Non‐Mandatory Approaches to Environmental Protection

The approach to environmental protection has been evolving from a regulation‐driven, adversarial 'government‐push' approach to a more proactive approach involving voluntary and often 'business‐led' initiatives to self‐regulate their environmental performance. This has been accompanied by increasing provision of environmental information about firms and products to enlist market forces and communities in creating a demand for corporate environmental self‐regulation by signaling their preferences for environmentally friendly firms. This paper provides an overview of the non‐mandatory approaches being used for environmental protection and surveys the existing theoretical literature analyzing the economic efficiency of such approaches relative to mandatory approaches. It also discusses empirical findings on the factors motivating self‐regulation by firms and its implications for their economic and environmental performance. It examines the existing evidence on the extent to which information disclosure is effective in generating pressures from investors and communities on firms to improve their environmental performance.     

Frontier Production Functions and Technical Efficiency Measures

The concept of technical efficiency is central to measuring the firm performance. The measurement of technical efficiency has proved difficult and complex, and the literature provides a range of methodologies. This paper reviews the various methodologies for measuring technical efficiency and offers a comparison between established methods of measurements. The discussion is literary with less mathematical jargons and equations. The objective of this paper is not to be exhaustive, but to be up‐to‐date and to provide a significant discussion on some of the core methods of measuring technical efficiency.     

A quantile-based approach for relative efficiency measurement

Two popular approaches for efficiency measurement are a non-stochastic approach called data envelopment analysis (DEA) and a parametric approach called stochastic frontier analysis (SFA). Both approaches have modeling difficulty, particularly for ranking firm efficiencies. In this paper, a new parametric approach using quantile statistics is developed. The quantile statistic relies less on the stochastic model than SFA methods, and accounts for a firm's relationship to the other firms in the study by acknowledging the firm's influence on the empirical model, and its relationship, in terms of similarity of input levels, to the other firms. Copyright © 1999 John Wiley & Sons, Ltd.     

Statistical Inference in Nonparametric Frontier Models: The State of the Art

Efficiency scores of firms are measured by their distance to an estimated production frontier. The economic literature proposes several nonparametric frontier estimators based on the idea of enveloping the data (FDH and DEA-type estimators). Many have claimed that FDH and DEA techniques are non-statistical, as opposed to econometric approaches where particular parametric expressions are posited to model the frontier. We can now define a statistical model allowing determination of the statistical properties of the nonparametric estimators in the multi-output and multi-input case. New results provide the asymptotic sampling distribution of the FDH estimator in a multivariate setting and of the DEA estimator in the bivariate case. Sampling distributions may also be approximated by bootstrap distributions in very general situations. Consequently, statistical inference based on DEA/FDH-type estimators is now possible. These techniques allow correction for the bias of the efficiency estimators and estimation of confidence intervals for the efficiency measures. This paper summarizes the results which are now available, and provides a brief guide to the existing literature. Emphasizing the role of hypotheses and inference, we show how the results can be used or adapted for practical purposes.     

Nonlinearity, ComputationaL Complexity and Macroeconomic Modelling

In this paper we survey and appraise the main contributions to solving and stabilising non‐linear equation systems typically found in Economics. We are keen wherever possible to draw distinctions and limiting cases between different solution methods, define acceleration strategies and encourage the use of hybrid or switching algorithms. Both large‐scale traditional macroeconomic models as well as smaller non‐linear analytical models are considered.     

MEASUREMENT AND DETERMINANTS OF MULTI-FACTOR PRODUCTIVITY: A SURVEY OF LITERATURE

This paper surveys and synthesizes fast-growing literature on the measurement and determinants of multi-factor productivity (MFP). We identify three strands of the literature to measure MFP: the first is growth accounting, which decomposes observed economic growth into the contribution of factor inputs and technological change, called the Solow Residual; the second is index number methods, accompanied by frontier techniques; the third is growth regressions and econometric methods used to estimate productivity across countries and regions. We keep our focus on assessing the major strengths and weaknesses of commonly used methods for MFP measurement and categorize existing literature on the determinants of MFP growth into macroeconomic and institutional factors. We attempt to provide a reassessment and thematic survey of literature on the drivers of aggregate productivity, enabling policymakers to formulate effective economic policy.     

Non‐linear Dynamics in the Infinite Time Horizon Model

Using results from the theory of non‐linear dynamical systems, it has been shown that optimal growth theory can provide new explanations for business cycles and for international differences in growth and development. The present survey concentrates on this aspect of optimal growth theory, that is on the possibility of fluctuations and non‐uniqueness in models of optimal capital accumulation. It is a selective survey on non‐linear dynamics in infinite time horizon models of optimal growth.     

