Introduction
Within the context of pronounced demographic ageing and increasing migration flows in Europe, focus has been placed on the impact of migrants on overall population change and labour force as well as on their integration into the labour market. In this context, the present chapter presents and discusses: key demographic characteristics; labour market barriers and enablers; and employability opportunities for migrants, refugees and asylum seekers (MRAs) for a selected panel of EU economies, namely the Czech Republic, Denmark, Finland, Greece, Italy, Switzerland and the UK. The aforementioned economies have participated in the Horizon 2020 EU project, SIRIUS – Skills and Integration of Refugees, Migrants and Asylum Applicants in European Labour Markets. In the analysis that follows, the acronym SIRIUS is directly equivalent with a reference to these economies.
The first section of this chapter focuses on the demographic characteristics of the population in the SIRIUS economies. In this context, the chapter offers a comparative view between the native population and the foreign-born population of the SIRIUS economies. In addition, key demographic characteristics of the labour force of the SIRIUS economies are analysed, as well as the impact of post-2014 migration flows on these characteristics. The second section of the chapter presents key characteristics regarding the MRAs in the various SIRIUS economies. In addition, in this chapter, the employability opportunities of MRAs are assessed using relevant panel-data models. Finally, the third section of the chapter focuses on the economies that attract new workforce, that is, MRAs. In this context, a relevant methodological framework is put forward in order to econometrically detect the labour ‘absorbing’ economies as well as the labour ‘absorbing’ sectors in each economy. Additionally, two new composite indices are introduced to identify the sectors and the occupations, respectively, of an economy which have simultaneously high growth potential and required educational attainment level compatible to the MRAs’ educational attainment level.
Migration, overall population change and labour force in the SIRIUS economies
Foreign-born population and overall population change
Over the recent period (2010–2020), international migration has become the main demographic component of population change in Europe (Coleman, 2008; van Nimwegen and van der Erf, 2010; Murphy, 2016; European Commission, 2020). In particular for the EU27 as a whole, during 2010–2020, population decline was prevented due to positive net migration, that is, the difference between inward and outward migration flows, which has compensated for the excess of the number of deaths over that of births, the so-called natural change. In addition, the contribution of international migration to an increasing population change results from migrants, either foreign nationals or foreign-born persons, when the migratory phenomenon is examined on the basis of the country of birth.
There are two main reasons for this. First, foreign-born population exhibits positive net migration, which contrasts with the negative net migration of natives. In other words, the direct effect of international migration on the overall population change is negative for the natives, given that outward exceed inward flows, whereas the net migration of foreign-born population leads to increases in population of the receiving countries (Bagavos, 2021). Second, in the majority of the European countries, the number of births and deaths to natives are very close, implying a limited or often negative natural change, and therefore a shrinking contribution of natives to the overall population change (Coleman, 2008; Salzmann et al, 2010). On the opposite, foreign-born population, because of its relatively young age structure, there is a higher number of births than deaths, which implies a positive indirect effect of foreign-born population on the overall population change.
There is an additional point which merits particular attention. In demographic terms, foreign-born population is not a typical population, since changes in its total size over time are solely determined by deaths and net migration, but not by births, which, by definition, are classified as native-born persons. Therefore, there is a significant difference between changes in the foreign-born population and contribution of the foreign-born population to the overall population size and growth. In practice, the difference between births and deaths to foreign-born population does not represent the natural change of foreign-born population but the natural change attributable to foreign-born population or, put differently, the natural change of the
Migration has its proper dynamic, but its impact on population change is additionally related to the demographic situation prevailing in the receiving countries (MacKellar and McNicoll, 2019). In that respect, given the current European context of low fertility settings and pronounced population ageing, the impact of foreign-born population on the size of the overall population of European countries is more pronounced than what would have been observed in a context of population growth. Consequently, there are strong reasons to believe that the foreign-born migration of the post-2014 period has had a relevant impact on population change in Europe, since it has occurred in a framework of stagnation and slowdown in the overall population growth.
Figure 2.1 displays the contribution of foreign- and native-born population to the overall population change in the seven countries involved in the SIRIUS project for the 2014–2019 period. It shows that the foreign-born population has been the driving force behind changes in the overall population as this population has attenuated overall population decline in Greece and Italy; or turned the expected population stagnation in Czech Republic and the UK into population increase and the expected population
Differentials between foreign- and native-born population in the contribution to the overall population change mainly results from different schemes as regards net migration (Table 2.1). Indeed, results confirm that the net migration of the foreign-born population is of positive sign, which is in contrast with the negative figure occurring for the native-born. Pronounced diversities are additionally observed in terms of the natural change attributable to each population group. In contrast to what is observed for natives, where the corresponding contribution is, in the largest majority of cases, of a negative sign, the excess of births over that of deaths among foreign-born population implies an increase in the overall country’s population. It is also worth noticing that the indirect effect of migration related to births and deaths of the foreign-born population is of relevant importance as it accounts for around 30 per cent of the total effect of foreign-born population on the overall population change. Seen in relation to the net migration of foreign-born population, the indirect effect implies that, for every 100 net foreign-born migrants, there are almost 45 persons added to the overall population through births and deaths to foreign-born population.
