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Abstract

This study offers new insights into the phenomenon of overeducation by showing that the overeducation rates among immigrants and the wage returns of overeducated immigrants are closely linked to their admission classes. The overeducation rate in Denmark is highest among immigrants from countries that became members of the EU after 2003, 61% of whom are overeducated as compared to 24% of natives. Controlling for demographic and educational characteristics, citizens from these new EU countries, as well as reunified family members, refugees, and students, are highly overeducated compared to natives, while this is not the case for citizens from the Nordics and older EU countries, that is, those that joined the EU before 2003. Furthermore, overeducated higher-educated citizens from the Nordics and from older EU countries only suffer minor wage losses, while other admission classes typically earn between 17% and 36% less than if they had work appropriate to their educational levels. For highly educated refugees, the gap is even larger. These results emphasize the importance of the differences in immigrants’ outside options (e.g., wages and living conditions in the home country) and the admission requirements they face. The results also highlight the potential gains for immigrants as well as their host countries of acknowledging immigrants’ educational skills.

Keywords

integration education wage effects residence permit type

1. Introduction

Many Western countries face an ageing population with more elderly and fewer working-age individuals. Immigrants are often mentioned as a solution to this problem, as they are typically young when they arrive and residence permits can be allocated to workers with skills that are in short supply in the general population. However, existing studies show that immigrants often work in jobs that require fewer educational skills than they actually possess and that overeducation is a more common phenomenon among immigrants than among natives (Green, Kler and Leeves 2007; Wald and Fang 2008; Lindley 2009; Dell’Aringa and Pagani 2011; Nielsen 2011; Joona, Gupta and Wadensjö 2014). Returns to overeducation are also generally found to be smaller for immigrants than for natives (Green, Kler and Leeves 2007; Chiswick and Miller 2008, 2010; Wald and Fang 2008; Lindley 2009; Dell’Aringa and Pagani 2011). A potential productivity loss therefore occurs, since overeducated immigrants could be more productive if their jobs matched their educational levels, and preventing this loss could benefit both the immigrants and their host countries. Knowledge of the societal gain that can be made by reducing immigrants’ overeducation is scarce, but a recent study estimates that the gain may be substantial, namely, that the Danish gross domestic product (GDP) would increase by 1 billion DKK (0.16 billion US$) if 10,000 unskilled people in Denmark obtained an (average) education (Skaksen 2018).

Immigrants are more likely to be overeducated than natives due to a lack of transferability of home-country educational skills (Chiswick and Miller 2010) and employers’ uncertainty about the quality of the education received abroad by immigrants (Li and Sweetman 2014). Immigrants may also lack the other skills (such as local language proficiency) needed to benefit fully from their education when they are in their host countries (Budría and Martínez de lbarreta 2020). In Australia, for example, immigrants with English-speaking backgrounds have been found to face similar overeducation rates and earnings as natives, while other immigrants face less favorable outcomes (Kler 2007; Wen and Maani 2018; Maani and Wen 2021). Furthermore, discrimination may prevent immigrants from obtaining a job on equal terms with natives (Bertrand and Mullainathan 2004), while the weaker networks on which they draw can imply less efficient job searches (Damm 2014; Kalfa and Piracha 2018).

Why people migrate, what incentives migrants have to stay in the host country, and under what conditions they are allowed to stay in the host country are central topics in the migration literature (Bodvarsson and Van den Berg 2009). However, these considerations are seldom discussed in relation to the phenomenon of overeducation, which is surprising, given its close link to understanding immigrants’ motivations to work in a foreign country despite being overeducated. Immigrants’ admission classes constitute a key aspect of this discussion, since these set the conditions for immigrants’ access to the host country as well as their rights and duties during their stay. Granting residence permits through different admission classes is a policy tool for politicians to manage migration. Nonetheless, to the best of my knowledge, this is the first study to examine overeducation based on admission class.

The central question examined in this analysis is as follows: to what extent are there differences in overeducation patterns according to immigrants’ admission class? Differences in both overeducation rates and wage returns are explored, and possible explanations for these patterns are discussed.

