Data and Code for Organized Crime and Economic Growth: Evidence from Municipalities Infiltrated by the Mafia
Principal Investigator(s): View help for Principal Investigator(s) Alessandra Fenizia, GWU; Raffaele Saggio, UBC
Version: View help for Version V1
Name | File Type | Size | Last Modified |
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Replication package March2024 | 05/15/2024 07:28:PM |
Project Citation:
Fenizia, Alessandra, and Saggio, Raffaele. Data and Code for Organized Crime and Economic Growth: Evidence from Municipalities Infiltrated by the Mafia. Nashville, TN: American Economic Association [publisher], 2024. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2024-05-28. https://doi.org/10.3886/E197843V1
Project Description
Summary:
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This paper studies the long-run economic impact of dismissing city councils infiltrated by organized crime. Applying a matched difference-in-differences design to the universe of Italian social security records, we find that city council dismissals (CCDs) increase employment, the number of firms, and industrial real estate prices. The effects are concentrated in Mafia-dominated sectors and in municipalities where fewer incumbents are re-elected. The dismissals generate large economic returns by weakening the Mafia and fostering trust in local institutions. The analysis suggests that CCDs represent an effective intervention for establishing legitimacy and spurring economic activity in areas dominated by organized crime.
Scope of Project
Subject Terms:
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City Council Dismissals;
Mafia;
Policy Evaluation;
government corruption
JEL Classification:
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D73 Bureaucracy; Administrative Processes in Public Organizations; Corruption
G38 Corporate Finance and Governance: Government Policy and Regulation
K42 Illegal Behavior and the Enforcement of Law
D73 Bureaucracy; Administrative Processes in Public Organizations; Corruption
G38 Corporate Finance and Governance: Government Policy and Regulation
K42 Illegal Behavior and the Enforcement of Law
Geographic Coverage:
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Italy
Time Period(s):
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1991 – 2016
Data Type(s):
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census/enumeration data;
program source code
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