Efficiency gains from reallocating human capital between China’s state and non-state sectors

ABSTRACT

This study is among the first to use household survey data from 1988 to 2019 to demonstrate how human capital misallocation between China’s state and non-state sectors has evolved and to estimate efficiency gains from reallocation over time. Our results show that human capital allocation between sectors has been converging to, diverging from, and re-converging to an optimal level, implying that the Chinese labour market has been gradually approaching efficiency during its market-oriented transition. Lower-educated labours had been more misallocated between the two sectors than higher-educated ones. Efficiency gains from reallocating human capital between sectors depend on the degree of misallocation at each stage and the gains are comparable to contemporaneous domestic gross research and development expenditures.

KEYWORDS:

• Human capital misallocation
• efficiency gains
• state sector
• non-state sector
• China

JEL CLASSIFICATION:

• J31
• J24
• P31
• O15

Acknowledgement

This work was supported by the National Social Science Fund of China (18BJY051) and the Fundamental Research Funds for the Central Universities. The authors thank David Peel and the anonymous reviewers for their valuable comments and suggestions.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

¹ The total factor productivity (TFP) of China’s state sector has been increasing during the transition but is still lower than the non-state sector’s (Brandt, Biesebroeck, and Zhang 2012).

² Brandt, Biesebroeck, and Zhang (2012) report that the TFP loss from within-province distortions due to misallocation of employment between state and non-state sectors are less than 3% from 1988 to 2006. Ge and Li (2019) find that the TFP of industrial enterprises have increased by 37% in 1998 and 30% in 2007 after reallocating human capital across enterprises with different ownerships.

³ Most studies estimate TFP increases by reallocating the number of employment (i.e. labour quantity) rather than the units of human capital (i.e. labour quality) across industries, firms, or regions (Brandt, Tombe, and Zhu 2013; Gong and Hu 2013; Gai et al. 2015; Li and Wang 2021).

⁴ Previous studies employing macro-level data show the labour misallocation across sectors and regions over two decades (Brandt, Biesebroeck, and Zhang 2012; Ye and Robertson 2018); although, they do not reflect the heterogeneities at micro-level. Some studies further use firm-level data (Gong and Hu 2013; Gai et al. 2015; Ge and Li 2019); although, they only focus on a short Chinese transition period due to limited data availability (mostly from 1998 to 2007). Vollrath (2014) and Ma, He, and Li (2018) use the individual-level data but they focus on estimating the misallocation across industries. In addition, Vollrath (2014) does not include China and Ma, He, and Li (2018) only include data in 2007 and 2013.

⁵ If the more capable individuals are more likely to work in the state sector and the capability is hard to control in estimations, then endogeneities arise.

⁶ Following Zhao (2002), we use the same identification variable and estimate the probability of individuals choosing to work in the state sector. The inverse Mills Ratio estimated in the first stage is then put into (2(2) $\mathrm{l}\mathrm{n}{w_i}_j=\alpha +\beta secto{r}_j+X_i^{\prime }\gamma +{\delta }_i+{\theta }_i+{\mu }_i+{\epsilon }_{ij}$(2) ) for the second stage regression.

⁷ The economy gains would be higher if we examine the misallocation of both human capitals and physical capitals.

⁸ We also estimate efficiency gains by setting α equal to 0.2 and 0.45, respectively, and the results are robust.

Additional information

Funding

The work was supported by the National Office for Philosophy and Social Sciences [18BJY051]; Fundamental Research Funds for the Central Universities.