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With Eric Arias, Emile Badran, Robert A. Blair, Ali Cheema, Thiemo Fetzer, Guy Grossman, Dotan Haim, Rebecca Hanson, Ali Hasanain, Ben Kachero, Dorothy Kronick, Benjamin Morse, Robert Muggah, Matthew Nanes, Tara Slough, Nico Ravanilla, Jacob N. Shapiro, Barbara Silva, Pedro C. L. Souza, Lily Tsai, and Anna Wilke.

How can societies effectively reduce crime without exacerbating adversarial relationships between the police and citizens? In recent decades, perhaps the most celebrated innovation in police reform has been the introduction of community policing, where citizens are involved in building channels of dialogue and improving police-citizen collaboration. Despite the widespread adoption of community policing in the United States and increasingly in the developing world, there is still limited credible evidence about whether it realistically increases trust in the police or reduces crime. Through simultaneously coordinated field experiments in a diversity of political contexts, this book presents the outcome of a major research initiative into the efficacy of community policing. Scholars from around the world uncover whether, and under what conditions, this highly influential strategy for tackling crime and insecurity is effective. With its highly innovative approach to cumulative learning, this project represents a new frontier in the study of police reform.

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This dataset, based on records obtained by Freedom of Information Act request, tracks United States Immigration and Customs Enforcement (ICE) individual enforcement actions from September 2023 through July 2025. The dataset includes five tables, linked by anonymized individual identifiers, tracking ICE encounters, detainer requests, arrests, detentions, and removals. The dataset creates new opportunities for descriptive and causal research on recent changes in immigration enforcement policy in the United States.

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Prominent theories of conflict argue that belligerents fight to capture valuable rents (“prizes”) such as oil and other resources. Yet, we show that armed groups rarely attack sites with the most oil above or below ground, oil terminals and wells; only pipelines lead to conflict. To explain this finding, we integrate crisis bargaining and Blotto games. In our model, armed groups attack to steal oil and signal strength; anticipating the government’s defenses, these groups rarely target the biggest prizes, which are more fortified. Consistent with our model, we show that armed groups strategically randomize where they attack pipelines and that local and export prices for fuel have different effects on violence, because only export prices affect the government’s willingness to buy off would-be attackers. We revamp the prize logic for conflict to generate more accurate predictions and, in so doing, provide a rare real-world validation of a Blotto model.

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In an effort to increase women's access to justice for crimes of violence against them, governments globally have implemented reforms including gender quotas in police hiring, women-run counseling centers, and mandates for women officers to exclusively handle cases of gender-based violence. The paper discusses the outcomes of such reforms, using a study conducted in Madhya Pradesh, India, as an example. This study introduced "women's help desks" in police stations, spaces designated for women officers to interact with women complainants. The results were mixed: while more incidents were reported and some police attitudes shifted, the reforms didn't significantly increase crime reporting by women or the arrest rates. The paper highlights the spectrum of gender-based reforms from integration (mixing women and men officers) to separation (exclusive spaces or stations for women), noting mixed results in effectiveness. It suggests that while certain reforms may increase report filings, they may not always result in arrests or change in police action, highlighting the complexity of addressing violence against women within the justice system.

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How does conflict affect firms' investment decisions? Past results are mixed: a third of studies we reviewed report null or mixed correlations; some suggest conflict increases investment. We rationalize these results, arguing that armed conflict has divergent effects depending on firms’ exposure to violence. Conflict can deter investment by disrupting production or raising uncertainty. Yet, conflict can encourage investment by hampering government oversight. We argue each mechanism operates over different geographic extents. We use data from the mining sector to test these claims and report three main results. Firms operating at conflict sites dramatically reduce investments. By contrast, firms operating in territory surrounding conflict, but at a remove from fighting, actually increase investment. Firms far from violence see a small negative effect. These divergent responses cannot be inferred from aggregate flows: we show conflict depresses aggregate investment, but this reflects responses among firms far from fighting.

Eric Arias, Emile Badran, Robert A. Blair, Ali Cheema, Thiemo Fetzer, Guy Grossman, Dotan Haim, Rebecca Hanson, Ali Hasanain, Ben Kachero, Dorothy Kronick, Benjamin Morse, Robert Muggah, Matthew Nanes, Tara Slough, Nico Ravanilla, Jacob N. Shapiro, Barbara Silva, Pedro C. L. Souza, Lily Tsai, and Anna Wilke.

