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The Experience of Rising Inequality in Russia and China During the Transition



This paper examines the changes in regional and sectoral inequality that accompanied economic transformation in Russia and China throughout the 1990s. The experiences of the two countries are widely viewed as having been polar opposites. While the Soviet collapse had adverse consequences for many parts of the post-Soviet population, the Chinese experience produced a continuing rise of average living standards. Nevertheless, both countries experienced a drastic increase in economic inequality. In both cases, regional inequalities rose more sharply than inequalities across sectors but within regions. In particular, major urban centers gained dramatically relative to the hinterlands. Also, in Russia as in China, those sectors exercising the largest degrees of monopoly power gained the most (or lost the least) in relative terms. In both countries, the respective position of finance improved greatly, while that of agriculture declined. The decline of agriculture in China, however, was not as precipitous as in Russia, and certain sectors, such as education and science, maintained their position in China in a way that was not possible for them in Russia.
The Experience of Rising Inequality in Russia and China during the Transition
James K. Galbraith, Ludmila Krytynskaia and Qifei Wang
UTIP Working Paper No. 23
February 2, 2003,,
A paper prepared for a Conference on
Globalization and Development Problems
5th International Meeting of Economists
Havana, Cuba
February 10-14, 2003
The collapse of the Soviet Union and the acceleration of economic reforms in the Peoples
Republic of China were hallmark events of the 1990s. The Soviet collapse had adverse
consequences for many parts of the post-Soviet population -- including sharply rising mortality
rates -- even as the country underwent a transition to apparent multiparty democracy. Meanwhile
the Chinese experience produced a continuing rise of average living standards, with political
change (mainly at the local level) only within the framework of continuing rule by the Chinese
Communist Party. Thus the experiences of the two countries are widely viewed as having been
polar opposites.
Nevertheless, in both Russia and China, economic inequality rose sharply. In both countries,
regional inequalities rose more sharply than inequalities across sectors but within regions. In
particular, major urban centers gained dramatically, relative to the hinterlands. In both countries,
moreover, there was a considerable reorientation of sectoral advantage, in both cases toward
those sectors exercising the largest degrees of monopoly power. In both countries, the relative
position of finance improved sharply, while that of agriculture declined. However the decline of
agriculture in China was not as precipitous in China as in Russia, and certain sectors, such as
education and science, maintained their position in China in a way that was not possible for them
in Russia.
1. Introduction
The collapse of the Soviet Union and the acceleration of economic reforms in the Peoples
Republic of China were hallmark events of the 1990s. In important respects, moreover, they are a
study in contrasts. Economic liberalization produced chaos, hyperinflation, industrial collapse,
and privation in post-Soviet Russia, whereas the Chinese experienced sustained economic growth
and continuing, visible improvement in living standards. On the political front, Russia acquired
the trappings of parliamentary democracy, with an independent commercial press. Meanwhile
China continued under one-party rule guided by the Chinese Communist Party, and an
independent media has not been permitted to exist.
The post Soviet economic implosion stemmed from several main sources: decentralization
and the physical breakup of the country, a complete collapse in investment (much of it
unproductive, to be sure, but a provider of jobs and income nevertheless), and the cataclysmic
effect of lower trade barriers on consumerswillingness to continue to purchase home-made
goods. Industrial production fell by nearly half. Meanwhile hyper-inflation devalued the savings
of the Russian public, leaving many destitute, and both governments and enterprises sharply
reduced social services of all kinds. The adverse consequences for many parts of the post-Soviet
population included rising mortality rates, especially among older men, attributed in part to the
stresses surrounding economic dislocation, in part to material impoverishment, and in part to the
decline in the provision of health care in post Soviet Russia.
These misfortunes occurred even as the country underwent political transition. The
transition was not smooth: born in the collapse of a coup detat, it involved the bloody
suppression of parliament in 1993. A riot of independent press in the early 1990s became
consolidated, after a fairly short time, under the substantial control of a small number of media
oligarches. The transition from Yeltsin to Putin was a stage-managed event, elevating a figure
who had no previous political standing. And the country has suffered an ongoing war in
Chechnya, with catastrophic effects on the people of that region. Nevertheless, the Russian
population today lives under a formal multi-party democracy, with political liberties never before
available to it.
Meanwhile Chinas GDP per capita roughly quadrupled over twenty years of economic
reform, and although growth rates were undoubtedly higher in the early and mid 1980s than in the
somewhat turbulent 1990s, economic development and transformation were abundantly visible
throughout the country. Chinese growth was fueled by rising agricultural productivity in the first
phase of reforms, and then by the development of light industry under the rubric of township and
village enterprises, as well as heavy investment in housing and urban infrastructure. The effects
are apparent everywhere in the country, though less so in the heavy-industrial Northeast
(Manchuria) than in the esport-oriented South.. Growth was financed largely by internal savings,
which amounted to over 35 percent of income in the middle 1990s, and it was also facilitated by a
vast expansion of Chinas external trade, known as the open-door policy, culminating in Chinas
admission to the WTO.
