Inference in the spatial autoregressive efficiency model with an application to Dutch dairy farms

This article extends the conventional spatial autoregressive efficiency model by including firm characteristics that may impact efficiency. This extension allows performing the typical inference in spatial autoregressive models that involves the derivation of direct and indirect marginal effects, with the latter revealing the nature and magnitude of spatial spillovers. Furthermore, this study accounts for the endogeneity of the spatial autoregressive efficiency model using a lag spatial lag efficiency component, which makes inference to be performed in a long-run framework. The case study concerns specialized Dutch dairy farms observed over the period 2009–2016 and for which exact geographical coordinates of latitude and longitude are available. The results reveal that the efficiency scores are spatially dependent. The derived marginal effects further suggest that farmers’ long-run efficiency is driven by changes in both their own and their neighbors’ characteristics, highlighting the existence of motivation and learning domino effects between neighboring producers

A generalized true random-effects model with spatially autocorrelated persistent and transient inefficiency

This study extends the generalized true random-effects model to account for spatial dependence in persistent and transient inefficiency. For this purpose, a model with spatially autocorrelated persistent and transient inefficiency components is specified. Additionally, spatial dependence is also modeled in the noise component to account for uncontrolled spatial correlations. The proposed model is applied to a panel dataset of Wisconsin dairy farms observed between 2009 and 2017 and estimated using Bayesian techniques. Apart from the traditional output-input quantities, the utilized dataset also contains information on the exact location of farms based on their latitude and longitude coordinates as well as on environmental factors. The empirical findings suggest low levels of both persistent and transient inefficiency for farms. Additionally, all components exhibit spatial dependence with its magnitude being more than double for persistent inefficiency

Measurement of production inefficiency in a technology and inefficiency heterogeneity setting

This study shows how the estimates of production inefficiency and of the marginal effects of its determinants can be distorted if not accounting for technology and inefficiency heterogeneity. This is achieved by employing a hierarchical stochastic frontier model with random parameters both in the production frontier and in the inefficiency distribution and comparing its results with a conventional frontier model. German dairy farming is used as a case study and estimation is performed in a Bayesian framework. The results reveal significant differences in the inefficiencies and the calculated marginal effects of its determinants across the two models. Specifically, it is shown that inefficiency is overestimated when heterogeneity is not accounted for. An inflation of the means and the variances of the marginal effects is also observed, with the latter result suggesting that technology heterogeneity dominates inefficiency heterogeneity. According to Bayes factors, the employed hierarchical frontier model is favoured by the data when compared to the conventional frontier model

Organizational forms and technical efficiency of the dairy processing industry in Southern Brazil

The objective of this article is to assess the determinants of the technical efficiency of dairy processing firms in Southern Brazil while accounting for their different organizational forms, namely cooperatives and investor-owned firms. The data from 243 milk processors in southern Brazil, including firm structure, management capacity, and organizational choice of dairies, were analyzed. A production frontier is specified to estimate technical efficiency and identify its potential driving sources. Bayesian techniques are used to estimate the model. An average efficiency of 77% indicates that the actual output is 23% below its potential, which implies that output could, on average, be increased by approximately 31.6%, under ceteris paribus conditions. Economies of scale were also detected. The analysis reveals that the management capacity within companies is the main determinant of efficiency. Idle capacities of processing plants are an important source of inefficiencies and cooperatives are more efficient than investor-owned firms, despite their transaction costs potentially being higher and the five vaguely defined property rights inherent to the traditional cooperatives which they must overcome. Knowledge about the cooperatives\u2019 objectives other than profit maximization would provide a more realistic comparison against investor-owned firms. This study assessed the determinants of the efficiency levels of dairy processing companies in an emerging economy using a unique own dataset with data collected at a plant level. Based on the results, manifold managerial and political implications have been derived that can benefit the dairy industry of developing and emerging economies

The Impact of Agri-Environmental Policies and Production Intensification on the Environmental Performance of Dutch Dairy Farms

This study examines the impact of policies and intensification on the environmental performance of Dutch dairy farms in the period 2001-2010 using a hyperbolic distance function. The results indicate that the change from the Mineral Accounting System to the combination of the Application Standards Policy with decoupled payments has not significantly changed farms’ hyperbolic efficiency. Farms receiving agri-environmental and animal welfare payments are less hyperbolically efficient than those that do not, highlighting greater decreases in desirable outputs than decreases in undesirable outputs. Finally, intensification increases hyperbolic efficiency, suggesting that intensive practices may increase production without harming the environment

Spatial spillovers on input-specific inefficiency of Dutch arable farms

Traditional benchmarking implicitly assumes that decision making units operate in isolation from their peers. For arable production systems in particular, this assumption is unlikely to hold in reality. This paper quantifies spatial spillovers on input-specific inefficiency using data envelopment analysis and a second-stage bootstrap truncated regression model. The bootstrap algorithm is extended to allow for the estimation of the parameter of the spatial weight matrix, which captures the proximity between producers. The empirical application concerns Dutch arable farms for which latitudes and longitudes are available. The average inefficiency across years was 3.87% for productive inputs and 2.98% for damage abatement inputs under variable returns to scale. For productive inputs technical inefficiency, statistically significant spillover effects from neighbours' age and their degree of specialisation depended on the type of the spatial weight matrix used (inverse distance ork-nearest neighbours). Statistically significant spillover effects of subsidy payments were adverse while statistically significant spillover effects from insurance payments were beneficial. For damage abatement inputs technical inefficiency, statistically significant adverse effects were found for neighbours' age and subsidy payments and beneficial effects from neighbours' insurance payments and their degree of specialisation

Analysing inefficiency in a non-parametric spatial-dynamic by-production framework : A k-nearest neighbour proposal

This paper accounts for spatial effects by benchmarking farms against their k-nearest neighbours (KNN) and measuring their inefficiency in a non-parametric dynamic by-production setting. The optimal number of neighbours (Formula presented.) against which farms are compared corresponds to the value of (Formula presented.) that maximises the Moran I test for spatial autocorrelation of the good and the bad output of the farms' two sub-technologies. The inefficiency scores for farms' good output, variable inputs, investments and bad outputs are then computed and compared with those calculated based on a global technology, which benchmarks all farms together. The application focuses on an unbalanced panel of specialised Dutch dairy farms over the period 2009–2016 that contains information on their exact geographical locations. The results suggest that the inefficiency scores exhibit statistically significant differences between the KNN and the global model. Specifically, the inefficiencies are generally deflated when a KNN technology is considered, suggesting that ignoring spatial effects can overestimate inefficiency