7 Causes of Changing Inequality in the World
- James K. Galbraith
p. 90↵When we move toward an analysis of inequalities in the wider world, we are required to cope with far more complex and uncertain data, and at the same time to seek simpler and more abstract theories. With some 220 extant countries, if each one spawned its own narratives, as the rise of inequality in the United States has done, we would never get anywhere. But to come up with a theory that has common application across many countries, we need measurements of inequality across countries and through time that are reasonably comprehensive and reasonably reliable—and this is a major challenge.
What Do We Know about Inequality in the Whole World?
Leaving aside efforts to construct a single measure of inequality for the world’s population (see Chapter 4), there are a number of major data sets that have collected Gini coefficients for a wide range of countries and years, almost entirely restricted to the period since 1950, and for the most part to much more recent years.
The great early effort along these lines was by Klaus Deininger and Lyn Squire at the World Bank, who in 1995 released a compendium of over 700 “high-quality” Gini coefficients, along with many others that they deemed less reliable. p. 91↵The coefficients came from many sources, some public sector but many based on surveys conducted by nongovernmental research organizations. Coverage was sparse and weighted to the rich countries; even 700 coefficients spread unevenly over 220 countries will leave many with little or no reported information. Concepts differed; the measures were sometimes gross and sometimes net of tax, sometimes based on household units and sometimes on persons, sometimes based on income and sometimes on expenditure. As a result, it was very difficult for researchers using the Deininger-Squire (DS) data set to arrive at consistent and credible conclusions as to what the data actually showed.
The DS data set has since been incorporated into the work of the World Institute for Development Economics Research (WIDER) of the United Nations University in Helsinki, which has added greatly to the database. Problems of coverage have been reduced, but the difficulties of differing concepts and uncertain comparability across measures remain. The DS and WIDER efforts are perhaps best viewed as vital repositories of past studies, rather than as polished comparative data sets. They are compendia of work done by hundreds of different research teams around the world over the years; it is not a criticism to state that when the underlying measures and calculations differ, the resulting data have to be treated with caution.
The World Bank has since moved on, and now publishes a “Gini coefficient” as part of the World Development Indicators (WDI) reported annually by the Bank. The actual genesis of and concepts underlying these coefficients are not as clearly distinguished as they might be—for instance, expenditure and income measures, which are definitely not comparable, are presented side by side. And the coverage is very sparse, so that the WDI cannot be considered a serious comparative research data set.
The Luxembourg Income Study (LIS) take a different approach, one of meticulous comparison of micro data sets p. 92↵accumulated from the original sources and available for micro studies of all kinds. Summary measures of household income inequality (market, gross, and net) from the LIS are considered to be among the most trustworthy available for comparative research. But coverage (though growing) is still small by world standards, with an emphasis on a handful of recent years in the wealthier countries.
Thomas Piketty of the Paris School of Economics and his associates Emmanuel Saez, Anthony Atkinson, and Gabriel Zucman attempt to build measures of top income shares from tax data in a selection of countries. These measures are not measures of inequality, since they reflect just a single point (the share in total taxable income of the top 10 percent or 1 percent, or 0.1 or 0.01 percent) of the distribution. But they are a useful complement to inequality measures, since the movement of the top shares reflects, to a degree, the overall movement of income inequality.
The advantage of the top share data sets is a long run of data for a few of the world’s wealthiest countries, including the United States, the United Kingdom, France, and Germany. Disadvantages include the fact that there are only twenty-nine countries in the data set, that it is restricted to countries with income tax records, and that comparability across countries is limited by differences in the definition of income and in the effectiveness of tax enforcement. Comparability across time is also something to be treated cautiously, since countries constantly rewrite their legal definitions of taxable income.
Moving in a different direction, Frederick Solt of the University of Iowa has produced a very large Standardized World Income Inequality Data set (SWIID), giving about 7,000 estimates each of market and net income inequality for 174 countries in a recent update. The SWIID has achieved wide acceptance; it was used, for instance, in recent studies by the International Monetary Fund. But some scholars remain skeptical, since the SWIID draws on many distinct sources and is not based in all cases on actual measurement. Rather, many p. 93↵reported observations are generated by imputation—by filling in missing values based on observations in neighboring places and neighboring times. This makes statistical work with the SWIID problematic, since there are fewer independent observations than the data set reports. The SWIID appears broadly consistent with the actual surveys on which it is based, but does exhibit some strange behavior, in countries and for years where actual observations are sparse, often in the early or late years of a series.
