What is the value of a social network? 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 use a rich 'digital trace' dataset to link the migration decisions of millions of individuals to the topological structure of their social networks. We find that migrants systematically prefer 'interconnected' networks (where friends have common friends) to 'expansive' networks (where friends are well connected). A micro-founded model of network-based social capital helps explain this preference: migrants derive more utility from networks that are structured to facilitate social support than from networks that efficiently transmit information.

The COVID-19 pandemic has devastated many low- and middle-income countries (LMICs), causing widespread food insecurity and a sharp decline in living standards. In response to this crisis, governments and humanitarian organizations worldwide have mobilized targeted social assistance programs. Targeting is a central challenge in the administration of these programs: given available data, how does one rapidly identify the individuals and families with the greatest need? This challenge is particularly acute in the large number of LMICs that lack recent and comprehensive data on household income and wealth. Here we show that non-traditional "big" data from satellites and mobile phone networks can improve the targeting of anti-poverty programs. Our approach uses traditional survey-based measures of consumption and wealth to train machine learning algorithms that recognize patterns of poverty in non-traditional data; the trained algorithms are then used to prioritize aid to the poorest regions and mobile subscribers. We evaluate this approach by studying Novissi, Togo's flagship emergency cash transfer program, which used these algorithms to determine eligibility for a rural assistance program that disbursed millions of dollars in COVID-19 relief aid. Our analysis compares outcomes - including exclusion errors, total social welfare, and measures of fairness - under different targeting regimes. Relative to the geographic targeting options considered by the Government of Togo at the time, the machine learning approach reduces errors of exclusion by 4-21%. Relative to methods that require a comprehensive social registry (a hypothetical exercise; no such registry exists in Togo), the machine learning approach increases exclusion errors by 9-35%. These results highlight the potential for new data sources to contribute to humanitarian response efforts, particularly in crisis settings when traditional data are missing or out of date.

One-fifth of the world's population lives in countries affected by fragility, violence and conflict, impeding long-term economic growth. However, little is known about how firms respond to local changes in security, partly because of the difficulty of measuring firm activity in these settings. This paper presents a novel methodology for observing private sector activity using mobile phone metadata. Using Afghanistan as the empirical setting, the analysis combines mobile phone data from over 2,300 firms with data from several other sources to develop and validate measures of firm location, size, and economic activity. Combining these new measures of firm activity with geocoded data on violent events, the paper investigates how the private sector in Afghanistan responds to insecurity. The findings indicate that firms reduce presence in districts following major increases in violence, that these effects persist for up to six months, and that larger firms are more responsive to violence. The paper concludes with a discussion of potential mechanisms, firms' strategic adaptations, and implications for policymakers.

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 greater use of mobile money, and are more likely to employ 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.

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.

The COVID-19 pandemic has devastated many low- and middle-income countries (LMICs), causing widespread food insecurity and a sharp decline in living standards. In response to this crisis, governments and humanitarian organizations worldwide have mobilized targeted social assistance programs. Targeting is a central challenge in the administration of these programs: given available data, how does one rapidly identify the individuals and families with the greatest need? This challenge is particularly acute in the large number of LMICs that lack recent and comprehensive data on household income and wealth. Here we show that non-traditional “big” data from satellites and mobile phone networks can improve the targeting of anti-poverty programs. Our approach uses traditional survey-based measures of consumption and wealth to train machine learning algorithms that recognize patterns of poverty in non-traditional data; the trained algorithms are then used to prioritize aid to the poorest regions and mobile subscribers. We evaluate this approach by studying Novissi, Togo’s flagship emergency cash transfer program, which used these algorithms to determine eligibility for a rural assistance program that disbursed millions of dollars in COVID-19 relief aid. Our analysis compares outcomes – including exclusion errors, total social welfare, and measures of fairness – under different targeting regimes. Relative to the geographic targeting options considered by the Government of Togo at the time, the machine learning approach reduces errors of exclusion by 4-21%. Relative to methods that require a comprehensive social registry (a hypothetical exercise; no such registry exists in Togo), the machine learning approach increases exclusion errors by 9-35%. These results highlight the potential for new data sources to contribute to humanitarian response efforts, particularly in crisis settings when traditional data are missing or out of date.

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 report on an experiment examining why default options impact behavior. Working with one of the largest private firms in Afghanistan, we randomly assigned each of 949 employees to different variants of a new default savings account. Employees assigned a default contribution rate of 5% are 40 percentage points more likely to contribute than employees assigned to a default contribution rate of zero; to achieve this effect through financial incentives alone would require a 50% match from the employer. Our design permits us to rule out several common explanations for default effects, including employer endorsement, employee inattention, and a lack of awareness about how to switch. Instead, we find evidence that the default effect is driven largely by a combination of present-biased preferences and the cognitive cost of calculating alternate savings scenarios. Default assignment also causes employees to develop savings habits that outlive our experiment: they are more likely to believe that savings is important, less likely to report being too financially constrained to save, and more likely to make an active decision to save at the end of our trial.