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.     

Statistical Approaches for Non‐parametric Frontier Models: A Guided Tour

A rich theory of production and analysis of productive efficiency has developed since the pioneering work by Tjalling C. Koopmans and Gerard Debreu. Michael J. Farrell published the first empirical study, and it appeared in a statistical journal (Journal of the Royal Statistical Society), even though the article provided no statistical theory. The literature in econometrics, management sciences, operations research and mathematical statistics has since been enriched by hundreds of papers trying to develop or implement new tools for analysing productivity and efficiency of firms. Both parametric and non‐parametric approaches have been proposed. The mathematical challenge is to derive estimators of production, cost, revenue or profit frontiers, which represent, in the case of production frontiers, the optimal loci of combinations of inputs (like labour, energy and capital) and outputs (the products or services produced by the firms). Optimality is defined in terms of various economic considerations. Then the efficiency of a particular unit is measured by its distance to the estimated frontier. The statistical problem can be viewed as the problem of estimating the support of a multivariate random variable, subject to some shape constraints, in multiple dimensions. These techniques are applied in thousands of papers in the economic and business literature. This ‘guided tour’ reviews the development of various non‐parametric approaches since the early work of Farrell. Remaining challenges and open issues in this challenging arena are also described. © 2014 The Authors. International Statistical Review © 2014 International Statistical Institute     

WHY IS THERE A LACK OF EVIDENCE ON KNOWLEDGE SPILLOVERS FROM FOREIGN DIRECT INVESTMENT?

Empirical analyses of knowledge spillovers from foreign direct investment (FDI) offer mixed results; they find positive, neutral and negative FDI spillover effects. This lack of evidence mainly comes from the results of firm‐level panel data analysis. This is important since this approach seems to be the most appropriate for estimating FDI spillovers. The paper takes a look at recent substantive and methodological developments in FDI spillover analysis, which have brought some more optimistic results with regard to FDI spillovers, and can help in further development in this field. The main substantive development relates to the introduction of a broad variety of sources of firm heterogeneity (foreign affiliates as well as local firms) in the analysis. Others include differentiation between vertical (inter‐industry) and horizontal (intra‐industry) spillovers, and host country absorptive capacity for knowledge spillovers. Methodological developments relate to distinguishing between technological/knowledge and productivity spillovers, improvement of modelling and estimation methods, and an increased amount and quality of data.     

ECONOMETRICS FOR GRUMBLERS: A NEW LOOK AT THE LITERATURE ON CROSS‐COUNTRY GROWTH EMPIRICS

Abstract Since the seminal contribution of N. Gregory Mankiw, David Romer and David N. Weil in 1992 the growth empirics literature has used increasingly sophisticated methods to select relevant growth determinants in estimating cross‐section growth regressions. The vast majority of empirical approaches, however, limit cross‐country heterogeneity in production technology to the specification of total factor productivity, the ‘measure of our ignorance’. In this survey, we present two general empirical frameworks for cross‐country growth and productivity analysis and demonstrate that they encompass the various approaches in the growth empirics literature of the past two decades. We then develop our central argument, that cross‐country heterogeneity in the impact of observables and unobservables on output as well as the time‐series properties of the data are important for reliable empirical analysis.     

WHAT'S TO KNOW ABOUT LABORATORY EXPERIMENTATION IN ECONOMICS?

Abstract Experimental economics has grown as a discipline from near non‐existence 50 years ago to a full‐fledged field within economics in the present. Much of experimental economics research involves experimental methods as a tool, applied to problems in other fields of economics. However, some of this research is inward looking, focusing on questions of the methodology of experimental economics. In this note, I briefly discuss two methodological issues in experimental economics that might benefit from meta‐analysis: the pool from which experimental participants are drawn (university undergraduate students versus other populations) and the scale of monetary incentives faced by participants (large, small or hypothetical).     