Differences between foreign- and native-born population in their contribution to the overall population change and in their natural change (annual averages, per 1,000)
| Contribution due to: | Difference in natural change due to the diversity in: | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Natural change | Net migration | ||||||||
| Foreign-born | Native-born | Overall natural change | Foreign-born | Native-born | Overall net migration | Fertility and mortality | Age structure | Total | |
| Denmark | 1.7 | –0.5 | 1.2 | 4.7 | –0.2 | 4.5 | 0.5 | 15.1 | 15.6 |
| Greece | 0.9 | –3.8 | –2.9 | 2.0 | –2.3 | –0.3 | 2.5 | 9.9 | 12.3 |
| Finland | 1.1 | –1.5 | –0.4 | 3.1 | –0.5 | 2.6 | 3.9 | 15.7 | 19.6 |
| Switzerland | 3.1 | –0.7 | 2.4 | 8.1 | –1.2 | 6.9 | 3.0 | 8.8 | 11.8 |
| United Kingdom | 2.2 | 0.1 | 2.4 | 4.6 | –0.2 | 4.4 | 2.2 | 14.2 | 16.3 |
| SIRIUS 5 | 2.1 | –0.5 | 1.6 | 4.6 | –0.6 | 4.0 | 2.5 | 12.7 | 15.3 |
Findings plotted in Table 2.1 additionally challenge the idea that the contribution of migration to the overall population change, through its
Foreign-born population and shifts in the size of the labour force
Slowdown in population growth and population ageing are both phenomena that challenge Europe, and therefore SIRIUS economies as well, in terms of the subsequent shifts in the number of economically active persons as reflected in the labour force, that is, the number of employed and unemployed persons. In recent times, those demographic transformations have taken place in a context of increasing migration flows and stocks, which, along with current upward trends in the participation in the labour market of both native females and natives aged between 55 and 64 years, inevitably affect the size and the age structure of the labour force (Hilgenstock and Kóczán, 2018; Spielvogel and Meghnagi, 2018a, 2018b; Bagavos, 2019).
There are two dimensions affecting the total size of the labour force. The first one is of demographic nature and has to do with the size and the age structure of the working age population (most frequently defined as those aged 15 to 64). The second is socioeconomic and refers to the propensity of persons to participate in the labour market, or equally the wish to have a job, and it is reflected in the participation rates, that is, the ratio of the labour force to the working age population. Changes in the size of the labour force results from shifts in population and in the participation rates and, therefore, migration affects the labour force of the receiving country through those two components. The relatively young age structure of migrant population coupled with a relatively high share of migrants aged 15 to 64 compared to the total migrant population impact on the demographic dimension of the labour force. In addition migrants’ shares compared to the overall population and their level of participation rates affects the overall participation rate, that is, the participation rate of the entire working age population (Cully, 2011).
Changes (percentage) in the size of the labour force between 2014 and 2020*
Note: *2019, rather than 2020, for the UK
Source: First author’s calculations based on Eurostat’s data (Eurostat, 2021g)Changes (percentage) in the size of the labour force between 2014 and 2020*
Note: *2019, rather than 2020, for the UK
Source: First author’s calculations based on Eurostat’s data (Eurostat, 2021g)Changes (percentage) in the size of the labour force between 2014 and 2020*
Note: *2019, rather than 2020, for the UK
Source: First author’s calculations based on Eurostat’s data (Eurostat, 2021g)A decomposition exercise allows to detect the components behind the countries’ diversity as regards the role of migration for the changes in the size of the labour force over time (Table 2.2). In general, there is a larger diversity among countries regarding the aforementioned impact of migration on the overall population change, than on the size of the overall labour force. In particular, the impact of foreign-born population on the labour force of the receiving countries is of demographic nature, as migrants’ participation rates do not significantly vary over time (Table 2.2a). In addition, a further decomposition of the population effect plotted in Table 2.2a, by distinguishing the effect due to the size of the working age population and the one attributable to the age structure of the working age population (last two columns of Table 2.2b), shows that the population effect, for both migrants and natives, relies largely on changes in the size of the working
The components of the contribution of foreign- and native-born persons to the shifts in the size of the labour force (as percentage of the labour force in 2014)
| a. Population and participation effects | ||||||
|---|---|---|---|---|---|---|
| Foreign-born | Native-born | |||||
| Effects due to: | Effects due to: | |||||
| Participation | Population | Total | Participation | Population | Total | |
| Czech Republic | 0.2 | 0.9 | 1.1 | 2.1 | –2.7 | –0.6 |
| Denmark | 0.5 | 0.7 | 1.2 | 2.7 | 1.2 | 3.9 |
| Greece | –0.4 | –2.3 | –2.7 | 1.4 | –3.3 | –1.9 |
| Italy | –0.7 | 0.8 | 0.1 | 1.9 | –4.2 | –2.3 |
| Finland | 0.3 | 1.6 | 1.9 | 3.4 | –3.4 | 0.0 |
| Switzerland | 0.8 | 2.9 | 3.7 | 0.9 | –0.1 | 0.8 |
| United Kingdom | 0.6 | 2.1 | 2.7 | 1.2 | –0.6 | 0.6 |
| SIRIUS 7 | 0.1 | 1.3 | 1.4 | 1.6 | –2.2 | –0.6 |
| b. Effects related to migrants’ origin, gender and the size of the working age population | ||||||
|---|---|---|---|---|---|---|
| Foreign-born | Native-born | Foreign-born | Native-born | |||
| Effect due to: | Participation effect | Effect due to the size of the working age population |
||||
| EU27 | Non-EU27 | Men | Women | |||
| Czech Republic | 0.4 | 0.8 | 0.6 | 1.5 | 0.9 | –4.2 |
| Denmark | 0.3 | 0.9 | 1.3 | 1.4 | 0.8 | 1.2 |
| Greece | –0.6 | –2.0 | 0.3 | 1.1 | –2.4 | –2.3 |
| Italy | –0.3 | 0.4 | 0.4 | 1.4 | 0.7 | –2.9 |
| Finland | 0.4 | 1.5 | 1.9 | 1.5 | 1.6 | –3.8 |
| Switzerland | 2.6 | 1.2 | 0.2 | 0.7 | 3.2 | 0.0 |
| United Kingdom | 1.7 | 1.1 | 0.0 | 1.3 | 2.1 | –0.5 |
| SIRIUS 7 | 0.8 | 0.7 | 0.3 | 1.3 | 1.3 | –1.7 |
In the meantime, the increasing participation of native women – but also of native men in Denmark and Finland – in the labour market (Table 2.2b) leads to an upward trend in the overall participation rates of natives2 (Table 2.2a); with the noticeable exception of Switzerland and the UK, this implies a participation rate effect on the overall labour force which exceeds the one attributable to the migration effect (Table 2.2a). However, the overall labour force decreases in Italy and Greece, as the increase in the labour market participation of native women does not counterbalance native population decline and the migration effect is either negligible (in Italy) or negative (in Greece).
