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Double Exposure examines the role of film in shaping social psychology's landmark postwar experiments. We are told that most of us will inflict electric shocks on a fellow citizen when ordered to do so. Act as a brutal prison guard when we put on a uniform. Walk on by when we see a stranger in need. But there is more to the story. Documentaries that investigators claimed as evidence were central to capturing the public imagination. Did they provide an alibi for twentieth century humanity? Examining the dramaturgy, staging and filming of these experiments, including Milgram's Obedience Experiments, the Stanford Prison Experiment and many more, Double Exposure recovers a new set of narratives.

This paper analyzes the method of social experiments. The assumptions that justify the experimental method are exposited. Parameters of interest in evaluating social programs are discussed. The authors show how experiments sometimes serve as instrumental variables to identify program impacts. The most favorable case for experiments ignores variability across persons in response to treatments received and assumes that mean impacts of a program are the main object of interest in conducting an evaluation. Experiments do not identify the distribution of program gains unless additional assumptions are maintained. Evidence on the validity of the assumptions used to justify social experiments is presented.

This paper introduces an innovative test of search and matching models using the exogenous variation available in experimental data. We take an off-the-shelf search model and calibrate it to data on the control group from a randomized social experiment. We then simulate a program group from a randomized experiment within the model. As a measure of the performance of the model, we compare the outcomes of the program groups from the model and from the randomized experiment. We illustrate our methodology using the Canadian Self-Sufficiency Project (SSP), a social experiment providing a time-limited earnings supplement for Income Assistance recipients who obtain full-time employment within a 12-month period. We find two features of the model are consistent with the experimental results: endogenous search intensity and exogenous job destruction. We find mixed evidence in support of the assumption of fixed hours of labor supply. Finally, we find a constant job destruction rate is not consistent with the experimental data in this context.

Families, primarily female-headed minority households with children, living in high-poverty public housing projects in five U.S. cities were offered housing vouchers by lottery in the Moving to Opportunity program. Four to seven years after random assignment, families offered vouchers lived in safer neighborhoods that had lower poverty rates than those of the control group not offered vouchers. We find no significant overall effects of this intervention on adult economic self-sufficiency or physical health. Mental health benefits of the voucher offers for adults and for female youth were substantial. Beneficial effects for female youth on education, risky behavior, and physical health were offset by adverse effects for male youth. For outcomes that exhibit significant treatment effects, we find, using variation in treatment intensity across voucher types and cities, that the relationship between neighborhood poverty rate and outcomes is approximately linear.

Intelligent Tutoring Systems (ITSs) have a great potential to effectively transform teaching and learning. As more efforts have been put on designing and developing ITSs and integrating them within learning and instruction, mixed types of results about the effectiveness of ITS have been reported. Therefore, it is necessary to investigate how ITSs work in real and natural educational contexts and the associated challenges of ITS application and evaluation. Through a systematic literature review method, this study analyzed 40 qualified studies that applied social experiment methods to examine the effectiveness of ITS during 2011–2022. The obtained results highlighted a complicated landscape regarding the effectiveness of ITS in real educational contexts. Specifically, there was an “intelligent” regional gap regarding the distribution of countries where ITS studies using social experiment methods were conducted. Compared to learning performance, relatively less attention was paid to investigating the impact of ITS on non-cognitive factors, process-oriented factors, and social outcomes, calling for more research in this regard. Considering the complexities and challenges existing in real educational fields, there was a lack of scientific rigor in terms of experimental design and data analysis in some of the studies. Based on these findings, suggestions for future study and implications were proposed.

Internet is a very attractive technology for the implementation of experiments, both in order to obtain larger and more diverse samples and as a field of economic research in its own right. This paper reports on an experiment performed both online and in the laboratory, designed to strengthen the internal validity of decisions elicited over the Internet. We use the same subject pool, the same monetary stakes and the same decision interface, and control the assignment of subjects between the Internet and a traditional university laboratory. We apply the comparison to the elicitation of social preferences in a Public Good game, a dictator game, an ultimatum bargaining game and a trust game, coupled with an elicitation of risk aversion. This comparison concludes in favor of the reliability of behaviors elicited through the Internet. We moreover find a strong overall parallelism in the preferences elicited in the two settings. The paper also reports some quantitative differences in the point estimates, which always go in the direction of more other-regarding decisions from online subjects. This observation challenges either the predictions of social distance theory or the generally assumed increased social distance in internet interactions.