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Honorable Mention, APSA Comparative Politics Section Luebbert Best Article Prize

Commentary: “Community policing in the developing world.” By Santiago Tobón. Science.

Is it possible to reduce crime without exacerbating adversarial relationships between police and citizens? Community policing is a celebrated reform with that aim, now adopted on every continent. Yet, the evidence base is limited, studying reform components in isolation in a limited set of countries, and largely silent on citizen-police trust. We designed six field experiments with Global South police agencies to study locally-designed models of community policing, with coordinated measures of crime and the attitudes and behaviors of citizens and police. In a preregistered meta-analysis, we find that these interventions led to mixed implementation, largely failed to improve citizen-police relations, and do not reduce crime. Structural changes may be required for incremental police reforms such as community policing to succeed.

Mohammed Bukar, Benjamin Crisman, Anthony Etim, Chad Hazlett, and Jiyoung Kim.

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The reintegration of former members of violent extremist groups is a pressing policy challenge. Governments and policymakers often have to change minds among reticent populations and shift perceived community norms in order to pave the way for peaceful reintegration. How can they do so on a mass scale? Previous research shows that messages from trusted authorities can be effective in creating attitude change and shifting perceptions of social norms. In this study, we test whether messages from religious leaders – trusted authorities in many communities worldwide – can change minds and shift norms around an issue related to conflict resolution: the reintegration of former members of violent extremist groups. Our study takes place in Maiduguri, Nigeria, the birthplace of the violent extremist group Boko Haram. Participants were randomly assigned to listen to either a placebo radio message or to a treatment message from a religious leader emphasizing the importance of forgiveness, announcing the leader’s forgiveness of repentant fighters, and calling on followers to forgive. Participants were then asked about their attitudes, intended behaviors, and perceptions of social norms surrounding the reintegration of an ex-Boko Haram fighter. The religious leader message significantly increased support for reintegration and willingness to interact with the ex-fighter in social, political, and economic life (8 to 10 percentage points). It also shifted people’s beliefs that others in their community were more supportive of reintegration (6 to 10 percentage points). Our findings suggest that trusted authorities such as religious leaders can be effective messengers for promoting peace.

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Scholars of the resource curse argue that reliance on primary commodities destabilizes governments: price fluctuations generate windfalls or periods of austerity that provoke or intensify conflict. 350 quantitative studies test this claim, but prominent results point in different directions, making it difficult to discern which results reliably hold across contexts. We conduct a meta-analysis of 46 natural experiments that use difference-in-difference designs to estimate the causal effect of international commodity price changes on armed conflict. We show commodity price changes, on average, do not change conflict risks. However, this overall effect comprises cross-cutting effects by commodity type. In line with theory, we find price increases in labor-intensive agricultural commodities reduce conflict, while increases in the price of oil, a capital-intensive commodity, provoke conflict. We also find that prices changes for lootable artisanal minerals provoke conflict. Our meta-analysis consolidates existing evidence, but also highlights gaps for future research to fill.

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Social scientists have long sought to explain why people donate resources for the good of a community. Less experiment in Nigeria, we tested two campaigns that encouraged people to try reporting corruption by text message. Psychological theories about how to shift perceived norms and how to reduce barriers to action drove the design of each campaign. The first, a film featuring actors reporting corruption, and the second, a mass text message reducing the effort required to report, caused a total of 1181 people in 106 communities to text, including 241 people who sent concrete corruption reports. Psychological theories of social norms and behavior change can illuminate the early stages of the evolution of cooperation and collective action, when adoption is still relatively rare.

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MPSA Pi Sigma Alpha Best Article Award

How are civilian attitudes toward combatants affected by wartime victimization? Are these effects conditional on which combatant inflicted the harm? We investigate the determinants of wartime civilian attitudes towards combatants using a survey experiment across 204 villages in five Pashtun-dominated provinces of Afghanistan—the heart of the Taliban insurgency. We use endorsement experiments to indirectly elicit truthful answers to sensitive questions about support for different combatants. We demonstrate that civilian attitudes are asymmetric in nature. Harm inflicted by the International Security Assistance Force (ISAF) is met with reduced support for ISAF and increased support for the Taliban, but Taliban-inflicted harm does not translate into greater ISAF support. We combine a multistage sampling design with hierarchical modeling to estimate ISAF and Taliban support at the individual, village, and district levels, permitting a more fine-grained analysis of wartime attitudes than previously possible.

