Spatial nonstationarity in the stochastic frontier model: An application to the Italian wine industry

Vidoli, Francesco; Cardillo, Concetta; Fusco, Elisa; Canello, Jacopo

doi:10.1016/j.regsciurbeco.2016.10.003

Cited by 39 publications

(21 citation statements)

References 37 publications

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“…In light of the above findings, it is reasonable to expect asymmetric reactions among wine producers in the years immediately following the 2008. Such asymmetry is likely to be heavily influenced by local environmental factors, considering the strong linkages existing between agricultural activities and the territory, and the key role played by soil, climatic, and morphological characteristics, but also by intangible factors, such as historical traditions and the local learning processes, in explaining the performance dynamics of most farm holdings (Beebe, Haque, F I G U R E 2 Labor productivity, wine industry, manufacturing industry and Italian economy, period 2004-2016 [Color figure can be viewed at wileyonlinelibrary.com] Jarvis, Kenney, & Patton, 2012;Morrison & Rabellotti, 2017;Turner, 2009;Vidoli, Cardillo, Fusco, & Canello, 2016).…”

Section: An Application To the Italian Wine Industrymentioning

confidence: 99%

Investigating space‐time patterns of regional industrial resilience through a micro‐level approach: An application to the Italian wine industry

Canello

Vidoli

2020

Journal of Regional Science

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This paper introduces a new methodology to identify space‐time patterns of regional resilience using a micro‐level approach. The novel empirical tool combines geographically weighted regression with panel stochastic frontier analysis with endogenous covariates. The analysis is implemented on a panel of farm holdings operating in the Italian wine industry, focusing on the impact of a major institutional change. The results show the effectiveness of the new procedure in identifying geographical clusters of wine producers who reacted to the shock in similar ways. The responses are found to be homogeneous within specific territories and heterogeneous between regions.

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Section: An Application To the Italian Wine Industrymentioning

confidence: 99%

Investigating space‐time patterns of regional industrial resilience through a micro‐level approach: An application to the Italian wine industry

Canello

Vidoli

2020

Journal of Regional Science

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“…Spatial dependence in technical efficiency can also be found, for instance, in agricultural production (e.g. Areal et al, 2012, Vidoli et al, 2016, and Mate-Sánchez-Val et al, 2017, or when we are examining the efficiency of production units of a single firm they follow similar procedures of action designed by the holding they belong to.…”

Section: Introductionmentioning

confidence: 99%

A new stochastic frontier model with cross-sectional effects in both noise and inefficiency terms

Orea

Álvarez

2019

Journal of Econometrics

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This paper develops a new stochastic frontier model that allows for cross-sectional (spatial) correlation in both the noise and inefficiency terms. The model proposed is useful in efficiency analyses when there are omitted but spatially-correlated variables and firms benefit from best practices implemented by other (adjacent) firms. Unlike the previous literature, our model can be estimated by maximum likelihood using standard software. The model is illustrated with an application to the Norwegian electricity distribution sector.

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“…Spatial dependence has been recently introduced also into stochastic frontier models, see Druska and Horrace (2004), Schmidt et al (2009)., Areal et al (2012)., Glass et al (2014Glass et al ( , 2016., Adetutu et al (2015). and Vidoli et al (2016).. However, the above references still assume the hypothesis of an aggregate production function, ignoring the effect of unobserved spatial heterogeneity.…”

Section: Introductionmentioning

confidence: 99%

Modelling spatial regimes in farms technologies

2018

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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. Keywords Unobserved heterogeneity • Spatial dependence • Cobb-Douglas production function • Olive production JEL codes D24 • C14 • Q12

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Spatial nonstationarity in the stochastic frontier model: An application to the Italian wine industry

Cited by 39 publications

References 37 publications

Investigating space‐time patterns of regional industrial resilience through a micro‐level approach: An application to the Italian wine industry

Investigating space‐time patterns of regional industrial resilience through a micro‐level approach: An application to the Italian wine industry

A new stochastic frontier model with cross-sectional effects in both noise and inefficiency terms

Modelling spatial regimes in farms technologies

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