Research

Summer Youth Employment Programs are known to have significant impacts on youth outcomes based on lotteries from oversubscribed programs. But most cities cannot use a lottery design due to heterogeneity across youth and jobs. How can programs achieve efficiency and equity under alternative assignment mechanisms? Using hiring platform data, we study youth application and employer selection behavior to explore these design challenges. We find large mismatches between the distribution of youth versus jobs leaving 10% to 25% of positions unfilled. Moreover, employers were nearly twice as likely to select white youth relative to their representation in the applicant pool. This disparity persisted when controlling for other demographics, the number and timing of applications, and job readiness. Our findings reveal that workforce development programs may perpetuate in equities in the absence of simple random assignment. Using a job matching algorithm, we show that placing just 30% of positions by lottery can improve both equity and efficiency.

This paper estimates short-run employment effects of California’s sectoral minimum wage for fast-food restaurants. On April 1, 2024, covered limited-service chains faced a $20 hourly floor while the statewide minimum wage was $16 (up from $15.50 in 2023). Using establishment-level mobility data and a difference-in-differences design restricted to the counties without local wage ordinances in CA, we proxy on-site staffing with weekly long-duration device visits (>4 hours). Baseline estimates indicate an average 8% decline in on-site staffing intensity at treated outlets relative to comparable full-service and retail establishments. An event-study shows no pre-trends and a gradual, persistent post-announcement decline,

consistent with anticipatory staffing adjustments. Placebo estimates for exempt “enclosed” venues are null, and effects are similar across urban and rural locations. The results imply that a sectoral wage floor induces meaningful reductions in on-site staffing intensity at continuing establishments, with adjustments beginning before implementation rather than through immediate large-scale workforce changes.

Vickrey (1969) bottleneck model predicts that time-varying tolls induce departure-time shifting only where schedule flexibility permits. We test this prediction using New York City’s Congestion Relief Zone (CRZ), implemented January 5, 2025, which created a discrete $6.75 price jump at four pre-announced clock-time boundaries. Applying a difference-in-discontinuities design to hourly mobile-device visit data across Manhattan and Boston establishments, we find behavioral responses at exactly the two boundaries where the Vickrey model predicts them. Overnight establishment visits increase 5.9 percent at the weekday 5 a.m. boundary, where early-arrival costs are modest for shift workers and delivery personnel. Weekend evening establishment activity increases 9.2 percent at the 9 p.m. boundary, where late-arrival costs are low for leisure visitors extending their stay. Estimates at the weekday 9 p.m. and weekend 9 a.m. boundaries are precisely zero, consistent with high institutional penalties at those cutoffs. The asymmetry across boundaries to identical $6.75 differential, demonstrates that threshold placement relative to schedule technology, not price magnitude, governs congestion pricing effectiveness.

This paper examines how California’s 2024 fast-food minimum wage ($20 under AB-1228) affected consumer behavior. Using high-frequency transaction data from ten national chains and a difference-in-differences design, I find that customer visits and transactions fell by 6–7 percent, while revenue declined by about 5 percent, implying higher spending per visit and partial price pass-through. Income-resolved data show sharp redistribution: higher-income consumers spend 2–3 percent more per visit despite visiting less, whereas low-income customers show little change. Distinguishing composite from income-specific effects reveals that consumer-side incidence is heterogeneous and borne disproportionately by higher-income households.

Last decade has seen noteworthy local policy decisions, especially a trend in decentralisation of wage determination. Considering local policy changes are aimed at the local areas where boundaries are porous, there is a need for detailed and accurate geographic and time information. Using the establishment location and mobile-device location data by SafeGraph, this study explores how the labor market responds to local minimum wage ordinances. I use the difference-in-differences approach to estimate the effect of increase in minimum wage on the variation in duration of visits at a location which can be used as a proxy to employment hours. I find a decrease in employment hours when there is a proportionate increase in local minimum wage and an increase in distance travelled from home with an increase in minimum wage. The study further demonstrates that the local labor market, especially in the non-tradeable sector, is more responsive to the changes in local minimum wage than the state binded minimum wage changes.

This paper explains why the Opportunity Zone (OZ) program produced no measurable gains for residents despite large capital inflows. Using ACS and LODES data through 2022, we replicate and extend prior work and find precise null effects on resident employment, poverty, income, and workplace employment in OZ tracts. We then show that these null effects arise from the labor-market network structure. Commuting-flow networks reveal that OZ tracts occupy peripheral, highly segregated positions with low in-degree centrality, high structural constraint, and limited connectivity to high-income labor markets.

The prior literature finds no negative relationship between minimum wage and employment in the U.S., especially for the non-tradable industry. The argument hinges on the use of the contiguous region to study the minimum wage variation by controlling for economic shock which might be correlated with the minimum wage changes. I use mobile-device location data to study cross-area movement for labor market zones when local minimum wage changes. I use the spatial and temporal differences in the minimum wages to find the decrease in visits at the establishment located that experienced an increase in minimum wages controlling for the home destination of the visitors. Moreover, this decrease in visits depends on the distance between the destination and the home location of the visitor. Further, I use home Census Block Group (CBG) characteristics to predict the low-wage visitor at an establishment.