economic transformation and earnings inequality in china and taiwan

Economic transformation and earningsinequality in China and Taiwan

John A. Bishopa,*, Jong-Rong Chioub

aDepartment of Economics, East Carolina University, Greenville, NC 27858, USAbTam Kang University, Taiwan, ROC

Received 31 January 2003; received in revised form 7 April 2004; accepted 1 May 2004

Abstract

The possibility of a shared development path, together with a common language and culture,

suggests that it is useful to examine the determinants of earnings in Taiwan and China. Using data

from the Chinese Household Income Project and official household income survey data of Taiwan we

estimate the distribution of earnings in Taiwan (1978–1995) and China (1988–1995). In spite of

differences in the trend in earnings inequality, we find converging levels and returns to education

between the two countries. In contrast, returns to experience appear not to be converging, most likely

due to China’s dramatic economic restructuring.

# 2004 Elsevier Inc. All rights reserved.

JEL classification: D31; J31; P20

Keywords: Distribution; Earnings determinants; Transitional economies

1. Introduction

Market-oriented economic reform, which began in 1978, has brought varied and

substantial changes to the Chinese economy. Among which, the change in the wage

system is the most noticeable and economic inequality has become an important concern.

Before the reform, China was a completely centrally planned economy in which the labor

was bureaucratically allocated and wages were administratively regulated. The egalitarian

wage system eliminated or minimized wage differences across regions, occupations and

Journal of Asian Economics 15 (2004) 549–562

* Corresponding author. Tel.: þ1 252 328 6756; fax: þ1 252 328 6743.

E-mail address: [email protected] (J.A. Bishop).

1049-0078/$ – see front matter # 2004 Elsevier Inc. All rights reserved.

doi:10.1016/j.asieco.2004.05.009

genders. However, things changed dramatically since the recent market-oriented economic

reform and inequalities in the distribution of income in China began to appear. This is

partly due to the policy of ‘allowing some to get rich earlier’ on the premise that ‘advanced

and richer region could help less advanced and poorer region and both get rich together

later.’ It is generally recognized (cf. Nee, 1989) that as the economy transitions from a

redistributive economy to market economy, producers are paid more closely according to

their individual productivity. Thus, we anticipate more income disparity under the more

flexible wage system in the new market-coordinated economy.

China shares with many other developing countries the characteristic of dual

economy. The rapidly growing modern capital-intensive sector is traditionally clustered

around the cities, while the traditional manual agriculture in the rural China remains

little changed. The coexisting modern industrial sector and backward agricultural sector

result in a large gap in income between rural and urban areas. For example, according to

State Statistical Bureau data, the income of the rural population was 1205 real yuan per

capita (base year ¼ 1995) in 1988, about 46% of the real 1988 urban income of 2552

yuan per capita. The gap has only widened over time with a rural average income in 1995

of 1578 yuan as compared 3893 yuan per capita in the urban areas (see Zhao, 2001). If

one considers only the coastal regions, which have received a higher priority in the

economic development, more economic freedom, and more financial assistance than the

provinces in inland China, these urban–rural differences would be even larger. Con-

sidering the uneven development throughout China, we restrict our study to urban areas

of China.

If we examine Taiwan’s post-war experience we find a similar level of economic

development as that of the late 1970s in China. The Taiwanese economy soared in the

1960s while at the same time income inequality dropped. Among those factors explaining

the Taiwan success story, labor absorption into non-agricultural sectors plays an important

role (see Kuo, Ranis, & Fei, 1981). Although the change in the wage structure of Taiwan is

not as dramatic as that of China, it did contribute toward lower income inequality in the

early years. Thus, at first glance Taiwan’s development path appears similar to that of

China in that urban areas absorb labor from agricultural sector so that the impact on

economy growth is quite similar; however, we note that development in China is occurring

with increasing income inequality.

The possibility of a shared development path, together with a common language and

culture suggest is useful to examine the determinants of earnings in Taiwan and China.

Data comparable to the People’s Republic of China for Taiwan (Survey of Personal

Income) is available beginning in 1979. To gain some further insight into the possibility of

a common development path, we provide separate earnings estimates for the most

developed part of the China, the southeastern coastal province of Guangdong.

