Time-varying Technical Inefficiency Research Articles

Farm-heterogeneity and persistent and transient productive efficiencies in Ethiopia’s smallholder cereal farming

Abstract This paper does an empirical comparison of time-invariant and time-varying technical inefficiency measures obtained from an econometric estimation of different panel data stochastic production frontier models. It estimates four panel data specifications of frontier models widely used in empirical applications using a panel dataset from the Ethiopian cereal farming sector. The empirical results show that estimates of both the magnitude and the individual farms’ rankings of persistent and transient productive efficiencies differ considerably across models and based on their agro-ecological zones location. The results further show that the cereal growing farms experience much more transient inefficiency as compared to persistent inefficiency.

On the estimation of technical and allocative efficiency in a panel stochastic production frontier system model: Some new formulations and generalizations

In this paper we propose some alternative formulations and estimation of technical and allocative inefficiency in the presence of some exogenous variables in the context of a panel stochastic frontier model which includes time-invariant firm effects (heterogeneity) along with time-varying technical inefficiency and random noise. These exogenous variables are used to explain technical and allocative inefficiency as well as firm heterogeneity. The presence of these exogenous variables allows us to relax some of the assumptions made in a recent paper by Lai and Kumbhakar (2019). These variables also allow to add flexibility in estimating the model parameters as well as both technical and allocative inefficiency and costs therefrom. More specifically, the incidental parameters problem associated with firm heterogeneity in the production function as well in the first-order conditions of cost minimization can be avoided by parameterizing them in terms of the exogenous variables. We propose and implement model comparison based on Bayes factors and marginal likelihood.

Evaluación de la eficiencia de las universidades estatales colombianas

<p class="subtitulo1">Esta investigación presenta el análisis de la evolución de los niveles de eficiencia técnica de las universidades estatales colombianas. Para ello se toma la información del Sistema de Indicadores de Gestión propuesto por el SUE y el MEN, evaluado en las 32 universidades públicas durante el período 2003-2012. Previo a la estimación de la eficiencia, se obtienen los índices de resultados de capacidad, formación e investigación, a través de los puntajes factoriales del análisis factorial. La estimación de la eficiencia técnica en los índices de resultados de formación e investigación en función del índice de resultados de capacidad, se hace bajo un modelo de fronteras estocásticas de producción con datos panel. El modelo asume los efectos de ineficiencia técnica variantes en el tiempo. La clasificación de las universidades se establece con las predicciones lineales de la regresión cuantíl, en los cuartiles .25, .50 y .75, que definen las universidades con eficiencia alta, media y baja, respectivamente. Los resultados de esta investigación indican que los índices de formación e investigación están determinados positivamente por los recursos financieros, la planta física y la planta profesoral, esta última con mayor influencia. </p>

A “true” random effects stochastic frontier analysis for technical efficiency and heterogeneity: Evidence from manufacturing firms in Ethiopia

This study examines the technical efficiency of the Ethiopian manufacturing sector using establishment-level census panel data over the period of 2000 to 2009. The “true” random effects stochastic frontier model (Greene, 2005a,b), which can disentangle time-varying technical inefficiency from time-invariant unobserved heterogeneity, and the conventional fixed and random effects models are used to estimate efficiency for the aggregated and individual industry groups. The results indicate that efficiency estimates are sensitive to model specifications of firm-specific unobserved heterogeneity. We find a significant gap in efficiency estimates between the “true” random effects model and the fixed and random effects models, which would imply considerable heterogeneity of manufacturing firms in Ethiopia. Our results suggest that firm-specific heterogeneity would be particularly significant in the food and beverages, non-metals, and furniture industries. We also show that the production of the Ethiopian manufacturing sector is largely responsive to changes in intermediate inputs compared to labor and capital inputs. The estimated technical efficiency considerably varies across firms within an industry suggesting a significant potential for improving efficiency in the sector. We discuss that the major problem for the variation in efficiency is the inability of firms to operate at their full production capacity, which was mainly caused by shortage of raw material supply. Generally, it is important to differentiate between inefficiency and unobserved heterogeneity in a stochastic frontier framework when firms operate under diverse social, industrial and environmental conditions.

