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The 2008 Trajectories and Origins survey once again provides the basis for an original study of the socio-economic integration of migrants in France. In this article, the aim is not to compare immigrants to the rest of the population in France but to examine why some have better access to employment and earnings than others. The authors look at the role of pre-migration skills by differentiating ‘professional’ skills from those not specific to a particular occupation, such as language skills, and ask which ‘travels’ best. Their findings provide valuable insights for public policies regarding migrants’ inclusion in the labour market.

1Since the 2000s, French migration policy has sought to address the twin challenges of achieving better quantitative control of migration flows and establishing a more selective policy designed to achieve better integration of the immigrant labour force. For example, less than 3 years after the passing of the immigration control Act of 26 November 2003, which was mainly designed to stop clandestine immigration, the government deemed it necessary to further reform the legislation on right of asylum and the entry and residence of foreign nationals. This resulted in the immigration and integration Act of 24 July 2006. Its main aim was to adapt immigration flows to France’s absorption capacity and economic needs and to replace ‘imposed’ immigration with ‘chosen’ immigration. But questions soon emerged as to how well the ‘chosen’ immigrants were able to integrate into the labour market and especially what role might be played by a migrant’s pre-migration human capital, depending on its degree of transferability.

2In this regard, numerous studies have shown that the probability of finding work, and the (relative) earnings from that work both depend on the immigrant’s initial skills (see OECD, 2014 for a full overview of the literature). ‘Skills’ can be defined as ‘the bundle of knowledge, attributes, and capacities that can be learned and that enable an individual to successfully and consistently perform an activity or task, whether broadly or narrowly conceived, and can be built upon and extended through learning’ (OECD, 2012). So to design a policy on immigration and integration, one must identify the skills immigrants bring to the host country and how the labour market makes use of them. Studies that have addressed this issue find that, on average, immigrants are less likely to be in employment than the native-born (Algan et al., 2010; Breem, 2013; OECD, 2018) and more likely to be overqualified for the job they have (Duleep and Regets, 1997; Chiswick and Miller, 2008; Beckhusen et al., 2013; Hirsch et al., 2014; Fortin et al., 2016; Lê and Okba, 2018). It therefore seems important to measure the extent to which migrants’ pre-migration human capital is underused in France and consider how best to develop an immigration and integration policy that will limit this waste as far as possible.

3Lack of data is often a major obstacle for research on immigrant integration. Usually, the available data (from population censuses or employment surveys) concern only the immigrant’s situation in the host country. They include very little information on the immigrant’s job market status, income, or level of education before they arrived. The skill indicator most often used in the literature is educational level, but this only partly reflects a person’s stock of human capital as it takes no account of where they got their education or their work experience in their country of origin. In France, the Trajectories and Origins survey (TeO, INED–INSEE, 2008) can be used to reconstruct immigrants’ employment and education situations before they migrated.

4This article contributes to the literature on immigrant integration by using TeO survey data to introduce a more precise measure of skills acquired in the countries of origin and examine the impact of those skills on the quantitative and qualitative aspects of integration. Access to employment represents the quantitative aspect, while integration quality is reflected in earnings, how well the type of work obtained in the host country matches the ‘imported’ human capital, and the overqualification that can result from a mismatch. [1] We start from classic models of human capital accumulation (Becker, 1964; Mincer, 1974) to focus on the role of professional skills (those that can only be used in a particular occupational setting) and general skills (those that are useful in looking for work and adapting to the norms of a new society and labour market, and which can be readily transferred from one sector to another).

5The aim here is to assess the chances of success of an immigrant who has not acquired additional education in the host country and to look at pre-migration factors that may foster and improve a migrant’s integration into the host country’s labour market.

I – Main factors for migrants’ economic integration: review of the literature

6Numerous studies conducted since the early 1980s have looked at migrants’ economic performance in host countries. Inclusion in the labour market is an important aspect of integration and has been widely studied. One reason for the interest in this issue is the high private and social costs of unemployment and overqualification among immigrants. Some studies have sought to measure the probability of finding work in the host country. A much larger proportion, however, have examined the labour market’s capacity to provide jobs that match immigrants’ professional skills. Whichever approach is used, the studies find that integration takes time and that in the short term immigrants are ‘penalized’ either in terms of access to employment or in terms of the ‘quality’ of their job compared to the native-born (Fougère and Safi, 2009; Fullin and Reyneri, 2011; Ballarino and Panichella, 2015). For the long term, Chiswick’s seminal article of 1978 found that immigrants in the United States were able to close the earnings gap with native-born workers within 10 to 15 years, but this optimistic conclusion aroused considerable controversy. The initial results proved unstable once account is taken of cohort effects (Borjas, 1985, 1995; Antecol et al., 2006), panel data (Hu, 2000; Lubotsky, 2007; Kaushal et al., 2016) or the selective nature of return migration (Edin et al., 2000; Constant and Massey, 2003). The most recent research shows that the earnings gap between migrants and native-born citizens does tend to shrink with length of residence. When short- and long-term effects are combined, one gets a U-shaped curve for immigrants’ integration in the job market. At first, immigrants are obliged to take work for which they are overqualified, but with time they manage to get better-paid jobs. The best-educated and those who held top-level jobs in their countries of origin seem to catch up the fastest (Rooth and Ekberg, 2006; Chiswick and Miller, 2009; Hirsch et al., 2014). However, even when they have been in the host country for a long period, immigrants seem to only partly catch up with the native-born in terms of earnings (Dustmann and Glitz, 2011).

7The economic literature has concluded that immigrants’ human capital gives a lower yield than native-borns’, in terms of access to employment, earnings, and a job that matches their skill profile (Aleksynska and Tritah, 2013). However, this finding is more or less marked depending on the host country, the migrant’s sociodemographic profile, and their reason for coming to the host country. A high proportion of publications on immigrant integration therefore focus on the reasons for this difference in yield. They identify several factors that contribute to the frequency of mismatch between skills and employment.

1 – Specific national or regional features of human capital

8This obviously covers language (Borjas, 1994, Chiswick and Miller, 2010) and qualification subject area (law, medicine, literature, etc.), but it also includes such assets as knowledge of how the job market and administrative system function (Ballarino & Panichella, 2015). Thus in the host country, immigrants find it difficult to use some of the skills they acquired in their country of origin. For example, difficulties can arise in the host country from implicit or explicit non-recognition of foreign qualifications. Such limitations on the country-to-country transferability of human capital make it more likely that an immigrant will find himself or herself in a job requiring a lower educational level than the one they have. Also, there are geographical variations in the skill qualities acquired at a given educational level: the training provided in developed countries adapts more easily to technological progress (Kiker et al., 2000). As a result, the observed overeducation among immigrants (i.e. the gap between a person’s educational attainments and that required for the job they are doing) correlates with the development gap between their country of origin and the host country (Sanroma et al., 2015; Fortin et al., 2016).

