Liberalization of European migration and the immigration of skilled people to Sweden

Migration policies can have a strong impact on which immigrants choose to move to another country and their skill levels, whether intended or not (Bianchi 2013). These policies can in turn affect innovation development in the country to which those immigrants move. For example, Kerr and Lincoln (2010) find that higher levels of entry by those holding an H-1B visa¹ increase employment among immigrants in science and engineering as well as patenting by inventors with surnames that appeared to be Indian and Chinese. Although some researchers have tried to investigate the impact of changes of migration policy on the composition of skills among immigrants, most of them focus on the impact of specific policy changes and immigrant groups (Chen 2005; Kato and Sparber 2013). Empirical evidence on the impact of more general liberalization of migration policies is still scarce.

One of the biggest changes in migration policy history in Europe is the liberalization of migration through the European Economic Area (EEA) agreement between the European Union (EU) and the European Free Trade Association (EFTA) countries in 1994. The agreement, which originally involved 17 countries, has grown in importance with the large expansion of the EU. In short, the EEA allows for the free movement of individuals among its member countries. However, little is known about how the European integration process has affected the skill composition of immigrants in the member countries. One earlier study similar as our study is from Huber and Bock-Schappelwein (2014), who find that the EEA has a positive impact on the education level of permanent migrants to Austria.

However, it is far from clear that the results in Austria can be generalized to other EEA countries. In particular, we examine the case of Sweden. The similarities between Sweden and Austria are, at first glance, striking. They have a similar population size and proportion of foreign born as well as generous welfare benefits (OECD 2016a, 2016b).² High welfare ambitions in both Austria and Sweden raise rewards to low-skilled workers for migrating to those countries over other countries. However, Austria and Sweden differ, for example, when it comes to the country of origin for immigrants.³

The effect of liberalization of migration has a theoretically ambiguous effect in terms of the selection of immigrants at different skill levels. Theory highlights the distribution of skills and rewards to skills in the receiving and sending countries, as well as migration costs that in turn determine the self-selection behavior of migrating individuals under free migration (Borjas 1987; Grogger and Hanson 2011). Our prediction from a theoretical framework based on Grogger and Hanson (2011) is that the average skill level for immigrants to Sweden decrease under free migration.

We examine this issue empirically based on a unique database that links inventors to the general population in Sweden (Jung and Ejermo 2014; Zheng and Ejermo 2015). We examine the effect of the full liberalization of migration on the skill composition of new immigrants in Sweden who originated in the EU-15.⁴ We exclude Denmark and Finland from the treatment group, as they have been part of a free-labor mobility agreement among the Nordic countries since 1954. Sweden’s accession to the EEA means that all EU-15 citizens could move to Sweden freely. We use difference-in-differences (DiD) estimations to evaluate the impact of liberalization on the skill composition of immigrants. This method means that the treatment group (i.e., EU-15 citizens) is compared in terms of skill composition (education, probability of becoming an inventor) both against themselves before treatment and relative to the trend in the control groups.

The results of our study indicate that the EEA had short-term negative effects on the selection of immigrants from the EU-15 with respect to their education level. This is manifested as both an increased probability that EU-15 immigrants had a low level of education and a decreased probability that they were highly educated. However, it is also clear that those effects are temporary, as estimated lead and lag models show the effect to be statistically significant only in the first years after the application of liberalization. Moreover, with respect to patenting, we see no systematic effects in our main result of interest after controlling for other variables. Our results are consistent with the theoretical assumption that negative selection driven by reduction in migration costs mainly applies to low-skilled immigrants in the studied case.

The rest of the paper is structured as follows. Section 2 summarizes Swedish migration history. Section 3 reviews previous studies examining the impact of migration policy selection of migrants. Section 4 discusses the theoretical background. Section 5 presents the database and descriptive statistics. Section 6 describes the methods used. Section 7 reports the results of the empirical analysis. The final section draws conclusion based on the study.