To answer this question empirically, the “realized matches” approach is used to judge whether there is a match between work and educational qualifications (Hartog 2000). In this approach, the “required” educational level for a given job is determined empirically based on a large number of actual job matches — in our case, the comprehensive administrative registers for the full population in Denmark between the ages of 25 and 64 years old. A linear probability model is used to estimate the gaps in overeducation levels among all immigrant admission classes and natives, and differences in wage gaps are estimated by ordinary least squares (OLS) including a rich set of control variables. The data include information about the admission classes of all immigrants arriving in Denmark since 1997.

The analysis shows that rates of overeducation are higher among immigrant employees of all admission classes, except refugees and reunified refugee families, than among Danish natives (henceforth, “natives”). The highest shares are found among citizens from countries that became European Union (EU) member states after 2003 (henceforth, “new EU countries”), students, and immigrants with work permits, followed by citizens from old EU countries (i.e., those who joined the EU before 2003), Nordic citizens, and family members reunified with natives. The incidence rates are low among refugees and their family members because fewer in this group have been educated above the basic level (primary and secondary school) than in the other groups. Refugees who have been educated to above the basic level also experience higher overeducation rates than natives. Wage returns for the overeducated are generally lower for immigrants than natives but vary substantially by education type and immigrants’ admission classes. Overeducated immigrants who have completed vocational education (henceforth, overeducated vocationally-educated immigrants) and natives experience considerable wage loss (14–25%). In contrast, overeducated natives who have completed higher education (henceforth, overeducated higher-educated natives) face low or no wage loss. Overeducated immigrants who have completed higher education (henceforth, overeducated higher-educated immigrants) generally earn 17–36% less when they are overeducated, with citizens from the Nordics and the old EU countries being prominent exceptions. These results emphasize the need for more attention to be given to the transferability and use of the qualifications of immigrants, in general, and immigrants from new EU countries, in particular. The results are relevant for old EU countries as well as other highly developed countries that need skilled labor to assist their ageing populations.

The rest of the paper is organized as follows: Section 2 discusses the analytical framework, and section 3 describes the data. Section 4 presents the identification and estimation strategy that leads to the results in section 5 and the conclusions drawn in section 6.

2. Analytical Framework

2.1 Measuring Overeducation

It is crucial to know which qualifications a specific job requires to judge whether there is a match between work and educational qualifications. The “required” educational level has been measured in three different ways: self-assessed, job analysis, and realized matches. The self-assessed measure uses questions given to employees regarding the educational level required to carry out a specific job. The job analysis uses information from occupational classifications to translate jobs into required years of schooling. Realized matches use the “typical” educational level among employees in each job.

Each of these three measures of the “required” education level (Hartog 2000) has pros and cons. Although different educational measures have been used, there has been substantial agreement in the results regarding returns to years of schooling (Leuven and Oosterbeek 2011). Typically, the returns to required schooling are 8–10%, while returns to overeducation are smaller (4–5%) and returns to undereducation are negative and smaller in size (−4 to –3%).

The availability of data is often decisive in determining which calculation method is used to determine the required educational level for a given job. With the existence of large administrative registers in Nordic countries, “realized matches” is an attractive approach that has been used in several former Nordic studies (Malchow-Møller, Munch and Skaksen 2007; Nielsen 2011; Joona, Gupta and Wadensjö 2014; Skaksen 2016; Schultz-Nielsen and Skaksen 2017; Clausen and Skaksen 2018).

Nielsen (2011) is the first to study overeducation among immigrants in Denmark using a large representative sample of immigrants. Focusing on immigrants with more than basic education for the period 1995 to 2002, she found overeducation rates of 15% and 33% if the mean and modal (most frequent) durations of educations, respectively, were used to assess the required education for each type of job. Schultz-Nielsen and Skaksen (2017) used five education types rather than just duration of education and found an overeducation rate in 2015 of 26% among immigrants with more than basic education and 22% among all immigrants.

The five education types are (1) primary and secondary school (“Basic ED”), (2) vocational education (“VE”), (3) short-cycle higher education (“short-CHE”), (4) medium-cycle higher education (“medium-CHE”), and (5) long-cycle higher education (“long-CHE”). This measure is expected to provide a more accurate picture than using years of education, which is the most commonly used measure in the international literature (Duleep and Regets 1981; Leuven and Oosterbeek 2011). In this paper, a robustness check is performed where education is measured by duration.