China reacted to the political upheavals of the late 1980s very differently from the Soviet
Union. The confrontation at Tienanmen Square in June, 1989 led to intervention by the military
and to bloody battles on the streets of Beijing, which were followed by a wave of repression.
Since the early 1990s, the repression eased for most of the Chinese population, although open
dissent against the system is still met with harsh measures. Formal political change has occurred
mainly at the local level, and only within the framework of continuing rule by the Chinese
Communist Party.
Thus the experiences of the two countries are widely viewed as having been polar
opposites. Certainly many observers in both China and Russia feel this way, and in this authors
experience most agree that the Chinese path was the superior one. The Russian experience is
cited, repeatedly, by senior Chinese economists as the model to avoid; if there is sentiment in
China favoring Russias model of political openness at the perceived economic cost this author
has never heard it expressed. And in Russia, there is envy of Chinese economic success. Even
though Russians are generally persuaded that the Soviet system could not – as of 1991 – have
been reformed successfully along Chinese economic lines, most believe (and with good reason)
that the process of economic transition was catastrophically mismanaged in post Soviet Russia.
This paper takes a new look at the Chinese and Russian economic experiences in the
1990s. Rather than a comparison of the broad macroeconomic aggregates, such as economic
growth, inflation, and industrial production, whose contours are well known, we examine here the
changing patterns of economic activity in both countries. We lay particular emphasis on
measurements of economic inequality, a key concern especially in countries with a history of
communism, and a driving force in the social development of any country. We also examine the
pattern of relative gains and losses in two dimensions: regions and sectors. That is, we look at
the changing spatial distribution of economic activity in both countries, and in the relative
prosperity of different branches of activity.
We find that there are, in fact, major similarities between the economic experiences of
Russia and China in the 1990s, when looked at in this way. In both countries, 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 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.
The next section describes methods and data, along with a brief overview of related work.
Sections three and four describe the economic experiences of Russia and China, respectively.
Section five presents brief conclusions.
2. Methods and Data.
Previous work on inequality in both Russia and China relies on sample surveys. These do
exist; surveys have been conducted in both countries on a reasonably regular basis. And surveys
do show rising economic inequality to have been a signal characteristic of both countries in the
time of liberalization. Nothing presented here is novel in that respect.
Nevertheless, the survey approach to the study of inequality suffers from disadvantages.
In the Russian case, questions have been raised about discrepancies between survey-based and
macroeconomic measures of income and consumption (Sheviakov and Kiruta 2002), who indicate
that the rise in inequality captured in survey measures may be overstated. In China, we are able to
find broad measures of urban and rural income inequality up through the mid 1990s (reported in
Riskin et al., 2001). But such work, however valuable, goes rapidly out of date.
Our approach relies not on surveys, but 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 bureau, 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 Chinese data experience some
changes in category structure over the years, which affects the continuity of our measures. They
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.
Our method is to compute the between-groups component of Theils T statistic across
province-sector cells for both Russia and China. Theils 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 mathematical properties of Theils T have been explored in detail elsewhere, and we
need not repeat that discussion here (Conceicao and Galbraith 2000, Conceicao, Galbraith and
Bradford 2001). Theils T has properties that make it attractive for this type of calculation; in
particular it is possible to sum row and column elements so as to arrive at cross-sector and cross-
province measures of inequality. It is also possible to look directly at the contribution to overall
inequality of each cell, sector or province, and to gauge the change in that contribution from year
to year. This provides a very convenient way to visualize the winners and losers in a process of
economic change, as we will show.
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
Regional Sectoral
Inequality Trends in Russia, 1990-2000
3. The Case of Russia.
The Russian transition was marked by two years of profound crisis: the industrial collapse
and hyper-inflation of 1991 and the financial collapse of 1998. Measures of inequality reveal the
impact of these crises, particularly that of 1991. When measured across 1232 province-sector
cells, inequality in Russia doubled between 1991 and 1992. After that inequality stabilized, for six
years. But 1998 to 1999, inequality rose another 39 percent, and it continued to rise into the year
2000. Table 1 gives the values of this measure.
Table 1. A Theil measure of Inequality for Russia, 1990-2001
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
0.031 0.035 0.070 0.059 0.071 0.076 0.068 0.065 0.068 0.095 0.102
Theils T statistic measured across 1232 province-sector cells in the Russian Federation. Source data: Goskomstat
Figure 1 decomposes this measure of inequality into its two principal dimensions, namely
sectoral and geographic, using data reported by Goskomstat at the sector and province levels.
Figure 1. Russian inequality by Region and by Sector, 1990-2001.
As Figure 1 reveals, inequality increased across both dimensions, and especially during the
critical transition crisis of 1991-1992. Moreover, inequality across regions increased by
considerably larger values than did inequality across sectors. This suggests a strong geographic
element in rising stratification in Russia: choice of place mattered more than choice of occupation
or industry. We shall, nevertheless, see shortly that economic fate of places in Russia had a great
deal to do with the spatial distribution of economic sectors.