Policy Points Social policies might not only improve economic well‐being, but also health. Health policy experts have therefore advocated for investments in social policies both to improve population health and potentially reduce health system costs. Since the 1960s, a large number of social policies have been experimentally evaluated in the United States. Some of these experiments include health outcomes, providing a unique opportunity to inform evidence‐based policymaking. Our comprehensive review and meta‐analysis of these experiments find suggestive evidence of health benefits associated with investments in early life, income support, and health insurance interventions. However, most studies were underpowered to detect health outcomes. Context Insurers and health care providers are investing heavily in nonmedical social interventions in an effort to improve health and potentially reduce health care costs. Methods We performed a systematic review and meta‐analysis of all known randomized social experiments in the United States that included health outcomes. We reviewed 5,880 papers, reports, and data sources, ultimately including 61 publications from 38 randomized social experiments. After synthesizing the main findings narratively, we conducted risk of bias analyses, power analyses, and random‐effects meta‐analyses where possible. Finally, we used multivariate regressions to determine which study characteristics were associated with statistically significant improvements in health outcomes. Findings The risk of bias was low in 17 studies, moderate in 11, and high in 33. Of the 451 parameter estimates reported, 77% were underpowered to detect health outcomes. Among adequately powered parameters, 49% demonstrated a significant health improvement, 44% had no effect on health, and 7% were associated with significant worsening of health. In meta‐analyses, early life and education interventions were associated with a reduction in smoking (odds ratio [OR] = 0.92, 95% confidence interval [CI] 0.86‐0.99). Income maintenance and health insurance interventions were associated with significant improvements in self‐rated health (OR = 1.20, 95% CI 1.06‐1.36, and OR = 1.38, 95% CI 1.10‐1.73, respectively), whereas some welfare‐to‐work interventions had a negative impact on self‐rated health (OR = 0.77, 95% CI 0.66‐0.90). Housing and neighborhood trials had no effect on the outcomes included in the meta‐analyses. A positive effect of the trial on its primary socioeconomic outcome was associated with higher odds of reporting health improvements. We found evidence of publication bias for studies with null findings. Conclusions Early life, income, and health insurance interventions have the potential to improve health. However, many of the included studies were underpowered to detect health effects and were at high or moderate risk of bias. Future social policy experiments should be better designed to measure the association between interventions and health outcomes.

Randomized controlled trials (RCTs) enroll hundreds of millions of subjects and involve many human lives. To improve subjects’ welfare, I propose a design of RCTs that I call Experiment-as-Market (EXAM). EXAM produces a welfare-maximizing allocation of treatment-assignment probabilities, is almost incentivecompatible for preference elicitation, and unbiasedly estimates any causal effect estimable with standard RCTs. I quantify these properties by applying EXAM to a water-cleaning experiment in Kenya. In this empirical setting, compared to standard RCTs, EXAM improves subjects’ predicted well-being while reaching similar treatment-effect estimates with similar precision.

Many social experiments are run in multiple waves or replicate earlier social experiments. In principle, the sampling design can be modified in later stages or replications to allow for more efficient estimation of causal effects. We consider the design of a two-stage experiment for estimating an average treatment effect when covariate information is available for experimental subjects. We use data from the first stage to choose a conditional treatment assignment rule for units in the second stage of the experiment. This amounts to choosing the propensity score, the conditional probability of treatment given covariates. We propose to select the propensity score to minimize the asymptotic variance bound for estimating the average treatment effect. Our procedure can be implemented simply using standard statistical software and has attractive large-sample properties.

We analyse three Danish experiments with combinations of early and intensive active labour market policy. We find that frequent individual meetings between newly unemployed workers and their caseworkers have substantial (and significant) effects on employment rates in both the medium and long run. Group meetings or an \"activation wall\" show positive but insignificant effects. Based on information on the costs of running the experiments, active labour programmes, and public transfer payments, we analyse the impact on government budgets and we show that individual meetings improved budgets with up to 4,500 euros per unemployed worker. We also look at the impact for subgroups.