Re-estimating Physical Inputs and Sectoral Productivity Growth in Chinese Agriculture: Quantile Regression Approach

Owusu Samuel Mensah, Zhuang Jincai, Henry Asante Antwi, Thomas Bilaliib Udimal

Abstract


The desire to increase food production in the midst of an escalating population growth and international food demand has been a critical issue under discussion. This paper employed quantile regression approach to identify the heterogeneous effect of physical inputs on growth of Chinese agriculture for different production levels from 1978 to 2014. The results revealed that the yield effect of land, labor, agricultural machinery, irrigation, energy and fertilizer application differ in both patterns and magnitude across the selected quantile points and OLS estimate results. However, the effect of fertilizer application is significantly positive on crop and livestock production, which is 2-4 times higher than the contribution of land and irrigation. However, since increase in fertilizer application is posing a lot of threat to the environment and causing low quality of food production, the study proposes development of fertilizer-sensitive technologies and sensitization of farmers on the application of N fertilizers to reduce the menace.

Keywords: Agricultural growth; physical inputs; quantile regression; PR China


Full Text:

PDF

References


Bosworth, B., & Collins, S. M. (2008). Accounting for growth: comparing China and India. The Journal of Economic Perspectives, 22(1), 45-66.

Buchinsky, M. (1998). Recent advances in quantile regression models: a practical guideline for empirical research. Journal of human resources, 88-126.

Chen, C. (2005). An introduction to quantile regression and the QUANTREG procedure. Paper presented at the Proceedings of the Thirtieth Annual SAS Users Group International Conference.

Chen, Z., Huffman, W. E., & Rozelle, S. (2009). Farm technology and technical efficiency: Evidence from four regions in China. China Economic Review, 20(2), 153-161.

Fan, S. (1991). Effects of technological change and institutional reform on production growth in Chinese agriculture. American Journal of Agricultural Economics, 73(2), 266-275.

Fan, S., Fang, C., & Zhang, X. (2003). Agricultural research and urban poverty: The case of China. World Development, 31(4), 733-741.

Fan, S., & Pardey, P. G. (1997). Research, productivity, and output growth in Chinese agriculture. Journal of development Economics, 53(1), 115-137.

Fan, S., Zhang, L., & Zhang, X. (2004). Reforms, investment, and poverty in rural China. Economic Development and Cultural Change, 52(2), 395-421.

Fuglie, K., & Schimmelpfennig, D. (2010). Introduction to the special issue on agricultural productivity growth: a closer look at large, developing countries. Journal of productivity analysis, 33(3), 169-172.

Huang, J., & Ma, H. (2010). Capital formation and agriculture development in China: Rome, FAO.

Huang, J., & Rozelle, S. (2015). The Role of Agriculture in China’s Development: Performance, Determinants of Successes and Future Challenges Emerging Economies (pp. 67-88): Springer.

Johnson, D. G. (1998). China's rural and agricultural reforms in perspective: publisher not identified.

Kevin Z. Chen, D. S. F., Dr Suresh Chandra Babu, D., Gautam, M., & Yu, B. (2015). Agricultural productivity growth and drivers: a comparative study of China and India. China Agricultural Economic Review, 7(4), 573-600.

Koenker, R., & Bassett Jr, G. (1978). Regression quantiles. Econometrica: journal of the Econometric Society, 33-50.

Lin, J. Y. (1992). Rural reforms and agricultural growth in China. The American economic review, 34-51.

Ling, Z. (1991). Rural Reform and Peasant Income in China. The Impact of China's Post Mao Rural.

McMillan, J., Whalley, J., & Zhu, L. (1989). The impact of China's economic reforms on agricultural productivity growth. The Journal of Political Economy, 781-807.

Ni, Z.-X., Wang, D.-Z., & Xue, W.-J. (2015). Investor sentiment and its nonlinear effect on stock returns—New evidence from the Chinese stock market based on panel quantile regression model. Economic Modelling, 50, 266-274.

Nin-Pratt, A., Yu, B., & Fan, S. (2010). Comparisons of agricultural productivity growth in China and India. Journal of Productivity Analysis, 33(3), 209-223.

Po-Chi, C., Ming-Miin, Y., Chang, C.-C., & Shih-Hsun, H. (2008). Total factor productivity growth in China's agricultural sector. China Economic Review, 19(4), 580-593.

SARKAR, D., ROY, D., & CHATTOPADHYAY, K. S. (2013). Effect of farm mechanization on agricultural growth and comparative economics of labour and machinery in West Bengal.

Variyam, J. N., Blaylock, J., & Smallwood, D. (2002). Characterizing the distribution of macronutrient intake among US adults: a quantile regression approach. American Journal of Agricultural Economics, 84(2), 454-466.

Wu, S., Walker, D., Devadoss, S., & Lu, Y.-c. (2001). Productivity growth and its components in Chinese agriculture after reforms. Review of Development Economics, 5(3), 375-391.

Xu, B., & Lin, B. (2016). A quantile regression analysis of China's provincial CO 2 emissions: Where does the difference lie? Energy Policy, 98, 328-342.

Xu, M. (2012). Chinese Agricultural Growth in Post-reform Era Advances in Computer Science and Engineering (pp. 711-715): Springer.

Xu, X., & Jeffrey, S. R. (1998). Efficiency and technical progress in traditional and modern agriculture: evidence from rice production in China. Agricultural economics, 18(2), 157-165.

Zhang, B., & Carter, C. A. (1997). Reforms, the weather, and productivity growth in China's grain sector. American Journal of Agricultural Economics, 79(4), 1266-1277.

Zhang, W., Zhang, F., & Ma, L. (2007). The Fertilizer Situation and Outlook in China. Presentation at the Sino-German International Research Training Group, Stuttgart, Germany, 13.

Zhang, X., & Fan, S. (2001). Estimating crop-specific production technologies in Chinese agriculture: a generalized maximum entropy approach. American Journal of Agricultural Economics, 83(2), 378-388.

Zhu, H., Duan, L., Guo, Y., & Yu, K. (2016). The effects of FDI, economic growth and energy consumption on carbon emissions in ASEAN-5: Evidence from panel quantile regression. Economic Modelling, 58, 237-248.

Zhu, H., Guo, Y., & You, W. (2015). An empirical research of crude oil price changes and stock market in China: evidence from the structural breaks and quantile regression. Applied Economics, 47(56), 6055-6074.

Zhu, L. (1991). Rural Reform and Peasant Income in China. London: blacnillan.


Refbacks

  • There are currently no refbacks.