2 – Asymmetry of information in the labour market

9This applies to both employers and immigrant workers, and it adds to the likelihood of a mismatch between skills and employment. On the employers’ side, information asymmetry leads to a failure to acknowledge the immigrant’s professional experience. Immigrants may therefore have to start by accepting jobs they are overqualified for, so as to demonstrate their real productive capacities to local employers and later move up the occupational ladder (‘search and match theory’ in Chiswick and Miller, 2009). This particularly applies to newly arrived immigrants and those from countries whose institutions and labour markets are very different from those of the host country (Chiswick, 1978; Kogan, 2007). Immigrants, for their part, face greater challenges in accessing information about available jobs and the nature of those jobs than do the native-born. This is not just because immigrants possess less of the host-country-specific human capital that would enable them to gather and assimilate such information. It is also because, on average, they have a smaller network of social and professional contacts who could help them get into the job market (Hellerstein et al., 2008; Patacchini and Zenou, 2012), especially upon arrival. So, overall, immigrants have less choice than native-born workers and less knowledge on which to base their decisions.

3 – Liquidity constraints

10The availability of ready money is a common issue when living standards in the country of origin are low and the host country provides little or no welfare assistance to immigrants when they arrive. In most cases, the immigrant faces an urgent necessity of finding a job to cover their essential needs and will accept a job that is quickly available but most often low-skilled and poorly paid (Dustmann, 2000).

11There is also, of course, discrimination (Baert et al., 2017; Meurs, 2017; Beauchemin et al., 2018) and administrative barriers that can slow down the process of obtaining a work permit for some worker categories or make hiring immigrants a complicated process (Havrylchyk and Ukrayinchuk, 2017).

12Furthermore, the profiles of immigrants who arrive in host countries are shaped by a dual process of selection. On the one hand, there is a self-selection in that immigrants make rational decisions to migrate or not, comparing the costs and benefits of each choice for each possible destination. Studies of this question point out that the migrant’s profile plays a large part in the decision to migrate (Borjas, 1987, 1999; Brucker and Trubswetter, 2007; Haberfeld and Lundth, 2014). The self-selection phenomenon is more or less marked depending on the reason for migration (work, family, or humanitarian reasons). On the other hand, a growing number of host countries are restricting immigration, accepting immigrants based on more or less selective criteria, setting quotas, or allowing entry on the basis of the migrant’s skills or financial resources (Aydemir, 2013; Bianchi, 2013; Bertoli et al., 2015). A good illustration of this selection policy is the EU Blue Card, which is recognized in France. [2]

13All these factors (educational attainment and study field, quality and sector of work experience, degree of proficiency in the language, country of origin, reason for migrating, length of residence, and size of a social network) are of prime importance for analysing immigrants’ integration into the labour market in France. This study follows on from earlier ones, but it does not set out to compare situation of migrants with that of the native-born, a subject widely covered by the literature. Instead, it looks at those pre-migration factors that are conducive to integration in the host country’s job market. The career path the immigrant adopts in the short term has a powerful impact on his or her future economic integration. For immigration policy, it is important to find short-term measures that will help guide the immigrant’s career in the right direction as soon as they arrive. To do that, it is essential to understand what kind of pre-migration human capital has most value in the host country labour market. This study makes the distinction between professional skills (which can only be used in a particular professional context) and general skills (common to most occupations) and looks at their respective roles, not only in terms of access to employment but also job quality as reflected in earnings and social and professional recognition. The point is to understand whether the (presumably desired) match between employment and skills is valued in terms of earnings in the job market.

II – Data

1 – A subsample from the TeO survey

14This analysis is based on the TeO survey conducted jointly by INED and INSEE in 2008. The survey’s purpose was to identify the impact of origin on the living conditions and social trajectories of migrants in France. It was based on a usable sample of 21,761 individuals aged over 18, of whom 5,645 were immigrants who had arrived in France after age 16. [3] It is the only survey available that can be used to precisely measure professional skills acquired before migrating, by skill sector. It gives information not only about the educational qualifications attained abroad but also the field of study; these two items are particularly useful for constructing indicators of pre-migration skills.

15The aim is to isolate the impact of the human capital accumulated before arriving in France on an immigrant’s occupational success and to assess the chances of success in the host country’s labour market of an immigrant who has had no additional education. This study does not examine the interaction between pre-migration skills and those acquired through education or training in the host country. It only concerns migrants who completed all their education abroad. This gave a sample of 4,710 individuals aged 18 to 60 (after students had been withdrawn from the sample), amounting to 84% of those immigrants in the TeO survey who arrived in France after age 16. [4]

16Table 1 describes the main socio-economic characteristics of the immigrants in the sample. [5] The average age of the immigrants was 43.1 and their average length of residence in France was 16.9 years. Women made up slightly more than half the sample. Family immigration (spouses of French nationals and family reunification) was the most common reason for immigrating (33.8%). Immigration for work accounted for almost a quarter of the sample (23.9%). Migrants from Africa were the most numerous (43.7%), followed by Europeans (32.1%) and Asians (17.6%). Most of the immigrants had fairly low levels of education; only 21.7% had educational attainments beyond secondary school. Of those who had worked in their home countries (63.9% of the sample), only 11.2% had been in unskilled jobs. At the time of the survey, 62.1% of immigrants were in employment in France, but over 22% were in unskilled jobs. This supports the hypothesis of overqualification following migration.

2 – Defining general skills and professional skills

17Using the TeO survey, we identified skills directly linked to a specific professional sector, [6] either through the person’s work experience or through their field of study. General skills are harder to measure. They include such things as knowing how to look for and process information, analyse and solve problems, work independently or in a team, deal with the unexpected, assess results and make decisions, present ideas, argue, etc. Computer and language skills also count, of course. Knowledge of the host country’s language is probably the most important capital of all, valuable not only for finding information in the host country but also for making other skills more transferable from one country to another, thus favouring job market integration (Chiswick and Miller, 2010). The TeO survey data give a direct measurement of the language skills the respondents possessed on arrival in the host country, through an indicator combining self-assessed levels of understanding, reading, writing, and speaking. [7] For other kinds of general skills, which are harder to measure, two proxies were used: educational attainment (EA) and socio-occupational category (SOC). [8] We assumed that acquisition of general skills correlates strongly with these two indicators. One can readily imagine that it is easier for a highly qualified person, whatever their field of study, to find useful information in a job search and to adapt to a job not directly connected with their skill sector.

3 – Measuring the match between skills and employment

18Another important question is that of the quality of economic integration. This was measured by analysing the match between the immigrant’s profile and that of the job they currently had. Measuring overqualification raises the question of the reference situation. The educational level attained abroad and the SOC of pre-migration employment do not necessarily always match. Some people may not have found work on a level with their qualifications; others may have made professional progress and held jobs above their qualifications before migrating. A person may also have obtained a qualification but never worked, or the reverse. Three indicators were thus constructed, comparing:

  1. educational level achieved abroad with SOC of current job (SOC/EA overqualification);
  2. pre-migration SOC with current SOC (SOC/SOC overqualification);
  3. pre-migration skill sector with skill sector of current job (skill sector match).

