the migration-welfare nexus...migration regime such as the eu, benefit levels should act as a pull...
TRANSCRIPT
This paper was prepared for the 11th European Consortium for Political Research (ECPR) General
Conference, 6-9 September 2017, Universitetet i Oslo, Norway.
Abstract
Key words: Political economy; welfare state; government spending; immigration; Europe
Acknowledgements
I would like to thank Olaf van Vliet, Kees Goudswaard, Koen Caminada and Alexandre Afonso for their thoughtful
and insightful feedback and suggestions on previous versions of this paper. Additionally, this study is a part of
SOLID: Solidarity Under Strain and is funded by Interaction between Legal Systems (ILS 2.0).
The Migration-Welfare Nexus To what extent does immigration influence national
welfare state generosity in Europe?
Clare Fenwick
Department of Economics, Leiden University
The question of whether immigration undermines native support for welfare provision has received
considerable attention in the academic literature. Remarkably, only a few studies have explored if
changes in support are echoed by changes in policy, such as the retrenchment of the welfare state.
This study explores whether immigration plays an influential role in the determination of welfare
state generosity. It investigates the relationship between stocks of migrants, the foreign-born
population, on two different indicators for welfare generosity - social welfare spending as a
percentage of GDP, as is common convention in welfare state literature, and a welfare generosity
index. The results show that the foreign-born population has a positive and statistically significant
effect on social welfare spending, but no effect on the welfare generosity index. Crucially, the findings
provide no evidence to support the hypothesis that higher levels of immigration lead to reduced levels
of social welfare provision. On the contrary, immigration may lead to welfare state expansion rather
than retrenchment.
2
Introduction
Milton Friedman famously once said “You cannot simultaneously have free immigration and a welfare
state” (Friedman, 1999). Indeed, it is to be expected that increasing immigrant inflows can present new
difficulties and challenges for the welfare state and solidarity among citizens. Some authors go as far to
argue that increasing immigration in Europe will eventually lead to the Americanisation of European
welfare states and politics (Alesina et al., 2001, 2004; Freeman, 1986).
The welfare state can be understood as a mechanism, or a social arrangement, which enables a
population to deal with collective risks and reduce social inequality. In Europe, it is something that has
been closely linked with the development of nation-states and has contributed to the forging of bonds
between citizens. The welfare state does, however, restrict rights and benefits to insiders and thus
exclude outsiders. As a result, increasing immigration becomes a challenge for modern social security
institutions built on inclusion, integration and homogeneity (Mau & Burkhardt, 2009).
Consequently, immigration can expose the tensions between the inherently closed system of the welfare
state and the relatively open economies of developed nations. This led Freeman to conclude “that,
ultimately, national welfare states cannot coexist with the free movement of labour” (1986).
This issue is especially salient in light of the fact that immigration is consistently one of the most pressing
issues for citizens and currently forms a central pillar in electoral campaigns in Europe (Afonso & Devitt,
2016). In particular, Western European countries tend to have well established welfare states, but have
also been dealing with large-scale migration for several decades and migration discourse is often fuelled
with controversy. As Burgoon et al. wrote “public opinion regarding the economic and cultural impact
of immigrants tends to be negative” (2012). Crucially, some authors predict that weakening solidarity
due to increasing ethnic diversity will undermine the welfare state (for a survey of the literature see
Stichnoth & Van der Straeten, 2013).
Despite this, Castles & Schierup (2010) wrote that “immigration and growing ethnic diversity are
important – but often neglected – factors in the evolution of welfare systems in Europe”. In globalisation
literature, most authors investigate the impact of trade and capital on the welfare state but ignore its
third facet, the movement of people. This paper aims to provide insight into the migration-welfare nexus
through investigating whether or not immigration plays an influential role in the determination of
welfare state generosity in Europe.
Earlier quantitative research is mixed, it is not clear to what extent immigration impacts welfare
generosity as most researchers use only social welfare spending as a proxy for generosity (Gaston &
Rajaguru, 2013; Lipsmeyer & Zhu, 2011; Soroka et al., 2006; Soroka et al., 2016). Starke (2006) argues
that studies researching welfare policy change should complement expenditure data with additional
quantitative measures. Consequently, this study extends previous research through complementing
3
social welfare spending data with a welfare generosity index developed by Scruggs et al. (2004, 2014).
Moreover, the analysis includes the years following EU expansion and so hopes to shed light on the
speculations and predictions that EU enlargement would have negative consequences for the European
welfare state. My empirical findings suggest that there is no evidence to support the conclusion that
increasing immigration is detrimental or incompatible with European welfare states.
In the following section I examine the previous literature surrounding immigration and the welfare
state. This is followed by a section on the research design, which includes my hypotheses, data and
method. Then, I present the results and analysis before finally concluding the paper.
Immigration and Welfare in Contemporary Debate
“National welfare states, whatever their internal principles, exist in a global political economy”
(Freeman, 1986).
The relationship between national welfare states and globalisation is complex, and previous literature
has tended to focus on the impact of capital mobility and trade liberalisation rather than on increasing
mobility. The research that has been conducted on how immigration impacts the generosity of welfare
states is typically split into two competing camps. One side advocates that increasing inflows should
lead to the retrenchment of the welfare state (e.g. Alesina & Glaeser, 2004; Alesina et al., 2001; Beine et
al., 2015; Burgoon, 2014; Schmidt-Catran & Spies, 2016), while the other proposes that increasing
immigration should actually drive its expansion (e.g. Brady & Finnigan, 2014; Finseraas, 2008; Steele,
2016; Walter, 2010).
However, political economy theory proposes that – in theory – empirical evidence should find increasing
immigration leads to reduced levels of welfare generosity.
Median Voter Theory
In 1981, Meltzer and Richard developed a political economy model in order to demonstrate that demand
for redistribution by voters is dependent on the level of economic inequality. The model predicts that
when the mean income rises relative to the median income, then demand for redistribution will increase
and taxes will rise. This is based on the assumption that voters act with economic self-interest, that those
with an income lower than the median income choose candidates who favour higher taxes and greater
redistribution. Whereas, voters who have an income above the median desire lower taxes and less
redistribution (Meltzer & Richard, 1981).
Magni-Berton (2014), uses Meltzer and Richard’s median voter model to show how immigration can
reduce demand for redistribution. First, it is assumed that immigrants to a new country have a lower
income than the median voter there. Subsequently, due to an absence of voting rights for immigrants,
this means that the income level of the median voter does not change, but the general mean income of
4
the entire population does. Consequently, immigrants close the gap between the mean and median
income and subsequently reduce support for redistribution.1
Based on this theory and building on the concept that a higher proportion of immigrants in Europe are
considered low-skilled and work in lower-paid jobs (UN-DESA & OECD, 2013), I would expect that
immigration has led to reductions in welfare state spending and generosity. This notion is supported by
the welfare magnet hypothesis, which also relies on assumptions about the economic self-interest of
people.
The Welfare Magnet Hypothesis
The welfare magnet hypothesis proposes that if location choices made by immigrants are guided by
income-maximizing behaviour, then the generosity of a welfare state will act as a considerable pull-
factor in those decisions. As a result, states are expected to reduce their welfare effort in order to avoid
becoming a magnet for immigrants.
The hypothesis finds its roots in George J. Borjas’ seminal work. Borjas (1999) investigates the location
decisions made by immigrants arriving in the United States of America (US) using the 1980 and 1990
Public Use Microdata Sample (PUMS) of the U.S. census. He finds evidence to suggest that immigrant
welfare recipients in the US are more heavily clustered in welfare-generous states than natives or
immigrants who do not receive welfare benefits. Borjas suggests this is because of migration costs;
immigrants have already decided to incur these costs and so choosing the state that offers the highest
benefits is costless. Whereas for native welfare recipients, the cost of migration deters them from
seeking out welfare-generous states.
Borjas concludes then that a relatively generous state becomes a “magnet” for immigrants and “will lead
to a very different geographic sorting of welfare recipients in the immigrant and native populations”
(1999). He does stress, however, that the statistical significance of his results is weak and that there may
be alternative explanations for the evidence.
To investigate the welfare magnet theory in a European context, De Giorgi and Pellizzari (2009) use data
from the European Community Household Panel (ECHP). They find that the relative generosity of a
welfare state influences the decisions of migrants. However, the results also show that the number of
migrants influenced by the welfare state of a particular country is minor when compared to other
reasons for emigrating. Regardless, the authors conclude that their findings may present serious
implications for further expansion of the EU.