Estimating Time-Varying Technical Inefficiency for a Panel of Telecommunications Operators: A Distance Function Approach

Estimators of relative efficiency commonly employed for regulatory purposes treat efficiency as fixed over time or constrain its time pattern to a given function. This paper aims to explore empirically the robustness of these assumptions. With this objective, the study compares results from standard panel estimators with estimators that allow for efficiency to vary freely over time. Evidence from a panel of 27 U.S. telecommunications operators over the period 1990 – 2003 shows significant variation of efficiency over time, suggesting that an estimator which constrained such variation could potentially produce misleading results. Results also confirm that standard panel estimators tend to overestimate inefficiency.

Metafrontier frameworks for the study of firm-level efficiencies and technology ratios

This paper uses the concept of a metafrontier to compare the technical efficiencies of firms that may be classified into different groups. The paper presents the basic analytical framework necessary for the definition of a metafrontier, shows how a metafrontier can be estimated using non-parametric and parametric methods, and presents an empirical application using cross-country agricultural sector data. The paper also explores the issues of technological change, time-varying technical inefficiency, multiple outputs, different efficiency orientations, and firm heterogeneity.

Technical Efficiency of Some Selected Manufacturing Industries in Bangladesh: A Stochastic Frontier Analysis

This paper investigates the technical efficiency of selected manufacturing industries of Bangladesh using a stochastic frontier production function approach suggested by Battese and Coelli (1992) applied to panel data. A feasible Cobb-Douglas stochastic frontier production function, which has time-varying technical inefficiency effects, was estimated. Two alternative distributions were used to model the random inefficiency term: a truncated normal distribution and a half-normal distribution. The estimated average technical efficiency for four groups of industries of Bangladesh over the reference period was 40.22% of potential output for the truncated normal distribution, whereas it was 55.57% of potential output for the half-normal distribution.

Stochastic Estimation of Firm Inefficiency Using Distance Functions

Econometric estimation of allocative and technical efficiency has frequently been carried out using a shadow cost function and its associated share or demand equations. Since the problem is formulated in terms of shadow prices, the effect of allocative inefficiency on input usage must be computed indirectly from input share or demand equations. As an alternative approach, we derive and estimate an input shadow distance system comprising the dual shadow input distance function and the price equations derived from the shadow cost minimization problem. Estimated shadow quantities provide direct estimates of the effect of allocative inefficiency on input usage. One can also easily calculate firm- and time-varying technical inefficiency by decomposing the residuals. We also compute returns to scale and the cost savings obtained by eliminating both types of inefficiency. Our approach is illustrated using a panel of U.S. railroads. Both the cost function and the distance function are valid representations of multioutput technologies. The arguments of the cost function are output quantities and input prices, while the arguments of the distance function are input and output quantities. The input distance function measures the extent to which the firm is input efficient in producing a given set of outputs, while the cost function assumes cost minimization by a firm which is input efficient. The shadow cost function is a generalization of the cost function and depends on output quantities and shadow (internal to the firm) input prices rather than actual (market) input prices. Thus, the shadow cost function assumes shadow cost minimization but not actual cost minimization. This function has frequently been employed in fixed-effects estimation of allocative efficiency (AE) and technical efficiency (TE). An observed input vector is technically efficient if it is on the isoquant of the observed output vector. An input vector is allocatively efficient if the radial contraction of the input vector to the isoquant is cost minimizing. Identification of parameters that measure AE can be achieved by the estimation of a shadow cost system comprising the actual cost equation and the share or input demand equations, which are expressed in terms of shadow prices and output quantities. Shadow cost systems have been estimated by Atkinson and Halvorsen (1984), Sickles, Good, and Johnson (1986), Eakin and Kniesner (1988), Kumbhakar (1992), and Atkinson and Cornwell (1994), among others. Given panel data, the deviation of shadow from actual input prices can be measured by price-specific inefficiency parameters that may vary across firms and over time. However, the estimation of the effect of

Separating Technical Change from Time-Varying Technical Inefficiency in the Absence of Distributional Assumptions ∗