The paper will proceed as follows. Section 2 describes the data and the changes in the

distribution of earnings that have occurred in Taiwan (1978–1995) and China (1988–1995)

over time. In order to better understand the similarities and differences between Taiwan and

China we estimate human capital models for both countries over time. It is widely

recognized that changes in returns to human capital can result in substantial changes

in earnings inequality and Section 3 presents the results of a Mincerian human capital

model. The final section provides some brief concluding remarks.

550 J.A. Bishop, J.-R. Chiou / Journal of Asian Economics 15 (2004) 549–562

2. Earnings data for China and Taiwan

2.1. Data description

The data used in this analysis come from the Chinese Household Income Project (CHIP).

This longitudinal study has been created to examine the dimensions of inequality in China

and its’ causes. The data set consists of both samples of the urban and the rural populations

of China. Each of the component consists of two data files, one in which the individual is

the unit of analysis and a second in which the household is the unit of analysis.

The empirical analysis is based on the survey data collected in 1988, during the period of

the stalled reforms when the economy was undergoing a comparatively high inflation, and

1995, when the pace of reform greatly accelerated after Deng Xiaoping’s famous south-

eastern tour and 14th Communist Party Congress in 1992. The availability of the data in

these two years allows us to examine the change in personal earnings determinations

accompanying the structural changes of the economy.

There are two prominent advantages of the CHIP data over alternative sources. First, the

survey is very comprehensive in the information provided on the respondent. Included in

the survey are questions concerning general demographic characteristics as well as income

and expenditure of individual/household. There are variables that indicate education, age,

gender, ethnicity and other personal characteristics. All types and sources of income and

expenditure information are collected. These types of questions are one of the reasons that

this data is excellent to use when evaluating wage differentials with respect to the change of

the economy.1

The second advantage of the CHIP data are the large sample sizes that comes from all

regions of China. The 1988 data contains 9009 urban households (with 31,827

individual members) in 10 provinces. The 1995 data contains 6931 urban households

(with 21,698 individual members) in 11 provinces (the original 10 plus Sichuan). We

restrict analysis to the urban sample for two reasons. First, the post-1988 reforms are

most pronounced in the urban areas. Second, many rural workers are farmers who do not

work for wages.

The data in this paper have been constrained to meet the following conditions: urban

individuals who were working at the time of the interview and aged between 20 and 59.

This creates a data set with 17,006 observations for 1988 and 10,846 observations for

1995, respectively. The urban sample distributed over Beijing and 10 of the country’s 30

provinces: Liaoning, Henan, Jiangsu, Anhui, Hubei, Guangdong, Shanxi, Gansu,

Yunnan, and Sichuan. Based on the geographical location and economic development,

they could be classified into four regions: Liaoning and Henan are in the northern region;

Jiansu and Guangdong are in the eastern and southern coastal regions, respectively;

Hubei and Anhui are in the central region and Shanxi, Gansu, Yunnan, and Sichuan are

in the western region.

1 There are two useful collections of papers using the CHIP data, The Distribution of Income in China, edited

by Griffin and Zhao (1993a, 1993b) and China’s Retreat from Equality, edited by Riskin, Zhao, and Li (2001a,

2001b). For a recent review of China’s Retreat, see Osterriech (2002).

J.A. Bishop, J.-R. Chiou / Journal of Asian Economics 15 (2004) 549–562 551

Starting from 1964, the Directorate-General of Budget, Accounting, and Statistics

(DGBAS), Executive Yuan, published the official household income survey data of Taiwan

biannual. In 1970, this survey data became annual. Microdata are available since 1976. A

stratified two-stage sampling method has been adopted. As with the CHIP data, the Taiwan

survey data is large enough and very comprehensive. The sample rate was 0.3% for 1975–

1977 and it was raised to 0.4% for 1978–1983. However, the sample size was fixed to

16,434 households for 1984–1994. The data is comprehensive in that general demographic

characteristics of respondents are included. We employ the same restrictions on this data as

for the China data.

