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.

Modelling spatial regimes in farms technologies / Anna Gloria Billé; Cristina Salvioni; Roberto Benedetti. - In: JOURNAL OF PRODUCTIVITY ANALYSIS. - ISSN 0895-562X. - ELETTRONICO. - 49:2-3(2018), pp. 173-185. [10.1007/s11123-018-0529-7]

Modelling spatial regimes in farms technologies

Anna Gloria Billé
Primo
;
2018

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.
2018
Modelling spatial regimes in farms technologies / Anna Gloria Billé; Cristina Salvioni; Roberto Benedetti. - In: JOURNAL OF PRODUCTIVITY ANALYSIS. - ISSN 0895-562X. - ELETTRONICO. - 49:2-3(2018), pp. 173-185. [10.1007/s11123-018-0529-7]
Anna Gloria Billé; Cristina Salvioni; Roberto Benedetti
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/853785
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