Modeling Unobserved Heterogeneity in New York Dairy Farms: One-Stage versus Two-Stage Models

Álvarez, Antonio; Corral, Julio del; Tauer, Loren W.

doi:10.1017/s1068280500001258

Cited by 30 publications

(32 citation statements)

References 30 publications

(28 reference statements)

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“…As expected, according to the literature (see, e.g., Alvarez et al, 2012, Alvarez anddel Corral, 2010;Sauer and Morrison, 2013), the TE of both groups was higher with the LCM than with the model assuming the same technology. The reason for this is that the LCM allows each farm's TE to be measured with respect to its own frontier (Alvarez and del Corral, 2010).…”

Section: Comparison Of the Te Scores With A Standard Sfa Modelsupporting

confidence: 86%

“…The finding that TE is higher if we take into account the effect of different technologies is in line with papers analysed the dairy sector in different countries (Alvarez-Del Corral, 2010;Alvarez et al, 2012;Emvalomatis, 2007;Sauer-Morrison, 2013). Thus, this finding appears to be general, irrespective of agricultural sectors or countries.…”

Section: Conclusion and Policy Implicationssupporting

confidence: 84%

“…LCMs assume that there are a finite number of groups underlying the data and estimate a different frontier for each of these groups (Alvarez et al, 2012).…”

Section: Theoretical Background and Empirical Modelmentioning

confidence: 99%

“…A growing body of literature has emphasised the importance of different technologies in estimating technical efficiency (TE) and technological change (TCH) in the dairy sector (Alvarez-Del Corral, 2010;Alvarez et al, 2012;Emvalomatis, 2007;Sauer-Morrison, 2013). …”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Heterogeneous technology, scale of land use and technical efficiency: The case of Hungarian crop farms

Baráth

Fertő

2015

Land Use Policy

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In this paper, we analyse the technical efficiency (TE) of Hungarian crop farms between 2001 and 2009 using conventional stochastic frontier analysis (SFA) and a latent class model (LCM). Our results suggest that technological heterogeneity plays an important role in Hungarian cereal, oilseed and protein (COP) crop producing farms, which are traditionally assumed to use homogeneous technologies. We identify two groups with different technologies using an LCM, and find that the scale of land use is one of the most important factors that differentiates our sample into unique technologies. Our results reveal that in the Hungarian COP sector there is less chance to increase performance through TE improvement than was earlier expected. The estimations also suggest that there is no room to improve productivity by increasing farm size, unless farms switch technologies. Consequently, agricultural policies for increasing productivity should concentrate on technological progress.

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Section: Comparison Of the Te Scores With A Standard Sfa Modelsupporting

confidence: 86%

Section: Conclusion and Policy Implicationssupporting

confidence: 84%

“…LCMs assume that there are a finite number of groups underlying the data and estimate a different frontier for each of these groups (Alvarez et al, 2012).…”

Section: Theoretical Background and Empirical Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Heterogeneous technology, scale of land use and technical efficiency: The case of Hungarian crop farms

Baráth

Fertő

2015

Land Use Policy

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“…Following Alvarez et al (2012) and based on the descriptive statistics in Table 3, we allow for two production technologies. Using the structural estimation procedure described below, we find that for stanchion operations α = 0.135 and γ = 0.174 and for milking parlors α = 0.107 and γ = 0.122.…”

Section: Productivity 221 Our Productivity Measurementioning

confidence: 99%

An Estimated Structural Model of Entrepreneurial Behavior

Jones¹,

Pratap²

2017

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Using a rich panel of owner-operated New York dairy farms, we provide new evidence on entrepreneurial behavior. We formulate a dynamic model of farms facing uninsured risks and financial constraints. Farmers derive nonpecuniary benefits from operating their businesses. We estimate the model via simulated minimum distance, matching both production and financial data. We find that financial factors and nonpecuniary benefits are of first-order importance. Collateral constraints and liquidity restrictions inhibit borrowing and the accumulation of capital. The nonpecuniary benefits to farming are large and keep small, low-productivity farms in business. Although farmers are risk averse, eliminating uninsured risk has only modest effects on capital and output. * We are grateful to

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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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Modeling Unobserved Heterogeneity in New York Dairy Farms: One-Stage versus Two-Stage Models

Cited by 30 publications

References 30 publications

Heterogeneous technology, scale of land use and technical efficiency: The case of Hungarian crop farms

Heterogeneous technology, scale of land use and technical efficiency: The case of Hungarian crop farms

An Estimated Structural Model of Entrepreneurial Behavior

Modelling spatial regimes in farms technologies

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