Identifying effects of farm subsidies on structural change using neural networks

Farm subsidies are commonly motivated by their promise to help keep families in agriculture and reduce farm structural change. Many of these subsidies are designed to be targeted to smaller farms, and include production caps or more generous funding for smaller levels of activity. Agricultural economists have long studied how such subsidies affect production choices, and resulting farm structure. Traditional econometric models are typically restricted to detecting average effects of subsidies on certain farm types or regions and cannot easily incorporate complex subsidy design or the multi-output, heterogeneous nature of many farming activities. Programming approaches may help address the broad scope of agricultural production but have less empirical measures for behavioral and technological parameters. This paper uses a recurrent neural network and detailed panel data to estimate the effect of subsidies on the structure of Norwegian farming. Specifically, we use the model to determine how the varying marginal subsidies have affected the distribution of Norwegian farms and their range of agricultural activities. We use the predictive capacity of this flexible, multi-output machine learning model to identify the effects of agricultural subsidies on farm activity and structure, as well as their detailed distributional effects.