My own effort along these lines is the Estimated Household Income Inequality (EHII) data set of the University of Texas Inequality Project. EHII is a collection of Gini coefficients for gross household income inequality. It is based on actual measurements of pay inequality in the industrial sector, using the between-groups component of Theil’s T statistic computed from the United Nations Industrial Development Organization (UNIDO) compilation of payroll and employment by industry for countries around the world. These are then converted into Gini format using the close statistical relationship between the measured T statistic and the original Deininger-Squire Gini measures, for about 430 overlapping observations. The result is a single-concept, consistent comparative data set with (at latest revision) 3,872 estimates for 149 countries. The EHII estimates track actual measures of gross household income inequality in many countries quite well, and with many more observations than can be garnered directly from surveys. We will use this data for comparative purposes in the following sections.
How Is Inequality Related to Economic Development?
Theories of economic development took off in the years following the Second World War, in part to meet the ideological challenges facing capitalism in the post-colonial countries during the Cold War. For those countries, communism offered a dual promise: rapid industrialization, as pioneered by the Soviet p. 94↵Union, and an egalitarian society run by representatives of the working classes and not by foreign firms or local puppets of the old masters. The communists also rejected social stratification on the basis of race or sex, liberating people of color and women from long histories of oppression. It was not obvious, to many observers, that capitalist society could prove itself an attractive alternative in a world where it was no longer considered good manners to impose the choice of economic system by brute force.
In this climate the economist Simon Kuznets offered an idea based on a simple model of industrial and structural change. Suppose one starts (as in the Northern states of the United States before the Civil War) with an agrarian society based on family farms and small freeholds. Then industrialization begins. Industry engenders and depends on cities, which grow up around the new factories and mills. Wages in factories must exceed the living one can earn on the farm, or workers will not accept that employment. So the cities are wealthier than the countryside. Inequality, originally very low, must increase as urbanization and industrialization proceed.
But, Kuznets then argued, there eventually will come a turning point. At some point, as agriculture becomes mechanized, the population of the countryside will diminish to a small fraction of the total. Then the inequalities that matter will no longer be those that distinguish the city from the hinterland, but those that exist within the cities. These, while initially high, will diminish as the working classes organize, vote, and create for themselves a world of unionized collective bargaining and, in the political sphere, social democracy and the welfare state. As income rises, inequality will decline, and the ultimate destiny of industrial capitalism is a society of tolerably egalitarian qualities, without the violence necessarily associated with communist revolution.
Kuznets’s idea was based on a core insight: the major forces affecting inequality in the process of economic development are not specific public policies, but the structural relations p. 95↵of different sectors in the economy as development unfolds. Certain aspects of the evolution of inequalities are inevitable. Two forces come into play: the relative weight in population and activity of high- and low-income sectors, and the differential in relative pay between them. If the historical process unfolds as Kuznets described, then the trajectory of inequality will follow an inverted U-curve, first rising and then falling as average income grows.
This insight may be modified if the initial or the terminal conditions are different from those that Kuznets assumed. For instance, suppose that instead of egalitarian homesteaders, the initial agriculture is one of large plantations worked by slave labor? In that case, industrialization might decrease inequality, even if the plantations persist, since the industrial element would comprise a previously nonexistent middle class. In that case, the “Kuznets curve” might be entirely downward sloping, with an egalitarian society emerging steadily in the course of growth, development, and emergent resistance to the most repellent features of the previous structure.
Or again, suppose that there emerges a trend toward globalization, under which some countries take the lead in providing advanced technologies, capital equipment, and services such as communications, insurance, and finance? In that case, inequality may rise in those advanced countries with further growth in income, which will flow in the first instance to the few, well-paid denizens of the advanced sectors. The Kuznets curve, having declined during an initial, national phase of industrialization, will now rise in the richest countries as the new international phase takes shape. In a paper in 2000, Pedro Conçeicão and this author christened this possibility the “Augmented Kuznets Curve” (Figure 7.1). It appears to fit the evidence quite well for the United States, the United Kingdom, and Japan.
How does the broader evidence fit the Kuznets curve? Many economists, using DS or WIDER, have concluded that p. 96↵the fit is poor. The University of Texas Inequality Project (UTIP) team, using measures of pay inequality from the UTIP-UNIDO data set, takes a more favorable view. Kuznets himself stressed that his theory was one related to pay, rather than to income, and so it is reasonable to focus on this type of data. The UTIP-UNIDO data suggest that most countries are on a declining Kuznets surface, but that China is on an upward-sloping surface for the traditional reasons, while a few advanced countries, including the United States, are again on an upward-sloping surface for the novel reasons just given. Underrating Simon Kuznets is not a good idea.