In addition, Razin and Wahba (2015) develop two theoretical models, the first a free-migration regime
and the second a restricted-migration regime, and then test them empirically and find support for the
welfare-magnet hypothesis. They conclude that in a free-migration regime, a generous welfare state
1 For more political economy models involving immigration, taxes and redistribution see Hansen (2003), Ortega (2004), Felbermayr and Kohler (2007), Nannestad (2007) and Razin and Sadka (2012).
5
attracts unskilled migrants while skilled migrants are deterred. Thus, they conclude that in a free-
migration regime such as the EU, benefit levels should act as a pull factor within the EU labour market.
The welfare magnet hypothesis is crucial in the debate on how migration will impact welfare generosity
because in particular, the public and politicians have become increasingly concerned that the welfare
systems in Europe will be negatively affected through welfare migration. One such result of this, is the
hypothesis that governments may enter into strategic interactions with neighbouring districts, states or
countries in a race to provide the lowest levels of welfare to avoid becoming a magnet for poor
immigrants. This has been dubbed the “Race-to-the-Bottom”.
In America, Schram and Krueger (1994) and Brueckner (2000) both present evidence to show that
strategic interactions between states have taken place, and both papers conclude that it is due to belief
in welfare magnets.
Likewise in Europe, Kvist (2004) argues that EU 15 member states have been engaging in strategic
interactions and, in light of further EU enlargement, concludes that this may intensify in the future.
Similarly, Dahlberg and Edmark (2008) investigate whether or not there is a race-to-the-bottom
between municipalities in Sweden. They conclude that if a neighbouring municipality reduces welfare
spending by 100 SEK, then a municipality reduces their welfare spending by approximately 41 SEK.
Although, benefit levels between municipalities or US states are more transparent than benefit levels
between countries and may explain the differing evidence on strategic interactions. Interestingly, based
on a comparative analysis of the EU-15 and 7 non-EU OECD countries, van Vliet (2010) determines that
rather than a social race-to-the-bottom, social expenditures in the EU-15 have converged and increased
on average.
In addition, using EU-LFS data, Skupnik (2014) find no evidence of a race-to-the-bottom in benefit levels
due to the mediating effect of transitional labour market restrictions on new EU members. Since 2014
however, these restrictions have all been lifted, and so the impact of immigration on benefit levels may
have changed.
However, whether or not a social race-to-the-bottom is taking place, there is a hypothesis that also
supports the belief that increasing immigration should lead to reduced levels of welfare generosity, just
through a different mechanism.
The Anti-Solidarity Hypothesis
“The individuals who agree to share according to need have to experience a sense of solidarity that
comes from common membership in some human community.” (Freeman, 1986)
Solidarity is considered important for the survival of the welfare state – to support this, previous
research has found that solidarity is significantly and positively related to support for the welfare state
(Burgoon et al., 2012; Crepaz, 2007; Kymlicka & Banting, 2006). Consequently, the anti-solidarity
6
hypothesis proposes that increasing racial heterogeneity challenges solidarity and thus undermines
support for the welfare state.
Alesina at. al. (2001) argue that ethnic diversity is an crucial factor for explaining why the US does not
have a welfare state similar to those found in Europe. They find that if the probability of two people
drawn at random from a population will belong to different ethnic groups increases by just one
percentage point, then social spending as a percentage of GDP is expected to reduce by 7.5 percentage
points. Following their research in America, Alesina at. al. (2001) argue that as Europe’s heterogeneity
increases because of immigration, rising ethnic divisions will be used as a challenge to generous welfare
states.
Alesina and Glaeser (2004) further their argument through expanding their analysis to 54 countries and
directly comparing the US and Europe. They find a negative correlation between racial fractionalisation
and social welfare spending. They conclude that European countries, in particular those in Scandinavia,
are largely homogenous and have generous levels of welfare state spending. and argue that generous
welfare states are contingent on a homogeneous society because solidarity between citizens depends
on common linkages, such as culture and language. Alesina and Glaeser (2004) suggest that increasing
immigration in Europe, potentially through the expansion of the EU, will challenge Europe’s
comparatively generous welfare states, as they find it has in the US.
Focussing on European OECD Countries, Mau and Burkhardt (2009) believe that the conclusion that
migration poses a threat to European welfare states is over-exaggerated. While they find a negative
influence of ethnic diversity on support for the welfare state, it is very weak and when controlling for
certain factors, such as GDP and unemployment, there is a mediating influence. Mau and Burkhardt use
five different measures for ethnic diversity; “ethnic fractionalisation, the proportion of foreign
population, foreign-born population, non-Western foreign-born population and migration inflow”
(2009).
Additionally, Finseraas (2009) finds no evidence for the anti-solidarity hypothesis. Instead, he
determines that it is xenophobia which undermines support for the welfare state. 2 He finds evidence to
suggest that voters with typically left-wing views on redistribution but right-wing views on immigration
tend to follow their immigration preferences at the ballot box. The parties that tend to support tough
stances on immigration are usually right-wing, and those same parties typically advocate for reductions
in welfare state generosity.
2 There is a large body of research that focuses on prejudice, racism and xenophobia, which investigates people’s
tendencies to favour an in-group as reasons to why ethnic diversity may reduce support for social welfare and redistribution. This is also related to a strand of literature on trust and the trust of outside groups where there are a variety of empirical, observational and experimental studies (Banting & Kymlicka, 2006; Crepaz, 2007; Nannestad, 2007; van der Meer & Tolsma, 2014).
7
Magni-Berton (2014) uses data from the 2008 European Values Survey covering 45 European countries,
and finds that immigration reduces support for redistribution primarily through concerns of expected
competitiveness on the labour market, which is increased when people believe there is a high number
of immigrants, while the impact of native’s solidarity with immigrants is comparatively weak.
Furthermore, Burgoon (2014) finds that the relationship between stocks of foreign-born and support
for redistribution is conditional upon the level of economic integration of immigrants; the less
economically integrated immigrants are, the more likely natives are to oppose redistribution.
Support for Redistribution: The Compensation Hypothesis
The flip-side of the anti-solidarity hypothesis is the compensation hypothesis, which advocates that as
immigration is perceived to increase the risk of income loss, then support for redistribution should
increase as a result.
Proponents argue that governments in open economies expand the welfare state order to insure citizens
against the risks posed by globalisation. For example, Finseraas (2008) finds evidence to support the
compensation hypothesis. Using the European Social Survey, he shows that individuals who believe
immigration lowers average wages are more likely to support higher benefit levels.
In Switzerland, Walter (2010) finds differences between globalisation ‘losers’ and ‘winners’. She shows
that globalisation ‘losers’ are more likely to experience feelings of economic insecurity and also support
greater expansion of the welfare state. Moreover, Walter’s results show that whether a person is a
globalisation ‘loser’ or ‘winner’ is highly dependent on their skill level, ‘losers’ typically have lower levels
of education than that of ‘winners’.
Looking at survey data from 17 European countries, Burgoon et al. (2012) find that exposure to
increasing immigration at the occupational-level leads to greater support for government redistribution
because it can raise individual economic uncertainties. Whereas, at the national level, they find that an
increasing foreign-born population has little to no effect on an individual’s support for increasing
welfare benefits.
Migration and Social Policy
“We should consider how and whether broad public attitudes putatively shaped by immigration
actually influence party and policymaking agendas and ultimately revenue and spending policies of
states.” (Burgoon et al., 2012)
Much of the previous literature focuses on the demand-side of welfare - how demand/support for
redistribution changes as immigration increases. There is much less research, particularly in Europe,
focused on the supply-side of welfare, and whether supplied levels of social welfare – social spending
and generosity - have increased or decreased as a result of immigration.
To test this, Soroka et al. (2006) combine two OECD social spending databases to cover 18 OECD
8
countries over the period 1960-2000. They investigate the impact of changes in stocks of foreign-born
on changes in social welfare spending – changes being the current year minus the preceding year. They
find that in countries with higher rates of immigration, welfare spending grows less than in countries
with limited migration. Notably, they do not find that spending decreases in countries with higher rates
of immigration, just that the rate of growth in welfare spending slows.
