Title: Essays on Applications of Dynamic Models
Description: In many real-world settings, individuals face a dynamic decision problem: choices in the present have an impact on future outcomes. It it important for researchers to recognizing these dynamic forces so that we are able to fully understand the trade-offs an individual faces and to correctly estimate the parameters of interest. I study dynamic decision making in three diverse contexts: residential choice of families in New Zealand, search strategies of ridesharing drivers in Texas, and welfare participation of single mothers in Michigan. In each of these, I motivate the analysis using a theoretical model, and bring the model to the data to estimate parameters and evaluate testable implications. In the first chapter, I ask: how do schools affect where families choose to live and does their effect contribute to residential segregation? I study these questions using unique administrative microdata from Auckland, New Zealand, an ethnically diverse -- but segregated -- city. I develop and estimate a dynamic model of residential choice where forward-looking families choose neighborhoods based on their children's schools, local amenities, and moving costs.

Previous studies typically estimate school quality valuations using a boundary discontinuity design. I leverage attendance zones in this setting to also generate reduced form estimates using this methodology. The structural model estimates show that the valuation of school quality varies by the child's school level and the family's ethnicity; the reduced form approach, however, cannot capture this heterogeneity. Moreover, I find that the reduced form estimates are aligned only with white families' valuations of quality. The model estimates also show that families experience a high disutility from moving houses if it results in their child changing school. In counterfactuals, I show that residential segregation increases as the link between housing and schools weakens. In the second chapter, co-authored with Vinayak Iyer, we ask: what drives the efficiency in ridesharing markets? In decentralized transportation markets, search and match frictions lead to inefficient outcomes. Ridesharing platforms, who act as intermediaries in traditional taxi markets, improve upon the status quo along two key dimensions: surge pricing and centralized matching.

We study how and why these two features make the market more efficient; and explore how alternate pricing and matching rules can improve outcomes further. To this end, we develop a structural model of the ridesharing market with four components: (1) dynamically optimizing drivers who make entry, exit and search decisions; (2) stochastic demand; (3) surge pricing rule and (4) a matching technology. Relative to our benchmark model, surge pricing generates large gains for all agents; primarily during late nights. This is driven by the role surge plays in inducing drivers to enter the market. In contrast, centralized matching reduces match frictions and increases surplus for consumers, drivers, and the ridesharing platform, irrespective of the time of the day. We then show that a simple, more flexible pricing rule can generate even larger welfare gains for all agents. Our results highlight how and why centralized matching and surge pricing are able to make the market more efficient. We conclude by drawing policy implications for improving the competitiveness between taxis and ridesharing platforms.

In the third chapter, co-authored with Lucas Husted, we ask: does removing families from welfare programs result in increased employment? Using detailed administrative data from Michigan, we study a policy reform in the state's TANF program that swiftly and unexpectedly removed over 10,000 families from welfare while quasi-randomly assigning time limits to over 30,000 remaining participants. We motivate our analysis using a dynamic model of welfare benefits usage. Consistent with economic theory, removing families from welfare increases formal labor force participation by roughly 4 percentage points (20\% over control group mean), with increases in annualized earnings of roughly \$500. However, despite this, the majority of families remain formally unemployed after welfare removal, and using quantile regressions we show that even the highest percentile wage gains fail to offset the loss in welfare benefits. The policy even affects families who are far from exhausting their time-limited benefits.

Under a dynamic model, families have an incentive to bank benefits for future use -- an effect we observe in the data. Overall, our findings provide evidence that, contrary to their stated goals, welfare reform measures that either kick families off welfare or make welfare harder to access could possibly deepen poverty.