Economic Indicators and Social Networks: New approaches to measuring poverty, prices, and impacts of technology

Collecting data to inform policy decisions is an ongoing global challenge. While some data collection has become routine, certain populations remain difficult to reach. From targeting social protection programs in densely-populated urban areas to reaching the ``last mile'' of infrastructure coverage, data collection and service delivery go hand-in-hand. Understanding the populations that live in urban communities as well as remote villages can help to tailor the design, targeting, and implementation of development programs. New sources of information have the potential to improve awareness of the needs and preferences of individuals, households, and communities./nThe goal of this dissertation is to provide multiple vantage points on the role that data, community input, and individual preferences can play in informing development policy. The empirical investigation presented in this dissertation covers two studies in Liberia and one in the Philippines. The unifying theme of the three chapters is the exploration of new sources of information about hard-to-reach populations./nIn the first chapter, I seek to describe and explain how community members would prefer to see a cash grant program targeted. Cash grant programs are widely popular. However, targeting of these, as well as other social protection programs in densely-populated urban areas, is a challenging undertaking. I take a first principal's approach to assessing individual preferences for targeting. I find that individuals express a clear preference for selecting community members that are similar to themselves. This holds true in the first stage of the study when I asked people to nominate knowledgeable community members to target a social protection program. It also holds true when I asked community members to target a cash grant. The presence of homophily, widely observed in social networks, is an important factor to consider when leveraging private information from individuals./nIn the second chapter, I present an empirical analysis of the determinants of cellular network adoption in the context of community cellular networks. The networks were installed in seven remote locations of the Philippines. Prior to the study, these locations had been overlooked by mobile network operators thus did not have reliable mobile phone service. I leverage a unique scenario where rich socio-economic data were collected prior to the installation of the cellular networks. Using this data, I examine demographics, economic welfare, and access to information prior to network launch. To examine determinants of network adoption, I present an empirical investigation of the household characteristics that correlate with cellular network adoption on the extensive (any usage) and intensive margins (volume of calls and texts). I find that wealth is a key driver of network usage. Social network position, however, does not appear to influence cellular network usage. Taken together, the findings of Chapter Two present encouraging evidence for the potential for cellular networks in remote localities as well as a cautionary tale of the potential for cellular networks to advantage wealthy households via greater access to outside social networks./nThe third chapter focuses on another angle of understanding hard to reach communities. The challenge of collecting high-quality, timely data on prices is at the forefront of assessing and responding to microeconomic and macroeconomic conditions throughout the world. By using high-frequency data collected through a mobile application, I analyze tens of thousands of individual price observations collected at hundreds of locations in Monrovia, Liberia. I show that these data can be used to construct composite market indices that mirror government price indices./nAs a whole, the chapters of this dissertation are intended to push the edges of how researchers and policymakers approach the understanding of social networks and economic indicators in urban and rural localities.

Advisor: Blumenstock, Joshua E.