Soroka et al. (2016) build on their previous research through separating social spending into nine
different sub-categories. They find that there are different effects in different spending categories and
that the areas most affected are those subject to moral hazard or rhetoric about moral hazard. Overall
though, they find further support to suggest that increases in immigration lead to smaller increases in
social welfare spending. However, the suitability of the model used by Soroka et al. (2016) has been
debated in previous literature (Plümper et al., 2005).
In a comparative study across 15 European countries from 1971-2007, Lipsmeyer and Zhu (2011)
investigate the impact of immigration on unemployment benefits – measured as replacement rates - and
find that domestic political pressures are more important for explaining variation in unemployment
compensation.
Gaston and Rajaguru (2013) use data on government social expenditures and migration from the
OECD's Social Expenditure database (SOCX) and the Continuous Reporting System on Migration
(SOPEMI) database and find no negative relationship between migration and social spending. Instead,
they determine that depending on the countries included in the sample, immigration can have a positive
effect on social welfare spending.
Research Design: Hypothesis
The relationship between migration and the welfare state is complex, as evidenced by the mixed results
and conclusions in the literature. However, there are two key directions in which this analysis could
turn.
Based on the theoretical arguments of the median voter, the welfare magnet hypothesis, the anti-
solidarity hypothesis, and the social “race-to-the-bottom”, I would hypothesis that increased
immigration leads to a reduction in welfare effort. Indeed, Soroka et al. (2006, 1016) find evidence to
suggest immigration reduces growth in social welfare spending.
However, the compensation hypothesis predicts the opposite result, and following empirical results
from Gaston and Rajaguru (2013) and Lipsmeyer and Zhu (2011), which fail to find support for the anti-
solidarity hypothesis, I could also hypothesise that increased immigration leads to an increase in welfare
effort.
Consequently, I formulate two hypotheses to examine in my analysis:
9
Hypothesis 1: Increased immigration reduces support for the welfare state. This change in
demand is reflected in reduced social spending and generosity.
Hypothesis 2: Increased immigration increases demand for greater redistribution. This change
in demand is reflected in increased social spending and generosity.
Research Design: Data
To test these hypotheses, I primarily draw on Organisation for Economic Cooperation and Development
(OECD) data, with spending from the OECD’s Social Expenditure (SOCX) Database (2017b), which covers
26 European countries3 that fall within the EU and/or the Schengen area, between the years of 1990 and
2015. Additionally, I have included the social welfare generosity index from the Comparative Welfare
Entitlements Dataset (CWED), developed by Scruggs, et al. (2014), which is available for 16 European
countries4.
I also use data on stocks of foreign-born from the OECD (2017a). Data on migration such as stocks of
foreign-born or immigration rates are consistently different between various sources. These differences
may arise from varying definitions between sources and countries, for example whether or not
returning citizens are included in immigration inflow data. Moreover, measurements may be taken at
varying times of the year between countries.
Data have also been gathered from the Quality of Government Dataset (Teorell et al., 2017), the ICTWSS:
Database on Institutional Characteristics of Trade Unions, Wage Setting, State Intervention and Social
Pacts in 51 countries between 1960 and 2014 (Visser, 2016), the KOF Globalization Index database
(Dreher, 2006), the Comparative Political Data Set (Armingeon et al., 2016) and the World Bank (2017).
The Dependent Variable Problem: operationalising social welfare generosity
The commonly used proxy indicator for welfare generosity or welfare effort is social spending as a
percentage of gross domestic product (GDP)(Allan & Scruggs, 2004). There are clear advantages to this
measure; for example, authors believe it provides a good indication of the generosity of a welfare state
as a whole, there is no need to correct for inflation and exchanges rates, and it is well recorded so data
is readily available for the majority of European countries over an extended period of time.
These measures have been criticised and there is a debate in the literature about whether or not this
measure is a suitable indicator for depicting welfare generosity (Allan & Scruggs, 2004; Caminada et al.,
2010; Clasen & Siegel, 2007; Esping-Anderson, 1990; Green-Pedersen, 2004; Starke, 2006; van
Oorschot, 2013; Wang & van Vliet, 2016).
3 Austria, Belgium, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Lithuania, Luxembourg, Netherlands, Norway, Poland, Portugal, Slovakia, Slovenia, Spain, Sweden, Switzerland, United Kingdom. 4 Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland and United Kingdom
10
First, Allan & Scruggs (2004) argue that levels of spending are not directly relevant to the levels of
protection provided because changes in the number of beneficiaries, such as higher unemployment and
an aging population, can mask changes at the individual level and therefore may not reflect policy
changes. An example is early 1980s Britain where the Conservative Government rolled back individual
entitlements but aggregate spending actually increased. However, authors often argue that these issues
can be mediated by carefully chosen control variables (Allan & Scruggs, 2004; Scruggs, 2008; Stichnoth
& Van der Straeten, 2013; Wang & van Vliet, 2016).
Second, there are differences in the tax treatment of transfers and social benefits between countries -
such as varying tax structures and/or income tax exemptions. This could mean that levels of disposable
income of benefit recipients vary despite the same levels of social spending. The tax system is
increasingly used as an alternative transfer mechanism - a notable example is the United Kingdom’s
Working Families Tax Credit – and gross spending data do not capture this. Unfortunately, the data for
net social spending, which accounts for tax expenditures, is sparse. Consequently, this makes comparing
social protection systems using spending more difficult (Allan & Scruggs, 2004; Caminada et al., 2010).
Third, welfare effort is not completely restricted to the public domain; there is a large variety of private
arrangements that can act as substitutes to public programmes.
Finally, it may be that retrenchment in areas such as pensions may not be visible immediately because
they have a long phase-in period and do not affect the current beneficiaries but have a large effect on
the entitlements of future recipients (Pierson, 1996; Starke, 2006).
Taking into account the dependent variable problem, I use two different dependent variables to
approach the question of how immigration influences social welfare generosity. Initially, I use social
welfare spending as a percentage of GDP as is common convention. I then take the welfare generosity
index from the Comparative Welfare Entitlements Dataset (CWED) 5 developed by Scruggs et al. (2004)
in order to compare and contrast the results of the two different indicators for welfare generosity.
The welfare generosity index contains information on the generosity of social benefits. It covers
unemployment insurance, sick pay insurance, and public pensions. Unemployment insurance only
covers national insurance provisions that are earned without income testing and so excludes
programmes such as the UK’s income-based Jobseeker’s Allowance or Germany’s unemployment
assistance. Sick pay insurance covers the benefits that are paid in the instance of short-term non-
occupational illness or injury. Public pensions covers only mandatory public programmes except the
nominally private Finnish earnings-related fund (Scruggs et al., 2014).
5 See Scruggs, L., Jahn, D., & Kuitto, K. (2014). Comparative Welfare Entitlements Dataset 2 Codebook. Version 2014-03. University of Connecticut & University of Greifswald. for further information on the dataset, the countries included, the index and its methodology.
11
Limitations of this index includes the lack of recent data, it extends from 1990 to 2010 and it is only
available for 16 European countries. Additionally, it does not cover aspects such as maternity leave
benefits, certain child/family benefits and publicly provided health insurance/universal healthcare.
Thus, the index could underestimate the generosity of some welfare states.
Table 1 shows social welfare spending as a percentage of GDP as an average over the period 1990-2015
for each country. France tops the table with 29% of its GDP spent on social welfare, on average.
Unsurprisingly, the Scandinavian countries – Sweden, Finland and Denmark - also have high social
welfare spending. Notably, Norway is somewhat lower, with 22% percent of its GDP spent on social
welfare on average – this is the same as Luxembourg, Spain and Hungary, countries that are not
traditionally associated with generous welfare states.
Overall, it appears that Western European countries, those that are traditionally associated with more
generous welfare states, do indeed have higher welfare spending as a percentage of their GDP. Although
the welfare state typologies do seem to exist (Esping-Anderson, 1990), with the Social Democratic and
Conservative welfare state typologies dominating the top half of the table. In contrast are the post-soviet
states and the Liberal welfare state typology (United Kingdom, Iceland and Ireland), they spend a much
lower percentage of their GDP on welfare.
When we compare welfare spending with Table 2, which shows the Total Welfare Generosity Index6, we
can see that they do not correspond exactly – the correlation is 0.4. Of particular note is Norway, which
leaps from 9th place in social welfare spending, to 1st place in the generosity index. Also interesting, is
Austria and Belgium. Both countries spend a similar amount of their GDP on welfare on average, but
when looking at the generosity index we can see that Belgium scores almost 8 points higher. However,
as discussed previously, this index could be improved through including a larger range of social
programmes, Austria may be generous in ways that the index does not capture.
