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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: Michael.chen@oecd.org. 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

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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

0.92

1.

04

0.91

1991

25

16.9

33

1665

29

82

2505

28

83

2924

29

67

2201

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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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