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Gender wage differences and illicit drug use: findings from Yunnan Province.

INTRODUCTION

A growing body of research explores gender wage differences in China. (1) Much of the research work has used either the Urban Household Survey 1988-92 (UHS) or the Chinese Household Income Project (CHIP). (2) The UHS, conducted by the Organisation of Urban Socio-Economic Survey of the National Bureau of Statistics of China, covered the urban population of 30 municipalities and provinces in China. The CHIP includes information on personal earnings and demographic characteristics of rural and urban areas between 1988 and 2002. Most researchers have reported that the gender wage gap has increased over time. The present work is based on a data set from Yunnan Province in southwestern China, which has a substantial rural population and a more traditional economy that is dominated by agriculture. It is important to assess whether earlier findings on urban coastal regions remain valid and applicable to this non-coastal province. Further, a data set of drug users was carefully considered and included in the present sample. Research on how drug use shapes wage differences in China is still limited. In the United States, work that examines this relationship yields mixed results. Therefore, this work will contribute to the growing literature on how wage differences in China are shaped by gender and drug use.

Over the past two decades, the Chinese economy has been characterised by rapid economic reform. (3) The transition towards a market-oriented economy accelerated after 1992. (4) Before these reforms, the egalitarian ideology held that equally productive workers, regardless of gender, should receive equal pay. (5) With market-driven economic reforms and the decentralisation of wage determination, gender wage discrimination now has some effects on the Chinese economy. (6) This research also examines how differences in education affect and shape wages. Zhang and Zhao noted that economic returns to education have been historically low in China. (7) However, in recent years, Park et al., and Knight and Song posited that the economic returns to education may be contributing to rising income inequality. (8) Researchers also debate whether the increasing privatisation of the Chinese economy will economically benefit women. Dong et al. argue that privatisation would widen the gender wage gap; however, Liu, Meng and Zhang posited that privatisation decreases gender discrimination. (9)

Besides rapid economic reform and problems of gender wage discrimination, China faces problems of drug use and a looming AIDS crisis. (10) Yunnan is a culturally diverse province and shares borders with Laos, Myanmar and Vietnam including the mountainous regions that have been designated as part of the golden triangle. This area accounts for much of the illicit drug production in the region and considerable efforts targeted at stemming drug use and trafficking into China have been made. In 1995, China adopted the Beijing Declaration and signed the Sub-Region Drug Control Program of Action. Much of these efforts have been developed in cooperation with Myanmar, Laos and Thailand as well as the United National Drug Control Programme (UNDCP). (11) Early research in the US generally reported that drug use has negatively impacted wages; (12) however, Kaestner found that young men who used cocaine and marijuana could expect as much as a 19 per cent higher wage compared to those who did not use illicit drugs. (13) Likewise, Gill and Michaels reported that young drug users enjoyed higher incomes compared to others. (14) Gill and Michaels suggested further research was needed to assess if this relationship remains valid in other data sets (the National Longitudinal Survey of Youth (NLSY) was used) and posited that drug use may appear to be different from what had been popularly conceptualised. (15) Specifically, most of those people who use drugs do not become habitual users and most quit taking drugs by age 35.16 In this sample, the typical narcotic used by respondents reporting drug use was heroin. It is important to better understand the nature of emerging wage inequalities in China, and given China's unique history of supporting gender parity in labour markets, it is also useful to examine and track how gender inequality is emerging as privatisation and educational opportunities expand.

BACKGROUND

Compared to China's coastal cities, Yunnan has had lower economic growth. The tobacco, agriculture, mining and tourism industry employ many workers in this province. (17) Overall, the Chinese economy enjoyed rapid growth with low inflation as well as a significant trade surplus in 2003. That year, China's labour force constituted approximately 59 per cent of its total population and unemployment was about 4.3 per cent. (18) China has dominated the global exports of many products ranging from electronic tools to footwear since the mid-1990s. This has spurred an influx of rural labour into the labour market. The labour force participation rate of Chinese urban women is extraordinarily high. Maurer-Fazio, Rawski and Zhang found near universal participation rates for urban women even dating back to the 1960s. (19) Further, Chinese women do not typically exit the labour market after marriage or the birth of their first child; therefore, there is little difference in the work experience between employed women and men of the same age cohort. (20)

Before market liberalisation, an egalitarian gender ideology prevailed in China. It was believed that all workers should be treated alike and rewarded similarly. (21) The Communist Party of China made a political mandate for egalitarian gender ideology and Chinese society began to adopt income equality for both genders. (22) However, following the decentralisation of wages and the opening up of markets, a gender wage gap emerged as well. Hughes and Maurer-Fazio posited that, between 1988 and 1995, men in urban areas earned 25 per cent more than women. (23) The wage gap in rural areas is thought to be even higher. By 1995, Maurer-Fazio, Rawski and Zhang found that rural men earned as much as 46 per cent higher than women. (24) Likewise, Dong et al., Gustafsson and Li, and Liu, Meng and Zhang reported significantly lower earnings for women compared to men. (25)

Besides rural/urban differences in wage determination, competing hypotheses have emerged with regard to how male and female workers have fared in privatised enterprises. Liu, Meng and Zhang found that privatisation decreases discrimination by gender. (26) Most researchers, however, maintain that privatisation has negatively impacted the earnings of women compared to men. Dong et al. argue that managers of privatised enterprises may be more willing than others to cede to the collective egalitarian ideology of the past. (27) Thus, they posited that privatisation has contributed to an increase in the gender wage gap and also discrimination against women in the labour market. (28) These researchers put forward the idea that decision-makers in privatised enterprises aim to reward productivity and are free to set wages to attract good workers. Employment in private enterprises, foreign-funded enterprises as well as those in self-employment has soared in recent years. Zhang et al. estimated that these sectors accounted for 19 per cent of employment by 2001; however, employment in state and government enterprises continues to dominate the market. (29)

