Skip to main content

Advertisement

SpringerLink
Log in

Pre-market discrimination or post-market discrimination: research on inequality of opportunity for labor income in China

  • Published:
Empirical Economics Aims and scope Submit manuscript

Abstract

Inequality of opportunity (hereafter “IO”) restricts the realization of social justice, and its mechanism has always attracted attention. Using China Labor-force Dynamic Survey (CLDS) data in 2012, 2014, and 2016, we fully consider the impact of easily neglected educational opportunities on income inequality and creatively get pre-market and post-market discrimination channels. Research shows that IO is a fundamental cause of employees’ income inequality in China. Male–female and urban–rural opportunity inequality can severally explain 31.66% and 17.16% of total IO. Using the optimized Oaxaca–Blinder decomposition method, we obtain that pre-market and post-market discriminations are the main paths of urban–rural and male–female opportunity inequality, respectively. What’s more, the above pathway has different characteristics in different income groups. Therein, affected by the asymmetric information and employer prejudice in the labor market, the proportion of post-market discrimination channels shows a downward trend as income increases. The conclusions provide empirical support for eliminating market discrimination and ensuring equality of opportunity. They also have enlightening significance for relevant policy formulation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

Notes

  1. “Global Wage Report 2018/19,” ILO official Web site, 2018–11-26, http://www.ilo.org/global/research/global-reports/global-wage-report/2018/lang-en/index.htm.

  2. As for “effort” variables affected by “circumstance” variables in most literature works, evidence is that the individual’s level of schooling is widely considered influenced by their family background (see, for example, Piketty 1995). In this regard, Roemer (1998) also takes the example, Asian students tend to study harder than others in the USA, to demonstrate that “individual ‘efforts’ are the reflection of ‘circumstances’. ”

  3. Unavoidable omissions have not virtually affected the conclusion, as follows: (1) If unobserved “circumstance” or “effort” variables are uncorrelated with variables in Eq. (12), then the estimated coefficient is unbiased. Moreover, measured IO is still a lower bound of the actual, and omitted “efforts” do not affect the measured value. (2) If unobserved “circumstances” are correlated with variables in Eq. (12), the estimated coefficient has captured the contribution of omitted “circumstances,” measured IO is higher than the observed value but closer to the actual. (3) If unobserved “effort” variables are correlated with observed “circumstances” in Eq. (12), the coefficient has incorporated the indirect effect of “circumstances” on omitted “efforts,” measured value is closer to the actual.

  4. The opinions are the author’s alone. Please refer to http://css.sysu.edu.cn for more information about the CLDS data.

  5. This processing is based on the following considerations: (1) The improved Oaxaca–Blinder method cannot decompose the values of inequality derived from the panel data, and the decomposition is almost all used for cross section data. (2) The key “circumstance” variable such as gender is time-invariant. We cannot estimate its effect when controlling individual fixed effects based on panel data, making it impossible to assess the exact counterfactual distribution to measure IO. (3) Representative research in measuring IO is almost all based on cross-section data (see, e.g., Bourguignon et al. 2007; Ferreira and Gignoux 2011; Palomino et al. 2019) or processes panel data into section data (see, e.g., Marrero and Rodríguez 2013). Thus, we also do this to guarantee that our conclusions are comparable.

  6. We take the employee as our research object for the following reasons: One of our primary purposes is to obtain the pre-market and post-market discrimination channels by decomposition method and compare their contributions to IO. However, the premise of getting the results of post-market discrimination by definition is that individuals have the job-seeking need when they enter the job market, or there would be no such thing as post-market discrimination. In other words, employers, self-employed, or individuals who are not in the job market are not exposed to post-market discrimination. It makes no sense for us to explore the discrimination against groups that are not subject to it, and of course, our conclusions are only for the employee group.

  7. Specific assignments are: the number of education years for illiteracy is 0; the number of education years for elementary school or private school is 6; the number of education years for junior high school is 9; the value of high school, vocational high school, technical school, and technical secondary school is 12; the number of education years for a college degree is 15; 16 for bachelor’s degree; 19 for master’s degree and above.

