Strategic Welfare Policies with Migration: A Theoretical Model and Empirical Evidence
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Maria Alessandra Antonelli
und
Valeria De Bonis
Abstract
We test the welfare magnet hypothesis for Europe. We modify the existing theoretical frameworks assuming that: (a) welfare services, intended as the output of welfare expenditure, not the poor’s income or social expenditure, enter the median voter’s utility function; (b) preferences depend on the position of the median voter in the income distribution; and (c) the total amount of welfare services provided may differ from the amount needed to finance them, because of inefficiencies in the transfer process. We then test the welfare magnet hypothesis for 22 European countries by estimating a reaction function corresponding to the generic form adopted by the literature, but using the variables inspired by the model. We find evidence of a positive strategic interaction among countries, which suggests a downward bias in the choice of the protection level because of welfare competition. The level of social protection also positively depends on GDP, the redistributive attitudes of residents and their weight in the population, vis-à-vis the migrants’ share, and the efficiency of social expenditure.
Calculation of the reaction function Eq. (16)
Substituting
With some calculations, we have:
Finally:
The Nash equilibrium
Given the reaction functions (16) and (17), the Nash equilibrium is:
Finally, we obtain the (18) in the text:
Analogously, for g2∗(19) we have:
Inefficiency and the equilibrium level of g (Claim 4)
By differentiating Eq. (27) or Eq. (28) one gets:
Source of primary data and reference period.
| Variable | Primary data and source | Reference years |
|---|---|---|
| g (SPPI) | Composite indicators (for primary data see Antonelli and De Bonis 2017, 2018) Source: OECD and Eurostat data | 2009–2011–2013–2015 for lagged value: 2007–2009–2011–2013 |
| PROX | Primary data: GDP in PPP US dollars Source: OECD | Biannual average values for GDP (2008–2009; 2010–2011; 2012–2013; 2014–2015) |
| MIG22 | Primary data: Immigrants Source: OECD International Migration Database | (Average values 2008–2009; 2010–2011; 2012–2013; 2014–2015) |
| KRM | Primary data: Immigrants source: OECD International Migration Database residents source: OECD P10 source: OECD Median income source: OECD | Biannual average values for all primary data (2008–2009; 2010–2011; 2012–2013; 2014–2015) |
| GDP per capita | GDP (PPP US dollars) source: OECD | Average values: 2005–2009; 2007–2011; 2009–2013; 2011–2015 |
| Population source: OECD | ||
| EFF | Index g for the output (see Antonelli and De Bonis 2017, 2018, 2019) for primary data. Source: OECD and Eurostat data | For g 2009–2011–2013–2015 |
| Net social public expenditure for the input. Source: SOCX OECD database | For net social public expenditure only biannual data available in OECD database (average values 2005–2009; 2007–2011; 2009–2013; 2011–2015 | |
| SEL | Primary data: means tested social benefits and total social benefits source: Eurostat | 2009–2011–2013–2015 |
Alternative weighing schemes
The alternative weighing scheme considered are:
A “neighbourhood” scheme based on geographical contiguity, assigning a weight, W, of
A “neighbourhood” scheme based on migration flows, that is, the ratio between the number of immigrants from country j to country i and the total number of immigrants from all other countries in the sample to country i, Mtot. We consider only immigration flows in line with the assumption in our model; Figlio et al. (1999) also justify this choice to avoid the negative weights that could derive when considering net flows, as well as attaching equal weights to jurisdictions with low immigration and emigration flows and to jurisdictions with high, offsetting flows in both directions (model 4). The results confirm what previously found for the MIG22 weight.
Panel FE with alternative weights.
| Panel fixed effects dependent variable: gti | ||
|---|---|---|
| Model 3 | Model 4 | |
| Wgt-1,j | 2.51937 (1.53727) | |
| Mtotgt-1,j | 9.16628 (3.95086)** | |
| KRM | 0.0000568445 (3.35610e-05)* | 0.0000658045 (3.42689e-05)* |
| GDP | 0.0000507884 (2.24078e-05)** | 0.0000432521 (2.35314e-05)* |
| EFF | 2.55211 (0.517754)*** | 1.72334 (0.479140)*** |
| LSDV R sq | 0.985299 | 0.983283 |
| Within R sq | 0.618461 | 0.566139 |
| F test for differing group intercepts | 22.0652 (p-value = 2.99575e-021) | 16.8069(p-value = 3.23006e-018) |
| Obs | 88 | 88 |
Notes: Std. errors in brackets. *, **, ***Are significant at the 10, 5 and 1% levels, respectively.
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