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Public sector reforms and their impact on the level of corruption: A systematic review

Author

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  • Giulia Mugellini
  • Sara Della Bella
  • Marco Colagrossi
  • Giang Ly Isenring
  • Martin Killias
Abstract
Background In spite of the large number of anti‐corruption reforms implemented in different countries, there has been little research that empirically and systematically assesses the impact of these efforts. Objectives The main objective of this review is to identify what works in curbing corruption in the public sector, by meta‐analyzing the findings of published and unpublished evaluations of different types of anti‐corruption interventions in different countries. The focus of this review is administrative corruption, namely corrupt acts involving civil servants in their dealings with their superiors, during the implementation of public policies, or while interacting with the public for service delivery. Political corruption (in the adoption of laws, regulations, and policies), and private‐to‐private corruption (involving only private actors) are excluded from this review. Search methods The literature search was conducted by querying three widely recognized electronic databases: RePEc, SSRN, and Web of Science. These databases are considered the most comprehensive in the socio‐economic field of research. The main grey literature repositories were also queried. Both published and unpublished studies were searched on the basis of specific combinations of keywords. The terms used to define queries were based on the “types of corruption”, “types of interventions/policies/reforms” and “study design” search strings. Specific conventions were used to “explode” or “truncate” keywords as appropriate. Screening of the references (i.e., snowballing) of the identified studies was also performed, and a reverse snowballing approach on Google Scholar was used. In order to ensure replicability, all searches were stored into Covidence, an online software developed by the Cochrane community for screening studies and extracting data for systematic reviews. Selection criteria Any study that included experimental evaluations (randomized controlled trials) of interventions developed for use in the public sector (e.g., public administration, education, health, etc.) to curb administrative corruption has been included in this review without any geographical or temporal limitations. Only studies written in or translated into English have been considered. Data collection and analysis Two review authors read the titles and abstracts of identified studies in order to determine their eligibility against the inclusion/exclusion criteria. When a title or abstract could not be included or rejected with certainty, the full text of the article was reviewed. In case of disagreement about whether or not a study should be included, the lead author (Giulia Mugellini), together with Martin Killias acted as arbitrator. The relevant information from identified studies was extracted independently by two review authors, following the guidelines of the Campbell Collaboration. The studies were assessed using the Cochrane Risk of Bias checklist as a basis. The effect size selected for the analysis was the Fisher's z‐score transformation of the partial correlation coefficient. For the meta‐analysis, random effect(s) models were estimated. Meta‐regression analysis models were then used to investigate the determinants behind the observed between‐ and within‐study heterogeneity. Ten different covariates were included in the meta‐regression models in order to control for the type of intervention, the type of corruption, the level of national income, the quality of the study and the type of participants involved in laboratory experiments. Results The initial literature search led to the identification of 70 studies. Approximately one‐third of the studies were excluded at the title/abstract stage because they either did not evaluate any anti‐corruption intervention but simply assessed the relationship between corruption and other phenomena, or because the study design was not based on randomized controlled trials. Another 14 studies were excluded only after a full‐text assessment. At this stage, the main reasons for exclusion were related to an unsuitable type of corruption (e.g., when the focus of the paper was political corruption, or private‐to‐private corruption instead of administrative corruption), the lack of regression output, or an unsuitable study design. At the end of the selection process, 29 studies resulted as eligible for inclusion. All the selected studies were written in English. The publication years ranged from 2007 to 2018. The majority of the selected studies (20) investigates the effect of anti‐corruption interventions in high‐ and upper‐middle income countries (Austria, Brazil, Canada, China, Germany, Italy, Mexico, the Netherlands, Thailand, the United Kingdom, and the United States). Nine studies focused on low‐ and low‐middle income countries (Burkina Faso, Burundi, Ethiopia, India, Indonesia, Pakistan, Tanzania, and Uganda). All of them were randomized experiments. Twenty‐five of these experiments were conducted in a laboratory, while four of them were field experiments. As to the type of outcome, the majority (18) of the selected studies addressed bribery (either active or passive), while 11 studies considered misappropriation of public resources (embezzlement). In terms of anti‐corruption interventions, 19 studies tested the effect of deterrence interventions, while 10 studies focused on policies based on organizational and cultural change. Overall, the meta‐analysis’ findings indicate that the identified interventions decrease the level of corruption. Results are statistically significant (p

Suggested Citation

  • Giulia Mugellini & Sara Della Bella & Marco Colagrossi & Giang Ly Isenring & Martin Killias, 2021. "Public sector reforms and their impact on the level of corruption: A systematic review," Campbell Systematic Reviews, John Wiley & Sons, vol. 17(2), June.
    • Handle: RePEc:wly:camsys:v:17:y:2021:i:2:n:e1173
    DOI: 10.1002/cl2.1173
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