This paper proposes an alternative model for separating technical change from time-varying technical inefficiency. The proposed formulation uses the general index, developed by Baltagi and Griffin (1988), to model technical change in the production frontier function and a quadratic function of time, as in Cornwell, Schmidt and Sickles (1990), to capture the temporal pattern of technical inefficiency. In such a setting, all parameters associated with the rate of technical change and the temporal pattern of technical inefficiency are identified separately. Moreover, the proposed formulation is independent of any distributional assumption concerning the one-sided error term associated with technical inefficiency, and it can be estimated in a single stage with non-linear FGLS. Empirical results based on a translog production frontier, and estimates of technical inefficiency and technical change are presented for the UK dairy sector over the period 1982–1992.

Estimating Technological Change Using a Stochastic Frontier Production Function Framework: Evidence from U.S. Firm-Level Data

This paper presents a methodology for estimating an index of technological change using firm-level data in a stochastic frontier production function model that takes into account time-varying technical inefficiency. In contrast to the Solow divisia index approach, econometric estimation of the index with panel data allows the researcher to separate technical progress from the stochastic measurement error. Applying the econometric methodology to a panel of 908 publicly-traded U.S. firms from the COMPUSTAT database, we find evidence of a significant downturn in general technological change for the period, 1970– 1989, whereas the divisia index methodology applied to the same data shows stagnation. When the sample is divided into Manufacturing, Services, and Miscellaneous categories we find that estimates of technological change for the three groups display markedly different stochastic behavior and that the Services group is the source of the downturn.

A Parametric Approach to Efficiency Measurement Using a Flexible Profit Function

Although the idea of measuring productive efficiency goes back to Farrell [8], the econometric estimation of it began with Aigner, Lovell, and Schmidt [1] and Meeusen and van den Broeck [17]. In Aigner, Lovell, and Schmidt [1] and Meeusen and van den Broeck [17], and their extensions which incorporate either cost minimizing or profit maximizing behavior, efficiency is measured relative to a frontier. An alternative to the frontier approach that started with Hopper [10] and Lau and Yotopoulos [15], is to measure efficiency (mostly allocative) without estimating any frontier. In the latter approach, allocative inefficiency (defined as the deviations of the first order conditions of profit maximization or cost minimization) is modeled through shadow (virtual) prices which are parametric functions of observed prices. The shadow price approach is extensively used in the literature on efficiency measurement because it has an advantage over the frontier approach, which requires distributional assumptions on the error terms-especially in cross sectional models. In empirical studies on the efficiency of regulated utilities, it has been argued that due to the presence of rate of return and other regulations, these firms optimize with respect to shadow prices instead of observed prices. These studies often fail to consider the possibility that the firms under question may be technically inefficient as well, which can affect allocation of inputs.1 This issue is important in both cost minimization and profit maximization cases. This paper uses a profit maximization framework and develops a generalized profit function approach that accommodates both technical and allocative inefficiencies in the context of a panel data model. The relationship between production technical inefficiency (loss of output due to technical inefficiency) and profit technical inefficiency (profit loss due to technical inefficiency) is derived for a flexible production function. Presently, it is believed that the derivation of this relationship is only possible for the self-dual production functions. We show that if the profit function is translog, some popular approaches used in modeling technical and allocative inefficiency are incorrect. In particular, we show that: (i) models that fail to include technical inefficiency and consider only allocative inefficiency yield biased and inconsistent parameter estimates; (ii) if technical inefficiency is neglected, the measure of technical change will be biased. The generalized profit function developed here is capable of distinguishing between technical change and time-varying technical inefficiency. Such a distinction is not possible if, for example, one uses

Production frontiers, panel data, and time-varying technical inefficiency

This paper uses a panel-data framework and models firm-specific technical inefficiency which is allowed to vary over time. The specification is flexible enough to accommodate increasing, decreasing, and time-invariant behavior of technical inefficiency. Time-varying firm- and input-specific allocative inefficiency is also incorporated. The estimation method suggested uses a parametric production function and cost-minimization hypothesis.