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Messing Up Texas?: A Re-Analysis of the Effects of Executions on Homicides

Executions in Texas from 1994–2005 do not deter homicides, contrary to the results of Land et al. (2009). We find that using different models—based on pre-tests for unit roots that correct for earlier model misspecifications—one cannot reject the null hypothesis that executions do not lead to a chan...

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Detalles Bibliográficos
Autores principales: Brandt, Patrick T., Kovandzic, Tomislav V.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4580630/
https://www.ncbi.nlm.nih.gov/pubmed/26398193
http://dx.doi.org/10.1371/journal.pone.0138143
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author Brandt, Patrick T.
Kovandzic, Tomislav V.
author_facet Brandt, Patrick T.
Kovandzic, Tomislav V.
author_sort Brandt, Patrick T.
collection PubMed
description Executions in Texas from 1994–2005 do not deter homicides, contrary to the results of Land et al. (2009). We find that using different models—based on pre-tests for unit roots that correct for earlier model misspecifications—one cannot reject the null hypothesis that executions do not lead to a change in homicides in Texas over this period. Using additional control variables, we show that variables such as the number of prisoners in Texas may drive the main drop in homicides over this period. Such conclusions however are highly sensitive to model specification decisions, calling into question the assumptions about fixed parameters and constant structural relationships. This means that using dynamic regressions to account for policy changes that may affect homicides need to be done with significant care and attention.
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spelling pubmed-45806302015-10-01 Messing Up Texas?: A Re-Analysis of the Effects of Executions on Homicides Brandt, Patrick T. Kovandzic, Tomislav V. PLoS One Research Article Executions in Texas from 1994–2005 do not deter homicides, contrary to the results of Land et al. (2009). We find that using different models—based on pre-tests for unit roots that correct for earlier model misspecifications—one cannot reject the null hypothesis that executions do not lead to a change in homicides in Texas over this period. Using additional control variables, we show that variables such as the number of prisoners in Texas may drive the main drop in homicides over this period. Such conclusions however are highly sensitive to model specification decisions, calling into question the assumptions about fixed parameters and constant structural relationships. This means that using dynamic regressions to account for policy changes that may affect homicides need to be done with significant care and attention. Public Library of Science 2015-09-23 /pmc/articles/PMC4580630/ /pubmed/26398193 http://dx.doi.org/10.1371/journal.pone.0138143 Text en © 2015 Brandt, Kovandzic http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Brandt, Patrick T.
Kovandzic, Tomislav V.
Messing Up Texas?: A Re-Analysis of the Effects of Executions on Homicides
title Messing Up Texas?: A Re-Analysis of the Effects of Executions on Homicides
title_full Messing Up Texas?: A Re-Analysis of the Effects of Executions on Homicides
title_fullStr Messing Up Texas?: A Re-Analysis of the Effects of Executions on Homicides
title_full_unstemmed Messing Up Texas?: A Re-Analysis of the Effects of Executions on Homicides
title_short Messing Up Texas?: A Re-Analysis of the Effects of Executions on Homicides
title_sort messing up texas?: a re-analysis of the effects of executions on homicides
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4580630/
https://www.ncbi.nlm.nih.gov/pubmed/26398193
http://dx.doi.org/10.1371/journal.pone.0138143
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