Externalities, efficiency, regulation, and productivity growth in the U.S. electric utility industry

This paper examines the decomposition of total factor productivity growth for firms subject to regulation, given the production of a bad output. The production of good and bad outputs provides benefits and costs to society. Corporate socially responsible firms recognize the cost to society of producing the bad output. The paper separates the production technology and regulation effects from both the scale and technical change components. The paper also examines the measurement and decomposition of productivity growth when not accounting for production of the bad output. Using a 1992–2000 panel of 34 U.S. investor-owned electric utilities, results indicate that improvements in the scale, efficiency change, and technical change components contributed to positive growth. Not accounting for production of the bad output led to, on average, an overestimation of both the rate of productivity growth, and the contributions of scale economies and technical change to changes in productivity growth.

Do we estimate an input or an output distance function? An application of the mixture approach to European railways

In this paper, we estimate parametric input and output distance functions and discuss how to estimate a mixture/latent class model (LCM) involving the output and input distance functions in the context of multi-input and multi-output production technology. The proposed technique is applied to a panel data on European Railways (1971–1994). This model allows us to identify determinants of the efficiency orientation, thereby providing useful information that can help researchers to choose between the input and the output-oriented approaches. In addition, we develop cross-indices that can be used to compute input (output) technical inefficiency from the estimates of output (input) distance function.

Investment and dynamic DEA

A dynamic version of data envelopment analysis (DEA) is developed in the present paper. Our model introduces investment in traditional DEA and imposes intertemporal cost minimization. Adding an intertemporal adjustment constraint into the cost minimization problem, we derive the relation between the DEA variables of the variable cost function and those of the primary production frontiers’ coefficients. The augmented DEA model can be solved using standard linear programming. This dynamic framework enables computing the production frontiers, measuring the productive efficiencies and evaluating the potential economies all in the presence of adjustment costs.

On measures of technical inefficiency and production uncertainty in stochastic frontier production model with correlated error components

Analysis of the behavior of technical inefficiency with respect to parameters and variables of a stochastic frontier model is a neglected area of research in frontier literature. An attempt in this direction, however, has recently been made. It has been shown that in a “standard” stochastic frontier model that both the firm level technical inefficiency and the production uncertainty are monotonically decreasing with observational error. In this paper we show, considering a stochastic frontier model whose error components are jointly distributed as truncated bivariate normal, that this property holds if and only if the distribution of observational error is negatively skewed. We also derive a necessary and sufficient condition under which both firm level technical inefficiency and production uncertainty are monotonically increasing with noise-inefficiency correlation. We next propose a new measure of the industry level production uncertainty and establish the necessary and sufficient condition for firm level technical inefficiency and production uncertainty to be monotonically increasing with industry level production uncertainty. We also study the limiting probabilistic behavior of these conditions under different parametric configuration of our model. Finally we carry out Monte Carlo simulations to study the sample behavior of the population monotonic property of the firm level technical inefficiency and production uncertainty in our model.

Technical efficiency in farming: a meta-regression analysis

A meta-regression analysis including 167 farm level technical efficiency (TE) studies of developing and developed countries was undertaken. The econometric results suggest that stochastic frontier models generate lower mean TE (MTE) estimates than non-parametric deterministic models, while parametric deterministic frontier models yield lower estimates than the stochastic approach. The primal approach is the most common technological representation. In addition, frontier models based on cross-sectional data produce lower estimates than those based on panel data whereas the relationship between functional form and MTE is inconclusive. On average, studies for animal production show a higher MTE than crop farming. The results also suggest that the studies for countries in Western Europe and Oceania present, on average, the highest levels of MTE among all regions after accounting for various methodological features. In contrast, studies for Eastern European countries exhibit the lowest estimate followed by those from Asian, African, Latin American, and North American countries. Additional analysis reveals that MTEs are positively and significantly related to the average income of the countries in the data set but this pattern is broken by the upper middle income group which displays the lowest MTE.

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.

