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Since China's accession of the World Trade Organization (WTO), its construction industry has attained unprecedented growth. However, for the sources of this enormous growth, a controversy regarding the total factor productivity growth (TFPG) still remains in production practice and extant studies. In view of this, the purpose of this paper is to measure TFPG and to explore its sources in the industry post-WTO accession.

This study presents an innovative source analysis of TFPG. Stochastic frontier approach is adopted to measure TFPG and to explore its sources by decomposing TFPG into technical progress (TP), technical efficiency change (TEC), allocative efficiency change (AEC) and scale efficiency change (SEC). Although China joined WTO in 2001, to provide an effective baseline, the study period is from 2000 to 2017.

The empirical results reveal that TFPG presented an overall downward evolutionary trend, but it still maintained a high growth post-WTO accession. From the perspective of decomposition, TP was the main source of TFPG. Furthermore, as a neglected source, interaction effects among TP, TEC, AEC and SEC have been demonstrated to have a significant influence on the cumulative TFPG.

To make the results be reliable, the authors discuss the empirical findings mainly by revealing the reasons behind the evolutions of TFPG and its sources. Based on these revealed reasons, government and policy makers can further refine and summarize some more detailed and targeted policy implications to improve TFPG.

By providing many empirical evidences to solve the aforesaid TFPG controversy, this paper, therefore, enriches the body of knowledge on growth theories, especially at the level of industrial economics.

A firm is technically efficient when it produces the maximum level of output for a given level of input on the assumption that technology is fixed. Although the above definition of technical efficiency has been around for decades, economists have, for the most part, been estimating average production functions (i.e. production functions that assume that all firms are technically efficient except for random noise), and then proceeding to make inferences regarding the potential of firms from this average production function.

Examines 88 academic member libraries of the Association of Research Libraries (ARL) to determine their relative cost efficiency, using stochastic frontier regression and data envelopment analysis (DEA) methods. Both methods give average ARL cost efficiencies of around 80 percent. This places academic ARL libraries in the same range of efficiency as other institutions, including for‐profit and non‐profit institutions. Many libraries are above 80 percent efficiency. For those below, some speculation is given for the lower efficiency. The lack of an output measure for the use of electronic sources may contribute to lower efficiency for a few libraries. Large staff size and a large number of serial subscriptions do predict lower efficiency, but this is not a necessary consequence. The DEA model allows us to determine increasing, constant, or declining returns to scale for research libraries. From this, it appears research libraries with expenditures between $10,000,000 and $20,000,000 are operating at the most efficient scale. Since the methods used are outside the repertoire of most LIS research, a conceptual explanation is provided.

Air carriers and aircraft manufacturers are investing in technologies and strategies to reduce fuel consumption and associated emissions. This chapter reviews related issues to assess airline fuel efficiency and offers various empirical evidences from our recent work that focuses on the U.S. domestic passenger air transportation system. We begin with a general presentation of four methods (ratio-based, deterministic frontier, stochastic frontier, and data envelopment analysis) and three perspectives for assessing airline fuel efficiencies, the latter covering consideration of only mainline carrier operations, mainline–subsidiary relations, and airline routing circuity. Airline fuel efficiency results in the short run, in particular the correlations of the results from using different methods and considering different perspectives, are discussed. For the long-term efficiency, we present the development of a stochastic frontier model to investigate individual airline fuel efficiency and system overall evolution between 1990 and 2012. Insight about the association of fuel efficiency with market entry, exit, and airline mergers is also obtained.

The purpose of this paper is to improve the estimation of the production frontier in cases where outliers exist. We focus on the case when outliers appear above the true frontier due to measurement error.

The authors use stochastic data envelopment analysis (SDEA) to allow observed points above the frontier. They supplement SDEA with assumptions on the efficiency and show that the true frontier in the presence of outliers can be derived.

This paper finds that the authors’ maximum likelihood approach outperforms super-efficiency measures. Using simulations, this paper shows that SDEA is a useful model for outlier detection.

The model developed in this paper is original; the authors add distributional assumptions to derive the optimal quantile with SDEA to remove outliers. The authors believe that the value of the paper will lead to many citations because real-world data are often subject to outliers.

The traditional predictor of technical inefficiency proposed by Jondrow, Lovell, Materov, and Schmidt (1982) is a conditional expectation. This chapter explores whether, and by how much, the predictor can be improved by using auxiliary information in the conditioning set. It considers two types of stochastic frontier models. The first type is a panel data model where composed errors from past and future time periods contain information about contemporaneous technical inefficiency. The second type is when the stochastic frontier model is augmented by input ratio equations in which allocative inefficiency is correlated with technical inefficiency. Compared to the standard kernel-smoothing estimator, a newer estimator based on a local linear random forest helps mitigate the curse of dimensionality when the conditioning set is large. Besides numerous simulations, there is an illustrative empirical example.