Table 1: Social Welfare Spending as a Percentage of GDP, average: 1990-2015, OECD SOCX
6 There are a different set of countries as the OECD dataset covers more European countries than the CWED.
Country Mean Country Mean Country Mean
France 29 Luxembourg 22 Czech Republic 18 Sweden 28 Spain 22 Switzerland 17 Finland 27 Hungary 22 Slovakia 17 Belgium 26 Greece 20 Ireland 17 Austria 26 Netherlands 21 Iceland 15 Denmark 26 Poland 21 Estonia 15 Germany 25 Slovenia 21 Latvia 14 Italy 24 Portugal 20 Norway 22 United Kingdom 19
Average 22
12
Table 2: Total Welfare Generosity, average: 1990 – 2010, CWED
Country Mean Country Mean Country Mean
Norway 42 Denmark 36 Portugal 31 Belgium 41 Finland 35 Italy 28 Sweden 41 Germany 35 Greece 28 France 38 Spain 34 United Kingdom 27 Netherlands 37 Austria 33 Switzerland 36 Ireland 31
Average 35
Also of interest, is how social spending and welfare generosity have changed over time. Figure 1 shows
how social welfare spending as a percentage of GDP has changed over time. It appears that most
European countries have seen steady increases in spending levels or stayed reasonable stable. Welfare
generosity on the other hand, as shown in Figure 2, appears to be more heterogeneous. Germany,
Denmark and Finland have seen steady declines in their generosity indexes. Others like Greece and Italy
have seen steady increases. Sweden has seen a dramatic decrease, while Ireland has seen a dramatic
increase.
Figure 1: Social Welfare Spending as a Percentage of GDP, 1990-2015
010
20
30
40
010
20
30
40
010
20
30
40
010
20
30
40
010
20
30
40
1990 2000 2010 2020 1990 2000 2010 2020 1990 2000 2010 2020 1990 2000 2010 2020 1990 2000 2010 2020
Austria Belgium Czech Republic Denmark Estonia
Finland France Germany Greece Hungary
Iceland Ireland Italy Latvia Luxembourg
Netherlands Norway Poland Portugal Slovakia
Slovenia Spain Sweden Switzerland United Kingdom
So
cia
l W
elfare
Sp
en
din
g a
s a
% o
f G
DP
Year
13
Figure 2: Welfare Generosity Index, 1990-2010
Explanatory Variable
As is common convention in the previous literature (e.g. Burgoon et al., 2012; Burgoon, 2014; Gaston &
Rajaguru, 2013; Mau & Burkhardt, 2009; Soroka et al., 2006, 2016), I use foreign born as a percentage
of the population, which serves as an indicator of the stock of migrants in a country, as the main
explanatory variable. The standard definition of foreign-born is “all persons who have ever migrated
from their country of birth to their current country of residence” (OECD, 2017a). People who were born
abroad as nationals of their current country of residence are included in the foreign-born data. Mau and
Burkhardt (2009) found that the percentage of foreign-born, as a proxy for ethnic diversity, was one of
the most useful indicators for explaining variation in attitudes towards the welfare state.
An alternative measure is net migration, as used by Lipsmeyer and Zhu (2011). However, Soroka et al.
(2006) argue that high emigration in some countries can, and does, mask considerable inflows of
migrants.
Table 3, displays the percentage of the population that is foreign born. There is considerable variation
between countries. To start, Luxembourg has the highest foreign-born population; unlike the majority
of other EU countries, most of the foreign-born in Luxembourg are other EU nationals (Eurostat, 2017;
Kollwelter, 2007). Switzerland is a similar case where just four EU nationalities - Italian, German,
Portuguese and French - make up almost half (49%) of the total foreign-born population (Nguyen,
2016).
25
30
35
40
45
25
30
35
40
45
25
30
35
40
45
25
30
35
40
45
1990 1995 2000 2005 2010 1990 1995 2000 2005 2010 1990 1995 2000 2005 2010 1990 1995 2000 2005 2010
Austria Belgium Denmark Finland
France Germany Greece Ireland
Italy Netherlands Norway Portugal
Spain Sweden Switzerland United Kingdom
Welfa
re G
ene
rosity In
de
x
YearGraphs by Country
14
Estonia’s high foreign-born population is somewhat of an outlier; it is because of the large number of
recognised non-citizens. These are mainly former Soviet-Union citizens and are permanent residents,
but have not acquired any other citizenship (Eurostat, 2017).
Finally, there are no surprise that Poland, Romania and Bulgaria have, on average, small foreign-born
populations; all are traditionally considered countries of emigration. Whereas Sweden, Germany and
Austria, traditionally considered countries of immigration, have relatively higher foreign-born
populations.
Table 3: Foreign-born as a percentage of the total population, average: 1990 – 2015, OECD
Country Mean Country Mean Country Mean
Luxembourg 36 France 11 Norway 9
Switzerland 24 Slovenia 11 Portugal 7
Estonia 17 Spain 10 Czech Republic 6
Austria 14 Greece 10 Denmark 6 Ireland 13 Netherlands 10 Finland 4 Belgium 12 Iceland 9 Slovakia 4 Germany 12 Italy 9 Hungary 3 Sweden 12 United Kingdom 9 Poland 2
Average 11
Figure 3 shows that over time, most Western European countries have seen a steady increase in their
foreign-born populations. Italy, Ireland and Spain are interesting because they are usually considered
countries of emigration, yet all three have seen large increases in their foreign-born populations. Italy,
Ireland and Spain all record both high immigration on non-nationals and emigration of nationals,
helping to push the foreign-born population higher (Eurostat, 2017). The only country to see their
foreign-born population reduce since 1990 is Estonia. The foreign-born populations of other
EU/Schengen countries have remained relatively stable.
15
Figure 3: Foreign-born as a percentage of the population, 1990-2015
Control Variables
Population Demographics. I control for certain population demographics by including the population
under 15 and the population over 64 in the model specification - as is advocated by Soroka et al. (2006,
2016). It is reasonable to assume that spending would increase as a larger proportion of the population
become dependent on working age tax payers. Infants are expected to affect health care and child-care
costs, school-age children affect education costs and the elderly impact spending on pensions (Soroka
et al., 2006). Pensions are typically the largest expenditure in social welfare budgets, this is due to a
combination of increased life expectancy and a decline in birth rates that cannot be mitigated by current
increases in the retirement age or immigration (Ervasti et al., 2012).
Alternatively, Gaston and Rajaguru (2013) use the dependency ratio to account for the impact of the
dependent population. In the sensitivity analysis, I substitute the population under 16 and over 64 and
find that the results are not altered.
Economic Controls. I control for the effect that a country’s domestic economic status may have on social
welfare generosity using three key variables.
First, I use GDP growth as Gaston and Rajaguru (2013) have done. They find that GDP growth has a
significant and negative association with social spending. They explain that “this reflects the fact that
010
20
30
40
010
20
30
40
010
20
30
40
010
20
30
40
010
20
30
40
1990 2000 2010 2020
1990 2000 2010 2020 1990 2000 2010 2020 1990 2000 2010 2020 1990 2000 2010 2020
Austria Belgium Czech Republic Denmark Estonia
Finland France Germany Greece Hungary
Iceland Ireland Italy Luxembourg Netherlands
Norway Poland Portugal Slovakia Slovenia
Spain Sweden Switzerland United Kingdom
Pe
rcen
tage
of F
ore
ign
-Born
16
the denominator (GDP) grows more slowly than the numerator (SOCX) when the economy slows and
social expenditures are politically difficult to decrease in a downswing of the business cycle” (Gaston &
Rajaguru, 2013). Moreover, a growing economy is expected to act as a “pull” factor and attract more
immigrants than one that is contracting (Massey, 1988).
Second, I include the national unemployment rate as a control for the domestic labour market as is done
by Gaston and Rajaguru (2013), Lipsmeyer and Zhu (2011) and Soroka et al. (2006, 2016). Higher
unemployment would indicate more spending on unemployment benefits. Plus, immigrants may take
the prospective job market into account when deciding on a destination country (Davanzo, 1978).
Third, I use female labour force participation as in Soroka et al. (2006, 2016). Female labour force
participation affects social spending through increased public childcare infrastructure to support
working mothers. It is also thought to be negatively related to migration as female labour acts as an
imperfect substitute for migrant labour (Afonso & Devitt, 2016; Soroka et al., 2006, 2016).