An implication of the finding that geography predominates in rising Russian inequality
concerns the welfare implications of rising inequality itself. Because cost-of-living is a regional
variable – housing, energy and food costs are place-specific – a changing pattern of relative
incomes across Russia was also accompanied to a considerable extent by co-respective changes in
the relative cost of living (Sheviakov and Kiruta provide a discussion). For this reason, rising
national measures of inequality should be treated with caution, as they will tend to overstate the
rise in inequality of place-specific living standards.
This point should not, however, be interpreted as intended to dismiss the importance of
rising inequality in the Russian Federation. It points rather to a need to analyze the political and
social dimensions of the increase. To the extent that money incomes diverge across places,
notwithstanding divergence in costs of living, the relatively impoverished places will lose
population to the relatively wealthy. And those who remain in places where money incomes are
lower will lose, to some extent, the economic capacity to interact with the rest of their own
country. Thus regional disparities tend to promote political regionalism. One aspect of this is the
potential for violent schisms, as the experience of the Caucasus demonstrates in Russia.
Figure 2 provides evidence on the particular pattern of place-specific gains and losses in
post Soviet Russia. The figure provides a series of stacked bar graphs, one for each year, where
each color block within a bar represents the Theil elementor weighted contribution to overall
inequality of each province, oblast, or krai in that year. Regions whose average income is above
the national average contribute a positive value to overall inter-provincial inequality; those whose
average income is below the national average contribute a negative value.
As the figure makes clear, the rising inter-regional inequalities in Russia occur in two
phases. The larger element is a general increase in dispersion in the transition crisis, whose
specific character is difficult to make out from the graph. As the 1990s wear on, however, this
resolves into a pattern of very rapidly rising relative incomes in just three places, whose color
blocks come to dominate the visual field on the right of the figure. These are the city of Moscow,
and the provinces of Khanty-Mansy and Tiumen. The latter two are lightly populated West
Siberian oil and gas regions, and in this way the figure tells very succinctly the main story of post
Soviet distribution in Russia.
Contribution to Inequality
T'90 T'91 T'92 T'93 T'94 T'95 T'96 T'97 T'98 T'99 T'00
Theil Elements - Russia
By Region
Contribution to Inequality
T'90 T'91 T'92 T'93 T'94 T'95 T'96 T'97 T'98 T'99 T'00
Agriculture Trade and food services Education Health, sporting and social services
Culture and arts Housing Communication Science
Management Finance, credit and insurance Construction Transportation
Industrial Production
Theil Elements -- Russia
By Sector
Figure 2: Inter-regional contributions to inequality in Russia,
Figure 3 presents a similar picture for the main sectors of the Russian economy (where,
unfortunately, energy production is subsumed in the larger sectoral category of industrial
production, which it comes by the end of the 1990s to dominate.) Transportation also
experiences a rising relative share, while the previously dominant sector of construction
experiences a loss of standing. There is a notable increase in the relative share of income in the
financial sector, an average earner in Soviet times. And there are major losses, especially in
agriculture, but also in science, culture and the arts, and education, health and sports. None of
this is, of course, mysterious to any observer of the Russian scene.
Figure 3. Inter-sectoral contributions to inequality in Russia.
Maps provide a useful way to visualize the spatial redistribution of wealth in Russia. In
the figures that follow, the regional Theil elements are arrayed in a color scheme Regions are
divided into ten groups, using natural breaks in the data to allocate regions to 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 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 in modern Russia. The color scheme is
geared to the values of inequality in the year 2000; thus the maps are designed to show the
evolution of inter-regional inequality in Russia toward their recent values. (For presentation
purposes, the Far East is not shown; unfortunately also, due to restrictions in the software,
Moscow City is not seen independently on these maps.)
Figure 4 thus presents the spatial pattern of inequalities in European Russia and West
Siberia in 1990. Overall inequality was much lower in this period than it later became; hence the
map is almost entirely in shades of green. Touches of yellow indicate the higher money incomes
in the Far North (costs were higher there too, of course), and light blue shows the lower incomes
of the Caucasus regions in the South. But the pattern is not extreme either way.
Figure 4. The Regional Distribution of Income in European Russia and West Siberia, 1990.
Figure 5 shows the developments as of 2000. A pattern of regional cleavage has emerged,
with a flood of wealth attributed to the lightly populated oil regions of Tiumen and Khanty-Mansy
(and also to Moscow City, which is not shown). Of equal significance is the stark relative decline
of the Russian South, the scene of course of many conflicts including the Chechen war.
Figure 5. The Regional Distribution of Income in European Russia and West Siberia, 2000.
As a final exercise in this vein, Figures 6 and 7 present regional and sectoral data together in a
single graph. The device is a stacked line graph. Each of the 89 regions is represented by a line,
whose value at each of fourteen points on the x-axis is given by the contribution to overall
inequality in Russia of the sector represented at that point. The provinces are arrayed by the size
of their total contribution to inequality (from bottom to top), and the values are cumulated, so
that the height of the stack at any sector represents total contribution to inequality of that sector.
