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Can the European Structural Funds shape migration flows?
Pellegrini, G.1; Tarola, O.1; Cerqua, A.2; Ceccantoni, G.1 1Sapienza University of Rome, 2University of Westminster
Dr Augusto Cerqua University of Westminster Email: [email protected] Website: sites.google.com/site/augustocerqua
Contact
• Cerqua, A., Pellegrini, G. (2016). Are we spending too much to grow? The case of Structural Funds. Mimeo (Sapienza University).
• Gabszewicz, J. & Tarola O. (2011). Migration, wage differentials and fiscal competition. Migration, 65.
• Gabszewicz, J. & Zanaj, S. (2014). Migration: a burden or a blessing for natives?. Center for Research in Economic Analysis, University of Luxembourg
• Gabszewicz, J., Tarola, O., & Zanaj, S. (2015). Migration, wages and income taxes. International Tax and Public Finance, 1-20.
• Lee, D. S. & Lemieux T. (2010). Regression Discontinuity Designs in Economics. Journal of Economic Literature, American Economic Association, vol. 48(2), 281-355, June.
• Pellegrini, G., Terribile, F., Tarola, O., Muccigrosso, T., & Busillo, F. (2013). Measuring the effects of European Regional Policy on economic growth: A regression discontinuity approach. Papers in Regional Science, 92(1), 217-233.
• Thistlethwaite, D. L., & Campbell, D. T. (1960). Regression-discontinuity analysis: An alternative to the ex post facto experiment. Journal of Educational psychology, 51(6), 309.
References
The cost of moving abroad is usually attached to the distance from the origin and receiving regions but, beyond the physical distance, there is a social and economic gap that cannot be neglected. Despite the complex and entangled reality of this phenomenon, we recognize two main issues to be considered: the public goods and services supply (we use a wider definition, including also primary goods and services belonging to the social environment of the citizens) and the labour market condition. We define a simple model of labour mobility and fiscal competition with a role for SF. There exist two regions, home H and other region F, with F being assumed smaller in terms of its population. Citizens can freely move from one region to the other. Far from Gabszewicz Tarola and Zanaj (2015), we assume here that population consists of two types of individuals, one with high preferences for public good at home and the other with low preferences for this public good, thereby introducing the so called relative preferences. Each resident compares the post-tax amount of money earned at home with…
Introduction
Migration has been studied from social, economic and institutional points of view, which mainly have in common the attempt to explain push and pull factors, potential and real impact on the economic fundamentals of countries and regions as well as effectiveness of migration policies. Our work tries to answer to the following questions: what is the impact of regional policies on migration flows among countries, regions and jurisdictions, differing in size, productive efficiency and population willingness to move? Besides, what is the relationship, if any, between SF and migration?
Research Question
Our analysis is based on the so called Regression Discontinuity Design (see, inter alia, Thistlethwaite and Campbell, 1960; Lee and Lemieux, 2010). We exploit the allocation rule of regional EU transfers: regions with a per capita GDP level below 75% of the EU average are qualified for Objective 1 funds. We claim that non eligible regions, with a per capita GDP just above the 75% threshold (not receiving a considerable amount of European SF), are a very good comparison group to those just below this cut off point (receiving Objective 1 funds, called "treated" regions), thus representing a valid "counterfactual scenario". The use of RDD overcomes the inverse causality problem, which implicitly affects this kind of analysis.
Methodology
We use NUTS-2 regional data on EU payments for Structural and Cohesion Funds from 1989 to 2011, collected on the base of the work done for the European Commission, Directorate General for Regional and Urban Policy, work package 14c: regression discontinuity design. To account for net migration, we use census data on the composition of the population in 2001 and 2011 in EU15 regions (Nuts 2 level) in order to catch significant differences which can be ascribed, at least in part, to the effect of regional policies financed by SF (i.e. increased public facilities supply in the region). Furthermore, using census data allow us to escape from problems regarding the fragmented definition of migrant across EU countries that often constitute a limiting factor for the analysis.
Data
Structural and Cohesion Funds (SF) represent the main regional policy implemented in Europe, with the aim of reducing economic gaps across areas and providing to all European regions appropriate economic and social standards. The effects of those policies affect the attractiveness of regions and territory which ends up to influence the choice of where to work and live made by workers, both European and not European. Among the effects of the policies financed by SF, we devote our attention to those on migration flows. This is the focus of our analysis: we aim to evaluate if there is a causal relationship between the amount of SF and the net migration per region. The Figures 1 and 2 show the high spatial heterogeneity of migration flows across Europe. The migration patterns are not obvious, and the growth rates of foreign population in the period 2001-2011 seems higher in the peripheral areas of the EU.
Theoretical Model
Figure 1. Share of the population with a nationality different from that of the host country (NUTS-2 regions in the EU-15)
International Seminar
Evaluating the impact of European Structural Funds:
is the transfer intensity effective?
Aula Gini – Faculty of Political Sciences, Sociology, Communication
Sapienza, University of Rome - November 27th, 2015
Program
Chair: U. Triulzi (DISSE, Sapienza, University of Rome)
14.45 Introduction:
M. Hristcheva (Head of Unity Evaluation and European Semester, DG
Regio, UE Commission)
Chair: D. Vidoni (DG Regio, UE Commission)
15.00 G. Pellegrini (Sapienza, University of Rome) and A. Cerqua (University
of Westminster):
Measuring the impact of intensity of treatment using RDD and
covariates: The case of EU Structural Funds
15.35 Discussion:
S. O. Becker (University of Warwick)
G. De Blasio (Bank of Italy)
16.00 Coffee Break
Chair: P. Casavola (UVAL-Dipartimento per lo Sviluppo e Coesione)
16.15 D. Bondonio (Università del Piemonte Orientale):
The impact of EU Structural Funds on regional growth: Estimating dose
response treatment effects through statistical matching
16.50 Discussion:
R. T. Greenbaum (Ohio State University)
E. Patacchini (Sapienza, University of Rome and Cornell University)
17.15: General Discussion
17.30 Conclusions
K. Stryczynski (Deputy Head of Unity, DG Regio, EU Commission)
Evaluating Effects of EU Cohesion Policy Competition for the best evaluations and evaluation proposals Best Evaluation Proposals (Strand B)
…the one obtained in the other region, including the cost to be incurred due to migration, while taking into account preferences for home (res. other region) public goods. The government in each region maximizes the tax revenue in order to provide the largest possible amount of public goods. Regions are assumed to play a two-stage game. In the first stage, each government is assumed to set its tax, taking into consideration the possible migration flow initiated as a consequence of its fiscal pressure. In the second stage, residents in H and F decide whether to stay in their own region or to migrate. We solve the game thereby determining the equilibrium configuration in the two regions. Finally, we introduce regional policies and evaluate their role in shaping the migrations flows between regions. To this aim, we define a setup where thanks to SF each of the two areas can benefit from regional policy tools, thereby enriching the bundle of public goods made available in the market.
Figure 2. Growth rate of foreigner population during the period 2001-2011 (NUTS-2 regions in the EU-15). Note: Missing data are displayed in grey