Labour market barriers and enablers
To determine, first, the position of post-2014 MRAs in the labour market of their host country, and, second, the main features of the host countries’ labour markets focusing on the sectoral structure and the relevant skills and occupations, we conduct a comparative statistical analysis. In this context, the particular goals of this chapter as we have mentioned earlier are the investigation of: the MRAs flows for the SIRIUS countries and the MRAs’ integration opportunities into the corresponding labour markets; the SIRIUS countries labour markets’ sectoral and occupational specialisation; and the labour market determinants.
To investigate these goals, probabilistic panel data models have been employed to econometrically investigate how the flows of MRAs affect their employment opportunities in the labour market. Furthermore, dynamic panel data analysis has been undertaken to investigate the determinants of labour market dynamics for each economy.
Migrants, refugees and asylum seekers in SIRIUS countries: a comparative analysis
Switzerland retains by far the highest percentage share (24.96 per cent) of foreign nationals of all the SIRIUS countries. By foreign nationals we refer to people whose nationality is different from their country of residence. The United Kingdom, Italy, Denmark and Greece come next, while the Czech Republic and Finland are the countries with the lowest shares of foreign nationals (4.83 per cent and 4.43 per cent, respectively) (Figure 2.3).
Share of foreign nationals to total population (percentage), SIRIUS countries
Source: Eurostat (2018)Share of foreign nationals to total population (percentage), SIRIUS countries
Source: Eurostat (2018)Share of foreign nationals to total population (percentage), SIRIUS countries
Source: Eurostat (2018)
Distribution of foreign nationals according to their educational attainment level (percentage), SIRIUS countries, 2008–2016
Source: Eurostat (2018)Distribution of foreign nationals according to their educational attainment level (percentage), SIRIUS countries, 2008–2016
Source: Eurostat (2018)Distribution of foreign nationals according to their educational attainment level (percentage), SIRIUS countries, 2008–2016
Source: Eurostat (2018)
Mean activity rates of foreign nationals by educational attainment level (percentage), SIRIUS countries, 2008–2016
Source: Eurostat (2018)Mean activity rates of foreign nationals by educational attainment level (percentage), SIRIUS countries, 2008–2016
Source: Eurostat (2018)Mean activity rates of foreign nationals by educational attainment level (percentage), SIRIUS countries, 2008–2016
Source: Eurostat (2018)In addition, foreign nationals with tertiary education have higher activity rates than those that have attained upper or post-secondary educational levels who, in turn, have higher activity rates than foreign nationals with less than primary, primary or lower secondary educational attainment levels (Figure 2.5). The less noticeable differences in activity rates among foreign nationals of different educational attainment levels have been recorded in Greece. Indeed, in this country the mean 2008–2016 activity rate of foreign nationals with educational attainment levels 0–2 is 73.4 per cent, which is 1.87 per cent and 4.80 per cent lower than those of educational attainment levels 3–4 and 5–8, respectively.
With regard to the position of foreign nationals in the labour market of each country, in Switzerland foreign national employees represent, on average, 24.07 per cent of the country’s total employees. Italy, the United Kingdom and Greece come next with 9.37 per cent, 9.25 per cent and 8.03 per cent, respectively. Finland and the Czech Republic rank last, in terms of the foreign nationals’ participation in the country’s labour market, with 2.47 per cent and 1.70 per cent, respectively (Figure 2.6).
