Top Management Team Heterogeneity,Strategic Change and Operational Performance*

This study examines the role of top management team (TMT) heterogeneity in facilitating strategic change. Based on the upper echelons literature, we argue that heterogeneous management teams are better able to handle the simultaneous and conflicting demands of refocusing the organization strategically and keeping up operational performance. We expect this to be true only for teams that are heterogeneous with respect to factors directly related to job requirements, however. Data were collected from 92 full TMTs of hospitals in Spain that were confronted with institutional pressures that challenged their current strategies. In support of our hypotheses, the results show job‐related TMT heterogeneity moderates the relation between strategic change and operational performance. No moderating effect is found for non‐job‐related TMT heterogeneity.     

Testing Parameters in GMM Without Assuming that They Are Identified

We propose a generalized method of moments (GMM) Lagrange multiplier statistic, i.e., the K statistic, that uses a Jacobian estimator based on the continuous updating estimator that is asymptotically uncorrelated with the sample average of the moments. Its asymptotic χ² distribution therefore holds under a wider set of circumstances, like weak instruments, than the standard full rank case for the expected Jacobian under which the asymptotic χ² distributions of the traditional statistics are valid. The behavior of the K statistic can be spurious around inflection points and maxima of the objective function. This inadequacy is overcome by combining the K statistic with a statistic that tests the validity of the moment equations and by an extension of Moreira's (2003) conditional likelihood ratio statistic toward GMM. We conduct a power comparison to test for the risk aversion parameter in a stochastic discount factor model and construct its confidence set for observed consumption growth and asset return series.     

Why the changing American economy calls for twenty-first century learning: answers to educators' questions

While struggling with the current pressures of educational reform, some educators will ask whether their efforts make economic sense. Questioning the future makeup of the nation's workforce, many wonder how the educational system should be tempered to better prepare today's youth. This chapter answers educators' and parents' questions around the effect of fluctuations in the American economy on the future of education. The authors offer reassurance that good jobs will always be available, but warn that those jobs will require a new level of skills: expert thinking and complex communication. Schools need to go beyond their current curriculum and prepare students to use reading, math, and communication skills to build a deeper and more thoughtful understanding of subject matter. To explain the implications of the nation's changing economy on jobs, technology, and therefore education, the authors address a range of vital questions. Citing occupational distribution data, the chapter explores the supply and range of jobs in the future, as well as why changes in the U.S. job distribution have taken place. As much of the explanation for the shift in job distribution over the past several decades is due to the computerization of the workforce, the authors discuss how computers will affect the future composition of the workforce. The chapter also addresses the consequences of educational improvement on earnings distribution. The authors conclude that beyond workforce preparedness, students need to learn how to be contributing members of a democracy.     

Generalized Method of Moments With Many Weak Moment Conditions

Using many moment conditions can improve efficiency but makes the usual generalized method of moments (GMM) inferences inaccurate. Two‐step GMM is biased. Generalized empirical likelihood (GEL) has smaller bias, but the usual standard errors are too small in instrumental variable settings. In this paper we give a new variance estimator for GEL that addresses this problem. It is consistent under the usual asymptotics and, under many weak moment asymptotics, is larger than usual and is consistent. We also show that the Kleibergen (2005) Lagrange multiplier and conditional likelihood ratio statistics are valid under many weak moments. In addition, we introduce a jackknife GMM estimator, but find that GEL is asymptotically more efficient under many weak moments. In Monte Carlo examples we find that t‐statistics based on the new variance estimator have nearly correct size in a wide range of cases.     

Toward an “Awareness” of the Relationship between Task Performance and Own Verbal Accounts of that Performance

The existence of learning without awareness has been debated for many years. Learning without awareness is said to occur when an individual''s behavior has been affected without that individual being aware of the conditions affecting the behavior, of the relationship between those conditions and the behavior, or of the fact that the behavior has changed. This paper describes a series of experiments investigating this phenomenon. The findings support the existence of “learning without awareness.” However, it is argued that the term “awareness” should be discarded as it is misleading. Instead, the results of the experiments are discussed in terms of behavior for which the individual does not provide a complete verbal account.