Individuals whose highest obtained education corresponds to completing basic ED (9–12 years of schooling) are considered to have the lowest education level, since these individuals have not acquired competencies that qualify them for specific jobs. VE (typically 14 years of education) is considered a higher educational level that provides job qualifications. Short-CHE typically involves around 15–16 years of education, medium-CHE corresponds to a bachelor's degree (17 years), and long-CHE corresponds to a master's or PhD degree (18 or 21 years).

The classification of job type follows the Danish version (DISCO) of the International Standard Classification of Occupations. I use the four-digit classification, which gives a total of 505 different job types. The required education for job functions in Denmark can be classified into five levels, depending on the actual educational profiles of all employees in each job function (Clausen and Skaksen 2018). The required level of education in a job function is:

“basic ED” if under 50% of employees have completed vocational or higher education.

“VE” if under 50% of employees have completed higher education but more than 50% have completed vocational or higher education.

“short-CHE” if under 50% of employees have completed medium- or long-CHE but more than 50% have completed higher education.

“medium-CHE” if under 50% of employees have completed long-CHE but more than 50% have completed medium- or long-CHE.

“long-CHE” if more than 50% of employees have completed long-CHE.

If a person's educational level exceeds the necessary level for their job, they are classified as overeducated (O_i = 1 and 0 otherwise). Similarly, if a person's educational level is below the necessary level for their job, they are classified as undereducated (U_i = 1 and 0 otherwise).

As the phenomenon of overeducation among immigrants is much more pronounced than undereducation in the same group, I focus on overeducation in this analysis. I use the same method as Schultz-Nielsen and Skaksen (2017) and Clausen and Skaksen (2018) and contribute to the literature by calculating (a) immigrants’ overeducation rates for different admission classes and (b) wage returns for overeducated immigrants from different admission classes, educational types, and lengths of stay.¹

2.2 Why Does Overeducation Occur?

Several economic theories offer explanations as to why overeducation (or more generally, a mismatch between education and jobs) may occur. According to human capital theory (Becker 1962, 1964), overeducation may occur because some employees compensate for their lack of other skills with more formal education. Job-matching theory emphasizes that information is scarce and job searches are costly; hence, employees do not know the specifications of a job before they start, and employers do not know the exact qualifications of their employees (Burdett 1978; Mortensen 1988). Meanwhile, job competition theory predicts that hiring an individual with a higher level of education can imply lower training costs for employers, making more highly educated employees attractive if salaries are based on job function (Thurow 1975).

Studies focusing on immigrants’ overeducation highlight that immigrants are more often overeducated than natives (Green, Kler and Leeves 2007; Wald and Fang 2008; Lindley 2009; Dell’Aringa and Pagani 2011; Nielsen 2011; Joona, Gupta and Wadensjö 2014). Explanations on the supply side include the lack of transferability of home-country educational skills (Chiswick and Miller 2010) and labor market experience (Friedberg 2000), poor quality of home-country education (Bratsberg and Terrell 2002), and insufficient host-country language proficiency (Budría and Martínez de lbarreta 2020). Other studies emphasize that a weaker network can imply a less efficient job search (Damm 2014; Kalfa and Piracha 2018). Demand-side explanations focus on employers’ uncertainty about the quality of the education immigrants receive abroad (Li and Sweetman 2014). Furthermore, discrimination may prevent immigrants from obtaining a job on equal terms with natives (Bertrand and Mullainathan 2004). Another important explanation is that differences in economic conditions between home and host countries (Aleksynska and Tritah 2013) may influence immigrants’ migration decisions and incentivize them to accept overeducation.

2.3 Admission Class: Migration Motives and Admission Conditions

In neo-classical theory, the migration decision is described as a type of human capital investment whereby individuals settle in the area where the net returns to human labor are highest in order to maximize their lifetime earnings (Becker 1962; Sjaastad 1962). More recent work has shown that migration motives may be more complex (e.g., seeking asylum and family reunification), but nearly all models rely on the basic assumptions that migration is driven by spatial and distributional differences (Bodvarsson and Van den Berg 2009; Dustmann and Görlach 2016). Denmark, the host country considered in the current analysis, is a highly developed country with a labor market characterized by low wage dispersion and a high “minimum” wage²; thus, it is more attractive to migrants from low-income than high-income countries and to low-skilled rather than high-skilled individuals (Hansen, Schultz-Nielsen and Tranæs 2017).