  8. We put health into the income equation and take it as the “effort” by definition for the following reasons. (1) Health is an essential part of human capital and undoubtedly affects personal income (see, e.g., French 2012). (2) The health of individuals is mostly under their control, such as through good living habits, etc. (see, e.g., Rosa Dias 2009; Donni et al. 2014; Fajardo-Gonzalez 2016). According to Rosa Dias (2009), about 79% of health inequality for British adults is due to individuals’ control factors. Donni et al. (2014) also estimate that controllable factors account for around 70%.

  9. Generally speaking, employees who have obtained a bachelor’s degree also have a high school degree, that is, the diploma effect has an accumulation effect. To avoid double counting, when calculating the diploma effect of a certain stage of education, it is necessary to exclude the influence of the diploma effect of the previous stage of education. Take the master’s degree as an example, its diploma effect is \(e^{{\left( {1.099 - 0.667} \right)}} - 1\).

  10. See Wooldridge (2000, Chapter 17), and we need at least one identification variable that influences selection but not the overall model’s dependent variable. The dependent variables of the overall model and the selection equation in this paper are individual wage and whether the individual chooses to work, respectively. We think that the number of children of individuals will affect their labor force participation but almost has no effect on their wages, according to the existing research (see Mroz 1987; Wooldridge 2000; Schwiebert 2015). Hence, we consider it the identification variable.

  11. Combining related research, white-collar employees are: “persons in charge of communist party, government, enterprises, and institutions,” “personnel and related personnel,” and “professional and technical personnel.” Blue-collar employees are: “manufacturing and related personnel,” “social production service and life service personnel,” “production and auxiliary personnel of agriculture, forestry, animal husbandry and sideline fishery,” and “other employees who are inconvenient to classify.”

References

  • Arrow KJ (1973) The theory of discrimination. In: Ashenfelter O, Rees A (eds) Discrimination in labor markets. Princeton University Press, Princeton, pp 3–33

    Google Scholar 

  • Bauer TK, Dross PJ, Haisken‐DeNew JP (2005) Sheepskin effects in Japan. Int J Manpow 26(4):320–335. https://doi.org/10.1108/01437720510609528

  • Björklund A, Jäntti M, Roemer JE (2012) Equality of opportunity and the distribution of long-run income in Sweden. Soc Choice Welfare 39(2–3):675–696

    Article  Google Scholar 

  • Bourguignon F, Ferreira FH, Menéndez M (2007) Inequality of opportunity in Brazil. Rev Income Wealth 53(4):585–618

    Article  Google Scholar 

  • Cappelen AW, Sørensen EØ, Tungodden B (2010) Responsibility for what? Fairness and individual responsibility. Eur Econ Rev 54(3):429–441

    Article  Google Scholar 

  • Checchi D, Peragine V (2010) Inequality of opportunity in Italy. J Econ Inequal 8(4):429–450

    Article  Google Scholar 

  • Chetty R, Hendren N, Kline P, Saez E (2014) Where is the land of opportunity? The geography of intergenerational mobility in the United States. Q J Econ 129(4):1553–1623

    Article  Google Scholar 

  • Coate S, Loury GC (1993) Will affirmative-action policies eliminate negative stereotypes? Am Econ Rev 83(5):1220–1240

    Google Scholar 

  • Conde-Ruiz JI, Ganuza JJ, Profeta P (2022) Statistical discrimination and committees. Eur Econ Rev 141:103994

    Article  Google Scholar 

  • Davillas A, Jones AM (2020) Ex ante inequality of opportunity in health, decomposition and distributional analysis of biomarkers. J Health Econ 69:102251

    Article  Google Scholar 

  • Démurger S, Gurgand M, Li S, Yue X (2009) Migrants as second-class workers in urban China? A decomposition analysis. J Comp Econ 37(4):610–628

    Article  Google Scholar 

  • Donni PL, Peragine V, Pignataro G (2014) Ex-ante and ex-post measurement of equality of opportunity in health: a normative decomposition. Health Econ 23(2):182–198

