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Is pro-growth institutional reform also pro-poor? : State-owned Enterprises
reform, inequality and poverty in urban China1
By Xiaobao Chen2, OECD Development Centre
Abstract:
Institutional reform in a transition economy, which aims to enhance long run economic growth by
liberalising factor market, can actually worsen inequality and poverty at least in the short run.
Evidence suggests that a pro-growth state-owned enterprises (SOEs) reform, which allows market
forces to determine workers’ pay according to their ability and improve the efficiency of the
Chinese economy, appears to have been joined with a decline in economic growth and an increase
in urban income inequality and poverty. Meng (2004), for example, observed that there are
increasing income gains at the top end of the urban income distribution and reductions at the lower
end in China since 1995 (the year when SOEs reform started). This is a great contrast to the
previous period (1988-1995) when income grew by 53% for the top 3% of household and 20% for
the bottom 20% despite the income gap between them widens (Zhao and Li 1999). The traditional
‘equity vs. efficiency’ trade-off exists in a market economy seems to be taking shape rapidly in
urban China particularly since the initiation of SOEs reform. This paper investigates the effects of
pro-growth state-owned enterprises (SOEs) reform on China’s urban inequality and poverty during
the period of rapid SOEs restructuring by testing two models of transition by Aghion et al (1994,
1997), using Urban Household Survey data. Main findings show that the speed and manner in
which layoff of redundant workers is implemented is pivotal in determining the extent of inequality
and poverty. The gradualist approach by which the reform was experimented locally then promoted
nation-wide helped to reduce poverty incidence. However, the inequality is worsening as
fundamentally, a reward system of ‘pay according to ability’ in the labour market for higher
productivity has been introduced which allows wage differential between the skilled and unskilled
to widen. This wage differential exists more prominently among SOEs (within sector) than between
SOEs and private enterprises (between sectors), because the wage has been allowed to be
determined freely and not fixed which enables the efficient SOEs to restructure without
privatisation, hence becoming more profitable and able to reward its workers who have
higher efficiency a higher wage. The social welfare functions of the SOEs are now shifted
towards the private individuals. This substantially worsened the welfare of the SOEs
workers at least in the short run as a well-functioned social security system for the billions
take time to be established. 1 Zero draft. Please do not quote or circulate. Comments welcome. 2 Contact: [email protected]. The views expressed in this paper are those of the author and do not necessarily represent those of the OECD Development Centre.
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Table of Contents
SECTION 1: INTRODUCTION ................................................................................3
SECTION 2 BACKGROUND ....................................................................................7
2.1 CONCEPTS, MEASUREMENTS AND DATA LIMITATION ...........................7
2.2 URBAN GROWTH .................................................................................................9
2.3 URBAN INEQUALITY ........................................................................................10
2.4 URBAN POVERTY..............................................................................................12
SECTION 3 THEORETICAL CONSIDERATIONS ............................................14
3.1 POVERTY-GROWTH-INEQUALITY TRIANGLE ...........................................14
3.2 GROWTH AND INEQUALITY LITERATURE .................................................15
3.3 PRO-GROWTH SOES REFORM & INEQUALITY ..........................................16
SECTION 4 EMPIRICAL EVIDENCE ..................................................................21
4.1 DECOMPOSITION OF POVERTY REDUCTION............................................21
4.2 SOES REFORM AND INEQUALITY.................................................................22
4.3 SOES REFORM, WELFARE AND GROWTH ..................................................33
4.4 SOES REFORM AND POVERTY.......................................................................35
SECTION 5 CONCLUSION ....................................................................................37
APPENDIX A: AGHION AND BLANCHARD TRANSITION MODEL (1994) ....39
REFERENCE .............................................................................................................41
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Section 1: Introduction Despite the impressive Chinese average GDP growth rate of over 8% per annum (1980-
2000), China’s income inequality has been rising fast as well. The urban Gini coefficient of
China has increased drastically from 0.16 in 1978 to 0.34 in 2002 (UNDP 2005). Above all,
it is estimated that around 11 million people were laid off in urban China annually by
State-Owned Enterprises since their reform3 that commenced in 1995. Several different
estimates suggest that 12 million state sector workers were laid off in 1999 alone which
accounts for an additional 7 percent of the urban labour force4 (Fan 2000 and Appleton,
Knight, Song and Xia, 2001). Many of them did not have high reemployment probability at
least in the short term and had contributed to the new acute phenomenon of 'urban
poverty’5 as they have to survive on compensatory subsistence payment as low as $100 a
year and have inadequate welfare provision such as health care and housing6. The total
number of jobless workers buying the unemployment insurance rose from 79.3 million in
1998 to 103.42 million in 2000 according to the Ministry of Labour and Social Security.
Fang et al (2002) shows by decomposition analysis of poverty change 7 that urban
poverty actually increases between 1995 and 1999, the period of extensive SOEs reform,
despite the economic growth. Depending on the poverty line used, the urban poverty rate
may have reached as high as 9% in 2002. This is due to the worsening income distribution
that reduces the gains of growth to be trickled down to the poor. Meng (2001) observed that
there are increasing income gains at the top end of the urban income distribution and slow
down of income gains at the lower end in China particularly since 1995. This is a great
contrast to the previous period (1988-1995) when income grew by 53% for the top 3% of
household and 20% for the bottom 20% though the income gap between them widens
3 Although SOEs reform also liberalizes other factor market, eg capital, and give rises to some ‘asset stripping’ which also affects the inequality landscape, this paper focuses on labour market liberalization as the main impact channel via which SOEs reform affects inequality and poverty in China. Since majority of SOEs are located in the urban areas, I focus on urban inequality. 4 It should be noted that this paper focuses on the period of extensive SOEs reform, 1995-1999. There has been some improvement in employment prospect in recent years especially after China’s entry into the WTO. For example, according to official figure: during the 2001-2005 periods, more than 180 million workers laid off from state-owned enterprises found new jobs (MOLSS, Jan19th , 2006). However, given the growing urban inequality (alongside rural-urban inequality) and the deepening of SOEs reform, this study and its findings are of high policy relevance. 5 Until the mid-1990s, poverty in China was a largely rural phenomenon. The Government’s antipoverty policies almost exclusively targeted the rural poor. 6 Even in a more prosperous city like Shanghai, unemployed workers only have a Minimum Living Allowance of around 280 yuan ($34) a month (NBS). 7 Bourguignon (2004) shows that poverty is the net outcome of interactions between growth and inequality. The Poverty-growth-inequality triangle will be elaborated in the theory section.
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(Zhao and Li 1999). According to the recent urban household survey conducted in 2002 by
CASS, the income of the richest 5 percent of urban residents was nearly 13 times that of
the poorest 5 percent.
The alarming poverty and inequality trends pose an antithesis to the period before the
SOEs reform. According to World Bank, 270 million Chinese had been lifted out of
absolute poverty between 1979 and 19958. China was then the finest example of pro-poor
growth. The poor at the bottom of the income distribution were able to participate and
share the benefits of growth. China’s ‘Open Door’ policy was perceived as embracing
globalisation and was advocated as the key to a successful poverty reduction by the Neo-
liberals.
Perhaps one might ask why bother with carrying out SOEs reform in the first place, if
China were growing over 8 percent per annum according to the official statistics?
The reasons are simple: development of the non-state sectors such as the TVEs since
1979 has spurred high economic growth. For example, the share of industrial gross output
value by non-SOEs swelled from less than 20% in 1980 to about 75% in 2000. This poses
fierce competition for the SOEs, which has incurred soaring financial losses partly because
most of them are plagued by heavy burdens of welfare provisions as in a planned economy9.
In 1995, the year in which the SOEs reform was initiated, half of the Chinese SOEs were
loss-making while they still employed 76% of total urban employment and produced only
35% of the industrial output while consuming 71% of government revenues (Qian 97). It
creates allocative inefficiency as it crowds out resources that would have otherwise become
available for the more efficient non-state sector and has great productive inefficiency as
they employ excess labour that enjoys employee tenure (often life-long), narrow wage
differentials and extensive employer-provided welfare benefits (housing, pensions, health
care, education) and thus has little incentive to be productive.
SOEs reform if implemented successfully will reduce worker’s lifetime welfare ties to
their employers, thereby providing them with more freedom or mobility to change jobs and
achieve higher earning potential (enhance income mobility in the long run) as the SOEs
reform may allow market forces to determine workers’ pay according to their ability. This
8 Meanwhile, at least 100 million more people were living in poverty in the world excluding China. 9 Depending on the type of industry, a quarter to a half of SOEs assets may be tied up in non-productive activities such as provision of housing and health care.