Estimating production frontiers and efficiency when output is a discretely distributed economic bad

This article studies the estimation of production frontiers and efficiency scores when the commodity of interest is an economic bad with a discrete distribution. Existing parametric econometric techniques (stochastic frontier methods) assume that output is a continuous random variable but, if output is discretely distributed, then one faces a scenario of model misspecification. Therefore a new class of econometric models has been developed to overcome this problem. The Delaporte subclass of models is studied in detail, and tests of hypotheses are proposed to discriminate among parametric models. In particular, Pearson’s chi-squared test is adapted to construct a new kernel-based consistent Pearson test. A Monte Carlo experiment evaluates the merits of the new model and methods, and these are used to estimate the frontier and efficiency scores of the production of infant deaths in England. Extensions to the model are discussed.     

Testing for Linearity

The problem of testing for linearity and the number of regimes in the context of self‐exciting threshold autoregressive (SETAR) models is reviewed. We describe least‐squares methods of estimation and inference. The primary complication is that the testing problem is non‐standard, due to the presence of parameters which are only defined under the alternative, so the asymptotic distribution of the test statistics is non‐standard. Simulation methods to calculate asymptotic and bootstrap distributions are presented. As the sampling distributions are quite sensitive to conditional heteroskedasticity in the error, careful modeling of the conditional variance is necessary for accurate inference on the conditional mean. We illustrate these methods with two applications — annual sunspot means and monthly U.S. industrial production. We find that annual sunspots and monthly industrial production are SETAR(2) processes.     

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.

BANKING SECTOR PERFORMANCE,PROFITABILITY, AND EFFICIENCY: A CITATION-BASED SYSTEMATIC LITERATURE REVIEW

This study presents a citation-based systematic literature review on banking sector performance, particularly in terms of profitability, productivity, and efficiency. Specifically, the study aims to identify the leading sources of knowledge in terms of the most influential journals, authors, and papers. The paper presents a content analysis of the 100 most cited papers. In total, 1996 peer-review papers were found relevant in the Scopus database by using a comprehensive list of keywords. The results show that the Journal of Banking & Finance appears to be the leading journal in terms of publication count and citations. Based on total citations, Allen Berger is the most prolific author. The most cited paper is “Problem loans and cost efficiency in commercial banks” by Allan Berger and Robert DeYoung. The content analysis of the top 100 papers identifies five essential themes: determinants of efficiency, methodology, ownership, financial crises, and scale economies. In terms of estimation approaches, 74% of papers employed frontier analysis, which includes 34% parametric and 40% nonparametric methods, and remaining 26% have used financial ratio analysis. Additionally, stochastic frontier and data envelopment analysis are widely used in parametric and nonparametric methods, respectively. An intermediate approach is extensively adopted for the specification of inputs and outputs.     

Measuring Cost Efficiency in European Banking: A Comparison of Frontier Techniques

This paper investigates the consistency of efficiency frontier methods on European banking samples. We measure the cost efficiency of banks from five European countries (France, Germany, Italy, Spain, Switzerland) with three approaches: stochastic frontier approach, distribution-free approach, and data envelopment analysis. We compare means, correlation coefficients, two public policy issues, and the correlation with standard measures of performance. In general, we conclude in favor of the lack of robustness between approaches, even if there are some similarities in particular between parametric approaches. We do, however, observe some correlation between all frontier approaches and standard measures of performance.     

Overeducation in the Labour Market

Abstract.  This paper presents a review of the literature on overeducation. The paper assesses the consistency of overeducation within the context of a number of theoretical frameworks including Human Capital Theory (HCT) and Assignment Theory. The analysis goes on to discuss the various measurement controversies associated with the study of overeducation in order to provide an assessment of the extent to which the impacts of the phenomenon represent an economic reality as opposed to a statistical artefact. After reviewing the literature, it is concluded that the impacts of overeducation are likely to be non‐trivial and that the phenomenon may potentially be costly to individuals and firms, as well as the economy more generally. The existence of overeducation also raises some doubts with respect to the validity of some of the central assumptions and predictions of HCT that are unlikely to be fully explained by gaps in the standard wage equation framework.     