Political Institutions. “One of the strongest generalisations that can be made about the origins and
growth of the welfare state is that where trade unions and social democratic parties are strong, the
welfare state has thrived” (Freeman, 1986). It is generally thought that left-leaning governments, who
traditionally have electoral ties to the working-class and unions, will support greater redistribution
(Armingeon & Giger, 2008; Lipsmeyer, Philips, & Whitten, 2017) – although the importance of partisan
politics is still debated (Allan & Scruggs, 2004; Pierson, 1996; Starke et al., 2014).
Freeman (1986) argues that migrant labour threatens the welfare state through dividing the working
class and thus breaking the unity of organised labour movements. Moreover, some authors find that a
strong left or strong trade unions can counteract the potential negative effects of diversity on welfare
generosity (Lipsmeyer et al., 2017; Lipsmeyer & Zhu, 2011; Taylor-Gooby, 2005).
As a result, I control for the ideology of the government in power by including the percentage of cabinet
posts held by social democratic and other left-wing parties, weighted by the number of days in office in
a given year as done by Soroka et al. (2006, 2016) and Lipsmeyer and Zhu (2011).
As an alternative, Gaston & Rajaguru (2013) use a 1 to 5 scale to account for cabinet ideology, with 1
being a hegemony of right-wing parties and 5 being a hegemony of left-wing parties. In the sensitivity
analysis, I switch the percentage of cabinet posts held by social democratic and other left-wing parties
for the cabinet ideology indictor and find that the results do not change.
In addition, I use trade union density – net union membership as a share of wage and salary earners in
employment - as a control for the bargaining power of domestic labour as done by Lipsmeyer and Zhu
(2011) and Soroka et al. (2006, 2016).
Economic Globalisation. Globalisation has been argued to both reduce public spending and increase it
(Gaston & Rajaguru, 2013; Iversen & Cusack, 2000; Lipsmeyer & Zhu, 2011). Thus, to control for
economic globalisation, I use the KOF economic globalisation indicator, as do Gaston and Rajaguru
17
(2013). The KOF economic globalisation indicator ranges between 0 and 100, with higher values
indicating a higher degree of economic globalisation. “Economic globalisation is here defined as the long
distance flows of goods, capital and services as well as information and perceptions that accompany
market exchanges. It is measured by actual flows of trade and investments, and by restrictions on trade
and capital such as tariff rates” (Dreher, 2006).
An alternative indicator for economic globalisation is trade openness as used by Lipsmeyer and Zhu
(2011). Trade openness is measured as the sum of exports and imports of goods and services as a share
of GDP. I use this instead of the KOF in the sensitivity analysis and the results remain unchanged. Soroka
et al. (2006, 2016) do not account for globalisation in their model specification.
Research Design: Method and Model Specification
A common practice for this type of study is to use the de facto Beck-Katz standard – panel-corrected
standard errors with country fixed effects and a lagged dependant variable – as done by Gaston and
Rajaguru (2013), Lipsmeyer and Zhu (2011) and Soroka et al. (2006, 2016).
However, it is argued that the lagged dependent variable is a considerable source of bias known as
Nickell bias (Nickell, 1981). It is reasoned that the lagged dependent variable is highly correlated with
the dependent variable and thus causes an upward bias in the standard errors. Subsequently, the
estimation model does not provide an accurate coefficient for the key explanatory variable. Therefore,
for the empirical analysis I use the more appropriate panel-corrected standard errors with country and
year fixed effects and Prais-Winsten correction for serial correlation of errors as recommended by
Plümper et al. (2005).
In addition, I lag the explanatory variable and all the control variables by one year - Gaston and Rajaguru
(2013) do the same, while Lipsmeyer and Zhu (2011) and Soroka et al. (2006, 2016) lag a selection of
their variables. One key reason is that it can help mitigate endogeneity issues arising from reverse
causality. Furthermore, in the case of certain variables it makes theoretical sense; policy decisions can
take time to be reflected in spending levels - by lagging the variables, this can be better taken into
account.
Finally, I use country and year fixed effects. By using country fixed effects, I hope to not only control for
welfare states that typically spend more because of how they were constructed/built-up over the years
but also to capture cultural influences that may determine a country’s tendency towards favouring a
more Bismarckian or Beveridgean welfare state. Moreover, by using year fixed effects I aim to account
for external shocks that may have taken place in certain years; for example, the expansion of the
European Union, the lifting of labour market restrictions on new member states and the financial crisis.
18
Results and Discussion
The estimate in column 1 of Table 4 indicates that higher immigration leads to higher social spending.
Levels of foreign-born are positively and significantly associated with levels of social spending; a 1
percent increase in foreign-born is associated with, on average, a 0.235 percent increase in social
welfare spending, ceteris paribus.
These findings are similar to Gaston and Rajaguru (2013) who also use social spending as a percentage
of GDP and conclude that “immigration has a relatively modest effect on welfare state spending”. On the
other hand, Gaston and Rajaguru also find that when they reduce their sample to 11 European countries
the effect disappears. Whereas, I find a highly significiant effect with 16 European countries, and when
that sample is expanded then the magnitude of the effect lessens but the result still remains highly
significant.
The crucial difference between our two models is the way we correct for serial correlation. Gaston and
Rajaguru (2013) use a lagged dependent variable, whereas I use the Prais-Winsten correction -
advocated by Plümper et al. (2005) as the more appropriate method. Moreover, Gaston and Rajaguru
(2013) do not control of population demographics in their model, nor the unemployment rate.
A first explanation is that my results lend support for the compensation hypothesis, Gaston and Rajaguru
(2013) also use their results to say they find some support for the compensation r exposure effect. Some
previous literature focusing on support for redistribution found that if natives feel economically
insecure when exposed to increased movement of labour, particularly when exposed at an occupational
level, then they support more compensation and greater redistribution from the government (Burgoon
et al., 2012; Finseraas, 2008; Walter, 2010). Thus, the higher spending associated with immigration
could be an indication that this demand is being reflected in policy decisions.
However, when I replace the dependent variable with the generosity index, the coefficient for foreign-
born is not statistically significant. I find no effect for either increased or decreased generosity as a result
of increasing immigration. It is interesting that the results for these two indicators of welfare generosity
are different – it suggests that the two indicators are in fact, measuring different things.
Instead of the increase in spending suggesting support for the compensation hypothesis, spending may
increase for a different reason. Perhaps the foreign-born population costs the welfare state more in
terms of education or child-care costs, especially considering immigrants in Europe tend to have more
children than the native population (Boeri, 2010; Freeman, 1986). Moreover, it is possible that
immigration is linked to increased spending through increased unemployment, either of natives or
immigrants, rather than increased generosity.
On the other hand, the foreign-born population on the EU is, as a whole, younger than the native
population and is less likely to use the health system or draw a pension (Eurostat, 2017). Just as social
spending is a flawed indicator, so is the welfare generosity indicator – with certain programmes not
19
included in its make-up. Consequently, the lack of a statistically significant relationship between foreign-
born and the welfare generosity index does not mean that the increase in social spending is not
indicative of increasing generosity.
Table 4: Effect of Foreign Born on Social Welfare Spending as a Percentage of GDP and the Welfare Generosity Index, 1990-2010
(1) (2)
Variable Spending Generosity
Foreign-bornt-1 0.235*** 0.009 (0.070) (0.085) Population under 15t-1 0.574** -0.007 (0.275) (0.250) Population over 64t-1 0.302** 0.046 (0.122) (0.151) Unemployment ratet-1 0.122** -0.046 (0.058) (0.048) GDP growtht-1 -0.194*** -0.019 (0.048) (0.049) Female labour force participationt-1 0.015 0.330*** (0.055) (0.050) Left Seatst-1 0.006*** 0.002 (0.002) (0.003) Union Densityt-1 0.092* 0.216*** (0.050) (0.055) KOF - economict-1 0.044 -0.024 (0.038) (0.043) Intercept 2.041 11.542* (6.660) (6.592) Country Dummies YES YES
Year Dummies YES YES
N 234 234
Adj R2 0.941 0.973 Standard errors in parentheses * p < .1, ** p < .05, *** p < .01
The difference in the effect of foreign-born on spending and generosity is not unique to the explanatory
variable. For example; it is not surprising that the size of the population over 64 years old has a
significant and positive impact on social spending considering pensions tend to be the biggest
expenditure for welfare states. However, there is no statistically significant effect on the generosity
index. Likewise for those under the age of 15 and for the unemployment rate.