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will fundamentally improve efficiency of the transformed enterprises, the labour market
and generate growth for the economy.
However, Klasen (2003) points out that in the short to medium term, policies that reduce
severe distortions, improve incentives for producers and free up factor markets in order to
increase the participation of poor households in growth (in the long run) are often
necessary but not sufficient conditions for promoting pro-poor growth. The introduction
of a more flexible wage setting system will widen wage differentials and may worsen
inequality at least in the short run10. This could be further exacerbated by frictions in the
labour market and also by the absence of well-functioned welfare and skill training
systems11. OECD (2006) finds that public spending on health and education may be too
low and inefficient to meet China’s development needs. Official spending12 in these areas,
along with culture and science, amounted to the equivalence of 5.5% of GDP in 2002
compared with an average of 28.2% for OECD countries. Hence, in addition to improving
incentives, more direct support is often needed to enable the poor to participate in the
growth process and to make use of the improved incentives. The inequality of opportunities
or access to health and education is not only a direct welfare loss, but also will endanger
China’s economic growth as productivity is lost due to diminished human capital.
In order to identify types of growth policy that is pro-poor, one needs to study how a
pro-growth institutional reform, which aims to improve incentives, could affect the
inequality and poverty (at least in the short run).
This paper focuses on answering two questions: First, the relationship between growth,
inequality and poverty in urban China from 1995 to 1999. i.e. is there a drastic change in
the poverty-inequality-growth relationship during the intensive SOEs reform period?
Second, how does Chinese SOEs reform (pro-growth) actually affect inequality and
poverty? To do this, I make use of the theories of transition proposed by Aghion and
10 Given that China has joined the WTO and favours FDIs that increasingly demands higher skill, China may experience a faster widening wage differential between the skilled and unskilled. Not to mention the fierce competition that MNCs may inflict upon SOEs and result in more closure of SOEs hence exacerbating poverty. 11 The rising income inequality means a lot of the poor have little or no access to basic education and medical healthcare whose prices are increasing drastically because of massive privatizations in those two sectors in the late 1990s. 12 The fast growth in Chinese public spending has been most marked in infrastructure investment and in public administration.
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Blanchard (1994) and Aghion and Commander (1999). The existing empirical literature is
scant13 and does not integrate well with theories. For example, Meng (2001) examines the
economic restructuring’s impact on urban inequality and she rarely introduces any theory
but just embarks on regression analysis to find out who are most affected by SOEs reform.
These rather ad-hoc studies also suffer data problems, as the urban household survey data is
not widely accessible and reliable. This paper attempts to document and integrate theories
available on transition and empirical literature on urban inequality in China in the light of
recent development.
The plan of the paper is as follows. Section 2 describes background concept,
measurement and data to evaluate the gravity of urban inequality and poverty for 1995-
1999, the intensive SOEs reform period. Section 3 reviews the literature and put forth
major theories. Section 4 attempts first to produce some empirical evidence on the
relationship between growth, inequality and poverty in urban China and second to examine
the impact of SOEs reform on inequality and poverty as implied by the two theoretical
models. Section 5 concludes this study with some policy recommendations.
13 Most studies on inequality in China centre around rural-urban and regional inequality. The few literature on changes in urban income distribution in China focuses mainly on the period up to the mid-1990s. eg. Zhao and Li (1999) advocated that urban housing reform was the main cause of inequality.
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Section 2 Background
2.1 Concepts, measurements and data limitation
a) Concepts:
‘Poverty is pronounced deprivation in well being’ (WDR 2000/1). It is when the poor do
not have the basic needs such as food, shelter, education and health care. But more
importantly, the poor are very vulnerable to adverse events outside their controls, let alone
having the power to voice their opinions. The deprivations restrict what Amartya Sen calls
the “capabilities that a person has, that is, the substantive freedoms he or she enjoys to
lead the kind of life he or she values.’’
A person is considered poor if his consumption or income level falls below some
minimum level necessary to meet basic needs. This minimum level is usually called the
"poverty line’’ which is multi-dimensional, incorporating both an income poverty line for
needs that can be met monetarily and non-monetary lines for other needs.
Absolute poverty differs from relative poverty as the poverty line of the former is
established in terms of some well-defined basic needs while the later is measured by the
fixed proportion of some income standard in the population, for example the mean or
median income (Bourguignon 2004). Absolute poverty line is more widely used in Less
Developed Countries and I will use it in the case of urban China.
Inequality is a broader concept than poverty as it is defined as the disparities in relative
income across the whole population, i.e. not only the censored distribution of individuals or
households below a certain poverty line. Incomes at the top and in the middle of the
distribution may be just as important to us in perceiving inequality as those at the bottom
(Litchfield 1999).
Klasen (2005) stresses the importance of non-income dimensions (health and education)
and measures of well-being which is of particular relevance to China given its serious
disparity in health and education provision across income groups and inadequate funding
by the government (OECD 2006). In this paper, the main focus is on the income or (more
precisely wage inequality) instead of the wealth inequality. Disparities in health and
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education which will eventually affect individual capabilities will also be discussed in
section 4.3.
b) Measurement:
Inequality has numerous measures. The most widely used is Gini coefficient, which
range from 0 to 1 of scale, with 1 represents total inequality and this provides an easy
comparison. However, the main drawback of Gini is that it is responsive only to changes in
income of the middle class rather than among the rich or poor. Theil index is a
decomposable measure, which allows the comparison of between-group and within-group.
It suffers similar problem as the Gini. In this paper, Gini and income deciles will be used to
give a fuller picture of the whole distribution.
Poverty line was mentioned previously and the most common measure is the US $1 a
day by the World Bank. Other measures include poverty headcount and poverty gap based
on poverty line. The headcount ratio measures the fraction of population below the poverty
line while the poverty gap ratio is defined as the ratio of the average of income (or extra
consumption) needed to get all poor people to the poverty line. It shows the depth of
poverty. In this paper, mainly these two measures will be used.
c) Data Limitations:
Data on income distribution and labour is not easily accessible and consistent in China.
Two major sources include Household Survey14 (1988, 1995, 1999, and 2002) by income
distribution research team of the Institute of Economics, Chinese Academy of Social
Science (CASS) and urban household survey (UHS) by National Bureau of Statistics
(NBS). Often the access to underlying household data from the surveys for all regions and
all years are limited and even when they are available, it is not possible to have complete
dataset for all the years covered (1995-1999) across all regions due to the local differences
in statistical practice. Under-representation of the high-income household in the household
14 The questionnaires designed by CASS were in a relatively consistent manner for the three years indicated and provide a good basis for a comparative study. The sample size for the three years is 8,992, 6,930, and 4,493 households, respectively. For 1988 the survey mainly covers ten provinces, including Beijing, Shanxi, Jiangsu, Liaoning, Anhui, Henan, Hubei, Guangdong, Yunnan, and Gansu. In 1995, Sichuan province is added to the previous ten provinces. The six provinces included in the 1999 survey are Beijing, Jiangsu, Liaoning, Henan, Sichuan, and Gansu. Thus, only five provinces were surveyed in all three years.
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survey15 is another major challenge which might lead to an underestimation of income
inequality. Hence, at some point, one may need to adapt some results from individuals or
organisations that have better access to Chinese data and had done some solid studies in
this area. In this paper, mainly the urban household survey data16 from National bureau of
statistics (NBS), which is largest and most representative survey of Chinese households17,
and data from ministry of labour and social security is used. It is also supplemented by
results from a number of empirical studies in this field including the one by Meng (2001),
which makes use of CASS Household Survey data.
The NBS survey has income and consumption measures with relatively comparable
definitions for most years. However, one issue worth noting is that the urban data are
sampled on the basis of urban household registration. This means that rural-registered
migrants living in the cities are not enumerated, and this may have some consequences for
interpretation of urban incomes and inequality.
2.2 Urban Growth
From figure 1, one can see that China’s GDP per capita growth correlates closely with
the growth of urban income per capita. There is a marked slowdown in growth of the two
between 1995 and 1999 (the extensive SOEs reform period) though the slowdown is more
moderate for GDP per capita. Indeed, the average annual growth rate for GDP per capita
and urban income per capita are 9.3 % and 20.7% (1990-1994) while they grow at a slower
pace of 7.7% and 11.1% (1995-1999) respectively.
Despite the slowdowns, GDP and urban income per capita in 1999 are 1.3 and 1.4 times
of the level in 1995. Moreover, the growth slowdown seems to be mitigated after 1999 as
GDP per capita and urban income per capita increase on average by 7.8% and 11.5%
between 2000 and 2004.
