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We extend Vuong’s (1989) model-selection statistic to allow for complex survey samples. As a further extension, we use an M-estimation setting so that the tests apply to general estimation problems – such as linear and nonlinear least squares, Poisson regression and fractional response models, to name just a few – and not only to maximum likelihood settings. With stratified sampling, we show how the difference in objective functions should be weighted in order to obtain a suitable test statistic. Interestingly, the weights are needed in computing the model-selection statistic even in cases where stratification is appropriately exogenous, in which case the usual unweighted estimators for the parameters are consistent. With cluster samples and panel data, we show how to combine the weighted objective function with a cluster-robust variance estimator in order to expand the scope of the model-selection tests. A small simulation study shows that the weighted test is promising.

We show how to use a smoothed empirical likelihood approach to conduct efficient semiparametric inference in models characterized as conditional moment equalities when data are collected by variable probability sampling. Results from a simulation experiment suggest that the smoothed empirical likelihood based estimator can estimate the model parameters very well in small to moderately sized stratified samples.

The author proposes analyzing the dynamics of income positions using dynamic panel ordered probit models. The author disentangles, simultaneously, the roles of state dependence and heterogeneity (observed and non-observed) in explaining income position persistence, such as poverty persistence and affluence persistence. The author applies the approach to Chile exploiting longitudinal data from the P-CASEN 2006–2009. First, the author finds that income position mobility at the bottom and the top of the income distribution is much higher than expected, showing signs that income mobility in the case of Chile might be connected to economic insecurity. Second, the observable individual characteristics have a much stronger impact than true state dependence to explain individuals’ current income position in the income distribution extremes.

Strategic adaptation in complex environments with frequent changes must balance the search for innovative opportunistic ventures and conscious pursuit to achieve established goals and outcomes. This creates a tension between attempted efficiency gains from tight strategic controls that avoid diversion of corporate resources and the facilitation of dispersed initiatives in search for business opportunities. To assess this conundrum, the authors present an interactive strategic control model that combines planning and participative strategy-making with interactive control processes. This combination of management practices arguably creates an adaptive system that drives the upside performance outcomes from a guided adaptation of opportunistic insights. Various hypotheses are developed and tested based on survey data from among the 500 largest firms in Denmark. The results suggest a direct relationship between interactive controls, strategic planning, and participative leadership on upside performance outcomes. Moreover, the positive effect from interactive controls on the upside potential is enhanced by participative decisions.

I propose a general framework for instrumental variables estimation of the average treatment effect in the correlated random coefficient model, focusing on the case where the treatment variable has some discreteness. The approach involves adding a particular function of the exogenous variables to a linear model containing interactions in observables, and then using instrumental variables for the endogenous explanatory variable. I show how the general approach applies to binary and Tobit treatment variables, including the case of multiple treatments.

We examine the roles of sample initial conditions and unobserved individual effects in consistent estimation of the dynamic binary response panel data model. Different specifications of the model are estimated using female welfare and labor force participation data from the Survey of Income and Program Participation. These include alternative random effects (RE) models, in which the conditional distributions of both the unobserved heterogeneity and the initial conditions are specified, and fixed effects (FE) conditional logit models that make no assumptions on either distribution. There are several findings. First, the hypothesis that the sample initial conditions are exogenous is rejected by both samples. Misspecification of the initial conditions results in drastically overstated estimates of the state dependence and understated estimates of the short- and long-run effects of children on labor force participation. The FE conditional logit estimates are similar to the estimates from the RE model that is flexible with respect to both the initial conditions and the correlation between the unobserved heterogeneity and the covariates. For female labor force participation, there is evidence that fertility choices are correlated with both unobserved heterogeneity and pre-sample participation histories.