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Policy debates on strategies to end extremist violence frequently cite poverty as a root cause of support for the perpetrating groups. There is little evidence to support this contention, particularly in the Pakistani case. Pakistan’s urban poor are more exposed to the negative externalities of militant violence and may in fact be less supportive of the groups. To test these hypotheses we conducted a 6,000-person, nationally representative survey of Pakistanis that measured affect toward four militant organizations. By applying a novel measurement strategy, we mitigate the item nonresponse and social desirability biases that plagued previous studies due to the sensitive nature of militancy. Contrary to expectations, poor Pakistanis dislike militants more than middle-class citizens. This dislike is strongest among the urban poor, particularly those in violent districts, suggesting that exposure to terrorist attacks reduces support for militants. Long-standing arguments tying support for violent organizations to income may require substantial revision.

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APSA Experiments Section Best Book Award

Reviewed in Nature

Assessing the properties of research designs before implementing them can be tricky for even the most seasoned researchers. This book provides a powerful framework—Model, Inquiry, Data Strategy, and Answer Strategy, or MIDA—for describing any empirical research design in the social sciences. MIDA enables you to characterize the key analytic features of observational and experimental designs, qualitative and quantitative designs, and descriptive and causal designs. An accompanying algorithm lets you declare designs in the MIDA framework, diagnose properties such as bias and precision, and redesign features like sampling, assignment, measurement, and estimation procedures. Research Design in the Social Sciences is an essential tool kit for the entire life of a research project, from planning and realization of design to the integration of your results into the scientific literature.

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The history of science is littered with violations of the human rights of research participants. One response has been regulation. In the United States, the Belmont Report lays out ethical principles for human subjects research, which form the foundation of modern-day institutional review boards. According to the Belmont Report, ethics committees and investigators need to determine whether the benefits of the research outweigh the costs and whether the costs unevenly burden particular groups. However, the assumptions underlying such assessments can be flawed. Without evidence about the causal effects of social science research on participant welfare, researchers risk causing unexpected harms or missing unexpected benefits.

Biz Herman ⓡ Amma Panin ⓡ Nicholas Owlsley ⓡ Graeme Blair ⓡ Alex Dyzenhaus ⓡ Elizabeth Iams Wellman ⓡ Allison Grossman ⓡ Ken Opalo ⓡ Anisha Singh ⓡ Hannah Alarian ⓡ Lindsey Pruett ⓡ and Yvonne Tan. (Author order randomized.)

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In this piece, we draw on our interdisciplinary experiences to develop a set of questions for RCT research in the Global South, suggesting ways to involve scholars and research staff who hail from the study site at every research stage. We maintain these interactions are not one-off exchanges, but rather opportunities to foster meaningful collaboration. We see such efforts as complementary to institutional efforts to recruit and retain graduate students and junior faculty from the Global South. We organize this piece by four distinct, yet interrelated research stages: idea generation, planning, implementation, and dissemination.

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In an effort to assess the generalizability of treatment effects across contexts, scholars (or teams of scholars) are increasingly conducting experiments around the same research questions in multiple country and subnational contexts. In this chapter, we categorize recent and ongoing efforts to conduct cross-context experiments into three types: “uncoordinated,” “coordinated, sequential,” and “coordinated, simultaneous.” We discuss some practical trade-offs across these types, arguing that coordinated cross-context designs offer the most promise for meta-analyses. We then draw attention to four areas in which the current approaches arguably all fall short in facilitating cumulative learning about treatment effects and treatment effect heterogeneity across contexts. We conclude by proposing some ways forward to continue improving our approach to learning about generalizability across contexts.

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Eliciting honest answers to sensitive questions is frustrated if subjects withhold the truth for fear that others will judge or punish them. The resulting bias is commonly referred to as social desirability bias, a subset of what we label sensitivity bias. We make three contributions. First, we propose a social reference theory of sensitivity bias to structure expectations about survey responses on sensitive topics. Second, we explore the bias-variance trade-off inherent in the choice between direct and indirect measurement technologies. Third, to estimate the extent of sensitivity bias, we meta-analyze the set of published and unpublished list experiments (a.k.a., the item count technique) conducted to date and compare the results with direct questions. We find that sensitivity biases are typically smaller than 10 percentage points and in some domains are approximately zero.