Table 1

Socio-economic profiles of the immigrants in the sample

Table 1
Socio-economic characteristics Number of respondents % (weighted data) Average age 43.1 years Average length of residence 16.9 years Reason for immigrating Work 1,093 23.9 Spouse of French national 766 16.9 Family reunification 846 16.9 Refugee 638 9.9 Other 1,367 32.4 Sex Female 2,592 53.4 Origin Europe 1,419 32.1 Africa 1,947 43.7 Asia 1,105 17.6 Other 239 6.6 Educational attainment Upper secondary or less 3,748 78.3 2 years’ higher education 283 5.4 More than 2 years‘ higher education 679 16.2 In work Yes 3,044 62.1 Pre-migration SOC (a) Unskilled 457 11.2 Skilled manual or clerical (b) 1,290 29.2 Intermediate occupation 773 16.3 High-level occupation 370 7.2 Did not work 1,821 36.1 Current SOC Unskilled 1,114 22.3 Skilled manual or clerical 1,273 24.9 Intermediate occupation 410 8.0 High-level occupation 246 5.9 Is not in work 1,667 38.8 Level of French Poor 3,774 79.5 Presence of child or children in the household Yes 3,678 77.1 Member of an association Yes 938 18.2 Region of residence Île-de-France 1,895 41.3

Socio-economic profiles of the immigrants in the sample

Table 1

(cont‘d). Socio-economic profiles of the immigrants in the sample

Table 1
Socio-economic characteristics Number of respondents % (weighted data) Mean wage (in euros) Unskilled 1,135 Skilled manual or clerical 1,347 Intermediate occupation 1,919 High-level occupation 3,011 Share of friends from same origin Over 50% 1,470 29.47 Have met friends within the past 2 weeks No 859 18.7 Sample size 4,710 100.0

(cont‘d). Socio-economic profiles of the immigrants in the sample

(a) The International Standard Classification of Occupations (ISCO) was used to define the SOCs.
(b) The ‘skilled’ category includes manual and clerical workers at the same level on the socio-occupational scale. According to Articles 2 and 3 of the Act of 3 July 1978 on employment contracts, the difference between manual worker contracts and clerical worker contracts is based solely on the manual or clerical nature of their work. The distinction is of no importance for our study.
Source: TeO survey; authors’ calculations.

19SOC/EA match is assessed by a normative analysis based on the use of an ‘objective’ criterion: the educational level required for certain job categories in a classification of occupations. Other authors have used this method to construct indicators of a match between EA and the current job’s ranking in the occupation scale. Examples are the O*NET database (Chiswick and Miller, 2010) and comparisons between the International Standard Classification of Education and the International Standard Classification of Occupations, such as those regularly produced by the OECD. The equivalence scale used for this study is shown in Table 2. Two other methods are used in the literature. The first, more subjective, is based on workers’ self-assessment of the degree of match between their EA and what their job actually requires (Duncan and Hoffman, 1981). The other is purely statistical, based on comparing the mean or modal EA of workers in each occupation (corrected by standard deviation) with that of the individual (Verdugo and Verdugo, 1989; Kiker et al., 2000), reflecting the process of adjusting to the labour market. The drawback of this last method is that it is sensitive to fluctuations in job market vacancies. For example, in periods of high unemployment, a broader trend towards overqualification can be observed, pulling the mean or modal educational level downwards: this is because highly educated people cannot find the jobs they want and accept less skilled ones. If an immigrant’s EA matches this mean or modal level negatively affected by an economic downturn, one may mistakenly conclude that the person is not overqualified for the job. The opposite may occur in periods of labour shortage. The method used here is sensitive neither to variations in individuals’ perceptions (as with self-assessment) nor to variations in employment opportunities.

Table 2

Equivalences used to construct the EA/SOC overqualification variable

Table 2
Educational attainment (EA) Socio-occupational category (SOC) Less than upper secondary certificate Unskilled Upper secondary certificate Skilled manual or clerical Two years’ higher education Intermediate occupation More than 2 years’ higher education High-level occupation

Equivalences used to construct the EA/SOC overqualification variable

20Regarding the SOC/EA and SOC/SOC comparisons, binary variables were constructed with a ‘Yes’ value if there is a match or an upgrading on the occupational ladder and a ‘No’ value for overqualification (a job at a lower skill level or no job at all). Having no job in the host country is routinely counted as overqualification. This is based on the principal that anyone, even someone with no educational qualification and no work experience, is eligible for some job, since there is a multitude of very low-skilled jobs in the economy. One can hardly classify an unemployed immigrant who already had no job and no qualifications in their home country as having an occupation match (and therefore as economically integrated). [9]

21Skill sector match is measured by a binary variable indicating whether an immigrant’s occupation in the host country matches the professional skills they acquired in the country of origin. For those without work experience before migrating, their professional skill corresponds to their field of study. No account is taken of SOC. For example, for a primary care doctor now working as a nurse, the skill sector match variable will get a ‘Yes’ value. Although they are overqualified, their having found a job in the health sector reflects some degree of recognition of their ‘health’ skills.

III – Methodology

22The econometric approach adopted involves three stages. First, one estimates the probability of being in work, for the immigrant population, using a logit-type model:

24where the Jobi variable is dichotomous (1 if the person is in employment, otherwise 0). The Profi variable refers to the professional skills of an individual i, according to their work experience in their country of origin or, if they have never worked, their field of study. The Geni vector represents general skills, measured in terms of knowledge of the language, educational level, and pre-migration job status. It is assumed that general skills will have a positive effect on employability and that professional skills will have more varied effects because such skills may be more or less transferable depending on their characteristics.

25In some professional sectors, poor employability may be due not only to a low level of skill transferability but also to a lack of employment opportunities in that sector. To measure the effect of professional skills independently of the impact of employer demand, a control is performed using the regional level of theoretical job opportunities for each professional sector. For this, we constructed an indicator of opportunities for employment (OE): [10]

27where Eij is the total number of jobs in sector i in region j and Oij is the number of people possessing the skills of sector i in region j. One advantage of this indicator is that it is symmetrical. It ranges from −1 to 0 if job opportunities are poor and from 0 to 1 in the contrary case. The 0 value applies when supply and demand are exactly equal.

28Based on the literature (Section II), we incorporated into the first model some additional control variables describing the immigrants’ personal characteristics. We added age, sex, the presence of a child in the household (which can affect people’s cost–benefit assessments in their employment decisions), length of residence, origin, degree of sociability (friends, association membership), reason for migrating, dependence on ethnic networks (using as indicator the percentage of friends of the same origin), but also place of residence (with two values: Île-de-France, where the job market is considered most buoyant, vs. all other regions). We expect a significant positive effect for age (it also reflects the impact of total length of work experience, which cannot be directly measured from the survey data), length of residence (which should correlate with post-migration work experience), the sociability indicators, and indicators of a favourable job market. By contrast, we expect a negative effect from dependence on ethnic social network, which may be associated with a lesser investment in accumulating human capital in the host country (Duleep and Regets, 1999).

