Although there is now widespread evidence of substantial variability in economic agents' responses to economic drivers in many applied economics fields, this variability has been largely overlooked by econometric agricultural production models. This article sets out to fill this gap by providing methodological contributions and empirical results. First, we consider panel data multicrop models featuring random intercept and slope parameters to account for the heterogeneous responses of crop producers to economic drivers. Second, we show that Monte Carlo expectation‐maximization algorithms are particularly well‐suited to estimating this type of model. Third, based on an application of our empirical modeling framework with a sample of French grain crop producers, we demonstrate substantial variability in farmers' responses to economic incentives. Fourth, we use the estimated model and a simple “statistical calibration” procedure to build farm‐specific simulation models, which are then used to evaluate the effects of the rapeseed price increase induced by European Union (EU) biofuel support. Our simulation results demonstrate that ignoring the variability in the considered farmers' responses to the economic incentives results in significant overestimation of the increases in rapeseed yield levels and variable input use levels induced by EU biofuel support, as well as significant underestimation of the variability in the congruent increases in rapeseed acreages.
Null crop acreages raise pervasive issues when modelling acreage choices with farm data. We revisit these issues and emphasize that null acreage choices arise not only due to binding nonnegativity constraints but also due to crop production fixed costs. Based on this micro-economic background, we present a micro-econometric multi-crop model that consistently handles null acreages and accounts for crop production fixed costs. This multivariate endogenous regime switching model allows for specific crop acreage patterns, such as multiple kinks and jumps in crop acreage responses to economic incentives, that are due to changes in produced crop sets. Currently available micro-econometric multi-crop models, which handle null acreages based on a censored regression approach, cannot represent these patterns. We illustrate the empirical tractability of our modelling framework by estimating a random parameter version of the proposed endogenous regime switching micro-econometric multi-crop model with a panel dataset of French farmers. Our estimation and simulation results support our theoretical analysis, the effects of crop fixed costs and crop set choices on crop acreage choices in particular. More generally, these results suggest that the micro-econometric multicrop model presented in this article can significantly improves empirical analyses of crop supply based on farm data.
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