Past research findings are mixed with regard to whether human capital, specifically education, is rewarded in the Chinese labour market. Meng and Kidd, for example, argue that labour markets do not reward human capital; rather non-market factors (especially institutionalised wage settings of the past) are still the most important factor in shaping wages. (30) Gustafsson and Li found that young women with little education did not realise the economic benefit of schooling and, in fact, experienced greater gender wage discrimination as China began economic reforms. (31) On the other hand, Maurer-Fazio argues that education is increasingly important in determining wages even for new entrants in the labour market. (32) Park et al. (2002) and Knight and Song (2003) posit that the rapid rise in income inequality in recent years has been shaped by rising returns to education. (33) Yet, educational attainment in rural China is still relatively low. (34) Zhang et al. found that the average level of education for the rural population in 1996 was 5.2 years (higher for men (6.64 years) than women (3.63 years). (35) Utilising urban household surveys conducted by the National Bureau of Statistics of China, Zhang et al. found that by 2001, the mean number of years of schooling was 11.8 years. (36) Notably, Zhang et al. found that 28 per cent of workers had a college education or higher and only 2.9 per cent of workers had a primary school education or less. (37)

A growing body of research explores gender differences in the Chinese labour market. Few researchers, however, explore how variation in marital status may precipitate these differences. This is undoubtedly due to the lack of information on marital status in many data sets. For example, Coady and Wang argue that the significance of returns to personal characteristics in influencing inequality had declined and that researchers should move beyond individual characteristics in better understanding wage differences. (38) Notably, their model does not include the respondents' marital status as measurement of wage differences.

Men typically enjoy higher economic returns associated with being married compared to women, and the phenomenon of earnings difference also holds true in South Korea and Japan. (39) Married women do not enjoy the same economic benefits that married men do. (40) Headlee and Elfin argue that 30 per cent of the wage gap in the US may be solely attributed to the marital status of men. (41) Loscocco and Wang argue that women in China, as in the US, bear primary responsibility for household labour as well as childcare. (42) Headlee and Elfin, and Blau and Beller argue that employers compensate married men more to help them support families and children. (43) We posit that gender differences in the economic returns from being married hold true in modern China as well.

DRUG USE AND INCOME

Currently, there is no research that explores how drug use affects variation in income in China. Research findings from the US are mixed with regard to how drug use impacts income. The early works tended to show that drug use negatively impacted earnings; however, more recent research reports that young drug users earned more than others. (44) Kandel, Chen and Gill also found a positive relationship between illicit drug use and income among younger workers (those under 30). (45) However, they also reported that, by age 35, there were no cumulative effects of illicit drug use on earnings. (46) Therefore, the use of illicit drugs may impact earnings differently depending on the age of the user. Gill and Michaels suggest that drug use might help individuals cope with personal problems, allowing them to function better in the labour market. (47)

There are few works that delve into how gender differences in drug use may affect earnings. Unfortunately, respondents in this study were not asked why they used illicit drugs; therefore, it is difficult to deduce what drug use means to users or how they perceive the correlation between drug use and employment. Basic ordinary least-squares (OLS) regression was utilised in this work where log annual income was a dependent variable.

In light of the past literature, we test the following hypotheses:

* Given the move towards an open market, we expect that gender differences in the Chinese labour market will mirror those that have appeared in other labour markets. (48) The rise of a market economy generally exists without a political mandate for gender equality and this was the case in the past in China, gender differentials, we posit, will characterise wage differences among workers. Further, we are persuaded by the work of Whalley and Zhang, Dong et al. and Ng; therefore, we similarly expect that economic returns to education and experience will be higher for men than women. (49)

* We do not expect to observe significant income differences among labour market participants who currently use illicit drugs compared to others, all else being equal. Given that the mean age of the sample was 32, we are persuaded by the work of Kandel, Chen and Gills and do not expect to observe a significant relationship between reported drug use and income. (50) Drug use may negatively impact educational attainment and work experience and illicit drug use is related to delayed entry into marriage. (51) In our model, we capture the partial effects of drug use on income after taking into account these variables.

Sample Selection

Data for this research project are from a large population-based survey which covered the entire province of Yunnan. The data set is cross-sectional and was collected in 2003. In order to ensure representation of drug users, a three-stage sampling procedure was utilised. First, counties in the province were rank-ordered in terms of how many people were known (registered) drug users (the vast majority being heroin users). From this sampling frame, eight counties were selected with priority given to counties with higher concentrations of drug use. Second, all rural townships and urban neighbourhoods in each of the eight selected counties were ranked according to government estimates (Centres for Disease Control and Prevention, aka CDC) by the number of known drug users. From this list, five townships and neighbourhoods were selected. Again, priority was given to the selection of those townships and neighbourhoods that had higher concentrations of known drug users. Thus, the primary sampling units consisted of 40 townships and neighbourhoods. Within these townships and neighbourhoods, individual respondents (aged 18 and older) were selected using disproportionate probability sampling. (52) This was accomplished utilising information on household registration rosters and confidential registrations of drug users. Interviewers visited selected individuals and informed them of their right to refuse to participate, how they would be compensated if they did participate, as well as inviting them to be a part of the study. The refusal rate of participants to be a part of the study was low (3.3 per cent). Face-to-face interviews were conducted among respondents. Yunnan Province is one of the most ethnically diverse in all of China. With mountainous terrain, it has been cut off from much of the early economic development in China. The sample was restricted to respondents who were employed, at least 18 years of age and had a reported income. Income was defined as wage earnings from the past year; therefore, this measure did not include capital income or any kind of transfer money. Further, the assumption was that reported wage income was from legal sources. As past research tended to exclude those who are not self-employed, we adopted the following example.