2.2. Earnings distributions

Fig. 1a–c present the earnings Lorenz curves and Gini coefficients for Taiwan,

Guangdong, and urban China. For Taiwan, the period 1978–1995 was a continuation

of its trend toward smaller Gini coefficients (i.e., lower inequality). By examining the

Gini coefficients, we observe that while 1978–1983 was a period of stable inequality, the

period 1983–1988 saw a substantial decline in the Gini from 0.2894 in 1983 to 0.2687 in

1988. Furthermore, earnings inequality continued to decline after 1988, with the 1995

Gini falling to 0.2570. Why has Taiwan’s earnings inequality continued to fall? Lin

and Chu (2000) and Bourguignon, Fournier, and Gurgand (2001) explain the greater

equality by greater equality in education and by greater labor market participation by

women.

Fig. 1b presents the earnings Lorenz curves and Gini coefficients for urban Guangdong

province. In 1988 Taiwan and Guangdong showed nearly an equal degree of inequality as

measured by the Gini coefficient. But while Taiwan experienced declining inequality,

Guangdong’s Gini rose from 0.2625 in 1988 to 0.2853 in 1995.

The most dramatic change in earnings inequality occurred in urban China (Fig. 1c).

Between 1988 and 1995 the earnings Gini rose from 0.2241 to 0.2799, a 25% increase in

inequality. Clearly, China’s move to an emerging market economy has been accompanied

by a nearly unprecedented increase in inequality; Gustafsson and Li (2001a, 2001b, p. 36)

suggest that among industrialized countries only the UK matches China’s magnitude of

increasing inequality.2

The debate regarding growing income inequality in China revolves around the question

of whether it is growth itself that caused the rise in income inequality or are a specific set of

policies or reforms to blame. Riskin, Zhao, and Li (2001a, 2001b) argue that it was not

growth per se but the reform policies, yet they recognize the interactions between the two.

Zhao (2001) argues that ‘‘disorder policies’’ that tolerate corruption, insider appropriation

of state assets, monopoly, and rent seeking have had an injurious effect on the distribution

of income.

2 The highly publicized increase in the U.S. Gini coefficient in the late 1970s was less than 10%, 0.39–0.42

(Bishop, Formby, & Smith, 1991). Deninger and Squire (1996) provide a comprehensive list of Gini coefficients.

The report Gini’s as larger as 0.62 (Brazil) and as small as 0.18 (Bulgaria). China and Taiwan fall in between

these two extremes.


00.10.20.30.40.50.60.70.80.9

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

19781995

Cu

mula

tive P

erc

en

t o

f E

arn

ing

s

Cumulative Percent of WorkersNote: Taiwan’s earnings Gini;

1978, 0.2904 (.0020); 1983, 0.2894 (.0019);

1988, 0.2687 (.0017); and 1995, 0.2570 (.0017)

00.10.20.30.40.50.60.70.80.9

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

19881995

Cu

mula

tive

Perc

en

to

fE

arn

ing

s

Cumulative Percent of WorkersNote: Guangdong’s earnings Gini;

1988, 0.2625 (.0066); 1995, 0.2853 (.0080)

00.10.20.30.40.50.60.70.80.9

1

mu

lati

ve P

erc

en

t o

f E

arn

ing

s

19881995

Cu 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Cumulative Percent of WorkersNote: China’s earnings Gini;

1988, 0.2241 (.0023); 1995, 0.2799 (.0025)

(a)

(b)

(c)

Fig. 1. (a) Taiwan’s earnings distribution, (b) Guangdong’s earnings distribution, and (c) China’s earnings

distribution.


3. Estimating returns to human capital

3.1. Summary statistics

Table 1 presents summary statistics for Taiwan, urban Guangdong Province, and urban

China. For Taiwan we consider four years of data, 1978, 1983, 1988, and 1995. For

Guangdong and China we consider 1988 and 1995. We provide summary data on workers’

age, schooling, gender, and earnings. The last two rows of Table 1 provide the CPI and

sample sizes.

The first row of Table 1 provides information on workers’ ages. In Taiwan we observe a

slightly U-shaped age profile, with average age beginning and ending at about 36 years but

dipping as low as 35 years in 1993. Urban Guangdong province shows the most rapid

increase in workers’ average age, rising by nearly 2 years from 37.5 years in 1988 to 39.4

years in 1995. In contrast, urban China shows the slowest increase in workers’ average age,

rising by 1.1 years from 37.6 years in 1988 to 38.7 years in 1995.

The second row of Table 1 provides workers’ average years of schooling. For Taiwan,

average years of schooling increased steadily from 9.5 years in 1978 to 11.7 years in 1995.