Figure 7.1 The Augmented Kuznets Curve
How Do Political Systems, Violence, Revolution, and War Affect Inequality?
If there are world forces that affect the rise or decline of economic inequality, does that mean that local conditions and p. 97↵institutions are unimportant? Of course not. For an appropriate analogy, consider a coastal area ravaged by a massive storm. The extent of the damage will depend in part on the strength of the storm. But it will also depend on the lay of the land, and on the strength of the levees, dikes, and ocean gates that may be in place when the storm hits. Similarly, as the world economy is swept by violent forces, the effect on individual countries will depend in part on their institutions and on their policies—on whether they accept or resist.
With a good comparative data set, such as EHII or UTIP-UNIDO, it becomes possible to assess the effect of particular political systems and of distinct events, such as war and revolutions, on the course of inequality. However, to make useful conclusions about these matters, one also needs a good source of information about political systems, wars, and revolutions. These data sets are largely the province of political scientists, who developed them for other purposes. In the case of the major data sets covering political systems (the POLITY data sets) there is a problem, which is that the scale runs from “authoritarian” to “democratic,” grouping communist and fascist regimes, or military dictatorships, in the same category. But it is clear that with respect to inequality, these two types of authoritarianism are quite different.
Hsu (2008) addressed this problem by developing a categorical data set of regime types by country and year, using a wide range of descriptors to capture the ideology and institutional characteristics of different countries at different times. This permits the data to indicate whether there are significant differences between countries at different times, according to their political regimes.
It turns out, not surprisingly, that there are significant differences between levels of inequality observed in countries with different political systems. Communist countries (in their day) had low inequality, as Cuba does to the present day. The social democratic governments of northern Europe retained low inequalities at least into the first p. 98↵decade of the 2000s, although values may have changed in recent years in certain cases. Islamic republics have somewhat lower degrees of inequality than their income levels would otherwise suggest. On the other hand, military regimes and one-party non-communist dictatorships tend to have inequality measures on the high side. When military regimes and dictatorships come to an end, inequality is generally much higher than it was before, and the restoration of democracy does not immediately, or automatically, bring a reduction. It takes a long time (if ever) for a newly established democratic government to begin to reduce inequalities incurred under a previous regime, as elected governments in South Africa, Brazil, Chile, and elsewhere have discovered.
It is also possible to assess the effect on inequality of historical events within particular countries. There was, for instance, a spectacular rise in inequality in the countries of Eastern Europe and the former Soviet Union when the Cold War ended and the Soviet Union broke up. Revolutions are rare events in modern data, but we note a sharp decline in inequality in Iran following the revolution there. There also appears to have been, as a general rule, declining inequality in periods just before right-wing coups d’état, and rising inequality thereafter; this was the experience of Chile before and after 1973, of Argentina before and after 1976, and of numerous other experiences that may be tracked in the data.
How Do Interest Rates, Growth, and Saving Affect Inequality?
Most theories of increasing inequality explored so far have been microeconomic; their core idea is that outside forces, such as technology or trade, buffet incomes through the mediation of particular markets for labor time and capital assets. Kuznets’s theory is meso-economic, meaning that it relates to structural change across grand categories of economic activity and development.
p. 99↵In 2014 Thomas Piketty offered a simple macroeconomic theory of rising inequality, based on two “fundamental laws.” The first was based on the fact that the ownership of financial assets is concentrated, and so if income on financial assets rises faster than income in general, then the inequality of income should increase. If we call income on financial assets (which is their interest rate) r, and the growth rate of income g, Piketty argued that typical value for r is around 5 percent per year, while that for g is closer to 2 percent, over the long run. Thus, r > g. We will return to this later.
A high interest rate surely favors creditors, and a low one favors debtors. It is equally sure that “people who have money to lend tend to have more money, than people who do not have money to lend.”1 So we should expect periods of high interest rates to favor the rich and periods of low interest rates to favor the poor. We shall discuss some global evidence for this view in the following section.
What Has Been the Role of Financialization in Changing Inequality?
Financialization is a clumsy name for an ongoing shift in the authority over economic activity from national governments to financial actors—for the rise in power of the banks, and for the international integration of financial markets.