VARIABLE CONSTRUCTION

Respondents were asked if they had ever used any illicit drugs, if they were currently using any drugs, and if they had ever in their lifetime been in a drug treatment or detox programme. The possible response options were dichotomised as "Yes" (1) or "No" (0). The years of education variable was constructed in line with Zhang et al. (53) The survey data included information on the highest education completed. The number of years of schooling for different levels of education was assumed as follows: elementary school attained equals six years; junior high school attained equals nine years; senior high school attained equals 12 years; vocational and those with two or three years of college attained equals 15 years; and those with at least four additional years of education in college equals 16 years. College was a dummy variable where "(1)" equalled those with four years or more of college education and "(0)" included all other education levels. Experience was defined as a worker's years of potential labour market experience operationalised as age minus years of schooling minus six. (54) Age was calculated by subtracting the date of birth from 2003. Marital status was coded as married "(1)" or not married "(0)". Urban metropolitan status was coded as living in an urban area (which includes urban neighbourhood, suburbs and urban fringe areas) "(1)" or rural villager "(0)". Respondents were asked, "In your opinion, what was your household socioeconomic status during your childhood?" Those who reported that their childhood socioeconomic status was either good or very good were assigned a variable value of "(1)". Respondents who reported their childhood socioeconomic status as very poor, poor, or average were assigned a variable value of "(0)" for socioeconomic status (SES). If respondents reported being a member of the Communist Party or the Communist Youth League, they were considered Communist Party members, and assigned as "(1)".

We controlled the type of employment. Respondents were categorised as either self-employed "(7)" or working for the government "(1)", a state enterprise "(2)", a collective enterprise "(3)", a private enterprise "(4)", a foreign joint venture "(5)", or identified as an entrepreneur/owner (with hired employees) "(6)". Again, we did not include in our original sample those who were self-employed or those who self-identified as an entrepreneur/owner (N = 9). We operationalised this variable as a dummy variable where those who worked for the government or state "(1)" were compared to all others (0).

Respondents were asked if they were currently using any drugs, with "Yes" assigned as "(0)" or "No" assigned as "(1)". Those who identified themselves as Han, were assigned as "(1)" and those who identified themselves as non-Han were assigned "(0)". Agricultural workers were coded as "(1)" while all other occupations were coded as "(0)".

LIMITATIONS OF DATA AND METHODS

While present data offers insights into how drug use may affect income in modern China, there are limitations that must be noted. First, drug use is a sensitive subject. Thus, there may be some question about the veracity of the data. Collecting information on any hard-to-reach population, in this case drug users, poses unique problems. Furthermore, respondents were not asked what kinds of illicit drugs they used nor why they used drugs, hence little causal interpretation may be gained in terms of whether drug use influenced differences in income or whether differences in income shaped drug use. Reported income was assumed to be from a legal source. Information about possible illicit income is not known. We used a cross-sectional sample of individuals in one province, namely Yunnan, which is noted for drug use. As the data set is limited to this province, the findings cannot be generalised to any larger population.

Results

There were 2,157 respondents in the sample. Of these, 61 per cent were male and the mean age of the sample was 32.14 with a range between 18 and 55 (see Table 1). Most of the respondents (72 per cent) were married and one-fourth (24 per cent) were single. Among other marital status categories (all coded as not married), there were 29 respondents (or 0.67 per cent) who lived together, 96 respondents (2.23 per cent) who were divorced, 19 respondents (0.44 per cent) who were separated, and 26 respondents (0.60%) who were widowed. Approximately a third of the respondents (34 per cent) lived in an urban neighbourhood. Few agricultural workers, about 8 per cent, lived in an urban area. Most respondents (85 per cent) reported that they had never used drugs. 13 per cent of the respondents had, in their lifetime, been in a drug treatment or detox programme. Only 4.3 per cent of the sample size, were current drug users. As the coefficients for most variables did not change considerably regardless of which drug variable was included in the model, we reported only coefficients that included current drug use because this would determine the contemporaneous effects of using drugs. Further, since current income was the dependent variable, current drug use may capture the present effects of such use. Initially, the variable was introduced whether the respondent had ever been in a drug treatment or detox programme. In the end, this variable was excluded because of multicolinearity between using drugs and having sought drug treatment. It is possible that drug users who do not seek treatment do not admit to drug use. The correlation for the whole sample for treatment and having done drugs was .911. The correlation for treatment and doing drugs now was .441. It appears that the simple correlation was sufficiently strong to merit excluding treatment from the model. Again, a panel data set that included mainly drug users could yield results that explain the effects of drug treatment on income differences.

Agricultural workers (46 per cent) made up the largest single share in terms of occupation in this sample. There were 32 types of occupations as identified by respondents in the sample (other distributions ranged from .02 per cent to 14 per cent). Virtually all the agricultural workers were rural villagers. Few of them (7 per cent) reported that their childhood socioeconomic status was good or very good. Most respondents (81 per cent) were not members of the Communist Party. Most respondents (62 per cent) were self-employed. Three per cent of the respondents worked for the government, 7 per cent worked for a state enterprise, 3.5 per cent worked for a collective enterprise, 17 per cent worked for a private enterprise, and 2.5 per cent identified themselves as entrepreneurs/owners. Most respondents (65 per cent) were highly likely to identify themselves as belonging to the Han ethnicity. Altogether 26 ethnic categories were chosen; however, few respondents belonged to any one of these categories (variable values ranged from .05 per cent to 5.54 per cent). Few respondents (14 per cent) were illiterate. Most reported that their highest grade of school completed was either elementary school (33 per cent) or junior high school (38 per cent). 7 per cent of the respondents had attained senior high education. Only 2 per cent of the respondents had completed two or three years of college education, and even a smaller percentage (0.6 per cent) had completed four or more years of college; however, 5 per cent of the respondents had a vocational education. The mean number of years of education completed was 8.63 years. For our sample consisting of both rural and urban dwellers, the mean number of years of schooling in 2003 was 8.63 years, which falls midway between the educational figures of rural and urban areas reported in the study by Zhang et al. (55) Less than 1 per cent of the respondents had a college degree and this figure aligns with that as reported by Zhang et al. for rural areas in China. (56) We found significant differences in the mean values of many variables between men and women in the model, such as years of education, experience, "drugs now" (current drug user), "drugs ever" (have been a drug user before), drug treatment and urban. Therefore, regression findings are presented separately for the sample as a whole and for women and men.