Environmental, social, and governance (ESG) issues have recently received much attention. This research investigates the daily performance of socially responsible investment (SRI). To do that, the authors construct portfolios consisting of the SRI, non-SRI, and matched non-SRI. The portfolios can be compared with the market benchmark based on α adjusted asset pricing models. Due to using high-frequency data, the authors use ARCH/GARCH to deal with time-varying volatility. Moreover, the authors also utilized Fama–MacBeth pooled regression to confront the SRI stocks and the non-SRI counterpart. In sum, the findings of this study confirm the superior performance of the value-weighted (VW) SRI portfolio against the market. On a head-to-head basis, the SRI yields a higher return than the non-SRI. The results are robust in the quarterly analysis. It is essential for investors that put their money in socially responsible (SR) portfolios to either promote sustainable development or chase a return on it.

The world in which companies operate today is volatile, uncertain, complex, and ambiguous, thus subjecting contemporary forms to an array of risks that challenge their viability in an increasingly competitive landscape. Organizations that cling to their traditional ways of operating impede their ability to survive while those able to embrace evolving changes and lever their strategic response capabilities (SRCs) will thrive against the odds. The possession of such capabilities has become a prominent explanation for effective adaptation to the impending changes but is rarely analyzed and tested empirically. Strategic adaptation typically assumes innovation as an important component, but we know little about how the innovative processes interact with the firm’s SRCs. Hence, this study investigates these implied relationships to discern their effects on organizational performance and risk outcomes. It explores the effects of SRCs and the role of innovation as intertwined adaptive mechanisms supporting strategic renewal that can attain superior performance and risk effects. The relationships are analyzed based on a large sample of US manufacturing firms over the decade 2010–2019. The study reveals that firms possessing effective SRCs have the ability to exploit opportunities and deflect risky situations to gain favorable performance and risk outcomes. While innovation indeed plays a role, the precise nature and dynamic effect thereof remain inconclusive.

In elite professional firms, minorities are actively recruited but struggle to move upward. The authors argue that initiatives aimed at general skill development can have unintended consequences for firm diversity. Specifically, the authors contend that approaches that win partner support through motivational significance and interpretive clarity provide a more effective avenue to skill development for minorities, who have less access than White peers to informal developmental opportunities. The authors also argue that a longer “partnership track,” which imposes a time limit on skill development, will benefit minority professionals. Using data on 601 offices of large US law firms in 1996 and 2005, the authors investigate the effects of five developmental initiatives and partnership track length on the representation of African-Americans, Latinxs, and Asian-Americans among partners. Observed effects are consistent with expectations, but patterns vary across racial-ethnic groups.

The purpose of this paper is to compare and analyze the modern productivity estimation techniques, namely, Levinsohn and Petrin (LP, 2003), Ackerberg Caves and Frazer (ACF, 2006), Wooldridge (2009) and Mollisi and Rovigatti (MR, 2017) on unit-level data of 32 Indian industries for the period 2009-2015.

The paper first analyzes different issues encountered in total factor productivity (TFP) measurement. It then categorizes the productivity estimation techniques into three logical generations, namely, traditional, new and advanced. Next, it selects four contemporary estimation techniques, computes the industrial TFP for Indian states by using them and investigates their empirical outcomes. The paper also performs the robustness check to ascertain, which estimation technique is more robust.

The result indicates that the TFP growth of Indian industries have differed greatly over this seven-years of period, but the estimates are sensitive to the techniques used. Further results suggest that ACF and Wooldridge yield the consistent outcomes as compared to LP and MR. The robustness test confirms Wooldridge to be the most robust contemporary technique for productivity estimation followed by ACF and LP.

To the authors’ knowledge, this is the first study that compares the contemporary productivity estimation techniques. In this backdrop, this paper offers two novelties. First, it uses advanced production estimation techniques to compute TFP of 32 diverse industries of an emerging economy: India. Second, it addresses the fitment of estimation techniques by drawing a comparison and by conducting a robustness test, hence, contributing to the limited literature on comparing contemporary productivity estimation techniques.