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New scientific research article by Lajos Baráth & Raushan Bokusheva in American Journal of Agricultural Economics



State-contingent production technology formulation:
Identifying states of nature using reduced-form econometric models of crop yield


Open Access article – American Journal of Agricultural Economics – First published: 16 August 2023


Conducting experiments can be time consuming and expensive, and may not always be reasonable. Therefore, empirical research often derives structural parameters based on observational data and reduced-form econometric models. The state-contingent approach presents a consistent conceptual framework for analyzing producer decisions under uncertainty. However, application of this structural modeling approach has been hampered by data constraints, particularly the lack of information for mapping producers’ stochastic outputs onto a set of the states of nature representing different uncertain events. Consistent mapping of uncertainty is particularly critical in the context of multiple output production where weather shocks often have different effects across crops and in microeconometric analyses when unobserved farm heterogeneity may confound the effect of uncertainty. Our study demonstrates how the application of reduced-form approaches can overcome constraints of structural econometric modeling associated with the lack of relevant data and presents an approach for identifying states of nature in the context of multiple output production using reduced-form econometric models of crop yield. In an empirical application based on Hungarian farm accountancy data, we demonstrate that the proposed approach allows a consistent mapping of production uncertainty in crop farming, utilizes panel data structure, and controls for potential endogeneity due to unobserved farm heterogeneity. We anticipate the presented approach to be useful for developing further the state-contingent approach and to stimulate further studies combining the strengths of structural approaches and reduced-form models.


Keywords: identification problem, production analysis under uncertainty, quasi-experimental data, state-contingent approach, structural and reduced-form models