An increasing number of decisions are guided by machine learning algorithms. But when consequential decisions are encoded in algorithms, individuals may strategically alter their behavior to achieve desired outcomes. This paper develops an empirical approach that adjusts decision algorithms to anticipate manipulation. By explicitly modeling incentives to manipulate, our approach produces decision rules that are stable under manipulation, even when the rules are fully transparent. We stress-test this approach through a large field experiment in Kenya. When implemented, linear strategy-robust decision rules outperform standard linear models such as LASSO.

When a policy prioritizes one person over another, is it because they benefit more, or because they are preferred? This paper develops a method to uncover the values consistent with observed allocation decisions. We estimate how much each person benefits from an intervention, and then reconcile the allocation with (i) the welfare weights assigned to different people; (ii) heterogeneous treatment effects of the intervention; and (iii) weights on different outcomes. We demonstrate this approach by analyzing Mexico’s PROGRESA anti-poverty programme. The analysis reveals that while the programme prioritized certain subgroups—such as indigenous households—the fact that those groups benefited more implies that the programme did not actually assign them a higher welfare weight. We also find evidence that the policy valued outcomes differently from households. The PROGRESA case illustrates how the method makes it possible to audit existing policies, and to design future policies that better align with values.

How do social networks influence the decision to migrate? Prior work suggests two distinct mechanisms that have historically been difficult to differentiate: as a conduit of information, and as a source of social and economic support. We disentangle these mechanisms using a massive “digital trace” dataset that allows us to observe the migration decisions made by millions of individuals over several years, as well as the complete social network of each person in the months before and after migration. These data allow us to establish a new set of stylized facts about the relationship between social networks and migration. Our main analysis indicates that the average migrant derives more social capital from “interconnected” networks that provide social support than from “extensive” networks that efficiently transmit information.

We report on an experiment examining why default options impact behavior. By randomly assigning employees to different varieties of a salary-linked savings account, we find that default enrollment increases participation by 40 percentage points—an effect equivalent to providing a 50 percent matching incentive. We then use a series of experimental interventions to differentiate between explanations for the default effect, which we conclude is driven largely by present-biased preferences and the cognitive cost of thinking through different savings scenarios. Default assignment also changes employees’ attitudes toward saving, and makes them more likely to actively decide to save after the study concludes.

We provide evidence that violence reduces the adoption and use of mobile money in three separate empirical settings in Afghanistan. First, analyzing nationwide mobile money transaction logs, we find that users exposed to violence reduce use of mobile money. Second, using panel survey data from a field experiment, we show that subjects expecting violence are significantly less likely to respond to random inducements to use mobile money. Finally, analyzing nationwide financial survey data, we find that individuals expecting violence hold more cash. Collectively, this evidence suggests that violence can impede the growth of formal financial systems.