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In this paper, we consider a simultaneous modeling of good and bad outputs. We use an input distance function (IDF) with endogenous inputs as well as endogenous bad outputs, which is novel in the literature. Moreover, we model input efficiency to depend on the production of bad outputs which allows us to investigate whether emissions of pollutants (bad outputs) are related to technological performance (technical efficiency). We also model production of each bad output with a spatial structure separately, each depending on production of good outputs, inputs and other exogenous variables. These bad output production functions allow us to estimate both direct and indirect effects of good output on the production of bad outputs, which may be of special interest because they show the cost (to the society) in terms of releasing pollutants to the environment in order to increase production of good outputs. We apply the new technique to a data set on U.S. electric utilities with four bad outputs, three inputs and two good outputs. We used a Bayesian technique to estimate the model which is a system consisting of the input distance function, reduced form equations for each input, dynamics of inefficiency and bad output production technology—separately for each. Empirically, bad outputs are found to affect inefficiency positively. Percentage increases in inefficiency due to a percentage increase in each bad output are found to vary from 0.225% to 0.42%. Energy prices are found to be positively related to inefficiency. From the spatial specifications of bad outputs, we find that the spillover effects of increasing production of good outputs account for the majority of the total effect, indicating that neighborhood effects are more important than own effects. This means, the neighboring utilities played a crucial role indicating “contagion” of practices.

We model productivity and inefficiency jointly, instead of modeling and estimating either only productivity or only inefficiency with many variable and quasi‐fixed inputs. In the first model, we use a multi‐step procedure. We use the proxy variable method based on the first‐order condition (FOC) of expected profit maximization with respect to the single variable input to take care of the endogeneity problem arising from both productivity and inefficiency. To separate mean inefficiency from mean productivity we assume them nonparametric functions of different sets of exogenous variables. In the second model, we consider a novel system consisting of the production function and the FOCs of expected profit maximization for the multiple variable inputs. Distributional assumptions are made on all the random errors associated with the production function, the FOCs, productivity, and inefficiency functions in the second model. We use the Colombian food manufacturing data as an application of our model.

In this paper, we introduce a new model to estimate efficiency by generalizing the state-of-the-art panel stochastic frontier model, the salient feature of which is decomposition of inefficiency into a persistent and a transient component. The proposed model introduces an autoregressive process to allow for temporal dependence in transient inefficiency. Both firm heterogeneity and persistent inefficiency components are allowed to be correlated with some exogenous and endogenous covariates in the model. Our model solves the endogeneity problem and it also introduces determinants of both persistent and transient inefficiency. Since the transient component is autoregressive, the likelihood function is not available in closed form. To address this problem we use the Maximum Simulated Likelihood and (Simulated or Bayes) Generalized Method of Moments method to estimate the parameters and several other quantities of interest, including transient and persistent inefficiency. Since the model is dynamic and accommodates determinants of inefficiency, it is useful to production managers who wish to identify how much of their present inefficiency is affected by past inefficiency, as well as how and in what ways efficiency can be improved. We use Norwegian electricity distribution data to showcase an application of our model.

In this paper we use an innovative and nonstandard approach to model and estimate markups and market power. The approach uses a regression framework with determinants as well as a random component. We use this innovative tool to investigate the level of market power in Norwegian industries and regions. Norway is an interesting case study because in Norway prices on most consumer goods and services are higher than in similar countries. Given that many studies show substantial and an increasing trend in markup, it is naturally interesting to investigate the market power in different industries and different regions in Norway. We use an unbalanced panel collected by The Norwegian Tax Administration for the period 2000–2018 to address this issue. We find low and non-increasing market power in Norway, which is different from other countries. Further, we find that market powers decrease with firm-size, increase with geographical industrial concentration and decrease with rural location.

The panel data on World Health Organization (WHO) member countries were utilized to estimate efficiency of world health systems. The results revealed that efficiency rankings for health services of countries differ substantially depending on whether the goal of healthcare provision is output maximization or health cost minimization.

