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.
Highlights
Theory supports the idea that firms do not operate on the basis of a common production function, i.e., that firms do not use homogeneous technology (Nelson and Winter 1982; Dosi 1988), a global production function is typically proposed in most empirical studies; it is assumed that production technology is invariant over space and across firms
Once the spatial regimes are identified by applying the iterative procedure described above, i.e., after controlling for spatial heterogeneity, we have to deal with spatial dependence among the observations
The long-term interactions among sitespecific environmental variables and farmer decision making about technology have contributed to developing local specific varieties and technologies, to give rise to a patchy technological landscape characterized by the presence of a discrete number of spatial regimes in technologies
Summary
Theory supports the idea that firms do not operate on the basis of a common production function, i.e., that firms do not use homogeneous technology (Nelson and Winter 1982; Dosi 1988), a global production function is typically proposed in most empirical studies; it is assumed that production technology is invariant over space and across firms. Many empirical studies control for the possibility of heterogeneous technologies by classifying farms into groups on the basis of a priori exogenous information about their technological characteristics, and subsequently estimate separate different production functions for each group This classification is based on either some a priori information (e.g., location of farms, etc.) or the application of cluster analysis (Álvarez et al 2008).
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