Cohesion Policy, tourism and culture in Italy: a regional policy evaluation

ABSTRACT

European Union (EU) Cohesion Policy aims to reduce regional disparities between countries. The tourism sector has played a strategic role in this policy in the last years. The 2007–13 period developed a new vision focused on the link between tourism and enhancing cultural and natural resources. This paper evaluates this policy by analysing whether EU funds have positively impacted tourism and culture. The synthetic control method is used for 20 NUTS-2 Italian regions in the period 1998–2018. We identify regions exposed to the ‘convergence’ objective as treated units and regions not exposed as counterfactual. Results show positive effects on culture.

KEYWORDS:

• European Union Cohesion Policy
• tourism
• culture
• synthetic control method

JEL:

• R10
• R58
• Z10
• Z32

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

Notes

1. In 2016, the Tourism Satellite Accounts in Europe showed that Spain recorded the highest tourism gross value added (€236,131 million). This represents 27% of the tourism gross value added of the EU. Following Spain are Germany (€105,252 million, 12% of the EU total), Denmark (€89,041 million), Italy (€87,823) and the UK (€83,492 million) (European Union, 2019). In terms of international tourism flows, during the pre-covid period, Italy was the third destination in Europe after France and Spain (United Nations World Tourism Organization (UNWTO), 2020). In 2020, it recorded a decrease of 61%, with −90% in April 2020 alone. However, competitor countries such as Turkey and Spain showed a higher decline in the same period: −69% and −77%, respectively (https://www.unwto.org/unwto-tourism-recovery-tracker).

2. An exception is only represented recently by Di Cataldo (2017), Barone et al. (2016) and Albanese et al. (2021).

3. Regions with a GDP per capita < 75% of the EU average. Successively these regions have been renamed as ‘convergence’ objective.

4. For a complete overview of these studies, see Biagi et al. (2021b).

5. Eye@RIS3 visualizes public investment priorities for innovation across Europe. For more information, see https://s3platform.jrc.ec.europa.eu/map/.

6. This approach has already been used in many applications and fields. For an extensive overview of the method and previous applications, see Biagi et al. (2021).

7. For a review of this topic, see Xu et al. (2020).

8. The STATA command synth is used.

9. The recent STATA command synth_runner is used.

10. P-values are available from the authors on request.