JOURNAL ARTICLE
Burdens, bribes, and bureaucrats: the political economy of petty corruption and administrative burdens.
Published In: Journal of Public Administration Research & Theory, 2024, v. 34, n. 4. P. 481 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Nieto-Morales, Fernando; Peeters, Rik; Lotta, Gabriela 3 of 3
Abstract
This article investigates the relationship between administrative burdens—defined as the psychological, learning, and compliance costs citizens experience in bureaucratic encounters—and petty corruption, specifically bribery, in Mexico's public sector. Analyzing over 63,000 reported interactions across 20 types of bureaucratic encounters using multilevel logistic regression, the study finds that higher administrative burdens significantly increase the likelihood of (attempted) bribery. It also identifies that bribery is more prevalent in encounters characterized by greater street-level bureaucratic discretion and those initiated by citizens ("on-demand"), while the availability of exit options (alternatives to public services) reduces bribery risk. However, the moderating effects of discretion, on-demand interactions, and exit options on the burden-bribery relationship are complex and sometimes counterintuitive, suggesting that discretion and citizen agency can both mitigate and exacerbate bribery depending on the type of administrative burden. The study contributes novel empirical evidence linking administrative burdens to petty corruption in contexts of weak institutions and endemic corruption, while acknowledging limitations related to measurement, reporting bias, and the focus on a single country.
Additional Information
- Source:Journal of Public Administration Research & Theory. 2024/10, Vol. 34, Issue 4, p481
- Document Type:Article
- Subject Area:Social Sciences and Humanities
- Publication Date:2024
- ISSN:1053-1858
- DOI:10.1093/jopart/muae010
- Accession Number:180119568
- Copyright Statement:Copyright of Journal of Public Administration Research & Theory is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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