15 This is due to insufficient legal protection of private property made some people reluctant tot reveal their incomes, whether they have earned their income legitimately. 16 The National Statistics Bureau of China conducts large-scale annual household surveys in rural and urban areas. The surveys cover all 30 provinces. They usually include 30,000 to 40,000 households in urban areas and 60,000 to 70,000 in rural areas. The NSB uses a two-tier stratified sampling scheme to draw a representative random sample of the population. Each household remains in the survey for three consecutive years, and keeps a record of their income and expenditure. 17 Unfortunately, I do not have access to household level data, so grouped or aggregate levels data are often used.
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Source: Urban Household Survey (NBS) and CEIC database
In contrast, majority of the CEEs had only managed to recover to within a few points
plus or minus their pre-transition levels with the exception of Poland which had its GDP
stood at 22% above its pre-transition (1988) level by 1999 (Keane and Prasad 2002).
2.3 Urban Inequality
Ravallion and Chen (2004) estimates that China’s national Gini coefficient for income
distribution rose from 0.28 in 1982 to 0.4518 in 2002 which is similar to calculations by the
Income Inequality Project conducted by the Economic Research Institute of the Chinese
Academy of Social Sciences. If the migrant population in urban areas is included, the
national Gini coefficient for income distribution in 2002 was 0.46 (UNDP 2005). Hence,
inequality in China is quite alarming given it has reached the high level of inequality in
such a short time.
In table 1, urban Gini coefficient is the only type of Gini that is rising since 1995. It
increases from 0.28 to 0.295 in 1999. Both the rural and regional Gini have been declining
in general though rural Gini is still the biggest out of the three.
18 Were out-of-salary and unlawful incomes such as taxation evasion, monopoly rent and group consumption taken into account, national Gini coefficient would be much higher.
Figure 1. GROWTH RATE OF GDP AND URBAN INCOME PER CAPITA
0
6
12
18
24
30
36
42
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
yoy, % change
0
2
4
6
8
10
12
14
Urban income per capita (left axis) GDP per capita (right axis)
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Table 1 TOTAL, URBAN, RURAL AND REGIONAL GINI COEFFICIENTS FOR CHINA: 1990-1999
Year Total Gini Urban Gini Rural Gini Regional
Gini
1990 0.294 0.230 0.310 -
1991 0.294 0.240 0.307 -
1992 0.301 0.250 0.313 -
1993 0.310 0.270 0.329 -
1994 0.324 0.300 0.321 -
1995 0.328 0.280 0.342 0.222
1996 0.375 0.284 0.323 0.215
1997 0.379 0.292 0.329 0.216
1998 0.386 0.300 0.337 0.209
1999 0.397 0.295 0.336 0.216
Source: National Bureau of Statistics
Furthermore, the average urban Gini for the 1995-1999 period is 0.29 which is higher than
0.27, that of the previous period (1990-1995).
Although Gini method has been a convenient means of indicating general income
inequality, change in the Gini coefficient could not well reflect the change in income
distribution of specific decile and not sensitive to the change in the percentage of total
income owned by the low-income groups.
Figure 2 CHANGE IN DISPERSION OF REAL PER CAPITA HOUSEHOLD INCOME IN URBAN CHINA:
1981-1999
Figure 2 adapted from Meng (2001) which is based on Household Survey (CASS)
illustrate the urban inequality by the ratio of mean incomes of the tenth and the first income
decile. Between 1991 and 1995, there is an increase in labour mobility across urban regions
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and the rate of return to different levels of skills widened (Knight and Song 1999). Hence,
the ratio of the tenth (richest) to the first (poorest) income deciles increased from 295 % to
378 %. The ratio of the first income decile to the medium income decile was reduced from
60 to 54% while the tenth income decile to the medium income decile increased from 177
to 202%. According to Gustafsson and Li (1999), the increase in urban income inequality
in this period was due to the increased regional dispersion.
However, from 1995 to 1999, income ratio of the tenth to the first decile increased
further from 378% in 1995 to 459% in 1999. More crucially, the relative income at the
bottom end of the distribution further reduced from 54% of medium income decile in 1995
to 47% in 1999 while the average income at the top end of the income distribution
continued to rise. The rate of urban income growth on the other hand has slowed down as
indicated previously for the 1995-1999 period.
Hence, one can observe that the income distribution tends to worsen further for those at
the bottom decile (though there is a continual increase in income growth for the top income
earners, despite the slowdown19 in overall urban income growth) since 1995.
2.4 Urban Poverty
Table 2 is adapted from Fang (2002), which was based on Urban Household Survey
(NBS) from 1992-1998. P0 represents poverty headcount while P
1 shows the poverty gap
ratio and P 2 shows the Foster-Geer-Thorbecke index.
Table 2. URBAN POVERTY FOR CHINA AS A WHOLE
Source: Fang et al (2002) based on urban household survey
19 One may need to take into account the macroeconomic conditions of this post-reform period especially when there was Asian Financial Crisis in 1998 which slows growth by cutting back on Chinese exports and results in employment reductions for some who lose income.
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Generally, poverty incidence declined dramatically from 1992 to 1995 and then
increased when SOEs reforms were implemented. For the US $1.0 per day poverty line,
about 2% of urban residents were poor in 1998, barely change from 1992. One should bear
in mind that the survey does not consider the ‘floating population’ (Knight 2003) i.e. the
rural migrants working in the city, which stands at 100 to 200 million in 1999.
Compared to the annual average of 18% urban income per capita growth rate from 1992
to 1998, the rate of reduction for the number of people living under US$1.0 a day is really
low, implying the rapid economic growth has not trickled down to the people at the bottom
who are most poor. The index of P1 and P 2 which capture income gaps between the
income of the poor and the poverty line, show more rapid reductions which implies that a
lot of the poor moved closer to the poverty line.
When the US$1.5 per day line is used, the poverty incidence is much higher. In 1992,
nearly 14% of urban population had consumption less than US$1.5 a day. By 1998, the
percentage dropped to about 9%. Nevertheless, during the intensive SOEs reform period
between 1996 and 1998, all measures show an increase in poverty despite moderate growth
in real income. For example, P0 rises from 8.41% in 1996 to 9.21% in 1997. All calculated
rates of poverty incidence are higher than those by World Bank regardless of poverty lines.
Similar results are found by Chen and Wang (2001). They illustrated that poverty does
not increase if the poverty line higher than $1.5/per day is used which means that the
economic slowdown really hurts the most vulnerable people at the bottom of the income
distribution.
In a nutshell, both urban inequality and poverty are rising fast in China, accompanying a
decline in GDP and urban income growth (1995-1999). As observed from the income
decile analysis, the pattern of inequality tends to favour the rich and marginalize the poor.
This might prevent the trickle-down effect of growth.
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Section 3 Theoretical Considerations
In this section, I will explore the theories regarding growth, inequality and poverty as
well as how SOEs reform affects inequality and poverty.
3.1 Poverty-growth-inequality Triangle
Bourguignon (2004) points out that poverty reduction in a given country and at a given
point of time is fully determined by the rate of growth of the mean income of the
population and the change in the distribution of income. In “Poverty-Growth-Inequality
(PGI) Triangle” in figure 3, a development strategy such as poverty reduction, which is the
central goal of development, is thus completely decided by the rate of growth and
distributional changes in the population.
Figure 3: ARITHMETIC IDENTITY: POVERTY-GROWTH-INEQUALITY TRIANGLE
Source: Bourguignon (2004)
The real challenge to establishing a development strategy for reducing poverty lies in the
interactions between distribution and growth, and not in the relationship between poverty
and growth on one hand and poverty and inequality on the other, which are essentially
arithmetic. There is little controversy among economists that growth is essential for
(income) poverty reduction under the assumption that the distribution of income remains
more or less constant. In fact, several evidence points in this direction (Deininger-Squire
1996, Dollar and Kraay 2001, Ravallion 2002, Bourguignon 2003). Likewise, much
evidence suggests that a worsening of the distribution tends to increase poverty.
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3.2 Growth and inequality literature
As indicated earlier, the key issue in establishing a development strategy is whether
growth and distribution are independent of each other or, strongly inter-related. For
example, 1) does faster growth tend to reduce inequality or on the contrary, to increase it?
2) Could too much inequality in a given country act to slow or, to accelerate growth?
