https://orcid.org/0000-0002-3350-7276
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ABSTRACT
We contribute to the analysis of the impact of European Union funds on European regional development. We find that the European funds have a significantly positive effect on regional economic growth in the European Union. This result is obtained both with ordinary least squares (OLS), and with two-stage least squares (2SLS) using the presence of environmentally protected areas as an instrument. Furthermore, we find that interregional spillovers are important: a significant part of the favourable effect seems to take place in nearby regions rather than in the recipient region.
ACKNOWLEDGEMENTS
We received helpful comments and suggestions from Julia Bachtrögler, Nicolas Debarsy, Daniel Dujava, Ugo Fratesi, Slavomír Hidas, Peter Huber, Martin Lábaj, Mikuláš Luptáčik, Aleksander Łaszek, Katarína Rimegová, Vicente Rios, Alena Sabelová and Stella Slučiaková; the seminar participants at Brunel University, Narodowy Bank Polski, Slovenská Národná Banka, University of Economics in Bratislava and Kazakh-British Technical University; the conference attendees at the European Public Choice Society, Royal Economics Society, Slovak Economics Association, Warsaw International Economic Meeting, European Workshop for Political Macroeconomics, Austrian Economics Association, conference on ‘Economic Prospects for the European Union: Challenges for Economic Policy until the End of the Decade’ in Düsseldorf, the Trexima conference on 25 years of transforming centrally planned economies in Bratislava, the 2nd BORDERS workshop at Ghent University, and the Hungarian Regional Science Association conference; and the four anonymous referees as well as the associate editor.
DISCLOSURE STATEMENT
No potential conflict of interest was reported by the authors.
Notes
1. We use the terms ‘Cohesion Policy’, ‘EU funds’ and ‘regional policy’ interchangeably throughout the paper because they are broadly similar (though not entirely identical). When referring to Cohesion Policy, we refer not only to the EU’s Cohesion Fund (CF) but also to other related funding instruments such as the European Regional Development Fund (ERDF), the European Social Fund Plus (ESF+), the European Agricultural Fund for Rural Development (EAFRD) and the European Maritime, Fisheries and Aquaculture Fund (EMFAF).
2. The EEC was the original name of what later came to be known as the European Community (EC) and finally the European Union (EU).
3. See https://cohesiondata.ec.europa.eu/EU-Level/Historic-EU-payments-regionalised-and-modelled/tc55-7ysv. The dataset contains information from the following funds: ERDF, European Social Fund (ESF), CF, EAFRD, European Maritime and Fisheries Fund (Roemisch, Citation2017).
4. Most previous analyses used programming-period averages, or used these averages to intrapolate and estimate annual figures. An example of an innovative approach to constructing annual regional data is Roemisch (Citation2017), who used annual national project-level data to create estimates of regional-level annual data, since project names often contained information about the recipient region. Among the more recent studies that use annual data similar to ours are Rodríguez-Pose and Garcilazo (Citation2015) and Di Cataldo and Monastiriotis (Citation2020).
5. Cerqua and Pellegrini (Citation2018) also use the new yearly data. However, they restrict their analysis to the regions of the EU-15 and 1994–2006 period.
6. In principle, the same issue applies to the end of each programming period. However, for the 1994–99 and 2000–06 budgets, the end-of-budget spending overlaps with spending allocated during the first two to three years of the next period, which also appears in our data. It is only the beginning-of-budget spending in the 2014–20 programming period that our data set misses.
7. Only information on spending by the various EU funds (ERDF, CF, ESF and EAFRD) is available in annual frequency. A breakdown by sectors is available for whole programming periods only.
8. The Cohesion Policy spending could be correlated with investment in physical capital. However, the correlation coefficient reported in is relatively low at 0.1669.
9. The new member states were ineligible for Cohesion Policy before joining the EU. We therefore assign them zero values of Cohesion Policy funds in the pre-accession years. Given that the regions in the new member states objectively had no receipts from Cohesion Policy in the pre-membership period, this solution seems appropriate. Assigning them zero EU funds values rather than treating the pre-membership years as missing observations increases the variation in the data, thus helping improve the quality of our estimation by adding the before–after dimension. Moreover, missing values would be appropriate if we did not know the actual figures. This is clearly not the case here.
10. Council Directive 79/409/EEC (subsequently updated by the Council Directive 2009/147/EC) and Council Directive 92/43/EEC, respectively. While the Birds Directive was in place already since 1979, it was the adoption of the Habitats Directive in 1992 that laid the foundations for the creation of environmentally protected areas. For further details, see ‘Natura 2000’, European Commission, http: //ec.europa.eu/environment/nature/natura2000/index_en.htm
11. The member states can also grant conservation status to species and areas under national law if they wish to protect areas that are not eligible for Natura 2000 protection. For further details, see https: //ec.europa.eu/environment/nature/natura2000/sites/index_en.htm
12. We are grateful to an anonymous referee for suggesting this possibility.
13. The designation used depends on whether the site receives protection under the Birds Directive or the Habitats Directive, respectively. The practical implications are broadly similar, however.