Participation of foreign nationals in the country’s labour market (percentage), SIRIUS countries, 2008–2016
Source: Eurostat (2018)Participation of foreign nationals in the country’s labour market (percentage), SIRIUS countries, 2008–2016
Source: Eurostat (2018)Participation of foreign nationals in the country’s labour market (percentage), SIRIUS countries, 2008–2016
Source: Eurostat (2018)
Mean employment rates of foreign nationals, by age group (percentage), SIRIUS countries, 2008–2016
Source: Eurostat (2018)Mean employment rates of foreign nationals, by age group (percentage), SIRIUS countries, 2008–2016
Source: Eurostat (2018)Mean employment rates of foreign nationals, by age group (percentage), SIRIUS countries, 2008–2016
Source: Eurostat (2018)
Mean employment rates of foreign nationals by educational attainment level (percentage), SIRIUS countries, 2008–2016
Source: Eurostat (2018)Mean employment rates of foreign nationals by educational attainment level (percentage), SIRIUS countries, 2008–2016
Source: Eurostat (2018)Mean employment rates of foreign nationals by educational attainment level (percentage), SIRIUS countries, 2008–2016
Source: Eurostat (2018)During the post-2014 migration period, an inflow of MRAs from various parts of the world passed through Greece and Italy and on to the Central
Net migration rate, SIRIUS countries, 2008–2016
Source: Eurostat (2018)Net migration rate, SIRIUS countries, 2008–2016
Source: Eurostat (2018)Net migration rate, SIRIUS countries, 2008–2016
Source: Eurostat (2018)With regard to the net migration rate of the SIRIUS countries, the highest increase was recorded in Finland, although it fell in 2016, breaking its 2012–2015 upward trend. Switzerland and Italy experience high net migration rates, with downward trends, though, while the UK’s net migration rate records an almost 36 per cent increase in 2014 (compared to 2013) – which
As regards the asylum seekers per 1,000 persons, in 2015 Finland, Switzerland and Denmark faced, proportionally, the greatest inflow of asylum seekers among the SIRIUS countries (5.9, 4.6 and 3.7, respectively). Greece, on the other hand, faced an increase in 2016 (4.6 asylum seekers per 1,000 persons). The economies of UK and Italy received a relatively small inflow of asylum seekers, that is, 1.05 and 1.4, respectively. In contrast, the Czech
First time asylum seekers per 1,000 persons, SIRIUS countries, 2014–2016
Source: Eurostat (2018)First time asylum seekers per 1,000 persons, SIRIUS countries, 2014–2016
Source: Eurostat (2018)First time asylum seekers per 1,000 persons, SIRIUS countries, 2014–2016
Source: Eurostat (2018)
Mean annual number of first instance decisions on asylum applications (per 1,000 persons), SIRIUS countries, 2008–2013, 2014–2016
Source: Eurostat (2018)Mean annual number of first instance decisions on asylum applications (per 1,000 persons), SIRIUS countries, 2008–2013, 2014–2016
Source: Eurostat (2018)Mean annual number of first instance decisions on asylum applications (per 1,000 persons), SIRIUS countries, 2008–2013, 2014–2016
Source: Eurostat (2018)The ratio of positive to total first instance decisions has also increased over this period (Figure 2.12).4 Greece is the country with the highest percentage relative increase, namely 134.41 per cent.
Finally, in the Czech Republic, Denmark, Greece and Finland, there has been an increase in the mean annual number of first residence permits per 1,000 persons over the period 2014–2016, compared to the period 2008–2013. However, the highest number still remains in the United Kingdom (10.57 first residence permits per 1,000 persons), followed by Denmark and the Czech Republic with 7.24 and 5.82 residence permits respectively (Figure 2.13).
Ratio of positive to total final decisions on asylum applications, SIRIUS countries, 2008–2013, 2014–2016
Source: Eurostat (2018)Ratio of positive to total final decisions on asylum applications, SIRIUS countries, 2008–2013, 2014–2016
Source: Eurostat (2018)Ratio of positive to total final decisions on asylum applications, SIRIUS countries, 2008–2013, 2014–2016
Source: Eurostat (2018)
Mean annual number of first residence permits (per 1,000 persons), SIRIUS countries, 2008–2013, 2014–2016
Source: Eurostat (2018)Mean annual number of first residence permits (per 1,000 persons), SIRIUS countries, 2008–2013, 2014–2016
Source: Eurostat (2018)Mean annual number of first residence permits (per 1,000 persons), SIRIUS countries, 2008–2013, 2014–2016
Source: Eurostat (2018)Various panel data models have been employed in an attempt to capture the determinants that directly influence, either positively or negatively, the employment opportunities of MRAs in the various labour markets. More precisely, using random effects panel data models and stepwise backward elimination we will uncover the fundamental determinants of employment opportunities for the MRAs. Next, using panel data probability models and stepwise backward elimination we will estimate the statistically significant factors that increase or decrease the probability of MRAs to integrate in the SIRIUS economies’ labour markets through employment.
On the other hand, there are some low-skilled occupations that were found to have a negative and statistically significant impact on the employment rate. More precisely, based on our analysis, MRAs with occupations such as technicians and associate professions, service and sales workers, skilled agricultural, forestry and fishing workers, plant machine operators and assemblers and elementary occupations have statistically significantly decreased employment opportunities, since these professions are considered to be saturated in the various labour markets. As a result, a general finding is that SIRIUS labour markets prefer educated male MRAs that have significant occupational skills, such as managers, professionals, and so on.
Employability of migrants, refugees and asylum seekers in the SIRIUS economies
In this section, we analyse further the integration capabilities of the MRAs in the countries of interest (the Czech Republic, Denmark, Finland, Switzerland, the UK, Greece and Italy). We identify the SIRIUS economies and the sectors of economic activity that could be considered as being ‘labour absorbing’. To do so, we proceed at multiple levels. Specifically, two complementary methodological frameworks have been used to investigate the aforementioned topic.
First, to detect the labour ‘absorbing’ economies we need a modelling framework that can deal with both the interlinkages and spillovers among different economies. Therefore, following the related econometric literature (Pesaran et al, 2004) by using the Global Vector AutoRegressive (GVAR) model, we model the dynamic interlinkages and the potential spillover effects among the various SIRIUS economies. In this context, the results of the GVAR estimation pinpoint the labour absorbing economies in the dataset. Next, using the VAR/VEC framework, we investigate if there are any specific labour absorbing sectors, that is, primary, secondary, manufacturing and tertiary sectors, that correspond to the NACE Rev. 2 classification A, B-F, C, and G-U (see Table A.1 in the Appendix), respectively, for all the SIRIUS economies. The implicit assumption here is that there is labour mobility across the various sectors, but not necessarily across the various economies. Based on our two-step approach, the first step provides evidence for the total economy, whereas the second step provides evidence for the sectoral dimension of the economy. Therefore, a labour absorbing economy, identified in the first step, implies that the economy in total could attract more labour from the rest of the economies in order to increase its production. On the other hand, a labour absorbing sector, identified in the second step, implies that this specific sector could attract, independently, more labour from the rest of the sectors in order to increase its production. The fundamental difference in the second step is that the labour attracted by a sector comes directly from the labour force of the respective economy, whereas in the first step the labour attracted by an economy comes both from the rest of the economies, as well as from the respective economy.
