Blog
Tracking the Rise of Inequality in China and Russia

by James K. Galbraith

I t is well-known that economic inequality rose drastically in Russia during the transition (Sheviakov and Kiruta 2001). For China, Khan et al. (1999) report a 42.5% increase in a Gini measure of household income inequality in China between 1988 and 1995 alone. But the information provided by sample surveys on this topic is necessarily of a very general kind, and there are limitations of data. For Russia no study assesses the joint effect of regional and sectoral income changes, while for China, as Benjamin et al. (2004: 7) note, with one exception ‘there are no studies that track inequality... on anything approximating a continuous basis.’ And equally there are limitations of method. As Wu and Perloff state (2004: 1) ‘...the Gini index only reflects some aspects of the underlying income distribution. A large amount of information is lost.’ angle2004-2_Page_04_Image_0001.jpgangle2004-2_Page_04_Image_0001.jpg

angle2004-2_img1.jpg

The research of the University of Texas Inequality Project, at http://utip.gov.utexas.edu, takes a new look at the Chinese and Russian transitions. Drawing on official data sources and associated classification schemes, we calculate our own measurement of economic inequality. In particular, we look at the changing spatial distribution of economic activity in both countries, and in the relative prosperity and impoverishment of different economic sectors. We have found this to be a versatile and robust way to measure changing patterns of economic gain and loss, especially useful in environments where annual survey data are unavailable, incomplete, or problematic.​angle2004-2_Page_04_Image_0001.jpg​​

In both countries, we confirm that inequality rose as economic liberalization proceeded. In both, regional inequalities rose dramatically, creating major new divisions across geographic space. In both countries, certain sectors gained relative position, notably those which were apparently able to exploit new-found market power to create and retain economic rents. Of these, finance, utilities and transport were the most important in China, and finance and energy production (counted as part of industrial production in the official statistics) were dominant in Russia. However, China’s advantage shows up in two important respects. Unlike in Russia, the region with the greatest gains is a major population center. And incomes in education and the social sectors have held up far better in China than in the Russian Federation, a fact that surely reflects differences in the fiscal capacities of the two nations.angle2004-2_Page_04_Image_0002.jpg

Our approach relies on the regularly gathered official measures of income by region and sector. In Russia, this information is collected and published by Goskomstat, the state statistical committee, mainly in annual hard copy publications. Russian data take the form of payroll and employment figures for fourteen major economic sectors, in each of 89 distinct geographic entities (province, city, oblast, krai). There are 1232 province-sector cells in our data set for Russia, for each of eleven years from 1990 through 2000, inclusive. In China, data at a sufficient level of detail are published annually in the China Statistical Yearbook, and are available in electronic format. For the year 2000 we have data for each of 16 sectors for 30 provinces in China, or 480 sector-province cells. The data extend back to 1987 on a reasonably consistent annual basis, and it is possible to extend the analysis as far back as 1979 with more highly aggregated information.angle2004-2_img2.jpg

Our method is to compute the between-groups component of Theil’s T statistic across provincesector cells for both Russia and China. Theil’s T is a very simple measure of inequality, relying only on two bits of information about each cell: its weight in total population (or employment), and the ratio of average income within the cell to average income in the country as a whole. The properties of Theil’s T have been explored in detail elsewhere (Conceição and Galbraith 2000; Conceição, Galbraith and Bradford 2001); suffice to say they are highly attractive for this type of calculation; in particular we have found that changes in the betweengroups component of a distribution are usually a very robust instrument for changes in the underlying distribution. It is also possible to look directly at the contribution to inequality of each cell, sector or province, and to gauge the change in that contribution from year to year.

