Is small still beautiful? A comparative study of rice farm size and productivity in China and India

Purpose - The farm size-productivity relationship has long been the subject of debate among development economists. Few studies address this issue for China, and those that do only with outdated data sets poorly representing the current situation after the past decade of rapid change, which includes the rapid development of land rental markets, village labor out-migration and use of farm machines. Meanwhile, many studies have researched this relationship for Indian, which is undergoing similar changes except for the development of active land rental markets. The purpose of this paper is to measure the farm size-productivity relationship under the situations of rapid transformation in China and India. Design/methodology/approach - Based on the data of 325 Jiangxi and 400 Allahabad rice farmers in 2011, the survey covered multiple plots of each household in one/multiple growing season(s). The authors use the production function approach and the yield approach, and control for farmland quality, imperfect factor markets, and farm size measurement error, to identify the farm size-productivity relationship. Findings - The regressions show that land yields increase with plot size both by season and over the year in China. Thismay be one of the reasons that farmsizes are growing in some areas. In India, however, the inverse farm size-productivity relationship is observed by the study, despite recent changes. Moreover, land yields increase with farm machine use in both China and India. This result contributes to the debate over whether mechanization improves yields or just expands the land frontier. Originality/value - The paper empirically estimates the farm size-productivity relationship under rapid agrarian transformation in both China and India based on a unique data set collected by the authors in a detailed primary survey. The paper considers measurement error in the analysis, which adds values to this type of analysis.