Modeling Heterogeneous Farm Responses to European Union Biofuel Support with a Random Parameter Multicrop Model

Obafèmi Philippe Koutchadé, Alain Carpentier and Fabienne Femenia

American Journal of Agricultural Economics, 2018, vol. 100, issue 2, 434-455

Abstract: 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.

Keywords: European Union biofuel support; Monte Carlo expectation-maximization algorithms; multicrop micro-econometric models; random parameter models (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (16)

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Persistent link: https://EconPapers.repec.org/RePEc:oup:ajagec:v:100:y:2018:i:2:p:434-455.

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