Surprisingly, Guangdong’s schooling closely matches Taiwan, rising from 10.5 years in

1988 to 11.7 years in 1995. Urban China’s average workers’ education grew quite rapidly

during the 1988–1995 period from 10.6 years in 1988 to 12.0 years in 1995. In fact, the

average education level in urban China actually exceeds Taiwan’s in 1995.3

Rows 3 and 4 of Table 1 provide more detail on changes in worker education by

examining the percent of workers with high school and college degrees. By 1995 we find a

Table 1

Summary statistics

Taiwan Guangdong China

1978 1983 1988 1995 1988 1995 1988 1995

Age 36.1 35.0 35.1 36.4 37.5 39.4 37.6 38.7

Years of schooling 9.5 10.2 10.8 11.7 10.5 11.7 10.6 12.0

Percent high school 41.8 46.9 53.0 62.7 51.1 66.1 48.9 66.8

Percent college 9.3 10.3 10.0 12.3 6.2 7.4 6.3 8.5

Percent of male 72.3 65.6 62.4 59.6 51.6 52.6 52.1 53.2

Real male earningsa 11377 14884 26124 37527 526 999 371 559

Real female earnings 7060 8864 16007 24579 437 840 312 465

Male/female 1.61 1.68 1.63 1.53 1.20 1.19 1.19 1.20

CPI 61.6 97.7 100.0 130.8 100.0 227.9 100.0 227.9

Sample size 15811 17499 17355 16114 2009 875 17006 10846

a Taiwan earnings in real NTD, Chinese earnings in real RMB.

3 How do these education and aging trends impact inequality? Lin and Chu (2000) note that rising education

levels in Taiwan are associated with greater inequality. Bishop, Formby, and Smith (1992) find that U.S. states

with higher education levels tend to have more unequal distributions. Greater inequality may be generated as

some are left behind with low education. Bishop et al. also find that population aging is associated with greater

inequality.


higher percentage of high school graduates in Guangdong (66.1%) and China (66.8%) than

in Taiwan (62.7%). However, Taiwan’s percentage of college graduates (12.3%) is

significantly higher in 1995 than either Guangdong (7.4%) or China (8.5%).

Rows 5–8 focus on differences in labor market participation and earnings by gender. We

find a steady decline in the percentage of Taiwanese workers who are male, falling from

72.3% in 1978 to 59.6% in 1995. In Guangdong and urban China as a whole the percent

male is only slightly above 50% in both years. Real male earnings (row 6) in Taiwan grew

by 230% between 1978 (11,377 NTD) and 1995 (37,527 NTD), while real Taiwanese

female earnings (row 7) grew by about 250% (7060 NTD to 24,579 NTD) over the same

time period.

Focusing on the 1988–1995 time period we find that real male earnings in Taiwan grew

by 44%, while real female earnings grew by 54%. For urban Guangdong, real male

earnings grew from 526 RMB in 1988 to 999 RMB in 1995, or 90%. For females in

Guangdong, earnings grew by 92% from 437 RMB to 840 RMB. Finally, for urban China

male earnings grew from 371 RMB to 559 RMB, or more than 50%. Female wages in

China grew by 49% between 1988 and 1995 from 312 RMB to 465 RMB.4

The male–female earnings ratio is given in row 8. For Taiwan, the decade between 1978

and 1988 shows very little change in the male–female earnings ratio (1.61 in 1978 and 1.63

in 1988); however, the relative earnings of females did increase between 1988 and 1995. As

expected the male–female earnings ratio is significantly smaller in both Guangdong and all

of urban China. For both Guangdong and urban China, male earnings exceed female

earnings by approximately 20%. How do these earnings gaps compare internationally?

Estimates provided by Waldfogel (1998) place China’s earnings gap in line with the

Scandinavian countries; Taiwan’s earnings gap is slightly larger than that in the U.S. but

significantly smaller than Japan’s.

It is interesting to note that the unadjusted earnings gap in China did not change between

1988 and 1995 even as China moved to a more market-oriented economy with greater

earnings inequality.5 In the next section we examine trends in returns to education,

experience and gender as possible explanations for changing levels of earnings inequality.