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Researchers need to select high-quality research designs and communicate those designs clearly to readers. Both tasks are difficult. We provide a framework for formally “declaring” the analytically relevant features of a research design in a demonstrably complete manner, with applications to qualitative, quantitative, and mixed methods research. The approach to design declaration we describe requires defining a model of the world (M), an inquiry (I), a data strategy (D), and an answer strategy (A). Declaration of these features in code provides sufficient information for researchers and readers to use Monte Carlo techniques to diagnose properties such as power, bias, accuracy of qualitative causal inferences, and other “diagnosands.” Ex ante declarations can be used to improve designs and facilitate preregistration, analysis, and reconciliation of intended and actual analyses. Ex post declarations are useful for describing, sharing, reanalyzing, and critiquing existing designs. We provide open-source software, DeclareDesign, to implement the proposed approach.

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Measurement error threatens the validity of survey research, especially when studying sensitive questions. Although list experiments can help discourage deliberate misreporting, they may also suffer from nonstrategic measurement error due to flawed implementation and respondents’ inattention. Such error runs against the assumptions of the standard maximum likelihood regression (MLreg) estimator for list experiments and can result in misleading inferences, especially when the underlying sensitive trait is rare. We address this problem by providing new tools for diagnosing and mitigating measurement error in list experiments. First, we demonstrate that the nonlinear least squares regression (NLSreg) estimator proposed in Imai (2011) is robust to nonstrategic measurement error. Second, we offer a general model misspecification test to gauge the divergence of the MLreg and NLSreg estimates. Third, we show how to model measurement error directly, proposing new estimators that preserve the statistical efficiency of MLreg while improving robustness. Last, we revisit empirical studies shown to exhibit nonstrategic measurement error, and demonstrate that our tools readily diagnose and mitigate the bias. We conclude this article with a number of practical recommendations for applied researchers. The proposed methods are implemented through an open-source software package.

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About a half century ago, in 1965, Warner proposed the randomized response method as a survey technique to reduce potential bias due to nonresponse and social desirability when asking questions about sensitive behaviors and beliefs. This method asks respondents to use a randomization device, such as a coin flip, whose outcome is unobserved by the interviewer. By introducing random noise, the method conceals individual responses and protects respondent privacy. While numerous methodological advances have been made, we find surprisingly few applications of this promising survey technique. In this article, we address this gap by (1) reviewing standard designs available to applied researchers, (2) developing various multivariate regression techniques for substantive analyses, (3) proposing power analyses to help improve research designs, (4) presenting new robust designs that are based on less stringent assumptions than those of the standard designs, and (5) making all described methods available through open-source software. We illustrate some of these methods with an original survey about militant groups in Nigeria.

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List and endorsement experiments are becoming increasingly popular among social scientists as indirect survey techniques for sensitive questions. When studying issues such as racial prejudice and support for militant groups, these survey methodologies may improve the validity of measurements by reducing nonresponse and social desirability biases. We develop a statistical test and multivariate regression models for comparing and combining the results from list and endorsement experiments. We demonstrate that when carefully designed and analyzed, the two survey experiments can produce substantively similar empirical findings. Such agreement is shown to be possible even when these experiments are applied to one of the most challenging research environments: contemporary Afghanistan. We find that both experiments uncover similar patterns of support for the International Security Assistance Force (ISAF) among Pashtun respondents. Our findings suggest that multiple measurement strategies can enhance the credibility of empirical conclusions. Open-source software is available for implementing the proposed methods.

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The validity of empirical research often relies upon the accuracy of self-reported behavior and beliefs. Yet eliciting truthful answers in surveys is challenging, especially when studying sensitive issues such as racial prejudice, corruption, and support for militant groups. List experiments have attracted much attention recently as a potential solution to this measurement problem. Many researchers, however, have used a simple difference-in-means estimator, which prevents the efficient examination of multivariate relationships between respondents’ characteristics and their responses to sensitive items. Moreover, no systematic means exists to investigate the role of underlying assumptions. We fill these gaps by developing a set of new statistical methods for list experiments. We identify the commonly invoked assumptions, propose new multivariate regression estimators, and develop methods to detect and adjust for potential violations of key assumptions. For empirical illustration, we analyze list experiments concerning racial prejudice. Open-source software is made available to implement the proposed methodology.