29The next step was to consider the ‘qualitative recognition’ of the individual’s human capital in the French labour market. We estimated two models, testing successively the probability of not being overqualified after migration and the probability of a skill sector match:

31where RECOGi, the variable of interest here, shows:

  • no downgrading versus overqualification, based on comparing either current SOC with pre-migration SOC (Model 2), or current SOC with pre-migration EA (Model 3);
  • or the match versus mismatch between the pre- and post-migration skill sectors (Model 4). [11]

32The first two groups of models (Equations 1 and 2) are based on logistic regression, so results can be interpreted in terms of odds ratios (OR).

33Lastly, we measured the impact on salary of skills acquired abroad and the degree of match between professional skills and the characteristics of employment in the host country. To account for labour market participation or non-participation, we estimated an earnings equation with a Heckman-type selection model (Heckman, 1978), using the maximum likelihood method (Nawata and Nagase, 1996): [12]

34Participation equation

36Earnings equation

38where Jobi is the selection variable equal to 1 if the person has a positive waged income; ui and εi both have a mean of 0, with standard deviations of 1 and δ respectively and with correlation ρ.

39To take into account the characteristics of the job and of the company, both of which can make wages fluctuate considerably, we added some additional control variables to the objective equation (Equation 3): number of employees, current SOC, type of work schedule, working time, and industry sector.

IV – Impact of pre-migrations skills on immigrants’ economic integration [13]

1 – Impact on access to employment

40Table 3 shows the results of the model testing immigrants’ probability of being in work (Equation 1). After controlling for personal and local characteristics, our findings confirm the importance of proficiency in French, which facilitates access to the job market. It multiplies the chances of being in work by more than 1.5 compared to someone with poor mastery of the language. The table also shows that it is the person’s pre-migration professional skill sector and not their EA as such that affects the probability of being in work. The coefficients associated with EA are non-significant: those with the highest educational qualifications do not perform better than the less educated in finding employment. By contrast, professional skills acquired before migrating (either by work experience or by training or education) seem to be worth more in the job market, but to widely varying degrees depending on the field of specialization. Some occupations transfer well, while others show no significant difference compared to zero professional skills. For example, people specialized in electricity and electronics have 1.2 times more chance of being in employment than those specialized in hotels and catering, and 2.6 times more than those with no professional skills or non-transferable skills (the coefficients for this last group are not significantly different from those from the ‘no skills’ reference group). Employability is also high for professionals in health, personal services, and social work.

41For the control variables, overall, the results are also as expected. For example, the chances of being in employment decline with age (1.02 less for every additional year of working) but increase with length of residence in France (1.02 times more chance for every additional year of residence). Men are 3.2 times more likely than women to be in work. Origin is also a significant factor: Europeans are more likely to be in work than other nationalities. This may be because, for example, skills are more easily transferable between European countries, which are closer in culture. The reason for migrating also affects the probability of being in work. Those who migrated for work reasons are, on average, 1.4 times more likely to be in employment than those who came for family reasons. Living in the Île-de-France region multiplies employability by 1.68.

42Lastly, a migrant’s skill capital plays a predominant role in their labour force integration. Figure 1A shows the impact of language skills and some professional skills on the probability of being in work, by length of residence. [14] Professional skills have more impact than language skills. For an immigrant with readily transferable professional skills, e.g. in the health sector, the difference in terms of probability is 13 points higher than for a migrant with no professional skills or only poorly transferable ones. This gap persists over time: it is 11 points after 35 years’ residence. This shows that poor transferability of skills tends to persist and affect the immigrant’s long-term economic integration. As to linguistic skills, arriving in France with a good level of French increases employment probability by only 5.1 points. This effect is slightly weaker (4.4 points) for those with transferable skills and shrinks slightly more quickly with length of residence. Although it does not have as much impact as transferable skills, language proficiency plays a slightly greater role in the integration process for those who have no professional skills of value in the job market.

Table 3

Characteristics affecting the probability of having a job and the probability of not being overqualified for current employment (logit binomial model)

Table 3
Model 1 Model 2 Model 3 Model 4 Probability of Probability of not being overqualified: being in work: Yes vs. No In work vs. Not in work SOC/SOC SOC/EA Probability of skill sector match: Yes vs. No OR p value OR p value OR p value OR p value Pre-migration professional skills (a) (Ref.: None, Models 1–3; Ref.: Transport, Model 4) Electricity, electronics 2.65* .09 1.37*** < .01 0.81 .80 1.79 .91 Personal services 2.48** .03 1.24*** < .01 1.16 .17 3.89 .83 Health 2.45** .01 0.63 .51 1.23** .05 5.03 .81 Social worker 2.29* .06 0.74 .18 0.92 .80 0.61 .91 Hotels and restaurants 2.13* .09 0.54 .79 1.21* .09 1.51 .93 Environment 2.05 .55 1.31* .08 1.08 .61 0.57 .96 Driver 1.90 .51 1.78*** < .01 0.99 .65 1.96 .91 Manufacturing 1.71 .40 1.07*** < .01 0.95 .49 0.63 .98 Teaching 1.67 .63 0.17*** < .01 0.87 .97 0.61 .97 Administration 1.66 .49 0.41** .02 0.65** .05 1.12 .96 Art 1.64 .68 0.44 .19 1.30** .03 2.14 .89 Police, army, security 1.37 .77 0.27** .12 0.57 .17 0.59 .96 Commerce 1.37 .46 0.17*** < .01 0.84 .86 0.73 .99 Agriculture 1.25 .28 0.31*** < .01 0.99 .39 0.54 .95 Age Number of years 0.98*** < .01 0.97*** < .01 0.99*** < .01 1.01 .45 Years in France Number of years 1.02*** < .01 1.03*** < .01 1.01** .02 0.99** .04 Job opportunities Coefficient between –1 and 1 0.92 .53 1.09 .52 1.07 .63 1.69*** < .01 Level of French (Ref.: Good) Poor 0.66*** < .01 0.72*** < .01 0.73*** < .01 0.89 .33 Education (Ref.: Upper secondary or less) More than 2 years’ higher education 0.92 .48 1.67*** < .01 0.30*** < .01 2.09*** < .01 2 years’ higher education 1.06 .33 1.30 .98 0.19*** < .01 1.41 .89 Sex (Ref.: Female) Male 3.17*** < .01 2.49*** < .01 2.35*** < .01 1.26* .07 Presence of children (Ref.: Yes) No 1.15* .08 1.05 .54 1.13 .11 1.09 .45 Origin (Ref.: Europe) Africa 0.60** < .01 0.51*** < .01 0.53*** < .01 0.71*** < .01 Asia 0.65* .09 0.68 .46 0.66* .09 0.83 .33 Other 0.77 .75 0.77 .55 0.88 .15 1.24* .05

Characteristics affecting the probability of having a job and the probability of not being overqualified for current employment (logit binomial model)

Table 3

(cont‘d). Characteristics affecting the probability of having a job and the probability of not being overqualified for current employment (logit binomial model)

