key: cord-1011766-a3783mx0
authors: Crescenzi, Riccardo; Di Cataldo, Marco; Giua, Mara
title: It's not about the money. EU funds, local opportunities, and Euroscepticism
date: 2020-06-15
journal: Reg Sci Urban Econ
DOI: 10.1016/j.regsciurbeco.2020.103556
sha: 126d5b9fd86d492e7c275248dc47e22db18f82b5
doc_id: 1011766
cord_uid: a3783mx0

Growing Euroscepticism across the European Union (EU) leaves open questions as to what citizens expect to gain from EU Membership and what influences their dissent for EU integration. This paper looks at the EU Structural Funds, one of the largest and most visible expenditure items in the EU budget, to test their impact on electoral support for the EU. By leveraging the Referendum on Brexit held in the United Kingdom, a spatial RDD analysis offers causal evidence that EU money does not influence citizens' support for the EU. Conversely, the analysis shows that EU funds mitigate Euroscepticism only where they are coupled by tangible improvements in local labour market conditions, the ultimate objective of this form of EU intervention. Money cannot buy love for the EU, but its capacity to generate new local opportunities certainly can.

In order to elicit citizens' preferences for the EU we leverage the Brexit 112 vote. Therefore, our paper also contributes to the literature on the deter- The NUTS classification (Nomenclature of Territorial Units for Statistics) is a system used to divide the EU territory in homogeneous units for statistical purposes. The NUTS1 level represents major socio-economic areas, often corresponding to the national level. The NUTS2 level identifies sub-national regions (often with administrative autonomy) and is used to determine eligibility for EU Cohesion Policy funds. regions classified as 'less developed'-and hence entitled to receive the highest 148 form of EU financial support -were West Wales and the Valleys in Wales, 149 and Cornwall and the Isles of Scilly in England (Figure 1 ). These two re-150 gions, the poorest of the country, are those with a regional GDP For the 2014-2020 period, the UK is the second largest net contributor 178 3 This is exemplified by the fact that UK national expenditure for 'Economic affairs' in the richest region of the country, the London metropolitan area, is comparable to the amount invested in Wales (£711 per person and £751 per person, respectively, in 2014).

Data on UK Government spending retrieved from https://www.gov.uk/government/collections/public-expenditure-statisticalanalyses-pesa. to Cardiff, Wales' capital. This city acts as 'managing authority' for all EU 214 funds in the Welsh Nation, that is, it is responsible to receive funds from 215 Brussels and redistribute them within Wales. While most of the beneficiary-216 4 Unlike other European countries, UK NUTS2 regions are used exclusively for EU funding purposes, having no administrative or political meaning (Gripaios and Bishop, 2006) . This makes local areas belonging to neighbouring NUTS2 regions more similar than in other countries, as the regional boundaries used for EU funds eligibility are often unrelated to any social, political or cultural characteristics. 5 We are thankful to Julia Bachtrögler for kindly sharing these data with us. 4.2) and our preferred specifications are the latter, i.e. excluding Cardiff.

A further issue with beneficiary-level data is that they only cover approx-224 imately 60% of total EU funds to Wales. The remaining 40% is either not Wales. This pattern can be further appreciated in Figure A1 in the Appendix, Some of the funds reporting the Welsh Government in Cardiff as beneficiary has been geocoded in the area where the money has been spent by exploiting the description of the projects. As an example, one of the largest project in the data is described as the 'Dualling of the A465 between Tredegar and Brynmawr '. While this is officially recorded with the Welsh Government (Department for Economy, Science & Transport) as beneficiary, it was possible to locate the investment in West Wales, in the exact place where the A465 road is. funding is missing, being expenditure distributed across several locations 264 within Cornwall. As a result, the information in our possession does not 265 provide sufficient evidence that Cornwall would be a setting suitable for a 266 causal RDD analysis. Therefore, it is discarded as an additional case-study. Table A2 in the Appendix. form of EU aid) is smoothly distributed across the boundary and its impact 320 is isolated from any possible confounding factor, provided that assignment 321 to the treatment cannot be manipulated.

Our spatial forcing variable is hence the geographical distance from the 

Where R w is the share of Remain votes in the Brexit Referendum in ward 

Where U w represent the socio-economic and labour market dynamism of 353 local areas, to which EU regional policy is intended to contribute and that The results of the test are reported in Table 1 . For all variables we find 373 no evidence of a significant difference across the border. This increases our 374 confidence that the empirical setting fulfils the requirement for an RDD, The sample may be composed by all wards of Wales, or by wards within 391 50km or 10km from the border on both sides. Our preferred estimates are 392 obtained with third-order polynomials of distance, following the AIC criteria.

As shown in Table 2 , in all these different specifications the coefficient 394 of the treatment dummy is not statistically significant. We find no average 395 treatment effect, or no evidence that Welsh wards located in the region re-396 ceiving higher EU funds have voted comparatively more for either 'Remain'

The balancing test has been conducted also for different samples -all Wales and 10km from the border. The results report no systematic difference between treatment and control groups. The only significant element in these samples is human capital, marginally significant at 10% level. As a robustness test, we have replicated all our main estimates with the inclusion of human capital as control in the regressions. All key findings of the paper are confirmed. These results are available upon request from the authors. The visual representation of this result is illustrated in Figure 3 . The . By running a simple OLS analysis they find that EU regional development funds at NUTS2 level are not significantly associated with UK voters' decisions at the Referendum on Brexit. 1),(4) ), all wards located 50 km or less from the treatment border (columns (2),(5)), all wards located 10 km or less from the treatment border (columns (3),(6)). Cardiff wards excluded. Models estimated with polynomials of order one (columns (1)-(3)) or order three (columns (4)-(6)) interacted with forcing variable and treatment variable. As the main objective of EU regional policy is the promotion of 'smart, sustainable and inclusive' growth in recipient territories (European Commission, 2014), improvements in the economy and the generation of employment opportunities represent the expected outcome of policy interventions.