Cost efficiency of Slovenian water distribution utilities: an application of stochastic frontier methods

This study estimates cost inefficiency and economies of scale of Slovenian water distribution utilities over the 1997–2003 period by employing several different stochastic frontier methods. The results indicate that significant cost inefficiencies are present in the utilities. An introduction of incentive-based price regulation scheme might help resolve this problem. However, the inefficiency scores obtained from different cost frontier models are not found to be robust. The levels of inefficiency estimates as well as the rankings depend on the econometric specification of the model. The established lack of robustness can be at least partly explained by different ability of the models to separate unobserved heterogeneity from inefficiency. Newly proposed true fixed effects model (Greene, J Econom 126:269–303, 2005; J Prod Anal 23(1):7–32, 2005) appears to perform better than the conventional panel data models with respect to distinguishing between unobserved heterogeneity and inefficiency. On the other hand, different models produce fairly robust results with respect to estimates of economies of output density, customer density and economies of scale. The optimal size of a company is found to closely corresponds to the sample median. Economies of scale are found in small-sized utilities, while large companies exhibit diseconomies of scale.

Assessing multi-dimensional performance: environmental and economic outcomes

This study examines economic performance, environmental performance, and regulatory activity for plants in three industries: pulp and paper, oil, and steel. Stochastic frontier production function models show significant deviations from production efficiency. Older plants are less efficient in production, but perform no worse on emissions. Plants spending more on pollution abatement tend to do worse on both production efficiency and emissions. Stricter local regulatory pressure is associated with somewhat lower emissions, but has mixed effects on production efficiency. Positive correlations between SUR residuals for emissions and production efficiency suggest unmeasured plant-level characteristics that drive both economic and environmental performance.

Stochastic frontier models with multiple time-varying individual effects

This paper proposes a flexible time-varying stochastic frontier model. Similarly to Lee and Schmidt [1993, In: Fried H, Lovell CAK, Schmidt S (eds) The measurement of productive efficiency: techniques and applications. Oxford University Press, Oxford], we assume that individual firms’ technical inefficiencies vary over time. However, the model, which we call the “multiple time-varying individual effects” model, is more general in that it allows multiple factors determining firm-specific time-varying technical inefficiencies. This allows the temporal pattern of inefficiency to vary over firms. The number of such factors can be consistently estimated. The model is applied to data on Indonesian rice farms, and the changes in the efficiency rankings of farms over time demonstrate the model’s flexibility.

Model selection in stochastic frontier analysis with an application to maize production in Kenya

This paper shows how to compute the standard errors for partial effects of exogenous firm characteristics influencing firm inefficiency under a range of popular stochastic frontier model specifications. We also develop an R²-type measure to summarize the overall explanatory power of the exogenous factors on firm inefficiency. The paper also applies a recently developed model selection procedure to choose among alternative stochastic frontier specifications using data from household maize production in Kenya. The magnitude of estimated partial effects of exogenous household characteristics on inefficiency turns out to be very sensitive to model specification, and the model selection procedure leads to an unambiguous choice of best model. We propose a bootstrapping procedure to evaluate the size and power of the model selection procedure. The empirical application also provides further evidence on how household characteristics influence technical inefficiency in maize production in developing countries.

Measuring performance in the presence of stochastic demand for hospital services: an analysis of Belgian general care hospitals

Since demand for hospital services is subject to substantial variability, the relationship between uncertain demand, excess capacity, hospital costs and performance should be investigated thoroughly. In this paper a waiting time indicator to proxy hospital standby capacity is incorporated into a multi-product translog cost function for Belgian general care hospitals. The indicator is derived from queuing theory and improves on the conventionally used (inverse of the) occupancy rate. The multi-product stochastic frontier specification allows calculation of cost elasticities and marginal cost of seven hospital departments, as well as the degree of economies of scale and scope and enables identification of differences in efficiency.

Debreu’s coefficient of resource utilization, the Solow residual, and TFP: the connection by Leontief preferences

Debreu’s coefficient of resource utilization is freed from individual data requirements. The procedure is shown to be equivalent to the imposition of Leontief preferences. The rate of growth of the modified Debreu coefficient and the Solow residual are shown to add up to TFP growth. This decomposition is the neoclassical counterpart to the frontier analytic decomposition of productivity growth into technical change and efficiency change. The terms can now be broken down by sector as well as by factor input.

The productivity performance of Irish dairy farms 1984–2000: a multiple output distance function approach

This study measures productivity growth on Irish dairy farms over the period 1984–2000. A total factor productivity index is constructed for the dairy system and is decomposed into technical change, efficiency change, and changes in scale efficiency. This is achieved by estimating a stochastic output distance function model of the production technology in use on Irish dairy farms. Overall, productivity on Irish dairy farms grew by 1.2% per annum over the sample period.