Immigration policy (including the opportunity to obtain temporary or permanent residence) is also an important aspect of migration decisions (Bodvarsson, Simpson and Sparber 2013). Furthermore, temporary resident permits, as opposed to permanent ones, reduce immigrants’ incentives to invest in host country-specific skills and thereby improve their future earnings in the host country (Dustmann 1993; Duleep and Regets 1999; Zwysen 2019). A recent study by Adda, Dustmann and Görlach (2022) focused on the interplay between return migration, human capital accumulation, and wage assimilation. The authors illustrated the effects of policy schemes on immigrants’ selection and behavior and showed how immigrants’ return plans affect their reservation wages and make them willing to accept jobs that natives will not do as long as first, the immigrants in question plan to return and second, consumption is cheaper in the home country than in the host country.

As in most other countries, immigrants can obtain residence in Denmark for work, study, family, and humanitarian reasons (Organisation of Economic Co-operation and Development [OECD] 2023). The rights to enter and remain in Denmark are regulated through the Aliens Act setting out the criteria for each admission class and associated requirements, which are designed to respect international agreements (Nilas 2021). Studies show that refugees in high-income countries generally have lower employment rates than other immigrants, and this is also the case in Denmark (Brell, Dustmann and Preston 2020). The regulation for family reunification is based on the principle of family unity and the right to family life stemming from conventions on human rights, while refugees (humanitarian reasons) are protected by the UN 1951 Convention (Christensen et al. 2006). To obtain a residence permit for work, immigrants must fulfill several conditions. In the Danish case, they must typically have a job offer and receive a salary above a certain level (in 2017, a yearly salary of 408,800 DKK (∼65,900 US$)). Meanwhile, students can obtain residence when they are admitted to a Danish educational institution, typically a university. As Denmark is a Nordic country and a member of the EU, citizens from EU/EEA and Nordic countries face fewer restrictions to access to Denmark than other immigrant groups. There is free mobility for citizens within the Nordic countries, and EU citizens have the right to immigrate for three months and look for work in other member states and stay if they find a job (Nilas 2021).

Conditions in home countries vary considerably for immigrants of different admission classes, as shown in Table 1 (next section), and the average GDP per capita varies from 3,800 US$ for refugees to 65,200 US$ for Nordic citizens. This economic gap underlines that the alternative to staying in Denmark varies for immigrants of different admission classes and (all else being equal) immigrants from wealthier countries have less incentives to accept working as overeducated than immigrants from less wealthy countries. However, admission classes may also vary in terms of other circumstances immigrants find important, for example, educational institutions and language distance from the host country. Furthermore, for refugees, it is often impossible to return to the home country, and migrating to a third country might not be possible either (Hatton 2009). Reunified family members can be regarded as “tied movers”: the migration decision is based on the “benefits” that accrue to the whole family and not just the immigrant's own job market opportunities (Mincer 1978). The family social network may help the reunified family to find a job but not necessarily one of an appropriate level (Damm 2014; Kalfa and Piracha 2018). Student permits are often temporary, which makes investments in host country-specific skills less attractive. However, students’ contact with the local educational system (and language) may help them overcome the problem of the transferability of education across borders.

Table 1.
Characteristics of Natives and Immigrants Depending on Type of Initial Residence Permit, 2017.

