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Job Corps is the United State’s largest and most comprehensive training program for disadvantaged youth aged 16–24 years old. A randomized social experiment concluded that, on average, individuals benefited from the program in the form of higher weekly earnings and employment prospects. At the same time, “young adults” (ages 20–24) realized much higher impacts relative to “adolescents” (ages 16–19). Employing recent nonparametric bounds for causal mediation, we investigate whether these two groups’ disparate effects correspond to them benefiting differentially from distinct aspects of Job Corps, with a particular focus on the attainment of a degree (GED, high school, or vocational). We find that, for young adults, the part of the total effect of Job Corps on earnings (employment) that is due to attaining a degree within the program is at most 41% (32%) of the total effect, whereas for adolescents that part can account for up to 87% (100%) of the total effect. We also find evidence that the magnitude of the part of the effect of Job Corps on the outcomes that works through components of Job Corps other than degree attainment (e.g., social skills, job placement, residential services) is likely higher for young adults than for adolescents. That those other components likely play a more important role for young adults has policy implications for more effectively servicing participants. More generally, our results illustrate how researchers can learn about particular mechanisms of an intervention.

In recent years, a wide range of psychosocial health interventions have been implemented among military service members and their families. However, there are questions over the evaluative rigor of these interventions. We conducted a systematic review of this literature, rating each relevant study (k = 111) on five evaluative rigor scales (type of control group, approach to participant assignment, outcome quality, number of measurement time points, and follow-up distality). The most frequently coded values on three of the five scales (control group type, participant assignment, and follow-up distality) were those indicating the lowest level of operationally defined rigor. Logistic regression results indicate that the evaluative rigor of intervention studies has largely remained consistent over time, with exceptions indicating that rigor has decreased. Analyses among seven military sub-populations indicate that interventions conducted among soldiers completing basic training, soldiers returning from combat deployment, and combat veterans have had, on average, the greatest evaluative rigor. However, variability in mean scores across evaluative rigor scales within sub-populations highlights the unique methodological hurdles common to different military settings. Recommendations for better standardizing the intervention evaluation process are discussed.

This chapter investigates the effects of gender stereotypes on expectations about expertise and task assignments. We present a theoretical model that predicts and explains the pervasive and self-reinforcing effects of gender-based stereotypes on expected knowledge and task assignments in groups. In the model, stereotypes influence expertise recognition, which influences tasks assignments. Task assignments provide group members with task experience and expertise. Expertise influences expertise recognition, making the model cyclical. Expertise gained from task experience also affects stereotypes, creating a cycle that reinforces stereotypes. We describe findings from a program of research designed to examine ways of breaking this self-reinforcing cycle, which investigates the effectiveness of various types of expertise claims made by people with expertise, that is inconsistent with stereotypical expectations. We consider the implications of our theory and data for effects of status on evaluation of expertise claims in work groups.

In the past decade, microfinance institutions (MFIs) have experienced a boom in innovations of lending products, partly fueled by donors who see microfinance as the next promise to alleviate poverty. Examples of these new products are the combination of credit with health or life insurance, business and health education, savings products, and the adoption of (or conversion to) individual loan liability. The add-on features generally aim at reducing the vulnerability of clients while contributing to asset creation, hence improving repayment rates and the sustainability of the service. The product innovations typically result from organizations striving to extend outreach, increase impact, and promote sustainability. As in other industries, MFIs typically decide whether to adopt new strategies based on other MFIs’ success with the innovations. Many new microlending products and approaches continue to be developed. However, MFIs must generally rely on qualitative and descriptive case studies and anecdotal evidence on the effectiveness of these innovations to decide whether to implement the new strategies. The usual case study approach does not provide tangible evidence that can enable other organizations to know what changes can be expected if they were to adopt similar product changes.

Teams’ emotional skills can be more than the sum of their individual parts. Although theory emphasizes emotion as an interpersonal adaptation, emotion recognition skill has long been conceptualized as an individual-level intelligence. We introduce the construct of team emotion recognition accuracy (TERA) – the ability of members to recognize teammates’ emotions – and present preliminary evidence for its predictive validity. In a field study of public service interns working full-time in randomly assigned teams, taken together positive and negative TERA measured at the time of team formation accounted for 28.1% of the variance in team performance ratings nearly a year later.

