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Lost in Corruption. Evidence from EU Funding to Southern Italy

Abstract

Windfall government revenues may generate an increase in the occurrence of corruption by reducing the degree of political accountability. This paper empirically investigates the relationship between the accrual of large financial transfers from a central level of government and the incidence of corruption at the local level. To this purpose we analyze the case of EU funding to Southern Italy and exploit within municipality variation in the flow of funds between 2007 and 2014. Our estimates show a statistically significant positive effect of transfers on corruption crimes: in the absence of EU funds disbursements the yearly number of white collar crimes in the South of Italy would have been 4% lower.

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Notes

  1. 1.

    See, for instance: New York Times on Slovakia; evidence from Romania; New York Times on Bulgaria.

  2. 2.

    For example, in September 2014 the court of Palermo started a trial against the manager of a training company accused of having subtracted about 15 million euro of EU funds for fictitious courses, while in October 2014, the director general for EU policies of the Abruzzi region was arrested for corruption (la Repubblica).

  3. 3.

    Details on the functioning of the policy can be found on the EC dedicated webpage.

  4. 4.

    The EC Regional Policy refers to this as the principle of “additionality” of the EU funds, by which contributions from the Funds must not replace public or equivalent structural expenditure by a Member State in the regions concerned. Yet, concerns have been raised that the EU structural funds may have relaxed the local budget constraint partly substituting the resources collected at the local level (Del Bo 2016).

  5. 5.

    For more-developed regions, additional factors are considered such as the share of skilled population, the share of school leavers and the distance between the regional GDP and an hypothetical GDP, obtained multiplying the actual population by the GDP per capita of the richest NUTS 2 region.

  6. 6.

    Details on the functioning of the audit mechanism for the 2007–2013 programming period can be found in the EC Regulation No 1828/2006.

  7. 7.

    Co-financing for the period under analysis is regulated by the Council Regulation No. https://eur-lex.europa.eu/legal-content/IT/ALL/?uri=CELEX%3A32006R1083. In the case of Italy, co-financing from EU should amount at most to 75% for the regions included in the Convergence objective (Puglia, Campania, Calabria and Sicily) and to 50% for the other regions. At the national level co-financing is regulated by the budget law for 2007 (law n. 296/2006) which envisaged that most of the resources should come from the State budget. As for the remaining that is on local budgets, the relevant administrative level is the regional one (or that of autonomous provinces that in the case of Italy have a status comparable to that of the regions). Moreover, regions do not use locally collected revenues for the need of the EU co-financing, rather, they use transfers from the central government.

  8. 8.

    We exclude projects that are managed at the national or regional level (which represent less than 3% of the projects implemented in 2007–2014 and amount to around 20% of the EU funds addressed to Italian regions) and only split across municipalities those that are managed at the province level (which represent about 2% of the projects located in the South of Italy and amount to 5% of the remaining funds).

  9. 9.

    Because of the EU N+2 rule payments referring to the 2007–2013 programming cycle could be made up until 2015.

  10. 10.

    The fact that most muncipalities received no EU funds and that white collar crimes diplayed such small variability implies that if we estimate our fixed effects model on the sub sample of municipalities in the Centre and North we lose about 90% of the observations, therefore remaining with a very selected sample. The correlation between EU funds and crimes that we estimate on that sample is a non significant –0.01, while it is a positive and significant \(0.076^{**}\) in the specification without municipality fixed effects, on a sample that mantains about 33% of the total number of observations.

  11. 11.

    In particular, the distribution of the error terms would not be normal and the model would likely predict negative values, which are conceptually unfeasible.

  12. 12.

    The results in Table 3, columns 1 and 2 are essentially unchanged when we employ the restricted set of observations, i.e. 6009.

  13. 13.

    Our specification allows for a flexible relationship between population and the effect of funds. Yet, our results remain essentially unchanged if we impose linearity, i.e. if we consider funds per capita as explanatory variable.

  14. 14.

    In an alternative specification, whose results we do not report for brevity, we estimate the same model of equation (1) using the standardized amount of the explanatory variable and find that a one standard deviation increase in the amount of funds generates an increase in the number of white collar crimes of about 11%.

  15. 15.

    Specifically, we refer to the TASI, a locally collected real estate tax.

  16. 16.

    The data we use for the indicator of local efficiency refer to 2014. For the overlapping sample of municipalities, the indicator correlates well with the measure of local efficiency calculated by Barone and Mocetti (2011) and with the index proposed by Giacomelli and Tonello (2015). When aggregated at the province level the indicator correlates well with the established proxies for public sector quality, such as Giordano and Tommasino (2011) and Nifo and Vecchione (2014).

  17. 17.

    Law 190 of 2012, see ANAC for its application to municipal governments.

  18. 18.

    Specifically, we consider (i) revenues from local real estate and waste taxes and (ii) the total tax revenue managed at the local level which may include also the revenue from an additional income tax decided by the municipality and collected at the central level.

  19. 19.

    Note that the sample significantly shrinks because data on other crimes are only available until 2011.

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Correspondence to Lucia Rizzica.

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We thank Audinga Baltrunaite, Emanuele Ciani, Federico Cingano, Tommaso Orlando, Giuliana Palumbo, Paolo Sestito, Alessandra Staderini, Stefania Zotteri, and seminar participants at the Bank of Italy (Rome, 2016), the European Urban Economic Association (Copenhagen, 2017), the CesIfo Workshop on Place-based Policies (Venice, 2017), the European Regional Science Association (Groningen, 2017), the Italian Association of Labor Economists (Cosenza, 2017), the OECD (Paris, 2017), the University of Salerno (Salerno, 2018), the University of Siena (Siena, 2018) and the University of Naples (Naples, 2018) for comments and suggestions. The views expressed in the paper are those of the authors only and do not involve the responsibility of the Bank of Italy.

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De Angelis, I., de Blasio, G. & Rizzica, L. Lost in Corruption. Evidence from EU Funding to Southern Italy. Ital Econ J 6, 355–377 (2020). https://doi.org/10.1007/s40797-020-00123-2

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Keywords

  • Regional transfers
  • Corruption
  • EU funds

JEL Classification

  • D7
  • H3
  • H7