GENDER WAGE DIFFERENCES

The model shows, controlling for all other variables, that men earned substantially higher income (19 per cent) than women in Yunnan. In this sample, men earned, on average, 7,109 yuan per year, while women earned 4,727 yuan. In 2003, women's income was 66.5 per cent that of men's income. Data on the number of hours worked was not included in the data set, so caution must be taken in interpreting income differences among respondents. Our empirical work supports that of Hughes and Maurer-Fazio, who reported a 25 per cent gender wage gap. (57)

We found that both women and men benefited economically by completing additional years of education and by working outside of agriculture. Being married and coming from a higher SES was also positively associated with income for men while working in an urban area was positively associated (at .06 level) with income for women. Results show that women earned an extra 11 per cent per year of education while men earned 9 per cent more per year; however, the difference by gender is not statistically significant. Therefore, the data do not support the hypothesis that economic returns to education and experience would be higher for men than women. This finding supports the work of Maurer-Fazio and others (Park et al. ; Knight and Song) but is at odds with the proposition by Gustafsson and Li that women with little education do not realise the significant economic benefit to schooling. (58)

Women who worked in a state enterprise did not have significantly higher wages than others. Past research is mixed in this area with some arguing that privatisation decreases gender discrimination; (59) however, others have posited that privatisation negatively impacts women's income. (60) The negative impact from being in agriculture was greater for women (-53 per cent) than for men (-46 per cent). Men gained a substantial income premium for being married (24 per cent). Coming from a higher SES significantly benefited men (28 per cent); however, this variable was not a significant one in precipitating income differences for women. Finally, for women, being in an urban area was positively associated with income at the .06 level. Urban working women reported an income that was 15 per cent higher than rural women's income, all else being equal.

Being married was positively associated with income as was having a family background with a higher SES. In this sample as a whole, being married increased one's income by 20 per cent and, for men, being married increased income by 24 per cent. Being married was not a significant variable in shaping income differences among women, all else being equal. Therefore, the data failed to support the hypothesis that being married would be positively associated with income for men in China, all else being equal.

Finally, working in agriculture reduced reported income by 51 per cent, all else being the same. Working for a state enterprise increased income by 8 per cent for the sample as a whole and brought a higher return for women (12 per cent) than for men (6.4 per cent). However, this difference was not statistically significant, again in part, because few respondents in our sample were working in a state enterprise. Likewise, being a member of the Communist Party or being Han did not significantly impact income.

WAGE DIFFERENCES AND DRUG USE

Even though care was taken to over-represent drug users in our sample, few respondents (4.3 per cent) currently used drugs. More men than women used drugs (see Table 2). The original sample excluded self-employed workers, and drug use was positively associated with higher income. Only one other variable, i.e., whether working in agriculture, impacted income as much. Drug users registered a 49 per cent increase in income, all else being equal. Female drug users reported a 109 per cent increase in income over other women, all else being equal, which was significantly higher than the benefit reported by male drug users (41 per cent). Our results provide empirical support for the hypothesis that drug use is positively related to income in Yunnan Province. Overall, drug users had a substantially higher income (49 per cent) than those who were not currently using drugs. This finding is in line with work by Kandel, Chen and Gill who found a positive relationship between illicit drug use and income. (61) We could not establish causation in the relationship between income and drug use. In future, researchers may want to explore the complex nature of this relationship. Furthermore, it must be emphasised that the drug use patterns in the region where this data were collected may not mirror those of the whole of China. Panel data would hence be very useful for studying causation between drug use and income.

SUMMARY AND DISCUSSION

The findings of this research about gender wage differences and illicit drug use are consistent with those found by other researchers for urban China. (62) We found empirical support for the proposition that men would earn more on average than women, and that married men would enjoy a wage benefit over others. We found that men earned 19 per cent more than women, holding other variables in the model constant. Alternatively, women earned 84 per cent of what men earned, controlling for background variables. This is in line with Khan's work that reported women's earnings were 80 per cent those of men's earnings. (63) Among self-employed workers, men earned 23 per cent more, all else being equal. Our findings also concur with the earlier work by Hughes and Maurer-Fazio and others which concluded that decentralisation and the opening up of markets in China led to the emergence of a gender gap. (64)

Like Maurer-Fazio, Park et al., and Knight and Song, we found that additional years of education were positively associated with income. (65) This provides empirical support for the hypothesis that modernisation has brought changes in economic returns to education. Specifically, we found that men had a 9 per cent return to a year of education compared to an 11 per cent return for women. Although not a statistically significant difference, women may enjoy a higher economic return with the completion of each additional year of education compared to men because better-educated women (and men) may be more likely to work in "modern" businesses which should tend to discriminate less than others. Clearly, if this relationship holds in the future, it becomes increasingly important to emphasise the importance of education for women in China. These results provide empirical support for a human capital understanding of labour market differences. Specifically, after controlling for the effect of other variables, education is critical in shaping wage differences. Human capital theory posits that such workers are more productive than others and this will yield higher economic returns, all else being the same. Returns to education noted in this work were higher than those reported by Maurer-Fazio, who found that men enjoyed a 2.9 per cent return for each year of education and a 4.5 per cent return for women; however for those under 30, rates of return to education were higher for both men (6.4 per cent) and women (6.8 per cent). (66) Maurer-Fazio relied on the CHIP 1988 data and their model, and included only potential experience, potential experience squared, and years of schooling as variables. (67) Given the differences in the model and an additional 15 years passage in time, our results differed; however, we both found a significant positive return to education as well as a higher return to years of schooling for women than men.