随机前沿方法的研究进展与展望

本文对随机前沿方法发展的历史沿革及其在中国的应用做了评述。围绕随机前沿方法的基本特征,首先阐述了其启蒙与产生的轨迹,然后对使用前提、分布假定、估计方法、异方差等基本问题以及效率的时变性、外生影响等研究的理论进展及相关应用做了梳理,并对全要素生产率的变化做了分解,评述了随机前沿方法在经验分析中的优势与作用,总结了国内区域与行业经济增长中随机前沿方法的应用与不足,最后对其研究发展做了展望。     

Applying efficiency measurement techniques to the measurement of productivity change

Deterministic frontier analysis (DFA), stochastic frontier analysis (SFA), and data envelopment analysis (DEA) are alternative analytical techniques designed to measure the efficiency of producers. All three techniques were originally developed within a cross-sectional context, in which the objective is to compare the efficiencies of producers. More recently all three techniques have been extended for use in a panel data context. In the latter context it is possible to measure productivity change, and to decompose measured productivity change into its sources, one of which is efficiency change. However when efficiency measurement techniques, particularly SFA, have been applied to panel data, it has infrequently been made clear what the objective of the analysis is: the measurement of efficiency, which may vary through time as well as across producers, or the measurement and decomposition of productivity change. In this paper I explore the use of each technique in a panel data context. I find DFA and DEA to have achieved a more satisfactory reorientation toward productivity measurement than SFA has.     

The Distribution and Redistribution of Opportunity

Recent theories of distributive justice focus on individual opportunities as the appropriate standard for distributive judgements. To translate this philosophical view into concrete public policy, three branches of economic literature have arisen, which we draw together here and critically assess. These concern ( i ) the measurement of individuals' opportunity sets, ( ii ) the measurement of the degree of inequality present in a distribution of opportunities, and ( iii ) the design of redistribution mechanisms intended to increase the degree of 'opportunity equality'. The purpose of the exposition is to unify the three themes, to give a flavour in non‐technical terms of what is going on in these very active literatures, and to indicate areas which remain open for research.     

A general framework for frontier estimation with panel data

The main objective of the paper is to present a general framework for estimating production frontier models with panel data. A sample of firms i = 1, ..., N is observed on several time periods t = 1, ... T. In this framework, nonparametric stochastic models for the frontier will be analyzed. The usual parametric formulations of the literature are viewed as particular cases and the convergence of the obtained estimators in this general framework are investigated. Special attention is devoted to the role of N and of T on the speeds of convergence of the obtained estimators. First, a very general model is investigated. In this model almost no restriction is imposed on the structure of the model or of the inefficiencies. This model is estimable from a nonparametric point of view but needs large values of T and of N to obtain reliable estimates of the individual production functions and estimates of the frontier function. Then more specific nonparametric firm effect models are presented. In these cases, only NT must be large to estimate the common production function; but again both large N and T are needed for estimating individual efficiencies and for estimating the frontier. The methods are illustrated through a numerical example with real data.     

The Components of Output Growth: A Stochastic Frontier Analysis

This paper uses Bayesian stochastic frontier methods to decompose output change into technical, efficiency and input changes. In the context of macroeconomic growth exercises, which typically involve small and noisy data sets, we argue that stochastic frontier methods are useful since they incorporate measurement error and assume a (flexible) parametric form for the production relationship. These properties enable us to calculate measures of uncertainty associated with the decomposition and minimize the risk of overfitting the noise in the data. Tools for Bayesian inference in such models are developed. An empirical investigation using data from 17 OECD countries for 10 years illustrates the practicality and usefulness of our approach.     

PRODUCTIVITY AND EFFICIENCY ANALYSIS SOFTWARE: AN EXPLORATORY BIBLIOGRAPHICAL SURVEY OF THE OPTIONS

The software available to implement and carry out efficiency analysis is crucial for the diffusion of efficiency frontier techniques among applied researchers and policy makers. The implementation of up‐to‐date productivity and efficiency analysis is indeed important to advance our knowledge in many fields, ranging from the public and regulated sectors to the private ones. This contribution fills a gap in the existing literature and surveys the currently available options to estimate a variety of frontier methodologies using either general or dedicated programs. We outline directions for future research.     

MEASURING PRODUCTIVITY

Abstract Quantifying productivity is a conditio sine qua non for empirical analysis in a number of research fields. The identification of the measure that best fits with the specific goals, as well as being data driven, is currently complicated by the fact that an array of methodologies is available. This paper provides economic researchers with an up‐to‐date overview of issues and relevant solutions associated with this choice. Methods of productivity measurement are surveyed and classified according to three main criteria: (i) macro/micro; (ii) frontier/non‐frontier and (iii) deterministic/econometric.