Database of Municipal Corruption Cases in Italy (2015–2023)

Description

This dataset provides a municipality-year database on corruption-related events reported by Italian municipalities with population equal or greater 15000 inhabitants for the period 2015–2023. The dataset combines manually collected information from the annual RPCT reports published by municipalities with administrative identifiers and contextual socio-economic variables. Corruption-related variables are derived from the annual report of the municipal officer responsible for corruption prevention and transparency (RPCT), using standardized ANAC reporting items. The dataset also includes municipality identifiers, resident population and taxable income per capita.

Steps to reproduce

This dataset was constructed at the municipality-year level for Italian municipalities with at least 15,000 residents over the period 2015–2023. To reproduce the dataset, researchers should proceed as follows: 1. Identify the target municipalities and create a municipality-year panel for the period 2015–2023. 2. Retrieve municipal identifiers and contextual variables: - ISTAT municipality code and resident population from ISTAT “Demografia in Cifre”; - municipality-level taxable income per capita from the open data published by the Italian Ministry of Economy and Finance, Department of Finance. 3. For each municipality and year, retrieve the annual report of the Municipal Anti-Corruption and Transparency Officer (RPCT) from the “Amministrazione trasparente” section of the municipality’s official website. These reports follow the standard template defined by the Italian National Anti-Corruption Authority (ANAC). 4. Extract and code the following items from the ANAC-RPCT annual report: - public_contracts_corr_event = 1 if a corruption event is reported under item 2.B.2 (“Public procurement/contracts”), 0 otherwise; - any_corr_event = 1 if a corruption event is reported in at least one sub-item of item 2.B (items 2.B.0, 2.B.00, 2.B.1, 2.B.2, 2.B.3, 2.B.4, 2.B.5, 2.B.6, 2.B.7, or 2.B.8), excluding item 2.B.9 (“No corruption events occurred”), 0 otherwise; - crim_or_disc_liability = 1 if criminal or disciplinary liability related to corruption events is reported, based on item 12.A up to 2018 and item 12.B from 2019 onward, 0 otherwise. 5. Harmonize municipality names and administrative codes across years. In cases of mergers, suppressions, denomination changes, or code changes, reconstruct the series when possible; otherwise leave the relevant cell blank. 6. Do not impute missing values. Missing information is left blank in the dataset. Additional details on coding rules, source harmonization, and extraction reliability are provided in the accompanying README, CODEBOOK, and METHODS files.

Institutions

• University of Rome Tor Vergata

  Lazio, Rome
• University of Siena

  Tuscany, Siena
• University of Pisa

  Tuscany, Pisa

Categories

Funders

• Ministero dell'Università e della Ricerca

  Grant ID: PRIN 2022 project "Corruption risk in municipal governments: the managerial perspective" (Project code 2022E9C3ZE; CUP E53D23006220006)

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