Joint estimation of technical efficiency and production risk for multi-output banks under a panel data cost frontier model

This article generalizes production risk from a single output production function to a multiple output cost frontier, which is able to examine input-oriented technical efficiencies and production risk simultaneously in the context of a panel data. Furthermore, the joint confidence interval estimates for technical efficiencies are constructed by means of multiple comparisons with the best approach. Whether taking production risk into account or not offers quite dissimilar implications in terms of the average technical efficiency measure and the identification of multiple efficient banks achieving the optimal cost frontier. It is suggested that inferences drawn on the basis of the confidence intervals of technical efficiency provide much more fruitful and insightful information than the point estimation alone. Bank specific risk parameters are found to be highly and positively correlated with fixed-effect estimates, implying that the more risk-averse a bank is, the more technically efficient it will be.

Confidence statements for efficiency estimates from stochastic frontier models

This paper is an empirical study of the uncertainty associated with technical efficiency estimates from stochastic frontier models. We show how to construct confidence intervals for estimates of technical efficiency levels under different sets of assumptions ranging from the very strong to the relatively weak. We demonstrate empirically how the degree of uncertainty associated with these estimates relates to the strength of the assumptions made and to various features of the data.     

A Review and Empirical Comparison of Bayesian and Classical Approaches to Inference on Efficiency Levels in Stochastic Frontier Models with Panel Data

This paper appliesa large number of models to three previously-analyzed data sets,and compares the point estimates and confidence intervals fortechnical efficiency levels. Classical procedures include multiplecomparisons with the best, based on the fixed effects estimates;a univariate version, marginal comparisons with the best; bootstrappingof the fixed effects estimates; and maximum likelihood givena distributional assumption. Bayesian procedures include a Bayesianversion of the fixed effects model, and various Bayesian modelswith informative priors for efficiencies. We find that fixedeffects models generally perform poorly; there is a large payoffto distributional assumptions for efficiencies. We do not findmuch difference between Bayesian and classical procedures, inthe sense that the classical MLE based on a distributional assumptionfor efficiencies gives results that are rather similar to a Bayesiananalysis with the corresponding prior.     

One-stage estimation of the effects of operational conditions and practices on productive performance: asymptotically normal and efficient,root-<Emphasis Type="Italic">n</Emphasis> consistent StoNEZD method

Understanding the effects of operational conditions and practices on productive efficiency can provide valuable economic and managerial insights. The conventional approach is to use a two-stage method where the efficiency estimates are regressed on contextual variables representing the operational conditions. The main problem of the two-stage approach is that it ignores the correlations between inputs and contextual variables. To address this shortcoming, we build on the recently developed regression interpretation of data envelopment analysis (DEA) to develop a new one-stage semi-nonparametric estimator that combines the nonparametric DEA-style frontier with a regression model of the contextual variables. The new method is referred to as stochastic semi-nonparametric envelopment of z variables data (StoNEZD). The StoNEZD estimator for the contextual variables is shown to be statistically consistent under less restrictive assumptions than those required by the two-stage DEA estimator. Further, the StoNEZD estimator is shown to be unbiased, asymptotically efficient, asymptotically normally distributed, and converge at the standard parametric rate of order n ^−1/2. Therefore, the conventional methods of statistical testing and confidence intervals apply for asymptotic inference. Finite sample performance of the proposed estimators is examined through Monte Carlo simulations.     

Productive Efficiency of the Swine Industry in Hawaii: Stochastic Frontier vs. Data Envelopment Analysis

Improving productive efficiency is an increasingly important determinant of the future of the swine industry in Hawaii. This paper examines the productive efficiency of a sample of swine producers in Hawaii by estimating a stochastic frontier production function and the constant returns to scale (CRS) and variable returns to scale (VRS) output-oriented DEA models. The technical efficiency estimates obtained from the two frontier techniques are compared. The scale properties are also examined under the two approaches. The industry's potential for increasing production through improved efficiency is also discussed.     

