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Even more than 50 years after the Civil Rights Act of 1964 prohibited discrimination toward a number of groups in employment settings in the United States, workplace discrimination remains a persistent problem in organizations. This chapter provides a comprehensive review and analysis of contemporary theory and evidence on the nature, causes, and consequences of discrimination before synthesizing potential methods for its reduction. We note the strengths and weaknesses of this scholarship and highlight meaningful future directions. In so doing, we hope to both inform and inspire organizational and scholarly efforts to understand and eliminate workplace discrimination.

The purpose of this study is to investigate how brand communities collectively react towards brand transgressions, an area where previous research has been scant.

This study adopts a netnographic approach in studying the reactions of one particular brand community and its reactions to a marketer-initiated brand transgression.

Building on coping theory, we find evidence of brand community coping, a temporally bounded process in which the community seeks to come to terms with and even overturn the transgression. Overall, we define the brand community coping process as unfolding through three overlapping and temporally bounded stages of (1) making the problem communal, (2) exploring the problem’s meaning, and (3) co-creating responses.

Studies of consumer coping particularly in cases of brand transgressions have predominantly adopted an individualistic approach to coping, or have treated communities as coping resources for individual consumers. This study is the first study to truly look at brand communities’ collective coping endeavors. We also offer managerial implications by questioning the overtly positive tone of brand co-creation literature and underline potential threats to marketers when consumers decide to use their co-creative practices to punish the marketer.

The purpose of this paper is to examine how research teams serve as building blocks for collaboration at a field level, and how these building blocks are assembled by a network of interacting organizations. The field setting is a medical sciences consortium in Australia established to encourage collaborative and entrepreneurial research among government, industry, research centers and university units. This consortium is examined as a case study. The analysis demonstrates how collaboration evolved at three interacting levels: research team, organization and interorganizational field.

The main findings are: (1) Intellectual property (IP) acts as the key orienting agent in this field to align the behavior of various stakeholders and leverage collaborative and entrepreneurial activity. (2) Tensions between the different ways that the commercial and public sector actors value IP serve to structure the interfaces among the consortium, the member organizations and the research teams. (3) The consortium is a key infrastructural element in the creation of collaborative capital in the Australian biotechnology field studied. The main contribution of the study is to highlight the nature of collaborative capital at a field level and begin to explore its implications.

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.

In this paper, we study partial identification of the distribution of treatment effects of a binary treatment for ideal randomized experiments, ideal randomized experiments with a known value of a dependence measure, and for data satisfying the selection-on-observables assumption, respectively. For ideal randomized experiments, (i) we propose nonparametric estimators of the sharp bounds on the distribution of treatment effects and construct asymptotically valid confidence sets for the distribution of treatment effects; (ii) we propose bias-corrected estimators of the sharp bounds on the distribution of treatment effects; and (iii) we investigate finite sample performances of the proposed confidence sets and the bias-corrected estimators via simulation.

We make use of rich microdata from the Belgian MEqIn survey, which contains detailed information on individual consumption, public consumption inside households, and time use. We explain the observed household behavior by means of a collective model that integrates marriage market restrictions on intrahousehold allocation patterns. We adopt a revealed preference approach that abstains from any functional form assumptions on individual utility functions or intrahousehold decision processes. This allows us to (set) identify the sharing rule, which governs the intrahousehold sharing of time and money, and to quantify economies of scale within households. We use these results to conduct a robust individual welfare and inequality analysis, hereby highlighting the important role of detailed consumption and time use data.