China is currently in a new phase of transition from high-speed growth to high-quality growth, and accurate estimation of element outputs is essential for the smooth progress of the transition. Using the back-fitting method, this study constructed a model of a spatiotemporal-varying elasticity production function to estimate the factor-output elasticity from 1993 to 2017 in 31 Chinese provinces. Nonparametric kernel density method was applied to describe the spatiotemporal evolution characteristics of factor-output elasticity. The results show that the factor-output elasticity of different provinces shows a nonlinear change trend over time and between regions. Overall, the elasticity of labor output shows a decreasing trend, the elasticity of capital output shows an increasing tendency, the eastern region has the lowest level of labor-output elasticity, but the highest level of capital-output elasticity. The western region has the highest level of labor-output elasticity but the lowest level of capital-output elasticity. On the whole, regions with higher resilience in labor output gradually shift towards the West, while capital shifts towards the East.

1. [1] C. Bai and Z. Qian, “Analysis on the influencing factors and changing reasons of China’s capital income share – Based on the provincial panel data,” J. of Tsinghua University, Vol.24, No.104, pp. 137-147+160, 2009 (in Chinese).
2. [2] L. Wang and L. Yuan, “Directed Technical Change, Industrial Structural Transformation and Factor Shares in China,” Economic Research J., Vol.53, No.11, pp. 115-131, 2018 (in Chinese).
3. [3] S. Zhang, B. Xu, and W. Gu, “Statistical and Economic Tests for the Time-Varying Elasticity Production Function Model,” Statistical Research, Vol.28, No.6, pp. 91-96, 2011 (in Chinese).
4. [4] D. Acemoglu, “Directed Technical Change,” The Review of Economic Studies, Vol.69, No.4, pp. 781-809, 2002.
5. [5] D. Acemoglu and V. Guerrieri, “Capital Deepening and Non-Balanced Economic Growth,” J. of Political Economy, Vol.116, No.3, pp. 467-498, 2008.
6. [6] C. Antonelli, “Technological congruence and the economic complexity of technological change,” Structural Change and Economic Dynamics, Vo.38, pp. 15-24, 2016.
7. [7] S. Bentolila and G. Saint-Paul, “Explaining Movements in the Labor Share,” Contributions in Macroeconomics, Vol.3, No.1, 2003.
8. [8] N. Giammarioli, J. Messina, T. Steinberger, and C. Strozzi, “European Labor Share Dynamics: An Institutional Perspective,” Economics Working Papers, Vol.13, 2002.
9. [9] Y. Huang, Y. Liu, Y. Wu, and W. Li, “Economic Growth and Regional Inequality in China: Effects of Different Levels of Education,” Economic Research J., Vol.48, No.4, pp. 94-105, 2013 (in Chinese).
10. [10] G. Liu, Y. Liu, and J. Zhang, “Heterogeneous growth path and mode transformation of China’s provincial economies – On the traditional decomposition deviation of the source of economic growth,” J. of Management World, Vol.35, No.6, pp. 39-55, 2019 (in Chinese).
11. [11] X. Tan and X. Chao, “Re-Measuring Chinese Factor Income Distribution,” Modern Economic Science, Vol.38, No.6, pp. 7-16+122, 2016 (in Chinese).
12. [12] P. Adrjan, “The mightier, the stingier: Firms’ market power, capital intensity, and the labor share of income,” MPRA Paper, Article No.83925, 2018.
13. [13] A. Bassanini and T. Manfredi, “Capital’s grabbing hand? A cross-industry analysis of the decline of the labor share in OECD countries,” Eurasian Business Review, Vol.4, No.1, pp. 3-30, 2014.
14. [14] A. Guscina, “Effects of Globalization on Labor’s Share in National Income,” IMF Working Papers, No.294, 2006.
15. [15] L. Karabarbounis and B. Neiman, “The Global Decline of the Labor Share,” The Quarterly J. of Economics, Vol.129, No.1, pp. 61-103, 2014.
16. [16] J. Zhang, “Comprehensive analysis of economic benefits during the Seventh Five Year Plan Period – Calculation of the contribution rate of various factors to conomic growth,” Economic Research J., Vol.4, pp. 8-17, 1991 (in Chinese).
17. [17] S. Zhang, C. Chen, D.-H. Huang, and L. Hu, “Measurement of factor price distortion: A new production function method with time-varying elasticity,” Technological Forecasting and Social Change, Vol.175, Article No.121363, 2022.
18. [18] S. Zhang and B. Xu, “Analysis of China’s unbalanced economic growth,” The J. of Quantitative and Technical Economics, Vol.32, No.3, pp. 94-110, 2015 (in Chinese).
19. [19] C. Brunsdon, A. S. Fotheringham, and M. E. Charlton, “Geographically weighted regression: A method for exploring spatial nonstationarity,” Geographical Analysis, Vol.28, No.4, pp.281-298, 1996.
20. [20] B. Huang, B. Wu, and M. Barry, “Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices,” Int. J. of Geographical Information Science, Vol.24, No.3, pp. 383-401, 2010.
21. [21] C. Hu and S. Zhang, “Space-time heterogeneous elastic production function and regional total factor productivity measurement,” Statistics and Information Forum, Vol.34, No.6, pp. 51-57, 2019 (in Chinese).
22. [22] C. Wei and C. Mei, “Back fitting estimation of semi-parametric spatial variable coefficient regression model,” Practice and Understanding of Mathematics, Vol.36, pp. 177-184, 2006 (in Chinese).
23. [23] J. Zhang, G. Wu, and J. Zhang, “The estimation of China’s provincial capital stock: 1952–2000,” Economic Research J., Vol.10, pp. 35-44, 2004 (in Chinese).
24. [24] T. Hastie and R. Tibshirani, “Generalized Additive Models,” Chapman and Hall/CRC, 2004.
25. [25] S. Zhang, Y. Liu, and D.-H. Huang, “Contribution of factor structure change to China’s economic growth: evidence from the time-varying elastic production function model,” Economic Research-Ekonomska Istraživanja, Vol.33, No.1, pp. 2919-2942, 2020.
26. [26] S. Zhang, C. Zhu, X. Li et al., “Sectoral heterogeneity, industrial structure transformation, and changes in total labor income share,” Technological Forecasting and Social Change, Vol.176, Article No.121509, 2022.