Purchasing Power Parity and the Taylor Rule

It is well known that there is a large degree of uncertainty around Rogoff's consensus half‐life of the real exchange rate. To obtain a more efficient estimator, we develop a system method that combines the Taylor rule and a standard exchange rate model to estimate half‐lives. Further, we propose a median unbiased estimator for the system method based on the generalized method of moments with non‐parametric grid bootstrap confidence intervals. Applying the method to real exchange rates of 18 developed countries against the US dollar, we find that most half‐life estimates from the single equation method fall in the range of 3–5 years, with wide confidence intervals that extend to positive infinity. In contrast, the system method yields median‐unbiased estimates that are typically shorter than 1 year, with much sharper 95% confidence intervals. Our Monte Carlo simulation results are consistent with an interpretation of these results that the true half‐lives are short but long half‐life estimates from single‐equation methods are caused by the high degree of uncertainty of these methods. Copyright © 2014 John Wiley & Sons, Ltd.     

Efficiency measurement when producers control pollutants: a non-parametric approach

This paper treats efficiency measurement when some outputs are undesirable and producers control pollutants by end-of-pipe or change-in-process abatement. A data envelopment analysis framework that compares producers with similar pollution control efforts is proposed. First, my approach avoids arbitrary disposability assumptions for undesirable outputs. Second, the model is used to evaluate the interplay between pollution control activities and technical efficiency. I compare my approach to the traditional neo-classical production model that does not incorporate undesirable outputs among outputs, and to Färe et al.’s (Rev Econ Stat 71:90–98, 1989, J Econom 126:469–492, 2005) well-known model that incorporates bads. I evaluate the common assumption in the literature on polluting technologies, that inputs are allocatable to pollution control, and apply U.S. electricity data to illustrate my main point: Although my empirical model specifications are in line with the literature on polluting technologies, they rely on inputs that play an insignificant role in controlling nitrogen oxides (NO_x) emissions. Consequentially, there are no reasons to expect the efficiency scores of the traditional model to differ from the efficiency scores of the other two models that account for resources employed to pollution control. Statistical tests show that my model, which explicitly takes pollution control efforts into account, produces efficiency scores that are not statistically different from the traditional model’s scores for all model specifications, while Färe et al.’s model produces significantly different results for some model specifications. I conclude that the popular production models that incorporate undesirable outputs may not be applicable to all cases involving polluting production and that more emphasis on appropriate empirical specifications is needed.     

Distorted prices and producer efficiency: the case of Romania

This research aims at shedding empirical light on the relative efficiency of small-scale maize producers in Romania. Farmers in transition countries still face heavily distorted price systems resulting from imperfect market conditions and socioeconomic and institutional constraints. To capture such distortions we formulate a stochastic shadow-cost frontier model to investigate the systematic input-specific allocative inefficiency. We further adjust the underlying cost frontier by incorporating shadow price corrections and subsequently reveal evidence on farm specific technical inefficiency. Different models are estimated due to the imposition of curvature correctness and the effects on the individual efficiency estimates are shown. The empirical results show a relative high technical efficiency of the small-scale farmers but relatively poor scores on systematic input price efficiency. The usage of extension services as well as agricultural training on the farm level are found to have a positive effect on the technical efficiency level of the farms. All model specifications further agree on the negative effect on efficiency with respect to the use of insecticides. The imposition of functional concavity on the shadow cost frontier leads to relative differences in the efficiency estimates of up to 240%.      

Small‐scale Inference: Empirical Bayes and Confidence Methods for as Few as a Single Comparison

Empirical Bayes methods of estimating the local false discovery rate (LFDR) by maximum likelihood estimation (MLE), originally developed for large numbers of comparisons, are applied to a single comparison. Specifically, when assuming a lower bound on the mixing proportion of true null hypotheses, the LFDR MLE can yield reliable hypothesis tests and confidence intervals given as few as one comparison. Simulations indicate that constrained LFDR MLEs perform markedly better than conventional methods, both in testing and in confidence intervals, for high values of the mixing proportion, but not for low values. (A decision‐theoretic interpretation of the confidence distribution made those comparisons possible.) In conclusion, the constrained LFDR estimators and the resulting effect‐size interval estimates are not only effective multiple comparison procedures but also they might replace p‐values and confidence intervals more generally. The new methodology is illustrated with the analysis of proteomics data.     