The sectors are arrayed along the x-axis in accord with their total contribution to inequality, so
that reading along the axis provides a guide to the relative wealth and poverty of different
economic activities in Russia. The charts present data for 1990 and for 2000.
Stacked Plot ( 88v*14c)
Figure 6. Province and Sector Inequality in Russia, 1990.
Figure 7. Province and Sector Inequality in Russia, 2000
The figures illustrate three fundamental points. First is the very great scale of increasing
inequality in Russia over a decades time. Second, there is the reorganization of sector ranks.
Most notably, whereas in 1990 agricultural incomes were at the middle of the Soviet income
distribution, a decade later they were at the bottom. Meanwhile the finance sector had moved up,
surpassing science and management, with strong gains especially in Moscow and Saint
Petersburg. Third, the high relative incomes in construction and industrial production in modern
Russia are due to extraordinary relative gains in just a handful of places; in many places in the
country these activities are not high-income. This is consistent with the view that the energy sub-
sector has come to dominate prosperous industrial activity in todays Russia.
4. The Case of China.
The Chinese transition to a socialist market economybegan with the liquidation of the
Great Proletarian Cultural Revolution in 1979 and the re-institution of the Household
Responsibility System for Chinese agriculture. (For a brief history, see Galbraith and Lu, 2000)
There followed a period of rapid agricultural productivity growth, with consequent surplus
population which became absorbed in light industry (township and village enterprises). In the
early 1980s special economic zones began the process of opening Chinas coastal cities to foreign
investment and inward capital flow, a process which also facilitated technology transfer to
Chinese industry.
The tremendous success of the Chinese reforms in the 1980s led to large increases in
living standards throughout the country, and a very substantial reduction of absolute poverty.
Food deprivation virtually disappeared. However economic slowdown at the end of the decade
produced an inflation, particularly in food prices, which contributed to the discontent of urban
populations. This factor played a role in building popular support for the political movement for
democracy of 1989, which eventuated in the bloody battles of June 4 in Beijing.
Following the profound political shock of 1989, Chinese economic reform continued but
along revised lines. Continuing decentralization devolved power from the center to the provinces;
sectoral liberalization devolved power toward industries whose strategic position involved
elements of monopoly power. Meanwhile the post-Tienanmen government particularly
encouraged the municipal authorities of Shanghai (whence the top officials came) to pursue grand
plans to restore that city to its position of financial pre-eminence in Asia, while the government
also embarked on an extraordinary redevelopment of the capital city.
All of the above developments have visible effects on the pattern of income distribution in
the Peoples Republic of China over the 1990s
Gini and Theil Measures
T' Inequality
Theil's T statistic across sector-province cells
Gini Coefficients (Survey Data)
Figure 8 presents an overall measure of income inequality in China, calculated across
sector-province cells. It is superimposed over two standard, sample-based measures of income
inequality in China, one urban and one rural. All show approximately similar patterns, with
particularly sharp increases in the early-middle 1990s, however the survey data end in 1995. A
drawback of the sector-province measure is that there was a redefinition of sectors in 1994, which
almost surely causes the extent of the increase in 1994 to be overstated; nevertheless, that there
was a sharp increase in Chinese inequality in 1994 is not in doubt. The extension of the Theil
measure through 2000 shows that inequality seems to have stabilized somewhat in China after
1996, though at high levels by historical standards for the country.
Figure 8. Measures of Income Inequality in China, 1987-2000.
Figure 9 presents a regional and sectoral decomposition of inequality in China. It shows
four measures that can be computed easily from data based on province-sector cells: inequality
between sectors, and inequality between provinces. As the figure illustrates, the pattern of
Chinese inequality resembles that in Russia, insofar as the spatial dimension of rising inequality
dominates the sectoral dimension. Similar considerations therefore apply. On the one hand,
differences in regional costs of living undoubtedly mean that real living standards have not
diverged as much as money incomes. On the other hand, however, a pronounced spatial pattern
of income inequality sets up powerful incentives for internal migration, with resulting pressures on
housing, social services, and unemployment.
Inequality Between Sectors and Between Provinces
Between Sectors
Between Provinces
T' Inequality Overall
Theil's T' Inequality Measure
87 88 89 90 91 92 93 94 95 97
Guangdong Shanghai Beijing Inner Mongolia Zhejiang Tianjin
Jiangsu Fujian Tibet Yunnan Qinghai Ningxia
Xinjiang Hainan Gansu Guangxi Guizhou Shandong
Jilin Shanxi Shaanxi Anhui Jiangxi Hebei
Hunan Hubei Liaoning Sichuan Henan Heilongjiang
Contribution Across Provinces
Figure 9. Regional and Sectoral Patterns of Inequality in China, 1987-2000.
The contribution of each province to Chinese inequality is illustrated in Figure 10, using
the same principles shown for Russia in Figure 2. Provinces whose income is below (above) the
national average contribute a negative (positive) quantity to the Theil index, based on distance
below (above) the average and population weight.
