Trade Policy and Access to Intermediate Inputs: Quantifying the Welfare Costs of a Fertilizer Shortage [Job Market Paper] (with Devaki Ghose and Ana Fernandes)
The availability of critical intermediate inputs, such as fertilizer, is increasingly constrained by trade restrictions. Since most countries import the bulk of their fertilizer, nationwide shortages have become a key policy concern. The potential consequences of such large-scale disruptions, however, are understudied, as the existing experimental evidence can precisely measure local yield responses to fertilizer use but ignores general equilibrium effects. To make progress on this question, we leverage the case of Sri Lanka—a highly fertilizer-dependent economy that abruptly banned all chemical fertilizer imports in May 2021. Using high-frequency firm-level trade data, administrative production records, novel remote-sensing yield estimates, and eventstudy designs, we document sharp declines in fertilizer imports, agricultural yields, and exports of fertilizer-intensive crops. A quantitative spatial model of trade and agriculture estimates the ban’s average welfare cost at 7.3%, with disproportionate losses for farmers (19.5%), estate workers (16.6%), and fertilizer-intensive regions. Model-implied partial-equilibrium elasticities match the experimental literature, whereas general-equilibrium elasticities are much smaller due to price and wage adjustments, indicating that partial equilibrium estimates overstate the effects of nationwide input shocks. The model also highlights interactions between domestic and trade policy: by reducing fertilizer use, the import ban effectively scaled down the country’s fertilizer subsidy program and its implied transfers from mobile workers to farmers, mitigating the former’s welfare losses by 56%.
De-concentrating Integration? Spatial Concentration, Trade Access, Local Fundamentals, and Structural Change [R&R, Journal of Urban Economics]
Why are populations more spatially concentrated in some countries than in others? And why does spatial concentration change over time? I investigate the relationship between international trade integration and the evolution of spatial concentration while controlling for the influences of local fundamentals (i.e. productivity and amenities) and structural change. To do that, I create a two-sector quantitative spatial model with non-homothetic preferences and multiple subnational regions that vary in terms of access to trade networks, local fundamentals, and the agricultural share of consumption. In the model, international integration increases the population of initially less populated regions because these regions are relatively more dependent on foreign imports. I estimate the model in two steps: first, I use model-implied trade gravity equations to estimate the global structure of trade costs; second, I estimate unobserved local fundamentals by matching the model to 2005 data on wages, population, and employment for 1611 regions across 192 countries. Counterfactual exercises indicate that integration tends to reduce spatial concentration. Furthermore, a model-driven accounting exercise shows that changes in trade access can explain 15% of the variation of the observed 1990-2005 change in spatial concentration in a sample of 44 countries.
Large Immigration Inflows and Structural Transformation across Space and Time (with Stelios Michalopoulos, Elie Murard, Elias Papaioannou, and Seyhun Orcan Sakalli)
Non-Tariff Measures in a Quantitative General Equilibrium Trade Model
Labor Reserves and Spatial Development across Sub-Saharan Africa (with Stelios Michalopoulos and Elias Papaioannou)
Selection on Ability, Firm Sorting, and the Gender Wage Gap over the Lifecycle: Evidence from Brazilian Administrative Data (with Gustavo Gonzaga, Kelly Santos, and Rodrigo R. Soares) [This project is an ongoing development building on our previous IZA Discussion Paper No. 10791.]
Gravity Implications of the Functional Form of Trade Costs for Realistic Distance Distributions
Safety of Navigation and International Trade (with Vitoria Rabello de Castro and Marcos Ribeiro Frazao)
Combining Pre-School Teacher Training with Parenting Education: A Cluster-Randomized Controlled Trial (with Berk Özler, Lia C.H. Fernald, Patricia Kariger, Christin McConnell, and Michelle Neuman), Journal of Development Economics, 2018, 133(C), 448-467.
We used a randomized, controlled study to evaluate a government program in Malawi, which aimed to support child development by improving quality in community-based, informal preschools through teacher training, financial incentives, and group-based parenting support. Children in the integrated intervention arm (teacher training and parenting) had significantly higher scores in assessments of language and socio-emotional development than children in preschools receiving teacher training alone at the 18-month follow-up. There were significant improvements in classroom organization and teacher behavior at the preschools in the teacher-training only arm, but these did not translate into improved child outcomes at 18 months. We found no effects of any intervention on child assessments at the 36-month follow-up. Our findings suggest that, in resource-poor settings with informal preschools, programs that integrate parenting support with preschools may be more (cost-) effective for improving child outcomes than programs focusing simply on improving classroom quality.
Field-Scale Rice Area and Yield Mapping in Sri Lanka with Optical Remote Sensing and Limited Training Data (with Mutlu Özdoğan, Sherrie Wang, Devaki Ghose, Ana Fernandes, and Gonzalo Varela), Remote Sensing, 2025, 17(17).
Rice is a staple crop for over half the world’s population, and accurate, timely information on its planted area and production is crucial for food security and agricultural policy, particularly in developing nations like Sri Lanka. However, reliable rice monitoring in regions like Sri Lanka faces significant challenges due to frequent cloud cover and the fragmented nature of smallholder farms. This research introduces a novel, cost-effective method for mapping rice-planted area and yield at field scales in Sri Lanka using optical satellite data. The rice-planted fields were identified and mapped using a phenologically tuned image classification algorithm that highlights rice presence by observing water occurrence during transplanting and vegetation activity during subsequent crop growth. To estimate yields, a random forest regression model was trained at the district level by incorporating a satellite-derived chlorophyll index and environmental variables and subsequently applied at the field level. The approach has enabled the creation of two decades (2000–2022) of reliable, field-scale rice area and yield estimates, achieving map accuracies between 70% and over 90% and yield estimates with less than 20% error. These highly granular results, which are not available through traditional surveys, show a strong correlation with government statistics. They also demonstrate the advantages of a rule-based, phenology-driven classification over purely statistical machine learning models for long-term consistency in dynamic agricultural environments. This work highlights the significant potential of remote sensing to provide accurate and detailed insights into rice cultivation, supporting policy decisions and enhancing food security in Sri Lanka and other cloud-prone regions.