Localized Innovation and Productivity Growth in Asia: An Intertemporal DEA Approach

Recent contributions to growth theory stress the importance of localized innovation for the performance of more backward countries. In earlier papers, analyses by means of DEA techniques confirmed this intuition. In this paper, we extend this type of analysis by relaxing the macroeconomic viewpoint adopted until now. New databases on output, labor and capital input in the agricultural and manufacturing sectors are developed for 40 countries. Using intertemporal DEA, it is found that changes in the global production frontier are localized at high levels of capital intensity. This result is stronger in agriculture than in manufacturing. Further, a decomposition of labor productivity growth in eight Asian countries for the period 1975–1992 into the effects of capital intensification, learning and innovation is made. The results suggest that there is a particular development path in which increases in capital intensity appear to be a prerequisite to benefit from international technology spillovers.JEL Classification: O14, O30, O40, O47     

SELECTION BIAS CORRECTIONS BASED ON THE MULTINOMIAL LOGIT MODEL: MONTE CARLO COMPARISONS

Abstract.  This survey presents the set of methods available in the literature on selection bias correction, when selection is specified as a multinomial logit model. It contrasts the underlying assumptions made by the different methods and shows results from a set of Monte Carlo experiments. We find that, in many cases, the approach initiated by Dubin and MacFadden (1984) as well as the semi-parametric alternative recently proposed by Dahl (2002) are to be preferred to the most commonly used Lee (1983) method. We also find that a restriction imposed in the original Dubin and MacFadden paper can be waived to achieve more robust estimators. Monte Carlo experiments also show that selection bias correction based on the multinomial logit model can provide fairly good correction for the outcome equation, even when the IIA hypothesis is violated.     

The Challenge Facing SMEs in the Eu’s New Member States

The paper considers the main threats and opportunities for SMEs in new member states, arising out of EU membership, including the implications for SMEs at the micro level. Whilst accession-related changes have important potential implications for firms of all sizes, the distinctive size-related characteristics of SMEs affect their ability to identify, cope with and respond to new sources of threat and opportunity. These potential impacts face new member countries at a relatively early stage in the development of market-based systems, which still contain many deficiencies (particularly in institutional terms) and a SME sector with many characteristics that reflect its fledgling status.     

A note on interactions-driven business cycles

Within the standard Keynesian multiplier framework, extended by a micro-model of interactive formation of individual consumption propensities, we demonstrate that socioeconomic interactions can lead to cyclical fluctuations in aggregate economic activity. The underlying micro-model of direct interactions is a version of Alan Kirman’s generic opinion formation model, with an additional feedback effect from macroscopic variables on the transition probabilities. Our model engenders cyclical fluctuations of economic variables, despite the fact that neither the Keynesian multiplier model nor Kirman’s model does so on its own.     

Measuring volatility with the realized range

Realized variance, being the summation of squared intra-day returns, has quickly gained popularity as a measure of daily volatility. Following Parkinson [1980. The extreme value method for estimating the variance of the rate of return. Journal of Business 53, 61–65] we replace each squared intra-day return by the high–low range for that period to create a novel and more efficient estimator called the realized range. In addition, we suggest a bias-correction procedure to account for the effects of microstructure frictions based upon scaling the realized range with the average level of the daily range. Simulation experiments demonstrate that for plausible levels of non-trading and bid–ask bounce the realized range has a lower mean-squared error than the realized variance, including variants thereof that are robust to microstructure noise. Empirical analysis of the S&P500 index-futures and the S&P100 constituents confirms the potential of the realized range.     

Pseudo empirical likelihood method in the presence of missing data

In this paper, we propose an estimator for the population mean when some observations on the study and auxiliary variables are missing from the sample. The proposed estimator is valid for any unequal probability sampling design, and is based upon the pseudo empirical likelihood method. The proposed estimator is compared with other estimators in a simulation study.     

Director expertise and co-option in industry superannuation funds?

