Author ORCID Identifier

https://orcid.org/0000-0002-2213-1437

Semester

Spring

Date of Graduation

2023

Document Type

Dissertation

Degree Type

PhD

College

Chambers College of Business and Economics

Department

Economics

Committee Chair

Bryan McCannon

Committee Co-Chair

Brad Humphreys

Committee Member

Brad Humphreys

Committee Member

Kole Reddig

Committee Member

Jakub Lonsky

Abstract

The first chapter examines the connection between gentrification and urban violence. I demonstrate a positive and plausibly causal relationship between urban redevelopment and gun violence in Philadelphia. As the underlying mechanism, I focus on gentrification's displacement effect on local drug markets. Treating the city as a spatial network of city blocks and using two-way fixed effects differences-in-differences estimators, I show the gentrification of one block increases violence across the surrounding neighborhood. I find that some 2,400 (8%) of Philadelphia's shootings between the years 2011 and 2020 can be attributed to spillover effects from the gentrification of drug blocks. This effect is nearly ten times stronger than that observed on blocks without high levels of drug crime. This study also contributes a new empirical measurement of gentrification drawn primarily from property sales, along with building, zoning, and alteration permit issuance and utilizes a novel nearest-neighbor network approach to identify spatial spillover effects.

The second chapter formalizes the synthetic difference-in-differences estimator for staggered treatment adoption settings, as briefly described in Arkhangelsky et al. (2021). To illustrate the importance of this estimator, I use replication data from Abrams (2012). I compare the estimators obtained using SynthDiD, TWFE, the group time average treatment effect estimator of Callaway and Sant'Anna (2021), and the partially pooled synthetic control method estimator of Ben-Michael et al. (2021) in a staggered treatment adoption setting. I find that in this staggered treatment setting, SynthDiD provides a numerically different estimate of the average treatment effect. Simulation results show that these differences may be attributable to the underlying data generating process more closely mirroring that of the latent factor model assumed for SynthDiD than that of additive fixed effects assumed under traditional difference-in-differences frameworks.

The third chapter is joint work with Dr. Bryan McCannon. In it, we exploit a novel data set of criminal trials in 19th century London to evaluate the impact of an accused’s right to counsel on convictions. While lower-level crimes had an established history of professional representation prior to 1836, individuals accused of committing a felony did not, even though the prosecution was conducted by professional attorneys. The Prisoners’ Counsel At of 1836 remedied this imbalance and first introduced the right to counsel in common law systems. Using a difference-in-difference estimation strategy we identify the causal effect of defense counsel. We find the surprising result that the professionalization of the courtroom led to an increase in the conviction rate, which we interpret as a consequence of jurors perceiving the trial as being fairer. We go further and employ a topic modeling approach to the text of the transcripts to provide suggestive evidence on how the trials changed when defense counsel was fully introduced.

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