Immigration to Sweden started to take off after World War II. In the 1950s and 1960s, high economic growth led to large inflows of immigrants for work reasons, who mainly originated in Nordic and Southern European countries (Bevelander 2000). In 1954, the five Nordic countries (Denmark, Finland, Iceland, Norway, and Sweden) enacted free movement in the entire area (Bevelander 2000; Stalker 2002). This treaty allowed large-scale emigration from the other Nordic countries to Sweden and especially Finns emigrated (Westin 2000; Helgertz 2010). However, labor migration from non-Nordic countries came to a halt in the mid-1970s following an economic slowdown, and refugee migration started to dominate immigration flows to Sweden, a trend that continued until recently.⁵ The European integration process started to change this, first with the implementation of the EEA in 1994, a free trade agreement that allows free mobility between the EFTA and the EU member countries, and then with Sweden’s membership in the EU along with Finland and Austria in 1995. The EEA can be characterized as a mark of change in policy toward more labor immigration, reinforced through EU membership.

Although total intra-EU migration comprised a small share (less than 2%) of total migration in the 1990s and 2000s (Cerna 2009), the free movement agreement may still have changed the composition of immigrants in member countries. This seems plausible in the case of Sweden because the composition of immigration may have changed as immigration policies in Sweden before 1995 mainly favored refugees and family-reunification movers. In Sweden, immigrants from the EU-15 accounted for 9–13% of all new immigrants between 2000 and 2015 (Statistics Sweden 2016, see Figure 4 in Appendix 1).

This trend toward a more integrated European labor market has continued with the enlargement of the EU to include Eastern European countries beginning in 2004 (EU-27) and special expert tax systems in Sweden aimed at attracting highly skilled labors (Mahroum 2001; Forskarskattenämnden 2013). The EU expanded dramatically in 2004, with 10 new member countries, and Sweden is one of the few EU-15 countries (the other two are the UK and Ireland) that allows free immigration for the new member countries (Ruhs 2016). This, however, had only a modest effect on immigration, as, according to Gerdes and Wadensjö (2008), about 10,000 migrant workers came to Sweden from the new EU member states.

Many studies examine how changes in migration policies have induced migration shifts but the results are ambiguous. For example, Mayda (2010) finds that migration quotas matter because they mitigate supply-side effects. Giordani and Ruta (2010) argue that restrictive immigration policies tend to worsen the skill composition of immigrants. Djajic (1989) indicates that, by relaxing qualitative restrictions (i.e., admission is based largely on skills) on immigration, the labor-importing country may in fact improve the quality of its labor force and reduce the flow of migrants across the border. Kato and Sparber (2013) find that restrictive immigration policy (tightening of H-1B visa grants for prospective undergraduate students) disproportionately discourages high-ability international students from pursuing education in the USA. By contrast, Chen (2005), based on a survey of Chinese graduate students, finds that master’s degree students who intended to migrate to the USA are likely to be negatively self-selected in a less restrictive migration regime and positively self-selected in a more restrictive regime. Borjas (1993) argues that the Canadian point system does not help in attracting more highly skilled workers from a particular source country compared with the USA, which does not use this kind of migration policy. However, it alters the national-origin mix of the immigrant flow, which reduces the proportion of immigrants from regions that generate flows of those who are low skilled. This explains why, on average, immigrants in Canada are more highly skilled than those in the USA.

European migration policies have changed drastically over the last two decades, creating large intra-European migration flows, especially from new EU member countries in Eastern Europe to old members with higher income levels in the past decade. These policy changes create natural experiments which provide opportunities to study the impact of migration policies. For example, Elsner (2013a) examines the impact of EU enlargement in 2004 on wage distribution for non-migrants in the sending country. Using Lithuanian household data and immigration data from the UK and Ireland, he finds that emigration significantly raised wages on average for stayers; however, the wage effect was only significant for male workers. In an accompanying paper (Elsner 2013b), he also finds that the effect was concentrated among young workers, whose wages increased by 6% over the period of 5 years, while the older workers were not affected. Moreover, he finds emigration has no significant effect on the wage distribution between high- and low-skilled workers. Nevertheless, Dustmann et al. (2015) find emigration has a positive effect on wages for high- and medium-skilled workers stayed in Poland but might have a negative effect on workers at the low end of skill distribution.

However, empirical evidence on how liberalization of migration among more economically integrated and developed European countries affects the skill composition of immigrants is sparse. Based on survey data on immigrants who arrived between 1988 and 2002, Huber and Bock-Schappelwein (2014) find that after Austria became a member of the EEA in 1994, the share of permanent migrants from the EEA to Austria with low education levels dropped compared to the change in the share of low-educated permanent migrants from other countries. They therefore argued that liberalizing migration may be an effective way to improve the skill structure of immigrants in countries that have a high share of low-skilled immigrants. By contrast, Beerli and Indergand (2014) find that the abolition of quotas for workers from European countries through a bilateral agreement with the EU in 2002 had a small but negative effect on the educational quality of immigrants to Switzerland. They attribute this decline to more selective immigration policies before 2002, which in turn stem from higher returns to education in Switzerland. The differing experiences of the two countries indicate that pre-reform migration patterns are quite important for understanding how liberalization changes migration flows.