A simple way to present the information is with a stacked bar graph, showing the contribution of each sector and region to inequality in each year. Figures 1 and 2 capture the increasing weight of finance, energy (1) and transportation in the increasingly deregulated economies of both countries. Sectors with average incomes below the national average contribute negative quantities to the Theil index, and so the graphs also show the deteriorating positions of agriculture and services. Figures 3 and 4 show how, in both countries, much of the increase in regional inequality owes to the relative rise of just three regions: the city of Moscow and the oil and gas districts of Tiumen and Khanty-Mansy in Russia, and the province of Guangdong alongside the municipalities of Beijing and Shanghai in China.

angle2004-2_img3.jpg

Maps provide a useful way to visualize the spatial redistribution of wealth. In figures 5 and 6, the regional Theil elements are arrayed in a colour scheme. Regions are divided into ten groups. The highest values, representing high shares of total income, are shown in red, with a shading to yellow for the second and third groups. Intermediate deciles, whose contribution to inequality is slight either because they have low population shares or incomes close to the national average, are shown in green. Blues indicate those regions with below average incomes and significant population shares: they are the centers of relative poverty. The colour schemes thus show the pattern of regional polarization that emerged in the period of transition and economic reform. (2) In both cases, a similar map made with data from ten or fifteen years earlier would not show the dramatic pattern of regional income polarization that presently exists.​angle2004-2_img4.jpg

It is no surprise that rising inequality should be a characteristic feature of transition from a socialist to a capitalist system. This is true whether the transition is or is not an economic success. In the absence of strong agricultural support programmes and social security systems, a particular feature of redistribution is a sharp decline in the relative income of the country-side. It seems that there is no mechanism that works effectively to offset this tendency in the transition to the market economy. Whether education, health care, and science suffer major losses of position under economic transition depends, on the other hand, on the tax system and public priorities of the government. China has protected these sectors and indeed expanded them in line with the growth of the Chinese economy overall. In Russia these sectors have suffered absolute and relative losses, with serious consequences for the health, education and culture of the population.


angle2004-2_img5.jpg

Studies of this kind are simple and inexpensive to carry out. We believe that they have a large potential to expand understanding of the changing patterns of income distribution in many contexts where survey data are insufficiently detailed, difficult to compare over time or between countries, or simply unavailable. In related work, we have analyzed the relationship of economic inequality to unemployment at the level of regions inside the European Union (Galbraith and Garcilazo 2004), with results that strongly contradict to received wisdom on the merits of ‘labour market reform.’ In a new paper, we have analyzed rising inequality in manufacturing pay in India under the reforms (Galbraith, Roy Chowdhury and Shrivastava 2004). In another, we have measured the effect of the information technology boom on income inequality between counties in the United States, with the striking finding that just four Western counties account for the entire increase in this measure in the late 1990s (Galbraith and Hale 2004). Finally, we have assembled a dense and consistent global data set for pay inequality from 1963 through 1999 (Galbraith and Kum 2003), and have used this data set as instruments to create a dense and consistent table of estimated coefficients of household income inequality (Galbraith and Kum 2004), with about four times the number of observations currently available from the widely-used data set of Deininger and Squire (1996). The web site of the University of Texas Inequality Project makes our data sets freely available, and we welcome the collaboration of researchers around the world who share our interest in these techniques.

(1) Energy is measured under ‘industrial production’ in Russia and under ‘Utilities’ in China.

(2) For presentation purposes, the Russian Far East is not shown; unfortunately also, due to limitations of the software, Moscow City and Shanghai not seen independently on these maps.

For detailed references see web site: http://utip.gov.utexas.edu

James K. Galbraith holds the Lloyd M. Bentsen Chair in Business/Government Relations at the LBJ School of Public Affairs at the University of Texas at Austin, where he directs the University of Texas Inequality Project.

Previous
Looking Beyond Averages in the Trade and Poverty Debate
Looking Beyond Averages in the Trade and Poverty Debate
Next
Highlights from the 2019 WIDER Development Conference
The World Bank recently estimated that two-thirds of all jobs in developing countries are at risk of automation...
Highlights from the 2019 WIDER Development Conference