Combining Matching and Nonparametric Instrumental Variable Estimation: Theory and An Application to the Evaluation of Active Labour Market Policies

We show how instrumental variable and matching estimators can be combined in order to identify a broader array of treatment effects. Instrumental variable (IV) estimators are known to estimate effects only for the compliers, representing a subset of the entire population. By combining IV with matching, we can estimate the treatment effects for the always‐ and never‐takers as well. Since in many cases these groups are the (endogenous) outcome of some assignment process, such estimates also help in judging the implications of such a selection process. In our application to the effects of participation in active labour market programmes in Switzerland, we find large and lasting positive employment effects for the compliers, whereas the effects for the always‐ and never‐participants are small. In addition, the compliers have worse employment outcomes without treatment than those who participate in the programme with or without the intervention under investigation. This suggests that the earlier assignment policy of the caseworkers was inefficient in that the always‐participants were neither those unemployed who would experience the highest expected treatment effects nor those unemployed who had the largest need for assistance. Copyright © 2014 John Wiley & Sons, Ltd.     

Framing the field of international human resource management research

This article presents an overarching framework of the international human resource management field. The framework has four different levels: Macro (encompassing countries, regions and industries), the Multinational Corporation, Unit (typically subsidiary) and Individual (including teams, employees and their family members). At each level, we make a distinction between Influencing Factors, the HRM Function (encompassing both the HR department and HR policies and practices), Proximal Outcomes and Distant Outcomes of HRM. The framework allows us to examine existing research and suggest avenues for future work.     

Relative importance of scheduled macroeconomic news for stock market investors

This paper investigates the relative importance of scheduled U.S. macroeconomic news releases for stock valuation. The study focuses on 11 macroeconomic announcements selected on the basis of the previous literature and the Bureau of Labor Statistics classifications of major economic indicators. The paper shows that five out of the 11 announcements have significant influence on stock valuation. These are the Employment Report, NAPM (manufacturing), Producer Price Index, Import and Export Price Indices, and Employment Cost Index. Of these six announcements, the Employment Report and NAPM (manufacturing) exert the greatest influence. The time of the announcement, measured by days from the beginning of the month to the release day, has a moderating impact on the relationship between macroeconomic announcements and its importance.     

Innovation and the circular economy: A systematic literature review

The circular economy emerged as an alternative model to the linear system, which now appears to be reaching its physical limitations. To transition to a circular economy, companies must not only be aware of but also engage in more sustainable practices. For such a transition, companies must rethink and innovate their business models and the ways they propose value to their clients while simultaneously considering environmental and social facets. This systematic literature review sought to map out from the company perspective the key topics interrelated with innovation and the circular economy, describing the internal and external factors to consider in such transition processes. Key lines of research were identified, and suggestions for future research and for facilitating movement toward a circular economy are provided. This work contributes to deepening the literature by identifying the priority areas concerning the circular economy and encouraging future research that meets international standards of excellence.     

Quality competition and entry deterrence: When to launch a second brand

We study the rationale for an incumbent to launch a second brand when facing potential entry into a market with quality‐differentiated products and a fringe producer. Depending on market size, the cost of a second brand and a potential entrant's setup cost the incumbent might use a second brand both when deterring and when accommodating entry. For low costs of brand proliferation, the high‐quality firm will prevent entry with limit qualities or multiple brands. The high‐quality incumbent will accommodate entry only if it cannot be prevented. Accommodation is always accompanied by an additional brand safeguarding the premium brand.     

Some general points on the $$I^2$$-measure of heterogeneity in meta-analysis

Meta-analysis has developed to be a most important tool in evaluation research. Heterogeneity is an issue that is present in almost any meta-analysis. However, the magnitude of heterogeneity differs across meta-analyses. In this respect, Higgins’ \(I^2\) has emerged to be one of the most used and, potentially, one of the most useful measures as it provides quantification of the amount of heterogeneity involved in a given meta-analysis. Higgins’ \(I^2\) is conventionally interpreted, in the sense of a variance component analysis, as the proportion of total variance due to heterogeneity. However, this interpretation is not entirely justified as the second part involved in defining the total variation, usually denoted as \(s^2\), is not an average of the study-specific variances, but in fact some other function of the study-specific variances. We show that \(s^2\) is asymptotically identical to the harmonic mean of the study-specific variances and, for any number of studies, is at least as large as the harmonic mean with the inequality being sharp if all study-specific variances agree. This justifies, from our point of view, the interpretation of explained variance, at least for meta-analyses with larger number of component studies or small variation in study-specific variances. These points are illustrated by a number of empirical meta-analyses as well as simulation work.     

Regularized conditional estimators of unit inefficiency in stochastic frontier analysis,with application to electricity distribution market

In stochastic frontier analysis, the conventional estimation of unit inefficiency is based on the mean/mode of the inefficiency, conditioned on the composite error. It is known that the conditional mean of inefficiency shrinks towards the mean rather than towards the unit inefficiency. In this paper, we analytically prove that the conditional mode cannot accurately estimate unit inefficiency, either. We propose regularized estimators of unit inefficiency that restrict the unit inefficiency estimators to satisfy some a priori assumptions, and derive the closed form regularized conditional mode estimators for the three most commonly used inefficiency densities. Extensive simulations show that, under common empirical situations, e.g., regarding sample size and signal-to-noise ratio, the regularized estimators outperform the conventional (unregularized) estimators when the inefficiency is greater than its mean/mode. Based on real data from the electricity distribution sector in Sweden, we demonstrate that the conventional conditional estimators and our regularized conditional estimators provide substantially different results for highly inefficient companies.