Job Market Paper
Entrepreneurs of Emotions: Evidence from Street Vending in India
Street vending is an important source of self-employment for the urban poor. I use primary observation, survey, and experimental data from Delhi to study this market. Partnering with street vendors to randomize both prices and the passersby they solicit to try to make sales, I find that even with identical goods, child vendors are 97% more likely to make a sale and earn more than twice that of adult vendors. Despite no differences in valuation for the goods, couples and female customers are 90% and 27% more likely to buy than male customers. Females and couples are 50% more likely to be targeted by vendors than males and are charged higher prices on average (4-38%) than males. I show that these findings are consistent with a model that incorporates altruism and a cost of refusal in the buyer's decision-making. I find that passersby are more altruistic towards children than adults in an incentivized dictator game. Additionally, requesting passersby to buy, increases the purchasing probability twofold for adult vendors and fourfold for child vendors. Survey data confirms that vendors target females or couples, over males, because they consider who would find it harder to refuse. The paper demonstrates that sellers leverage insights into consumer social preferences to inform their selling strategies, which can be effective in markets with personal selling.
Inaccurate beliefs about social norms can reduce informal interactions and adversely affect one's ability to deal with economic and health shocks. We run a randomized controlled trial with low-income workers in urban India who lack access to formal financial and healthcare support. We find that the majority of individuals underestimate their community’s willingness to engage around financial and mental health concerns. Belief correction leads to a large increase in the demand for network-based assistance. Additional survey experiments show that the effects are primarily driven by a reduction in the perceived costs of violating social norms. Implementation of a hypothetical choice experiment allows us to identify whether these costs are driven by concerns around signaling, reputation, or insensitivity. Then, we structurally estimate a network diffusion model to benchmark the predicted long-run effects of our intervention against counterfactual interventions. We predict that the large effects on engagement will not translate into a shift in equilibrium. We compute the strength of counterfactual interventions needed to sustain these effects and find that belief correction can be used to generate both the demand and funding for these policies.
We conduct a randomized experiment in 225 low-cost primary schools in Kenya using non-monetary incentives (certificates and badges) based on performance in Math and English. We randomize over 20,000 students to receive either individual-level, class-level, combined or no incentives. We find that class-level incentives raised test scores by 0.1-0.2 standard deviations (including on non-incentivized subjects), and student and teacher attendance by 14.5% and 6% respectively. Combined incentives are also effective in raising student performance. The effect of individual-level incentives on test scores is statistically indistinguishable from zero.
We study the impact of global expansions in mobile internet access between 2000 and 2018 on student outcomes. We link geospatial data on the rollout of 3G mobile technology with over 2 million student test scores from 82 countries. Our findings indicate that the introduction of 3G coverage leads to substantial increases in smartphone ownership and internet usage among adolescents. Moreover, changes in 3G coverage are associated with significant declines in test scores across all subjects, with magnitudes roughly equivalent to the loss of one-quarter of a year of learning. We find suggestive evidence that a reduction in feelings of belonging, ease of making friends, and self-efficacy may explain these impacts.
The Labor Market and Poverty Impacts of COVID-19 on South Africa: An update with NIDS-CRAM Wave 2
(with Ihsaan Bassier, Josh Budlender, and Rocco Zizzamia)
Accepted at: MIT Conference on Digital Experimentation (2023), Amazon Machine Learning Conference (2023), Amazon Economics Summit (2023)