I find that GDP growth is significant and positively associated with social spending - this is the same
result as Gaston and Rajaguru (2013) who explain that in an economic downturn, the denominator
(GDP) will grow more slowly than the numerator (social spending) and so is not unexpected. This
20
explanation also makes sense for why there is no statistically significant relationship between GDP
growth and the generosity index.
A particularly interesting result is female labour force participation, which has no significant effect on
social spending. Whereas for generosity, female labour force participation has a large, positive effect
and is highly statistically significant. Alongside arguments that female labour force participation
increases welfare generosity through the need for improved child-care infrastructure, Soroka et al.
(2016) suggest that it may also be linked to increased costs associated with a larger work force such as
training, employment insurance and leave. Moreover, in the medium to long-term, it is thought that
women’s participation in the labour force will increase women’s demands for redistribution (Huber &
Stephens, 2001).
Furthermore, the proportion of left cabinet seats is positively and significantly associated with social
spending, but appears to be unimportant for explaining welfare generosity. There is debate in the
literature about the impact of partisan politics on the expansion and retrenchment of the welfare state.
The effect of left seats on social spending would appear to support the Allan and Scruggs (2004) camp
that partisanship is important, while the impact of left seats on the generosity index would appear to
support Pierson (1996) and his new politics of the welfare state. So as Starke stated: "the debate on the
relevance of political parties and ideas […] is still far from settled" (2006)
Union density is positively and significantly associated with both social spending and the generosity
index. Although, the magnitude of the effect of union density is much greater for the generosity index
and the significance level is higher. It appears that union membership and support is important for social
spending and generosity, this is similar to the results found by Lipsmeyer and Zhu (2011) who argue
that domestic political pressures are important for explaining higher unemployment compensation in
an age of increasing immigration.
In contrast to Gaston and Rajaguru (2013) who find a statistically significant negative relationship
between economic globalisation and social spending, I find no significant impact of the KOF economic
globalisation indicator on either social spending or the generosity indicator.
Sensitivity Analysis
In this section, I discuss the results of various tests that were undertaken in order to check the
robustness of my results. In order to see if my results are sensitive to the control variables chosen, I
substitute a number of the variables with various alternatives that have also been chosen by other
authors. The results are shown in Table 5. The result for foreign-born appears to be robust; it is positive
and statistically significant for all the estimation models using social welfare spending, and for all the
estimation models using generosity is consistently statistically insignificant.
Table 5: Alternative control variables, 1990-2010
21
(1) (2) (3) (4) (5) (6) (7) (8) Variable Spending Generosity Spending Generosity Spending Generosity Spending Generosity Foreign-bornt-1 0.256*** 0.009 0.240*** 0.018 0.231*** 0.032 0.244*** 0.009 (0.074) (0.087) (0.069) (0.082) (0.072) (0.086) (0.066) (0.085) Population 0.588** -0.035 0.745*** 0.389 0.589** 0.007 under 15t-1 (0.262) (0.225) (0.282) (0.250) (0.266) (0.248) Population 0.438*** 0.106 0.337*** 0.142 0.308*** 0.047 over 64t-1 (0.124) (0.169) (0.118) (0.168) (0.117) (0.151) Unemployment 0.127** -0.047 0.146*** -0.017 0.119** -0.040 0.122** -0.048 ratet-1 (0.061) (0.047) (0.054) (0.053) (0.056) (0.048) (0.056) (0.048) GDP growtht-1 -0.194*** -0.018 -0.168*** -0.003 -0.168*** 0.003 -0.195*** -0.019 (0.049) (0.049) (0.049) (0.047) (0.044) (0.054) (0.047) (0.049) Female labour 0.004 0.331*** 0.003 0.322*** 0.032 0.335*** 0.012 0.329*** force participationt-1 (0.056) (0.053) (0.057) (0.049) (0.056) (0.061) (0.055) (0.050) Left seatst-1 0.006*** 0.002 0.006*** 0.001 0.007*** 0.003 (0.002) (0.003) (0.002) (0.003) (0.002) (0.003) Union densityt-1 0.103** 0.211*** 0.079 0.213*** 0.089* 0.213*** (0.050) (0.052) (0.049) (0.051) (0.048) (0.054) KOF - economict-1 0.034 -0.024 0.030 -0.040 0.042 -0.026 (0.037) (0.044) (0.035) (0.048) (0.038) (0.043) Dependency ratiot-1 0.191*** 0.023 (0.070) (0.067) Trade opennesst-1 -0.028* -0.035** (0.014) (0.016) Wage -0.041 -0.083 coordinationt-1 (0.081) (0.100) Government 0.164*** 0.066 ideologyt-1 (0.054) (0.072) Intercept 7.699 11.210** 6.102 11.880** 2.907 13.179* 1.906 11.545* (4.851) (5.626) (5.752) (4.876) (6.835) (6.822) (6.418) (6.587) Country Dummies YES YES YES YES YES YES YES YES Year Dummies YES YES YES YES YES YES YES YES N 234 234 234 234 234 234 234 234
Standard errors in parentheses * p < .1, ** p < .05, *** p < .01
The biggest change is that in the original estimation models, the KOF economic globalisation indicator
is not statistically significant. Yet, trade openness is statistically significant and negatively associated
with both social spending and welfare generosity. Furthermore, the effect of union density on spending
becomes insignificant when the KOF economic globalisation indicator is replaced with trade openness.
Despite these changes, the effect of foreign-born on spending and generosity remains stable.
In the original spending estimation the sample is restricted to the countries and date range found in the
CWED in order to ensure social spending and the generosity index can be compared. However, the OECD
provides spending and migration data on more European countries and for a larger time-frame. I have
expanded the sample step-by-step to see how the inclusion of more years and then the extra countries
affects the results.
Table 6 shows the original estimation with the two expanded samples. When I increase the years, the
magnitude of the effect of foreign-born on spending drops. This expansion in years means that the
22
results incorporate observations following the last round of EU enlargement and the last of the labour
market restrictions on Bulgarians and Romanians have been removed.
Successively, when the number of countries is then expanded to include Czech Republic, Estonia,
Hungary, Iceland, Luxembourg, Poland, Slovakia and Slovenia, the magnitude of the effect reduces again.
While the use of a more heterogeneous group dilutes the impact of foreign-born, the coefficient remains
statistically significant and positively associated with social welfare spending in both expanded samples.
Table 6: Effect of Foreign Born on Welfare Spending – expanding the sample
16 European OECD Countries
1990-2010
16 European OECD Countries 1990-
2015
24 European OECD Countries 1990-
2015
Original (2) (3) Variable Spending Spending Spending Foreign Bornt-1 0.235*** 0.204*** 0.142*** (0.070) (0.056) (0.037) Population under 15t-1 0.574** 0.515** 0.610*** (0.275) (0.237) (0.120) Population over 64t-1 0.302** 0.392*** 0.388*** (0.122) (0.122) (0.094) Unemployment ratet-1 0.122** 0.093*** 0.075*** (0.058) (0.035) (0.027) GDP growtht-1 -0.194*** -0.182*** -0.116*** (0.048) (0.032) (0.015) Female labour force participationt-1 0.015 -0.022 0.014 (0.055) (0.043) (0.026) Left Seatst-1 0.006*** 0.006*** 0.006*** (0.002) (0.002) (0.001) Union Densityt-1 0.092* 0.122*** 0.076*** (0.050) (0.039) (0.025) KOF - economict-1 0.044 0.060* 0.022 (0.038) (0.033) (0.016) Intercept 2.041 1.310 3.719 (6.660) (5.519) (2.888) Country Dummies YES YES YES Year Dummies YES YES YES N 234 297 403
Standard errors in parentheses * p < .1, ** p < .05, *** p < .01
Further Research and Limitations
I believe further research could be conducted on levels and changes. Soroka et al. (2006, 2016) focus on
changes in their estimation models and state that levels of immigration (as measured by the proportion
of the population that is foreign born) do not matter for explaining social spending. However, my
analysis suggests that is incorrect, and that levels of foreign-born are important for explaining variation
in social spending and should not be disregarded.
23
The analysis could be improved by investigating a greater number of years, different proxies and a
greater range of countries. It would be interesting to see if the results I have found are replicable across
a greater range of OECD countries and other indicators of welfare generosity.