Being married significantly enhanced the earnings of men; however, this effect did not hold for women. Married men earned 24 per cent more than unmarried men. Married men may be perceived, like in many developed economies, as "needing" a higher income. This research found that the return to being married was higher than that reported by Qian who used a sample from Beijing. He found that married men and women earned 11 per cent more than those who were not married. (68) While the models differ, the sign and direction of the coefficient for marital status were in line with other works. Whether or not employers in China prefer the stability of married workers, as suggested by Hughes and Maurer-Fazio, or whether unmarried workers will fare better in venture capital markets, must be explored in future work. (69)

Respondents coming from a higher socioeconomic status reported higher wages; however, this effect did not generally hold for women. Clearly, class differences are characteristic of modern China. Our work provides empirical support for the hypothesis that being raised in a more economically prosperous family brings labour market returns independent of other variables, especially for men. How class differences shape educational attainment, especially higher educational attainment, must be the focus of future work. Further, it is essential to better understand why the benefits of class appear to be gender-specific. Specifically, are women in China, regardless of socioeconomic status, adopting the notion (by whatever means) that success for men must come first before gender equality may be realised? Variables aimed to capture socioeconomic differences are also not always included in models to better understand gender wage differences in modern China. Again, we argue that this variable is critical to include in future models that explore gender wage differences in the Chinese economy.

Our data supports the work of Gill and Michaels and Kaestner that drug use is positively associated with income. (70) We cannot determine the causation, whether drug use leads to higher income or whether higher income leads to drug use; however, this is an interesting and important relationship to pursue in future work. Again, drug use in a region of China may not be representative of the whole of China. Yunnan borders countries which are tackling serious drug issues that remain a continuing problem. It is also important to note that virtually all respondents who admitted to ever having used drugs in Yunnan had undergone detoxification. China has a policy of compulsory detoxification that may well impact future occupational and wage opportunities--a consequence of drug use that may be explored in future work. Future work might include detailed questions regarding type of and life-course drug usage. This information would allow a better understanding of how drug use may shape wage differences. The lack of this information is a significant limitation of the current work.

Our work, using a data set for a large southwestern province is an initial attempt to address some of these issues. Like other data sets available in China, our data lack good information on work experience and number of hours worked. Nevertheless, our work provides empirical support for the work of others, especially with regard to positive returns to education and gender, as well as exploring how privatisation and drug use may shape differences in earnings in modern China. It also adds empirical support for the human capital theory given that education and experience are important variables in understanding wage differences, all else being equal. However, gender differences are emerging in the Chinese labour market. How researchers may theoretically understand the emergence of gender differences in this market, and the differences that are at odds with human capital theory, will be beneficial in grasping the interconnectedness of gender, markets, privatisation, opportunities, etc. Future data sets may include more information about the underground market economy and how this shapes reported wages. Further, more detailed data surrounding drug use including drug history, the effects of compulsory detoxification, the relationship between the intensity of drug use and income would allow a better understanding of how drug use impacts wage differences and how this varies by gender.

(1) See for example, John Whalley and Zhang Shunming, "A Numerical Simulation Analysis of (Hukou) Labour Mobility Restrictions in China", Journal of Development Economics no. 2 (2006): 1-65; Chen Jian, "Regional Income Inequality and Economic Growth in China", Journal of Comparative Economics 22, no. 2 (1996): 141-64; Dong Xiao-Yuan, Fiona MacPhail, Paul Bowles and Samuel Ho, "Gender Segmentation at Work in China's Privatized Rural Industry", World Development 32, no. 6 (2004): 979-98; Roger Gordon and David Li, "The Effects of Wage Distortions on the Transition: Theory and Evidence from China", European Economic Review 43, no. 1 (1999): 163-83; Ng Ying-Chu, "Economic Development, Human Capital, and Gender Earnings Differentials", Economics of Education Review 23, no. 6 (2004): 587-603.

(2) Li Shi, "Chinese Household Income Project, 2002", Inter-university Consortium for Political and Social Science Research (2009-2010).

(3) Ng, "Economic Development, Human Capital, and Gender Earnings Differentials".

(4) John Bishop, Luo Feijun and Wang Fang, "Economic Transition, Gender Bias, and the Distribution of Earnings", Economics of Transition 13, no. 2 (2005): 239-59.

(5) See Meng Xin and Paul Miller, "Occupational Segregation and its Impact on Gender Wage Discrimination in China's Rural Industrial Sector", Oxford Economic Papers 47, no. 1 (1995): 136-55.

(6) James Hughes and Margaret Maurer-Fazio, "Effects of Marriage, Education, and Occupation on the Female/Male Wage Gap in China", Pacific Economic Review 7, no. 1 (2002): 137-56; Scott Rozelle, Dong Xiao-Yuan, Zhang Linxiu and Andrew Mason, "Gender Wage Gaps in Post-reform Rural China", Pacific Economic Review 7, no. 1 (2002): 157-79.

(7) Zhang Junsen and Zhao Yaohui, "Economic Returns to Schooling in Urban China, 1988-1999", Paper presented at the 2002 meetings of the Allied Social Sciences Association, Washington, DC, 2002.

(8) See Albert Park, Song Xiaoqing, Zhang Junsen and Zhao Yaohui, "The Growth of Wage Inequality in Urban China, 1988 to 1999", Working paper, Peking University, Beijing (2002); John Knight and Lina Song, "Increasing Urban Wage Inequality in China", Economics of Transition 4, no. 11 (2003): 597-620.

(9) See Dong, MacPhail, Bowles and Ho, "Gender Segmentation at Work in China's Privatized Rural Industry"; Liu Pak-Wai, Meng Xin and Zhang Junsen, "Sectoral Gender Wage Differentials and Discrimination in the Transitional Chinese Economy", Journal of Population Economics 13, no. 2 (2000): 331-52.

(10) Yang Xiushi, Carl Latkin, David Celentano and Luo Huasong, "Prevalence and Correlates of HIV Risk Behaviors among Drug Users", AIDS and Behavior 10, no. 1 (2006): 70-94; Xiao Yan, Sibylle Kristensen, Sun Jiangping, Lu Lin and Sten Vermund, "Expansion of HIV/AIDS in China", Social Science and Medicine, no. 4 (2006): 24-68; Lu Lin, Jia Manhong, Luo Hongbing and Zhang Xiaolin, "Analysis of the First Round of HIV Behavioral Surveillance in Yunnan", Disease Surveillance 18, no. 11 (2003): 414-7.