A new technique is proposed for deriving the risk preference function under production risk and expected utility of profit maximization. The derivation depends on neither a specific parametric form of the utility function nor any distribution of the error term representing production risk. The proposed risk preference function is flexible enough to test different types of risk behavior and symmetry of the output distribution. Furthermore, our production risk specification allows for inputs with positive and negative marginal risk. The econometric model accommodates production risk, risk preferences and firm heterogeneity simultaneously. Norwegian salmon farming data are used as an application. Keywords: Production risk; risk aversion; heterogeneity; joint estimation; salmon aquaculture

A comparison is made of the different approaches used in the approximation of production efficiency such as deterministic, stochastic, parametric and non-parametric. The models are used to evaluate the time-varying technical efficiency of 15 Colombian cement firms from the period of 1968 to 1988. Results indicate the time structure of efficiency to differ significantly across the models.

I. INTRODUCTION It is important for a firm, no matter what it produces, to use the inputs in such a way that the cost of producing a given level of [...]

The use of two panel data models for measuring technical change is considered. Specifically, the time trend model and general index model were applied to approximate technical change in Swedish Social Insurance Offices between 1974 and 1987 on the basis of 366 observations annually. The models, which decomposed technical change into its 'pure and non-neutral components,' employed a single-input requirement function with four outputs.

We evaluate the efficiency of electricity distribution operators (DSOs) as providers of local public infrastructure. In particular, we consider two types of efficiency, i.e., short-term (transient) and long-term (persistent). We apply the recently developed four-component stochastic frontier model, which allows identifying determinants of the two types of efficiency, after controlling for firm heterogeneity and random noise, to a panel dataset of German DSOs observed during 2006-2012.Those DSOs operating in the eastern parts of Germany have undergone a profound restructuring after the reunification in 1990. We find that this was beneficial for their efficiency as they perform, on average, better in terms of persistent efficiency than DSOs in West Germany. Both eastern and western DSOs perform similarly well in terms of transient efficiency, which is expected as the sector is highly regulated. As such, we provide new insights on identifying the nature and sources of public infrastructure productive inefficiency, which is relevant for public policies.

We estimate firm productivity and markups in the presence of heterogeneous output and input prices. We assume that firms are monopolistically competitive in the output market and that state firms are favored and pay lower prices for inputs due to political connections. The proxy‐variable (or called the control‐function‐based) approach of structurally identifying production functions and productivity is customized to our new setting. Our approach solves the underidentification problem inherent in the standard proxy‐variable method. We investigate the Chinese transportation equipment industry and find evidence against perfect competition and price homogeneity. We also find that productivity and markup differentials and dynamics are consistent with the market‐oriented reforms in China.

Numerous studies have focused on estimating technical inefficiency in electricity distribution firms. However, most of these studies did not distinguish between persistent and transient technical inefficiency. Furthermore, almost none of the studies estimated the cost of input misallocation arising from non-optimal use of inputs. One reason is that the cost function (input distance function) typically used in the literature does not allow for the separation of technical inefficiency and allocative inefficiency. In this study, we estimate both the persistent and transient components of technical inefficiency and input misallocation of Norwegian electricity distribution firms, using panel data from 2000 to 2016. Our modeling and estimation strategy is to use a system approach, consisting of the production function and the first-order conditions of cost minimization. Input misallocation for each pair of inputs is modeled via the first-order conditions of cost minimization. We also estimate the costs of each component of technical inefficiency and input misallocation by deriving the cost function for a multi-output separable production technology. Our modeling and estimation strategy handles endogeneity of inputs. Finally, we allow for inclusion of determinants of persistent and transient technical inefficiency. Our results show that the costs of input misallocation of Norwegian electricity distribution firms are non-negligible.

