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This paper aims to analyze environmental Total Factor Productivity (TFP) change. Indeed, innovative environmental TFP measures are introduced through convex and non convex environmental production processes. Hence, the impacts of input and output quality change on environmental productivity variation are underscored. In addition, general decomposition of the new ratio- and difference-based environmental TFP measures is proposed. Finally, an empirical example is provided to illustrate these propositions.

This paper provides diagnostic tools for examining the role of influential observations in Data Envelopment Analysis (DEA) applications. Observations may be prioritized for further scrutiny to see if they are contaminated by data errors; this prioritization is important in situations where data-checking is costly and resources are limited. Several empirical examples are provided using data from previously published studies.

The result by Morrison (1985) and Morrison and Schwarzt (1994) that there is an one-to-one relationship between the rate of economic capacity utilization and ratio of cost elasticities at the temporary and the full equilibrium has been instrumental in past studies on measuring economic capacity utilization and on adjusting indexes of productivity growth for temporary equilibrium. In this paper, dynamic duality and comparative dynamics are employed to assess the behavior of cost elasticity along an optimal path to the steady state. The analysis suggests that no a priori relationship exists between economic capacity utilization and elasticity of cost. The absence of an a priori relationship between these two economic variables implies that theoretical and empirical results based on past notions about the dynamic behavior of cost elasticity may have to be reconsidered.

This paper estimates the determinants of cost inefficiency of several publicly operated passenger-bus transportation companies in India in terms of their ownership structure as well as other firm-specific characteristics. A panel data on publicly operated passenger-bus transportation companies is used to estimate a translog cost system with inefficiency. Inefficiency is specified in such a way that both its mean and variance are firm- and time-specific. For the estimation of production technology and cost inefficiency we have used a multi-step estimation procedure instead of the single-step maximum likelihood (ML) method. In the first step we estimate the translog cost system with heteroskedastic cost function without using any distributional assumptions on the error terms. The second stage uses the ML method to estimate the parameters associated with inefficiency, conditional on the parameter estimates obtained from the first stage. Finally, the residual of the cost function is decomposed to obtain firm-and time-specific measures of cost inefficiency, with ownership type and other firm-specific characteristics as explanatory variables.

This paper analyses the importance of scale economies by means of unbalanced plant-level panel data from three Norwegian manufacturing industries. Focus is on heterogeneous technologies, and unlike most previous work on micro data, the model description includes heterogeneity in both the scale properties (the slope coefficients) and the intercept term, represented by random coefficients in the production function. Three (nested) functional forms are investigated: the Translog, an extended Cobb-Douglas, and the strict Cobb-Douglas. Although constant or moderately increasing returns to scale is found for the average plant, the results reveal considerable variation across plants. Variations in both input and scale elasticities are to a larger extent due to randomness of the production function parameters than to systematic differences in the input mix.

Industrial policy, particularly through the provision of large-scale assistance to industry in the form of ‘tax holidays’ and subsidies to firms, is very important in China. A major contribution of this paper is to introduce firm-level measures of assistance directly into industry-level production functions determining firm output using Chinese firm-level panel data for 1998–2007 and analysing the impact of government assistance on TFP at the firm-level. Our results indicate inverted U-shaped gains from assistance: across the 26 industries considered, firms receiving assistance rates of 1–10, 10–19, 20–49 and 50+% experienced on average 4.5, 9.4, 9.2 and −3% gains in TFP level, respectively. We then decompose the growth of TFP and relate it to assistance and formal political connections between firms and the government. We find in general firms receiving assistance contributed relatively more to TFP growth than non-assisted firms. However, this was largely through new firms being ‘encouraged’ to start-up rather than through firms open throughout 1998 to 2007 improving. There is also evidence that closure rates were truncated as a result of assistance. Moreover, the better results for assisted firms was very much ‘driven’ by a sub-group that received assistance but had no formal political connections and were not State-owned.

The paper takes up inference in the stochastic frontier model with gamma distributed inefficiency terms, without restricting the gamma distribution to known integer values of its shape parameter (the Erlang form). The paper shows that Gibbs sampling with data augmentation can be used in a computationally efficient way to explore the posterior distribution of the model and conduct inference regarding parameters as well as functions of interest related to technical inefficiency.

This paper analyzes the productivity of farms across 370 municipalities in the Center-West region of Brazil. A stochastic frontier model with a latent spatial structure is proposed to account for possible unknown geographical variation of the outputs. The paper compares versions of the model that include the latent spatial effect in the mean of output or as a variable that conditions the distribution of inefficiency, include or not observed municipal variables, and specify independent normal or conditional autoregressive priors for the spatial effects. The Bayesian paradigm is used to estimate the proposed models. As the resultant posterior distributions do not have a closed form, stochastic simulation techniques are used to obtain samples from them. Two model comparison criteria provide support for including the latent spatial effects, even after considering covariates at the municipal level. Models that ignore the latent spatial effects produce significantly different rankings of inefficiencies across agents.