    Article  Google Scholar 

  • Fajardo-Gonzalez J (2016) Inequality of opportunity in Adult Health in Colombia. J Econ Inequal 14(4):395–416

    Article  Google Scholar 

  • Ferreira FH, Gignoux J (2011) The measurement of inequality of opportunity: theory and an application to Latin America. Rev Income Wealth 57(4):622–657

    Article  Google Scholar 

  • Ferrer AM, Riddell WC (2002) The role of credentials in the Canadian Labour Market. Can J Econ 35(4):879–905

    Article  Google Scholar 

  • Fleurbaey M, Schokkaert E (2011) Equity in health and health care. In: Pauly MV, McGuire TG, Barros PP (eds) Handbook of health economics. North-Holland, Waltham, pp 1003–1092

    Google Scholar 

  • French D (2012) Causation between health and income: a need to panic. Empir Econ 42(2):583–601

    Article  Google Scholar 

  • Fuller S, Vosko LF (2008) Temporary Employment and social inequality in Canada: exploring intersections of gender, race and immigration status. Soc Indic Res 88(1):31–50

    Article  Google Scholar 

  • Golley J, Zhou Y, Wang M (2019) Inequality of opportunity in China’s labor earnings: the gender dimension. Chin World Econ 27(1):28–50

    Article  Google Scholar 

  • Heckman JJ, Kautz T (2013) Fostering and measuring skills: interventions that improve character and cognition. SSRN Electron J

  • Jaeger DA, Page ME (1996) Degrees Matter: New Evidence on Sheepskin Effects in the Returns to Education. Rev Econ Stat 78(4):733–740. https://doi.org/10.2307/2109960

  • Jusot F, Tubeuf S, Trannoy A (2013) Circumstances and efforts: How important is their correlation for the measurement of inequality of opportunity in health? Health Econ 22(12):1470–1495

    Article  Google Scholar 

  • Krafft C, Alawode H (2018) Inequality of opportunity in higher education in the Middle East and North Africa. Int J Educ Dev 62:234–244

    Article  Google Scholar 

  • Kübler D, Schmid J, Stüber R (2018) Gender discrimination in hiring across occupations: a nationally-representative vignette study. Labour Econ 55:215–229

    Article  Google Scholar 

  • Lang K, Manove M (2011) Education and labor market discrimination. Am Econ Rev 101(4):1467–1496

    Article  Google Scholar 

  • Lefranc A, Pistolesi N, Trannoy A (2008) Inequality of opportunities vs. inequality of outcomes: Are western societies all alike? Rev Income Wealth 54(4):513–546

    Article  Google Scholar 

  • Li CL (2005) Prestige stratification in the contemporary China: occupational prestige measures and socio-economic index. Sociol Stud 02:74-102+244 (in Chinese)

    Google Scholar 

  • Li Y, Lv G (2019) Research on the source and channel of opportunity inequality in China. China Ind Econ 9:60–78 (in Chinese)

    Google Scholar 

  • Liu Q (2012) Unemployment and labor force participation in urban China. China Econ Rev 23(1):18–33

    Article  Google Scholar 

  • Marrero GA, Rodríguez JG (2013) Inequality of opportunity and growth. J Dev Econ 104:107–122

    Article  Google Scholar 

  • Meng X, Zhang J (2001) The two-tier labor market in urban China. J Comp Econ 29(3):485–504

    Article  Google Scholar 

  • Mora JJ, Muro J (2008) Sheepskin effects by cohorts in Colombia. Int J Manpow 29(2):111–121. https://doi.org/10.1108/01437720810872686

  • Mroz TA (1987) The sensitivity of an empirical model of married women’s hours of work to economic and statistical assumptions. Econometrica 55(4):765–799

    Article  Google Scholar 

  • Neal DA, Johnson WR (1996) The role of premarket factors in black-white wage differences. J Polit Econ 104(5):869–895

    Article  Google Scholar 

  • Olfindo R (2018) Diploma as Signal? Estimating sheepskin effects in the Philippines. Int J Educ Dev 60:113–119