Few studies address the impact of migration policy with respect to inventive behavior. One study we know is by Jahn and Steinhardt (2016). They examine the impact of immigration on innovation by exploiting a placement policy for ethnic German immigrants, who are mainly from the former Soviet Union. They find immigrants had no or even positive effect on innovation (number of patents), although the majority of immigrants are unskilled. This may be because quite high share of these immigrants had worked as engineers and they have less cultural and language problems compared with other immigrants.

The workhorse model for understanding selection patterns in immigration flows is Borjas’ (1987) model of self-selection of migrants. This model assumes that the costs of migration are positively related to the skill level. The model assumes that immigrants who move for work reasons will tend to move to a destination country that offers wages above those of their home country corresponding to their skill levels. Positive selection is defined as such when immigrants to the destination country are drawn from the upper tail of the income distribution in their home country and end up in the upper tail in the destination country. Conversely, negative selection is defined as the case where immigrants are from the lower tail of income distribution in the home country and end up in the lower tail in the destination country. Borjas (1987) shows that positive selection takes place if the correlation between the positions in the skill distribution to that of the income distribution in the home country and destination country are high and the income distribution at the destination country is higher. The opposite takes place if the correlation again is high but the distribution of income is lower in the destination country.⁶

Swedish income distribution is more compressed and low-skilled people are (generally) rewarded more generously than that of the majority of other countries in the world, including the EU-15 countries. Therefore, negative selection of immigrants to Sweden seems to be the main theoretical implication according to Borjas’ (1987) model. However, his model is based on an assumption of free mobility and increasing costs by skill level. This assumption is debatable, and we would like to have a further discuss about it.

The nature of the moving cost to Sweden and its relation to skills before and after the EEA are important because they influence the payoff involved in moving. The distribution of the moving cost for different skill levels before the EEA and the change in the distribution of the cost reductions implied by the EEA may differ by skill levels and is important for the outcome.

International migration cost includes actual moving costs (e.g., cost of visa application), information searching cost (e.g., time spent on searching the process of visa application), social networks cost (e.g., give up part of social network in the home country), and adoption costs (e.g., learning a new language and institutions) in the host country⁷ (McKenzie and Rapoport 2010). For citizens from the EU-15, it is hard to see that actual moving costs and social network costs are systematically related to the skill level. However, information searching costs and adoption costs are likely to be negatively related to an individual’s skills. For example, highly educated individuals are usually easier to find the necessary information for migration and better at learning a new language and institutions. Nevertheless, highly educated individuals also often demand more knowledge about institutions and may invest more in education to adopt to the new country. Total adaptation costs may not be fully known before someone moves and could vary from one individual to another, but it surely plays a role in the decision to move. Overall, it seems plausible that migration costs to Sweden are not systematically related to skills.

Importantly, the implication that costs rise with the skill level also seems to be at odds with generally observed migration patterns. They tend to show that selection is overall positive, even in cases where one would expect to observe more negative selection patterns, according to empirical data on world migration flows reported by Grogger and Hanson (2011). Grogger and Hanson (2011) develop an alternative model in which moving costs are considered fixed and unrelated to the skill level. In Appendix 2, we modify this model to account for our assumption that the fixed cost of moving is likely to be lowered more for low-skilled workers. The implication of this, combined with the assumption that the net wage difference for highly skilled workers relative to low-skilled workers does not increase in Sweden as compared to EU-15 countries, is that a higher share of EU-15 citizens with low skills could be attracted to Sweden after the liberalization of migration after the inception of the EEA in 1994.