Refugees and family EU-New EU-Old Family reuni. (natives) Family reuni. (others) Study Work Nordic All immigrants Natives

Men (0/1) 0.58 0.50 0.57 0.22 0.39 0.39 0.64 0.35 0.47 0.49

(0.49) (0.50) (0.50) (0.41) (0.49) (0.49) (0.48) (0.48) (0.50) (0.50)

Non-western (0/1) 0.99 0.05 0.07 0.82 0.94 0.74 0.67 0.00 0.50 0.00

(0.09) (0.22) (0.25) (0.38) (0.25) (0.44) (0.47) (0.00) (0.50) (0.00)

Age 37.60 34.49 38.56 43.20 38.49 34.75 37.14 36.58 37.57 44.91

(9.26) (7.69) (8.83) (7.29) (7.55) (5.95) (7.12) (8.24) (8.32) (10.67)

Median age at immigration 24.95 29.48 30.84 31.46 25.17 24.37 30.82 26.85 28.31 0.00

(9.93) (7.83) (8.05) (6.64) (8.24) (5.26) (6.22) (7.59) (0.47) (0.00)

Have one or more children (0/1) 0.55 0.38 0.44 0.57 0.71 0.54 0.51 0.53 0.51 0.44

(0.50) (0.49) (0.50) (0.50) (0.45) (0.50) (0.50) (0.50) (0.50) (0.50)

Cohabiting (0/1) 0.64 0.64 0.68 0.80 0.76 0.73 0.72 0.73 0.70 0.74

(0.48) (0.48) (0.47) (0.40) (0.43) (0.44) (0.45) (0.44) (0.46) (0.44)

Primary and secondary edu. (0/1) 0.39 0.17 0.09 0.25 0.36 0.10 0.08 0.07 0.19 0.17

(0.49) (0.38) (0.28) (0.43) (0.48) (0.30) (0.27) (0.26) (0.39) (0.38)

Vocational edu. (0/1) 0.31 0.18 0.16 0.36 0.35 0.18 0.11 0.12 0.22 0.38

(0.46) (0.38) (0.37) (0.48) (0.48) (0.38) (0.32) (0.33) (0.41) (0.49)

Short-cycle higher edu. (0/1) 0.05 0.08 0.06 0.06 0.05 0.07 0.03 0.06 0.06 0.06

(0.21) (0.27) (0.23) (0.24) (0.23) (0.26) (0.17) (0.23) (0.23) (0.25)

Medium-cycle higher edu. (0/1) 0.16 0.28 0.20 0.20 0.16 0.24 0.22 0.32 0.22 0.24

(0.37) (0.45) (0.40) (0.40) (0.36) (0.43) (0.42) (0.47) (0.42) (0.43)

Long-cycle higher edu. (0/1) 0.09 0.28 0.49 0.13 0.08 0.41 0.56 0.43 0.32 0.14

(0.29) (0.45) (0.50) (0.34) (0.27) (0.49) (0.50) (0.50) (0.46) (0.35)

Danish edu. (0/1) 0.51 0.25 0.32 0.39 0.45 0.64 0.17 0.51 0.39 1.00

(0.50) (0.43) (0.47) (0.49) (0.50) (0.48) (0.38) (0.50) (0.49) (0.00)

Hourly wage, 1,000 DKK 182.29 186.55 257.88 186.48 179.20 200.91 245.61 242.07 210.71 233.54

(61.18) (76.29) (173.69) (66.58) (56.86) (75.62) (136.88) (129.71) (113.00) (111.11)

YSM > 5 0.87 0.56 0.63 0.89 0.93 0.82 0.59 0.79 0.74 1.00

(0.34) (0.50) (0.48) (0.31) (0.26) (0.38) (0.49) (0.41) (0.44) (0.00)

GDP per capita, US$ 3,784 12,953 37,587 11,624 7,110 10,614 11,043 65,196 19,533 57,773

(4,045) (3,417) (13,971) (14,705) (7,667) (13,192) (13,996) (11,117) (20,951) (0.00)

Weight 1.27 2.05 1.61 1.29 1.36 1.31 1.68 1.46 1.54 1.00

(0.44) (0.83) (0.63) (0.34) (0.41) (0.52) (0.55) (0.61) (0.64) (0.00)