Figure 10. Inter-provincial contributions to inequality in China, 1987-1997.
Theil Element
78 79 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99
Transport Bank&Ins Utilities Govt
Health Scientific Social Services Real Estate
Education Others Geo. Prospecting Construction
Mining Farming Trade Manufacturing
Contribution By Sector
The picture that emerges from Figure 10 is, of course, of the dense concentration of high
incomes in just three places: Guangdong Province, and the municipalities of Shanghai and Beijing.
The first case is explicable, of course, by the fact that Guangdong is the major center for rapidly
growing export manufacturing in China. Shanghai and Beijing enjoyed particularly free rein in this
period to pursue rapid economic development and redevelopment, as any visitor to either city can
attest. What is striking is the extent to which these cases appear to dominate the rise in inter-
regional inequality in China, itself the dominant pattern in the rise of inequality in China generally
Figure 11 presents the inter-sectoral distribution of income in China. Unlike Russia, China
does not have a strong natural resource sector. Instead, the chief winners in the Chinese transition
have been industrial sectors with monopoly power: transportation and utilities. As in Russia, the
banking sector is a major winner, something that is visibly reflected in the construction of bank
towers across the country. Manufacturing in contrast emerges as a relative loser, while the
position of farming and trade, which was never strong, has also deteriorated. The position of
mining, formerly quite high in the Chinese pecking order, has fallen considerably.
Figure 11. Intersectoral contributions to inequality in China, 1978-1999.
Figures 12 and 13 present the evolution of inequality across provinces in China in a pair of
maps, organized on principles similar to those shown earlier for Russia. Superficially, the pattern
is quite similar: relative income gains are concentrated in relatively small narrow of the country.
However it should be noted that, in contrast to the Russian case, the great winners in China are
heavily populated. Guangdong province, in particular, holds well over eighty million people.
(Note that the city of Shanghai is not represented on these maps.
Contributions to Inequality
Well Below Average
Below Average
Low Neutral
Low Neutral
High Neutral
Above Average
Figure 12. The Regional Distribution of Income in China, 1987
Figure 13. The Regional Distribution of Income in China, 1997
Inequalites in China -- 1987 by Sector and Province
Inequality in China - 2000 by Sector and Province
Figures 14 and 15 present sector-province line graphs for China similar to those shown for
Russia in Figures 6 and 7. Notable details include the sharp fall in the relative position of
construction, formerly the best-paid activity in China, and the decline in the relative position of
manufacturing workers (IN in the 1987 figure, MA in that for 2000). On the other hand, the
position of science, health and education has held much better in China than was the case in
Russia. The rising relative position of almost all activities in the few top provinces is clearly
apparent in Figure 15.
Figure 14. Sector and Province Inequality in China, 1987.
Figure 15. Sector and Province Inequality in China, 2000.
Note: Sector IN is broken up into MA, UT, and MI in 1994, and SS is added at that date. See appendix for
5. Conclusions
Under the surface appearance of radical differences between the transition experiences of
Russia and China, disconcerting similarities can be found. In both countries sharp rises in
inequality coincided with macroeconomic crises. This was true of the industrial collapse of 1991
and the financial implosion of 1998 in Russia, and of the growth slowdown of 1993-1994 in China,
known euphemistically in China as the period of soft landing.In both countries, incomes
diverged more sharply on a regional than on a sectoral basis. In both, relative income rose most
sharply in the financial and political centers (Moscow, Beijing, Shanghai) and in the regions
providing hard currency export earnings (West Siberia, Guangdong). In both, economic
liberalization produced economic rents for those sectors enjoying monopoly power in the domestic
market (transportation, Utilities). And in both countries, the rise of finance capitalism produced
large relative and no doubt also absolute gains for those employed in the financial sectors.
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 programs and social security systems -- such as exist
in the United States and Europe -- a particular feature of redistribution is a sharp decline in the
relative income of the country-side. It is apparent that there is no market mechanism that works
effectively to offset this trend; despite all of the problems of agriculture in socialist countries,
socialism is evidently a system for the countryside.
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. Indeed a close analysis of changes in province-sector cells reveals that the
education sectors in Shanghai and Beijing are among the most rapidly gainers of relative size and
income in all of China during the late 1990s. In Russia these sectors have suffered absolute and
relative losses, with serious consequences for the health, education and culture of the population.
It seems certain that the continuing presence of control over the capital account, and the
corresponding suppression of capital flight from China, is a major factor in preserving the Chinese
capacity to act in the social sectors.
Pedro Conceição and James K. Galbraith, Constructing Long and Dense Time Series of Inequality
Using the Theil Statistic,Eastern Economic Journal, 26(1), 61-74, June 2000.
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Nested and Hierarchical Grouping Structures: Implications for the Measurement of Inequality
Through Time, With Data Aggregated at Different Levels of Industrial Classification, Eastern
Economic Journal, 27(4), Fall 2001, 491-514.
