{"dcterms:modified":"2025-04-01","dcterms:creator":"Harvard Dataverse","@type":"ore:ResourceMap","schema:additionalType":"Dataverse OREMap Format v1.0.1","dvcore:generatedBy":{"@type":"schema:SoftwareApplication","schema:name":"Dataverse","schema:version":"6.6 build 1829-192cdc4","schema:url":"https://github.com/iqss/dataverse"},"@id":"https://dataverse.harvard.edu/api/datasets/export?exporter=OAI_ORE&persistentId=https://doi.org/10.7910/DVN/VIXNNE","ore:describes":{"citation:datasetContact":{"citation:datasetContactName":"Ley, Sandra","citation:datasetContactAffiliation":"CIDE, Political Studies Division","citation:datasetContactEmail":"sandra.ley@cide.edu"},"citation:keyword":[{"citation:keywordValue":"organized crime"},{"citation:keywordValue":"criminal governance"},{"citation:keywordValue":"high-profile violence"},{"citation:keywordValue":"turf wars"},{"citation:keywordValue":"partisan vertical fragmentation"},{"citation:keywordValue":"election cycles"}],"author":[{"citation:authorName":"Trejo, Guillermo","citation:authorAffiliation":"University of Notre Dame, Department of Political Science","authorIdentifierScheme":"ORCID","authorIdentifier":"0000-0003-4286-2808 "},{"citation:authorName":"Ley, Sandra","citation:authorAffiliation":"CIDE, Political Studies Division","authorIdentifierScheme":"ORCID","authorIdentifier":"0000-0001-8141-9273"}],"citation:dsDescription":{"citation:dsDescriptionValue":"This article explains a surprising wave of lethal attacks by drug cartels against hundreds of local elected officials and party candidates in Mexico, 2007-2012. These attacks are puzzling because criminal organizations prefer the secrecy of bribery over the publicity of political murder. Scholars suggest that war drives armed actors to attack state authorities in search of protection or rents. Using original data of high-profile attacks in Mexico, we show that war need arguments underexplain violence. Focusing on political opportunities, we suggest that cartels use attacks to establish criminal governance regimes and conquer local governments, populations, and territories. We present quantitative and qualitative evidence showing that cartels took advantage of Mexico's political polarization and targeted subnational authorities who were unprotected by their federal partisan rivals. Cartels intensified attacks during subnational election cycles to capture incoming governments and targeted geographically adjacent municipalities to establish controls over large territories. Our findings reveal how cartels take cues from the political environment to develop their own de facto political domains through high-profile violence. These results question the widely shared assumption that organized criminal groups are apolitical actors.","citation:dsDescriptionDate":"2018-11-01"},"subject":"Social Sciences","citation:depositor":"Ley, Sandra","title":"Replication Data for: High-Profile Criminal Violence.  Why Drug Cartels Murder Government Officials and Party Candidates in Mexico","dateOfDeposit":"2018-11-01","citation:notesText":"This dataverse includes the relevant .do and .dta files for replicating the analyses reported in the manuscript. Please see the ReadMe file for more information on how to use these materials. ","language":"English","@id":"https://doi.org/10.7910/DVN/VIXNNE","@type":["ore:Aggregation","schema:Dataset"],"schema:version":"1.0","schema:name":"Replication Data for: High-Profile Criminal Violence. 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This site is dedicated to the replication data for the articles it publishes.\n<br><br>\nFor more information about contributing to the journal, read the <a href=\"https://www.cambridge.org/core/services/aop-file-manager/file/58ff2df042e2c86c065b28a3\">Instructions for Contributors</a>. Authors needing help with this repository can view the <a href=\"https://www.cambridge.org/core/services/aop-file-manager/file/58ff2afb19dbda5f061e39b4\">guide to using BJPolS Dataverse</a>.","schema:isPartOf":{"schema:name":"Harvard Dataverse","@id":"https://dataverse.harvard.edu/dataverse/harvard","schema:description":"<span><span><span><h3>Share, archive, and get credit for your data. 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