Raising Farm Income in India: What Does a Simultaneous Quantile Regression Approach Tell?^§

This paper focuses on the contribution of farm income component of farmers’ income and traces its determinants by Situation Assessment Survey of Agricultural Households 2012–13. This study analyses the determinants of farm income by Simultaneous Quantile Regression Model (SQRM) and highlights that contribution of farm income to double farmers’ income would not be attainable without considering the variability of impact of different factors across farm income groups. The distribution of farm income is highly skewed; explanatory variables do not influence farm income in a similar manner across all quantiles, and the failure of identification of the specificity of the predictor variables is prone to be affected by generalization. Results indicate that coefficients estimated by SQRM mostly retain signs but their magnitude and significance differ across quantiles of farm income. The results further indicate that loan outstanding per hectare and machinery hiring inversely impact farm income at lower quantiles of agricultural households contrary to, insignificant or positive impacts on higher quantiles of farm income households. From the policy point of view, it may be argued that it is more important to focus on the low farm income households rather than considering the entire farm income distribution aggregately. Alternative sources of income have an immense role in the upgradation of the economic situation of the agricultural households, a frontal strategy to raise farm income will naturally have to take into account the determinants of this income of different groups of farmers, and in particular, strategies must target specific issues faced by low farm income groups.