Table 3
Model 1 Model 2 Model 3 Model 4 Probability of Probability of not being overqualified: being in work: Yes vs. No In work vs. Not in work SOC/SOC SOC/EA Probability of skill sector match: Yes vs. No OR p value OR p value OR p value OR p value Reason for migration (Ref.: Work) Spouse of French national 0.73* .06 0.67** .02 0.63** .03 0.84 .75 Refugee 1.02** .02 0.80 .91 0.72 .86 0.70 .11 Family reunification 0.73* .08 0.69* .06 0.63** .03 0.65* .05 Other 0.73** .03 0.84 .33 0.73 .97 1.28*** < .01 Have met friends in the past 2 weeks (Ref.: Yes) No 0.85* .078 0.89 .19 0.85* .07 0.99 .95 Share of friends from same origin (Ref.: > 50%) Fewer 1.22** .01 1.20** .02 1.25** < .01 0.99 .91 Member of an association (Ref.: Yes) No 0.94 .49 0.99 .94 0.97 .68 1.03 .80 Region of residence (Ref.: Other) Île-de-France 1.68*** < .01 1.45*** < .01 1.59*** < .01 1.00 .99 Number of observations 4,710 4,710 4,710 3,576 General R2 (b) .17 .20 .19 .23 Overall significance test / Wald < .01 < .01 < .01 < .01

(cont‘d). Characteristics affecting the probability of having a job and the probability of not being overqualified for current employment (logit binomial model)

(a) To simplify the table, under ‘pre-migration professional skills’, only those sectors with significant coefficients have been included. For all results, see online appendix (in French):
(b) Cox and Snell (1989).
***p < .01. **p < .05. *p < .10.
Source: TeO survey; authors’ calculations.

2 – Impact on overqualification and skill sector match

43Table 3 shows the results of estimating the three overqualification indicators defined above. The likelihood of being overqualified (comparing pre-migration and post-migration SOC) varies widely according to type of professional skill (Model 2). Possessing certain professional skills improves the chances of upward occupational mobility or at least reduces the likelihood of downgrading. For example, former drivers were among those with the highest odds of an SOC/SOC match (1.78). It is also worth estimating differences in the likelihood of overqualification according to professional skill and comparing the ORs obtained. For people with skills in the administration field, the risk of being overqualified for their job in France was about 3.4 times greater than for people with skills in electronics or environment, but 2.3 times less than for teachers or salespeople. So pre-migration professional skills in some occupations are clearly more useful in France in terms of upward job mobility, whereas in some other occupations immigrants run a high risk of being downgraded compared to their pre-migration SOC.

44There are also differences between professions as regards French employers’ recognition of pre-migration EA (Model 3). But fewer sectors were concerned than in Model 2, and not the same ones. If we look only at the role of EA, without taking work experience into account, having qualifications in the fields of health, hotels and restaurants, or the fine crafts seems to reduce the risk of overqualification.

45It might be thought that skill sector match would be better in sectors where pre-migration SOC and EA are better recognized. But the results of Model 4 do not confirm this. They suggest there is no significant difference between sectors. However, skill sector match is very significantly better where there are good local employment opportunities for a particular profession (with 1.69 times better odds than where there is a shortage of vacancies for particular skills).

46Although EA did not have a significant impact on the probability of being in employment, it did affect the quality of the work. For example, highly qualified people had twice as much chance of having a job in their original skill sector, although not necessarily in their previous SOC. This is partly because some professionals, such as qualified doctors (whether or not they have already worked in that capacity), will find work in the health sector but in less highly qualified jobs.

47High EA can also reduce the probability of being overqualified when the measurement is based on a comparison of pre- and post-migration SOCs (dividing the measurement by 1.7 for those with higher-education qualifications compared to an upper secondary school certificate or less [Model 2]), but it is the reverse if overqualification is measured by the gap with the educational level (Model 3). This seems to suggest that the French labour market recognizes an immigrant’s training better if it is ‘confirmed’ by a corresponding work experience. French employers do not readily acknowledge educational qualifications earned abroad: overall, degrees and diplomas do not ‘travel’ as well as working know-how and experience.

48Linguistic skills do improve job quality in terms of not being overqualified (Models 2 and 3) but do not seem to affect skill sector match (Model 4).

49Analysis of marginal effects confirms the predominant role of professional skills compared to general skills (language proficiency and educational level) but shows that the impact of professional skills varies more widely. For example, the probability of being in the same SOC before and after migration is 22 points higher for people with skills in electronics than for teachers (Figure 1B), and this gap gets wider with length of residence. Not only do electronics specialists have a greater likelihood of getting a better-quality job, they also climb the career ladder faster. The trend is similar, but the differences slightly smaller, for the impact of professional skills on not being overqualified compared to EA (Figure 1C). By contrast, although language level always has a significant impact, its marginal impact on the probability of a skill match is much weaker (Figures 1B and 1C). For immigrants with transferable skills, there is a difference in probability of 3 points between those with good mastery of French and those with poor mastery; but for immigrants with poorly transferable skills, such as in teaching, the difference is only 1.7 points.

Figure 1

Marginal effects of pre-migration skills by length of residence

Figure 1

Marginal effects of pre-migration skills by length of residence

Reference: A 30-year-old woman of African origin, with children and a French spouse, speaking French poorly, with an upper secondary school certificate or less, living outside the Île-de-France region, and with a social circle of whom more than 50% are of the same origin.
Source: TeO survey; authors’ calculations.

50Unlike the previous models, which showed upward occupational mobility with length of residence in France, the probability of a skill sector match is negatively affected. The likelihood of such a match declines over time but remains high for the best-educated. The marginal impact of EA on the probability of a skill sector match (Figure 1D) is greater, with differences of about 8.8 points, between those with higher-education qualifications and those with an upper secondary school certificate or less. This gap increases slightly with length of residence, reaching 9.4 points after 35 years.

51The analysis of marginal impacts shows that professional skills have a significantly greater impact than general skills, not only in terms of access to work, but also in terms of upward mobility. All in all, our findings show that pre-migration professional skills (work experience and know-how) are of greater value in the French labour market than are general skills acquired before migration. Linguistic skills make professional skills more transferable, but the effect is modest.

3 – Impact on earnings

52To complete our qualitative analysis of economic integration, we now consider the pay awarded for skills acquired before migration, seeking also to measure the impact of a downgrade in SOC between country of origin and host country in terms of earnings. Table 4 shows the model’s objective equation results. We tested for the impact on immigrants’ earnings of all three indicators of mismatch: overqualification compared to the pre-migration SOC (Model 5), overqualification compared to EA (Model 6), and skill sector mismatch (Model 7).

53After controlling for the characteristics of employer company, job, and immigrant’s socio-demographic profile, the table shows that overqualification in terms of SOC reduces earnings by 8% (Model 5). This means that, all else being equal, those who manage to get their pre-migration working experience acknowledged in France earn more than those who have to accept a job for which they are overskilled. On the other hand, finding a job that matches one’s educational level has no impact on pay (Model 6), which once again highlights the poor level of recognition given to foreign educational qualifications in France. Lastly, the table shows that a skill sector match has a significant positive impact on an immigrant’s pay, amounting to 6% higher earnings (Model 7).