It is the first question that this paper is interested in addressing. Kuznets curve (1955)
shows that there is an ‘inverted-U’ relationship between growth and inequality, i.e. growth
would first lead to an increase and then a decrease in inequality. The uneven income
growth could occur because of skill-biased technical change for example as some sectors
take off first and there is a frenetic increase in demand for individuals with these skills. The
growth of economy is highly concentrated in these sectors and the income spread through
the economy as demands for all sorts of other goods and services rise. Moreover, it could
also be due to the transfer of labour from low productivity sectors to high-productivity
sectors. In Kuznets (1955)’s own words ‘if inequality between these two sectors was rather
more substantial than that within each sector, then inequality would first rise –as people
moved across sectors- and then fall as most of them found themselves in the new sector or
the economy reached a point where factor movement was equalizing returns across
sectors’. It is this kind of sectoral transfer of labour caused by productivity difference
and labour market institutional change initiated by SOEs reform that is examined in
this paper.
Kuznets theory was overruled by Deiniger and Squire (1996), which concludes that there
is no support for ‘inverted-U’ relationship in about 90% of the countries investigated when
tested on a country-by-country basis. There are too much country specificity in the way
growth affects distribution for any generalization to be possible.
As for whether inequality slows growth, both Persson and Tabellini (1994) and Alesina
and Rodrik (1994) showed that initial inequality seemed to be empirically associated with
lower growth rates. There are various hypotheses about progressive redistribution may be
growth enhancing. The imperfect credit market arguments indicate that redistributing
capital from capital-rich individuals to capital-poor and credit-constrained people increases
efficiency, investment and growth. Follow this line of reasoning; redistribution of human
capital is crucial for growth as it determines the productivity directly.
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3.3 Pro-growth SOEs reform & inequality
The above discussion and the literature seem to suggest that though economic growth is
the basis for increasing national income, it does not necessarily result in better distribution
or poverty reduction. Consequently, policies that merely concentrate on growth may only
be looking at part of the development problem. Policies, which promote growth as well as
improve the income distribution, will both promote pro-poor growth and poverty reduction
(Klasen 2005).
Moreover, Klasen (2003) points out that in the short to medium term, policies that
reduce severe distortions, improve incentives for producers and free up factor markets (pro-
growth) in order to increase the participation of poor households in growth (in the long run)
are often necessary but not sufficient conditions for promoting pro-poor growth.
Institutional reform such as SOEs reform in China, reduces the distortions that exist in
the labour market by providing the labourers with more mobility to change jobs and
achieve higher earning potential as market forces is introduced to determine workers’ pay
according to their ability. In the long run, more growth will be generated along with
enhanced income mobility. However, the dynamics of SOEs reform’s short to medium
term impacts on inequality and poverty is relatively unknown.
Indeed, SOEs reform may even have an underlying preference for greater inequality (at
least in the short run) because it needs to increase rewards to motivate the more able. It has
changed two aspects of the labour market i.e. 1) removal of barriers to labour mobility so
that more productive workers can be reallocated and 2) the widening of wage
differentials to reflect productivity differences. We should look for models of transition
that capture these two aspects of labour market transformations as an explanation for
increasing inequality under transition economies.
a) Aghion-Blanchard Model
Aghion-Blanchard model (1994) aims to develop an efficiency-wage based explanation
for costly labour adjustment between the old state and the new private sector for Central
Europe. It explains the first aspect of labour market change brought by SOEs reform.
In particular, they derive the following relationship for the speed of job creation in the
17
new private sector.
Model (based on assumptions in appendix A):
(A)
Subject to:
1=E+N+U; H=dN/dt= f (U) (B)
E is employment in the state sector, x is the constant marginal productivity (or average
product in this case) of the state sector, N is employment in the private sector while y is the
constant marginal productivity of the private sector. a is a parameter represents the strength
of the effect of profit on private sector job creation. r is the interest rate, H is private job
creation which is a function of profit per worker (equation 3, appendix A). Moreover, the
speed of private job creation, dN/dt=H (equation 7) is a function of unemployment. U is
unemployment, and total labour force is normalized to 1.
As seen from equation (6), the government is only concerned with efficiency and
chooses employment in the state sector to maximize the present discounted value of output.
The cost of job creation in the private sector is given by 2)(2
1H
ar.
Solving this maximization problem gives the following optimal unemployment, U**:
x= f’(U**) [ (y-(f(U**) /a) –x) /r] (C)
Equation C shows that closing one state job leads to a flow loss of x. The increase in
unemployment leads to a marginal increase in private job creation of f’(U**). The
additional flow output associated with a private job is equal to y, minus the marginal cost of
job creation, f (U**)/ar. Hence, the term in brackets gives the present value of replacing a
state job by a private job. The government should try to achieve U** and the associated
speed of closing, S**=f(U**). If U** is less than initial unemployment 0U , then the
optimal policy is not to start closing until unemployment has declined to U**.
This is illustrated more clearly by equation (6) and (8), i.e. the assumptions20 and the
20 For details, please refer to Appendix A.
18
figure 4:
Hsdt
dU −−= )1( λ , 000 1 NEU −−= (6)
)(]))1(
1(][
)([ Ufb
Urcy
caU
UaH ≡
−−−
+= (8)
λ shows proportion of workers in the state sector after restructuring. Equation (6)
indicates that unemployment dynamics depend on the flow into unemployment, i.e.
)1( λ−s (which in turn depends on speed of restructuring and the proportion of workers
losing their jobs in the process); and on the flow out of unemployment i.e. private job
creation, H. From equation 8, H depends on unemployment via wage (first term in brackets)
and tax channels (second terms in brackets).
Figure 4. DYNAMICS OF UNEMPLOYMENT UNDER EXOGENOUS RESTRUCTURING
Source: Aghion and Blanchard (1994)
From figure 4, one can observe that there is a maximum speed of restructuring. At low
level of unemployment, private job creation is initially positive and increasing but it
remains smaller than the flow into unemployment coming from restructuring, i.e. )1( λ−s .
At this initial stage, the effect of unemployment that dominates is the direct effect on wages,
the private job creation rises. As unemployment gets sufficiently large, the effect of
unemployment on taxes dominates the effect on wages and private job creation declines
and eventually at point C in the figure, the fiscal burden becomes so big that both the new
and the privatized sectors become unprofitable and close down. Hence, too fast a rate of
19
restructuring can lead to too high a level of unemployment and derail the transition. There
exist two equilibria, AU and BU . The lower equilibrium is stable, the higher one unstable.
As long as the initial level of unemployment, 0U , is less than BU , the economy converges
to the lower unemployment AU . At that unemployment rate, flows in and out of
unemployment are equal. Unemployment remains at AU until restructuring has been
achieved and the state sector has been fully transformed.
So how is this mechanism of sectoral reallocation of labour related with urban income
inequality?
Depending on the speed and sequence of the SOEs reform, the sectoral transfer of labour
(how much and at what speed) will create mainly a quantity effect on the inequality. It
arises because of changes in the distribution of attributes such as ownership in the
population of workers. Some workers are reallocated successfully from the SOEs sector to
the private sector where they enjoy higher wages while some end up unemployed in the
reallocation process and suffer absolute loss of income which increases the inequality.
The existence of optimal speed of restructuring and level of unemployment imply an
optimal level of inequality in the form of temporary loss of income (as a result of loss of
jobs) and welfare benefits that may ensure the success of privatization and re-employment.
Trying to increase the speed of restructuring of SOEs is not feasible as it may encounter
strong oppositions from SOEs employees as they lose welfare benefits as well as a heavier
welfare burden on the private sector.
b) Aghion-Commander Model
Aghion and Commander (1999) and Commander and Tolstopiatenko (1998a) introduce a
model21, which set up a general equilibrium model designed to capture the reallocation of
labour and hence of labour income across sectors. Their common view is that the
reallocation of workers from a public sector with a compressed wage distribution, to a
private sector with much higher wage inequality, accounts for the bulk of increased
earnings inequality during transition. Thus, an exploration of this model will be useful for
our understanding of the second mechanism of labour market transformation caused by
21 For details of this lengthy general equilibrium model, please refer to Aghion and Commander (1999). In this paper, I focus on basic intuition of the model and the empirical implication.
20
SOEs reform. I.e. the widening of wage differentials to reflect productivity differences.
There are several key assumptions in the model: I) there is a state sector (SOEs) and a
private sector. ii) In the SOEs, firms operate under a zero-profit constraint without capital
accumulation and wages are set equal to average product. iii) firms in the private sector
behave competitively. iv) the SOE is less efficient, demonstrated by a lower constant in the
Cobb-Douglas production function. v) SOEs face a probability of closure, and can choose
to restructure, in which case they shed excess labour and become like private firms. vi)
there is unemployment if hiring by private firms falls short of exits from state firms. vii)
Inequality within each sector is set exogenously in the model, and the authors assume a
higher level in the private sector. From the assumptions, it can be seen that the dynamics of
inequality are affected in the model by employment, labour incomes and income variations
in each sector. The impact of between sector values will depend on a set of parameters,
including relative productivities, restructuring and closure probabilities. Higher inequality
arises during the transition when this model is simulated.
