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An empirical study of openness and convergence in labor productivity in the Chinese provinces

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Abstract

Based on the theoretical framework of the Solow growth model, this paper employs a dynamic panel data approach to examine the impact of openness on growth and convergence in labor productivity in the Chinese provinces during the period 1984–2008. The study finds that regional openness has a significantly positive effect on regional growth in labor productivity in the Chinese provinces. When regional heterogeneity and regional openness are accounted for, the study finds fast conditional convergence in labor productivity across the Chinese provinces. As a byproduct, this study also estimates the structural parameters of the aggregate production function in the case of China. In sum, the major findings of this study lend strong support to the claim that openness promotes growth of labor productivity in China.

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Notes

  1. We should note that besides technology, B(t) should also capture factors such as resource endowments, institutions, culture and the like.

  2. See, for example, Barro and Sala-i-Martin (1995) or Romer (2006) for the derivation.

  3. Given the functional specification in (9), we can see that this model does not have much to say about the indirect effect of openness on growth through its impact on capital accumulation because the investment rate is also included as one of the explanatory variables.

  4. An extended GMM method of Blundell and Bond (2000), in which lagged first differences are also used as instruments for the levels equations, should work better than standard first-differenced GMM methods when the variables are highly persistent so that lagged values are only weakly correlated with subsequent first differences. However, we have not opted for the use of extended GMM method of Blundell and Bond (2000) in this analysis mainly because the series of the variables in our regressions are not very highly persistent and the Arellano-Bond GMM regressions currently used in the analysis are shown to be valid by passing the related Sargan and AR tests.

  5. Owing to missing data Chongqing and Hainan are not included in our sample.

  6. See Gundlach (1997). In this and the next paragraph, we mainly follow Gundlach (1997) for a discussion of the estimated values of the rate of technological progress and the rate of depreciation.

  7. Compared with Jefferson et al. (1992), Young (1995) takes account of human capital accumulation and focuses on the aggregate economy instead of individual industries.

  8. Given this, it then can be shown that all major regression results in this study are not sensitive to the chosen value of the depreciation rate if it is within the interval [0.03, 0.07]. Unlike Gundlach (1997), some other studies alternatively assume or estimate a different depreciation rate for each of the Chinese provinces. However, we do not follow this approach in the present study because this approach confronts us with an immediately related issue, i.e. the possibility of time-varying depreciation rates for any single province, which will take us too far afield given the main scope of the present study.

  9. Taking an explanatory variable as endogenous means that we assume it to be correlated with the current error term while taking it as predetermined means that we instead assume it to be uncorrelated with the current error term. We run different variants of the regression here mainly for a comparison purpose.

  10. Even in this RE regression the estimated coefficient on \( \ln y_{i,t - 1} \) is significantly lower than unity at the 5% level, suggesting conditional convergence across the 29 Chinese provinces over the sample period.

  11. The corresponding p-value of this estimate is 0.086.

  12. However, the Wald test rejects the null hypothesis that the coefficients on \( \ln (s) \) and \( \ln (n + g + \delta ) \) are equal in value but opposite in sign in most of the previous regressions in Table 1. This renders the estimation of the parameter α here based on the results in Table 2 less meaningful.

  13. See the notes below the tables for the details.

  14. Here we still follow the basic procedure of Mankiw et al. (1992) and Islam (1995) except that we include an openness variable in the model.

  15. See, for example, Barro and Sala-i-Martin (1995) or Romer (2006) for the derivation.

  16. The GMM regressions in Tables 3 and 4 also pass the Sargan test and the Arellano-Bond autocorrelation test of the residuals (orders one and two) at the 5% significance level. See the notes below the tables for the details.

  17. However, in most of the unrestricted regressions in Table 3, the Wald test rejects the null hypothesis that the sum of the coefficients on ln(s) and ln(h) are equal to the coefficient on ln(n + g + δ) in value but opposite in sign. This renders the estimation of the parameters α and φ here based on the results in Table 4 less meaningful.

  18. This paragraph draws on Madariaga and Poncet (2007)’s summary of Greenaway and Görg (2004)’s discussion of the issue.

  19. By the end of 2004, FIE’s employed 23 million Chinese, comprising about 10% of total manufacturing employment.

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  1. College of International Finance and Commerce, Shanghai International Studies University, No. 1550 Wenxiang Rd., Shanghai, 201620, China

    Yanqing Jiang

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Jiang, Y. An empirical study of openness and convergence in labor productivity in the Chinese provinces. Econ Change Restruct 45, 317–336 (2012). https://doi.org/10.1007/s10644-011-9120-1

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