In this paper, we examine the effect of infrastructure financing (public expenditure) and urbanization on productivity using a new concept. We call this concept Urban Kuznets curve (UKC) and define it as an inverted U-shaped relationship between per capita GDP and urbanization. We examine the UKC relationship from several angles. First, we explore its existence from a data driven approach (the scatter plot of log per capita GDP against urbanization). We then examine it after controlling for the input factors and a time trend. The first approach is ad hoc because no formal economic model is used. The second approach is grounded in the microeconomic theory with state-of-the-art econometric tools instead of using some popular ad hoc regressions where (log) per capita GDP is regressed on public expenditure and urbanization along with some other controls. In our pursuit of understanding the various channels through which urbanization may affect productivity, we use parametric and semiparametric partially linear econometric approaches. In the parametric approach we treat urbanization in the same way as inputs (capital, labor, etc.) and compute optimum levels of urbanization to examine whether the benefits of urbanization are exhausted for any of the Chinese provinces during our study period. In the semiparametric partially linear models, the UKC is detected from the estimated elasticity of urbanization without calculating the optimal levels of urbanization. For this study we use the Chinese province level panel data over the period 2005–2014 from three sectors, viz., agriculture, manufacturing, and services.

In this paper we estimate the short-run, long-run and overall efficiency of Norwegian electricity distribution companies for the period 2000-2013 controlling for both noise and company effects. Short-run inefficiency is the part of inefficiency that is allowed to adjust freely over time for each company, but long-run (persistent) inefficiency remains constant over time, although it is allowed to vary across companies. For robustness check we also consider two additional models in which either company effects are not controlled or these are treated as inefficiency. The production technology is represented by a translog input distance function in all three models. We find that technical change and returns to scale are quite robust across the models. However, the efficiency scores across the three models we consider are not correlated strongly. We conclude that the regulators and practitioners should take extra caution in using the proper model in practice, especially when the efficiency measures are used to reward/punish companies through incentives for better performance.

Six competing parametric models are used to determine whether similarities in the measures of total factor productivity growth, technical change, returns to scale, input elasticities and input and scale biases are present among these models. The models are used to estimate technical change and productivity in the Swedish cement industry over the period 1955-1979. The empirical results turn out to be model dependent and often conflicting.

A general assumption of the standard hedonic price model is that producers produce outputs/ services efficiently, and deviations from this situation are assumed to involve either overcharging or undercharging. Using the profit maximising behaviour, we derive an alternative hedonic model to show when prices are overcharged for services or products with the same characteristics. We argue that price charged by a firm depends on the price charged by the neighbouring firms giving rise to spatial dependence in prices. Based on this philosophy, we present a spatial-lag frontier hedonic pricing model. This new modelling framework is illustrated using data from Norwegian ski resorts in the winter season of 2014/ 2015 to examine whether one-day ski lift ticket prices are interrelated with other operators and to what extent the ski lift tickets are overcharged, if any.

This paper primarily reviews the panel stochastic frontier (SF) model in which the composite error term 𝜀ithas four components, i.e. 𝜀it=ti-?i+vit-uit,where ?iand uitare the persistent and transient inefficiency components, tiis the random firm effect and vitis the random noise. We discuss a variety of model specifications of the four-component SF panel model and estimation approaches developed mostly during the period 2014 to 2021. While discussing these models, we focus on the following issues: heterogeneity, endogeneity of inputs/output(s), inclusion of determinants of inefficiencies via their variances and dependence among the error components. Additionally, we examined models that introduce allocative inefficiency. Models that use both multi-step and system approaches to model allocative inefficiency are also examined.

Abstract The short-term relationships between the supply of farmed salmon and its market and biological determinants are not fully understood. In this article an econometric model of salmon supply is estimated exploiting monthly data on Norwegian salmon aquaculture. Our estimates indicate that supply has shifted over time due to innovations in several areas. We find that the price of farmed salmon has a limited effect on supplied quantity, giving a highly inelastic short-run supply elasticity. The biomass and seasonal factors are the main determinants of shifts in salmon supply in the short term.