Two main reasons for this outcome:
1) The sectoral shift of workers from the relatively low inequality SOEs to the
higher inequality private sector, and
2) Mean wages are higher in the private sector (due to its greater productivity).
Hence, Aghion and Commander Model (1999) intends to capture the SOEs reform’s
price effect on inequality, which occurs when there is a change in the value attached to
different attributes. It could be a direct consequence of widening wage differentials
between the SOEs and private sector workers. As the SOEs workers are reallocated in the
private sector, the value attached to their ownership changes and they are paid a higher
wage since private sector has a higher productivity.
21
Section 4 Empirical Evidence
In this section, I would provide some empirical evidence to support the theories in the
previous section in order to show 1) the relationship between growth, inequality and
poverty in urban China from 1995 to 1999, 2) how SOEs reform affects urban inequality of
income and welfare which influences growth and 3) how SOEs reform affects urban
poverty.
4.1 Decomposition of poverty reduction
From section 3, we know that changes in poverty is a net outcome of the interaction
between inequality and growth and in this subsection, decomposition analysis for three
different poverty measures are provided to examine the changing relationship between
growth, inequality and poverty in urban China.
Table 3 is adapted from Chen and Wang (2001), which shows the change in urban and
rural poverty between 1990 and 1999, is decomposed into the growth and redistribution
component. In this paper, we focus on dynamics of urban poverty. The growth component
shows the change in poverty due to a change in the economic growth while holding the
income distribution constant at the base year 1990. The distribution component refers to the
change in poverty due to a change in the income distribution while holding the economic
growth constant.
Table 3: DECOMPOSITION OF POVERTY LINE BY $1 A DAY POVERTY LINE, 1990-1999
Note: a negative number indicates a poverty reduction while a positive number indicates an increase in poverty
Source: Chen and Wang (2001) based on urban household survey (NBS)
22
Overall, there was a 0.46% reduction in urban poverty between 1990 and 1999 using $1
a day poverty line. The worsening income inequality resulted in a 2.61% increase in
poverty while economic growth reduced urban poverty by 0.95%. However, during 1996
and 1999, the period of intensive SOEs reform, we witness for the first time since China’s
reform started in 1979, an increase in urban poverty of 0.03% which is mainly a result of
worsening income distribution that contributed to a 0.35% increase in poverty despite the
0.19% reduction in poverty caused by economic growth. This is a stark contrast to previous
period of 1993-1996 when growths contributed to 0.3% in poverty reduction and mitigate
the negative effect of moderate inequality brought to poverty reduction. Hence, an overall
reduction in urban poverty of 0.25%.
One can infer that growth has been the dominant force behind reduction in poverty.
However, the power of economic growth to reduce poverty was substantially undermined
by the ever-worsening inequality. The SOEs reform seems to have reduced growth22 in the
short run as we can see the contribution by growth to poverty reduction decreased from
0.3% to 0.19% between 1996 and 1999. It has also enhanced inequality’s strength in
increasing poverty from 0.13% to 0.35%, almost three times increase from previous period!
Therefore, an inequality-reducing strategy must be in place to counteract the negative
impacts on income distribution caused by supposed growth-enhancing (long-run) SOEs
reform.
4.2 SOEs reform and inequality
a) Testing the Aghion-Blanchard Model Aghion-Blanchard model (1994) predicts that at a low unemployment, a slow
restructuring or a lower level of unemployment increase will result in a high private job
creation, as the flow into unemployment from restructuring is more easily absorbed by the
high private job creation. Hence, in the initial phase of adjustment, priority should be given
to private job creation. Indeed, that was what happened in China. Under the slogan of
‘grasping the large and letting go the small’ , small and medium SOEs which occupied
57% of SOEs employment were privatized at a local level in 1995 then promoted
22 It should be noted that China was indirectly affected by the Asian Financial Crisis of 1997/98 as her major Asian trading partners experienced devaluation and macroeconomic instability. This may have also reduced the fast pace of growth in China during that period.
23
nationwide, which was then followed by the restructuring of large SOEs in 1997. In
table 4 , urban unemployment between 1995 and 2000 in China (between 4.0-4.7% and
8.3%23) though high is not as serious as those of CEEs, which often exceed 10%, for
example, in Poland the unemployment increased by 14.2 % in 1992 since the reform
initiated in 1989. Private job creation seems to be on the rise as observed from steady
increase of private employment as a proportion of total urban employment. However, the
private job creation slowed down in 1999 and 2000 when unemployment reached new
heights, private employment growth rate was -2% in 2000. Unlike in many CEEs, private
sector already takes up nearly 10% of total employment at the eve of SOEs reform since
China promoted the gradual increment of private sector employment since 1978. This
probably explains why unemployment has not exceeded 10% (though approaching) like
many CEEs. However, absolute size and speed of unemployment increase is enormous,
around 12 million workers (7.8% of total urban workforce) were laid off from SOEs in
1999 according to the data in table 4 (which is based on wide consensus).
The exit rate from unemployment to employment is much higher than Poland too. It is
about 45% re-employment compares with 2.3% in Poland (Hu 1999). The re-employment
rate is widely agreed in China between the official and academic source, which ranges
from 40 to 60%. Moreover, Rawski (2002) points out that between 1995 and 2000, 13.5
million workers were absorbed into the private sector.
This confirms the theory which predicts a slower restructuring i.e. lower unemployment
will be accompanied by a higher exit rate due to higher private job creation.
Table 4 URBAN UNEMPLOYMENT RATE AND PERCENTAGE OF URBAN LAYOFF IN URBAN
UNEMPLOYMENT, 1995-1999.
1994 1995 1996 1997 1998 1999 2000
Urban employment: (in Millions) Total 164.1 169.5 171.7 173.4 155.7 152.4 146.6 State 108.9 109.6 109.5 107.7 88.1 83.4 78.8 Private 15.6 20.5 23.3 26.7 32.3 34.7 34 Private employment as % of total urban employment
9.51 12.09 13.57 15.40 20.75 22.77 23.19
23 Alternate figures for urban unemployment are used instead of official data which according to many scholars is not very reliable. The figures are generally higher than the official ones.
24
Unemployment (official)
2.6 2.9 3.0 3.1 3.1 3.1 3.1
Unemployment (alternate24)
3.6-4.1 4.0-4.7 4.9-5.9 5.6-6.9 7-8 8-9 8.3
Laidoff (%) 0.6 2.04 2.26 4.0 – 5.1 7.4 7.8 6.5
Labour productivity growth of SOEs (%)
7.38 4.79 6.09 11.89 11.51 3.51 ..
Sources: China Labour Statistical yearbook (various years), CEIC database and Rawski (2002)
However, there are a number of caveats. First, official urban unemployment is equivalent
to just ‘registered unemployed’ which does not include layoffs.
Instead a term ‘xiagang’ or furloughs is used for the layoffs (redundant employees). It is
not dissimilar to the tough unemployment eligibility requirements in Czech Republic,
which have led to a low official unemployment of 2.5% and many workers have dropped
out of the labour force. (Aghion and Blanchard 1994). This may create disguised
unemployment as indicated by Yang (1999) and one may under-estimate the
unemployment rate. If the laid-off is considered as unemployed, the unemployment could
reach more than 12 percent of the urban labour force in 1999 alone. It is estimated that the
number of furloughed urban workers could be more than 30 million that many of them face
sharply reduced economic circumstances and often encounter difficulty in re-entering the
urban labour market.
The ‘Xiagang’ or layoff means the old enterprises, government and insurance funds
continue to give out subsistence salary and other kinds of partial subsidies like continued
health care to compensate for the unemployed while provide a temporary means of living
in the process of finding new jobs. The subsistence salary and basic welfare only lasts for a
maximum of three years during which the unemployed are expected to actively seek re-
employment with the help of the local employment service centre. This motivates SOEs
workers to search for jobs more actively as they realize the government’s support is
temporary. In this way, the exit rate may be increased. Indeed, it is seen in China that the
24 Compiled from various reliable sources, sometimes range is given to show consensus. Estimated actual urban unemployment includes the unemployed who are registered, the laid-off workers who are still unemployed, and the jobless who have agricultural residence cards. For details please refer to Rawski (2002).
25
layoffs organize themselves and set up own business, which are often small and
encouraged by the government.
