Becoming an evidence‐based HR practitioner

Evidence‐based HR (EBHR) is a decision‐making process combining critical thinking with use of the best available scientific evidence and business information. We describe how to get started as an evidence‐based HR practitioner. Actively managing professional decisions is a key aspect of EBHR. Doing so involves making decisions, especially consequential or recurring ones, using practices supported by high‐quality research. We present a step‐by‐step set of approaches to becoming an evidence‐based HR practitioner: from getting started, through everyday practices and continuous learning to integrating EBHR into your organisation. In offering guidance for evidence‐based practice, this article underscores the connection between effective practice and organisational research.     

Distributional dynamics following a technological revolution

In this paper we explore the link between technological change and the dynamics of employment, production, and the distribution of earnings. Technological change not only advances society's collective capability but also changes the relative productivities of its members. The latter effect establishes the likely winners and losers from advances in productive capabilities, provides a mechanism that can generate cyclical fluctuations in output as well as employment, and determines the evolution of the earnings distribution.
Dynamique de la répartition à la suite d'une révolution technologique . Ce mémoire examine le lien entre le changement technologique et la dynamique de l'emploi, de la production et de la répartition des revenus. Le changement technologique n'augmente pas seulement la capacité collective d'une société mais modifie aussi les productivités relatives de ses membres. Ce dernier effet crée des gagnants et des perdants, déclenche un mécanisme qui peut générer des fluctuations cycliques tant dans le niveau de production que dans celui de l'emploi, et détermine l'évolution de la répartition des revenus.     

Modelling innovation support systems for regional development – analysis of cluster structures in innovation in Portugal

The present article offers a concise theoretical conceptualization and operational analysis of the contribution of innovation to regional development. The latter concepts are closely related to geographical proximity, knowledge diffusion and filters and clustering. Institutional innovation profiles and regional patterns of innovation are two mutually linked, novel conceptual elements in this article. Next to a theoretical framing, the article employs the regional innovation systems concept as a vehicle to analyse institutional innovation profiles. Our case study addresses three Portuguese regions and their institutions, included in a web-based inventory of innovation agencies which offered the foundation for an extensive database. This data-set was analysed by means of a recently developed principal coordinates analysis followed by a Logistic Biplot approach (leading to a Voronoi mapping) to design a systemic typology of innovation structures where each institution is individually represented. There appears to be a significant difference in the regional innovation patterns resulting from the diverse institutional innovation profiles concerned. These profiles appear to be region specific. Our conclusion highlights the main advantages in the use of the method used for policy-makers and business companies.     

A Brief Survey of Modern Optimization for Statisticians

Modern computational statistics is turning more and more to high‐dimensional optimization to handle the deluge of big data. Once a model is formulated, its parameters can be estimated by optimization. Because model parsimony is important, models routinely include non‐differentiable penalty terms such as the lasso. This sober reality complicates minimization and maximization. Our broad survey stresses a few important principles in algorithm design. Rather than view these principles in isolation, it is more productive to mix and match them. A few well‐chosen examples illustrate this point. Algorithm derivation is also emphasized, and theory is downplayed, particularly the abstractions of the convex calculus. Thus, our survey should be useful and accessible to a broad audience.     

Forecasting the term structures of Treasury and corporate yields using dynamic Nelson-Siegel models

We extend Diebold and Li’s dynamic Nelson-Siegel three-factor model to a broader empirical prospective by including the evaluation of the state space approach and by using nine different ratings for corporate bonds. We find that the dynamic Nelson-Siegel factor AR(1) model outperforms other competitors on the out-of-sample forecast accuracy, especially on the investment-grade bonds for the short-term forecast horizon and on the high-yield bonds for the long-term forecast horizon. The dynamic Nelson-Siegel factor state space model, however, becomes appealing on the high-yield bonds in the short-term forecast horizon, where the factor dynamics are more likely time-varying and parameter instability is more probable in the model specification.     

Prosecutorial Discretion at the EPA: Some Evidence on Litigation Strategy

This paper examines the determinants of the EPAs litigation strategy between 1977 and 1996 focusing on the tenure of Ann Gorsuch. Two hypotheses about this period are tested: (1) that changes in the EPAs litigation strategy were in fact an effort to reduce the expected penalty for violating environmental laws or (2) that the changes made in litigation strategy were consistent with efforts to reduce transaction cost. Contrary to previous research, I find no conclusive evidence of an overall shift toward business interests in EPA prosecutions. There is, however, clear evidence of shifts in the EPAs litigation strategy across administrations.     

Precautionary Demand for Education, Inequality, and Technological Progress

This paper offers an explanation for the evolution of wage inequality within and between industries and education groups over the past several decades. The model is based on the disproportionate depreciation of technology-specific skills versus general skills due to technological progress, which occurs randomly across sectors. Consistent with empirical evidence, the model predicts that increasing randomness is the primary source of inequality growth within uneducated workers, whereas inequality growth within educated workers is determined more by changes in the composition and return to ability. Increasing randomness generates a precautionary demand for education, which we show empirically to be significant.