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Indian leather industry has massive potential for generating employment and achieving high export-oriented growth. However, its economic performance has not been assessed much till date. The present paper attempts to fill in this gap by examining technical efficiency (TE) of individual leather producing firms for some years since the mid-1980’s. Analyzing the industry’s firm-level data through the two conventional tools, viz., data envelopment analysis and stochastic frontier analysis, the paper observes a significant positive association between a firm’s size and its TE, but no such clear relation between a firm’s age and TE. It also finds significant variation in TE across firms in different groups of states as well as under different organizational structures and observes some technological heterogeneity across states. Although, non-availability of panel data does not allow one to assess effects of economic reforms on the performance of the Indian leather firms, the average firm-level TE, however, seems to be on an increasing path, except for downswing in the immediate post-reform years.

Program instability, low levels of capital investment and uneconomical production rates have been recognized as significant problems in defense acquisition programs. In this paper, we use a dynamic programming model under uncertainty to investigate the relations among these problems. We show that capital investment and production rates that appear to be too low may be the result of cost minimizing responses by contractors to program instability. Attempts to change the level of capital investment or production rate on defense programs without regard to the nature of program instability may hinder efficient resource allocation.

This study measures the efficiency of government secondary schools in New South Wales, Australia, using a two-stage semi-parametric production frontier approach to schooling. In contrast to previous research comparing school performance with two-stage data envelopment analysis (DEA), we control for prior academic achievement of students by using a rich data set from 2008 to 2010. We employ detailed financial data for deriving the envelope for the efficient production frontier of the schools. Using Simar and Wilson’s (J Econ 136:31-64, 2007, J Prod Anal 36:205-218, 2011a) double bootstrap procedure for two-stage DEA, the study finds that schools with lower total student numbers, a higher average of years of service of teachers, a higher ratio of special education students that attracts extra government funding, and girls only do better than other schools. On the other hand, a negative influence comes from a school’s location in provincial and outer metropolitan areas. An important result is that the socio-economic background of students attending a school has no significant effect on their academic performance, whereas higher prior academic achievements have a positive and statistically significant impact on student achievement. These results are relevant to decision makers for the school sector, in particular for funding criteria contained in the Gonski (Review of funding for schooling - Final report (December). Canberra: Commonwealth Government of Australia, 2011) review report.

In this paper, we measure U.S. technology shocks by implementing a dual approach, which is based on price data instead of aggregate quantity data. By doing so, we find the relative volatility of technology shocks and the correlation between output fluctuation and technology shocks to be much smaller than those revealed in most real-business-cycle (RBC) studies. Our results support the findings of Burside et al. (Eur Econ Rev 40:861–869, 1996), who showed that the correlation between technology shocks and output is exaggerated in the RBC literature. This suggests that one should examine other sources of fluctuations for a better understanding of the business cycle phenomena.

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.

This paper provides an outline of possible uses of complete information on the facial structure of a polyhedral empirical production possibility set obtained by DEA. It is argued that an identification of all facets can be used for a characterization of basic properties of the empirical production frontier. Focus is on the use of this type of information for (i) the specification of constraints on the virtual multipliers in a cone-ratio model, (ii) a characterization of the data generation process for the underlying observed data set, and (iii) the estimation of isoquants and relevant elasticities of substitution reflecting the curvature of the frontier. The relationship between the so-called FDEF approach and the cone-ratio model is explored in some detail. It is demonstrated that a decomposition of the facet generation process followed by the use of one of the available (exponential) convex hull algorithms allows for an explicit identification of the facial structure of the possibility set in fairly large DEA data sets. It is a main point to be made that the difficulties encountered for an identification of all facets in a DEA-possibility set can be circumvented in a number of empirical data sets and that this type of information can be used for a characterization of the structural properties of the frontier.

This paper discusses early work on productivity change by Färe and Grosskopf and their co-authors. We illustrate the use of recent statistical results by revisiting and updating Färe et al. (1994, AER). We analyze 17 OECD countries, estimating productivity change and its sources as measured by Malmquist indices and various decompositions. We describe and use recent theoretical results to make inferences, thereby quantifying what is learned from the data as opposed to merely providing point estimates.

The paper is concerned with supply constraints in the provision of telecommunications services. As a measure of supply constraint we use the average waiting time for telephone connections. Duration models are employed to analyze a panel data set for 28 countries. In addition to economic variables, we consider the role of technical efficiency in causing supply constraints. Stochastic frontiers are used to determine the technical efficiency with which countries use labor and capital inputs to connect customers. When technical efficiency is included in duration models for waiting times until connection, we find that it is the major determinant.

This paper analyses trends in labour productivity and its underlying determinants in a panel of OECD countries from 1979 to 2002. Data Envelopment Analysis (DEA) is used to estimate a Malmquist measure of multifactor productivity (MFP) change. We decompose the growth in labour productivity into (i) net technological change (ii) input biased technical change (IBTC) (iii) efficiency change and (iv) capital accumulation. We analyse the effect of each of these factors in the transition towards the equilibrium growth paths of both labour productivity and per capita GDP for the OECD countries, controlling for the effects of different policies and institutions. The results indicate that on average gaps in productivity or income levels are narrowing down although there is no evidence to suggest that the entire OECD area comprises a single convergence “club”. Using kernel estimation methods we find that that labour productivity and per capita GDP are settling toward a twin peak (bimodal) distribution. Panel unit root tests over an extended (1960–2001) period provide general support for the convergence hypothesis. Analysis of the contributions of productivity growth within industries and sectoral composition changes show that aggregate productivity change is predominantly driven by ‘net’ within sector effects with very little contribution emerging from sectoral shifts (the ‘in-between’ static or dynamic effects resulting from higher or above average productivity industries gaining employment shares or low productivity industries losing shares).