Statistical inference on seemingly unrelated varying coefficient partially linear models

This paper is concerned with the statistical inference on seemingly unrelated varying coefficient partially linear models. By combining the local polynomial and profile least squares techniques, and estimating the contemporaneous correlation, we propose a class of weighted profile least squares estimators (WPLSEs) for the parametric components. It is shown that the WPLSEs achieve the semiparametric efficiency bound and are asymptotically normal. For the non‐parametric components, by applying the undersmoothing technique, and taking the contemporaneous correlation into account, we propose an efficient local polynomial estimation. The resulting estimators are shown to have mean‐squared errors smaller than those estimators that neglect the contemporaneous correlation. In addition, a class of variable selection procedures is developed for simultaneously selecting significant variables and estimating unknown parameters, based on the non‐concave penalized and weighted profile least squares techniques. With a proper choice of regularization parameters and penalty functions, the proposed variable selection procedures perform as efficiently as if one knew the true submodels. The proposed methods are evaluated using wide simulation studies and applied to a set of real data.     

Robust Small Area Estimation under Spatial Non‐stationarity

The effective use of spatial information in a regression‐based approach to small area estimation is an important practical issue. One approach to account for geographic information is by extending the linear mixed model to allow for spatially correlated random area effects. An alternative is to include the spatial information by a non‐parametric mixed models. Another option is geographic weighted regression where the model coefficients vary spatially across the geography of interest. Although these approaches are useful for estimating small area means efficiently under strict parametric assumptions, they can be sensitive to outliers. In this paper, we propose robust extensions of the geographically weighted empirical best linear unbiased predictor. In particular, we introduce robust projective and predictive estimators under spatial non‐stationarity. Mean squared error estimation is performed by two analytic approaches that account for the spatial structure in the data. Model‐based simulations show that the methodology proposed often leads to more efficient estimators. Furthermore, the analytic mean squared error estimators introduced have appealing properties in terms of stability and bias. Finally, we demonstrate in the application that the new methodology is a good choice for producing estimates for average rent prices of apartments in urban planning areas in Berlin.     

Asymptotic comparison at optimal levels of reduced‐bias extreme value index estimators

In this article we are interested in the asymptotic comparison, at optimal levels, of a set of semi‐parametric reduced‐bias extreme value (EV) index estimators, valid for a wide class of heavy‐tailed models, underlying the available data. Again, as in the classical case, there is not any estimator that can always dominate the alternatives, but interesting clear‐cut patterns are found. Consequently, and in practice, a suitable choice of a set of EV index estimators will jointly enable us to better estimate the EV index γ, the primary parameter of extreme events.     

Sales by multi‐product retailers

This paper examines the rationale underlying periodic price promotions, or sales, for perishable food products by supermarket retailers. Whereas previous studies explain sales in a single‐product context as arising from informational, storage cost, or demand heterogeneity, this study focuses on the central role of retailers as multi‐product sellers of complementary goods. By offering a larger number of discounted products within a particular category, retailers are able to attract a sufficient number of customers to offset the effect of lower margins on sale items by selling more high‐margin items. The implications that emerge from the resulting mixed‐strategy equilibrium are tested in a product‐level, retail‐scanner data set of fresh fruit sales. Hypotheses regarding the rationale and effectiveness of sales are tested by estimating econometric models that describe (1) the number of sales items per store, (2) the depth of a given sale, and (3) promotion effectiveness on store‐level demand. The results of this econometric analysis support the hypothesis that the breadth and depth of price promotions are complementary marketing tools, thus explaining how EDLP and HI‐LO store formats can exist in the same monopolistically competitive market equilibrium. Copyright © 2006 John Wiley & Sons, Ltd.     

Properties of Census Dual System Population Size Estimators

We study parametric and non‐parametric approaches for assessing the accuracy and coverage of a population census based on dual system surveys. The two parametric approaches being considered are post‐stratification and logistic regression, which have been or will be implemented for the US Census dual system surveys. We show that the parametric model‐based approaches are generally biased unless the model is correctly specified. We then study a local post‐stratification approach based on a non‐parametric kernel estimate of the Census enumeration functions. We illustrate that the non‐parametric approach avoids the risk of model mis‐specification and is consistent under relatively weak conditions. The performances of these estimators are evaluated numerically via simulation studies and an empirical analysis based on the 2000 US Census post‐enumeration survey data.     

