I am a Ph.D. candidate in the Department of Agricultural and Resource Economics at the University of California, Davis. My areas of research include development economics, firm productivity, trade, and migration. I also currently work as a consultant with the World Bank’s East Asia and Pacific Chief Economist’s Office and have previously worked with the Poverty and Equity Global Practice.

I am interested in the drivers of private sector development, particularly in developing countries, including how skills, trade, supply chains, and technology adoption affect firm productivity and resilience to shocks. My work applies advanced causal inference and machine learning methods to study these themes across a range of contexts. I am also interested in cross-border and rural-to-urban migration, especially Mexico–US labor flows, and how climate, economic, and policy-related shocks affect migration patterns.

I am on the 2025-26 job market.

Working Papers

Chawla, P. 2025. “Can Human Capital Improve Firm Resilience in Financial Crises? Evidence from the 1997 Indonesian Crisis”. Working paper. (Job Market Paper) SSRN Link

Summary

Do returns to human capital rise during crises? This paper examines whether Indonesia's INPRES school construction program in the 1970s improved firm resilience during the 1997 Asian Financial Crisis. I combine a difference-in-differences strategy with a shift-share instrument, exploiting variation in district INPRES intensity and the national share of treated working-age cohorts. I find that each additional school per 1,000 children raised post-crisis real labor productivity and output by 2.8 and 3.5 percent, respectively. These effects are not explained by pre-crisis differences in basic educational attainment. Instead, INPRES contributed to a pre-crisis shift of workers toward skill-intensive production work. Using reduced-form evidence and a simple model, I show that the resulting local abundance of skilled production workers helped keep their wages lower in high-INPRES districts, enabling plants to retain more skilled workers during the crisis.

Barriga-Cabanillas, O., Chawla, P., Redaelli, S. and Yoshida, N. 2023. “Updating Poverty in Afghanistan Using the SWIFT-Plus Methodology”. Policy Research Working Papers, 10616. World Bank, Washington, D.C. Link
Submitted

Summary

This paper uses a machine learning-based survey-to-survey imputation method (SWIFT-plus) to estimate poverty in Afghanistan following the Taliban's return to power in August 2021. A model trained on the 2019/20 Expenditure and Labor Force Survey is used to predict household consumption in the 2023 Afghanistan Welfare Monitoring Survey, a phone survey drawn from the same sampling frame. The results show that 48.3 percent of the population was poor as of April-June 2023, a 4 percentage point decline since the same months in 2020. This decline was driven by falling rural poverty, while urban poverty remained unchanged.

Research in Progress

“Predicting Mexico-to-US Migration with Machine Learning for Counterfactual Analysis,” with J. Edward Taylor
Conferences: AAEA & WAEA Joint Annual Meeting 2025 Poster

Summary

Reliable tools to predict migration are increasingly important amid rising climate and economic risks, and demographic shifts. Tree-based machine learning models can uncover complex, nonlinear relationships that conventional models often miss and can be used to simulate responses to shocks. Migration data are costly to collect, so models must perform well with readily available data. We first train a LightGBM model on an ideal dataset, a panel tracking the employment locations of 10,739 individuals from 1980 to 2007, and achieve high predictive accuracy. Using this as a benchmark, we then train a model on just four years of data without migration histories. By adding public weather data, this restricted model approaches benchmark performance (within 0.1 F1 score). Counterfactual shocks show that a 10% rise in temperature reduces migration by 13% the following year, a 10% increase in age lowers it by 17%, and a 10% drop in income by 18%.

“Firm Networks and Resilience to Shocks in Rural Markets,” with Daniel Putnam and Jess Rudder

“Is Technology Inappropriate for Developing East Asia?,” with World Bank EAP team

“Financial Literacy and Small Firm Performance in Uganda”, with Ester Agasha, Andrew Hobbs, Travis Lybbert, Nathalie Nyanga, and Bruce Wydick

“Local Economic Impacts of Cash Transfers to Refugees and Asylum Seekers in Mexico, Mauritania, and Moldova,” with Justin Kagin and J. Edward Taylor