(11) See United Nations Drug Control Programme (UNDCP), Sub-regional Plan of Action on Drug Control Programme 2009, at <http://www.esa.un.org/undcp> [17 Jan. 2012].

(12) Henrick Harwood et al., "Economic Costs to Society of Alcohol and Drug Abuse and Mental Illness: 1980", Report submitted to National Institute on Drug Abuse (Research Triangle Park, NC: Research Triangle Institute, 1984).

(13) Robert Kaestner, "The Effect of Illicit Drug Use on the Wages of Young Adults", Journal of Labor Economics 9, no. 4 (1991): 381-412.

(14) Andrew Gill and Robert Michaels, "Does Drug Use Lower Wages?", Industrial and Labor Relations Review 45, no. 3 (1992): 419-34.

(15) Ibid.

(16) Ibid.

(17) China National Bureau of Statistics, China Statistical Yearbook (Beijing: China Statistics Press, 2005).

(18) International Monetary Fund, International Financial Statistics (Washington, DC: International Monetary Fund, 2008).

(19) Margaret Maurer-Fazio, Thomas Rawski and Zhang Wei, "Inequality in the Rewards for Holding up Half of the Sky: Gender Wage Gaps in China's Urban Labour Market, 1988-1994", The China Journal 41 (1999): 55-88. See also Thomas Rawski, "Economic Growth and Employment in China" (New York: Oxford University Books, 1979); and Elizabeth Croll, Changing Identities of Chinese Women: Rhetoric, Experience, and Self-Perception in Twentieth-Century China (London: Hong Kong University Press, 1995).

(20) See Margaret Maurer-Fazio, "Earnings and Education in China's Transition to a Market Economy: Survey Evidence from 1989 and 1992", China Economic Review 10, no. 1 (1999): 17-40.

(21) Ng, "Economic Development, Human Capital, and Gender Earnings Differentials"; Meng Xin, "MaleFemale Wage Determination and Gender Wage Discrimination in China's Rural Industrial Sector", Labour Economics 5, no. 1 (1998): 67-89; Akira Iriye, Edward Lazzerini and David Kopf, The World of Asia (London: Harlan Davidson Inc., 1995).

(22) See Meng Xin and Paul Miller, "Occupational Segregation and its Impact on Gender Wage Discrimination in China's Rural Industrial Sector", Oxford Economic Papers 47, no. 1 (1995): 136-55; and Meng Xin, Labor Market Reform in China (New York: Cambridge University Press, 2000).

(23) Hughes and Maurer-Fazio, "Effects of Marriage, Education, and Occupation on the Female/Male Wage Gap in China".

(24) Maurer-Fazio, Rawski and Zhang, "Inequality in the Rewards for Holding up Half of the Sky".

(25) Dong, MacPhail, Bowles and Ho, "Gender Segmentation at Work in China's Privatized Rural Industry"; Bjorn Gustafsson and Li Shi, "Economic Transformation and the Gender Earnings Gap in Urban China", Journal of Population Economics 13, no. 2 (2000): 305 -29; Liu, Meng and Zhang, "Sectoral Gender Wage Differentials and Discrimination in the Transitional Chinese Economy".

(26) Liu, Meng and Zhang, "Sectoral Gender Wage Differentials and Discrimination in the Transitional Chinese Economy".

(27) Dong, MacPhail, Bowles and Ho, "Gender Segmentation at Work in China's Privatized Rural Industry"; see also Xu Wei, Tan Kok-Chiang and Wang Guixin, "Segmented Local Labor Markets in Post-Reform China", Environment and Planning A 38, no. 1 (2006): 85 -109.

(28) See also Ulla Grapard, "Theoretical Issues of Gender in the Transition from Socialist Regimes", Journal of Economic Issues 31, no. 3 (1997): 665-86; Susana Lastarria-Cornhiel, "Impact of Privatization on Gender and Property Rights in Africa", World Development 25, no. 8 (1997): 1317-33; Dong Xiao-Yuan, Paul Bowles and Samuel Ho, "The Determination of Employee Share Ownership in China's Privatized Rural Industries", Journal of Comparative Economics 30, no. 2 (2002): 415 -37; Shu Xiaoling, "Market Transition and Gender Segregation in Urban China", Social Science Quarterly 86, no. 5 (2005): 1299-323.

(29) Zhang Junsen, Zhao Yaohui, Albert Park and Song Xiaoping, "Economic Returns to Schooling in Urban China, 1988-2001", Journal of Comparative Economics 33, no. 4 (2005): 730-52.

(30) Meng Xin and Michael Kidd, "Labor Market Reform and the Changing Structure of Wage Determination in China's State Sector during the 1980s", Journal of Comparative Economics 25, no. 3 (1997): 403 -21. See also Raymond Byron and Evelyn Manaloto, "Returns to Education in China", Economic Development and Cultural Change 38, no. 4 (1990): 783-96; Robert Gregory and Meng Xin, "Wage Determination and Occupational Attainment in the Rural Industrial Sector of China", Journal of Comparative Economics 21, no. 3 (1995): 353-74.

(31) Gustafsson and Li, "Economic Transformation and the Gender Earnings Gap in Urban China". See also Daniel Millimet and Wang Le, "A Distributional Analysis of the Gender Earnings Gap in Urban China", The B.E. Journal of Economic Analysis and Policy 1 (2006): 20-5.

(32) Maurer-Fazio, "Earnings and Education in China's Transition to a Market Economy".

(33) Park, Song, Zhang and Zhao, "The Growth of Wage Inequality in Urban China, 1988 to 1999"; Knight and Song, "Increasing Urban Wage Inequality in China". See also Zhang, Zhao, Park and Song, "Economic Returns to Schooling in Urban China, 1988-2001".

