I'm an Assistant Professor in the Economics Department at the University of San Francisco. I teach microeconomics topics in both the undergraduate major and the Master's in Applied Economics.
My research studies the neighborhoods and employees most exposed to aggregate industrial transitions, such as climate adaptation and automation. Within these topics, I use data science, unconventional data sources, and leading methods in causal identification to inform current policy issues.
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Job Market Paper
As fracking has become the dominant method of oil and gas extraction in the US, the population living within 1 mile of a well has quadrupled to over 10 million. While environmental externalities of this extraction are most concentrated within 1 mile, profits predominantly flow out of the host county. Measuring the intensity and distribution of fracking impacts on adjacent neighborhoods requires outcomes with high spatial resolution. I study changes in total neighborhood income and population which are derived from machine learning models trained to identify urban growth in daytime satellite imagery. Coupled with a precise shale geology instrument, my microspatial approach identifies that fracking exposure as far as 20 miles away leads to a 2 percent decline in neighborhood income. The spatial gradient and associated mechanisms of this effect indicate that it is driven by local industrialization rather than direct environmental externalities. While this effect is exacerbated by more extraction, it completely attenuates in areas with strong environmental protections or employment specialization in relevant sectors.
We apply deep learning to daytime satellite imagery to predict changes in income and population at high spatial resolution in US data. For grid cells with lateral dimensions of 1.2km and 2.4km (where the average US county has dimension of 55.6km), our model predictions achieve R-sq values of 0.85 to 0.91 in levels, which far exceed the accuracy of existing models, and 0.32 to 0.46 in decadal changes, which have no counterpart in the literature and are 3-4 times larger than for commonly used nighttime lights. Our network has wide application for analyzing localized shocks.
Using a comprehensive five-year panel dataset of tens of thousands of employees in a large technology firm, we study how clustered team-level attrition impacts rates of promotions, bonuses and attrition among remaining employees. We deploy a novel identification strategy leveraging changes in the firm's stock vesting schedule to isolate random variation in clustering of voluntary attrition. While a change to smoother vesting schedules leads to smoother attrition patterns, our data does not show strong evidence that clustered attrition impacted remaining employee outcomes.
Rich data on the multi-dimensional task requirements of each occupation has sparked a breadth of economic literature examining the portability of human capital across the labor market. A primitive in such analyses is constructing a norm over the vectors of occupational skills to create a continuous measures of skill distance between occupation pairs. While the existing literature has centered around factor analysis and angular separation as the leading norms, I show that using a regression framework derived from an Eaton, Kortum, Roy model of occupation switching directly implies a novel, empirical norm which is disciplined by observed occupation switching patterns. This approach relieves key limitations of existing measures, such as linearity and the inability to distinguish directional differences in skill portability, and allows for an analysis of which skill dimensions are critical in the portability of human capital, and which are not. Implications for existing results on skill portability are discussed, along with immediate policy applications to alleviate adjustments costs of workers switching occupations mid-career. Skill portability measures are aggregated, showing that compositional changes in employment by occupation since 1976 have lead to increased overall skill portability. Finally, using this novel measure of skill portability, network analysis shows that the incidence of a recession on job loss across the occupation network is related to the severity and duration of employment effects overall and by occupation.
Federal Reserve Bank of Boston Current Policy Perspectives
In light of the weak labor market conditions in the United States from 2008 until recently, one might have expected that participation in alternative income-generating activities, such as informal side-jobs, would have increased during that period. By the same logic, participation in informal work should have declined more recently, as conditions in the formal labor market improved. However, recent technological innovations have created a number of new opportunities for engaging in informal work. Such innovations may have promoted structural increases in informal work participation; if so we would expect informal work participation to remain elevated or increase further even as the economy improves. To test these predictions the authors designed the Survey of Informal Work Participation, fielded within the Federal Reserve Bank of New York's Survey of Consumer Expectations. The survey was fielded in December 2013 and again in January 2015, on two separate, nationally representative samples. The first survey was designed mainly to assess the extent and intensity of participation in paid informal work activities and its determinants, the types of activities engaged in, and the extent to which such activities helped individuals to compensate for negative economic shocks during and after the recession. The second survey was designed to follow up on the main outcomes of the first and to determine whether the motivations for engaging in informal work and/or the types of people drawn to such work, had changed as the labor market improved.