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Passively collected big data sources are increasingly used to inform critical development policy decisions in low- and middle-income countries. While prior work highlights how such approaches may reveal sensitive information, enable surveillance, and centralize power, less is known about the corresponding privacy concerns, hopes, and fears of the people directly impacted by these policies --- people sometimes referred to asexperiential experts.To understand the perspectives of experiential experts, we conducted semi-structured interviews with people living in rural villages in Togo shortly after an entirely digital cash transfer program was launched that used machine learning and mobile phone metadata to determine program eligibility. This paper documents participants' privacy concerns surrounding the introduction of big data approaches in development policy. We find that the privacy concerns of our experiential experts differ from those raised by privacy and developmentdomain experts.To facilitate a more robust and constructive account of privacy, we discuss implications for policies and designs that take seriously the privacy concerns raised by both experiential experts and domain experts. 

Abstract 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. 

Abstract 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. 

Poverty maps derived from satellite imagery are increasingly used to inform high-stakes policy decisions, such as the allocation of humanitarian aid and the distribution of government resources. Such poverty maps are typically constructed by training machine learning algorithms on a relatively modest amount of “ground truth” data from surveys, and then predicting poverty levels in areas where imagery exists but surveys do not. Using survey and satellite data from ten countries, this paper investigates disparities in representation, systematic biases in prediction errors, and fairness concerns in satellite-based poverty mapping across urban and rural lines, and shows how these phenomena affect the validity of policies based on predicted maps. Our findings highlight the importance of careful error and bias analysis before using satellite-based poverty maps in real-world policy decisions. 

A key challenge in the design of effective anti-poverty programs is determining who should be eligible for program benefits. In devel- oping countries, one of the most common criteria is a Proxy Means Test — a simple decision rule that determines eligibility based on basic information about each household (for example, the number of rooms in the household, the number of children, whether there is indoor plumbing, and other observable characteristics) [1, 3, 4, 7]. At the core of each Proxy Means Test (PMT) is a machine learning algorithm that uses the short list of household characteristics to pre- dict whether the household should be deemed poor, and therefore eligible, or non-poor, and therefore ineligible [5, 6].