A common pattern in inequality measures around the world is the influence on the overall measure of inequality of increasing (and sometimes decreasing) incomes in the financial sector. This is hard to detect in survey data, which usually do not identify respondents according to whether they work in or out of finance. But it emerges very clearly when the between-groups component of a Theil index is calculated across sector categories, if (as is usually the case in national p. 100↵data sets) one of the included categories happens to be finance. In such data sets, one can read the effect of rising (and sometimes falling) incomes in finance directly from a table or chart. Or, it is often possible to infer the increasing importance of finance from geographic data sets, since most countries and regions have a “financial capital” where the bulk of incomes from that sector are reported. New York and London play that role in the West; Shanghai plays it in China; Moscow plays it in Russia, Sao Paulo in Brazil.
The financial sector influences inequalities in a second way, by concentrating the growth of investment, and therefore of the associated incomes, in a small quadrant of economic activity at any one time. This is a consequence of the herd mentality. At a particular moment, some sector becomes “hot” and all of the financial players rush for a “piece of the action.” Some will succeed; many will fail. And there will be a penumbra of shady and fraudulent players, who (if left unchecked) may bring major risks to the stability of the system. But the effect on inequality stems from the initial rush, which must inevitably concentrate resources into the hands of “superstars”—for a short time. In contrast, typical public-sector financing of the economy spreads activity around; that is the nature of politics. The gains are smaller but more widely shared, the durability may be greater, and inequality is much less likely to increase.
What Do Global Patterns Show?
Looking at global patterns of changing inequality is another way to illustrate the modern power of global finance. A study conducted on the UTIP data set analyzed the general tendency for inequality to change, year by year from the early 1960s forward. Until 1971, there was no general tendency that could easily be seen. Some countries showed rising inequality, others showed falling inequality, and a reasonable observer might p. 101↵conclude that differences in national policies were the main factors.
From 1971 until around 1980, overall, inequality in the world declined, with the narrow (but important) exception of the recession-riddled industrial West, where it started to rise. Declines were especially sharp in a band of countries extending from Iran to Iraq and across North Africa to Algeria—a group clearly tied together by their common role as producers of oil. But other commodity producers also did well, as did the debt-fueled developing countries in the southern cone of South America.
And in 1981, things changed again. Inequality started rising as a dramatic, general pattern almost everywhere. Inequality rose most sharply at first in Latin America and Africa, the epicenters of the world debt crisis. Only those countries that had remained aloof from commercial bank financing were immune: China, India, and Iran. In the 1990s, the center of rapidly rising inequality shifted to Eastern Europe and the former Soviet Union; and in the later 1990s it moved on to Asia, and notably to liberalizing India and to China. Here again, there was an exception: the foreign direct investment-powered”Tigers” of Southeast Asia, until the crisis that hit them in 1997.
What was going on emerges with striking clarity from this picture. In the year 1971, the stabilizing global financial framework created at Bretton Woods in 1944 collapsed. There followed an oil-and-commodity boom that reduced inequalities in the producing countries and increased them among the consumers. Then, in the 1980s, ultra-high interest rates and rolling debt crises reversed the balance of financial power. This now unquestionably favored the rich and crushed the poor, first in Latin America and Africa, then in the communist states, and finally in Asia.
From this pattern the power of global financial forces is evident. Only those countries that had avoided commercial international debt escaped the storm, and only for so long as p. 102↵they could or chose to maintain their independence. Their capacity to do that was very limited, in this era of globalization, neoliberalism, and what was called the “Washington consensus” for economic policy, namely to privatize, deregulate, open up to external competition, and cut public spending and taxes.
But then, in 2000, the wheel turned one more time. Thanks to the bursting of the stock market bubble in the United States and in the wake of the 9/11 attacks, interest rates were cut practically to zero. Commodity prices rose worldwide, especially oil. China continued to grow, providing a new source of demand to many peripheral producers. In much of South America, Russia, and eventually even China itself, inequality peaked and began to decline, even as these regions took their distance from the neoliberal consensus of the 1990s and from the international institutions that enforced it. This phenomenon again confirms the importance of common global forces, while suggesting that even under “capitalism”—provided the policies are not too savage—there is no necessary tendency for inequality to increase forever. Inequalities may or may not increase, depending on world conditions that are set, to a great extent though not exclusively, by the powers that control world financial systems.
