Focus on the 2015 inflow of refugees
5. A macro analysis to explain Belgium’s bad performance
5.4 How those results help to explain Belgium’s bad performance
In brief, the personal characteristics of a country’s immigrants, mainly their level of education, explain part of the discrepancies between countries in terms of labour market integration of immigrants and the gap with respect to natives. Labour market features nevertheless constitute powerful explanatory factors, in particular employment protection (positive impact), union density and replacement income rates in the event of unemployment.
Regarding integration policies, the most efficient appear those that are particularly designed for immigrants.
Once again, integration policies regarding education are key in that respect. Moreover, also incentives to stay for a long period could be beneficial since they induce immigrants to invest in human capital specific to the host country. Anti‑discrimination policies could also help even if they seem less efficient to tackle difficulties encountered by non‑EU immigrants.
Those results provide a consistent explanation of Belgium’s relatively poor performance in integrating immigrants into the labour market, especially for non‑EU born individuals. In fact, compared to the average of the countries analysed, Belgium is less likely to have immigrants who are high‑educated and more likely to attract low‑
educated foreigners. Its labour market rigidities could also be an explanatory factor. In addition, few policies are specifically designed to help immigrants find a job (see table 6).
Table 6
Comparing Belgium with the average
(average of observations used for regressions compared to the average for Belgium over the period 2006‑2018)
Average Belgium
Dependent variables (in pp)
Employment gap for first‑generation immigrants −6 −14
Employment gap for first‑generation non‑EU immigrants −8 −21
Participation gap for first‑generation immigrants −3 −8
Participation gap for first‑generation non‑EU immigrants −4 −12
Share among the population (in %)
Total first‑generation 12 15
Non‑EU first‑generation 7 8
Age 1
Total first‑generation 1.09 1.13
Non‑EU first‑generation 1.15 1.21
Gender 1
Total first‑generation 0.96 0.96
Non‑EU first‑generation 0.95 0.97
High level of education 1
Total first‑generation 1.06 0.85
Non‑EU first‑generation 1.02 0.74
Low level of education 1
Total first‑generation 1.43 1.62
Non‑EU first‑generation 1.50 1.85
Unemployment rate (in %) 9 8
EPL (index from 0 to 6) 2.6 2.7
Share of public employment (in %) 15 18
Share of self‑employment (in %) 14 13
Job tenure (in %) 43 48
Union (in %) 28 54
Net replacement rate (in %) 39 59
ALMP measures (in % of GDP) 0.47 0.51
MIPEX (index from 0 to 100)
Access to the labour market 61 60
General support for labour market mobility 61 92
Targeted support for labour market mobility 38 17
Workers’ rights 74 75
Family reunion 63 74
Access to education 39 42
Targeting needs in terms of education 51 65
New opportunities in education 30 51
Intercultural classes for all 47 85
Permanent residence 63 83
Access to nationality 48 65
Anti‑discrimination 61 78
Sources : Eurostat (LFS), EC, MIPEX, OECD, Visser, NBB calculations.
1 Those variables are the ratio with respect to natives proportions. Taking “Age” as an example, if the variable has the value of 1, it means that the share of immigrants at working age is identical to the share of natives at working age. If the value is below 1, it means that immigrants are less likely to be at working age than natives. If the value is above 1, it means that immigrants are more likely to be at working age than natives.
The two ‘negative’ factors, namely labour market institutions and level of education, complement one another : features of the Belgian labour market explain why our country attracts proportionately fewer high‑skilled migrants and / or low‑skilled migrants. While immigrants (including from non‑EU countries) in the countries studied are more likely to be high‑educated than natives, this is not the case in Belgium, where immigrants are 15 % less likely to be high‑educated (25 % for non‑EU immigrants). Cohen and Razin (2008) developed a theoretical model to find out the effect of a more generous social security system on immigrant education levels, and conducted an analysis across OECD countries. Assuming free access to the country, the effect would be negative : more generous social systems would tend to attract low‑skilled immigrants. Boeri et al. (2012) state that attracting talents depends mainly on the labour market, and wage premiums on education. While R&D spending induces greater inflows of highly skilled migrants, generous welfare benefits and strict employment protection attract more unskilled workers. This finding was corroborated by Eichhorst et al. (2017), who found that a more generous unemployment benefit system was negatively correlated with the presence of high‑skilled immigrants. These people are often in work and so contribute to the social security system, and a more generous system could in fact reduce their reasons for moving to a particular country. The same is found to be true for union representation, which could help improve labour conditions for the lower skilled and so mainly attract lower‑skilled immigrants.
Nevertheless, the literature is not unanimous in saying that individual decisions to move to a specific country are induced by its social system (Edo et al., 2018). Theoretically, that could be the case, but the most frequently quoted criteria are unemployment and wage differences compared to the country of origin, the existence of social networks, and regional proximity (Giulietti 2014). In 2018, Docquier et al. produced similar results showing that the size of the network of previous migrants and the average income per capita in the country are crucial determinants of the size of migration inflows.