James K. Galbraith and Lu Jiaqing, Sustainable Development and the Open-Door Policy in China,
A Paper from the Project on Development, Trade and International Finance. New York:
Council on Foreign Relations, 2000.
Goskomstat, Labor Statistics (Trud), various issues ,
Carl Riskin, Zhao Renwei, Li Shi, editors. China's retreat from equality : income distribution and
economic transition, Armonk, N.Y. : M.E. Sharpe, c2001.
Alexey Sheviakov and Alexander Kiruta, Economic Inequality, standards of living, and poverty of
population in Russia, Center for Socioeconomic Measurement, Russian Academy of Sciences and
State Committee of the Russian Federation on Statistics, 2002.
State Statistical Bureau, China Statistical Yearbook, Beijing: China Statistical Publishing House,
various years.
Sector Codes in Russia and China
IP Industrial Production
AG Agriculture
FO Forestry
CT Construction
TR Transportation
CM Communication
TS Trade and food services
HO Housing
HS Health, sporting and social services
ED Education
CA Culture and arts
SC Science
FI Finance, credit and insurance
MG Management
FA Farming, Forestry, Animal Husbandry and Fishery
MI Mining, and Quarrying
MA Manufacturing
UT Electricity, Gas and Water Production and Supply
CT Construction
GE Geological Prospecting and Water Conservancy
TR Transport, Storage, Post & Telecommunications
WS Wholesale and Retail Trade,& Catering Services
BA Banking and Insurance
RE Real Estate Trade
SS Social Services
HE Health Care, Sporting & Social Welfare
ED Education, Culture and Art, Radio, Film and Television
RD Scientific Research and Polytechnical Services
GT Government Agencies, Party Agencies and Social
ET Others
IN Industry
Note: Chinese industrial code IN becomes split into MI, MA, and UT in 1994; Sector SS is added
at that date. This should not affect calculations of inter-provincial or inter-sectoral inequality
presented here, as these are taken from standardized post-1994 definitions.
... The processes of territorial concentration of the economy and population and, more broadly, regional inequality in post-Soviet Russia have been studied in sufficient detail, both in economic studies and economic geography. Most of the studies focus on the level of federal subjects and generally confirm the general trend of convergence of regions from the second half of the 2000s 6 [4,16,31], which replaced a long period of growth in interregional differences in the 1990s and early 2000s [6,9,26]. The intensification of territorial inequality continued throughout the crisis period of the 1990s, which was then supported by economic growth. ...
... In addition, the number of firms reduces due to the closure of small uncompetitive companies, which leads to firms understanding the need for mutual cooperation in order to minimize costs and to use joint innovations. Having studied the transition period in the Russian and Chinese economies (1990 s), Galbraith et al. (2004) argue that the industries with the maximum level of concentration remained in a winning position and were less affected by the crisis, in terms of unemployment risk. ...
This paper studies the influence of diversification and specialization on one of the main indicators of the Russian labour market: unemployment growth. The purpose of the work is to find out which effects dominate in the Russian regions, Marshallian or Jacobs, and whether this predominance is stable for different time periods. We tested empirically the following hypotheses: 1) the dependence of the unemployment growth on the concentration or diversification is nonlinear due to possible overlapping effects of urbanization and localization; 2) the influence of the concentration or diversification on the unemployment growth depends on the time period. To test these hypotheses, we use nonparametric additive models with spatial effects. Both hypotheses found empirical confirmation, with each effect prevailing in different time periods: Marshallian effects were prevalent in 2008-2010, and 2013-2016, while Jacobs effects were prevalent in 2010-2013.
... It is important to note that as the end of the union drew closer, the economic equality in the country was relatively high, even though it was far from the proclaimed socialist ideal (Alexeev and Gaddy 1993;Bergson 1984;Matthews 1985;McAuley 1977). After the breakup of the union state, Russia experienced a massive rise in income inequality (Galbraith, Krytynskaia, and Wang 2004). ...
Previous research has either equated religion- and language-based group identities or asserted that their social effects are the same. This article proposes a novel differentiation between religious and ethnic self-identification that accounts for in-group income inequality and the social role of the group. The study argues that ethnicity-based identities tend to be associated with economic activities, thereby increasing the demand for income equality within such groups. Religious identities, on the contrary, are centered around noneconomic activities and have the ideological framework for reconciling material inequalities. The observable implication of this distinction is that the high-, low-, and middle-income categories of the multicultural society will display differential association with ethnic and religious identities. Ethnic groups will have lower in-group income inequality as a result of the exclusion of the poor and the departure of the rich. Religious groups, on the contrary, will have higher in-group income inequality due to the capacity of religion to accommodate both poor and rich. Relevant empirical tests from the ethnically and religiously diverse Russian North Caucasus region indicate support for the proposed theory.
... In addition, IID and public (particularly innovation) policies can play significant roles in efforts to lower socio-economic challenges such as inequality (the gap between the rich and poor) and exclusion (Galbraith, Krytynskaia, and Wang 2004;OECD 2011a;BRICS 2014;Keeley 2015), poverty (World Bank 2004;OECD 2011b;IMF 2014) and unemployment (World Bank 2005NDP 2011;Gupta 2013;Marcelle 2014;UNCTAD 2014), all of which impact on development. ...
