Mobile phone ubiquity in much of the developing world has turned from a question of when rather than if. Some of the poorest and most remote parts of the world are being connected to the global telecommunications network to enable an unprecedented ability to both observe and interact with previously hard-to-reach populations at scale. While many mobile phone owners adopt this technology for basic phone use, the connectedness this expansive ownership enables presents an opportunity to the study and practice of economic development that extend beyond simple peer-to-peer communication.
The modern information technology sector and its underlying network infrastructure presented this same opportunity during its own formation. The network was not only valuable for the communication it enabled, but also for the data it produced from those who utilized its services. It also serves as a platform for a deluge of information systems and services that have become a part of our everyday lives and has spurred significant economic growth over the past few decades. This "data revolution" is well underway in the developed economies but is diminishing in its returns, solving increasingly marginal problems. This same transformation is relatively nascent in developing economies where more salient challenges, such as poverty, have yet to be overcome. In this dissertation, we explore a data-driven approach that leverages mobile phone technology to better measure and address poverty in sub-Saharan Africa.
Our approach starts with the identification of a problem: in this case, poverty. In the first chapter, we apply novel machine learning methods to analyze roughly ten terabytes of data of mobile phone use from Rwanda's largest telecommunications operator to measure poverty at a national scale. We demonstrate that an individual's history of mobile phone usage can be used to infer his or her socioeconomic status. Using this individual model of mobile phone use and socioeconomic status, we can predict poverty and wealth across the entire network and accurately reconstruct national and regional distributions of wealth. Once we obtain this measure of poverty, we can then focus our efforts in regions that are most afflicted.
The second chapter helps moves us from diagnosis to a potential cure. Predictions may be helpful to provide some guidance on which regions or populations to target but does not provide much in the way of what to do to have impact. In three years of field research in poor regions of rural Kenya and Rwanda, it was clear that much of the world's poor thrive and survive on subsistence agriculture, but many of these farmers also own mobile phones. Having such a platform enabled the ability to provide potentially welfare-improving information at scale. This chapter presents the research design and analyzes the results of of six randomized controlled trials testing the welfare effects of sending hundreds of text message formulations encouraging agricultural experimentation to over 500,000 farmers in Kenya and Rwanda. Targeting farmers with the right messaging and delivery characteristics was a focus of these trials. We find statistically significant effects on agricultural technology adoption and high rates of return on welfare outcomes by providing information over this medium. This mirrors the digital advertising industry in many developed economies and reminds us that advertisements as information can have very large welfare effects in poor information environments.
The third chapter dives deeper into one of the six studies where the research design focused on information spillover in Rwanda where mobile phone ownership was about half of what it was in Kenya. We find that information does indeed spillover onto other farmers within the same group, and those farmers who don't have phones experience the largest percentage increases in adoptions when others within the same group receive a text message. This has large implications on the effectiveness and cost efficiency of information treatments to regions with lower mobile phone adoption. Not only were these interventions effective, they were also very inexpensive and resulted in network effects, further improving agricultural technology adoption, increasing food production and reducing poverty.
The chapters in this dissertation develop a theory and methods for understanding how to leverage mobile technologies to measure and reduce poverty. It serves as a guide for both research and practitioners to approach solving problems in development that is grounded in measurement, data, collaboration, impact and scale.
|Advisor:||Blumenstock, Joshua E.|
|Commitee:||Kremer, Michael, Miguel, Edward, Varian, Hal R.|
|School:||University of California, Berkeley|
|Department:||Information Management and Systems|
|School Location:||United States -- California|
|Source:||DAI-A 80/08(E), Dissertation Abstracts International|
|Subjects:||Information Technology, Economics, Sub Saharan Africa Studies|
|Keywords:||Agricultural technology adoption, Data revolution, Hard-to-reach populations|
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