(34) Until the late 1970s, the Bureau of Labour and Personnel controlled the wages of all urban workers in China and wage differences by education were very small. See Zhang, Zhao, Albert Park and Song, "Economic Returns to Schooling in Urban China, 1988-2001"; Meng X. and M. Kidd, "Labor Market Reform and the Changing Structure of Wage Determination in China's State Sector during the 1980s".

(35) Zhang, Zhao, Park and Song, "Economic Returns to Schooling in Urban China, 1988-2001".

(36) Ibid.

(37) Ibid.

(38) David Coady and Wang Limin, "Incentives, Allocation and Labour Market Reforms during Transition", Applied Economics 32, no. 4 (2000): 511-26.

(39) See Elizabeth Monk-Turner and Charlie Turner, "The Gender Wage Gap in South Korea", Journal of Asian Economics 15, no. 2 (2004): 415-24; Diane Davis and Helen Astin, "Life Cycle, Career Patterns and Gender Patterns and Stratification in Academe", in Storming the Tower, ed. Suzanne Lie and Virginia O'Leary (East Brunswick, NJ: GP Publishing, 1990); Joan Chrisler, "Teacher vs Scholar", in L. Collins, J. Chrisler and K. Quina, eds., Career Strategies for Women in Academe (Thousand Oaks, CA: Sage Publications, 1998).

(40) Orley Ashenfelter and Alan Krueger, "Estimates of the Economic Return of Schooling from a New Sample of Twins", NBER Working Paper #4143 (Cambridge, MA: NBER, 1991); Deborah Olsen, Sue Maple and Frances Stage, "Women and Minority Faculty Job, Professional Role Interests, Professional Satisfactions and Institutional Fit", Journal of Higher Education 66, no. 3 (1995): 267-84; Joan Williams, "Balancing Act", Chronicle of Higher Education 48 (2002): 1-9.

(41) Sue Headlee and Margery Elfin, The Cost of Being Female (Westport, CT: Praeger, 1996).

(42) Karyn Loscocco and Wang Xiu-Xia, "Gender Segregation in China", Sociology and Social Research 76, no. 3 (1992): 118-26.

(43) Headlee and Elfin, The Cost of Being Female; Francine Blau and Andrea Beller, "Trends in Earnings Differentials by Gender", Industrial and Labor Relations Review 41, no. 4 (1988): 513-29.

(44) Harwood et al., "Economic Costs to Society of Alcohol and Drug Abuse and Mental Illness: 1980"; Michael French, "The Effects of Alcohol and Illicit Drug Use in the Workplace", Journal of Employee Assistance Research 10, no. 2 (1993): 98-110; Gill and Michaels, "Does Drug Use Lower Wages?"; Richard Jessor, John Donovan and Frances Costa, Beyond Adolescence: Problem Behaviour and Young Adult Development (New York: Cambridge University Press, 1991).

(45) Denise Kandel, Devin Chen and Andrew Gill, "The Impact of Drug Use on Earnings: A Life-Span Perspective", Social Forces 74, no. 1 (1995): 243-70.

(46) Dale Heien and David Pittman, "The Economic Costs of Alcohol Abuse", Journal of Labor Economics 8 (1989): 381, 412; Zhang Zhiwei, "A Longitudinal Study of Alcohol and Drug Use in the Workplace", Dissertation Abstracts International no. 77 (2006): 3.

(47) Gill and Michaels, "Does Drug Use Lower Wages?".

(48) Blau and Beller, "Trends in Earnings Differentials by Gender"; Headlee and Elfin, The Cost of Being Female.

(49) Whalley and Zhang, "A Numerical Simulation Analysis of (Hukou) Labour Mobility Restrictions in China"; Dong, MacPhail, Bowles and Ho, "Gender Segmentation at Work in China's Privatized Rural Industry"; Ng, "Economic Development, Human Capital, and Gender Earnings Differentials".

(50) Kandel, Chen and Gill, "The Impact of Drug Use on Earnings: A Life-Span Perspective".

(51) Barbara Mensch and Denise Kandel, "Dropping Out of High School and Drug Involvement", Sociology of Education 61, no. 2 (1988): 95-113; Michael Newcomb and Peter Bentler, Consequences of Adolescent Drug Use: Impacts on the Lives of Young Adults (New York: Sage Publications, 1988); Kazuo Yamaguchi, and Denise Kandel, "On the Resolution of Role Incompatibility: Life Event History Analysis of Family Roles and Marijuana Use", American Journal of Sociology 90, no. 6 (1985): 1284-325.

(52) Richard Bilsborrow, Graeme Hugo, Amarjit Oberai and Hania Zlotnik, International Migration Statistics: Guidelines for the Improvement of Data Collection Systems (Geneva: International Labor Office, 1997).

(53) Zhang, Zhao, Park and Song, "Economic Returns to Schooling in Urban China, 1988-2001".

(54) Ibid.

(55) Zhang, Zhao, Park and Song, "Economic Returns to Schooling in Urban China, 1988-2001".

(56) Ibid.

(57) Hughes and Maurer-Fazio, "Effects of Marriage, Education, and Occupation on the Female/Male Wage Gap in China".

(58) Maurer-Fazio, "Earnings and Education in China's Transition to a Market Economy", China Economic Review 10, no. 1 (1999): 17-40; Park, Song, Zhang and Zhao, "The Growth of Wage Inequality in Urban China, 1988 to 1999"; Knight and Song, "Increasing Urban Wage Inequality in China"; Gustafsson and Li, "Economic Transformation and the Gender Earnings Gap in Urban China".

(59) Liu, Meng and Zhang, "Sectoral Gender Wage Differentials and Discrimination in the Transitional Chinese Economy".

(60) Dong, MacPhail, Bowles and Ho, "Gender Segmentation at Work in China's Privatized Rural Industry"; Xu, Tan and Wang, "Segmented Local Labor Markets in Post-Reform China".

(61) Kandel, Chen and Gill, "The Impact of Drug Use on Earnings: A Life-Span Perspective".

