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Technical progress and induced innovation in China: a variable profit function approach

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Abstract

We propose a new methodology to estimate empirically the input price-induced technical change and total factor productivity (TFP) growth in China. Our primary goal is to test Hicks’ induced innovation hypothesis by examining whether technical change in China has been induced by sharp increase in input prices that have accompanied its rapid economic growth. Utilizing the idea of a firm’s two-stage optimization problem, we develop a new parametric form of the variable profit function wherein the derived input demand and output supply functions can be easily constrained to be regular, and the functional structure is parsimonious in the number of parameters. Applying this methodology to Chinese time series data for 1986–2017, we find that not only is wage-induced innovation significant and quantitatively important, but also that it substantially buffered a decline in TFP growth before 2006 that would otherwise be substantial. Overall, we conclude that China’s economic growth is predominantly driven by wage-induced innovation along with massive injection of heavily subsidized physical inputs in public works and huge investment in industrial sectors.

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Notes

  1. See Lopez (1984, p. 358) and Karagiannis and Mergos (2000, p. 32).

  2. As a matter of fact, computable general equilibrium models typically do not use flexible functional forms.

  3. By “effectively global regularity,” it is meant that the regularity properties are satisfied in an unbounded region in input-output space, potentially including all points in the sample and all points corresponding to a higher level of output price. A good example of such model is Cooper, McLaren and Wong’s (2001) Modified Gorman Polar Form.

  4. To be consistent with the second stage problem Eq. (6), we admit that \(\left( {p_t,\,{{{\mathbf{w}}}}_{{{\mathrm{t}}}},\,{{{\mathbf{z}}}}_{{{\mathrm{t}}}},\,\overline v _{t - 1}} \right)\) should be the conditioning variables of the optimizer V*. Note however that \(\overline v _{t - 1}\) is not directly measurable whilst preliminary results indicate the incorporation of \(\left( {p_t,\,{{{\mathbf{w}}}}_{{{\mathrm{t}}}},\,{{{\mathbf{z}}}}_{{{\mathrm{t}}}}} \right)\) in V* is not statistically significant (based on the Wald test results). Therefore, we make the theoretical model more empirically operational by simplifying Eq. (7) as \({{{\mathrm{V}}}}^ \ast \left( {\widetilde {{{\mathbf{w}}}}_{{{{\mathrm{t - 1}}}}}{{{\mathrm{, }}}}t} \right)\).

  5. We have conducted the Chi-square based Wald tests to examine different forms of structural change (for example, including the trend variables t and t2, lagged input prices \({{{\tilde{\mathbf w}}}}_{{{{\mathrm{t - 1}}}}}^{}\) or the intercept terms of the supply/demand system). Results (available from the authors upon request) indicated that the form of structural change illustrated in Eq. (13) gave sensible parameter estimates and was supported by the data.

  6. See Ray and Segerson (1990, p.44).

  7. See Peeters and Surry (2000, p.61) and Esposti and Pierani (2008, pp. 14-15).

  8. See Chambers (1988, p.132).

  9. Following Kohli (1978 and 1993), we treat imports as an intermediate input of production, which is appropriate in the case of China. As indicated by Xu and Mao (2018), the share of imports that consist of intermediate inputs has increased from 67.6 to 78.6 % during 1995-2013. In addition, retail and domestic handling are still needed for most finished imported goods.

  10. Data on coal and oil consumption are net of that used for non-production purposes.

  11. We have estimated various models based on different specifications of three-period moving averages such as \(\widetilde {{{\mathbf{w}}}}_{{{{\mathrm{t - 1}}}}}\) = (\(\widetilde {{{\mathbf{w}}}}_{{{{\mathrm{t - 1 - }}}}\tau }\) + \(\widetilde {{{\mathbf{w}}}}_{{{{\mathrm{t - 2 - }}}}\tau }\) + \(\widetilde {{{\mathbf{w}}}}_{{{{\mathrm{t - 3 - }}}}\tau }\))/3 for τ ≥ 0. Based on the Bayesian information criterion, the preferred value of τ is zero, implying that Eq. (24) may be the preferred specification of long run lagged input prices.

  12. The superscript “n” indicates the last row (row and column) of the respective vector (matrix) has been annihilated.

  13. See Kohli (1993, p. 248).

  14. In notation, \({{{\mathrm{WIIE}}}}_{{{\mathrm{t}}}} = {{{\mathrm{E}}}}_{{{{\mathrm{{\Pi}}}}}^ \ast \widetilde {{{\mathrm{w}}}}_{{{{\mathrm{1t - 1}}}}}} \dot {\widetilde w}_{1t - 1} \cdot {\textstyle{{{\Pi}^ \ast } \over {p_t{{{\mathrm{Y}}}}_{{{\mathrm{t}}}}^ \ast }}}\) which measures the rate of technical change induced by an increase in the lagged long run wage rate \(\widetilde w_{1t - 1}\).

  15. See Chen et al. (2018). Holz (2019) shows that industrial policies in China have little or no effect on investment outcomes in industry.

  16. See Hsieh and Klenow (2009, p. 1405).

  17. See Wei et. al (2017, p. 50).

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Acknowledgements

MQZ acknowledges financial support from the National Natural Science Foundation of China (No. 71773102).

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Authors and Affiliations

  1. Department of Economics, FSS, University of Macau, Taipa, Macau SAR, China

    K. K. Gary Wong

  2. Ohio State University, Columbus, OH, USA

    Belton M. Fleisher

  3. Hunan University Center for Economics, Finance & Management Studies, Changsha, China

    Belton M. Fleisher

  4. Central University of Finance & Economics China Center for Human Capital & Labor Market Research; and IZA, Beijing, China

    Belton M. Fleisher

  5. MOE Key Laboratory of Econometrics, the Wang Yanan Institute for Studies in Economics, Xiamen University, Xiamen, 361005, Fujian, China

    Min Qiang Zhao

  6. Interdisciplinary Arts and Sciences, University of Washington Tacoma, Box 358436, Tacoma, WA, 98402, USA

    William H. McGuire

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  1. K. K. Gary Wong
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Correspondence to Min Qiang Zhao.

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Appendix

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Table 6

Table 6 Parameter estimates of the GVPF (asymptotic t ratios in parentheses)
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Gary Wong, K.K., Fleisher, B.M., Zhao, M.Q. et al. Technical progress and induced innovation in China: a variable profit function approach. J Prod Anal 57, 177–191 (2022). https://doi.org/10.1007/s11123-021-00626-9

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