Meta‐Analytic Research in Management: Contemporary Approaches,Unresolved Controversies,and Rising Standards

Early meta‐analyses in management research sought primarily to resolve seemingly conflicting findings by estimating a relationship’s population‐level effect size. Since then, management researchers have adopted increasingly sophisticated approaches that permit new theorizing, testing and comparing sophisticated models, and identifying boundary conditions. We summarize three of these approaches – i.e., qualitative meta‐analysis (QMA), meta‐analytic structural equation modeling (MASEM), and meta‐analytic regression analysis (MARA) – along with the special issue papers that adopt each approach. We conclude by raising three unresolved controversies that we believe deserve more attention and by offering our thoughts about how to maximize a meta‐analytic study’s chances for publication and impact.     

Do LGBT‐supportive corporate policies enhance firm performance?

Prior research provides evidence that lesbian, gay, bisexual, and transgender (LGBT)‐supportive corporate policies are related to important human resource functions, such as enhanced recruitment and retention. In addition, prior research indicates that investors view the adoption of such policies positively. We examine the firm‐performance mechanisms underlying favorable stock‐market reactions based on an integration of perspectives from corporate social responsibility and the business case for diversity. Specifically, we estimate a hierarchical linear model (HLM) to account for the nested nature of our data (firms nested within states) and find that (1) the presence of LGBT‐supportive policies is associated with higher firm value, productivity, and profitability; (2) the firm‐value and profitability benefits associated with LGBT‐supportive policies are larger for companies engaged in research and development (R&D) activities; and (3) the firm‐value and profitability benefits of LGBT‐supportive policies persist in the presence of state antidiscrimination laws. In supplemental analyses, we find that firms implementing (discontinuing) LGBT‐supportive policies experience increases (decreases) in firm value, productivity, and profitability. We are among the first to link LGBT‐supportive policies specifically to financial performance outcomes as well as to develop and test a multilevel model of these relationships. Our results have important implications for theory and research on LGBT issues in organizations, human resource managers, and policymakers.     

Compensation systems for green strategy implementation: parametric and non‐parametric approaches

This paper presents a design for compensation systems for green strategy implementation based on parametric and non‐parametric approaches. The purpose of the analysis is to use formal modeling to explain the issues that arise with the multi‐task problem of implementing an environmental strategy in addition to an already existing profit‐oriented strategy. For the first class of compensation systems (parametric), a multi‐task model is used as a basis. For the second class of compensation systems (non‐parametric), data envelopment analysis is applied.Copyright © 2003 John Wiley & Sons, Ltd. and ERP Environment     

Roles of Public Capital in Pakistan's Economy: Productivity, Investment and Growth Analysis

This study analyzes the role of public capital in Pakistan's economy, tracing the relationship between productivity of public capital and economic growth. We estimate a production function, with public capital as an inputs. The results indicate that the productivity (output elasticity) of aggregate as well as different components of public capital are sufficiently high. We also analyze substitutability and complementarity between public and private capital by estimating investment functions, revealing that public capital has worked as a substitute for private investment. The net effect of public capital on the national economy is analyzed by estimating reduced forms, with the result that public capital has a positive net effect on national product. The growth analysis shows that the contribution of public capital is declining over time.     

Adjusting for Non‐Response in Population‐Based Case‐Control Studies

In this paper we discuss the analysis of data from population‐based case‐control studies when there is appreciable non‐response. We develop a class of estimating equations that are relatively easy to implement. For some important special cases, we also provide efficient semi‐parametric maximum‐likelihood methods. We compare the methods in a simulation study based on data from the Women's Cardiovascular Health Study discussed in Arbogast et al. (Estimating incidence rates from population‐based case‐control studies in the presence of non‐respondents, Biometrical Journal 44, 227–239, 2002).     