A main finding is that the aggregate output of the UK has a statistically significant effect on the aggregate labour dynamics of the Czech Republic, Finland and Switzerland. This could be attributed to the strong interconnection between the UK and these economies mainly in terms of trade and financial relations. Another interesting finding is that the economies of the UK, Switzerland, Finland and the Czech Republic could be considered as being ‘labour absorbing’. In other words, based on our econometric analysis, these economies can attract extra workforce from the other SIRIUS economies. In this context, in these economies any potential future migration flows have increased potential of being integrated into their labour markets.
Next, at a sectoral level, another main finding is that the economies of Switzerland and Greece have the highest ‘labour absorbing’ capability for MRAs in the sense that all their sectors are characterised as being ‘labour absorbing’. Then, the economies of Finland and the Czech Republic have
Labour absorbing sectors
| Switzerland | Czech Republic | Denmark | Finland | Greece | Italy | United Kingdom | |
|---|---|---|---|---|---|---|---|
| Primary sector | + | + | + | + | + | + | |
| Secondary sector | + | + | + | + | + | ||
| Manufacturing sector | + | + | + | ||||
| Tertiary sector | + | + | + |
Employability indicators: a structural analysis
In this section, a quantitative analysis is also presented based on two composite indicators, that is, SIRIUS 1 and SIRIUS 2. SIRIUS 1 and SIRUS 2 are used to identify the sectors and the occupations, respectively, of an economy which have simultaneously high growth potential and required educational attainment level compatible to the MRAs’ educational attainment level. For the construction of both indicators, input-output analysis is used, which constitutes a widely used methodology appropriate for this type of investigation. The estimates are disaggregated by sector of economic activity
For each SIRIUS country the most dynamic sectors and occupations are determined and the MRAs’ integration potential is approached based on the similarity of their educational attainment level with the educational attainment level’s demand, at the sectoral and occupational levels, respectively.
In this research, a methodology of estimating the employability of MRAs for an economy is proposed. Based on the proposed methodology, an efficient model for the simulation of the labour market will be used, providing a method for the matching of educational attainment level of MRAs across the sectors and the occupations of the economy, aiming at the optimisation of the integration process.
First, the investigation of the labour market structural characteristics of the Czech Republic, Denmark, Greece, Italy, Finland, United Kingdom and Switzerland focuses on the employment structure at the level of sectors, occupations and educational attainment level. Then, two composite indicators, focusing on the sectoral structure of employment and on the occupational structure of employment, respectively, are introduced: the Growth Indicator for Sectors (GIS), and the Growth Potential Indicator for Occupations (GIO). The estimation of the GIS and the GIO is based on important indicators. These indicators are connected, on one hand with the structure and growth of employment at the sectoral and occupational level, respectively, and on the other, with the multiplying effect of sectors and occupations in the examined economy. The multiplying effect of economic sectors and occupations is estimated based on Input-Output Analysis (IOA). Then, for the comparison of the educational attainment level of MRAs with the educational attainment level of the employment for each country, two indicators expressing the similarity level are introduced: the Sectoral Structure Similarity (SSS) and the Occupational Structure Similarity (OSS). Finally, two composite indicators are used to identify the priorities, at the sectoral and occupational level, for MRAs’ integration in each economy: SIRIUS Indicator for Sectors (SIRIUS 1) and the SIRIUS Indicator for Occupations (SIRIUS 2).
Based on our findings, in the Czech Republic, the occupations with high employability potential are in the categories of elementary occupations, craft and related trades workers and clerical support workers. In Denmark, the occupations with high employability potential can be found in a wide range of occupations, such as craft and related trades workers, clerical support workers, service and sales workers. In Greece, the occupations with high employability potential are in the categories of skilled agricultural workers, plant and machine operators and assemblers, and elementary occupations. In Switzerland, the occupations with high employability potential are in
Conclusion
The main aims of this chapter were threefold. To begin with, the chapter presented the overall demographic situation in Europe by focusing on the economies participating in the SIRIUS project. In this context, the demographic role of post-2014 MRAs was assessed as well as their impact on these economies. The main demographic finding was that MRAs will significantly improve the labour force of the economies participating in the SIRIUS project, in terms of the working age population. Next, the chapter dealt with the barriers and enablers in the labour market of the economies participating in the SIRIUS project, acknowledging at the same time the key characteristics of the post-2014 MRA flows. In this context, the main findings were that the gender as well as the educational attainment level of MRAs are the most important factors that determine their employability opportunities. In other words, the analysis yielded that males with high educational level have increased opportunities to be integrated in the labour market of the SIRIUS economies. Finally, the chapter detected the labour absorbing economies and sectors in the countries participating in the SIRIUS project. Based on our findings, the economies of the UK, Switzerland, Finland and the Czech Republic could be considered as being ‘labour absorbing’, whereas the primary sector of each economy seems to be highly ‘labour absorbing’ in almost all the countries participating in the SIRIUS project.
