Essays in Development and Behavioral Economics

Abstract

This dissertation comprises three chapters in the fields of development and behavioral economics.

Chapter 1 uses a field experiment to study the effect of social media on voters in a large election in Tamil Nadu, India. I randomly invited study participants to join chat groups organized by political parties on WhatsApp, India's most-used social media platform. Unlike Facebook and Twitter, WhatsApp groups are relatively small, and users' feeds are neither curated by algorithms nor moderated by the platform. I find that joining a group increases political knowledge, improving participants' ability to distinguish true from false news. Moreover, the groups have an effect on political preferences, pushing participants toward the party affiliated with the WhatsApp group they joined. This effect is driven by moderates who were indifferent between parties at the baseline. To disentangle the effect of direct party messaging from the effect of messaging between individual users, I designed a second treatment arm that exposed participants to the posts of party officials but not the posts and replies of other group members. This reveals that the horizontal exchange between individuals is key: the treatment effects of party messaging alone are smaller than those of the full groups. I provide evidence on possible mechanisms underlying this difference. Participants assigned to the full groups both receive more messages and pay more attention to the messages they receive.

Chapter 2, co-authored with Michael Kremer, Xinyue Lin, and Gautam Rao, studies the endowment effect and collateralized loans. Collateral requirements play an important role in credit markets. This chapter shows that the endowment effect—the phenomenon where owing a good increases one's valuation of it—inhibits demand for loans which use a borrower's existing assets as collateral. Using a field experiment in Kenya, we show that borrowers instead strongly prefer loans collateralized using the new durable assets being financed by the loans themselves. They are willing to pay 9% per month higher interest for such Same-Asset Collateralized Loans (SACLs) despite the endowed and new assets being randomized, and thus similarly valued before ownership. Our findings imply that assets which are difficult to use as collateral—which cannot be financed by SACLs—will be invested in less, even if the borrower has other collateral. We argue that borrowers' preference for SACLs is driven by naivete: they initially perceive that they have little to lose when offered a SACL, but subsequently come to develop an attachment to the new asset, resulting in high repayment effort. Consistent with this, borrowers underestimate their future attachment to an asset before owning it, and SACLs do not have higher default rates despite having higher demand. We derive the conditions under which offering consumers SACLs increases or conversely decreases borrower welfare.

Chapter 3 is about time preference, news utility, and information avoidance. Take up of testing for chronic conditions, such as HIV and diabetes, is often low, with serious health consequences. I develop a model that describes why people might choose to put off learning information, even if the information is free and useful. If people are loss averse over news about their future welfare, the prospect of bad news will outweigh the prospect of good news in situations of uncertainty. This, when combined with present bias, may lead to information avoidance, even if the information would allow the decision maker to increase their welfare in the long run. As an example, I show how these preferences might influence decisions about medical testing and depress take-up rates of testing. People may put off getting tested due to the immediate psychological costs of learning their test results. I present results of survey experiments with hypothetical choices in Nairobi, Kenya and Chennai, India. Testing decisions are consistent with this model in the context of diabetes testing in Chennai but not HIV testing in Nairobi.