No. of obs. 9,887 11,506 12,298 8,608 5,805 7,788 7,564 5,853 69,309 1,590,957

	Refugees and family	EU-New	EU-Old	Family reuni. (natives)	Family reuni. (others)	Study	Work	Nordic	All immigrants	Natives
Men (0/1)	0.58	0.50	0.57	0.22	0.39	0.39	0.64	0.35	0.47	0.49
	(0.49)	(0.50)	(0.50)	(0.41)	(0.49)	(0.49)	(0.48)	(0.48)	(0.50)	(0.50)
Non-western (0/1)	0.99	0.05	0.07	0.82	0.94	0.74	0.67	0.00	0.50	0.00
	(0.09)	(0.22)	(0.25)	(0.38)	(0.25)	(0.44)	(0.47)	(0.00)	(0.50)	(0.00)
Age	37.60	34.49	38.56	43.20	38.49	34.75	37.14	36.58	37.57	44.91
	(9.26)	(7.69)	(8.83)	(7.29)	(7.55)	(5.95)	(7.12)	(8.24)	(8.32)	(10.67)
Median age at immigration	24.95	29.48	30.84	31.46	25.17	24.37	30.82	26.85	28.31	0.00
(9.93)	(7.83)	(8.05)	(6.64)	(8.24)	(5.26)	(6.22)	(7.59)	(0.47)	(0.00)
Have one or more children (0/1)	0.55	0.38	0.44	0.57	0.71	0.54	0.51	0.53	0.51	0.44
(0.50)	(0.49)	(0.50)	(0.50)	(0.45)	(0.50)	(0.50)	(0.50)	(0.50)	(0.50)
Cohabiting (0/1)	0.64	0.64	0.68	0.80	0.76	0.73	0.72	0.73	0.70	0.74
	(0.48)	(0.48)	(0.47)	(0.40)	(0.43)	(0.44)	(0.45)	(0.44)	(0.46)	(0.44)
Primary and secondary edu. (0/1)	0.39	0.17	0.09	0.25	0.36	0.10	0.08	0.07	0.19	0.17
(0.49)	(0.38)	(0.28)	(0.43)	(0.48)	(0.30)	(0.27)	(0.26)	(0.39)	(0.38)
Vocational edu. (0/1)	0.31	0.18	0.16	0.36	0.35	0.18	0.11	0.12	0.22	0.38
	(0.46)	(0.38)	(0.37)	(0.48)	(0.48)	(0.38)	(0.32)	(0.33)	(0.41)	(0.49)
Short-cycle higher edu. (0/1)	0.05	0.08	0.06	0.06	0.05	0.07	0.03	0.06	0.06	0.06
(0.21)	(0.27)	(0.23)	(0.24)	(0.23)	(0.26)	(0.17)	(0.23)	(0.23)	(0.25)
Medium-cycle higher edu. (0/1)	0.16	0.28	0.20	0.20	0.16	0.24	0.22	0.32	0.22	0.24
(0.37)	(0.45)	(0.40)	(0.40)	(0.36)	(0.43)	(0.42)	(0.47)	(0.42)	(0.43)
Long-cycle higher edu. (0/1)	0.09	0.28	0.49	0.13	0.08	0.41	0.56	0.43	0.32	0.14
(0.29)	(0.45)	(0.50)	(0.34)	(0.27)	(0.49)	(0.50)	(0.50)	(0.46)	(0.35)
Danish edu. (0/1)	0.51	0.25	0.32	0.39	0.45	0.64	0.17	0.51	0.39	1.00
	(0.50)	(0.43)	(0.47)	(0.49)	(0.50)	(0.48)	(0.38)	(0.50)	(0.49)	(0.00)
Hourly wage, 1,000 DKK	182.29	186.55	257.88	186.48	179.20	200.91	245.61	242.07	210.71	233.54
(61.18)	(76.29)	(173.69)	(66.58)	(56.86)	(75.62)	(136.88)	(129.71)	(113.00)	(111.11)
YSM > 5	0.87	0.56	0.63	0.89	0.93	0.82	0.59	0.79	0.74	1.00
	(0.34)	(0.50)	(0.48)	(0.31)	(0.26)	(0.38)	(0.49)	(0.41)	(0.44)	(0.00)
GDP per capita, US$	3,784	12,953	37,587	11,624	7,110	10,614	11,043	65,196	19,533	57,773
	(4,045)	(3,417)	(13,971)	(14,705)	(7,667)	(13,192)	(13,996)	(11,117)	(20,951)	(0.00)
Weight	1.27	2.05	1.61	1.29	1.36	1.31	1.68	1.46	1.54	1.00
	(0.44)	(0.83)	(0.63)	(0.34)	(0.41)	(0.52)	(0.55)	(0.61)	(0.64)	(0.00)
No. of obs.	9,887	11,506	12,298	8,608	5,805	7,788	7,564	5,853	69,309	1,590,957

Note: Means and std. deviations in parenthesis. YSM = years since migration.

