Middle Managers' Divergent Strategic Activity: An Investigation of Multiple Measures of Network Centrality

abstract    Using a sample of 89 mid-level managers in a US based urban hospital, this study investigates relationships among three measures of network centrality and managers' divergent strategic activity. While prior work has demonstrated a relationship between managers' boundary-spanning responsibilities and strategic activity, inadequate attention has been paid to managers' internal network position. Drawing from established theory, we consider expected network flows associated with three elements of the strategic renewal process. From this, we hypothesize and test relationships among managers' divergent activity and three measures of network centrality. Our findings suggest specific relationships between alternative forms of network centrality and particular elements of the strategic renewal process. Consistent with existing research, the findings also show boundary-spanning managers to be more strategically active than their non-boundary-spanning counterparts.     

Estimation of dynamic panel data models with sample selection

We propose a new method for estimating dynamic panel data models with selection. The method uses backward substitution for the lagged dependent variable, which leads to an estimating equation that requires correcting for contemporaneous selection only. The estimator is valid under relatively weak assumptions about errors and permits avoiding the weak instruments problem associated with differencing. We also propose a simple test for selection bias that is based on the addition of a selection term to the first‐difference equation and subsequent testing for significance of this term. The methods are applied to estimating dynamic earnings equations for women. Copyright © 2011 John Wiley & Sons, Ltd.     

Estimating panel data models in the presence of endogeneity and selection

We consider estimation of panel data models with sample selection when the equation of interest contains endogenous explanatory variables as well as unobserved heterogeneity. Assuming that appropriate instruments are available, we propose several tests for selection bias and two estimation procedures that correct for selection in the presence of endogenous regressors. The tests are based on the fixed effects two-stage least squares estimator, thereby permitting arbitrary correlation between unobserved heterogeneity and explanatory variables. The first correction procedure is parametric and is valid under the assumption that the errors in the selection equation are normally distributed. The second procedure estimates the model parameters semiparametrically using series estimators. In the proposed testing and correction procedures, the error terms may be heterogeneously distributed and serially dependent in both selection and primary equations. Because these methods allow for a rather flexible structure of the error variance and do not impose any nonstandard assumptions on the conditional distributions of explanatory variables, they provide a useful alternative to the existing approaches presented in the literature.     

Empirical economics acknowledges the cooperation of

Empirical economics acknowledges the cooperation of     

Understanding Error Structures and Exploiting Panel Data in Meta-analytic Benefit Transfers

A regression meta-analysis is a statistical summary of results from a set of empirical studies. While, a meta-analysis is typically used to drawn inferences regarding the collective insights from an empirical literature, a regression meta-analysis can also be used to predict outcomes as a substitute for the conduct of a new study. Within the nonmarket-valuation literature benefit transfers are a special case of prediction where value estimates collected for one purpose are used as a basis for predicting value for unstudied applications. Balancing against the prediction opportunities provided by a regression meta-analysis is the potential prediction error. This paper considers some of these issues in the estimation of a regression meta-analysis to support prediction of nonmarket values for applications where an original study does not exist. We do not purport to address all elements of the error structure and prediction issues, but to present a more coherent focus to enhance future research on the validity and reliability of benefit-function transfers, and ultimately assist in enhancing the credibility of benefit transfers to support policy analyses.     

Quasi-maximum likelihood estimation and testing for nonlinear models with endogenous explanatory variables

I propose a quasi-maximum likelihood framework for estimating nonlinear models with continuous or discrete endogenous explanatory variables. Joint and two-step estimation procedures are considered. The joint procedure is a quasi-limited information maximum likelihood procedure, as one or both of the log likelihoods may be misspecified. The two-step control function approach is computationally simple and leads to straightforward tests of endogeneity. In the case of discrete endogenous explanatory variables, I argue that the control function approach can be applied with generalized residuals to obtain average partial effects. I show how the results apply to nonlinear models for fractional and nonnegative responses.     

Panel data methods for fractional response variables with an application to test pass rates

We revisit the effects of spending on student performance using data from the state of Michigan. In addition to exploiting a dramatic change in funding in the mid-1990s and subsequent nonsmooth changes, we propose nonlinear panel data models that recognize the bounded nature of the pass rate. Importantly, we show how to estimate average partial effects, which can be compared across many different models (linear and nonlinear) under different assumptions and estimated using many different methods. We find that spending has nontrivial and statistically significant effects, although the diminishing effect is not especially pronounced.     

Influences on Sustainable Innovation Adoption: Evidence from Leadership in Energy and Environmental Design

The adoption of innovations associated with environmental sustainability has been a topic of growing interest among scholars. The research presented in this paper draws on Abrahamson's theoretical framework of fads and fashions to argue that dimensions of uncertainty and degree of external versus internal influence provide significant insights into firms' decisions to adopt sustainable building innovations. We develop three hypotheses, reflecting three views of adoption influence: fad, fashion, and efficient‐choice. We find that adoption of Leadership in Energy and Environmental Design (LEED) green building certification in the United States was more likely among firms similarly oriented toward end‐consumers and among firms strategically positioned as environmental leaders. These results provide support for the fad and efficient‐choice views of adoption, respectively. Contrary to expectations suggested by the fashion perspective, adoption was not more likely among firms located in states whose political leaders are more committed to environmental protection. Our findings offer important implications for practitioners and policy makers seeking to encourage sustainable building design. Copyright © 2011 John Wiley & Sons, Ltd and ERP Environment.     

Inverse probability weighted M-estimators for sample selection, attrition, and stratification

I provide an overview of inverse probability weighted (IPW) M-estimators for cross section and two-period panel data applications. Under an ignorability assumption, I show that population parameters are identified, and provide straightforward -consistent and asymptotically normal estimation methods. I show that estimating a binary response selection model by conditional maximum likelihood leads to a more efficient estimator than using known probabilities, a result that unifies several disparate results in the literature. But IPW estimation is not a panacea: in some important cases of nonresponse, unweighted estimators will be consistent under weaker ignorability assumptions.JEL Classification: C13, C21, C23I would like to thank Bo Honoré, Christophe Muller, Frank Windmeijer, and the participants at the CeMMAP/ESCR Econometric Study Group Microeconometrics Workshop for helpful comments on an earlier draft.