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Rural Labor Migration, Discrimination, and the New Dual Labor Market in China pp 97–114Cite as

Economic Returns to Schooling in Urban China: Ordinary Least Squares the Instrumental Variables Approach

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Abstract

In this paper, we examine the economic returns to schooling in urban China by using ordinary least squares (OLS) and instrumental variables (IV) methodologies. First, we find that OLS estimates of the returns to education are lower in China than in other transition economies, whereas IV estimates are higher in China. Second, we find that OLS, a method for estimating the returns to education without controlling for endogeneity bias, may underestimate the true rates of return for men. In addition, if we do not control for the endogeneity and sample selection biases, we may further underestimate the true rates of return for women. Finally, we find that OLS estimates of the returns to education for men are slightly higher than those for women. The IV estimates for women are higher than those for men, and this difference increases after correcting for selectivity biases.

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Notes

  1. 1.

    Heckman (2003) suggests that China invests much more on physical capital than human capital because of the difference in the returns to human and physical capital.

  2. 2.

    The following papers present estimates of the rates of returns to schooling in China using different data sets. These papers also examine previous studies on the returns to education in China. For example, one of the first-class studies examining prior studies is Zhang et al. (2005).

  3. 3.

    When we pool the 2004 and 2006 CHNS data, we cannot just pool the wage data for the 2 years to form the sample, because the nominal wages across the 2 years are not compatible in the estimation, and part of the wage differential may be simply the result of the different values across the years. In order to overcome this problem, we convert the 2003 wage data of each province into the 2005 wage data in the same province, considering the price increase (or decrease) of the province. More concretely, we adjust the 2005 wages in region i as \( w_{2005}^{i} = w_{2003}^{i} \times {{(p_{2005}^{i} } \mathord{\left/ {\vphantom {{(p_{2005}^{i} } {p_{2003}^{i} }}} \right. \kern-0pt} {p_{2003}^{i} }}) \), where \( w_{t}^{i} \) denotes the wage at time t (t = 2003 or 2005) in province i and \( p_{t}^{i} \) the price level at time i (t = 2003 or 2005) in provincei.

  4. 4.

    Source China State Statistical Bureau and China Ministry of Labour and Social Security (2006) China Labour Statistical Yearbook.

  5. 5.

    Our data cover 1,549 married females, of whom 534 are “nonparticipating” (doing housework) and 1,015 are “participating”. The CHNS questionnaire contains the following questions on work status: (1) Are you working presently? a. No; b. Yes. (2) Why are you not working? a. Seeking work; b. Doing housework; c. Disabled; d. Student; e. Retired; f. Other. For married women who choose “a. Seeking work” in the second question, we consider the decision on nonparticipation to probably be due to demand-side restrictions. For married women who choose “b. Doing housework” in the second question, we consider the decision on nonparticipation to be related to self-selection.

  6. 6.

    Share is the share of household members who are younger than 7 years or older than 65 years.

  7. 7.

    Heckman (1990) suggests that a variable excluded from the wages equation be included as a continuous variable in the participation equation. Zhang et al. (2005) use the shares of household members who are younger than 7 years, between 7 and 15 years, or older than 60 years as identifying variables.

  8. 8.

    Most existing studies in the literature use annual wages instead of hourly wages. This leads to a bias in the estimates. When we use annual wages to estimate the rate of returns to schooling in this paper, we find that the rates of returns for men and women are 7.11% and 6.88%, respectively. These rates are lower than those obtained on the basis of hourly wages and are consistent with Li (2003).

  9. 9.

    Following Halvorsen and Palmquist (1980), we compute these estimates using the following formula: \( (Exp(\beta ) - 1) \times 100 \).

  10. 10.

    Following Wooldridge (2002), we treat all the variables except schooling as exogenous, include all exogenous variables in the participation Eq. (8.4), and list all exogenous variables as instruments in estimating Eq. (8.5) using 2SLS.

  11. 11.

    The fact that the significant selectivity term \( \lambda \) is positive (negative) implies that the returns to schooling is underestimated (overestimated) if we do not control for sample selection bias. See Green (2002), Wooldridge (2002), and Arabsheibani and Mussurov (2007).

  12. 12.

    Table 8.6 shows that the selection term is significant in the wage equation but not in the schooling equation. These results are consistent with those of Arabsheibani and Mussurov (2007).

  13. 13.

    The term\( \lambda \) is negative in Arabsheibani and Mussurov (2007). The negative effect, they explain, suggests that on average, nonparticipating women have a comparative advantage over participating women. This, they suggest, may be associated with the rise in Islamic values that keep more women in their homes (even though women, on average, are endowed with characteristics associated with earnings advantages in the workplace).

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Authors and Affiliations

  1. Center for Macroeconomic Research, School of Economics, Xiamen University, Xiamen, People’s Republic of China

    Guifu Chen

  2. Faculty of Economics, Kobe University, Kobe, Japan

    Shigeyuki Hamori

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  1. Guifu Chen
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  2. Shigeyuki Hamori
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Correspondence to Guifu Chen .

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Chen, G., Hamori, S. (2014). Economic Returns to Schooling in Urban China: Ordinary Least Squares the Instrumental Variables Approach. In: Rural Labor Migration, Discrimination, and the New Dual Labor Market in China. SpringerBriefs in Economics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41109-0_8

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