Is public expenditure productive? Is there a shortfall or excess in public capital investment? We address these old issues in the light of new econometric tools. It is argued that the Cobb‐Douglas specification that ignores nonlinearity inherent in the functional relationship of the production technology causes incorrect estimates of input elasticities. To avoid possible model misspecification, we use Li‐Racine generalized kernel estimation. This procedure is used to estimate the returns to private capital, employment, and public capital in gross state product from a panel of 48 states for 17 years. In contrast to previous studies, we find that the return to public capital is positive and significantly different from zero.

The distinguishing feature of the study is in using a globally flexible functional form that permits one to calculate different types of elasticities under both constant and variable output hypotheses. The Symmetric Generalized McFadden cost function alongwith the output supply condition form the basis of the econometric model. To measure inputsubstitutabilities, we used Alien-Uzawa, Morishima, and Shadow elasticities of substitution. Empirical results, based on 300 farm households from West Bengal, India, show that fertilizer is most price sensitive input. It is a gross substitute for manure and human labor but complement to bullock labor. Manure is a gross substitute for all the inputs whereas human labor is gross complement to bullock labor. Using the Morishima measure we find that the fertilizer and bullock labor are complements when the price of the latterchanges. Similar complementary relationship is found between bullock and human labor due to changes in the price of human labor. All other inputs are Morishima substitutes. The Shadow elasticity of substitution estimates indicate that all factors are substitutes. The estimate of returns to scale indicates the presence of diminishing returns to scale.

This paper deals with modeling firm-specific technical change (TC), and technological biases (inputs and scale) in estimating total factor productivity (TFP) growth. Several dual parametric econometric models are used for this purpose. We examine robustness of TFP growth and TC among competing models. These models include the traditional time trend (TT) model and the general index (GI) model. The TT and the GI models are generalized to accommodate firm-specific TC and technological bias (in inputs and output). Both nested and non-nested tests are used to select the appropriate models. Firm-level panel data from the Japanese chemical industry during 1968- 1987 is used as an application.

This paper addresses estimation and decomposition of productivity change, which is mostly identified as technical change under constant (unitary) returns to scale (CRS). If the CRS assumption is not made, productivity change is decomposed into technical change and scale effects.Furthermore, if inefficiency exists, it also contributes to productivity change. Here we decompose productivity change into efficiency change, technical change, and scale effects. Three alternative approaches using parametric production, cost, and profit functions, which differ in terms of behavioral assumptions on the producers and data requirements, are considered.

This paper deals with the estimation of technical efficiency using pseudo panel data constructed from rotating panel data on Swedish crop farmers where each unit is observed for a maximum of four years. Farms are grouped into 89 cohorts, using their common time‐invariant characteristics, and the cohorts are used as an individual unit in the empirical analysis. The overall technical inefficiency is decomposed into a persistent and a residual component. An alternative model with time‐varying technical inefficiency is also considered. In addition to technical inefficiency, measures of returns to scale and technical change are also obtained.

This paper is concerned with the measurement of total factor productivity(TFP) growth and technical change in Swedish manufacturing industries during the period 1964-1989. Two alternative formulations of technical change, viz., the time trend model and the general index model are used for measuring TFP growth and technical change. Measures of these are embedded in a cost minimization framework where we estimate the translog cost function along with the associated cost share equations. The cost function accommodates industry-specific variability through an error component model. The models are estimated using the iterative seemingly unrelated regression method after some transformations to the cost function. Empirical results show that the general index model is superior to the time trend model when the pattern of technical change and total factor productivity growth are examined.