Efficiency vectors, efficiency and performance measures: new methods for ranking efficient organizational units

We present and discuss measures for analyzing productivity in deterministic frontier models. A new efficiency measure is introduced allowing for discrimination among efficient organizational units. In addition, a new performance measure for analyzing productivity of organizational units is presented. This measure fulfills various properties of efficiency measures but relaxes the indicator property. Both new approaches are based on the development of efficiency vectors which is a new vector measure for measuring efficiency. The vector components are efficiency measures related to subsets of a production possibility set. The new approaches are applied in the context of data envelopment analysis.

Estimating DEA technical and allocative inefficiency using aggregate cost or revenue data

In this paper, we address the question of Data Envelopment Analysis (DEA) evaluation of efficiency when aggregate cost or revenue data must be used. We show that the DEA technical inefficiency measure using total revenues as the single output variable or total costs as the single input variable equals the aggregate technical and allocative inefficiency. We employ this result to estimate allocative inefficiency and construct statistical tests of the null hypothesis of no allocative inefficiency analogous to those of the null hypothesis of no scale inefficiency. We illustrate our method using revenue and personnel data for the top U.S. public accounting firms over 1995–1998. Our empirical results indicate the existence of statistically significant allocative inefficiency in the public accounting industry.

The efficiency of Spanish arable crop organic farms,a local maximum likelihood approach

The methodologies that have been used in existing research to assess the efficiency with which organic farms are operating are generally based either on the stochastic frontier methodology or on a deterministic non-parametric approach. Recently, Kumbhakar et al. (J Econom 137:1–27, 2007) proposed a new nonparametric, stochastic method based on the local maximum likelihood principle. We use this methodology to compare the efficiency ratings of organic and conventional arable crop farms in the Spanish region of Andalucía. Nonparametrically encompassing the stochastic frontier model is especially useful when comparing the performance of two groups that are likely to be characterized by different production technologies.

Is competition really bad news for cooperatives? Some empirical evidence for Italian producers’ cooperatives

In this paper, we analyse the nature of the relationship between market power and technical efficiency for producers’ cooperatives. More specifically we test two hypotheses: first, we evaluate the extent to which increasing market pressure may help producers’ cooperatives to improve technical efficiency to guarantee positive profits; second, we test whether higher technical efficiency induces producers’ cooperatives to have a larger market share. These hypotheses are tested on a sample of Italian conventional and cooperative firms for the Wine Production and Processing sector, using both frontier analysis and dynamic panel techniques. The results support the hypothesis that increasing market pressure can affect positively the cooperatives′ efficiency, while gains in technical efficiency do not seem to have any impact on the cooperatives’ market share.

Flexible mixture modelling of stochastic frontiers

This paper introduces new and flexible classes of inefficiency distributions for stochastic frontier models. We consider both generalized gamma distributions and mixtures of generalized gamma distributions. These classes cover many interesting cases and accommodate both positively and negatively skewed composed error distributions. Bayesian methods allow for useful inference with carefully chosen prior distributions. We recommend a two-component mixture model where a sensible amount of structure is imposed through the prior to distinguish the components, which are given an economic interpretation. This setting allows for efficiencies to depend on firm characteristics, through the probability of belonging to either component. Issues of label-switching and separate identification of both the measurement and inefficiency errors are also examined. Inference methods through MCMC with partial centring are outlined and used to analyse both simulated and real data. An illustration using hospital cost data is discussed in some detail.

The cost implications of waste reduction: factor demand, competitiveness and policy implications

Manufacturing produces both good and “bad” outputs, such as waste, which have negative environmental effects. Economic (e.g., tax) and non-economic (e.g., reputation) incentives encourage firms to reduce waste. However, such practices are costly because decreases in output produced or increases in inputs used may accompany waste reduction. We employ a cost function approach to evaluate patterns of output and waste production and capital, labor, and materials use, for UK manufacturing plants. We find that costs of waste reduction generally imply increasing materials use and capital and labor input saving, but vary by county, region, and industry.

Incorporating correlation regimes in an integrated stressed risk modeling process

The practice of using stress tests to complement Value at Risk (VaR) estimates suffers from some limitations such as the lack of coherence between a statistical risk measure and a subjective one. On the other hand there is a wide consensus that using the same correlation matrix to design various stress tests is not likely to provide an accurate representation of relationship amongst risk factors in periods of market stress. In this paper we introduce a solution to these problems by explicitly considering different correlation regimes and incorporating the result of the stress test to the traditional market risk measurement models.