The re-employment programme assisted by the local government nationwide often
accompanies the programme of ‘xiagang’. The central government sets a target of 50% re-
employment of layoffs for the local government. By the third quarter of 1996, about 5
million laid-off workers had participated in ‘re-employment programmes’. In these
programmes, nearly 3 million workers took part in new career instruction and 1.1 million
received retraining. Of the 5 million, 4.5 million received unemployment benefits and 2
million received temporary relief. About 2.4 million were actually re-employed (Yang
1999). This probably explains why the figure for re-employment of SOEs workers is
relatively high. However, as argued by Rawski (2002), approximately 51.2 million workers
may have ended up in the informal sector; many of them are SOEs layoffs25. Hence,
unemployment and in particular under-employment could be underestimated.
Second, along with confusing classification of unemployment, there is a growing
diversification of enterprise ownership and forms of employment. Apart from state and
collective enterprises, there were ‘’cooperative enterprises’’, ‘’joint ownership enterprises’’,
‘’limited liability corporations,’’ share holding corporations’’ and enterprises owned and
operated by investors from Hong Kong, Macao, Taiwan province and foreign countries. All
these have made classification of private employment very difficult if not impossible.
Third, the urban unemployment rate does not include the rural migrants to the city who
experience transitory joblessness. The number of rural migrants is probably between 50 to
60 million (Solinger 1999). Therefore, urban unemployment could be underestimated.
Notwithstanding the classification and accounting difficulties, the observed changes in
employment conditions may not have straightforward implications for poverty or income
inequality as one assumes formal employment is superior to informal employment.
However, it suffices to say that one possible consequence of massive unemployment is to
push the unemployed into the lower end of income distribution as a result of loss in income
and thus the inequality widens. The exact quantity effect of SOEs reform on inequality is
25 Moreover, average annual absorption of workers into formal employment dropped from roughly 11.6 million during 1980/90 and 15.5 million during 1990/95 to a negative 3.8 million during 1995/2000.
26
difficult if not impossible to be measured and decomposed. The only thing close to it is to
see how unemployment distribution changes for income deciles, which will give us a
broad picture of how layoff as a result of restructuring has influenced people at different
income deciles. This is shown by Meng (2001) in figure 5. Unfortunately, only two years
comparison is available.
Figure 5 DISTRIBUTION OF HOUSEHOLDS WITH UNEMPLOYED MEMBERS
ACROSS INCOME DECILES
Source: Meng (2001) based on CASS survey (1995, 1999)
One can observe that the number of unemployed households falling into the lowest
income group (e.g. decile 1) increased drastically from 0.29% in 1995 to around 0.6% of
the survey samples in 1999. In contrast, there is a decline in the number of unemployed
household at the top end of the income distribution (e.g. decile 10) by 1999.
Unemployment has increased more drastically for household at lower income deciles
(decile 1-6) and it is not so serious for those at the top decile (decile7-10). This is
consistent with the dramatic increase in layoff from 34% of urban unemployment in 1995
to 57% which is equivalent to 23 millions people in 1999.
However, not all unemployed households fall into the lowest income group between
1995 and 1999. There is also two times increase in number of unemployed household at the
medium deciles (e.g. decile 6). Reduction in the household income from one member being
unemployed can be offset by income earned by other employed members. Nearly half of
the households with two or more unemployed members were located at the bottom 10
percentiles of the income distribution, and about 30 percent of these households were
27
concentrated at the lowest 5 percentiles of the distribution in 1999. In contrast, there were
only 25% of households with more than one member unemployed being located in the
bottom 10 percentiles of income distribution in 1995. It shows that the households were
more able to compensate for unemployment in 1995 than 1999.
The unskilled, but who are still employed has to compete with the unemployed who are
normally unskilled or possess low level of skill and their wage may fall due to the
increased supply of unskilled labour leading to a further widening of the income
distribution.
To sum up, the speed and scope of SOEs reform are crucial in determining the level of
unemployment and hence inequality via the quantity effect. The gradual nature of SOEs
reform as well as the reemployment scheme offered by the government seems to have
helped slow down the inequality a bit though the precise magnitude of reduction in
inequality widening is hard to quantify.
b) Testing the Aghion-Commander Model
The model predicts a higher mean wage in the private sector due to its greater
productivity. Moreover, sectoral shift of workers from the relatively low inequality SOEs
(state) sector (due to its compressed wage structure) to the higher inequality private sector
means between-sector inequality is the main determinant behind inequality.
From table 5, mean wage in the private sector is higher than that of the SOEs throughout
the period shown. Moreover, the mean wage of private sector has been higher than the
SOEs even before the SOEs reform in 199526. Furthermore, as reflected by the mean wage
ratio between the private sector and the SOEs (between sector wage differential or
inequality), there has actually been an accelerating decline in the mean wage ratio since
1995 mainly due to the faster rise of SOEs mean wage.
26 It may be partly due to the existence of private sector, which has higher productivity, before the SOEs reform and hence a higher mean wage (efficiency wage argument). Expansion of private sector in China came before SOEs reform as a key government strategy of economic reform since 1979, especially the development of Township-village enterprises (TVEs).
28
Table 5 MEAN WAGE OF WORKERS BY OWNERSHIP
Mean Monetary Wage (Yuan)
Year SOEs wage
Non-public sector wage
Wage ratio (non-public sector wage/SOE wage)
1991 2477 3468 1.4 1992 2878 3966 1.38 1993 3532 4966 1.4 1994 4797 6303 1.31 1995 5625 7463 1.33 1996 6280 8261 1.32 1997 6747 8789 1.3 1998 7668 8972 1.17 1999 8543 9829 1.15
Source: China Statistical Yearbook 2000
What is wrong then with the theory? Why is there a decline in the between-sector wage
differentials? Does it imply that the mean wage differences between sectors are not an
important factor driving changes in inequality?
The answers to these questions lie behind the institutional factors of which the theory
does not examine. We should look at the within-sector inequality in China during this
period first.
Park et al (2003) study the growth of wage inequality in urban China between 1988 and
1999. They show the price effect on inequality brought about by the ownership change is
insignificant for the post-reform period (1995-1999) and conclude that the lack of
ownership effect means the labour market changes largely encompassed the state sector
over this period. The sectoral transfer of labour out of the SOEs sector and changes in the
state-nonstate wage differentials were not the key determinants of overall wage inequality
growth.
They propose the within-sectoral wage inequality growth as the key factor for overall
wage inequality increase. Figure 6 illustrates the 90th-10th percentile log wage differences
for workers belong to three different ownership categories; SOEs, urban collectives and
non-public enterprises.
29
Figure 6 NINETIETH-TENTH PERCENTILE LOG WAGE DIFFERENTIAL, 1992-99
Source: Park et al (2003), calculations based on China urban labour survey 2001 (CULS)27
At the beginning of 1992 during the pre-reform period, the wage inequality within the
non-public sector was very large (e.g. 2 times in 1992), but declined drastically since then.
It was slowed down once the SOEs reform started and has only picked up a bit in the last
two years (e.g. 1.8 times in 1999). On the other hand, inequality within SOEs sector went
up steadily from 1.25 in 1992 to 1.8 in 1999. Hence, there is some sort of convergence
between the non-public sector wage differential and the SOEs wage. Keane et al (2001)
point out similar experience in Poland except there is increased variance of wages (earnings
inequality) within both the public and private sectors.
Given there has been a decline in the between-sector wage differentials and a rise in the
inequality within the SOEs sector, one is inclined to think that the mean wage differences
between sectors is not an important factor driving changes in inequality but rather there are
some kinds of changes in the wage setting system within the SOEs sector.
Indeed, first, there exist ‘SOEs restructuring in the absence of privatization’ - the
phenomenon suggested by Pinto et al (1993), based on the Polish and CEEs experience, in
China as well. If it were true, the model could be modified to include this caveat as
proposed by Keane (2002).
27 It is carried out by the Institute for Population and Labor Economics at the Chinese Academy of Social Sciences (CASS-IPS), working with provincial and municipal government statistical bureaus. The CULS was conducted in five cities: Fuzhou, Shanghai, Shenyang, Wuhan, and Xian. The cities were chosen to provide regional diversity and variation in the size of the state versus private sectors.
30
It happens when the SOEs in China behave as if they were private firms and set their
wages competitively to increase the incentive and hence the productivity of the state
worker so as to narrow the productivity gap with the private firms. This is often
implemented without privatization (defined by a change in ownership). Knight and Song
(2003) point out that there is a considerable difference in (standardised) mean wages
between profit-making and loss-making SOEs in 1995. The wage ratio grew substantially
between 1995 and 1999, in which year the wage difference between highly profitable and
just profitable firms was also large. This is due to the fact that the management has been
granted power to determine the wage of each worker within the enterprise (decentralised
wage setting system). Indeed, the managers link the wage level to profitability of the
enterprise, which is governed by the efficiency wage considerations i.e. higher wages
induce more efficiency hence an improvement in profit. Similarly, Commander and Dhar
(1998) report a substantial increase in the heterogeneity of wages across SOEs in Poland
between 1990 and 1994, with those that performed better in terms of sales offering higher
wages. The lesson is clear. Privatization per se would not necessarily lead to
restructuring, but rather that the nature of managerial incentives and financial
constraints are critical.