Furthermore, it would be interesting to further disaggregate the independent variable foreign born into
non-western and western as it is often the non-western foreign-born immigrants that are the most
visible, regularly encompassed in public debate and are often seen as the most ‘threatening’ or the most
likely to be utilising social welfare support (Sumino, 2014). Mau & Burkhardt (2009) found it was their
most relevant diversity indicator in the determination of individual support for the welfare state. Thus,
it would be interesting to determine if there is also an effect on welfare generosity.
In addition, Ortega (2004) states the importance of the make-up of skills in the immigrant population
for determining support for redistribution.7 As a result, it would be interesting in the future to include a
variable that identifies the relative skill composition of natives in comparison to immigrants arriving.
This is something other empirical papers have not done; however, Facchini and Mayda (2009) did use
it in order to determine individual attitudes towards immigrants. According to Ortega’s model, if the
majority of immigrants are skilled, relative to the natives, we should expect a less generous welfare
state.
These areas for further research also highlight certain limitations to my research. The data is only on 16
European countries, over the time period 1990-2010. Therefore, my results may not be generalisable to
other parts of the world, other selections of countries or other time periods.
Concluding Remarks
In this paper I have empirically examined the role that migration has to play in the determination of
welfare state generosity in Europe. I set out to investigate the relationship between stocks of
immigrants, as measured by the proportion of the population that is foreign born, and welfare state
generosity, as measured by social welfare spending as percentage of GDP and a welfare generosity index.
The initial results suggest that ethnic diversity has a positive, and statistically significant impact on
social welfare generosity, if spending data can be considered a good proxy for generosity. This provides
comparable results to authors who find support for the compensation hypothesis. However, these initial
results are questioned when I exchange the dependent variable for a welfare generosity index. This
alternative exploration suggests that there is little to no relationship between foreign-born and the
welfare generosity index. Hence, my results tell us that the two indicators are measuring different things
and thus authors should be careful of the conclusions they draw regarding welfare generosity from
either indicator.
7 Felbermayr and Kohler (2007) also use skill composition of immigrants in their political economy model.
24
For policy makers, these results should help shed some light on a topic troubled by xenophobia, racism
and discrimination. I hope what has been laid out here contributes towards evidence-based policy
making in the field of migration. It is important to note that immigration does not seem to be leading to
a race-to-the-bottom in Europe, nor should increased immigration mean that policy makers need to look
at benefits in neighbouring countries when drawing up their own welfare policies. Policy makers should
aim to continue delivering welfare benefits that work for improving social inequality and inclusion. This
is particularly crucial for the successful integration of migrants into society, and as immigration
numbers do not appear to be relenting, this will be fundamental for improving solidarity between
diverse populations.
My quantitative empirical study calls out for further research, particularly within the areas of further
disaggregating welfare generosity into component parts, such as labour benefits, health insurance,
sickness insurance, family and child benefits and so on. Additionally, more research within the realm of
how changes in migration patterns, such as the expansion of the EU, affect social welfare generosity
would be welcome to better understand the nuances behind the issue. Finally, I believe that this field of
study could benefit greatly from more innovative measures of social welfare generosity, as the results
from this study suggest that current methods are not necessarily presenting the true picture.
25
Bibliography
Afonso, A., & Devitt, C. (2016). Comparative political economy and international migration. Socio-Economic Review, 14(3), 591–613.
Alesina, A., & Glaeser, E. L. (Edward L. (2004). Fighting poverty in the US and Europe : a world of difference. Oxford University Press.
Alesina, A., Glaeser, E., & Sacerdote, B. (2001). Why Doesn’t the United States Have a European-Style Welfare State? Brookings Papers on Economic Activity, 2001(2), 187–254.
Allan, J. P., & Scruggs, L. (2004). Political Partisanship and Welfare State Reform in Advanced Industrial Societies. American Journal of Political Science, 48(3), 496–512.
Armingeon, K., & Giger, N. (2008). Conditional Punishment: A Comparative Analysis of the Electoral Consequences of Welfare State Retrenchment in OECD Nations, 1980–2003. West European Politics, 31(3), 558–580.
Armingeon, K., Isler, C., Knöpfel, L., & Weisstanner, D. (2016). Supplement to the Comparative Political Data Set – Government Composition 1960-2014. Bern: Institute of Political Science, University of Berne.
Banting, K., & Kymlicka, W. (2006). Multiculturalism and the Welfare State: Recognition and Redistribution in Contemporary Democracies. Oxford: Oxford University Press.
Beine, M., Burgoon, B. B., Crock, M., Gest, J., Hiscox, M., Mcgovern, P., … Thielemann, E. (2015). Measuring Immigration Policies: Preliminary Evidence from IMPALA. CESifo Economic Studies, 61(3), 527–559.
Boeri, T. (2010). Immigration to the Land of Redistribution. Economica, 77(308), 651–687.
Borjas, G. J. (1999). Immigration and Welfare Magnets. Journal of Labour Economics, 17(4), 607–637.
Brady, D., & Finnigan, R. (2014). Does immigration undermine public support for social policy? American Sociological Review, 79(1), 17–42.
Brueckner, J. K. (2000). Welfare reform and the race to the bottom: Theory and evidence. Southern Economic Journal, 66(3), 505–525.
Burgoon, B. (2014). Immigration, Integration, and Support for Redistribution in Europe. World Politics, 66(3), 365–405.
Burgoon, B., Koster, F., & van Egmond, M. (2012). Support for redistribution and the paradox of immigration. Journal of European Social Policy, 22(3), 288–304.
Caminada, K., Goudswaard, K., & Van Vliet, O. (2010). Patterns of welfare state indicators in the EU: Is there convergence? Journal of Common Market Studies, 48(3), 529–556.
Castles, S., & Schierup, C.-U. (2010). Migration and Ethnic Minorites. In F. G. Castles, S. Leibfried, J. Lewis, H. Obinger, & C. Pierson (Eds.), The Oxford Handbook of the Welfare State (pp. 278–291). Oxford: Oxford University Press.
Clasen, J., & Siegel, N. A. (2007). Comparative Welfare State Analysis and the’Dependent Variable Problem’. In J. Clasen & N. Siegel (Eds.), Investigating Welfare State Change: The “Dependent Variable Problem” in Comparative Analysis (pp. 3–12). Cheltenham: Edward Elgar Publishing Limited.
Crepaz, M. M. L. (2007). Trust Beyond Borders. Michigan: University of Michigan Press.
Dahlberg, M., & Edmark, K. (2008). Is there a “race-to-the-bottom” in the setting of welfare benefit levels? Evidence from a policy intervention. Journal of Public Economics, 92(5–6), 1193–1209.
Davanzo, J. (1978). Does Unemployment Affect Migration? Evidence from Micro Data. The Review of Economics and Statistics, 60(4), 504–514.
De Giorgi, G., & Pellizzari, M. (2009). Welfare migration in Europe. Labour Economics, 16(4), 353–363.
Dreher, A. (2006). Does Globalization Affect Growth? Evidence from a new Index of Globalization. Applied Economics, 38(10), 1091–1110.
Ervasti, H., Andersen, J. G., Friedburg, T., & Ringdal, K. (Eds.). (2012). The Future of the Welfare State: Social Policy Attitudes and Social Capital in Europe. Cheltenham: Edward Elgar Publishing Limited.
Esping-Anderson, G. (1990). The Three Worlds of Welfare Capitalism. Princeton: Princeton University Press.
Eurostat. (2017). Migration and migrant population statistics. Retrieved June 19, 2017, from http://ec.europa.eu/eurostat/statistics-explained/index.php/Migration_and_migrant_population_statistics
26
Facchini, G., & Mayda, A. M. (2009). Does the Welfare State Affect Individual Attitudes toward Immigrants? Evidence across Countries. The Review of Economics and Statistics, 91(2), 295–314.
Felbermayr, G. J., & Kohler, W. (2007). Immigration and Native Welfare. International Economic Review, 48(4), 731–760.
Ferrera, M. (2014). Solidarity in Europe after the Crisis. Constellations, 21(2), 222–238.
Finseraas, H. (2008). Immigration and Preferences for Redistribution: An Empirical Analysis of European Survey Data. Comparative European Politics, 6(4), 407–431.
