Two-stage inference in experimental design using dea : an application to intercropping and evidence from randomization theory

Neste artigo é proposto o uso de medidas de eficiência DEA, com retornos constantes à escala e input unitário, na análise de respostas multidimensionais não negativas de ensaios experimentais. A abordagem proposta concorda com a Análise de Variância (Covariância) clássica para respostas unidimensionais e simplifica a análise estatística para o caso multidimensional. Os melhores tratamentos indicados pela análise otimizam um output combinado, definido por preços sombra, que são as soluções dos problemas lineares de DEA. A abordagem é particularmente útil na análise de experimentos consorciados (plantio simultâneo de mais de uma cultura agrícola). São aqui discutidos dois exemplos. Os resultados são validados via Teoria de Aleatorização, de modo a estudar apropriadamente as questões de correlação e não-normalidade das medidas DEA nas diferentes parcelas experimentais. ________________________________________________________________________________________ ABSTRACTIn this article we propose the use of Data Envelopment Analysis (DEA) measures of efficiency, under constant returns to scale and input equal to unity, in the analysis of multidimensional nonnegative responses in the design of experiments. The approach agrees with the standard Analysis of Variance (Covariance) for univariate responses and simplifies the statistical analysis in the multivariate case. The best treatments provided by the analysis optimize a combined output defined by shadow prices, which are the solutions of the DEA problem. The approach is particularly useful for the analysis of intercropping (crop mixtures) experiments. In this context we discuss two examples. To properly address the issue of correlation and non-normality of DEA measurements in different experimental plots we validate the results via Randomization Theory

Computing confidence intervals for output oriented DEA models: an application to agricultural research in Brazil.

We define and model the research production at Embrapa, the major Brazilian institution responsable for applied agricultural research. The main theoretical framework is Data Envelopment Analysis - DEA. We explore the economic interpretation and the statistical properties of these models to compute confidence intervals for output oriented efficiency measurements, based on a parametric flexible model, defined by the truncated normal distribution. These results provide a better insight on the efficiency classification and allow comparison among the DMUs involved int ehe evaluation process taking into account inefficiency random variation

Stochastic non-smooth envelopment of data : semi-parametric frontier estimation subject to shape constraints

The field of productive efficiency analysis is currently divided between two main paradigms: the deterministic, nonparametric Data Envelopment Analysis (DEA) and the parametric Stochastic Frontier Analysis (SFA). This paper examines an encompassing semiparametric frontier model that combines the DEA-type nonparametric frontier, which satisfies monotonicity and concavity, with the SFA-style stochastic homoskedastic composite error term. To estimate this model, a new two-stage method is proposed, referred to as Stochastic Non-smooth Envelopment of Data (StoNED). The first stage of the StoNED method applies convex nonparametric least squares (CNLS) to estimate the shape of the frontier without any assumptions about its functional form or smoothness. In the second stage, the conditional expectations of inefficiency are estimated based on the CNLS residuals, using the method of moments or pseudolikelihood techniques. Although in a cross-sectional setting distinguishing inefficiency from noise in general requires distributional assumptions, we also show how these can be relaxed in our approach if panel data are available. Performance of the StoNED method is examined using Monte Carlo simulations.v2012o

A nonpaprametric approach to evaluate the impact of contextual variables on the agricultural research efficiency in Brazil.

In this paper we measure technical efficiency for each of Embrapa (Brazilian Agricultural Research Corporation) research centers. We model DEA efficiency as a function of contextual variables: revenue generation capacity, partnership intensity, improvement of administrative processes, and impact of technologies generated by the research centers. Production is modeled with random and inefficient errors, in a manner similar to stochastic frontiers. The assessment of significance for the set of contextual variables is carried out by means of linear programming and goodness of fit tests and has a nonparametric basis. We conclude that there is joint significance of all contextual variables

Performance evaluation using bootstrapping DEA techniques: Evidence from industry ratio analysis

In Data Envelopment Analysis (DEA) context financial data/ ratios have been used in order to produce a unified measure of performance metric. However, several scholars have indicated that the inclusion of financial ratios create biased efficiency estimates with implications on firms’ and industries’ performance evaluation. There have been several DEA formulations and techniques dealing with this problem including sensitivity analysis, Prior-Ratio-Analysis and DEA/ output–input ratio analysis for the assessment of the efficiency and ranking of the examined units. In addition to these computational approaches this paper in order to overcome these problems applies bootstrap techniques. Moreover it provides an application evaluating the performance of 23 Greek manufacturing sectors with the use of financial data. The results reveal that in the first stage of our sensitivity analysis the efficiencies obtained are biased. However, after applying the bootstrap techniques the sensitivity analysis reveals that the efficiency scores have been significantly improved.Performance measurement; Data Envelopment Analysis; Financial ratios; Bootstrap; Bias correction

A Probabilistic Approach for Assessing the Significance of Contextual Variables in Nonparametric Frontier Models: an Application for Brazilian Banks

This article presents an empirical application illustrating the use of a nonparametric frontier model relying on a probabilistic definition of the production frontier. The significance of the variable nonperforming loans in productive efficiency is assessed, for a sample of Brazilian banks, using the concepts of condicional and unconditional efficiency measures, in a context where it is not necessary to impose any particular distribution for the production data. The analysis is robust relative to the assumptions of separability.

The Performance of German Water Utilities: A (Semi)-Parametric Analysis

Germany's water supply industry is characterized by a multitude of utilities and widely diverging prices, possibly resulting from structural differences beyond the control of firms' management, but also from inefficiencies. In this article we use Data Envelopment Analysis and Stochastic Frontier Analysis to determine the utilities' technical efficiency scores based on cross-sectional data from 373 public and private water utilities in 2006. We find large differences in technical efficiency scores even after accounting for significant structural variables like network density, share of groundwater usage and water losses.Water supply, technical efficiency, data envelopment analysis, stochastic frontier analysis, structural variables, bootstrapped truncated regression

Conditional FDH efficiency, income dispersion and market imperfections: the case of the Brazilian agricultural sensus of 2006.

In this article we assess the effect of market imperfections and income inequality on rural production efficiency. The analysis is carried out using the notion of stochastic conditional efficiency computed in terms of free disposal hull (FDH) efficiency measurements. Free disposal hull and conditional FDH are output oriented with variable returns scale

Scale of operation, allocative inefficiencies and separability of inputs and outputs in agricultural research: Embrapa case study.

In this article we consider some properties of concern for research production at Embrapa. We address questions related to statistical tests for the scale of operation, the presence of allocative inefficiencies and separability of inputs and outputs. The production process is assessed by nonparametric methods with the use of a Data Envelopment Analysis frontier. The period of concern is 2002-2009. We conclude that Embrapa technology shows variable returns to scale, shows mild allocative inefficiencies in sub-periods, and is separable in inputs and outputs

Confidence intervals for DEA efficiency measurements applied to Embrapa´s research system: a bootstrap approach.

Neste artigo foi estudado o sistema de produção de pesquisa da Embrapa, a maior instituição brasileira de pesquisa agropecuária. A principal ferramenta teórica usada foi a Análise de Envoltória de Dados – DEA. Exploraram-se a interpretação econômica e as propriedades estatísticas desses modelos, para calcular intervalos de confiança para medidas de eficiência orientadas a output. Tomou-se como base um modelo paramétrico flexível, definido pela distribuição normal truncada. Os intervalos foram calculados por reamostragem. Estes resultados geraram melhores entendimentos sobre as medidas de eficiência e permitiram comparações entre as DMUs envolvidas na análise. O modelo considerou erros de ineficiência e erros aleatórios