However, as argued by Rawski (2002) and many others, there has been an almost
excessive rise in certain state sectors pay especially since the beginning of 1998, which has
almost become an instrument of expansionary macro-policy to boost consumption spending
in China. For example, health care, sports, education, culture, scientific research and
government agencies have seen great increase in worker’s income as the profit increase are
distributed in a form of bonus to income. The profit increase does not necessarily come
from improvement in efficiency. On the other hand, wage levels in more traditional
manufacturing industries, where older state-owned enterprises are concentrated, have
stagnated throughout the decade relative to other industries.
Table 6 (page 34) confirms this observation. It shows the average nominal wage of
workers in the SOEs by sectors and also the ratio between nominal wage of each sector and
overall average nominal wage (1990-2000). One can immediately notice that the average
nominal wages for SOEs workers in the traditional older SOEs strongholds are declining,
eg. manufacturing and construction industries, while the wage for SOEs workers working
in emerging industries such as banking and insurance, health care, scientific research sector
31
and government agencies are on the rise relative to others, partly due to increased
productivity but also state monopoly as well as being used as a tool to boost consumption.
Second, as Qian (1997) suggests, Chinese SOEs reform adopts a ‘Federalism’ approach.
There is an invention of wide varieties of ownership (as mentioned earlier in 4.2 a) to suit
the local conditions for the SOEs reform instead of the ‘Big-bang’ approach of
privatization used by some CEEs. The SOEs reform was very much localized as the local
government has better information about the SOEs, which they have been under their
supervision. This is made more feasible as the central government carried out fiscal and
financial reforms along with SOEs reform to harden the local government budget
constraints and also reduce their influence on local banks.
Hence, small SOEs are privatized for example in the form of share-selling to their former
employees and possibly outside private investors who then pour in new investment and
restructure the SOEs into a modern enterprise of higher efficiency, via setting competitive
wage to attract the more able as one of the way. The ownership is often not well defined28
and thus the enterprise not well classified29. In fact, the term ‘privatization’ is never used; it
is coined as ‘change of ownership’ instead. This can also result in ‘restructuring of the
former SOEs with some degree of privatization’.
The mechanism of intra-sectoral transfer could be further explored. As the fraction of
firms engaged in competitive wage setting grows (both through increases in the size of the
private sector and restructuring of SOEs), the relative demand for skilled labor will
increase in the economy as a whole. The wage differential within the SOEs sector and
(within) the private sector will both widen further. This may narrow the inequality between
SOEs and the private sector, as their productivity gap gets smaller.
Consequently, income inequality within SOEs sector may just reflect growing
differences in the economic performance (productivity) across each SOE over many
different regions, or in some cases, the monopoly power of certain industry. All these imply
that the between-sectors income inequality is narrowed, while intra-sector inequality is
widened.
28 It might be that the government is reluctant to show that it has given up the control of firms. 29 Aghion and Blanchard (1994) shows sometimes there is classification change instead of true change of employment by ownership, e.g. in Poland the cooperatives, which at the start of the reform accounted for 1.5 million employees was then reclassified as part of the private sector.
32
Tab
le 6
Ave
rage
nom
inal
Wag
e of
Wor
kers
by
Sec
tors
, Sta
te-o
wne
d un
its
-yua
n-
Ave
rage
of
all
15
sect
ors
Farm
ing,
Fo
rest
ry,
and
Fish
ery
Min
ing
Man
ufac
t-ur
ing
Ele
ctri
city
, G
as a
nd
Wat
er
Con
stru
ctio
n
Tra
nspo
rt,
Stor
age
&
Post
Who
lesa
le
& R
etai
l T
rade
B
anki
ng
&In
sura
nce
Rea
l E
stat
e
Hea
lth
Car
e,
Soci
al
Secu
rity
an
d So
cial
W
elfa
re
Geo
logi
cal
Pros
pect
&
Wat
er
Con
serv
e So
cial
Se
rvic
es
Edu
cati
on
Cul
ture
Art
R
adio
Film
&
TV
Scie
ntif
ic
Res
earc
h &
Po
lyte
chni
c Se
rvic
e
Gov
t, Pa
rty
Age
ncie
s &
Soc
ial
Org
1990
23
19.4
15
59
2763
22
89
2648
26
67
2697
20
28
2200
22
47
2263
24
63
2307
21
34
2411
21
15
1
0.67
1.
19
0.99
1.
14
1.15
1.
16
0.87
0.
95
0.97
0.
98
1.06
0.
99
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1991
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1995
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1.
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1997
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8570
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8303
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1999
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85.8
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N
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33
4.3 SOEs reform, welfare and growth
Besides changing the pattern of wage and employment, the SOEs reform also transforms
the welfare provision system in China. Before SOEs reform, it is not uncommon for the
SOEs to allocate half of their earnings to the provision of life long welfare such as
education and health care. Most individuals now have to pay for their own welfare-
provision30 (with some government subsidy) because of the SOEs reform, which stopped
provision of permanent welfare.
Table 7 shows that the share of education and health expenditure in total government
expenditure has been declining while the share of education and health expenditure in total
expenditure by urban individual has increased. Hence, it confirms the increasingly market
oriented trend in welfare expenditure31 since the SOEs reform.
Table 7. TRENDS IN EDUCATION-RELATED AND PUBLIC HEALTH EXPENDITURE (BY GOVERNMENT
AND INDIVIDUAL URBAN RESIDENTS), 1995-2003
1995 2000 2001 2002 2003
Total education-related expenditure (by government), RMB billion
141.2 256.3 305.7 349.1 385.1
Growth rate, % 22.9 12.7 19.3 14.2 10.3 Share in total expenditure, % 13.6 11.3 11.7 11.7 11.6 Urban education-related expenditure per capita (by individual), Yuan
312.7 627.8 690.0 902.3 934.4
Growth rate, %
24.7
10.7 9.9 30.8 3.6
Share in total expenditure of urban residents, %
8.8 12.6 13.0 15.0 14.4
Total health expenditure (by government), RMB billion
38.7 71.0 80.1 86.4 111.7
Growth rate, % 17.4 12.9 12.8 8.0 29.2 Share in total expenditure, % 3.7 3.1 3.1 2.9 3.4 Urban health expenditure per capita, (by individual), Yuan
110.1 318.1 343.3 430.1 476.0
Growth rate, % 32.8 29.5 7.9 25.3 10.7
Share in total expenditure of urban residents, %
3.1 6.4 6.5 7.1 7.3
Source: Various editions of the Finance Yearbook of China, Education Financing Statistics Reports by the Ministry of Education, Health Yearbook of China and urban household survey (NBS)
30 This is also consistent with the findings from OECD (2006) which shows that public spending on health and education may be too low and inefficient to meet China’s development needs.
34
This shift of welfare burden onto the individuals has affected persons across different
income deciles quite differently. Table 8 from Fang et al (2002) presents the level and
major components of per capita expenditure under the 10th, 20th, 80th, and 90th income
percentile in 1992 and 1998, as well as the corresponding percentage change over the
period.
Table 8. EXPENDITURES BY PERCENTILE
Source: Fang et al (2002) based on Urban Household Survey (NBS)
Note: The expenditures are in 1992 constant price.
Growth rates of expenditure among the top 10th and 20th of the population are 51.01%
and 56.73%, much higher than the growth rates in the bottom 10th and 20th, which are
14.03% and 17.42%, highlighting widening income and also non-income inequality.
The shares of expenditures on healthcare, education, and housing have increased
drastically across four different percentiles, largely as a consequence of SOEs reforms. The
magnitude of increase is most significant for healthcare expenditures due to increasing out-
of-pocket pay for visit. Alarmingly, for the bottom 10th of the population, the rates of
increase in expenditures on healthcare (e.g. 57.25% increase for the poorest 10th),
education, and housing have outpaced the rate of income growth (14.03%) leaving the poor
less resources to cope with risk and escape from the poverty trap. Another striking feature
is that the proportion of expenditure on education by the rich (14.44% in 1998 for the
richest 10th) is about twice of that by the poor (7.07% for the poorest 10th in 1998).
