UC Berkeley

The mission of development economics is, fundamentally, to explain why some communities are wealthier than others. This dissertation is a collection of two essays that make progress towards fitting together key pieces of the puzzle. My hope is that the findings will inform, not only researchers, but policymakers working to accelerate economic development.

To that end, the essays make three thematic contributions. First, they leverage data and experiments from the Middle East and North Africa (MENA). While MENA’s relevance to global markets is self-evident, to date economists have paid it less attention than other low- and middle-income regions. Second, the results advance the economic geography of development by exploring trade and forced migration. Third, the results advance the political economy of development by exploring corruption and the victims of conflict. These geographic and political factors are among the most important drivers of structural transformation, productivity growth, and poverty reduction.

In Chapter 1, joint with Nate Grubman and Jawaher Mbarek, I investigate corruption dynamics in international trade. Every year emerging markets import goods valued at more than $7 trillion, and in many countries shipments have to pass through corrupt customs administrations. Given these high stakes, policymakers require a deep understanding of both the causes and the effects of customs fraud. In addition, trade corruption can serve as a laboratory to study corruption writ large. One previously unexplored complexity is that bribe payers and bribe receivers often have repeated interactions. Given corruption’s characteristic counterparty risks and information asymmetries (not to mention the impossibility of contract enforcement), these long-running relationships likely matter for a wide variety of outcomes across a wide variety of contexts.

To pursue these general learning objectives, we overcome the data and identification challenges inherent to investigating bribery: we build an original dataset on Tunisian customs transactions using an audit study to directly observe bribes, and we leverage a natural experiment in which a computer algorithm randomly assigns customs officials to import shipments. There are three sets of results. First, we show that bribery and tax evasion are widespread, that bribery is collusive (not coercive), and that age (but not gender) predicts officials’ corruptibility. Second, in line with a straightforward Nash Bargaining model, we show that the length of official/trader relationships increases tax evasion but decreases bribe amounts. Third, we zoom out to consider the larger macroeconomic implications and show that, in terms of lost tax revenue, bribery costs the Tunisian government 0.7% of GDP or $80 per citizen.

In Chapter 2, joint with Edward Miguel, Sandra V. Rozo, Emma Smith, and Sarah Stillman, I investigate affordable housing for forced migrants. There are 82 million forced migrants in the world today, and the vast majority reside in “host communities:” they live, work, and attend school in the same neighborhoods as the citizens of their host country. As both a cause and an effect of this arrangement, many governments, IGOs, and NGOs support these refugees’ shelter through either direct provision or rental assistance. However, the evidence on the welfare implications of housing subsidies is inconclusive for refugees in particular and for high-poverty populations in general. On the one hand, studies have shown that shelter assistance has the potential to provide, not just a roof overhead, but a number of downstream benefits. On the other hand, some studies have found null or even negative results, and it is often unclear whether the positive results are due to direct housing-quality effects or indirect neighborhood effects.

To fill this knowledge gap, we ran a randomized controlled trial (RCT) testing a program that provides housing support to Jordan’s Syrian refugees. The subsidies are large (approximately $190 per month) and are not vouchers; households can only use them for their current housing, allowing us to be among the first to identify housing-quality effects absent confounding neighborhood effects. There are three sets of results. First, we estimate the positive impacts of the program. Recipients experience improvements in their living situations, as well as better education and credit-market outcomes. Second, we observe that, while the program does not cause movement of households, it does cause movement between households. Recipients welcome new individuals into their homes, likely so that family, friends, and neighbors can take advantage of the upgraded housing. Third, we estimate the negative impacts of the program and argue that these unintended consequences likely follow from the endogenous household formation. For example, recipients are less food secure, consistent with having more mouths to feed.

Together, these studies have generalizable, actionable insights. Knowing that dynamics matter for corruption can help reform-minded governments decide which levers to pull as they attempt to enhance transparency. And the large economic cost of trade corruption implies a high return on investment for the governments that succeed. Our RCT reveals both the upside potential and the downside risk of large-scale affordable-housing interventions. By refining their design, implementation, and cost-effectiveness, policymakers could improve these programs and the lives of the vulnerable communities they serve.