UC Davis

The chapters in this dissertation provide a way of tracing out a history of sorts; of howhouseholds and people coped with negative shocks in the past: through cooperative land tenure arrangements and collectives, and now: through technological platforms that follow the “economic laws of the market.” The questions I ask are: How do households adapt to new circumstances in developing countries? And how has the landscape of the labor market changed to incorporate new technology for these households to use? How has new technology and new data allowed us to say more about not just households’ economic lives, but their political lives as well?

In the first chapter, I study how ride-share applications can be used to send money from cities to rural areas in times of bad rural shocks. Rural-urban linkages have long been a topic of study in the developing world. Remittances are often a key driver of these linkages and can act as insurance against rural weather shock risk, in the absence of availability and access to formal insurance products. The emergence of new technologies, such as ride-share and mobile money platforms, can be potentially transformative at allowing remittance flows to adjust more quickly to adverse shocks. I use a dataset of Uber driver labor supply and a rich dataset of weather indicators to estimate the effect of adverse weather shocks in rural areas on Uber drivers in Kampala, Uganda. Since I do not have explicit information on migrant status and rural connection, I leverage an external dataset of Ugandan voter registration and train a gradient boosting classifier on Ugandan surnames to predict drivers' regions of origin. I develop a switching regression estimator to address the misclassification bias from the predictions. I find that a one standard deviation increase in the intensity of agricultural drought is associated with an increase of 5.1 hours online in the month of the event (a 6\% increase over average hours), providing suggestive evidence that Uber's flexibility is used to buffer against adverse weather shocks.

In the second chapter, I study ethnic voting in Uganda. A large literature on ethnic voting has shown that ethnic identity is a major element in voting preferences for many developing countries, and that higher fractionalization in ethnicity leads to lower public goods provision. It is often difficult to disentangle ethnic identity and shared economic goals that stem from living in the same location. This paper differentiates between the effects of a voter's ethnic identity and their location, on voting behavior. We estimate these effects by pairing voter registration data with election outcomes from polling stations throughout Uganda. We overcome the challenge of measuring ethnic identity by using a machine learning algorithm that exploits variation in surnames across ethnic and linguistic groups on the polling station level. In particular, we use the letter sequences in each individual voter's surname to predict their ethnicity. We then use these predictions as the explanatory variable of interest in a regression model with the outcome being support for the incumbent president in the 2016 general election. We find that ethnic voting preferences are robust to location effects, but that location tends to amplify preferences, especially when in an area with similar views, and with a history of preferences disfavoring the candidate.

In the third chapter, I explore an institution that existed before technology beganto shape developing economies: the cooperative farm. Even despite there being heterogeneity in ability in cooperative agriculture, these institutional forms have existed for many years, because of their ability to pool costs amongst its members. I look at how the success of recent policies aimed at privatizing these communal farms (where I take as a context, the PROCEDE reform in Mexico) are affected by market power in the supply chain. What I find is that a higher level of monopsony power in the supply chain leads to lower price passthrough, meaning that an increase in the world price of tortillas, for example, will not transmit completely to an increase in the price of corn. This implies less of an increase to privatization of the cooperative farm.