The large variations across income deciles means that those who can afford higher
education may enjoy higher income as they send out a signal that their productivity is
higher than those who are less educated and skilled. This may further worsen income
35
inequality cumulatively. Moreover, the growth rate may be slowed directly in consequence
of a diminished human capital that lowers productivity. That is why this issue has been on
the top of the government’s agenda for the 11th five-year national economic plan (2006-
2010).
36
4.4 SOEs reform and poverty
From above analysis, urban inequality of income and welfare have both worsened
substantially and the SOEs reform has increased the incidence of urban poverty and may
have deepened the depth of poverty.
Table 9 (Li 2001) shows the probability of poverty occurrence based on the health and
employment status of the urban sample. The data is based on the urban household survey of
National Bureau of Statistics in 2000 which mainly reflects the situation in 1999. The aim
is to examine the relationship between layoff by SOEs reform and probability of becoming
the poor by using the Probit model.
Table 9 URBAN POVERTY PROBABILITY ACCORDING TO EMPLOYMENT STATUS AND HEALTH
Employment status Poverty Probability
Total Sample Healthy Not Healthy
Working 3.60 3.47 8.61
Retired 3.33 3.06 4.30
Looking for work 12.00 11.52 25.00
Laid off 23.02 22.30 31.43
unemployed 20.87 18.69 50.00
Handicapped or ill 26.19 -- 26.19
Source: Li Shi (2001) based on urban household survey 200032 (NBS)
Note: layoff workers (Xiagang) by SOEs are not counted as unemployed officially as explained earlier.
From table 9, those who are laid off by the SOEs, unemployed or handicapped have the
highest chances of becoming the poor as their poverty occurrence probability (rate) are
23%, 21% and 26% respectively. Overall, those who are unhealthy have higher probability
of poverty. If someone has poor health and unemployed then his probability of becoming
poor is 50% more than ten times over the normal healthy and working person.
32 It covers five provinces and the capital .i.e. Liaoning, Jiangsu, Henan, Sichuan and Gansu and Beijing. Besides Beijing, it selects 12 cities out of the five provinces. It includes 5300 households, 4500 of them are registered urban resident and 800 of them are rural migrants to the urban areas.
37
Section 5 Conclusion
According to the Economic survey of China (OECD, 2005), the private sector in China
is producing well over half of GDP and an overwhelming share of exports in 2003. Private
companies generate most new jobs and are improving the productivity and profitability of
the whole economy. These successful economic progresses are to a great extent attributable
to the SOEs reform (which is still on-going and intractable in some regions). However, as
shown in this paper, SOEs reform has broken the traditional planned and equalitarian
income distribution mechanism. A market-driven income distribution system provides
more incentives for productivity and creativity, which has significant impacts on changes in
income inequality33 in China. In particular, it has made some workers worse off (at least in
the short run) not only in terms of income loss but also welfare such as health and
education. Increasingly, they have to cope with the instability of tenure and rising cost of
welfare provision34. Urban poverty has increased for the first time and the main culprit is
accelerating inequality that prevents the trickle-down effect of growth.
It is in this context that institutional reform such as that of the SOEs, which intends to
improve the efficiency of the factor market, needs to be implemented with care. Aghion
and Blanchard model (1994) show that the speed and manner in which layoff of redundant
workers is implemented is pivotal in determining the extent of inequality and poverty.
Excessive restructuring will prevent private job creation and thus deepen inequality, though
it is very difficult to trace the precise impact of SOEs layoffs on income distribution change.
The gradualist approach by which the reform was experimented locally then promoted
nation-wide helped to reduce poverty incidence in China. Empirical evidence from the test
of Aghion and Commander (1999) in China has shown that the wage differential exists
more prominently among SOEs (within sector) than between SOEs and private enterprises
(between sectors), This is due to the fact that the wage has been allowed to be determined
freely and not fixed which enables the efficient SOEs to restructure without privatisation,
hence becoming more profitable and able to reward its workers who have higher efficiency
a higher wage. It also enables monopoly industries or firms to distribute extra profits to its
workers, sometimes to evade tax.
33 On the other hand, Khor, Niny and John Pencavel (2005) calls for a closer look at the longer-term measures of income inequality. For example, if the rise in inequality in annual incomes is accompanied with more income mobility from year to year, income inequality measured over a longer interval of time may not have increased at all. Unfortunately, their study only covers 1990-1995 period and did not examine the period since the SOEs reform. 34 There is some evidence that large changes in inequality could have adverse political economy feedbacks to the sustainability of the reform.
38
The social welfare functions of the SOEs are now shifted to the private individuals as
evidenced by the decline in government expenditure towards health care and education and
the increasing share of health care and education in urban resident’s total private
expenditure. This substantially worsened the welfare of the SOEs workers at least in the
short run. The widening income distribution has meant that the ability to pay for the
medical and health service and social security varies across income groups and often those
at the bottom percentiles (often unemployed) find themselves particularly hard hit. This
widening inequality of opportunity could lead to further income disparity. The human
capital is diminished and economic growth could be slowed in the long run.
Some reservations of the paper: 1) Classification of the SOEs and layoffs are not clear
and this influences the theoretical predictions as wide range of ownerships are created and
those laid off are not counted as unemployment. The unemployment and underemployment
could be under-estimated. 2) This paper focuses on wage and income inequality exclusively
and there may be some extra earnings from sources other than wage such as earnings from
investing in the stock of SOEs (SOEs shares were sold to their own employee as a means of
privatisation and to get rid of their own financial burdens). Furthermore, illegal income
from bribes is always cited as a more serious concern. 3) Severity of the urban poverty
might have been greatly underestimated as the official statistics excludes the ‘floating’
population i.e. the rural immigrants to the cities. 4) There are some other reforms such as
fiscal and financial, going on at the time of SOEs reform and could have also worsened the
inequality. 5) Asian Financial Crisis in 1998 may have exerted some impacts on poverty in
urban China as most of her trading neighbours experience contraction in demand. 6) There
has been substantial macroeconomic improvement since China joins the WTO which
resulted in improved employment opportunities35.
Hence, a pro-growth institutional reform may not be pro-poor (at least in the short run)
and it needs to be accompanied by some form of direct income and welfare support to
mitigate the welfare loss at least in the short run and especially targeting those who are
most vulnerable including rural migrants to the city.
35 According to Ministry of Labour and Social Security (Jan 19th, 2006), China provided jobs for 9.7
million urban residents in 2005, compared with 9.8 million in 2004. During the 2001-2005 periods, more than 180 million workers laid off from state-owned enterprises found new jobs. In 2005, the number of unemployed people stood at 8.39 million, with a registered unemployment rate of 4.2 per cent, the same as the previous year.
39
Appendix A: Aghion and Blanchard transition model (1994) Assumptions: The state sector: 1=E+N+U (1) dE/dt = -s (2)
The economy consists of two sectors, the state sector with employment E and the private
sector with employment N. The labour force is normalized to one. U is unemployment. s is
the speed of restructuring of state firms, which is assumed to be under the control of the
government.
The new private sector: dN/dt =H = a (y-z-w) (3) w= b+ c( r+ H/U ) (4) H is private job creation, a is parameter, y is the constant average product of labour in the
private sector, w is the wage in the private sector and z are taxes per worker. Hence, private
job creation depends on profit per worker, the difference between the average product of
labour, y and direct and indirect labour costs, (w+z).
Private sector wages depend on labour markets conditions, b is unemployment benefits, r is
the interest rate, H/U is the ratio of hires to unemployment (the exit rate from
unemployment) and c is a constant.
3) Taxes and unemployment benefits Ub= (1-U)z (5) Higher unemployment, given unemployment benefits, leads to higher taxes per worker,
thus, ceteris paribus, higher unemployment decreases private job creation.
4) Unemployment and the speed of restructuring
Hsdt
dU −−= )1( λ , 000 1 NEU −−= (6)
(w-b)= [ca/(U+ca)][y+(r/a)U-(1/(1-U))b] (7) After the initial increase in unemployment, unemployment dynamics depend on the speed
of restructuring and on private job creation. )1( λ−s shows the flow into unemployment
40
which depends on the speed of restructuring i.e. s, and the proportion of workers losing
their jobs in the process ( λ shows proportion of workers in the state sector after
restructuring). The flow out of unemployment is equal to private job creation H. Private job
creation depends on unemployment via wages and taxes, solving for wages using equation
(3), (4) and (5), gives (7) and replacing w by its value from (7) and z by its value from (5)
means that private job creation is a function of unemployment:
)(]))1(
1(][
)([ Ufb
Urcy
caU
UaH ≡
−−−
+= (8)
41
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