Finseraas, H. (2009). Anti-Immigration Attitudes, Support for Redistribution, and Vote Choice in Europe. Norwegian Institute for Social Research, 1–25.
Freeman, G. P. (1986). Migration and the Political Economy of the Welfare State. American Academy of Political and Social Science, 485, 51–63.
Friedman, M. (1999). Q & A session with Milton Friedman at the 18th Annual Institute for Liberty and Policy Analysis (ISIL) World Libertarian Conference. San Jose: Co-sponsors - The Mackinac Center for Public Policy; the Atlas Economic Research Foundation.
Gaston, N., & Rajaguru, G. (2013). International migration and the welfare state revisited. European Journal of Political Economy, 29, 90–101.
Green-Pedersen, C. (2004). The Dependent Variable Problem within the Study of Welfare State Retrenchment: Defining the Problem and Looking for Solutions. Journal of Comparative Policy Analysis, 6(1), 3–14.
Hansen, J. D. (2003). Immigration and income redistribution in welfare states. European Journal of Political Economy, 19(4), 735–746.
Huber, E., & Stephens, J. D. (2001). Development and crisis of the welfare state: parties and policies in global markets. Chicago: The University of Chicago Press.
Iversen, T., & Cusack, T. R. (2000). The Causes of Welfare State Expansion: Deindustrialization or Globalization? World Politics, 52(3), 313–349.
Kollwelter, S. (2007). Immigration in Luxembourg: New Challenges for an Old Country. Retrieved June 19, 2017, from http://www.migrationpolicy.org/article/immigration-luxembourg-new-challenges-old-country
Kvist, J. (2004). Does EU Enlargement Start a Race to the Bottom? Strategic Interaction among EU Member States in Social Policy. Journal of European Social Policy, 14(3), 301–318.
Kymlicka, W., & Banting, K. (2006). Immigration, Multiculturalism, and the Welfare State. Ethics & International Affairs, 20(3), 281–304.
Lipsmeyer, C. S., Philips, A. Q., & Whitten, G. D. (2017). The effects of immigration and integration on European budgetary trade-offs. Journal of European Public Policy, 24(6), 912–930.
Lipsmeyer, C. S., & Zhu, L. (2011). Immigration, Globalization, and Unemployment Benefits in Developed EU States. American Journal of Political Science, 55(3), 647–664.
Magni-Berton, R. (2014). Immigration, redistribution, and universal suffrage. Public Choice, 160(3–4), 391–409.
Massey, D. S. (1988). Economic Development and International Migration in Comparative Perspective. Source: Population and Development Review, 14(3), 383–413.
Mau, S., & Burkhardt, C. (2009). Migration and welfare state solidarity in Western Europe. Journal of European Social Policy, 19(3), 213–229.
Meltzer, A. H., & Richard, S. F. (1981). Tests of a rational theory of the size of government. The Journal of Political Economy, 89(5), 914–927.
Nannestad, P. (2007). Immigration and welfare states: A survey of 15 years of research. European Journal of Political Economy, 23(2), 512–532.
Nguyen, D.-Q. (2016). Defining the 25% foreign population in Switzerland. Retrieved June 19, 2017, from https://www.swissinfo.ch/eng/society/migration-series--part-1-_who-are-the-25--foreign-population-in-switzerland-/42412156
Nickell, S. (1981). Biases in Dynamic Models with Fixed Effects. Source: Econometrica Econometrica, 49(6), 1417–1426.
OECD. (2017a). Migration - Foreign-born population. Retrieved August 9, 2015, from https://data.oecd.org/migration/foreign-born-population.htm
27
OECD. (2017b). Social Expenditure Database (SOCX). Paris. Retrieved from http://www.oecd.org/social/expenditure.htm
Ortega, F. (2004). Immigration and the Survival of the Welfare State (No. 815). Barcelona.
Pierson, P. (1996). The New Politics of the Welfare State. World Politics, 48(2), 143–179.
Plümper, T., Troeger, V. E., & Manow, P. (2005). Panel data analysis in comparative politics: Linking method to theory. European Journal of Political Research, 44, 327–354.
Razin, A., & Sadka, E. (2012). Tax competition and migration: The race-to-the-bottom hypothesis revisited. CESifo Economic Studies, 58(1), 164–180.
Razin, A., & Wahba, J. (2015). Welfare magnet hypothesis, fiscal burden, and immigration skill selectivity. Scandinavian Journal of Economics, 117(2), 369–402.
Schmidt-Catran, A. W., & Spies, D. C. (2016). Immigration and Welfare Support in Germany. American Sociological Review, 81(2), 242–261.
Schram, S. F., & Krueger, G. (1994). Welfare Magnets and Benefit Decline - Symbolic Problems and Substantive Consequences. Publius, 24(4), 61–82.
Scruggs, L. (2008). Social Rights, Welfare Generosity, and Inequality. In C. Anderson & P. Baramendi (Eds.), Democracy, Inequality, and Representation (pp. 62–90). New York: Russell Sage Foundation.
Scruggs, L., Kuitto, K., & Jahn, D. (2014). Comparative Welfare Entitlements Dataset 2. Version 2014-03.
Skupnik, C. (2014). EU enlargement and the race to the bottom of welfare states. IZA Journal of Migration, 3(15), 1–21.
Soroka, S., Johnston, R., & Banting, K. (2006). Immigration and Redistribution in a Global Era. In S. Bowles, P. Bardhan, & M. Wallerstein (Eds.), Globalization and Egalitarian Redistribution (pp. 261–288). Princeton: Princeton University Press and Russell Sage Foundation.
Soroka, S., Johnston, R., Kevins, A., Banting, K., & Kymlicka, W. (2016). Migration and welfare state spending. European Political Science Review, 8(2), 173–194.
Starke, P. (2006). The Politics of Welfare State Retrenchment: A Literature Review. Social Policy and Administration, 40(1), 104–120.
Starke, P., Kaasch, A., & Van Hooren, F. (2014). Political Parties and Social Policy Responses to Global Economic Crises: Constrained Partisanship in Mature Welfare States. Journal of Social Policy, 43(2), 225–246. Retrieved from http://www.journals.cambridge.org/abstract_S0047279413000986
Steele, L. G. (2016). Ethnic Diversity and Support for Redistributive Social Policies. Social Forces, 94(4), 1439–1481.
Stichnoth, H., & Van der Straeten, K. (2013). Ethnic diversity, public spending, and individual support for the welfare state: A review of the empirical literature. Journal of Economic Surveys, 27(2), 364–389.
Sumino, T. (2014). Does Immigration Erode the Multicultural Welfare State? A Cross-National Multilevel Analysis in 19 OECD Member States. Journal of Ethnic and Migration Studies, 40(3), 436–455.
Taylor-Gooby, P. (2005). Is the Future American? Or, Can Left Politics Preserve European Welfare States from Erosion through Growing Diversity? Journal of Social Policy, 34(4), 661.
Teorell, J., Dahlberg, S., Holmberg, S., Rothstein, B., Khomenko, A., & Svensson, R. (2017). The Quality of Government Standard Dataset, version Jan17. University of Gothenburg: The Quality of Government Institute.
UN-DESA, & OECD. (2013). World Migration in Figures. Retrieved from https://www.oecd.org/els/mig/World-Migration-in-Figures.pdf
van der Meer, T., & Tolsma, J. (2014). Ethnic Diversity and Its Effects on Social Cohesion. Annual Review of Sociology, 40, 459–478.
van Oorschot, W. (2013). Comparative Welfare State Analysis with Survey-Based Benefit Recipiency Data: The “Dependent Variable Problem” Revisited’. European Journal of Social Security, 15(3), 224–248.
van Vliet, O. (2010). Divergence within Convergence: Europeanization of Social and Labour Market Policies. Journal of European Integration, 32(3), 269–290.
Visser, J. (2016). ICTWSS Database. version 5.1. Amsterdam: Amsterdam Institute for Advanced Labour Studies (AIAS), University of Amsterdam.
Walter, S. (2010). Globalization and the welfare state: Testing the microfoundations of the compensation
28
hypothesis. International Studies Quarterly, 54(2), 403–426.
Wang, J., & van Vliet, O. (2016). Social Assistance and Minimum Income Benefits: Benefit Levels, Replacement Rates and Policies Across 26 OECD Countries, 1990-2009. European Journal of Social Security, 18(4), 333–355.
World Bank. (2017). World Development Indicators. Washington D.C. Retrieved from http://data.worldbank.org/