Organized crime and corruption: what are the consequences for Italian Cohesion Policy investments?

ABSTRACT

The paper evaluates the effect of illegal activities on Cohesion Policy implementation. This issue is fundamental in Italy, where large delays in expenditure risk to undermine the growth-enhancing effects of funds. To explain delays, we focus on two criminal behaviours: corruption and organized crime. By exploiting a two-step approach, an empirical analysis is carried out on Italian provinces, with a focus on Southern ones, between 2007 and 2015. The findings show that both crimes, impacting the efficiency of funds, cause delays in the implementation of Cohesion Policy. These consequences are higher if linked to corruption rather than to organized crime.

KEYWORDS:

• delay
• corruption
• criminal organizations
• Cohesion Policy
• public investments

JEL:

• D73
• D79
• E6

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Notes

1. Recipients were classified based on their legal nature, based on the ISTAT 2006 classification (see Appendix A in the supplemental data online and http://www.istat.it/it/archivio/6523).

2. This approach requires instrumenting the endogenous variables (in our case, corruption and index of organized crime by resorting to a set of instruments (z) characterized by three exclusion restrictions: (1) z is exogenous in the original equation; (2) z is relevant for explaining the endogenous variable; and (3) z has no direct effect on the dependent variable, it affects y only via its effect on the instrumented variable (Wooldridge, 2010).

3. The first-stage F-statistic is significant, thus confirming the validity of our instruments (Stock et al., 2002). The value of the coefficient of the endogenous variable lies within the confidence region obtained after applying the conditional likelihood ratio test statistics (Moreira, 2009), by supporting the robustness of the results to weak instrument issues. In each specification, the null hypothesis of the Sargan–Hansen’s J-statistic that the instruments are valid is not rejected. The overall accuracy of the models is supported by the Kleibergen–Paap rk LM statistics that reject the null hypothesis of under-identification.