How much does state aid mitigate employment losses? Local policy effects at a time of economic crisis

ABSTRACT

We evaluate the effectiveness of the most extensive experiment of income redistribution, that is, the European Union regional policy, at a time of economic crisis. By exploiting geographical discontinuities in fund eligibility, we analyse comprehensive data on all publicly funded Italian projects at the municipality level. We find a positive impact of localized support to firms with a sizable increase in employment and the number of plants in intensively treated areas. The result is an important policy lesson concerning the effects of place-based policies at a time of a long-lasting recession, such as that engendered by the Covid-19 crisis.

KEYWORDS:

• European Union regional policy
• spatial regression discontinuity design
• recession
• municipalities

JEL:

• C21
• H25
• R11

ACKNOWLEDGEMENTS

An earlier version of this study was published as a working paper under the title ‘Local Policy Effects at a Time of Economic Crisis’. The authors thank Rocío Titiunik, Mara Giua, Marco Di Cataldo and four anonymous reviewers for their very helpful discussions and comments; the discussants Guido De Blasio and Markku Sotarauta; and the seminar participants in Brussels, Cagliari, Rome, Cork and Berlin, where preliminary versions of the paper were presented. The authors also thank Simona De Luca who provided precious insights into the OpenCoesione database; and Delio Miotti for the support on the municipality data.

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

Notes

1. The passage of EU regional policy towards a place-based policy has experienced two main moments. The first was the influential Barca Report (Barca, 2009), which proposed and strengthened the paradigm of place-based policies for local and regional development, as opposed to the space-blind or place-neutral initiative: where the place-based approach reaffirms that place matters in terms of geography, institution, capability and resources, place-neutral policies, as business support initiatives, were given to firms irrespective of where they are located and of what sector they are in (Bentley & Pugalis, 2014). The second phase involved the transition to RIS3, focusing investment on innovation under the European Regional Development Fund (ERDF) programme for the period 2014–20.

2. One reason is that many of the main Cohesion Policy evaluation studies concern the two programming periods 1994–99 and 2000–06, characterized by a positive economic cycle, while the crisis period mainly concerned the Structural Funds programming cycle from 2007 to 2013.

3. State aid is defined by the EU as an advantage in any form whatsoever conferred on a selective basis to undertakings by national public authorities.

4. This aspect is politically central as uneven development reduces confidence in democratic institutions spurring the rise in populism (e.g., Bachtler et al., 2017).

5. The Convergence region status is determined at the Nomenclature of Statistical Territorial Units (NUTS)-2 level (regions at this level are defined by minimum and maximum population thresholds of 800,000–3 million inhabitants and correspond to administrative divisions in EU member states) and in advance for a whole programming period of seven years. In the programming period 2007–13, Convergence regions received €199 billion, plus part of the Cohesion Fund, which amounted to €69 billion (European Commission, 2007).

6. This was made clear by the National Strategic Framework (NSF) 2007–13, a document that affirmed a unitary regional policy that would merge both strands of regional policy – EU (funded by the Structural Funds) and domestic (funded by the Fund for Under-utilized Areas) – into a single, seven-year strategic framework (Polverari, 2013). The NFS 2007–13 ring-fenced 85% of the domestic regional policy budget to the eight Mezzogiorno regions (EPRC, 2010b) despite only four of them obtaining the Convergence status, that is, Campania, Apulia, Calabria and Sicily.

7. A similar reduction of resources was observed in Greece, the UK, Ireland, Latvia, the Netherlands, Spain and France (EPRC, 2010a, 2010b).

8. The form of aid is variable: grants, low-interest loans or interest rebates, state guarantees, the purchase of a share-holding or an alternative provision of capital on favourable terms, exemptions or reductions in taxes, social security or other compulsory charges, or the supply of land, goods or services at favourable prices (European Commission, 2006).

9. As the per capita GDP of Convergence regions becomes higher than 75% of the EU average, ‘phasing-in’ or ‘phasing-out’ transitional programmes are put in place, reducing the amount of funds available to former Convergence regions (Di Cataldo, 2017).

10. The main selection criteria of those areas were: low population density regions, areas in geographical isolation, NUTS-3 regions with fewer than 100,000 population which have either a GDP per capita of less than the EU average or which have an unemployment rate higher than 115% of the national average, and areas in serious relative decline. In any case, state aid in each country could not exceed an overall population coverage of 42% of the country population (European Commission, 2006).

11. A few types of state aid are allowed even outside of the boundaries of the state aid map. For instance, the de minimis regulation allows small amounts of aid – less than €200,000 over three rolling years – to be given to an undertaking for a wide range of purposes. Also aid for research, development and innovation (R&D&I) can fall outside the scope of Article 87 of the Treaty of Amsterdam due to the existence of innovation spillovers. Our data cover all forms of public support.