3.2. Earnings equations results

Table 2 presents the results for human capital regression models for earnings.6 Table 2a

proxies human capital with years of experience, experience squared, schooling and an

indicator for female and a female � years of schooling interaction term. Table 2b replaces

years of schooling with indicator variables for the various levels of schooling. Table 2a

includes results for Taiwan, 1978, 1983, 1988, and 1995, Guangdong, 1988 and 1995, and

all of urban China, 1988 and 1995. Table 2b excludes results from Guangdong province due

4 See Khan and Riskin (2001) (Table 2.2), for changes in price level in China.5 We note that Blau and Kahn (1996) attribute much of the gender earnings gap in the U.S. (relative to other

developed countries) to the high level of U.S. wage inequality.6 For other examples of earnings equations using the CHIP data see, Xie and Hannum (1996), Knight and

Song (1993, 2001), and Gustafsson and Li (2001a, 2001b). For a study of earnings in Taiwan, see Lin and Chu

(2000) and Bourguignon et al. (2001).


Table 2

Regression models for total earnings

Variable Taiwan Guangdong China

1978 1983 1988 1995 1988 1995 1988 1995

(a) Model 1: year of schooling

Intercept 10.21 (0.025) 10.76 (0.026) 11.18 (0.024) 11.79 (0.026) 6.76 (0.081) 8.11 (.141) 6.52 (0.023) 7.60 (0.039)

Experience 0.058 (0.002) 0.062 (0.002) 0.062 (0.001) 0.057 (0.001) 0.056 (0.005) 0.037 (0.005) 0.049 (0.001) 0.039 (0.001)

Experience squared �0.001 (0.00003) �0.0011 (0.00003) �0.0012 (0.00003) �0.0011 (0.00002) �0.0009 (0.0002) �0.0005 (0.0001) �0.0007 (0.00003) �0.0005 (0.00003)

Years of schooling 0.063 (0.001) 0.067 (0.002) 0.063 (0.001) 0.063 (0.002) 0.029 (0.005) 0.047 (0.010) 0.028 (0.001) 0.056 (0.002)

Interaction of female and

level of education

0.045 (0.002) 0.044 (0.002) 0.031 (0.002) 0.030 (0.002) 0.015 (0.008) 0.036 (0.012) 0.024 (0.002) 0.032 (0.003)

Female �0.948 (0.022) �0.977 (0.021) �0.817 (0.022) �0.772 (0.024) �.294 (0.082) �0.539 (0.144) �0.389 (0.020) �0.518 (0.038)

R2 0.35 0.38 0.36 0.35 0.14 0.14 0.34 0.33

(b) Model 2: education level

Level of education college

or above

0.755 (113) 0.828 (129) 0.772 (116) 0.801 (123) 0.309 (36) 0.592 (81)

Community college 0.527 (69) 0.565 (76) 0.504 (6) 0.488 (63) 0.232 (26) 0.456 (58)

Technical school 0.481 (62) 0.475 (61) 0.439 (55) 0.410 (51) 0.135 (14) 0.342 (41)

Senior middle school 0.363 (44) 0.376 (46) 0.312 (35) 0.296 (34) 0.044 (4) 0.211 (23)

Junior middle school 0.210 (23) 0.162 (18) 0.131 (14) 0.132 (14) 0.002a (0) 0.138 (15)

Interaction of gender and level

of education

Four-year college or above 0.669 (315) 0.667 (346) 0.500 (257) 0.444 (247) 0.320 (88) 0.354 (158)

Community college 0.575 (201) 0.596 (220) 0.453 (160) 0.394 (142) 0.327 (75) 0.380 (130)

Technical school 0.547 (180) 0.540 (175) 0.352 (121) 0.310 (105) 0.287 (53) 0.362 (102)

Senior middle school 0.478 (132) 0.408 (119) 0.282 (81) 0.254 (73) 0.261 (35) 0.255 (59)

Junior middle school 0.311 (68) 0.307 (60) 0.156 (33) 0.104 (27) 0.197 (22) 0.170 (36)

R2 0.34 0.38 0.37 0.38 0.34 0.34

aNot significant at 95% level.

55

6J.A

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15

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04

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–5

62

to small sample sizes in some of the education cells. As is typical in Mincerian earnings

models the dependent variable is the log of earnings.

