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Modelling spatial regimes in farms technologies

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  • A. G. Billé

    (Free University of Bolzano-Bozen)

  • C. Salvioni

    () (University of Chieti-Pescara)

  • R. Benedetti

    (University of Chieti-Pescara)

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.

Suggested Citation

  • A. G. Billé & C. Salvioni & R. Benedetti, 2018. "Modelling spatial regimes in farms technologies," Journal of Productivity Analysis, Springer, vol. 49(2), pages 173-185, June.
  • Handle: RePEc:kap:jproda:v:49:y:2018:i:2:d:10.1007_s11123-018-0529-7
    DOI: 10.1007/s11123-018-0529-7
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    Cited by:

    1. Burhan Can Karahasan, 2020. "Winners and losers of rapid growth in Turkey: Analysis of the spatial variability of convergence," Papers in Regional Science, Wiley Blackwell, vol. 99(3), pages 603-644, June.
    2. Jacopo Canello & Francesco Vidoli, 2020. "Investigating space‐time patterns of regional industrial resilience through a micro‐level approach: An application to the Italian wine industry," Journal of Regional Science, Wiley Blackwell, vol. 60(4), pages 653-676, September.
    3. Yiorgos Gadanakis & Francisco José Areal, 2020. "Accounting for rainfall and the length of growing season in technical efficiency analysis," Operational Research, Springer, vol. 20(4), pages 2583-2608, December.

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