Source: Administrative register information from Statistics Denmark.

Based on these considerations, it can be expected that immigrants are more often overeducated if they are migrants from poorer countries, refugees, or family reunified. However, immigrants who plan to stay in the host country for a longer period (e.g., refugees and family reunified) are expected to have lower overeducation levels in the long run since they have better incentives to invest in host country-specific skills than short-stay immigrants.

3. Data

3.1 Sample Construction

The analysis is based on deidentified Danish administrative data covering all natives and immigrants who were resident in Denmark on 1 January 2017.³ Information from a range of administrative registers at Statistics Denmark is included regarding demography, education, labor market attachment, hourly wages, and immigration history, including admission classes.

Demographic information for 1 January 2017 is available in the population register. The education register contains information about all education received in Denmark until October 2016. Information about education received abroad is gathered by Statistics Denmark, mainly through large surveys among immigrants, but also from sources such as the authorization register from the National Board of Health and the member register from the Danish Engineering Association.⁴ Job types and hourly wages in 2016 were reported to the tax authorities by employers.⁵ Definitions of all variables are provided in Supplemental Appendix Table A.1.

On 1 January 2017, 2,502,670 natives and 415,380 immigrants aged 25–64 lived in Denmark. The full sample of immigrants is restricted to the 270,507 who had arrived since 1997, as information regarding their residence permits is available. The sample is further restricted to those individuals who are employees and have a job and whose job category is known with certainty.⁶ This sample of 1,599,529 natives and 106,476 immigrants would be optimal for analysis; however, since information regarding the education immigrants received abroad is not always recorded in Denmark, I restrict the sample further to those immigrants for whom educational information is available. After imposing these restrictions, the sample consists of 69,309 immigrants and 1,590,957 natives, giving a total of 1,660,266 individuals. The sample selection criteria are shown in detail in Supplemental Appendix Table B.1. To adjust for the missing educational information among immigrants, a weight is imposed on everyone in the sample. For natives and immigrants with Danish education, the weight is set to 1, since their educational information is never missing. The sample selection and the weighting procedure for immigrants with a foreign education are described in further detail in Supplemental Appendix B.

A key variable in the analysis is the initial admission class. I concentrate on all immigrants who first arrived in Denmark after 1996 and find that their initial residence permits capture the original “reason” for their arrival. Admission class is organized into eight categories: (1) refugees and family, (2) “EU-new,” (3) “EU-old,” (4) family reunified with natives (“FR-N”), (5) family reunified with others (“FR-O”), (6) study, (7) work, and (8) Nordic. Refugees are classified together with family reunified since they have similar behavior in the labor market when controlling for gender differences (Schultz-Nielsen 2017). ⁷ Within the family reunified category, I distinguish between those who reunified with non-refugee immigrant family members (“FR-O”) and those who reunified with natives (“FR-N”), who are often in somewhat different economic situations (Schultz-Nielsen 2017). Immigrants who obtained residence based on free labor mobility within the EU are classified as “EU-Old” if they originate from a country that was an EU member before 2003 and “EU-New” if their country became a member after that date.⁸ All Nordic citizens (except Danish natives) are included in the “Nordic” category,⁹ as they do not need residence permits to stay in Denmark, while immigrants who have received a student visa or work-related residence permit are included in the “study” and “work” categories and also referred to as “students” and “workers.”

