Increasing the effectiveness of the Chinese grain subsidy: a quantitative analysis Academic Article uri icon

abstract

  • © 2018, Emerald Publishing Limited. Purpose: The purpose of this paper is to examine the grain production implications of alternative designs for China’s grain subsidy policy. In particular, the authors examine three subsidy designs including area-based subsidy, quantity-based subsidy and production-cost-based subsidy. Design/methodology/approach: To carry out the analysis, the authors develop a Chinese agricultural sector model (CASM) and an econometric, policy action–farmer response summary model. The CASM is used under a wide variety of subsidy level and basis experiments to generate pseudo data on farmer reactions to subsidies. Then a summary function model was estimated over those pseudo data that quantitatively summarized modeled farmer responses to different grain subsidy schemes. In turn, the summary functions were used to optimize the subsidy level such that it maximized grain production both within and across the area-based, quantity-based and cost-based subsidies. Regional implications were also developed. Findings: The authors found that the production-quantity-based subsidy is the most cost-effective in stimulating grain production among the subsidy schemes. The authors also argue that scheme complies with WTO regulations regarding product-specific support. The authors found that the areas where grain production was most affected were the traditional grain-producing regions. Originality/value: To the authors’ knowledge the authors have not seen a study of the Chinese grain subsidy program context that examined the effects of alternative subsidy schemes, nor one that developed estimates of the optimal subsidy level. In addition, the methodology is unique employing bottom-up, regionally disaggregated, sector modeling coupled with an aggregate pseudo data based summary function approach providing a new, original approach for analyzing agricultural policy design.

author list (cited authors)

  • Yi, F., & McCarl, B.

citation count

  • 3

publication date

  • November 2018