Productivity Growth, Efficiency and Outsourcing in Manufacturing and Service Industries

This paper is a survey of recent contributions to, and developments of, the relationship between outsourcing, efficiency and productivity growth in manufacturing and services. The objective is to provide a thorough and up–to–date survey that provides a significant discussion on data, as well as on the core methods of measuring efficiency and productivity. First, the readers are introduced to the measurement of partial and total factor productivity growth. Different parametric and non–parametric approaches to the productivity measurement in the context of static, dynamic and firm–specific modelling are discussed. Second, we survey the econometric approach to efficiency analysis. The issues of modelling, distributional assumptions and estimation methods are discussed assuming that cross–sectional or panel data are available. Third, the relationship between outsourcing and productivity growth in manufacturing and services is discussed. The correspondence between a number of hypotheses and empirical findings are examined. Examples of varieties of relevant empirical applications, their findings and implications are presented. Fourth, measurement of inputs and outputs in manufacturing and services are discussed. Finally, to promote useful research, a number of factors important to the analysis of outsourcing, efficiency and productivity growth in the service sector are summarised.     

Three Factors to Signal Non‐Response Bias With Applications to Categorical Auxiliary Variables

Non‐response causes bias in survey estimates. The unknown bias can be reduced, for example as in this paper by the use of a calibration estimator built on powerful auxiliary information. Still, some bias will always remain. A bias reduction indicator is proposed and expressed as a product of three factors reflecting familiar statistical ideas. These factors provide a useful perspective on the components that constitute non‐response bias in estimates. To illustrate the indicator, we focus on the important case with information defined by one or more categorical auxiliary variables, each expressed by two or more properties or traits. Together, the auxiliary variables may represent a large number of traits, more or less important for bias reduction. An examination of the three factors of the bias reduction indicator brings the insight that the ultimate auxiliary vector for calibration need not or should not contain all available traits; some are unimportant or detrimental to bias reduction. The question becomes one of selection of traits, not of complete auxiliary variables. Empirical examples are given, and a stepwise procedure for selecting important traits is proposed.     

GLOBALIZATION AND PRODUCTIVITY: A SURVEY OF FIRM‐LEVEL ANALYSIS

Abstract Recent empirical studies which utilize plant‐ or establishment‐level data to examine globalization's impact on productivity have discovered many causal mechanisms involved in globalization's impact on firms’ productivity. Because these pathways have been broad, there have been few attempts to summarize the several and detailed mechanisms of self‐selection and learning at the same time. This paper examines seven pathways so that the clear‐cut consequences of the broad picture of globalization become visible. This strategy is useful for detecting missing links within and across the existing studies as well as for finding possible synergy effects among different mechanisms. Insightful policy implications may be derived from the comprehensive comparisons between the seven different pathways of globalization.     

Conceptual estimating tool for technology-driven projects: exploring parametric estimating technique

This paper examines a parametric estimating technique applied to technology-driven projects. Parametric cost estimating is a widely used approach for bidding on a contract, input into a cost benefit analysis, or as the pre-planning tool for project implementation. Extensive literature reviews suggest that effective parametric estimating methodology is becoming an essential tool for technology-driven organizations. The use of parametric estimating in budgeting, scheduling, and control of projects will enhance the ability of project management organizations to effectively and efficiently utilize valuable resources. The benefit of parametric estimating is its use as an estimating model for better determining potential resource requirements during the project pre-planning and conceptual phase.     

Productivity and Trade Orientation in UK Manufacturing*

We show that improvements in aggregate productivity in UK manufacturing during the first years after the implementation of the Euro, by the UK's main trading partners in Europe, are determined by both market share reallocation and within‐company productivity growth. Furthermore, we outline a structural methodology for estimating parameters of a production function linking the unobservable productivity to endogenous company‐level trade orientation, investment and exit decisions. This allows us to back out consistent and unbiased estimates of productivity dynamics by trade orientation of companies within four‐digit UK manufacturing industries using FAME data over the period 1994–2001. Our estimates of productivity dynamics indicate that improvements in aggregate productivity were mainly driven by market share reallocations away from inefficient and towards efficient exporting companies alongside productivity improvements within non‐exporting companies.