Additional evidence on the impact of this policy change on the skill structure of immigrants in Sweden from the EU-15 can be obtained using a DiD regression approach. Here, we exploit the fact that, in 1994, only immigration from the EU-15, but not from outside the EU-15, was affected by Sweden’s accession to the EEA. We divide the data into a subset of region that was affected by the reform (the EU-15) and another subset that was not affected (“Other developed regions”). Our study period is 1990–1997, or alternatively 1990–2007, and we group the time period into a pre (1990–1993)- and a post (1994–1997 or 1994–2007)-membership period. We start in 1990 due to the availability of data on control variables (marriage and children).¹³ Moreover, inventor data in this dataset is complete until 2007. Extending the investigation period beyond 1997 allows us to distinguish whether any effects are short or long term.

where y _ijt is a dummy variable indicating whether an immigrant (i) from region (j) at the time of migration (t) is low, middle, or highly educated or becomes an inventor after migration. Y _t is a set of migration year-fixed effects. These pick up influences stemming from, for example, business-cycle variations that may influence the skills of immigrants. D _j is a vector of dummy variables for each region of origin that captures unmeasured region-fixed effects. X _i is a set of individual characteristics at the time of migration that may affect the probability of observing a certain level of education or of being an inventor. X _i includes the variables female (1 = female, 0 = male), age, age², marital status (1 = married, 0 = unmarried), and children (1 = at least having one child, 0 = no children) at the time of migration. These are included to control for skill differences that stem from gender, life-cycle effects, and socioeconomic background. H _jt is an index of weighted human capital based on the average number of years of schooling and estimated rates of return to education from the Penn World Tables (Feenstra et al. 2015). Since we observe region of immigrants and not their country of origin, we construct a weighted human capital measure for each region, based on population size.¹⁴ This index controls for supply-side and trends in skills effects that influence the skill level of immigrants. D_{t ≥ 1994} is a dummy variable with a value of 1 if an immigrant migrated in 1994 or later. D_{EU − 15} is a dummy variable with a value of 1 if an immigrant is from the EU-15. β _t , γ _j , λ, and ρ are parameters to be estimated; ε _ij is an error term. α is the central parameter of interest. This measures the average change in y _ijt on the treated relative to the untreated migrant groups. If α is statistically significantly different from 0, the examined skill for immigrants coming from the EU-15 in 1994 or later are found to be different from the EU-15 immigrants coming before 1994 and from immigrants from other regions.

The immigrant age is very stable at around 29–30 years on average, both before and after 1994. Immigrants are roughly balanced by gender from “Other developed regions” both before and after 1994. By contrast, male immigrants dominate strongly from the EU-15 both before and after 1994 (around 60%). Finally, it is as common for immigrants from the EU-15 as for immigrants from “Other developed regions” to have a child at the time of migration, with a slowly declining share after the EEA for both groups.

Our regressions use two basic models for each dependent variable. The first model excludes while the second model includes all control variables X _i and weighted human capital. The comparison of these two models is done to gain and understanding of how the control variables X _i and weighted human capital affect the skill composition of immigrants. For simplicity, we use the linear probability model (rather than probit or logit models), as we are only interested in the average treatment effect of the EEA, and these are directly given by our estimated coefficient.

This paper presents evidence on the change of skill structure of immigrants to Sweden from the EU-15 as the result of the implementation of the EEA in 1994, which allows free mobility among member countries. We use both education and inventor data to infer changes in skill levels.

Using DiD regressions, we find that the education level of immigrants from the EU-15 decreased following 1994 compared with immigrants from “Other developed regions” in the first years after the implementation of liberalization of migration within the EEA. However, the long-run effect is much weaker both by extending the post-1994 period to 2007 and investigating by lead and lag effects in our regression results. The results suggest that the effects of the EEA are largely transitory. In terms of the probability of an immigrant becoming an inventor, we initially see an increased probability, but the effect turns insignificant when we control human capital in the immigrants’ region of origin. This suggests that the growing number of inventors from the EU-15 is the result of its increasing human capital, but not the result of liberalization of migration. The education results are reconcilable with the assumption that moving costs decreased more for low-skilled people as a result from free migration in EEA.

It is difficult to generalize the impact of liberalization of migration on skill structure of immigrants in Sweden to other countries or other periods. This is because Sweden has some special features in terms of its migration policy (e.g., dominated by refugees and family-reunification movers since 1968) and economic situation (e.g., relatively high wages for low-skilled workers) compared with other countries. Nonetheless, the results indicate that for countries with a negative selection of migrants, liberalization of migration may have a negative effect on the skill structure of their new immigrants in the short-run, which they may need to prepare for. However, in the long-term, they may not need to worry too much about it.