    Article  Google Scholar 

  • Ordine P, Rose G (2011) Inefficient self-selection into education and wage inequality. Econ Educ Rev 30(4):582–597

    Article  Google Scholar 

  • Palomino JC, Marrero GA, Rodríguez JG (2019) Channels of inequality of opportunity: the role of education and occupation in Europe. Soc Indic Res 143(3):1045–1074

    Article  Google Scholar 

  • Piketty T (1995) Social Mobility and redistributive politics. Q J Econ 110(3):551–584

    Article  Google Scholar 

  • Roemer JE (1993) A pragmatic theory of responsibility for the egalitarian planner. Philos Public Aff 22(2):146–166

    Google Scholar 

  • Roemer JE (1998) Equality of opportunity. Harvard University Press, Cambridge

    Book  Google Scholar 

  • Rosa Dias P (2009) Inequality of opportunity in health: Evidence from a UK cohort study. Health Econ 18(9):1057–1074

    Article  Google Scholar 

  • Schady NR (2003) Convexity and sheepskin effects in the human capital earnings function: recent evidence for Filipino men. Oxford Bull Econ Stat 65(2):171–196

    Article  Google Scholar 

  • Schwiebert J (2015) Estimation and interpretation of a Heckman selection model with endogenous covariates. Empir Econ 49(2):675–703

    Article  Google Scholar 

  • Sewell WH, Shah VP (1968) Parents’ education and children’s educational aspirations and achievements. Am Sociol Rev 33(2):191

    Article  Google Scholar 

  • Shi X, Wei L, Fang S, Gao X (2018) Opportunity inequality in China’s income distribution. Manage World 3:27–37 (in Chinese)

    Google Scholar 

  • Solon G (1999) Intergenerational mobility in the labor market. Handbook of Labor Economics, pp 1761–1800

  • Spence M (1973) Job market signaling. Q J Econ 87(3):355

    Article  Google Scholar 

  • Spera C, Wentzel KR, Matto HC (2008) Parental aspirations for their children’s educational attainment: Relations to ethnicity, Parental Education, children’s academic performance, and parental perceptions of school climate. J Youth Adolesc 38(8):1140–1152

    Article  Google Scholar 

  • Wooldridge JM (2000) Introductory econometrics a modern approach. South-Western, Cincinnati

    Google Scholar 

  • Yamamoto Y, Matsumoto K, Kawata K, Kaneko S (2019) Gender-based differences in employment opportunities and wage distribution in Nepal. J Asian Econ 64:101131

    Article  Google Scholar 

  • Yang X, Gustafsson B, Sicular T (2021) Inequality of opportunity in household income, China 2002–2018. China Econ Rev 69:101684

    Article  Google Scholar 

  • Zhang L, Sharpe RV, Li S, Darity WA (2016) Wage differentials between urban and rural-urban migrant workers in China. China Econ Rev 41:222–233

    Article  Google Scholar 

Download references

Acknowledgements

We sincerely thank the editors for their hard work, and we are also very grateful to anonymous referees for their helpful suggestions. We are especially indebted to China Labor-force Dynamic Survey (CLDS) for providing data support. The opinions are the author’s alone. Please refer to http://css.sysu.edu.cn for more information about the CLDS data.

Funding

The funding was provided by National Social Science Foundation of China (Grant No. 18BJY213).

Author information

Authors and Affiliations

  1. Economics and Management School, Wuhan University, Wuhan, China

    Yue Yu & Chengkui Liu

Authors
  1. Yue Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chengkui Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chengkui Liu.

Ethics declarations

Conflict of interest

No conflict of interest exists in submitting this manuscript, and all authors approve the manuscript for publication. On behalf of my co-authors, I would like to declare that the work described was original research that has not been published previously and is not under consideration for publication elsewhere, in whole or in part. All the authors listed have approved the manuscript that is enclosed.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yu, Y., Liu, C. Pre-market discrimination or post-market discrimination: research on inequality of opportunity for labor income in China. Empir Econ 64, 2291–2313 (2023). https://doi.org/10.1007/s00181-022-02315-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00181-022-02315-4

Keywords

Search

Navigation