This study examines whether independent directors who possess financial expertise and are independent from the CEO (i.e., non-co-opted) are associated with improved outcomes for industry superannuation funds. Our results highlight that independence alone is insufficient to improve fund outcomes. Instead, we find that only non-co-opted independent directors benefit fund members in terms of higher performance and lower fees. Moreover, we find that independent directors' financial expertise is not associated with fund performance and fees. Our study has implications for regulators and superannuation funds who are currently debating the need for one-third independent directors on the board of Australian superannuation funds.     

Branding strategies,marketing communication,and perceived brand meaning: The transfer of purposive,goal-oriented brand meaning to brand extensions

This article develops and tests a conceptual model of the transfer process whereby perceived similarity organized around shared goals facilitates the transfer of knowledge and affect from a parent brand to an extension of that brand. Empirical results, based on two well-known brands and two hypothetical product extensions for each brand, demonstrate that the availability of well-formed, goal-derived categories associated with a parent brand establishes an organizing framework for consumers' assessments of similarity thatfacilitates the transfer of consumer knowledge and attitude from the parent brand to a brand extension in another product category. This facilitating effect of similarity does not occur in the absence of goal-derived categories. The results also reveal how marketing communication can be used to facilitate the transfer process by framing similarity in terms of common goals. Implications are discussed for the organization of consumer knowledge and affect across product categories and for understanding prior research findings on brand extension. Ingrid M. Martin (imartin@csulb.edu) is an associate professor of marketing at California State University at Long Beach. Her research has examined issues in the area of consumer goals as they guide structuring and processing marketing information, product choice and usage. Her research has been published in theJournal of Consumer Research, Journal of Marketing Research, Journal of Public Policy & Research, and five book chapters. David W. Stewart (david.stewart@marshall.usc.edu) is the Robert E. Brooker Professor of Marketing in the Marshall School of Business at the University of Southern California. He is the immediate past editor of theJournal of Marketing. Dr. Stewart has authored or co-authored more than 200 publications and seven books. Shashi Matta (matta@marshall.usc.edu) is a doctoral candidate in marketing at the Marshal School of Business, University of Southern California. His research interests include branding, and services marketing. Shashi’s research has been published in theJournal of Consumer Research.     

Linking thoughts to feelings: Investigating cognitive appraisals and consumption emotions in a mixed-emotions context

While emotions have been shown to have significant influence on various consumer behaviors, the cognitive appraisals linked to consumption emotions have not been fully explored. This research investigates how individuals' cognitive interpretations of situations correspond to the emotions they experience in these contexts. Using analysis of variance and multiple discriminant analysis, our results show a systematic relationship between cognitive appraisals and 10 consumption emotions. The author's findings offer theoretical insights into these consumption emotions, the appraisal/consumption emotion correspondence, basisversus subordinate-level category differences in emotions, and mixed consumption emotions. The findings provide a practical framework for academics and practitioners interested in better understanding and managing consumption emotions. Julie A. Ruth (Ph.D., University of Michigan) is an assistant professor of marketing in the School of Business—Camden at Rutgers University. Her research interests include affect and emotions, consumer relationships, and consumer response to brand strategies such as brand alliances and sponsorships. Frédéric F. Brunel (Ph.D., University of Washington) is an assistant professor of marketing in the School of Management at Boston University. His research interests include consumer perceptions of product design and aesthetics, consumer attitude and affect, and gender and sociocultural issues in consumption. Cele C. Otnes (Ph.D., University of Tennessee) is an associate professor of business administration in the College of Commerce and Business Administration at the University of Illinois, Urbana. Her research interests include consumer rituals, affect and consumer ambivalence, and gift exchange.     

Forecasting recovery rates on non-performing loans with machine learning

We compare the performance of a wide set of regression techniques and machine-learning algorithms for predicting recovery rates on non-performing loans, using a private database from a European debt collection agency. We find that rule-based algorithms such as Cubist, boosted trees, and random forests perform significantly better than other approaches. In addition to loan contract specificities, predictors that refer to the bank recovery process — prior to the portfolio’s sale to a debt collector — are also shown to enhance forecasting performance. These variables, derived from the time series of contacts to defaulted clients and client reimbursements to the bank, help all algorithms better identify debtors with different repayment ability and/or commitment, and in general those with different recovery potential.