Notes
The population component of the differences in the contribution of foreign- and native-born population to the overall population change thought the natural change, refers to the share of women of reproductive age as compared to the total population of each population group and the age structure of women of reproductive age in each
Results not presented here in detail indicate that this upward trend results from increasing participation rates of persons aged 55 to 64.
Part of this increase is due to the fact that asylum seekers are included in the 2016 inflows.
In all countries participating in the SIRIUS project, except Italy.
Note that the present chapter utilises official data on migration and labour without taking into consideration any irregular migration flows or irregular employment that could be present in the various economies.
Appendix
Classification of sectors of economic activity, NACE Rev. 2, 1-dig
| A | Agriculture, forestry and fishing |
| B | Mining and quarrying |
| C | Manufacturing |
| D | Electricity, gas, steam and air conditioning supply |
| E | Water supply, sewerage, waste management and remediation activities |
| F | Construction |
| G | Wholesale and retail trade, repair of motor vehicles and motorcycles |
| H | Transportation and storage |
| I | Accommodation and food service activities |
| J | Information and communication |
| K | Financial and insurance activities |
| L | Real estate activities |
| M | Professional, scientific and technical activities |
| N | Administrative and support service activities |
| O | Public administration and defence, compulsory social security |
| P | Education |
| Q | Human health and social work activities |
| R | Arts, entertainment and recreation |
| S | Other service activities |
| T | Activities of households as employers, undifferentiated goods- and services-producing activities of households for own use |
| U | Activities of extraterritorial organisations and bodies |
Specialisation index of occupations, 2016
| Czech Republic | Denmark | Greece | Italy | Finland | United Kingdom | Switzerland | |
|---|---|---|---|---|---|---|---|
| OC1 | 0.877 | 0.459 | 0.452 | 0.633 | 0.555 | 1.833 | 1.459 |
| OC2 | 0.797 | 1.318 | 0.994 | 0.761 | 1.246 | 1.313 | 1.288 |
| OC3 | 1.080 | 1.070 | 0.511 | 0.995 | 1.187 | 0.775 | 1.193 |
| OC4 | 0.969 | 0.812 | 1.089 | 1.251 | 0.609 | 0.982 | 0.861 |
| OC5 | 0.901 | 1.166 | 1.406 | 1.032 | 1.157 | 1.095 | 0.964 |
| OC6 | 0.382 | 0.515 | 3.199 | 0.680 | 0.933 | 0.339 | 0.857 |
| OC7 | 1.459 | 0.632 | 0.831 | 1.140 | 0.929 | 0.712 | 0.998 |
| OC8 | 1.853 | 0.686 | 0.810 | 0.917 | 1.047 | 0.690 | 0.496 |
| OC9 | 0.606 | 1.235 | 0.766 | 1.233 | 0.667 | 0.942 | 0.453 |
| OC0 | 0.509 | 0.684 | 3.104 | 1.881 | 0.675 | 0.506 | 0.000 |
Classification of sectors of economic activity, NACE Rev. 2, 2-dig
| Description | |
|---|---|
| A01 | Crop and animal production, hunting and related service activities |
| A02 | Forestry and logging |
| A03 | Fishing and aquaculture |
| B | Mining and quarrying |
| C10–C12 | Manufacture of food products, beverages and tobacco products |
| C13–C15 | Manufacture of textiles, clothing apparel and leather products |
| C16 | Manufacture of wood and of products of wood and cork, except furniture; manufacture of articles of straw and plaiting materials |
| C17 | Manufacture of paper and paper products |
| C18 | Printing and reproduction of recorded media |
| C19 | Manufacture of coke and refined petroleum products |
| C20 | Manufacture of chemicals and chemical products |
| C21 | Manufacture of basic pharmaceutical products and pharmaceutical preparations |
| C22 | Manufacture of rubber and plastic products |
| C23 | Manufacture of other non-metallic mineral products |
| C24 | Manufacture of basic metals |
| C25 | Manufacture of fabricated metal products, except machinery and equipment |
| C26 | Manufacture of computer, electronic and optical products |
| C27 | Manufacture of electrical equipment |
| C28 | Manufacture of machinery and equipment NEC (national electrical code) |
| C29 | Manufacture of motor vehicles, trailers and semi-trailers |
| C30 | Manufacture of other transport equipment |
| C31–C32 | Manufacture of furniture; other manufacturing |
| C33 | Repair and installation of machinery and equipment |
| D35 | Electricity, gas, steam and air conditioning supply |
| E36 | Water collection, treatment and supply |
| E37–E39 | Sewerage; waste collection, treatment and disposal activities; materials recovery; remediation activities and other waste management services |