12 In absence of GDP data at the ward level we rely on information about the unemploy- Table 3 . First, it can be noted that, again, the West Wales 1),(4) ), all wards located 50 km or less from the treatment border (columns (2),(5)), all wards located 10 km or less from the treatment border (columns (3),(6)). Cardiff wards excluded. Models estimated with polynomials of order one (columns (1)-(3)) or order three (columns (4)-(6)) interacted with forcing variable and treatment variable.

in Figure A2 . significant. This appears to confirm that the creation of labour opportunities 497 for the most disadvantaged and for the youngest tends to be linked with a 498 stronger support for EU membership in areas eligible for EU transfers.

As a fourth test, we attempt to minimise any bias that may have been 500 produced by spillovers driven by the possibility that wards from East Wales 501 13 Following Internatioanl Labour Organisation (ILO) definitions, long-term unemployment rate corresponds to people seeking employment for one year or longer. Youth unemployment refers to unemployment of the 18-24 year old population. 6)).

In one additional robustness test, we replace the West Wales treatment 520 dummy with our beneficiary variables in Table A8 . While this indicator only 521 covers a portion of all EU money spent in Wales (approximately 60%), as 522 shown in Table A1 the variable correlates well with the West Wales dummy. 523 We control again for Census characteristics and test the model for all Welsh 524 wards (columns (1), (3), Table A8 ) and all Welsh wards excluding Cardiff 525 (columns (2), (4), Table A8 ). When testing the relationship between benefi-526 ciaries of EU funds and the Brexit Referendum once again we find no evidence 527 that high recipients of EU resources have voted differently from less funded 528 areas, and we also confirm that highly-funded wards in which unemployment 529 has decreased more have voted Remain more.

Finally, we further test the robustness of the significance of our main 531 coefficients by introducing a bootstrapping procedure. When using Local 532 Authorities for standard errors clustering we have a maximum of 52 clusters, 533 which is a relative low number, equal or lower than the rule of thumb for the 534 minimum number of clusters for the standard clustering procedure (Bertrand et al., 2004) . We therefore replicate the estimates in Tables 2 and 3 Table A9 , report wild-bootstrapped t-statistics in paren-541 thesis. In terms of statistical significance, these estimates appear perfectly 542 in line with our main specifications in Tables 2 and 3. Table A10 , suggests where voters were aware of the EU 605 funds received by West Wales they were also more likely to relate improve-606 ments in local labour market condition to the effect of EU policies. 1),(4) ), all wards located 50 km or less from the treatment border (columns (2),(5)), all wards located 10 km or less from the treatment border (columns (3),(6)). Cardiff wards excluded. Models estimated with polynomials of order one (columns (1)-(3)) or order three (columns (4)-(6)) interacted with forcing variable and treatment variable. 1) ), all wards located 15 km or less from the treatment border (column (2)), all wards located 30 km or less from the treatment border (column (3)), all wards located 40 km or less from the treatment border (column (4)). Cardiff wards excluded. Models estimated with polynomials of order three interacted with forcing variable and treatment variable. 4)), all wards located 50 km or less from the treatment border (columns (2),(5)), all wards located 10 km or less from the treatment border (columns (3),(6)). Cardiff wards excluded. Models estimated with polynomials of order one (columns (1)-(3)) or order three (columns (4)-(6)) interacted with forcing variable and treatment variable. 4)), all wards located 50 km or less from the treatment border (columns (2),(5)), all wards located 10 km or less from the treatment border (columns (3),(6)). Cardiff wards excluded. Models estimated with polynomials of order one (columns (1)-(3)) or order three (columns (4)-(6)) interacted with forcing variable and treatment variable. Note: clustered standard errors at local authority level in parenthesis. *** p<0.01, ** p<0.05, * p<0.1. Forcing variable: distance in km from border between East Wales and West Wales. West Wales: dummy variable taking value 1 for all wards belonging to West Wales and The Valley. Samples: all wards of Wales excluding East Wales wards less than 10km from border (columns (1), (4)), all West Wales wards located 50 km or less from the treatment border and East Wales wards between 10 and 50km from treatment border (columns (2), (5)), all West Wales wards located 10 km or less from the treatment border and East Wales wards between 10 and 20km from border (columns (3), (6)). Controls refer to labour market and demographic ward characteristics taken from the Census. Note: clustered standard errors at local authority level in parenthesis. *** p<0.01, ** p<0.05, * p<0.1. Samples: all Wales wards (columns (1), (3)), all Wales wards excluding wards from Cardiff (columns (2), (4)). Controls refer to labour market and demographic ward characteristics taken from the Census. (1),(4)), all wards located 50 km or less from the treatment border (columns (2),(5)), all wards located 10 km or less from the treatment border (columns (3),(6)). Cardiff wards excluded. Models estimated with polynomials of order three interacted with forcing variable and treatment variable. (1),(4)), all wards located 50 km or less from the treatment border (columns (2),(5)), all wards located 10 km or less from the treatment border (columns (3),(6)). Cardiff wards excluded. Models estimated with polynomials of order one (columns (1)-(3)) or order three (columns (4)-(6)) interacted with forcing variable and treatment variable. 1),(4) ), all wards located 50 km or less from the treatment border (columns (2),(5)), all wards located 10 km or less from the treatment border (columns (3),(6)). Cardiff wards excluded. Models estimated with polynomials of order one (columns (1)-(3)) or order three (columns (4)-(6)) interacted with forcing variable and treatment variable.

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