Estimation and decomposition of inefficiency when producers maximize return to the outlay: an application to Norwegian fishing trawlers

This paper deals with estimation of a production technology where endogeneous choice of input and output variables is explicitly recognized. In particular, we assume that producers maximize return to the outlay (RO). For simplicity and tractability we start with a Cobb–Douglas transformation function with multiple inputs and outputs and show how the first-order conditions of RO maximization can be used to derive an estimating equation which is nothing but a partial input productivity equation. This equation does not suffer from the econometric endogeneity problem although the output and input variables are endogenous. First, we consider the case where producers are fully efficient allocatively but technically inefficient. The model is estimated using a single equation stochastic frontier approach. The model is then extended to allow allocative inefficiency and it is estimated as a system using generalized method of moment. Algebraic expressions are derived to decompose the effect of technical and allocative inefficiencies on RO. We also consider translog specifications that are estimated as (1) a single equation frontier model as well as (2) a system. We use a panel of Norwegian fishing trawlers data to estimate the model. Outputs are different species caught while inputs are labor and vessel size. We also control for number of days of operation, age of the vessel and year effects. Empirical results show that the average rate of RO is reduced by about 20 to 30 % due to technical inefficiency. On the other hand, average allocative efficiency is found to be about 78 %. The average overall efficiency is found to be around 60 %.     

Capacity shortfall and efficiency determinants in Brazilian airports: Evidence from bootstrapped DEA estimates

This paper reports on the use of different approaches for assessing efficiency related-issues in 63 major Brazilian airports. Starting out with the bootstrapping technique presented in Simar and Wilson (1998, 2004), several DEA estimates were generated, allowing the use of confidence intervals and bias correction in central estimates to test for significant differences in efficiency levels, returns-to-scale, and input-decreasing/output-increasing potentials. The findings corroborate anecdotal and empirical evidence regarding a capacity shortfall within Brazilian airports, where infrastructure slack is virtually inexistent, regardless of the airport type and location.     

Efficiency analysis of Norwegian district courts

This paper examines the efficiency of the Norwegian district courts with the aim of suggesting ways of improving this efficiency. Pooling the observations for the period 1983 to 1988 efficiency measures are calculated for each court using the nonparametric data envelopment analysis (DEA) method. The results show estimates of overmanning due to technical inefficiency. Comparisons are made between the specialized city courts and the generalized rural courts. When using the yearly observations Malmquist indices are calculated to estimate the rate of productivity change. Finally the paper addresses the questions of how the information from DEA can be used by the courts to become more efficient.The editor of this paper was Knox Lovell.     

Distribution-free Approximate Methods for Constructing Confidence Intervals for Quantiles

Quantile estimation is important for a wide range of applications. While point estimates based on one or two order statistics are common, constructing confidence intervals around them, however, is a more difficult problem. This paper has two goals. First, it surveys the numerous distribution-free methods for constructing approximate confidence intervals for quantiles. These techniques can be divided roughly into four categories: using a pivotal quantity, resampling, interpolation, and empirical likelihood methods. Second, a method based on the pivotal quantity that has received limited attention in the past is extended. Comprehensive simulation studies are used to compare performance across methods. The proposed method is simple and performs similarly to linear interpolation methods and a smoothed empirical likelihood method. While the proposed method has slightly wider interval widths, it can be calculated for more extreme quantiles even when there are few observations.     

Comparison of the h-index for different fields of research using bootstrap methodology

An important disadvantage of the h-index is that typically it cannot take into account the specific field of research of a researcher. Usually sample point estimates of the average and median h-index values for the various fields are reported that are highly variable and dependent of the specific samples and it would be useful to provide confidence intervals of prediction accuracy. In this paper we apply the non-parametric bootstrap technique for constructing confidence intervals for the h-index for different fields of research. In this way no specific assumptions about the distribution of the empirical h-index are required as well as no large samples since that the methodology is based on resampling from the initial sample. The results of the analysis showed important differences between the various fields. The performance of the bootstrap intervals for the mean and median h-index for most fields seems to be rather satisfactory as revealed by the performed simulation.     

Technological progress in the Brazilian agriculture

We analyze technical progress in the Brazilian agriculture in the period 1976–2016. Output is production value and inputs are expenses on land, labor, and other inputs. Data Envelopment Analysis (DEA) measures are used to capture technological progress via efficiency analysis. Filtering the data with a moving average and fractional regression we identify four periods with distinct behavior of technical efficiency under constant or decreasing returns to scale. From 1976 to 1988 and from 1996 to 2007 technical efficiency decreases at statistically significant rates of −1.74% and −0.26% respectively. From 1989 to 1995 and from 2008 to 2016 efficiency increases at statistically significant rates of 1.53% and 1.75% respectively. Efficiency of input use varies in these periods and is computed as a byproduct of the DEA analysis. We observe a more efficient usage of land and other inputs. A parametric fit using adjusted efficient inputs indicate the relative elasticities of 40.5%, 38.5% and 21.0% for other inputs, land and labor, respectively. Technological progress increases 3.73% in the period 1976–1988, decreases 1,58% between 1989 and 1995, increases 0.17% in the period 1996–2007 and increases 1.01% between 2008 and 2016. Technological inputs are statistically significantly influential on total factor productivity as indicated by an infinite distributed lag model. The long run elasticity effect is 39.5%. The effect of investments (public) in agricultural research has a significant elasticity of 0.835.     

