Hello! I am a Ph.D. candidate in the joint Business and Economics program at the University of Michigan. My research interests are in development and labor economics, with ongoing projects on the economics of migration and managerial skills in developing countries.
I am on the 2023/24 job market.
I document how international labor migration is used to cope with negative shocks, highlighting the role of search frictions and international macroeconomic conditions in shaping the migration response. Using administrative data on the universe of temporary migrant contracts from the Philippines, I show that typhoons increase international migration from affected municipalities. However, overseas wages of new migrant cohorts fall. The wage drop is driven by migrants leaving for low-wage countries and occupations, despite typhoon driven migrants being positively selected in terms of education. These patterns are consistent with congestion and search frictions in overseas contract markets: typhoons lower reservation wages and incentivize migrants to leave for lower paying overseas jobs. Strong international migrant demand dampens this response: typhoons lead to a larger migration increase without a proportionally large wage drop. As a result, the total migrant earnings response doubles when moving from median to 75th percentile migrant demand conditions. Similarly, households in typhoon affected regions receive more remittances when international migrant demand is high. These results suggest policies that increase the availability of overseas jobs in the wake of disasters can lead to substantial shock-coping gains.
How does income from international migrant labor affect the long-run development of migrant-origin areas? We leverage the 1997 Asian Financial Crisis to identify exogenous and persistent changes in international migrant income across regions of the Philippines, derived from spatial variation in exposure to exchange rate shocks. The initial shock to migrant income is magnified in the long run, leading to substantial increases in income in the domestic economy in migrant- origin areas; increases in population education; better-educated migrants; and increased migration in high-skilled jobs. 77.3% of long-run income gains are actually from domestic (rather than international migrant) income. A simple model yields insights on mechanisms and magnitudes, in particular that 23.2% of long-run income gains are due to increased educational investments in origin areas. Improved income prospects from international labor migration not only benefit migrants themselves, but also foster long-run economic development in migrant-origin areas.
We study the allocation and productivity consequences of training production line supervisors in soft skills via a randomized controlled trial. Consistent with standard practice for training investments within firms, we asked middle managers – who sit above supervisors in the hierarchy – to nominate members of their supervisory team for training. Program access was randomized within these recommendation rankings. Highly recommended supervisors experienced no pro- ductivity gains; in contrast, less-recommended supervisors’ productivity increased 12% relative to controls. This was not due to poor information or favoritism. Instead, consistent with the fact that supervisor turnover comes at a large effort cost to middle managers due to gaps in coverage and onboarding, middle managers prioritized retention over productivity impacts. Indeed, treated supervisors were 15% less likely to quit than controls; this gain was most pronounced for highly recommended supervisors. Misallocation of training can help explain the persistence of low managerial quality in firms.
Work in Progress
Internal Migration and the Geography of Unemployment and Self Employment [abstract]
A large fraction of workers in developing economies work in low-productivity self-employment. The self-employment rates vary widely within countries. I study the impact of migration frictions on the aggregate rate and distribution of self-employment. Focusing on Brazil, I document that, controlling for average income, self-employment rates are positively associated with the unemployment ratio of a region. Further, higher unemployment ratio is associated with lower relative earnings for the self employed. These patterns are consistent with unemployment risk in the wage-work sector incentivizing individuals to be self employed. I then develop an economic geography model with a self-employment sector and a wage-work sector that is subject to search-and-matching frictions. Individuals migrate based on location-specific productivity or preference draws. Lower migration frictions impact self-employment through two channels. First, workers can sort into locations with different self-employment rates, changing the aggregate self-employment rate. Second, sorting on productivity changes the equilibrium unemployment risk through impacting the average worker productivity and therefore the vacancy-posting incentives of firms. Changes in unemployment risk leads to changes in self-employment decision, affecting the distribution of self-employment. I estimate the model using Brazilian data to quantify the impacts of migration frictions.
Migration responses can be informative in assessing the impacts of place-based policies such as state specific minimum wages. Recent evidence suggests that minimum wage hikes raise the wages of low-income workers without substantial disemployment effects. In a simple spatial model, a clear prediction arise: states with rising minimum wages should observe rising inflows into minimum wage industries. However, existing work does not find that rising minimum wages are associated with an increase in the inflow to a state. In this paper, we propose one potential answer to this puzzle. We first show descriptively that minimum wage hikes tend to be spatially correlated and concentrated in high population states. Using a standard model of location choice, we demonstrate that estimates from a standard TWFE specification on aggregate migration flows can be hard to interpret in such a setting due to the bilateral nature of migration. Using our model, we motivate the estimation of a bilateral specification that consistently estimates an interpretable object: migration elasticity times the average effect of minimum wage hikes on the value of a state-sector. Using the LEHD Job-to-Job Flows Origin-Destination data and focusing on the restaurant industry, we find that minimum wage hikes increase the interstate flows to a location’s restaurant industry five years after the hike. However, it is unlikely a minimum wage change is equally valued across all subgroups. We find that the increase in flows is primarily concentrated in workers without a 4-year college degree and aged 35-65. Further, the increase in flows is primarily concentrated in large firms that can absorb the minimum wage hike. An aggregate TWFE specification delivers much smaller and imprecise estimates. The results suggest minimum wage hikes increase the value of a state's restaurant industry, at least in the short-run, implying that any disemployment effects are outweighed by the benefits of a rising wage. Further, our findings suggest estimates from standard specifications on aggregate flows can be misleading.
Good managers are extremely important for both worker satisfaction and well-being (and consequent workplace outcomes like attendance and retention) as well as for enabling workers to achieve higher productivity (and as a result to earn higher wages). However, identifying good managers, both among candidates at the time of hiring as well as among incumbents for the purposes of rewarding best practices and targeting training in deficient skills, is costly and difficult, particularly for low-margin labor-intensive manufacturing firms in developing countries. Over the last years, in a large garment manufacturing firm in India, we have developed and validated an instrument to comprehensively measure managerial skills, traits, and practices; estimated the contributions of each dimension of managerial quality to productivity; simulated gains from enhanced screening and training policies; and designed and tested training to remediate managerial deficiencies. We translated the aforementioned measurement and training tools into a tablet app-based platform that allows for self-administration and efficient scaling. In a large-scale experiment, we randomize the access to the skill measurement and training tool across 53 factories. We then test the benefit of using the tool on productivity, worker well-being, and gender composition of supervisors.