This paper aims to evaluate the technical efficiency of the health-care systems in 21 selected middle-income countries during the period (2000–2017) and determine the source of inefficiency whether it is transient (short run) or persistent (long run).

The study uses the stochastic frontier analysis technique through employing the generalized true random effects model which overcomes the drawbacks of the previously introduced stochastic frontier models and allows for the separation between unobserved heterogeneity, persistent inefficiency and transient inefficiency.

Persistent efficiency is lower than the transient efficiency; hence, there are more efficiency gains that can be made by the selected countries by adopting long-term policies that aim at reforming the structure of the health-care system in the less efficient countries such as South Africa and Russia. The most efficient countries are Vietnam, Mexico and China which adopted a social health insurance that covers almost the whole population with the aim of increasing access to health-care services. Also, decentralization in health-care has assisted in adopting health-care policies that are suitable for both the rural and urban areas based on their specific conditions and health-care needs. A key success in the implementation of the adopted long-term policies by those countries is the continuous monitoring and evaluation of their outcomes and comparing them with the predefined targets and conducting any necessary modifications to ensure their movement in the right path to achieve their goals.

Although several studies have evaluated the technical efficiency both across and within countries using non-parametric (data envelopment analysis) and parametric (stochastic frontier analysis) approaches, to the best of the authors’ knowledge, this is the first attempt to evaluate the technical efficiency of selected middle-income countries during the period (2000–2017) using the generalized true random effects stochastic frontier analysis model.

While firms in developing countries are increasingly adopting lean practices of inventory management, there is limited evidence showing the impact of lean practices on firm performance in countries such as India. Lean practices improve the financial performance of the firms through superior cost-reduction measures and operational efficiencies. This paper examines the impact of inventory leanness in Indian manufacturing firms on their financial performance.

The authors measure inventory leanness based on stochastic frontier analysis (SLA), apart from using conventional measures available in the literature. The authors analyze the impact of inventory leanness on the financial performance of firms by examining data for 12,334 unique Indian manufacturing firms for the period 2009–2018. The authors present a comparative analysis using different methods of inventory leanness and study the effects on firm performance.

First, the authors find that only 68 industries out of 411 industries follow lean practices, i.e. most industries do not follow lean practices. Second, the estimation results show that there exists a positive relationship between inventory leanness and firm performance. The results suggest that an inverted U-shaped relationship exists between inventory leanness and firm performance for the entire sample. In particular, 17% of the industries in the sample exhibit such a relationship, and it is sufficiently strong to show up in the average regression results for the entire sample.

The authors introduce a novel measure of inventory leanness named stochastic frontier leanness based on the SFA method used in production economics. It measures leanness by benchmarking the inventory levels against the industry “frontier”. Furthermore, the authors conduct an empirical study of the lean-financial performance relationship with a large panel dataset of Indian firms instead of the survey-based methods that were previously used in the literature.

The aim of this paper is to call the attention of public entities for the econometric frontier models which allows benchmarking the tax offices accurately. In the context of the European Maastricht, the European countries face public receipts shortage and to focus in the management of the tax agencies in order to maximize receipts. The procedure adopted in this paper allows benchmarking the tax agencies accurately.

The paper adopts the stochastic frontier model to benchmark the tax offices. This model is presently very popular in benchmarking exercises, based on its statistical framework.

The paper finds that the tax offices analysed vary along the sample and along the period.

The homogeneity of the tax offices and the short data set are the main limitations of the paper. Relative to the first, since they are managed by the same entity and perform the same task we can claim that they are similar, relative to the second critic, we cannot overcome it because of the secrecy related to this units.

The efficiency rankings are due to the way the tax offices handle costs and outputs. The way they handle the combination of output and inputs is explained in the literature by the principal‐agent relationship, by collective‐action problem, due to asymmetric information between different tax offices and X‐inefficiency. The public policy has to address these problems in order to upgrade the efficiency of the tax offices.

This is the first paper adopting the stochastic frontier model in tax offices.

The purpose of this paper is to provide evidence concerning wage differentials and the existence of gender pay discrimination in France, Germany, Italy, Spain and the United Kingdom.

The authors used the stochastic frontier approach as an alternative method to measure the relevance of gender discrimination. This methodology allows them to explain the differences between the potential and the observed wage that an individual could obtain, given his or her human capital endowment.

The authors found support for wage discrimination against women. The results show that a significant part of the gender wage gap in all the countries analysed is not attributable to differences in human capital endowment or personal and job‐related characteristics.

The definition of education in the data set is modest and broadly defined.

This paper contributes to the debate on gender discrimination by analysing the determinants of the gender gap using an innovative methodology in the gender gap literature: the stochastic frontier approach.