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 use machine learning methods to estimate how much each individual benefits from an intervention, and then reconcile its 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 program. The analysis reveals that while the program prioritized certain subgroups -- such as indigenous households -- the fact that those groups benefited more implies that they were in fact assigned a lower welfare weight. The PROGRESA case illustrates how the method makes it possible to audit existing policies, and to design future policies that better align with values.

An increasing number of decisions are guided by machine learning algorithms. In many settings, from consumer credit to criminal justice, those decisions are made by applying an estimator to data on an individual's observed behavior. But when consequential decisions are encoded in rules, individuals may strategically alter their behavior to achieve desired outcomes. This paper develops a new class of estimator that is stable under manipulation, even when the decision rule is fully transparent. We explicitly model the costs of manipulating different behaviors, and identify decision rules that are stable in equilibrium. Through a large field experiment in Kenya, we show that decision rules estimated with our strategy-robust method outperform those based on standard supervised learning approaches.

Religious adherence has been hard to study in part because it is hard to measure. We develop a new measure of religious adherence, which is granular in both time and space, using anonymized mobile phone transaction records. After validating the measure with traditional data, we show how it can shed light on the nature of religious adherence in Islamic societies. Exploiting random variation in climate, we find that as economic conditions in Afghanistan worsen, people become more religiously observant. The effects are most pronounced in areas where droughts have the biggest economic consequences, such as croplands without access to irrigation.

We provide new evidence on how insecurity affects firm behavior by linking data on violent conflict in Afghanistan to geo-stamped corporate mobile phone records. We begin by developing a method for observing firm location choice with phone data, and validate these measurements using independent sources of administrative and survey data. Next, we show that deadly terrorist attacks reduce the presence of firms in targeted districts by 4-6%. The effect includes both an increase in the local exit of existing firms following attacks and a decrease in new firm entry. We find large negative spillovers from attacks in provincial capitals on firm presence in nearby rural districts. After violence, employees in provincial capitals are 33% more likely to move to Kabul and 15% more likely to leave for another province.

Mobile phones reduce the cost of communicating with existing social contacts, but do not eliminate frictions in forming new relationships. We report the findings of a two-sided randomized control trial in central Tanzania, centered on the production and distribution of a "yellow pages" phone directory with contact information for local enterprises. Enterprises randomly assigned to be listed in the directory receive more business calls, make more use of mobile money, and employ more workers. There is evidence of positive spillovers, as both listed and unlisted enterprises in treatment villages experience significant increases in sales relative to a pure control group. Households randomly assigned to receive copies of the directory make greater use their phones for farming, are more likely to rent land and hire labor, have lower rates of crop failure, and sell crops for weakly higher prices. Willingness-to-pay to be listed in future directories is significantly higher for treated enterprises.

Many decisions that once were made by humans are now made using algorithms. These algorithms are typically designed with a single, profit-related objective in mind: Loan approval algorithms are designed to maximize profit, smart phone apps are optimized for engagement, and news feeds are optimized for clicks. However, these decisions have side effects: irresponsible payday loans, addictive apps, and fake news can harm individuals and society. This project develops and tests a new paradigm for prioritizing the social impact of an algorithmic decision from the start, rather than as an afterthought. The key insight is to leverage recent advances in machine learning -- which make it possible to predict who will benefit from a decision and how -- to design algorithms that balance those predicted benefits alongside traditional profit-related objectives.

Digital loans have exploded in popularity across low- and middle-income countries, providing short term, high interest credit via mobile phones. This paper reports the results of a randomized evaluation of a digital loan product in Nigeria. Being randomly approved for a loan (among those who otherwise would have been denied) substantially increases subjective well-being after an average of three months, but being randomly approved for a larger loan does not have any eect. Neither intervention signicantly impacts other measures of welfare, and we can rule out large impacts - either positive or negative - on income and expenditures, resilience, and women's economic empowerment.

We present experimental evidence on the economic impacts of mobile phone access. Our results are based on a randomized control trial in the Philippines, through which 14 isolated and previously unconnected villages were randomly assigned to either receive or not receive a new cellphone tower. Following a pre-analysis plan, we find that the introduction of mobile phones had large and significant impacts on household income and expenditure, particularly for wage workers. Mobile phone access also increased social connections within and between communities. However, there are no consistent impacts on market access, informedness, or subjective well being. In post-specified analysis, we find suggestive evidence that the improved economic conditions are driven by increases in migration, remittances, and self-employment. Working paper available by request.