| F | Construction |
| G45 | Wholesale and retail trade and repair of motor vehicles and motorcycles |
| G46 | Wholesale trade, except of motor vehicles and motorcycles |
| G47 | Retail trade, except of motor vehicles and motorcycles |
| H49 | Land transport and transport via pipelines |
| H50 | Water transport |
| H51 | Air transport |
| H52 | Warehousing and support activities for transportation |
| H53 | Postal and courier activities |
| I | Accommodation and food service activities |
| J58 | Publishing activities |
| J59–J60 | Motion picture, video and television programme production, sound recording and music publishing activities; programming and broadcasting activities |
| J61 | Telecommunications |
| J62–J63 | Computer programming, consultancy and related activities; information service activities |
| K64 | Financial service activities, except insurance and pension funding |
| K65 | Insurance, reinsurance and pension funding, except compulsory social security |
| K66 | Activities auxiliary to financial services and insurance activities |
| L68 | Real estate activities |
| M69–M70 | Legal and accounting activities; activities of head offices; management consultancy activities |
| M71 | Architectural and engineering activities; technical testing and analysis |
| M72 | Scientific research and development |
| M73 | Advertising and market research |
| M74–M75 | Other professional, scientific and technical activities; veterinary activities |
| N | Administrative and support service activities |
| O84 | Public administration and defense; compulsory social security |
| P85 | Education |
| Q | Human health and social work activities |
| R–S, T, U | Other service activities, activities of households as employers, activities of extraterritorial organisations and bodies |
Sectoral specialisation index, 2016
| Czech Republic | Denmark | Greece | Italy | Finland | United Kingdom | Switzerland | |
|---|---|---|---|---|---|---|---|
| A01 | 0.61 | 0.58 | 3.04 | 0.92 | 0.71 | 0.23 | 0.75 |
| A02 | 2.27 | 0.47 | 0.39 | 0.99 | 3.25 | 0.28 | 1.00 |
| A03 | 0.72 | 0.00 | 5.60 | 1.19 | 0.00 | 0.62 | 0.00 |
| B | 2.20 | 0.36 | 1.03 | 0.41 | 0.41 | 1.01 | 0.29 |
| C10–C12 | 1.10 | 0.94 | 1.52 | 0.94 | 0.66 | 0.58 | 0.76 |
| C13–C15 | 1.23 | 0.28 | 0.84 | 1.77 | 0.26 | 0.43 | 0.23 |
| C16 | 1.92 | 0.73 | 0.63 | 1.04 | 2.10 | 0.48 | 1.98 |
| C17 | 1.65 | 0.57 | 0.70 | 1.30 | 2.56 | 0.63 | 0.59 |
| C18 | 1.37 | 0.56 | 1.13 | 1.07 | 1.01 | 0.96 | 1.19 |
| C19 | 1.07 | 0.00 | 1.39 | 1.25 | 1.73 | 1.11 | 0.00 |
| C20 | 1.24 | 0.74 | 0.44 | 1.00 | 0.87 | 0.59 | 0.93 |
| C21 | 0.78 | 2.92 | 1.20 | 1.09 | 0.49 | 1.12 | 1.94 |
| C22 | 2.30 | 0.67 | 0.44 | 1.08 | 0.64 | 0.56 | 0.57 |
| C23 | 2.34 | 0.90 | 0.52 | 1.26 | 1.03 | 0.51 | 0.61 |
| C24 | 2.31 | 0.28 | 0.54 | 1.50 | 1.02 | 0.55 | 0.39 |
| C25 | 2.31 | 0.78 | 0.59 | 1.46 | 1.04 | 0.50 | 0.96 |
| C26 | 1.99 | 0.77 | 0.15 | 0.79 | 1.41 | 0.76 | 2.71 |
| C27 | 2.53 | 0.68 | 0.46 | 1.33 | 1.18 | 0.37 | 1.06 |
| C28 | 1.50 | 1.46 | 0.10 | 1.46 | 1.21 | 0.54 | 1.02 |
| C29 | 3.02 | 0.08 | 0.03 | 0.64 | 0.20 | 0.43 | 0.06 |
| C30 | 1.29 | 0.32 | 0.26 | 0.99 | 0.60 | 1.33 | 0.53 |
| C31–C32 | 1.57 | 0.71 | 0.53 | 1.27 | 0.50 | 0.59 | 0.66 |
| C33 | 1.33 | 0.62 | 0.33 | 1.13 | 1.20 | 1.25 | 0.56 |
| D35 | 1.44 | 0.74 | 1.10 | 0.79 | 1.01 | 0.85 | 0.86 |
| E36 | 1.34 | 0.52 | 1.02 | 0.87 | 0.56 | 1.07 | 0.18 |
| E37–E39 | 1.22 | 0.77 | 0.74 | 1.53 | 0.56 | 0.87 | 0.53 |
| F | 1.12 | 0.82 | 0.60 | 0.92 | 1.08 | 1.08 | 1.01 |
| G45 | 1.04 | 0.90 | 0.94 | 1.03 | 0.92 | 0.83 | 0.98 |
| G46 | 0.79 | 1.35 | 0.90 | 1.01 | 1.05 | 0.70 | 1.33 |
| G47 | 0.82 | 1.01 | 1.49 | 0.99 | 0.75 | 1.06 | 0.75 |
| H49 | 1.47 | 0.75 | 0.90 | 0.88 | 1.14 | 0.89 | 0.81 |
| H50 | 0.37 | 2.38 | 7.18 | 1.07 | 2.08 | 0.90 | 0.35 |
| H51 | 0.64 | 0.89 | 1.19 | 0.53 | 1.13 | 1.19 | 1.47 |
| H52 | 0.78 | 0.84 | 0.67 | 0.93 | 0.94 | 0.85 | 0.74 |
| H53 | 1.07 | 0.98 | 0.54 | 1.07 | 1.01 | 1.35 | 1.09 |
| I | 0.73 | 0.89 | 1.92 | 1.27 | 0.73 | 1.13 | 0.87 |
| J58 | 0.56 | 1.44 | 0.82 | 0.56 | 1.11 | 1.11 | 0.69 |
| J59–J60 | 0.95 | 1.45 | 0.78 | 0.70 | 0.94 | 1.65 | 0.95 |
| J61 | 1.05 | 0.89 | 1.54 | 0.98 | 0.93 | 1.17 | 1.18 |
| J62–J63 | 1.04 | 1.56 | 0.43 | 0.87 | 1.69 | 1.38 | 1.31 |
| K64 | 0.69 | 1.08 | 0.99 | 1.05 | 0.74 | 1.07 | 1.65 |
| K65 | 0.70 | 1.12 | 1.08 | 0.92 | 0.78 | 1.34 | 2.22 |
| K66 | 1.05 | 0.41 | 0.39 | 0.80 | 0.59 | 2.10 | 1.92 |
| L68 | 0.89 | 1.38 | 0.19 | 0.75 | 1.15 | 1.37 | 1.24 |
| M69–M70 | 0.60 | 0.75 | 1.16 | 1.22 | 0.76 | 1.29 | 1.57 |
