I am a Ph.D. candidate in Economics at Columbia University.
I am interested in studying the socio-economic impacts of digital technologies, especially social media, and its implications for regulation of digital platforms. I also work on ethnic conflict in low-income countries, investigating its causes and consequences, and evaluating interventions to reduce prejudice towards outgroups.
I am on the job market in AY 2022-23.
In my job market paper, I conduct a field experiment in India with an intervention to improve people's ability to identify misinformation and reduce their misperceptions about minorities.
Primary Fields: Development Economics, Political Economy
Secondary Fields: Industrial Organization
Abstract: Can people learn to identify misinformation, and to what extent can this mitigate its effects on beliefs, attitudes, and behavior? We conduct a large field experiment with an intervention aimed at improving people’s ability to identify misinformation and reduce their misperceptions about minorities. The experiment is done in India, where there has recently been high levels of misinformation on social media, a significant portion of which targets Muslims, the largest religious minority in the country. The intervention is to provide weekly digests containing summaries of fact-checks of viral misinformation, along with narrative explainers on the issues with a lot of misinformation around them. We find that the intervention increases people’s ability to correctly identify misinformation as false by eleven percentage points. However, it also decreases belief in true news by four percentage points. We estimate a structural model to disentangle the two mechanisms of impact—truth discernment, which is the ability to distinguish between false and true news; and skepticism, which changes the overall credulity for both false and true news. The impact is driven by both an increase in truth discernment and skepticism. The intervention also changes policy attitudes and behavior. Treated individuals are less likely to support discriminatory policies against Muslims and are more likely to pay for efforts to prevent harassment of inter-faith couples.
Abstract: I estimate the impact of ethnic violence on economic growth using data on Hindu-Muslim riots in India. For causal identification, I use shift-share instruments to isolate exogenous national shocks to violence from endogenous local shocks. On average, a riot reduces state GDP growth rate by 0.14 percentage points. To investigate mechanism, I estimate the dynamics of impact using the synthetic control method and compare it to theoretical predictions from a shock to social capital versus physical capital. This shows that the negative impact of violence is likely driven by a negative shock to social capital from higher animosity and discrimination among communities exposed to violence. This impact of violence on growth creates a vicious cycle when one also considers the effect in the opposite direction – lower growth leading to more violence. The multiplier due to this vicious cycle magnifies the impact of external growth shocks by 40 percent in equilibrium. The results highlight the importance of having strong institutions to manage conflict for the long-term prosperity of societies.
I use night lights data to construct a worldwide panel of city shapes and sizes. Existing methods define a city as contiguous pixels with high light intensity, using an arbitray intensity threshold to define the boundary. I develop a data-driven method to get the optimal city-specific light intensity threshold, using the gradient and the hessian of the light intensity field. This has two main benefits. First, it gives more accurate city boundaries across a wide range of city sizes allowing analysis over large regions. Second, it controls for the non-calibrated variation in sensitivity of satellite sensors across time, which allows for construction of a panel of city shapes, whereas previous approach could only construct cross-sections. Having a accurate measure of city boundaries is critical for research on topics such as segregation. Hence, this method provides a data foundation for large scale panel data analysis on such topics in urban and spatial economics in low-income countries, where standard measures of city boundaries—such as commuting zones—are not available.
In this project I explore the long-term impact of colonial institutions on contemporary ethnic relations by estimating the impact of British rule on residential segregation in India. I compare the levels of segregation in regions that were directly ruled by British versus those that were ruled be them indirectly through native kings who had high levels of autonomy. For causal identification, I use random deaths of kings without heirs (Iyer, 2010) and European Wars (Mukherjee, 2017) as instruments for direct rule.