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The chapter considers the ethical problems engendered by random assignment and privacy concerns in randomised controlled experiments and cluster randomised trials. The particular focus is on procedural, legislative and technical approaches to reducing or avoiding the problems. Examples are given from a variety of disciplines including health and education, though the main emphasis is on research in crime and delinquency.

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.

This paper tests whether employers responded particularly negatively to African American job applicants during the deep U.S. recession that began in 2007. Theories of labor queuing and social closure posit that members of privileged groups will act to minimize labor market competition in times of economic turbulence, which could advantage Whites relative to African Americans. Although social closure should be weakest in the less desirable, low-wage job market, it may extend downward during recessions, pushing minority groups further down the labor queue and exacerbating racial inequalities in hiring. We consider two complementary data sources: (1) a field experiment with a randomized block design and (2) the nationally representative NLSY97 sample. Contrary to expectations, both analyses reveal a comparable recession-based decline in job prospects for White and African American male applicants, implying that hiring managers did not adapt new forms of social closure and demonstrating the durability of inequality even in times of structural change. Despite this proportionate drop, however, the recession left African Americans in an extremely disadvantaged position. Whites during the recession obtained favorable responses from employers at rates similar to African Americans prior to the recession. The combination of experimental methods and nationally representative longitudinal data yields strong evidence on how race and recession affect job prospects in the low-wage labor market.

This chapter reviews the existing empirical evidence on how social insurance affects health. Social insurance encompasses programs primarily designed to insure against health risks, such as health insurance, sick leave insurance, accident insurance, long-term care insurance, and disability insurance as well as other programs, such as unemployment insurance, pension insurance, and country-specific social insurance programs. These insurance systems exist in almost all developed countries around the world. This chapter discusses the state-of-the art evidence on each of these social insurance systems, briefly reviews the empirical methods for identifying causal effects, and examines possible limitations to these methods. The findings reveal robust and rich evidence on first-stage behavioral responses (“moral hazard”) to changes in insurance coverage. Surprisingly, evidence on how changes in coverage impact beneficiaries’ health is scant and inconclusive. This lack of identified causal health effects is directly related to limitations on how human health is typically measured, limitations on the empirical approaches, and a paucity of administrative panel data spanning long-time horizons. Future research must be conducted to fill these gaps. Of particular importance is evidence on how these social insurance systems interact and affect human health over the life cycle.

In this paper, we present a critical survey of experiments on political clientelism and vote-buying. We claim that through randomization and control, field experiments represent an important tool for answering causal questions, whereas list experiments provide useful methods that improve the hard task of measuring clientelism. We show that existing experimental research gives answers to the questions of why clientelism is effective for getting votes and winning elections, who relies more on this strategy – incumbents or challengers – how much clientelism takes place, and who tend to be the favorite targets of clientelistic politicians. The relationship between clientelism and other illicit strategies for getting votes, such as electoral violence and fraud, has also been analyzed through experimental interventions. Experiments have also studied mechanisms and policies for overcoming clientelism. Finally, we show that external validity is a major source of concern that affects this burgeoning literature.

As businesses become more sophisticated and welcome new technologies, artificial intelligence (AI)-based methods are increasingly being used for firms' pricing decisions. In this review article, we provide a survey of research in the area of AI and pricing. On the upside, research has shown that algorithms allow companies to achieve unprecedented advantages, including real-time response to demand and supply shocks, personalized pricing, and demand learning. However, recent research has uncovered unforeseen downsides to algorithmic pricing that are important for managers and policy-makers to consider.

The overall goal of science is to build a valid and reliable body of knowledge about the functioning of the world and how applying that knowledge can change it. As personnel and human resources management researchers, we aim to contribute to the respective bodies of knowledge to provide both employers and employees with a workable foundation to help with those problems they are confronted with. However, what research on research has consistently demonstrated is that the scientific endeavor possesses existential issues including a substantial lack of (a) solid theory, (b) replicability, (c) reproducibility, (d) proper and generalizable samples, (e) sufficient quality control (i.e., peer review), (f) robust and trustworthy statistical results, (g) availability of research, and (h) sufficient practical implications. In this chapter, we first sing a song of sorrow regarding the current state of the social sciences in general and personnel and human resources management specifically. Then, we investigate potential grievances that might have led to it (i.e., questionable research practices, misplaced incentives), only to end with a verse of hope by outlining an avenue for betterment (i.e., open science and policy changes at multiple levels).

Using the Program for International Student Assessment (PISA) surveys (2003–2015), this chapter explores the relationship between the gender gap in math test scores and computer (digital devices) gaming, as a potential “swimming upstream” factor in the quest to close that gap. Using a decomposition based on a pooled hybrid specification, we attribute two to three points (from 13% to 29%) of the gender math gap to gender differences in the incidence and returns to intense gaming. The comparison of the negative versus positive girl-specific effects found for collaborative games versus single-player games suggest a potential role for gaming network effects.

This chapter analyzes a geographic quasi-experiment embedded in a cluster-randomized experiment in Honduras. In the experiment, average treatment effects of conditional cash transfers on school enrollment and child labor were large – especially in the poorest experimental blocks – and could be generalized to a policy-relevant population given the original sample selection criteria. In contrast, the geographic quasi-experiment yielded point estimates that, for two of three dependent variables, were attenuated. A judicious policy analyst without access to the experimental results might have provided misleading advice based on the magnitude of point estimates. We assessed two main explanations for the difference in point estimates, related to external and internal validity.

The use of behavioral insights and experimental methods has recently gained momentum among health policy-makers. There is a tendency, however, to reduce behavioral insights applications in health to “nudges,” and to reduce experiments in health to “randomized controlled trials” (RCTs). We argue that there is much more to behavioral insights and experimental methods in health economics than just nudges and RCTs. First, there is a broad and rich array of complementary experimental methods spanning the lab to the field, and all of them could prove useful in health economics. Second, there are a host of challenges in health economics, policy, and management where the application of behavioral insights and experimental methods is timely and highly promising. We illustrate this point by describing applications of experimental methods and behavioral insights to one specific topic of fundamental relevance for health research and policy: the experimental elicitation and econometric estimation of risk and time preferences. We start by reviewing the main methods of measuring risk and time preferences in health. We then focus on the “behavioral econometrics” approach to jointly elicit and estimate risk and time preferences, and we illustrate its state-of-the-art applications to health.