Table 4

Impact of pre-migration skills on earnings, MLE selection model(a)

Table 4
Model 5 Model 6 Model 7 Coefficient p value Coefficient p value Coefficient p value Constant 6.93*** < .01 6.84*** < .01 6.69*** < .01 Ln of length of residence in France (b) Ln of number of years 0.002*** < .01 0.002*** < .01 0.002*** < .01 Overqualification SOC/SOC (Ref.: Yes) No −0.06*** < .01 Overqualification SOC/EA (Ref.: No) Yes −0.01 .56 Skill sector match (Ref.: No) Yes −0.08*** < .01 SOC (Ref.: Intermediate occupation) High-level occupation 0.49*** < .01 0.52*** < .01 0.51*** < .01 Skilled manual or clerical −0.18*** < .01 −0.19*** < .01 −0.19*** < .01 Unskilled worker −0.27*** < .01 −0.29*** < .01 −0.28*** < .01

Impact of pre-migration skills on earnings, MLE selection model(a)

Table 4

(cont‘d). Impact of pre-migration skills on earnings, MLE selection model(a)

Table 4
Model 5 Model 6 Model 7 Coefficient p value Coefficient p value Coefficient p value Number of employees (Ref.: < 50) 50 or more 0.10*** < .01 0.10*** < .01 0.10*** < .01 Sector(c) (Ref.: Other) Offshore operations 0.47*** < .01 0.46*** < .01 0.43*** < .01 Financial 0.26*** < .01 0.27*** < .01 0.25*** < .01 Construction 0.17*** < .01 0.19*** < .01 0.18*** < .01 Transport and communications 0.18*** < .01 0.19*** < .01 0.18*** < .01 Health and social welfare 0.15*** < .01 0.16*** < .01 0.16*** < .01 Agriculture, hunting, forestry 0.14* .05 0.16** .03 0.15** .05 Manufacturing 0.14*** < .01 0.14*** < .01 0.14*** < .01 Public administration 0.12** .03 0.12** .03 0.10* .09 Type of work schedule (Ref.: Variable) Fixed 0.02 .34 0.02 .32 0.01 .45 Working time (Ref.: Part-time) Full-time 0.53*** < .01 0.54*** < .01 0.56*** < .01 Education (Ref.: Upper secondary or less) More than 2 years’ higher education 0.01 .81 0.03 .44 0.02 .54 2 years’ higher education 0.04 .24 0.05 .17 0.05 .15 Pre-migration SOC (Ref.: High-level occupation) Unskilled −0.11*** < .01 −0.04* .10 −0.04 .11 Skilled manual or clerical −0.06** .03 −0.03 .36 −0.03 .33 Intermediate occupation 0.04 .25 0.05 .14 0.05 .13 Level of French (Ref.: Good) Poor 0.02 .42 0.01 .55 -0.03 .11 Sex (Ref.: Female) Male 0.06** .01 0.06*** < .01 0.15*** < .01 Origin (Ref.: Europe) Africa −0.10*** < .01 −0.10*** < .01 −0.15*** < .01 Asia −0.06** .02 −0.06** .02 −0.11*** < .01 Other 0.08* .06 0.08** .05 0.04 .26 Location (Ref.: Other) Île-de-France 0.01 .46 0.02 .38 0.07*** < .01 Number of observations 4,710 t (d) −0.73 < 0.01 −0.71 < 0.01 −0.07 0.68 Log likelihood −3 829 −3 839 −3 835 AIC 7,834 7,853 7,847

(cont‘d). Impact of pre-migration skills on earnings, MLE selection model(a)

(a) The dependent variable is the log of the earnings. Models are estimated by maximum likelihood estimation (MLE).
(b) In the previous estimations, the model is a logit model. Classically, a direct linear relationship between probability and length of time is assumed. However, because the earnings equation is in log form, the earnings have to be introduced as a log.
(c) To simplify the table, for the ‘sector’ variable, only those sectors with significant coefficients have been included. All results are available on request.
(d) The correlations of error terms between the participation equation and the earnings equation are significant for all three models. This means there was a selection bias that had to be corrected. Only the corrected results are shown.
***p < .01. **p < .05. *p < .10.
Source: TeO survey; authors’ calculations.

54Among immigrants’ general skills, good proficiency in French has a positive impact on earnings, raising them by 3%. But the level of their EAs has no effect.


55This article has two aims. The first is to assess the impact of the level of human capital that immigrants have acquired before migration on their probability of finding work in their host country, making a distinction between generally applicable skills and skills specifically connected with a profession. It then assesses the quality of immigrants’ integration via recognition of their skills in terms of socio-occupational status, skill sector match, and earnings.

56We show that pre-migration human capital has a considerable impact on the probability of finding work and maintaining or improving socio-occupational status, and on earnings. However, the professional know-how and experience amassed before migrating are of greater value in the labour market than EA. All else being equal, those with higher-education qualifications do not seem to integrate better than the rest. The immigrants who integrate most easily are those with specific professional skills in sectors such as health, electricity and electronics, hotels and restaurants, and transport. EA is indirectly taken into account when backed up by work experience. Among general skills, language skills ensure better economic integration in both quantitative and qualitative terms. However, they have significantly less marginal impact than professional skills on the odds of finding work and of working in the same skill sector as in the home country.

57Unsurprisingly, some professional skills ‘migrate’ more easily than others. Professions that have culture-specific aspects or that cannot be practised in the host country without authorization (and therefore official recognition of the qualification concerned) are poorly transferable. Similarly, the human capital of migrants from European countries ‘travels’ better than that of migrants from Asia or Africa. This article shows that the negative impact of poor skill transferability on economic integration persists over time, with a slight tendency for the gaps to increase.

58These findings provide some useful material for thinking about immigration and integration policy design. The lasting negative impact of the poor transferability of some pre-migration skills revealed by our results suggests that immigrants’ short-term occupational paths determine and condition their future economic integration. If the aim of French immigration policy is rapid, good-quality economic integration (i.e. without the need for a period of training or education in the host country, and with a good match between the immigrant’s skills and his or her job), selection criteria should not be based solely on EA. The focus should be significantly more on professional skills, especially those most readily transferable. Special attention should also be paid to pre-migration work experience and language skills. A policy designed to improve transferability by better recognition of certain foreign qualifications and work experiences and by communication aimed at French employers would facilitate successful economic integration for immigrants, with long-term positive effects.

59This research could be taken further on a number of points. While our findings certainly show that different types of skills, particularly those related to different professional sectors, have different impacts on immigrants’ economic integration, they do not enable us to precisely rank professional skills according to their transferability. Such a classification would be useful for calibrating an immigrant integration policy. However, it would require more precise data on larger samples so as to describe immigrants’ socio-economic integration in greater detail.