Estimated Gross Household Income Inequality, Decade by Decade Averages
These maps (Figure 7.2) show the decade-by-decade averages of the EHII data set, for the 1970s and the early 2000s. Note the clear pattern of lower inequality in richer countries (apart from China at the time) and the shift toward higher average inequality values. The lowest inequalities today are shown in Scandinavia; the erstwhile low values for the United Kingdom, France, Central Europe, Canada, Australia, and China have all disappeared.2
The maps in Figures 7.3 to 7.6 show the annual percentage change in measures of pay inequality across industrial sectors, calculated from the UTIP-UNIDO data set, which is based directly on UNIDO’s Industrial Statistics, over selected six-year intervals. The pay data set is the raw material from which estimated household income inequalities were computed, so the two measures are very close. But there are more observations in the pay data, and they change more over time, so it is easier to use them to pick up some of the dramatic shifts in inequality that occurred at particular moments, in particular at the time of the oil boom in the 1970s, the debt crisis of the 1980s, and when the Soviet Union disbanded in the 1990s. An interesting feature of this data shows up in the final map (Figure 7.6): a worldwide tendency for inequalities to decline, although from high levels, in the early part of the 2000s.
Oil Boom and Oil Shock
Notice the declines in producing countries (Algeria, Libya, Iraq, Iran) and the increases among importers, notably India and the United States (Figure 7.3).
Debt Crisis in the Third World
Note the increasing inequality in most countries of South America, Africa, and Asia (Figure 7.4). Chile is only an apparent exception; that country had already experienced sharply rising inequality following the 1973 coup, and in the banking crisis year of 1982—similarly for Bolivia. In the United States, industrial pay inequalities also rose sharply in the late 1970s and in the early 1980s recessions; the peak for that period was in 1982, and pay inequalities declined slightly with economic recovery after 1983.
The dramatic collapse of the Soviet Union and of the regimes in its neighbors speaks for itself (Figure 7.5). Again note the declining pay inequality in the United States, as the information-technology boom got underway and the economy moved toward full employment. In the US case, pay inequalities fell, even though income inequalities rose to an unprecedented peak.
The Early 2000s: A Decade of Declining Inequalities
Note the declines in Russia, China, India, Indonesia, and much of Europe as well. The United States, once again in difficulties, shows rising industrial pay inequality in this period (Figure 7.6). In Brazil, pay inequalities seem to have risen, even though overall income inequalities tended to decline, quite sharply, after 2002. I have not inspected the case of New Zealand, a country that moved from a social-democratic model to freemarket neoliberalism in the 1980s, and did not back off in the first decade of the 2000s, as did South Africa and parts of South America.
We have taken a quick tour of a large world, in search of regularities in the movement of economic inequality, so far as it can be observed through the lens of a large, consistent data set. The following general conclusions appear to be in order.
First, when analyzed with reliable world data, Kuznets’s core insight remains valid. There is a trajectory of inequality in the course of economic development, structural change, and rising income. For most countries in the world today, growth reduces inequality and rich countries are more egalitarian than the poor. However, there are exceptions, notably at the low end of the scale—the rise of China, at least until recently, was accompanied by sharply rising inequality. And at the high end, as technology and finance emanate from a few of the richest countries to the entire world, the Kuznets curve appears once again to turn up.
p. 111↵Second, political institutions have been and in some cases remain a bulwark against rising inequalities. When they crumble, the associated violence can contribute to abrupt changes, which may be difficult to reverse. Rising inequalities can happen quite suddenly, whereas—with just a few revolutionary exceptions—reducing them is a matter of patient progress over the years.
Third, global financial forces and changing financial conditions have played a powerful role affecting economic inequalities around the world over the past fifty years, especially since the breakup of the stabilizing framework of Bretton Woods in 1971.
Fourth, when we look at a large group of countries spanning the entire world, there appears to be no single permanent trend to inequality, neither down (as Kuznets surmised for the long run) nor up (as Piketty argues from a much smaller group). Instead, the great swing upward of income inequalities appears to have been mostly a phenomenon of the years from 1980 to 2000. After 2000, the trend stops, and though inequalities remained high, there was a tendency for them to decline in numerous widely separated countries. In South America, most notably, inequality and also poverty declined in many countries, including Brazil and Argentina, following crises that forced or enabled policy changes. Lower interest rates and better commodity prices appear to have been strong factors, as well as a retreat in many places from the free-market orthodoxies of the prior two decades.