12. Considering NUTS-2 regions, Becker et al. (2018) estimated the employment effect of EU regional policy before and after the crisis. They found that the effects are weaker during the crisis than before. However, they do not really isolate the impact of the Great Recession as they pool together the programming periods 2000–06 and 2007–13. A similar result is found by Bachtrögler (2016).

13. By analysing only state aid projects, we isolate the projects expected to impact firm outcomes directly. However, we cannot exclude the possibility that some other projects, especially those concerning the realization of public works, might indirectly affect firm outcomes. In the robustness section, we will consider all project categories to gauge the robustness of our estimates.

14. During the period 2007–15, 91 Italian municipalities were suppressed and 36 new municipalities were created, but only one of these new municipalities was created in the Mezzogiorno. We use the 2011 municipality map (the year of the census) with 8092 municipalities.

15. These figures do not include the 2273 projects at the national level or with a localization abroad. See Appendix A in the supplemental data online for the detailed construction of the sample.

16. Another possibility would be to use commitments rather than actual payments. However, commitments include also funds that may be cancelled (decommitted) or not entirely completed.

17. The use of workplace employment rather than the employment of resident people allows the consideration of job creation as a proxy for the broader local development of the treated areas (Giua, 2017).

18. Considering the NACE Rev. 2, ASIA includes all plants with the exception of those classified as section A, ‘agriculture, forestry and fishing’; section O, ‘public administration and defense, compulsory social security’; division 94, ‘activities of membership organizations’; section T, ‘activities of households as employers; undifferentiated goods- and services-producing activities of households for own use’; section U, ‘activities of extraterritorial organizations and bodies’; public institutions; and non-profit institutions.

19. Other causes of the delays in expenditure were the negative effects of the crisis on the ability of both public authorities and private enterprises to find co-financing, political discontinuity in national and regional governments, and inadequate administrative capacity combined with the excessive complexity of procedures (European Commission, 2016b).

20. Although Lazio’s economy is arguably stronger than that of the Mezzogiorno regions, we keep Lazio’s municipalities in the main analysis because of the border Lazio shares with Campania, as well as because Lazio’s NUTS-3 regions bordering with Campania, that is, Latina and Frosinone, have a GDP per capita similar to those of Abruzzo and Molise. In one of the robustness exercises reported in the robustness section, we remove the Lazio’s NUTS-3 regions not bordering with Campania and find that our estimates are not much affected.

21. When studying treatment assignments that change discontinuously at a geographical border, it is common for multiple administrative or political borders to perfectly overlap (Keele et al., 2017). Therefore, this might imply that the outcomes of interest might be affected not only by the jump in the eligibility to receive different intensity of state aid, but also by ‘irrelevant’ regional-specific treatments (e.g., differences in regional taxation or administrative differences). Note, however, that, as Cohesion Policy funds were largely used to compensate the fall in ordinary spending in Southern regions, the assumption that the estimate of β represents a good approximation of the causal effect of state aid is strengthened. Further, the tax base of the regional tax on productive activities (Imposta Regionale Attività Produttive – IRAP), which is the tax applied to all taxpayers engaging in productive or professional activities, is centrally set and regional governments cannot exploit full autonomy in changing it (Lagravinese et al., 2018). During the period 2007–15 there was little in-between variation in the tax base of IRAP for the regions under analysis; therefore, it is unlikely that this significantly affected firms’ behaviour. Lastly, the use of intra-regional discontinuities as an instrument additionally lessens worries about the validity of this assumption.

22. By differentiating the outcome variable we lower the variance in the SRDD estimator (Lee & Lemieux, 2010) taking into account pre-treatment differences in the dependent variable. Further, we condition on pre-treatment income and firm variables to account for the potential bias due to pre-treatment differences (Frölich & Huber, 2019).

23. A potential alternative is to use the Euclidean distance to the boundary (i.e., the shortest distance to the boundary from each municipality centroids) as forcing variable. However, as shown by Keele and Titiunik (2015), using a one-dimensional distance metric can lead to bad matches.

24. Considering that in 2015 the average number of employees was 2043.67, the average number of plants was 668.93 and the average population was 9455.50, we find that, on average, each municipality increased employment by 92.39 units (2043.67 – 2,043.67/(1 + 0.0473)), which multiplied by the number of municipalities suggests additional employment for 185,795 units (92.39*2011) and an estimated cost per job of €25,504 ((€249.20*9455.50*2011)/185,795).

25. The precise geographical disaggregation of the 2014–20 projects was not available at the time of the analysis.