... 6 [4,16,31], сменивший длительный период роста межрегиональных различий в 1990-е и в начале 2000-х гг. [6,9,26]. Усиление территориального неравенства продолжалось на протяжении всего кризисного периода 1990-х гг., а затем было поддержано ростом экономики. ...
Full-text available
The aim of the study is to identify general trends in the process of spatial concentration / deconcentration of the population and economy (according to GDP) in the countries of the European Union and in Russia in 2007–2015. An attempt was made to identify the role of the Global Cities in this process. The study was performed at two spatial levels – the statistical division grids NUTS2 and NUTS3 (for the EU) and the level of the subjects of the Russian Federation. The degree of concentration and its dynamics were estimated based on the analysis of the Theil Index. The contribution of Global Cities was determined through Theil index decomposition. It is shown that the demographic concentration at the NUTS3 level was more intense than at the NUTS2 level and in almost all countries. The decrease in territorial economic inequality at the NUTS2 level in the period did not lead to convergence at the NUTS3 level. There was economic divergence in the period 2009–2015. These results confirm trends previously identified by other researchers. It has been established that the contribution of Global Cities to the processes of economic and demographic concentration turns out to be positive in both cases. It is stronger than the contribution of the others territories to the concentration of the population, and is almost equal, but opposite to the deconcentration in the economy, observed in others territories. It is concluded that global cities in Russia make a multidirectional contribution to the process of population concentration and economical deconcentration, which is atypical for the EU countries and similar to those states that also have economic difficulties (for example, Greece and Portugal).
... Therefore, although wealth is accumulating rapidly in the hands of a very small number of people in both countries, much of that wealth represents the conversion of extremely high incomes into wealth (available at: https:// Economic inequality in household incomes is also accompanied by growing regional and sectoral inequality in Russia and China [57]. 5 Markedly divergent post-communist inequality patterns suggest that the rise in inequality is not inevitable and point to the importance of policies, institutions, and ideology in shaping inequality. ...
Full-text available
Objective To compare the specifics of the ldquopublic protection responsesrdquo to the deepening of marketisation in Russia and China and to the strengthening of ldquomarket fundamentalismrdquo in Western countries. Methods The methodology is based on the concept of ldquodouble movementrdquo developed in the works of Karl Polanyi and on the categorical apparatus of the authorrsquos theory of institutional X and Ymatrices. Results It is shown that since the 1980s in most countries of the world a process of liberalisation of national economies has been taking place including the active introduction of market institutions in various spheres of social life. In Russia and China this process is known as postsocialist ldquomarket reformsrdquo. However after the global financial and economic crisis of 2007ndash2008 which showed once again the instability of the market economy and the continuous growth of social inequality there have been widespread and continuing attempts to strengthen public control over spontaneous market forces. A similar process took place in the 1930s in Europe and the United States after the Great Depression and Karl Polanyi then called it a ldquodouble movementrdquo or ldquocountermovementrdquo. He described it as a public response to the expansion of the market ldquoaimed at protecting human life and naturerdquo. The ldquodouble movementrdquo has both its positive perspectives and risks. The main risks as Polanyi noticed were the spread of populist ideologies in societies including fascism and the associated threat of social instability. The consideration of the Polanyian approach with the categorical apparatus of the theory of institutional XndashYmatrices revealed the specificity of the ldquodouble movementrdquo in Russia and China compared to the capitalist countries of the West. It is shown that in Russia and China the scale of state participation in the economy and social control over the market compared with Western countries is significantly higher which makes the economic development of these two countries more stable and predictable in the context of the continuing ldquoera of uncertainty.rdquo The specific risks of ldquodouble movementrdquo for these countries were also identified associated with excessive strengthening of the unitary principle in the political system and an ldquooverdoserdquo of collectivist ideas to the detriment of personal aspirations and values. Scientific novelty Identification of the specific features of the ldquopublic responserdquo to excessive marketisation in countries where either X or Yinstitutional matrices dominate. Practical significance The results obtained can be used as theoretical and illustrative material in courses on institutional economics and economic sociology as well as for examining the implications of various and differing institutional designs of national economic policies.
... In addition to the large body of literature on rural-urban income inequality, inter-andintra provincial inequality, as an inevitable outcome of market driven reforms, is another subject of heated debate (Tsui, 1993 andGustafsson and Li, 2002;Shorrocks and Wan 2005;Fan and Sun, 2008;Gries and Redlin, 2008;Hao and Wei, 2010;Li and Wei 2010). Measurements by Galbraith, Krytynskaia and Wang (2004) show that much of the rise could be attributed to the relative gains of just one province and two municipalities: Guangdong, Shanghai and Beijing. Major losers in regional terms include the Northeast (Manchuria) and the Southwest (Sichuan). ...