| M71 | 0.95 | 1.25 | 1.26 | 1.26 | 1.80 | 1.23 | 2.02 |
| M72 | 1.06 | 1.00 | 0.34 | 0.70 | 2.29 | 0.98 | 1.37 |
| M73 | 1.39 | 1.12 | 0.80 | 0.66 | 1.06 | 1.38 | 1.04 |
| M74–M75 | 1.01 | 1.21 | 0.49 | 1.15 | 0.95 | 1.58 | 1.08 |
| N | 0.58 | 0.91 | 0.57 | 1.04 | 1.05 | 1.13 | 0.91 |
| O84 | 0.93 | 0.79 | 1.31 | 0.81 | 0.66 | 0.88 | 0.67 |
| P85 | 0.85 | 1.19 | 1.06 | 0.89 | 0.94 | 1.39 | 1.00 |
| Q | 0.63 | 1.61 | 0.54 | 0.74 | 1.54 | 1.20 | 1.27 |
| R–S–T–U | 0.81 | 1.23 | 0.76 | 1.04 | 1.36 | 1.26 | 1.14 |
Bagavos, C. (2019) ‘The importance of foreign migration for shifts in the size of the labour force of European countries: Methodological insights and contemporary evidence from SIRIUS and from selected non-SIRIUS countries’, Working Paper, No 2, SIRIUS Project.
Bagavos, C. (2022) ‘How Much Does Migration Affect Labor Supply in Europe? Methodological Insights and Contemporary Evidence from the European Union and Selected European Countries’, International Migration Review. Doi: 10.1177/01979183221115148
Coleman, D. (2008) ‘The demographic effects of international migration in Europe’, Oxford Review of Economic Policy, 24(3): 452–476.
Cully, M. (2011) ‘How much do migrants account for the unexpected rise in the labour force participation rate in Australia over the past decade?’, paper presented at the Australian Conference of Economists. Available at: https://www.homeaffairs.gov.au/research-and-stats/files/migrants-account-unexpected-rise-labour-force-rate-aus-2000-2010.pdf
European Commission (2020) Report from the Commission to the European Parliament, the Council, the Economic and Social Committee and the Committee of the Regions on the Impact of Demographic Change, Brussels COM(2020) 241 Final.
Eurostat (2021a) Population change: Demographic balance and crude rates at national level, Brussels: European Commission. Available at: https://ec.europa.eu/eurostat/cache/metadata/en/demo_r_gind3_esms.htm
Eurostat (2021b) Population on 1 January by age group, sex and country of birth, Brussels: European Commission. Available at: https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=migr_pop1ctz
Eurostat (2021c) Live births by mother’s age and country of birth, Brussels: European Commission. Available at: http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=demo_facbc&lang=en
Eurostat (2021d) Deaths by age, sex and country of birth, Brussels: European Commission. Available at: https://data.europa.eu/data/datasets/ukwszrhxhlwj9tpvcz5idq?locale=en
Eurostat (2021e) Immigration by age group, sex and country of birt, Brussels: European Commission. Available at: https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=migr_imm3ctb&lang=en
Eurostat (2021f) Emigration by age group, sex and country of birth, Brussels: European Commission. Available at: https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=migr_imm3ctb&lang=en
Eurostat (2021g) Population by sex, age, country of birth and labour status, Brussels: European Commission. Available at: https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=lfsa_pgacws&lang=en
Hilgenstock, B. and Kóczán, Z. (2018) ‘Storm clouds ahead? Migration and labor force participation rates in Europe’, IMF Working Paper, WP/18/148.
MacKellar, L.F. and McNicoll, G. (2019) ‘International migration: Approaches, issues, policies. Introduction to the online collection of articles’, Population and Development Review, 45: e12246. https://doi.org/10.1111/padr.12246
Murphy, M. (2016) ‘The impact of migration on long-term European population trends, 1950 to present’, Population and Development Review, 42(2): 225–244.
Pesaran, M.H., Schuermann, T. and Weiner, S. (2004) ‘Modeling regional interdependencies using a global error correcting macroeconometric model’, Journal of Business and Economic Statistics, 22(2): 129–162.
Salzmann, T., Edmonston, B. and Raymer, J. (eds) (2010) Demographic Aspects of Migration, Wiesbaden: VS Verlag für Sozialwissenschaften.
Spielvogel, G. and Meghnagi, M. (2018a) ‘The contribution of migration to the dynamics of the labour force in OECD countries: 2005–2015’, Working Paper, No. 203, OECD Social, Employment and Migration Working Papers.
Spielvogel, G. and Meghnagi, M. (2018b) ‘Assessing the role of migration in European labour force growth by 2030’, Working Paper, No. 204, OECD Social, Employment and Migration Working Papers.
van Nimwegen, N. and van der Erf, R. (2010) ‘Europe at the crossroads: Demographic challenges and international migration’, Journal of Ethnic and Migration Studies, 36(9): 1359–1379.