Common breaks in means and variances for panel data

This paper establishes the consistency of the estimated common break point in panel data. Consistency is obtainable even when a regime contains a single observation, making it possible to quickly identify the onset of a new regime. We also propose a new framework for developing the limiting distribution for the estimated break point, and show how to construct confidence intervals. The least squares method is used for estimating breaks in means and the quasi-maximum likelihood (QML) method is used to estimate breaks in means and in variances. QML is shown to be more efficient than the least squares even if there is no change in the variances.     

Application of a double bootstrap to investigation of determinants of technical efficiency of farms in Central Europe

The paper provides one of the first applications of the double bootstrap procedure (Simar and Wilson 2007) in a two-stage estimation of the effect of environmental variables on non-parametric estimates of technical efficiency. This procedure enables consistent inference within models explaining efficiency scores, while simultaneously producing standard errors and confidence intervals for these efficiency scores. The application is to 88 livestock and 256 crop farms in the Czech Republic, split into individual and corporate.

Analysis of Hybrid Life-tests in Presence of Competing Risks

The mixture of type-I and type-II censoring schemes, called the hybrid censoring scheme is quite common in life-testing or reliability experiments. In this paper, we consider the competing risks model in presence of hybrid censored data. Under this set up, it is assumed that the item may fail due to various causes and the corresponding lifetime distributions are independent and exponentially distributed with different scale parameters. We obtain the maximum likelihood estimators of the mean life of the different causes and derive their exact distributions. Using the exact distributions, all the moments can be obtained. Asymptotic confidence intervals and two bootstrap confidence intervals are also proposed. Bayes estimates and credible intervals of the unknown parameters are obtained under the assumptions of independent inverted gamma priors of the mean life of the different causes. Different methods have been compared using Monte Carlo simulations. Onereal data set has been analyzed for illustrative purposes. Part of the work was supported by a grant from the Natural Sciences and Engineering Research Council.     

Measuring technical and allocative efficiencies for banks in the transition countries using the Fourier flexible cost function

The transition economies are known to have quite different market structures from the market economies. State-owned banks accounts for a major part of the financial sector in East European countries before the transition period. Since the input prices of the sector are frequently under the control of those governments, the misallocated resources may incur the loss of economic efficiency. This paper attempts to gauge the technical and allocative efficiency using unbalanced panel data of 340 banks from 14 transition countries under the framework of the Fourier flexible shadow cost function. Accommodating technical and allocative efficiencies simultaneously, as suggested by Atkinson and Cornwell (Int Econ Rev 35:231–243, 1994a) and Kumbhakar and Wang (J Econom 134:317–340, 2006a), avoids potential specification errors and leads to consistent parameter estimates. The average total cost savings resulting from greater technical and allocative efficiency are around 28.31 and 7.13%, respectively. Foreign-owned banks are found to be the most economically efficient. The enforcement of financial liberalization does gradually improve upon the sample banks’ technical efficiency. The allocative inefficiency arises from over capitalization and excess funds. Scale diseconomies appear to prevail in the sample states with a few exceptions.     

Estimating farmers’ productive and marketing inefficiency: an application to vegetable producers in Benin

This paper estimates the technical and marketing inefficiency of a sample of urban vegetable producers in Benin. Marketing inefficiency is defined as the failure of farmers to achieve better marketing output and is reflected in lower output price indices. The study proposes a Russell-type measure of inefficiency using a directional distance function that accounts simultaneously for the expansion of outputs and price indices and the contraction of variable inputs. A truncated bootstrap regression is used in the second stage to consistently analyze factors that underlie differences in inefficiency. The first-stage results suggest that vegetable producers are more inefficient with respect to marketing than production. The second-stage results indicate that technical inefficiency is affected by the production environment and private extension services. Marketing inefficiency is affected by the type of marketing arrangements.