Essays on Technology, Decision-Making, and Economic Development

Abstract

Understanding how individuals make decisions under constraints is central to the study of economic development. Technological, financial, and social constraints shape economic behavior in ways that influence access to opportunities, policy effectiveness, and long-run welfare. This dissertation explores these constraints in Sub-Saharan Africa through three essays. The first examines technological constraints by developing a generalized framework for evaluating the causal impact of mobile network access, addressing measurement challenges in geospatial data and providing a method for studying steady-state impacts of mobile connectivity. The second examines financial constraints to clean energy adoption, using a randomized experiment in Cameroon to assess the effectiveness of subsidies in promoting biogas technology uptake and highlighting a disconnect between stated intent and actual adoption. The third analyzes social constraints by investigating how shifts in national over ethnic identity influence labor market outcomes in Sub-Saharan Africa, leveraging national football team victories as an exogenous shock to identity. Together, these essays contribute to our understanding of how constraints shape economic behavior in Sub-Saharan Africa and provide evidence to inform the design of policies that promote economic development. Chapter 1 addresses a critical gap in the literature on mobile connectivity by developing a broadly applicable method to estimate its causal effects. While prior studies have largely focused on the rollout of telecom networks or relied on unusually high-quality data, this chapter introduces an approach that enables evaluation in steady-state conditions where network access has stabilized, using widely available data sources. I employ a Longley-Rice Irregular Terrain Model to predict mobile signal strength and implement a regression discontinuity design at the threshold for basic mobile access. A key challenge in geospatial analyses using Demographic and Health Surveys (DHS) data is coordinate displacement, which protects respondent privacy but introduces measurement error that can bias estimates. To address this, I construct a machine learning-based proxy that significantly improves signal strength estimation, reducing the root mean squared error by 25.56% to 52.31% at the margin. Monte Carlo simulations demonstrate that this correction improves classification around the treatment threshold and reduces bias in treatment effect estimates, particularly in noisy environments. Applying this framework to DHS data, I validate the approach using women's phone ownership rates and estimate the impact of network access on infant mortality. The results support the framework’s reliability and highlight its potential for future research on mobile network connectivity. Chapter 2 (co-authored with Anna L. Berka, Cornelis Gardebroek, and Niccolò F. Meriggi), examines financial constraints in the adoption of complex energy technologies. While subsidies have been shown to facilitate the adoption of low-cost, intuitive technologies, their effectiveness in promoting more complex, capital-intensive innovations remains less understood. In this chapter, I present evidence from a clustered randomized controlled trial in rural Cameroon, where households were offered varying levels of subsidies for biodigester construction, a technology that enables biogas production. The results indicate that 25% and 45% subsidies increased contract signing by 15 and 20 percentage points, respectively, yet few of these agreements resulted in actual biodigester construction. The 45% subsidy increased completed constructions by 3 percentage points, while the 25% subsidy had no significant effect. The findings reveal a disconnect between initial adoption intent and actual follow-through, suggesting that financial incentives alone may be insufficient for promoting complex technology adoption. We identify household characteristics associated with successful adoption and argue that hyper-targeting of subsidies could enhance policy effectiveness in promoting capital-intensive technologies. Chapter 3 explores how social identity influences economic behavior, particularly in ethnically diverse societies. Using national football team victories as an instrument, I examine how shifts in national over ethnic identification affect labor market outcomes in Sub-Saharan Africa. Previous research has documented that national victories increase nationalistic sentiment, but this chapter provides new evidence that these shifts are particularly pronounced in regions where ethnic majorities face higher relative unemployment. In addition, the observed shift persists for at least 45 days, suggesting longer-term effects on identity. Further, I present suggestive evidence that national identity is related to ethnic unemployment disparities, which I interpret through the lens of in-group favoritism in labor markets. These findings illustrate how collective experiences contribute to nation-building, shaping identity, economic behavior, and potentially mitigating labor market inequality in ethnically fractionalized societies. Together, these essays illustrate how technological, financial, and social constraints shape economic behavior in developing contexts. By advancing empirical methods for assessing the impacts of technological constraints, refining our understanding of financial barriers to technology adoption, and uncovering the economic effects of identity shifts, this dissertation contributes to both economic theory and policy design. The findings enhance our understanding of development challenges and provide insights that can inform more effective interventions and strategies for fostering inclusive economic growth.