Appendix - The sensitivity of the results

60Additional estimates were made to test the robustness of the results. First, it can reasonably be assumed that some people who do not find a job they consider suitable when they arrive prefer to wait and find a better opportunity. Also, as mentioned in the literature review, the time required for taking all the necessary steps to get a work permit and gather information about the labour market can have an impact on employment rates among recent arrivals but is not directly related to their skill level. Again, the dual information asymmetry between employer and employee may have an impact on the quality of the match between pre-migration skills and the nature of the migrant’s job in the host country. For these reasons, the migrant’s first months in France may not be propitious for finding work on a level with the skills acquired in the home country. To test this hypothesis, we again estimated Equation 1 (probability of being in work: Table A.1, Model 8) and Equation 2 (skill sector match: Table A.2, Model 11; and Table A.3, Model 14) on a sub-sample that excluded those who had been in France for less than 2 years. These new estimates confirmed the robustness of the previous results.

61A second series of tests concerned migrants with no work experience and no educational qualifications before immigrating. Previously, in constructing the indicators of a match between skills and employment, we had decided to include people with no job skills in the country of origin. Assuming these people could at least qualify for unskilled jobs, we considered that anyone without a job in the host country was overqualified. This is a debatable choice, as it amounts to equating this situation (having no job in the host country) with the more classic sense of overqualification (current job less skilled than the one held in the country of origin). We tested the robustness of our skill matching models on a subsample that excluded all those who had no professional skills before arriving in France (Table A.2, Model 10; Table A.3, Model 13). We also correlated these sensitivity tests by excluding all those who had no pre-migration professional skills and had been in France for less than 2 years (Table A.2, Model 12; Table A.3, Model 16). Once again, the additional estimations show the results to be stable.

62Lastly, the extensive scope of the TeO survey naturally led us to wonder whether integration conditions were stable across the different cohorts in the 2008 survey. The main analysis in our study merges several cohorts of immigrants, controlling only for length of time in France; this amounts to assuming they encountered similar conditions for integration when they arrived in France. We therefore made new estimates with a more uniform subsample, excluding those who had been in France for more than 20 years (a more detailed analysis was not possible given the sample’s small size). The results of these estimates (Models 9, 13, and 17) once again highlight the strong stability of the results.

Table A.1

Characteristics affecting the probability of immigrants being in work, by length of time in France

Table A.1
Model 8 More than 2 years’ residence Yes vs. No Model 9 Less than 20 years’ residence Yes vs. No OR p value OR p value Pre-migration professional skills (Ref.: None) Electricity, electronics 2.63* .09 4.35* .07 Personal services 2.50** .03 3.30** .05 Health 2.47** .01 3.38*** < .01 Social worker 2.27* .07 2.78* .08 Hotels, restaurants 2.17* .09 2.59* .09 Education (Ref.: Upper secondary or less) More than 2 years’ higher education 1.07 .31 1.19 .17 2 years’ higher education 0.91 .46 0.85 .19 Level of French (Ref.: Good) Poor 0.67*** < .01 0.62*** < .01 Control variables (a) Yes Yes Number of observations 4,533 2,971 R² .17 .25 Wald < .01 < .01

Characteristics affecting the probability of immigrants being in work, by length of time in France

(a) The control variables used are the same as for Table 3.
Note: To simplify the table, under ‘pre-migration professional skills’, only those sectors with significant coefficients have been included. All results are available on request.
*** p < .01. ** p < .05. * p < .10.
Source: TeO survey; authors’ calculations.
Table A.2

Characteristics affecting the probability of immigrants not being overqualified compared to pre-migration socio-occupational category (SOC)

Table A.2
Model 10 Model 11 Model 12 Model 13 With skills More than With skills Less than SOC/SOC match 2 years’ residence and more 20 years’ residence Yes vs. No SOC/SOC match than 2 years’ SOC/SOC match Yes vs. No residence Yes vs. No SOC/SOC match Yes vs. No OR p value OR p value OR p value OR p value Pre-migration professional skills (Ref.: Transport for Models 10 and 12; None for Models 11 and 13) Driver 2.17*** < .01 1.79*** < .01 2.22*** < .01 2.89** .01 Personal services 1.31*** < .01 1.31*** < .01 1.39*** < .01 2.12 .17 Electricity, electronics 1.71*** < .01 1.33*** < .01 1.70*** < .01 1.68 .14 Manufacturing 1.25*** < .01 1.08*** < .01 1.27*** < .01 1.17* .09 Environment 1.57* .06 1.24* .09 1.51* .07 2.88 .21 Administration 0.45** .05 0.40** .02 0.46** .02 0.47** .02 Agriculture 0.36*** < .01 0.29*** < .01 0.35*** < .01 0.45 .14 Police, army, security 0.33** .03 0.27** .02 0.35** .04 0.37 .36 Teaching 0.20*** < .01 0.17*** < .01 0.20*** < .01 0.24* .09 Commerce 0.20*** < .01 0.17*** < .01 0.19*** < .01 0.22* .09 Goods handling 1.04* .09 0.95* .07 1.18** .04 0.93* .07 Transport Ref. 0.83 .23 Ref. 1.02 .18 Education (Ref.: Upper secondary or less) More than 2 years’ higher education 1.67*** < .01 1.68*** < .01 1.70*** < .01 1.759 < .01 2 years’ higher education 1.29 .98 1.32 .89 1.32 .93 1.29 .896 Level of French (Ref.: Good) Poor 0.71*** < .01 0.74*** < .01 0.75*** < .01 0.73 < .01 Control variables Yes Yes Yes Yes Number of observations 3,570 4,533 3,421 2,971 R² .21 .20 .21 .24 Wald < .001 < .001 < .001 < .01

Characteristics affecting the probability of immigrants not being overqualified compared to pre-migration socio-occupational category (SOC)

Note: Model 10: those with no pre-migration skills are excluded; Model 11: those who had been in France less than 2 years are excluded; Model 12: those who had no pre-migration skills and had been in France less than 2 years are excluded; Model 13: immigrants who had been in France for more than 19 years are excluded.
To simplify the table, under ‘pre-migration professional skills’, only those sectors with significant coefficients have been included. All results are available on request.
*** p < .01. ** p < .05. * p < .10.
Source: TeO survey; authors’ calculations.
Table A.3

Characteristics affecting the probability of not being overqualified compared to pre-migration educational attainment

Table A.3
Model 14 With skills SOC/EA Yes vs. No Model 15 With more than 2 years’ residence SOC/EA Yes vs. No Model 16 With skills and more than 2 years’ residence SOC/EA Yes vs. No Model 17 With less than 20 years’ residence SOC/EA Yes vs. No OR p value OR p value OR p value OR p value Pre-migration professional skills (Ref.: Transport for Models 14 and 16; None for Models 15 and 17 Health 1.43* .09 1.21** .04 1.47* .08 1.48** .05 Hotels, restaurants 1.40 .14 1.22* .06 1.48* .09 1.40* .10 Art 1.51* .06 1.32** .02 1.60** .04 1.34 .14 Administration 0.76** .02 0.62** .03 0.74** .01 0.73* .01 Transport Ref. 0.758 .76 Ref. 0.78 .55 Education (Ref.: Upper secondary or less) More than 2 years‘ higher ed. 0.30*** < .01 0.30*** < .01 0.31*** < .01 0.294 .05 2 years‘ higher ed. 0.19*** < .01 0.20*** < .01 0.21*** < .01 0.15 < .01 Level of French (R ef.: Good) Poor 0.73*** < .01 0.74*** < .01 0.75*** < .01 0.67 < .01 Control Yes Yes Yes Yes Number of observations 3,570 4,533 3,421 2,971 R² .19 .18 .18 .23 Wald < .001 < .001 < .001 < .001

Characteristics affecting the probability of not being overqualified compared to pre-migration educational attainment

Note: Model 14: those with no pre-migration skills are excluded; Model 15: those who have been in France less than 2 years are excluded; Model 16: those who had no pre-migration skills and have been in France less than 2 years are excluded; Model 17: immigrants who have been in France for more than 19 years are excluded.
To simplify the table, under ‘pre-migration professional skills’, only those sectors with significant coefficients have been included. All results are available on request.
*** p < .01. ** p < .05. * p < .10.
Source: TeO survey; authors’ calculations.