Why do some secessionist claims turn violent and others stay peaceful? This study elucidates the role of inequality and diversionary tactics of states in secessionist violence. Horizontal inequality increases the grievances of minorities and fuels rebellion. States with high vertical inequality prefer to suppress peripheries instead of increasing redistribution and alleviating their material grievances. States shun redistributing toward peripheral regions because sharing with one group prompts demands for redistribution among other groups, including the dominant group. Fearing resource reallocation at the national scale and potential loss of their elevated social status, the elites opt for violent solutions for secessionist crises. Using a new dataset on self-determination movements I test these conjectures and find strong support for them.
This book develops the first new, liberal theory of economic justice to appear since John Rawls and Ronald Dworkin proposed their respective theories back in the 1970s and early 1980s. It does this by presenting a new, liberal egalitarian, non-Marxist theory of exploitation that is designed to be a creature of capitalism, not a critique of it. Indeed, the book shows how we can regulate economic inequality using the presuppositions of capitalism and political liberalism that we already accept. In doing this, the book uses two concepts or tools: a re-conceived notion of the ancient doctrine of the just price, and my own concept of intolerable unfairness. The resulting theory can then function as either a supplement to or a replacement for the difference principle and luck egalitarianism, the two most popular liberal egalitarian theories of economic justice of the day. It provides a new, highly-topical specific moral justification not only for raising the minimum wage, but also for imposing a maximum wage, for continuing to impose an estate tax on the wealthiest members of society, and for prohibiting certain kinds of speculative trading, including trading in derivatives such as the now infamous credit default swap and other related exotic financial instruments. Finally, it provides a new specific moral justification for dealing with certain aspects of climate change now regardless of what other nations do. Yet it is still designed to be the object of an overlapping consensus—that is, it is designed to acceptable to those who embrace a wide range of comprehensive moral and political doctrines, not only liberal egalitarianism, but right and left libertarianism too.
The paper provides a critical assessment of an authoritative study “From Soviets to oligarchs” (2017) on evolution of economic inequality in Russia by F. Novokmet, T. Piketty and G. Zucman where Russia is portrayed as a country with abnormally polarized income distribution by international standards. The author examines main quantitative results obtained by Piketty’s team, describes peculiar methodological features of their measuring procedure and analyzes how they dissect available empirical data sets. A general conclusion is that the trio uses an unconventional methodology that does not allow to apply equivalence scales; their argument suffers from internal inconsistencies (different units of observation and different definitions of income are used); they misunderstand the nature of data that form a base for their calculations (simulated estimates are perceived as raw survey data, post-tax incomes as pretax ones, etc.); deduction and declaration coefficients that they impose on tax data are empirically improbable; their final estimates of income inequality for Russia are higher than empirically realistic ones approximately by one third (Gini coefficients 0.5—0.6 instead of 0.3—0.4 by other researchers).
Full-text available
This paper deepens and extends the conclusion by Conceição and Galbraith [2000] that, under some very general conditions, the dynamics of overall inequality can be captured using only the between sector component of the Theil index. This paper explores the fractal properties of the Theil index, and presents a more compelling empirical illustration. The fractal property of the Theil index results directly from its characteristic of perfect decomposability, which allows for the separation of inequality into between- and a within-groups components, provided that the groups are mutually exclusive and completely
This paper proposes the application of the between-group component of the Theil index to data on wages, earnings, and employment by industrial classification, in order to measure the evolution of wage or earnings inequality through time. We provide formal criteria under which such a between-group Theil statistic can reasonably be assumed to give results that also track the (unobserved) evolution of inequality within industries. The advantage of this approach lies in the widespread availability of data from which long and dense time-series of inequality may be constructed. We conclude with an empirical application to the case of Brazil, an important developing country for which satisfactory Gini coefficients are scarce, but for which a between-industries Theil statistic may be computed on a monthly basis as far back as 1976.
China's retreat from equality : income distribution and economic transition, Armonk
  • Carl Riskin
  • Zhao Renwei
  • Li Shi
Carl Riskin, Zhao Renwei, Li Shi, editors. China's retreat from equality : income distribution and economic transition, Armonk, N.Y. : M.E. Sharpe, c2001.
Economic Inequality, standards of living, and poverty of population in Russia, Center for Socioeconomic Measurement, Russian Academy of Sciences and State Committee of the Russian Federation on Statistics
  • Alexey Sheviakov
  • Alexander Kiruta
Alexey Sheviakov and Alexander Kiruta, Economic Inequality, standards of living, and poverty of population in Russia, Center for Socioeconomic Measurement, Russian Academy of Sciences and State Committee of the Russian Federation on Statistics, 2002.
Sustainable Development and the Open-Door Policy in China, A Paper from the Project on Development, Trade and International Finance
  • K James
  • Lu Galbraith
  • Jiaqing
James K. Galbraith and Lu Jiaqing, Sustainable Development and the Open-Door Policy in China, A Paper from the Project on Development, Trade and International Finance. New York: Council on Foreign Relations, 2000.