(62) Zhang, Zhao, Park and Song, "Economic Returns to Schooling in Urban China, 1988-2001".

(63) F. Blau and L. Khan, "Understanding International Differences in the Gender Wage Gap", Journal of Labor Economics, 21 (2003): 106-44.

(64) Hughes and Maurer-Fazio, "Effects of Marriage, Education, and Occupation on the Female/Male Wage Gap in China"; Maurer-Fazio, Rawski and Zhang, "Inequality in the Rewards for Holding up Half of the Sky"; Dong, MacPhail, Bowles and Ho, "Gender Segmentation at Work in China's Privatized Rural Industry"; Gustafsson and Li, "Economic Transformation and the Gender Earnings Gap in Urban China"; Liu, Meng and Zhang, "Sectoral Gender Wage Differentials and Discrimination in the Transitional Chinese Economy".

(65) Maurer-Fazio, "Earnings and Education in China's Transition to a Market Economy"; Park, Song, Zhang and Zhao, "The Growth of Wage Inequality in Urban China, 1988 to 1999"; Knight and Song, "Increasing Urban Wage Inequality in China".

(66) Maurer-Fazio, "Earnings and Education in China's Transition to a Market Economy".

(67) Ibid.

(68) Qian Yingyi, "Enterprise Reform in China: Agency Problems and Political Control", Economics of Transition 4, no. 2 (1996): 427-47.

(69) Hughes and Maurer-Fazio, "Effects of Marriage, Education, and Occupation on the Female/Male Wage Gap in China".

(70) Gill and Michaels, "Does Drug Use Lower Wages?"; Kaestner, "The Effect of Illicit Drug Use on the Wages of Young Adults".

Elizabeth Monk-Turner (eturner@odu.edu) is Professor of Sociology in the Department of Sociology and Criminal Justice at Old Dominion University. She received her PhD in Sociology from Brandeis University. Her research interests include gender disparities in labour markets, female commercial sex work and subjective well-being.

Charlie G. Turner (cgturner@odu.edu) is Emeritus Professor of Economics at Old Dominion University and the University of Colorado Denver. He received his PhD in Economics from Harvard University. His research interests are in international trade and finance and predictors of subjective well-being.

Xiushi Yang (xyang@odu.edu) is Professor of Sociology in the Department of Sociology and Criminal Justice, Old Dominion University. He obtained his PhD in Sociology with a concentration in population studies from Brown University. His research interests include migration, urbanisation, gender and HIV risk behaviours.

Huasong Luo (huasong00@yahoo.com.cn) is Dean of the School of Tourism and Geography at Yunnan Normal University. He obtained his doctoral degree from Kunming Technology University. His research interests include demography and human geography.
TABLE 1
MEAN VARIABLE VALUES (AND STANDARD DEVIATION)
FOR VARIABLES IN THE MODEL, 2003, FULL SAMPLE
(INCLUDES SELF-EMPLOYED AND OWNERS) *

                   All       Men     Women

Variables

Years (ED)          8.63     8.57     8.73
                   (2.72)   (2.73)   (2.69)

College              .006     .008     .004
                    (.08)    (.09)    (.04)

  Experience       16.18    16.98    14.81
                   (7.99)   (7.81)   (8.08)

  Gender             .61
                    (.49)

  Drugs Now          .04      .07      .007
                    (.20)    (.25)    (.08)

  Drugs Ever         .15      .23      .02
                    (.35)    (.42)    (.14)

  Drug Treatment     .13      .20      .01
                    (.33)    (.40)    (.12)

  Marital Status     .72      .70      .74
                    (.45)    (.46)    (.39)

  Urban              .34      .31      .39
                    (.47)    (.46)    (.49)

  High SES           .07      .06      .08
                    (.26)    (.25)    (.28)

State                .09      .10      .08
                    (.29)    (.30)    (.27)

Party                .19      .17      .21
                    (.39)    (.38)    (.41)

Han                  .65      .65      .64
                    (.48)    (.48)    (.48)

Agri                 .46      .46      .47
                    (.50)    (.50)    (.50)

Age                32.14    32.63    31.35
                   (7.92)   (7.59)   (8.33)

Iwage               8.11     8.19     7.99
                   (1.03)   (1.04)    (.99)

N                   2157     1359     798

Notes: * Mean variable values that were
significantly different by gender were years
of education, experience, drugs now, drugs
ever, drug treatment, and urban.

TABLE 2
LOG INCOME REGRESSED ON INDEPENDENT VARIABLES
IN MODEL, 2003, MEN AND WOMEN

                       All        Men       Women

Variables

Intercept              6.99      7.24      6.87
                       (.13)     (.18)     (.21)

Years (ED)              .09 *     .09 *     .11 *
                       (.01)     (.01)     (.02)

College                 .06       .06       .17
                       (.18)     (.21)     (.39)

  Experience            .02 **    .02       .03
                       (.01)     (.02)     (.02)

  Experience Squared   -.0004    -.0004    -.0004
                       (.0003)   (.0004)   (.0006)

  Gender                .19 *
                       (.05)

  Drugs Now             .49 *     .41 *     1.09 *
                       (.15)     (.17)      (.43)

  Marital Status        .20 *     .24 *      .12
                       (.06)     (.08)      (.11)

  Urban                 .08       .05        .15
                       (.05)     (.06)      (.08)

  High SES              .22 *     .28 *      .12
                       (.09)     (.12)      (.12)

State                   .08       .064       .12
                       (.06)     (.07)      (.11)

Party                   .04       .09       -.02
                       (.05)     (.07)      (.08)

Han                     .003      .04       -.04
                       (.05)     (.07)      (.08)

Agri                   -.51 *    -.46 *     -.53 *
                       (.08)     (.10)      (.15)

[R.sup.2]               .216      .188       .237

N                       1411      868        543

Notes: * significant at the .05 level.

** significant at the .07 level.

Standard errors are in parentheses.
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Publication:China: An International Journal
Article Type:Report
Geographic Code:9CHIN
Date:Apr 1, 2013
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