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The authors argue that organization design needs to play a more active role in the explanation of differential performance and outline a set of ideas for achieving this both in theoretical and empirical research. Firms are heterogeneous in terms of (1) how well they do things, capturing persistent productivity differences, and (2) how they do things – and both reflect firms’ organization design choices. Both types of heterogeneity can be persistent, and are interdependent, although they have typically been studied separately. The authors propose a simple formal framework – the “aggregation function framework” – that aligns organization design thinking with the emphasis on performance heterogeneity among firms that is characteristic of the strategy field. This framework allows for a more precise identification of how exactly organization design may contribute to persistent performance differences, and therefore what exactly are the assumptions that strategy and organization design scholars need to be attentive to.

In this chapter, the author outlines the link between organization design and competitive strategy, focusing on rivalry. A firm’s organization design choices can affect its competitive advantage as well as the strategic decisions of its rivals. Therefore, organization design can influence the nature and intensity of competitive interactions between firms. To illustrate this effect, the author focuses on the literature on divisionalization and offers a set of propositions as examples. Taken together, the author makes three main observations: (1) a firm’s competitive position and objectives are reflected in its organizational choices; (2) heterogeneity in competitive position and objectives lead to heterogeneity in organization design choices across firms; and (3) organization design and competitive strategy are interdependent processes. The author concludes by discussing the implications for strategy and management research and pointing out some opportunities for future research.

This chapter develops a framework for quantile regression in binary longitudinal data settings. A novel Markov chain Monte Carlo (MCMC) method is designed to fit the model and its computational efficiency is demonstrated in a simulation study. The proposed approach is flexible in that it can account for common and individual-specific parameters, as well as multivariate heterogeneity associated with several covariates. The methodology is applied to study female labor force participation and home ownership in the United States. The results offer new insights at the various quantiles, which are of interest to policymakers and researchers alike.

Institutional rules and economies of scale can create incentives for firms to make inframarginal decisions when offering fringe benefits. We examine how such incentives might affect a firm's offer of health insurance.

We develop and estimate an empirical model of the firm's offer of health insurance that includes incentives created by rules and economies of scale. We quantify the behavioral manifestations from rules and costs as recruiting difficulty in areas outside those in which compensation is set and the percentage of high-skilled jobs in the firm and use the California Health and Employment Surveys (CHES) to estimate the model.

We show a 10–13 percentage point increase in the probability of a firm offering workers health insurance in jobs outside of those in which compensation is being set, if the recruiting difficulty lies in mid- or high-skilled positions. This increase is about twice the size of the increase associated with recruiting difficulty in the position in which compensation is negotiated.

A failure to control for the influence of inframarginal decision making when estimating the wage-insurance tradeoff helps produce wrong-signed estimates.

By bringing institutional rules and economies of scale into the framework of a firm's offer of fringe benefits, we help move the focus of the fringe benefit-wage tradeoff away from the individual level.

Identification in a regression discontinuity (RD) design hinges on the discontinuity in the probability of treatment when a covariate (assignment variable) exceeds a known threshold. If the assignment variable is measured with error, however, the discontinuity in the relationship between the probability of treatment and the observed mismeasured assignment variable may disappear. Therefore, the presence of measurement error in the assignment variable poses a challenge to treatment effect identification. This chapter provides sufficient conditions to identify the RD treatment effect using the mismeasured assignment variable, the treatment status and the outcome variable. We prove identification separately for discrete and continuous assignment variables and study the properties of various estimation procedures. We illustrate the proposed methods in an empirical application, where we estimate Medicaid takeup and its crowdout effect on private health insurance coverage.

Purpose – Emerging work on returnee entrepreneurs has done little to examine how these individuals coordinate the resources they need to exploit their opportunities. Existing research has recognized the role of context, but this has been quite limited. The chapter provides a novel analytical framework that integrates a resource orchestration perspective with recognition of the heterogeneity of context.

Design/Methodology – The authors build upon returnee entrepreneurship, strategic entrepreneurship theory, and theories relating to context and spillovers to distinguish the implications of temporal, institutional, social, and spatial dimensions of context for resource selection and coordination.

Findings – The authors identify a range of research themes relating to each context. The authors also discuss methodological issues relating to both qualitative and quantitative research.

Originality/Value – The intention is to spur further entrepreneurship, strategy, and international business research.

The simplest approach to measuring racial composition is to calculate only the proportion of White students in the total undergraduate enrollment (%White). This was the most common approach in earlier studies of student body racial diversity (see, e.g., Astin, 1993).² A slightly different alternative, at least conceptually, is to calculate the percentage of students of color or racial minorities (students who reported their racial/ethnicity to be non-White). Recent studies are more likely to use this alternative (see, e.g., Antonio, 2001; Gurin, Dey, Hurtado, & Gurin, 2002; Terenzini, Cabrera, Colbeck, Bjorklund, & Parente, 2001). This measure centers the focus of analysis on students of color and thus may be more advantageous than “%White” for interpreting findings. Still, the basic assumption underlying both approaches is that as the proportion of White students decline and by definition the proportion of students of color increase, the student body necessarily becomes more diverse. Accordingly, these approaches presume a zero-sum game for diversity on college campuses, where gains for one group come at the expense of other groups.