14. In fact, only six regions had no protected sites in 2014: Guadeloupe, Martinique, French Guiana, Réunion, Inner London and Outer London. The first four of these are French overseas territories located outside of Europe, the remaining two are regions of an entirely urban nature.
16. These are published by the European Commission, https://ec.europa.eu/environment/nature/ natura2000/data/index_en.htm. The precise geographical coordinates of all Natura 2000 sites were required to allocate the sites to individual NUTS-2 regions and also to compute the area of each site. The source of the coordinates is the European Environmental Agency (EEA), http://www.eea.europa.eu/data-and-maps/data/natura-1; and http://bd.eionet.europa.eu/activities/Natura_2000
17. See http://www.eea.europa.eu/data-and-maps/data/biogeographical-regions-europe-3 and https://ec.europa.eu/eurostat/web/gisco/geodata, respectively.
18. For example, some Natura 2000 sites are constituted by rivers. If the river is also the border of a region and forms a bend, then the centroid of the site may be located in an adjacent region.
19. The moderate correlation can undermine the strength of our findings. Nevertheless, as we discuss below, the first-stage F-statistic is well above the rule of thumb value of 10 in all instances.
20. See https://ec.europa.eu/environment/nature/natura2000/ and https://ec.europa.eu/regional_policy/en/policy/themes/environment/. We are grateful to an anonymous referee for suggesting this relationship between environmental conservation status and EU funding.
21. We are grateful to an anonymous referee for also suggesting exploring this possibility.
22. Mankiw et al. (Citation1992) use 0.05. Using 0.05 would result in the loss of two observations, Thessaly in Greece in 2000 and Nord-Est in Romania in 2012, which recorded a negative population growth rate of 5%.
23. See https://ec.europa.eu/regional_policy/en/policy/what/investment-policy/. We are grateful to an anonymous referee for suggesting including this dummy as a regressor.
24. The use of a fixed effects model is supported by the result of a Hausman test: the test statistic (for the baseline Solow model augmented to include the ratio of the EU funds to GDP) is 734.11 (p = 0.00).
25. This contrasts with the previous findings of Becker et al. (Citation2013), Rodríguez-Pose and Garcilazo (Citation2015) and others. A possible reason for our findings deviating from the previous literature is that we use country-level rather than regional information on institutional quality.
26. As we argue in the Introduction, this kind of bias could be driven by the fact that slowly growing regions receive more funds because they remain eligible for Cohesion Policy funding for longer. In contrast, fast-growing regions quickly lose eligibility for transfers.
27. As an alternative, we split the sample into two subsamples according to the Convergence Objective eligibility. These results (available from the authors upon request) show a strong and statistically significant effect of EU spending in the less-developed regions and a slightly weaker but still positive effect in the rest of the regions; as before, the effect appears more robust when using the number of protected sights as the instrument.
28. It is possible that the EU funds affect regional development with a delay rather than contemporaneously. This could be due either to delays in disbursing the funds or because the investments themselves take time to build and bear fruit. Therefore, we also re-estimated the previous results – OLS and 2SLS alike – with all the EU funds lagged by one year, and again with all regressors lagged by a year. These regressions results (available from the authors upon request) are very similar to those reported with contemporaneous regressions.
29. We also conduct regressions with a spatial weight matrix based on k-nearest neighbours, with k = {5; 10; 15; 25; 50}. These results (available from the authors upon request) indicate that our model is robust to the choice of the spatial weight matrix.
30. We also use a spatial weight matrix based on a cut-off value of the quartiles of great circle distances for each region specifically, which results in an equal number of non-zero values for each region. The difference in the results is negligible.
31. The following remote NUTS-2 regions were dropped: Madeira, Azores, Canary Islands, Ceuta, Melilla, Guadeloupe, Martinique, French Guiana and Réunion. For example, the French overseas territory of Réunion (in the Indian Ocean) is more likely to be economically influenced by mainland France rather than the nearest EU region, which is Cyprus.
32. We are mindful of the identification problems when using IV in spatial models, as highlighted by Gibbons and Overman (Citation2012). In particular, the methodology for dealing with endogenous variables in a panel spatial model is not well-established. Therefore, rather than present results that might be questionable and potentially weak, we prefer to implement one contribution at a time. In the preceding section, the objective was to compare OLS and 2SLS estimates. Now we compare estimates obtained with OLS with those based on a spatial model. The 2SLS analysis has established that the OLS estimates may be biased downwards. Therefore, the SDM results probably also underestimate the true effects of Cohesion Policy in a spatial framework.
33. We conducted additional robustness checks by replacing the distance-based weight matrix with considering n-closest neighbours (with n = 5, 10, 15, 25 and 50) and omitting the interaction term between the EU funds and institutional quality. These results (see the supplemental data online) suggest that the effect of the Cohesion spending hovers at the limits of statistical significance. The results of the spatial model, therefore, need to be taken with caution.