Rows 2 and 3 of Table 2a provide the experience coefficients. We find very stable

experience coefficients in Taiwan, ranging between 0.057 and 0.062. Guangdong’s return

to experience in 1988 is very similar to Taiwan (0.056) and all of urban China shows

somewhat lower returns to experience in 1988 (0.049). However, both Guangdong and

urban China show declines in the return to experience over time. This suggests that the

skills that older workers acquired in a socialist economy have declined in value during the

transition to a more market-oriented labor market.

Fig. 2 plots the returns to experience for Taiwan, Guangdong, and urban China for 1988

and 1995. As expected each profile shows diminishing returns to additional years of

experience. In the case of Taiwan and Guangdong 1988 we find that earnings decline for

older workers with more than 25–30 years of experience. Examining the experience

profiles for China, we find a uniform decline in experience over time. While returns to

experience clearly decline in urban China between 1988 and 1995, this decline is most

dramatic in Guangdong. This is illustrated in Fig. 2 by the abrupt downward shift in returns

to experience in Guangdong between 1988 and 1995.

Row 4 provides the years of schooling coefficient for males. For Taiwan, this male

schooling coefficient is remarkably stable over time, ranging between 0.063 and 0.067. We

note that this is very close to the consensus estimate for the U.S. (cf. Krueger and Lindahl,

2001). While in 1988 the education coefficients for both urban Guangdong and urban

China are much smaller than in Taiwan, the returns to male education increased sharply

between 1988 and 1995 as the Chinese market economy emerged. For Guangdong, the

schooling coefficient increased from 0.029 to 0.047 and for China the schooling coefficient

increased from 0.028 to 0.056. To put rapid increase in the return to education in

perspective, in 1988 the male return to schooling in China was only 44% of that of

Taiwan, but by 1995 China’s return to schooling had grown to almost 90% of that of

Taiwan.

0

10

20

30

40

50

60

70

80

90

100

0 5 10 15 20 25 30 35

Years of Experience

Exp

erie

nce

Pre

miu

m

Taiwan '88

Taiwan '95

China '88

China '95

Guangdong '88

Guangdong '95

Fig. 2. Experience profiles: Taiwan, China, and Guangdong.


In order to control for differences in return to education by gender we interact female

with schooling (row 5). In all cases the interaction coefficient is positive, implying that in

Taiwan, urban China, and Guangdong returns to schooling are higher for females than for

males. In 1988 Taiwanese females earned 9.4% (6.3% þ 3.1%) per year of schooling, while

their Chinese counterparts earned 7.1% (2.8% þ 2.4%) per year of schooling. By 1995, the

return to schooling for Chinese females had grown to 8.8% or 95% of the return earned by

Taiwanese women.

Row 6 provides the coefficients for the female indicator variable. Together with the

female � schooling interaction term (row 3) we can obtain an estimate how the female

earnings penalty that varies with years of schooling. For example, the 1995 female

coefficient in Taiwan is �0.772, but as females earn higher returns to education this

penalty only directly applies to females without any schooling. Fig. 3 plots the female

penalty for Taiwan 1978 and 1995 and China 1988 and 1995. Recall from Table 1 that the

unadjusted earnings penalties for females are approximately 20% in China and 60% in

Taiwan. Fig. 3 shows that these penalties clearly vary with the level of education, with

lower educated females suffering larger earnings penalties that higher educated women. In

fact, we find little or no earnings penalty for college-educated Chinese women in both 1988

and 1995. Over time we find a decline in the Chinese female earnings penalty. In contrast,

in Taiwan lower educated women enjoy declining penalties over time while there is a small

increase in the earnings penalty for the most educated women.

Table 2b provides the model coefficients for a set of educational indicator variables. The

female interaction coefficient measures the additional educational premium earned by

females. In parentheses below the coefficient value is the percentage premium above the

omitted primary education level.7 For females this is the combined or total effect. We

exclude the Guangdong only sample from this table due to small sample sizes in some

education classes.

