This dissertation examines the interplay between law, policy, and decision-making within criminal justice and employment.
The first chapter investigates the interplay between predictive algorithms and human discretion in parole decision-making. It examines how deviations from algorithmic recommendations affect recidivism rates, revealing that increased supervision levels—when tailored by professional judgment—significantly lower recidivism. These findings underscore the importance of strategic oversight in enhancing the effectiveness of parole supervision.
The second chapter examines gender discrimination in hiring, using data from the blind auditions of the TV show The Voice. It highlights a significant own-gender bias, with coaches showing a clear preference for artists of the opposite gender. This study not only exposes subtle biases in decision-making but also employs sophisticated machine learning techniques to consider additional factors like team composition and performance dynamics, offering a comprehensive view of the selection process.
In the third chapter, the dissertation revisits the study by Stevenson and Wolfers (2006) concerning the impact of unilateral divorce laws on domestic violence and suicide rates. Utilizing modern econometric tools to correct for potential biases in earlier research, this reevaluation finds no significant impact of these laws on the rates of suicide and intimate partner violence, challenging prior conclusions and suggesting a more complex interaction between law and social outcomes.
These studies collectively enhance our understanding of how law and policy shape human behavior and societal structures, highlighting the significant roles of discretion, bias, and law.
","abstract_html":"<p>This dissertation examines the interplay between law, policy, and decision-making within criminal justice and employment.</p> <p>The first chapter investigates the interplay between predictive algorithms and human discretion in parole decision-making. It examines how deviations from algorithmic recommendations affect recidivism rates, revealing that increased supervision levels—when tailored by professional judgment—significantly lower recidivism. These findings underscore the importance of strategic oversight in enhancing the effectiveness of parole supervision.</p> <p>The second chapter examines gender discrimination in hiring, using data from the blind auditions of the TV show<em> The Voice</em>. It highlights a significant own-gender bias, with coaches showing a clear preference for artists of the opposite gender. This study not only exposes subtle biases in decision-making but also employs sophisticated machine learning techniques to consider additional factors like team composition and performance dynamics, offering a comprehensive view of the selection process.</p> <p>In the third chapter, the dissertation revisits the study by Stevenson and Wolfers (2006) concerning the impact of unilateral divorce laws on domestic violence and suicide rates. Utilizing modern econometric tools to correct for potential biases in earlier research, this reevaluation finds no significant impact of these laws on the rates of suicide and intimate partner violence, challenging prior conclusions and suggesting a more complex interaction between law and social outcomes.</p> <p>These studies collectively enhance our understanding of how law and policy shape human behavior and societal structures, highlighting the significant roles of discretion, bias, and law.</p>","abstract_has_math":false,"creators":["Assamidanov, Anuar"],"institution":null,"degree_name":"Economics, PhD","degree_level":"Open Access Dissertation","degree_discipline":"School of Social Science, Politics, and Evaluation","degree_department":null,"school":null,"contributors":["Scott Cunningham","Fernando Lozano"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-01-01T08:00:00Z","date_published":"2024-01-01T08:00:00Z","updated_at":"2026-07-24T01:40:49Z","subjects":["Economics"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarship.claremont.edu/cgu_etd/848","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Scott Cunningham","Fernando Lozano"]},{"key":"dc:creator","label":"Author","values":["Assamidanov, Anuar"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2024-09-18T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["School of Social Science, Politics, and Evaluation"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Open Access Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Economics, PhD"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Economics"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarship.claremont.edu/cgu_etd/848"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This dissertation examines the interplay between law, policy, and decision-making within criminal justice and employment.
The first chapter investigates the interplay between predictive algorithms and human discretion in parole decision-making. It examines how deviations from algorithmic recommendations affect recidivism rates, revealing that increased supervision levels—when tailored by professional judgment—significantly lower recidivism. These findings underscore the importance of strategic oversight in enhancing the effectiveness of parole supervision.
The second chapter examines gender discrimination in hiring, using data from the blind auditions of the TV show The Voice. It highlights a significant own-gender bias, with coaches showing a clear preference for artists of the opposite gender. This study not only exposes subtle biases in decision-making but also employs sophisticated machine learning techniques to consider additional factors like team composition and performance dynamics, offering a comprehensive view of the selection process.
In the third chapter, the dissertation revisits the study by Stevenson and Wolfers (2006) concerning the impact of unilateral divorce laws on domestic violence and suicide rates. Utilizing modern econometric tools to correct for potential biases in earlier research, this reevaluation finds no significant impact of these laws on the rates of suicide and intimate partner violence, challenging prior conclusions and suggesting a more complex interaction between law and social outcomes.
These studies collectively enhance our understanding of how law and policy shape human behavior and societal structures, highlighting the significant roles of discretion, bias, and law.
"]},{"key":"dc:title","label":"Title","values":["Essays on Crime and Law"]}]}],"canonical_facts":{"dc:contributor":["Scott Cunningham","Fernando Lozano"],"dc:creator":["Assamidanov, Anuar"],"dc:date.available":["2024-09-18T07:00:00Z"],"dc:description.abstract":["This dissertation examines the interplay between law, policy, and decision-making within criminal justice and employment.
The first chapter investigates the interplay between predictive algorithms and human discretion in parole decision-making. It examines how deviations from algorithmic recommendations affect recidivism rates, revealing that increased supervision levels—when tailored by professional judgment—significantly lower recidivism. These findings underscore the importance of strategic oversight in enhancing the effectiveness of parole supervision.
The second chapter examines gender discrimination in hiring, using data from the blind auditions of the TV show The Voice. It highlights a significant own-gender bias, with coaches showing a clear preference for artists of the opposite gender. This study not only exposes subtle biases in decision-making but also employs sophisticated machine learning techniques to consider additional factors like team composition and performance dynamics, offering a comprehensive view of the selection process.
In the third chapter, the dissertation revisits the study by Stevenson and Wolfers (2006) concerning the impact of unilateral divorce laws on domestic violence and suicide rates. Utilizing modern econometric tools to correct for potential biases in earlier research, this reevaluation finds no significant impact of these laws on the rates of suicide and intimate partner violence, challenging prior conclusions and suggesting a more complex interaction between law and social outcomes.
These studies collectively enhance our understanding of how law and policy shape human behavior and societal structures, highlighting the significant roles of discretion, bias, and law.
"],"dc:identifier":["https://scholarship.claremont.edu/cgu_etd/848"],"dc:subject":["Economics"],"dc:title":["Essays on Crime and Law"],"thesis:degree_discipline":["School of Social Science, Politics, and Evaluation"],"thesis:degree_level":["Open Access Dissertation"],"thesis:degree_name":["Economics, PhD"]},"updated_at":"2026-07-24T01:40:49Z"}