = Continental Europe, including Switzerland
(i.e., non-Anglophone European Union, excluding the Nordic countries, which are reported
separately, and Switzerland) = Austria, Belgium, France, Germany, Greece, Italy,
Luxembourg, Netherlands, Portugal, Spain and Switzerland; EU* = Austria and Germany
(Ginis below 30), EU** = EU excluding Austria and Germany, FSU = Former Soviet
Union=Armenia, Azerbaijan, Estonia, Georgia, Kazakhstan, Kyrgyz Republic, Latvia,
Lithuania, Moldova, Mongolia, Tajikistan, Russian Federation, Ukraine and Uzbekistan; In =
India, Jp = Japan, LA = Latin America = Argentina, Bolivia, Brazil, Chile, Colombia, Costa
Rica, Dominican Republic, Ecuador, El Salvador, Guatemala, Haiti, Honduras, Mexico,
Nicaragua, Panama, Paraguay, Peru, Uruguay and Venezuela; No = Nordic Countries =
Denmark, Finland, Norway and Sweden; NA = North Africa = Algeria, Egypt, Morocco and
Tunisia; Not classified in regions = Cambodia, Canada, Djibouti, Ethiopia, Iran, Israel,
Jordan, Lao PDR, Philippines, Timor-Leste, Turkey and Yemen; OECD-1 = Anglophone
OECD and EA1* = Australia, Ireland, New Zealand, United Kingdom and United States
(Canada was not included in Anglophone OECD as its income distribution is different from
the rest of this group; in any case, it is officially bilingual, with almost one-fourth of its
population having French as its mother tongue), and Singapore and Hong Kong; OECD-2 =
OECD countries with the lowest inequality = Japan, the Nordic countries, and Korea and
Taiwan (as discussed above, in some inequality statistics includes part of the EU, especially
Germany and Austria); Ru = Russia, SS-A = Sub-Saharan Africa (excluding middle-income
Southern Africa “SAf”) = Benin, Burkina Faso, Burundi, Cameroon, Cape Verde, Central
African Republic, Chad, Congo, Rep., Cote d'Ivoire, Gabon, Gambia, The Ghana, Guinea,
Guinea-Bissau, Kenya, Lesotho, Malawi, Mali, Mauritania, Mozambique, Niger, Nigeria,
Papua New Guinea, Rwanda, Senegal, Sierra Leone, Swaziland, Tanzania, Togo, Uganda,
Zambia, and Zimbabwe; SA = South Asia = Bangladesh, India, Pakistan, and Sri Lanka; SAf
= middle-income Southern Africa = Botswana, Namibia, and South Africa, US = United
States.
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