Fig. 4a and b plot the male and female education premiums for Taiwan and China. We

plot only one year (1995) for Taiwanese males as we find relatively little change in the

education premiums over time. The earnings benefits of education are higher in Taiwan at

every educational class than in China. However, between 1988 and 1995 China shows a

dramatic increase in the benefits of education. For example, returns to college to males

more than double from 36% in 1988 to 81% in 1995. Over time the returns to the different

00.20.40.60.8

1

0 6 8 10 12 14 16Years of Schooling

Fem

ale

Pen

alty

China '88China '95Taiwan '78Taiwan '95

Fig. 3. Female earnings penalties by years of schooling.

7 These are the ‘‘unlogged’’ values of the regression coefficients.


levels of education in China have become very close to those in Taiwan, with the notable

exception of college.

Fig. 4b shows the female education premiums. As expected the female education

premiums are significantly larger than the male education premiums for each country and

each year. A rapid increase of the Chinese female education premiums together with a

small decline in its Taiwanese counterpart leads to nearly identical returns to education in

both countries, again with the notable exception of college.

0

20

40

60

80

100

120

140

Junior High Senior High Tech. Some College College

Education Level

Ed

uca

tio

n P

rem

ium

Taiwan '95China '88China '95

0

50

100

150

200

250

300

350

Junior High Senior High Tech. Some College College Grad

Education Level

Ed

uca

ton

Pre

miu

m

Taiwan '95Taiwan '78China '88China '95

(a)

(b)

Fig. 4. (a) Male education premiums and (b) female education premiums.


4. Conclusion

In this paper we study labor market earnings for Taiwan and China to investigate the

possibility of a shared development path. To gain some further insight into the possibility of

a common development path, we provide separate earnings estimates for the most

developed part of the China, the southeastern coastal province of Guangdong. The data

for our study comes from the Taiwan Survey of Personal Income Distribution (1978, 1983,

1988, and 1995) and the Chinese Household Income Project (1988 and 1995). During this

time period China’s economy experienced a dramatic shift from a planned socialist

economy to an emerging market economy.

We find that earnings inequality continued to decline in Taiwan in the 1980s and

early 1990s. In contrast, the urban China earnings Gini coefficient increased by nearly

25% between 1988 and 1995. Likewise, Guangdong Province experienced an increase

in earnings inequality; in 1988 Guangdong had an earnings Gini very similar to

Taiwan, but by 1995 its earnings Gini was more than 10% larger than Taiwan’s. While

China’s rapid growth rates that occurred after economic liberalization appear to be

similar to those that Taiwan enjoyed during its emergence as a modern economy, the

initial conditions (i.e., China’s planned economy) were quite different. Thus, unlike

Taiwan, China’s economic development has not been accompanied by declining

inequality.

Between 1988 and 1995 Taiwan and all of urban China both experienced a nearly 50%

increase in real earnings. In contrast, urban Guangdong Province enjoyed a near

doubling of earnings. Average levels of education are similar in all three regions for

both years. In fact, China’s average level of education in 1995 actually exceeds that of

Taiwan; however, Taiwan has 50% more college graduates. All three regions show signs

of aging populations.

To help better understand these changes in earnings and inequality we examined

changes in the returns to human capital. For Taiwan, we find that male returns to

education are stable over time and closely approximate those in the United States. In

addition, we find stable returns to experience. We do, however, find falling female

penalties at the bottom of the education scale, which together with a slight increase in

the earnings penalty among higher educated women, is consistent with Taiwan’s

declining earnings inequality.

For China, we find surprisingly little difference between the human capital estimates

for all of China and the most developed part, Guangdong. Our findings show rapidly

rising returns to education in China and declining returns to experience. These findings

are consistent with an environment of rising earnings inequality between 1988 and

1995.

As expected, we find that the ratio of male to female earnings in China is much lower

than in Taiwan. We find that the shift from a planned economy to an emerging market

economy, with its greater degree of wage inequality, has resulted in a deterioration of

Chinese women’s relative earnings for all but college-educated women. Thus, a decline in

the Taiwanese female earnings penalty at most education levels combined with rising

female earnings penalties in China has resulted in a narrowing of the gap between relative

female earnings in the two countries.


In sum, in spite of differences in the trend in earnings inequality, we find converging

levels and returns to education between the two countries. In contrast, returns to experience

appear not to be converging, most likely due to China’s dramatic economic restructuring.

Acknowledgements

The authors would like to thank an anonymous referee for useful comments that

improved the paper. The usual caveat applies.

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