  • [1]
    Overqualification refers to the situation where a person possesses a higher skill level than is necessary for their job.
  • [2]
    The European Blue Card is a work permit issued to highly qualified people from non-EU countries on the condition that they have a job contract or a promise of employment at a pay level at least 1.5 times the mean gross income of the EU member countries or, for occupations with a labour shortage, 1.2 times that mean. After a certain time, Blue Card holders can apply for a permanent residence permit in an EU member country. This arrangement is recognized by all EU countries except Denmark, Ireland, and the United Kingdom. The conditions for issuing the card are the same in all countries.
  • [3]
    In the TeO survey, an immigrant is defined as any person born outside France and legally resident in France (definition established by the Haut-Conseil à l’Intégration [High Council on Integration]).
  • [4]
    One might reasonably ask whether this choice creates a selection bias: immigrants finding it harder to convert their pre-migration human capital into a suitable job might choose to pursue more education in France. However, of the 533 immigrants removed from the sample (those who had arrived after age 16 and had taken training or qualifying education in France), 333 had arrived before age 25, and 331 had never worked abroad. This suggests that their decision to take an education course was due more to a desire to continue their studies than to a mismatch between their pre-migration skills (which in most cases were very poor) and job opportunities in France.
  • [5]
    All analyses were performed using the weighting variable found in the TeO survey.
  • [6]
    A person’s professional skills are defined by their training, experience, and job content, not the sector in which they work or have worked. For example, an accountant working in a hospital will be classed as having skills in accounting and finance rather than in health.
  • [7]
    In the TeO survey, migrant respondents assessed their own level of French on arrival. The data producer supplied a combined indicator, with four grades for how well the arriving immigrant knew French (‘very well’, ‘well’, ‘not very well’, and ‘not at all’). To limit the subjective nature of the self-evaluation, the variable was recoded with only two categories, grouping ‘very well’ with ‘well’ and ‘not very well’ with ‘not at all’.
  • [8]
    The risk of multicollinearity between these two variables and professional skills is low: Cramér’s V is no greater than 0.32.
  • [9]
    Tests run on a sample that excluded those who were not in employment before or after immigrating (see Appendix) confirm the robustness of the result.
  • [10]
    This indicator is calculated from the TeO survey data using weighted individual variables.
  • [11]
    Skill sector match is tested only for those migrants who had acquired occupational skills before arriving in France.
  • [12]
    Self-employed people were excluded from the sample.
  • [13]
    A sensitivity analysis is provided in the Appendix.
  • [14]
    In Figures 1B and 1C, no professional skills is compared with the skills that our results found to be most transferable. The trend remains the same for the other transferable skills; the figures are not shown but are available on request.
file_downloadRelated documents

The objective of this article is to differentiate the role played by transversal skills and professional skills, accumulated by migrants in their home country, on their chances of getting a job in France, as well as on the adequacy between different job characteristics (socio-professional category and wage) and pre-migratory skills. To quantify the impact of the transferability of human capital, we use the data from the Trajectories and Origins survey (2008). We show that pre-migration human capital plays an important role both in obtaining employment and in maintaining or furthering one’s socio-occupational position, as well as in remuneration. Using several indicators of immigrant integration, quantitative and qualitative, we show that, with the exception of language skills, other transversal skills do not play the expected role. Conversely, professional skills allow for better economic integration. Nevertheless, we show that certain pre-migratory professional skills are not transferable and hence they are associated with low economic integration, the effect that is persistent over time.

  • immigration
  • economic integration
  • pre-migration human capital
  • France

Le rôle du capital humain prémigratoire dans l’intégration économique des immigrés en France : Compétences métier vs compétences transversales

L’objectif de cet article est de différencier le rôle joué par les compétences transversales et les compétences métier accumulées à l’étranger, sur les chances d’accéder à un emploi en France, ainsi que sur l’adéquation de cet emploi et du niveau de salaire avec les compétences prémigratoires. Pour quantifier l’impact de la transférabilité du capital humain, les données de l’enquête Trajectoires et origines (TeO, 2008) sont utilisées. Le capital humain prémigratoire joue un rôle important, aussi bien sur les chances d’accès à un emploi que pour le maintien ou la progression de la position socioprofessionnelle, ainsi que pour la rémunération des immigrés. En utilisant plusieurs indicateurs d’intégration des immigrés, quantitatifs et qualitatifs, on s’aperçoit qu’à l’exception des compétences linguistiques, les autres compétences transversales ne jouent pas le rôle attendu en tant que vecteur d’intégration. À l’inverse, les compétences métier permettent une meilleure intégration économique. Par ailleurs, les effets négatifs d’un faible niveau de transférabilité de certaines compétences métier sur l’intégration économique se maintiennent.


El papel del capital humano pre-migratorio en la integración económica de los inmigrantes en Francia: competencias profesionales versus competencias transversales

El propósito de este artículo es diferenciar el papel que desempeñan las competencias transversales y las competencias profesionales adquiridas antes de la inmigración, sobre las oportunidades de acceder a un empleo en Francia, así como sobre la adecuación del empleo y de su nivel de remuneración a las competencias premigratorias. Para cuantificar el impacto de la transferibilidad del capital humano, utilizamos los datos de la encuesta Trayectorias e orígenes (TeO, 2008). El capital humano pre-migratorio desempeña un papel importante tanto en las oportunidades de acceder a un empleo que en el mantenimiento o en la progresión socio-profesional, así como en la remuneración de dicho empleo. A partir de varios índices de integración, cuantitativos y cualitativos, se observa que, con excepción de las competencias lingüísticas, las competencias transversales no juegan el papel esperado como vector de integración. Por el contrario, las competencias profesionales permiten una mejor integración económica. Por otra parte, se mantiene el efecto negativo de un bajo nivel de transferibilidad de ciertas competencias profesionales sobre la integración económica.


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Nadiya Ukrayinchuk
LEM, CNRS, UMR 9221, Université de Lille and Institut Convergences Migrations, CNRS, ANR-17-CONV-0001
Xavier Chojnicki
LEM, CNRS, UMR 9221, Université de Lille and Institut Convergences Migrations, CNRS, ANR-17-CONV-0001
Translated by
Harriet Coleman
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Uploaded on on 04/12/2020
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