We discuss the two most popular frameworks for identification, estimation and inference in regression discontinuity (RD) designs: the continuity-based framework, where the conditional expectations of the potential outcomes are assumed to be continuous functions of the score at the cutoff, and the local randomization framework, where the treatment assignment is assumed to be as good as randomized in a neighborhood around the cutoff. Using various examples, we show that (i) assuming random assignment of the RD running variable in a neighborhood of the cutoff implies neither that the potential outcomes and the treatment are statistically independent, nor that the potential outcomes are unrelated to the running variable in this neighborhood; and (ii) assuming local independence between the potential outcomes and the treatment does not imply the exclusion restriction that the score affects the outcomes only through the treatment indicator. Our discussion highlights key distinctions between “locally randomized” RD designs and real experiments, including that statistical independence and random assignment are conceptually different in RD contexts, and that the RD treatment assignment rule places no restrictions on how the score and potential outcomes are related. Our findings imply that the methods for RD estimation, inference, and falsification used in practice will necessarily be different (both in formal properties and in interpretation) according to which of the two frameworks is invoked.

Researchers in economics and other disciplines are often interested in the causal effect of a binary treatment on outcomes. Econometric methods used to estimate such effects are divided into one of two strands depending on whether they require unconfoundedness (i.e., independence of potential outcomes and treatment assignment conditional on a set of observable covariates). When this assumption holds, researchers now have a wide array of estimation techniques from which to choose. However, very little is known about their performance – both in absolute and relative terms – when measurement error is present. In this study, the performance of several estimators that require unconfoundedness, as well as some that do not, are evaluated in a Monte Carlo study. In all cases, the data-generating process is such that unconfoundedness holds with the ‘real’ data. However, measurement error is then introduced. Specifically, three types of measurement error are considered: (i) errors in treatment assignment, (ii) errors in the outcome, and (iii) errors in the vector of covariates. Recommendations for researchers are provided.

Relative to the randomized controlled trial (RCT), the basic regression discontinuity (RD) design suffers from lower statistical power and lesser ability to generalize causal estimates away from the treatment eligibility cutoff. This chapter seeks to mitigate these limitations by adding an untreated outcome comparison function that is measured along all or most of the assignment variable. When added to the usual treated and untreated outcomes observed in the basic RD, a comparative RD (CRD) design results. One version of CRD adds a pretest measure of the study outcome (CRD-Pre); another adds posttest outcomes from a nonequivalent comparison group (CRD-CG). We describe how these designs can be used to identify unbiased causal effects away from the cutoff under the assumption that a common, stable functional form describes how untreated outcomes vary with the assignment variable, both in the basic RD and in the added outcomes data (pretests or a comparison group’s posttest). We then create the two CRD designs using data from the National Head Start Impact Study, a large-scale RCT. For both designs, we find that all untreated outcome functions are parallel, which lends support to CRD’s identifying assumptions. Our results also indicate that CRD-Pre and CRD-CG both yield impact estimates at the cutoff that have a similarly small bias as, but are more precise than, the basic RD’s impact estimates. In addition, both CRD designs produce estimates of impacts away from the cutoff that have relatively little bias compared to estimates of the same parameter from the RCT design. This common finding appears to be driven by two different mechanisms. In this instance of CRD-CG, potential untreated outcomes were likely independent of the assignment variable from the start. This was not the case with CRD-Pre. However, fitting a model using the observed pretests and untreated posttests to account for the initial dependence generated an accurate prediction of the missing counterfactual. The result was an unbiased causal estimate away from the cutoff, conditional on this successful prediction of the untreated outcomes of the treated.

This article attempts to bridge and contribute to three related lines of inquiry: the effect of economic organization on cultural diversity; the origins of career specialism; and the contrast between market and firm as alternative modes of governance. In particular, I use the natural experiment engendered by the transformation of Hollywood from the firm-based studio system to the contemporary market system to test the claim that typecasting-driven restrictions on generalist identities in an internal labor market are comparable in their significance to those found in the external labor market (Faulkner, 1983; Zuckerman, Kim, Ukanwa, & von Rittmann, 2003). Results support this claim and thereby suggest that incentives for experimentation by employers in internal labor markets counterbalance the greater control over work assignments enjoyed by independent contractors in the external labor market.