Moreover, while migrants selectivity increases over time throughout the world (Rayp et al., forthcoming), recent literature shows that migration policies based on skill selectivity are not efficient, and that push and pull factors, such as geographical proximity or cultural similarities, are more relevant explaining the magnitude and the structure of migration flows (Antecol et al., 2003 ; Bélot and Hatton, 2012). Rayp et al. (forthcoming) recently confirm previous findings. Computing a unique indicator of migration policies in 42 OECD and non‑OECD countries from 1990 to 2014, they find that skill selectivity has a weak effect on the scale and structure of migration flows.
According to our analysis, some factors in Belgium should favour better integration outcomes compared to other countries. First, Belgium provides easier access to permanent residence. Combining data on bilateral migration desires from 140 origin countries and data on policies in 38 destination countries over the period 2007‑2014, Beine et al. (2019) find that, in addition to labour market features, access to nationality and permanent residence may also influence migrant inflows. More precisely, those factors increase the perceived attractiveness of a destination country. Belgium also scores higher than the average regarding anti‑discrimination policies and access to education including targeting needs.
6. Conclusion
The aim of this second part of the report is first to provide a clear understanding of which factors could have an impact on the employment or participation gaps between first‑generation immigrants and natives and, secondly, to find an explanation for Belgium’s poor performance compared to other EU countries. To do so, we created a new dataset including EU countries over the period 2006‑2019 and merging information from different sources allowing us to test 25 explanatory variables for employment and participation gaps between first‑generation (non‑EU) immigrants and natives. Those variables are : personal characteristics of immigrants (age, gender, level of education (high or low)), history of migration (share among the population), economic environment (unemployment rate), labour market features (EPL, public employment, self‑employment, job tenure, union density, net replacement rate, labour market policy measures) and integration policy indicators (12 MIPEX sub‑indicators).
The findings once again show that education is a key factor in explaining employment and labour market participation gaps between first‑generation immigrants and natives. When focusing on non‑EU immigrants, the results are however less robust. On the one hand, a high level of education (based on self‑reporting from Labour Force Survey) is less beneficial for a non‑EU immigrant, probably because of the diploma recognition issue. On the other hand, being low‑educated is less detrimental for them too. The potential explanation could be that they are more active in low‑skilled sectors and are more inclined to accept lower wages than natives.
This increases their chance of getting a job compared to natives.
The over‑representation of immigrants, and in particular non‑EU immigrants, in low‑paid jobs is also reflected in the results obtained for net replacement income rate. A high replacement income in the event of unemployment increases the effect of the unemployment trap among immigrants and the effect is more pronounced for non‑
EU immigrants.
Regarding employment protection on regular contracts, our findings support the view expressed in the literature that a higher level of protection reduces the gap in labour market integration between immigrants and natives.
Immigrants, usually less aware of employment protection regulations, are also less likely to claim their rights, and this makes it cheaper for employers to hire immigrants than to hire natives.
Labour market rigidities, such as a high level of job tenure, make it more difficult for individuals who are not yet active to enter the labour market, because of lower turnover among firms. The degree of union density also tends to favour established workers rather than unemployed or new entrants. As immigrants are over‑
represented among both categories, a higher level of union density widens the gap with natives in terms of both employment and labour market participation.
Because of their low time variability, results on migrant integration policies should be considered with caution.
Nevertheless, some interesting results show up from the analysis. Activation policies and general support for a better access to the labour market tend to widen the labour market integration gap between immigrants and natives. In order to significantly improve labour market outcomes of immigrants, targeted policies tend to be more efficient.
Access to education is significantly and positively associated with the labour market integration of immigrants compared to natives, and this result is true for all types of immigrants. Design of educational policies specifically targeted to immigrants is also beneficial even after controlling for country and year fixed effects. The positive impact disappears however when looking at employment of non‑EU immigrants. Non‑EU immigrants are temporarily kept away from the labour market to upgrade their skills, so that the insignificant effect on the employment rate could be counterbalanced by a positive impact on the quality of their jobs.
Policies designed to induce immigrants to stay in the country for a longer period tend to reduce the employment and labour market participation gaps with respect to natives. In that respect, the most powerful policy is providing easier access to permanent residence, while the other indicators, family reunion and access to nationality, do not always give significant results.
Finally, anti‑discrimination policies are efficient in reducing the labour market integration gap between immigrants and natives when we consider total first‑generation immigrants. However, the positive impact is less clear for non‑EU immigrants. As for employment activation policies or education policies, discrimination policies are maybe not targeting immigrants enough, as they are often designed in common with other potential characteristics leading to discrimination such as gender, age, handicap, etc.
Those results provide a consistent explanation of Belgium’s relatively poor performance in integrating immigrants into the labour market. Compared to the average of the countries analysed, Belgium is less likely to have immigrants who are high‑educated and more likely to attract low‑educated foreigners. Its labour market rigidities could also be an explanatory factor. In addition, few policies are specifically designed to help immigrants find a job. While Belgium scores high on the aggregate MIPEX (4th among all EU countries), it performs badly regarding targeted support for immigrants. However, some policies should be in favour of labour market integration of immigrants, namely, easier access to permanent residence, the high level of access to education and targeting needs in that respect and strong anti‑discrimination policies, even though some improvements are still possible for the two latter compared to best performer, in particular regarding education policies.