This paper deals with the estimation of efficiency. Efficiency is measured relative to the cost frontier which is defined as C*(W, Y) = minX{W'X|f(X) ≥ Y, X≥ 0} where Wis the vector of input prices, Xis the vector of inputs, Yis the actual output produced and f() is a function of the production technology. The condition f(X) ≥ Y, X≥ 0 implies that the maximum possible output, given X≥ 0, can never be less than the actual output. A firm is defined as cost inefficient if its actual cost exceeds the minimum cost given by the cost frontier, which is to be estimated by using data on actual costs, output and input prices. We first consider a panel data model where cost inefficiency is decomposed into a permanent component and a residual component. The model can be viewed as a one‐way error component model (ECM) with an additionalerror term introduced to capture the residual component of cost inefficiency. Second, the ECM is generalized to accommodate firm‐specific variances for the permanent inefficiency component. Finally, we propose a multistep procedure to estimate the cost function parameters and to predict cost inefficiency. The model is applied to examine efficiency in the production of electricity by 10 major investor‐owned electricity utilities in Texas during 1966–85. The production technology is represented by a cost function that is assumed to be the same for every utility (except for heterogeneous intercepts), but it is allowed to shift over time owing to changes in technology. Empirically we find evidence for permanent and residual cost inefficiencies. Furthermore, the data support the ECM with firm‐specific variances and rejects the homoscedastic ECM. Cost inefficiency is found to vary widely across utilities, ranging from 8% to 36% with a mean of 27%. Technical change, defined as the rate of cost diminution over time with everything else unchanged, is found to be quite small (less than ±1%), especially during 1971–85.

This paper develops a model to examine labor-use efficiency associated with the effieciency wage theory and technical effieciency associated with the frontier production model. The generalixed modelcombines these two approaches and derives measures of technical and labor-use efficiency as well as their effects on productivity growth. It also seperates technical inefficiency from industry heterogenity. the model is applied to estimate loss of output due to technical and labor-use inefficiency for 18 two-digit U.S manufacturing industries using time series and cross-sectional data.

This paper uses a flexible (translog) production function to estimate efficiency of 227 farms from West Bengal, India. We consider estimation of technical and allocative inefficiencies using a profit maximising framework which accommodates both endogenous and exogenous inputs. The maximum likelihood method of estimation developed in this paper is based on the production function and the first‐order conditions of profit maximisation. Farm‐specific technical and allocative inefficiencies are also estimated. Empirical results show that the mean level of technical efficiency is 75.46% while the best farm is 85.87% efficient (technically). So far as allocative efficiency is concerned the majority of the farms are found to be under‐users of the endogenous inputs, viz., fertiliser, manure, human and bullock labour.

It is formally proven that if inefficiency (u) is modelled through its variance, considered as a function of exogenous variables z, then the marginal effects of zon technical inefficiency (TI) and technical efficiency (TE) have opposite signs in the typical setup with a normally distributed random error and an exponentially or half-normally distributed u. This is true for both conditional and unconditional TIand TE. An example is provided to show that the signs of the marginal effects of zon TIand TEmay coincide for some ranges of z. If the real data comes from a bimodal distribution of u, and a model is estimated with an exponential or half-normal distribution for u, the estimated efficiency and the marginal effect of zon TEcould be wrong. Moreover, the rank correlations between the true and the estimated values of TEcould be small and even negative for some subsamples of the data. This is a warning that in the case when the true (real life) distribution of the inefficiency is bimodal, commonly used standard SFA models could lead to wrong policy recommendations. The kernel density plot of the residuals is suggested as a diagnostic plot. The results are illustrated by simulations.

We characterize the types of interactions between foreign direct investment (FDI) and economic growth, and analyze the effect of institutional quality on such interactions. To do this analysis, we develop a class of instrument-based semiparametric system of simultaneous equations estimators for panel data and prove that our estimators are consistent and asymptotically normal. Our new methodological tool suggests that across developed and developing economies, causal, heterogeneous symbiosis and commensalism are the most dominant types of interactions between FDI and economic growth. Higher institutional quality facilitates, impedes or has no effect on the interactions between FDI and economic growth.