Modelling Spatial Regimes in Farms Technologies

TL;DR

This paper introduces a two-step spatial econometric method to identify local production regimes among farms, improving understanding of environmental and decision-making heterogeneity for better policy guidance.

Contribution

It develops a novel two-step approach to endogenously identify spatial regimes and model spatial dependence in farm technology analysis.

Findings

Identification of spatial regimes enhances understanding of farm heterogeneity.

The method improves model accuracy for policy and extension services.

Application to Italian olive farms demonstrates practical usefulness.

Abstract

We exploit the information derived from geographical coordinates to endogenously identify spatial regimes in technologies that are the result of a variety of complex, dynamic interactions among site-specific environmental variables and farmer decision making about technology, which are often not observed at the farm level. Controlling for unobserved heterogeneity is a fundamental challenge in empirical research, as failing to do so can produce model misspecification and preclude causal inference. In this article, we adopt a two-step procedure to deal with unobserved spatial heterogeneity, while accounting for spatial dependence in a cross-sectional setting. The first step of the procedure takes explicitly unobserved spatial heterogeneity into account to endogenously identify subsets of farms that follow a similar local production econometric model, i.e. spatial production regimes. The second step consists in the specification of a spatial autoregressive model with autoregressive disturbances and spatial regimes. The method is applied to two regional samples of olive growing farms in Italy. The main finding is that the identification of spatial regimes can help drawing a more detailed picture of the production environment and provide more accurate information to guide extension services and policy makers.