3.2 Descriptive Statistics

Table 1 shows the unweighted distribution of characteristics in the sample, where natives constitute the majority (1,590,957 persons) and immigrants constitute the minority (69,309 persons), and each admission class represents between 5,800 and 12,300 immigrants. The gender composition is even for natives and EU-new, as men constitute 49–50% of the sample. There are more men than women among workers, EU-old, and refugees (including family members), while the opposite is the case for students, Nordic citizens, FR-N, and FR-O. Natives, Nordic citizens, EU-new, and EU-old are almost exclusively of Western origin, while the majority of refugees, students, workers, FR-N, and FR-O tend to be of non-Western origin.¹⁰

Refugees, students, and FR-O have a median age close to 25 years old when they arrive in Denmark, whereas the median age of immigrants in other admission classes is closer to 30. Half of all immigrants have children, with the share being lowest among the EU-new group and highest among the FR-O group. Seventy percent of the immigrants are married or cohabiting, the share being highest for the FR-N and FR-O groups, given they already had a family member in Denmark when they arrived.

The educational composition also varies according to immigrants’ admission classes, as discussed in more detail below. For now, it is sufficient to note that the share of basic-educated (those who have completed primary or secondary schooling as the highest level of education) is highest among refugees, followed by FR-O. The share that received education in Denmark is highest among refugees, students, reunified family (“FR”), and Nordic citizens, who are also generally younger upon arrival.

The highest average hourly wage (salary before tax) is 258 DKK (∼ 42 US$) and is obtained by EU-old immigrants, closely followed by natives and citizens from the Nordics and workers.¹¹ Perhaps ironically, these immigrants are also among the groups were fewest have stayed in Denmark for more than five years. However, as described earlier, immigrants’ length of stay is expected to be closely linked to their admission classes and life situations. Therefore, it is expected that the largest shares of immigrants who have received Danish education will be those with family ties in Denmark and refugees, while the lowest shares will be found among workers and citizens from EU countries. Table 1 confirms this pattern. GDP per capita in immigrants’ home countries is, on average, highest (65,200 US$ in 2017) among citizens from Nordic countries, followed by natives and EU-old. The average GDP per capita is close to 11,000–13,000 US$ among students, workers, FR, and EU-new, the latter admission class being the most homogenous as measured by the lower standard deviation. As expected, the refugees’ home countries are the poorest (with an average GDP per capita of 3,800 US$). The average weight is, as mentioned earlier, equal to 1 for natives and varies from 1.27 for refugees to 2.05 for EU-new.

Using these weights, the educational level of each admission class is shown in Figure 1. The educational levels of immigrants and natives are important in any discussion of overeducation. First, overeducation requires education above the basic level, and second, the probability of being overeducated may vary with education types and educational systems.

Figure 1.

Educational levels among immigrant employees aged 25–64 by admission class, %.

Refugees represent the largest share of immigrant employees with basic education is found among refugees (43%), followed by FR-O (40%) and FR-N (28%), respectively, while workers and Nordic citizens (8%) and EU-old (9%) represent the lowest shares. Correspondingly, the largest shares of immigrants with long-CHE are workers (56%) and EU-old (49%), followed by Nordic citizens (43%).

4. Empirical Analysis

The probability of being overeducated can be analyzed in a setup where the dependent variable, O_i, is a dummy variable taking the value of 1 if the individual is overeducated and 0 otherwise:

O_{i} = α_{0} + \sum_{c} α_{1 c} A_{i}^{c} + \sum_{t} α_{2 t} E_{i}^{t} + π X_{i} + ϵ_{i}

(1)

where

A_{i}^{c}

and

E_{i}^{t}

are vectors of indicator variables.

A_{i}^{c}

= 1 if individual i has admission class c, and

E_{i}^{t}

= 1 if individual i has education type t and 0 otherwise.

The nine admission classes c of residence are: (1) refugees and family, (2) EU-new, (3) EU-old, (4) FR-N, (5) FR-O, (6) study, (7) work, (8) Nordic, and (9) natives (used as baseline).

Individuals with basic education are not overeducated. Consequently, I disregard the individuals with basic education in this first model and only consider four types t of education: (1) VE, (2) short-CHE, (3) medium-CHE, and (4) long-CHE.

$X_{i}$ is a vector of personal characteristics including gender, experience, and educational field, these being the most basic characteristics that may also impact natives’ probability of being overeducated. As noted in section 2.3, circumstances other than these basic characteristics may vary among immigrants of different admission classes. This empirical analysis does not aim to control for all potential varying circumstances, but simply to estimate the difference between each admission class and natives when controlling for these basic characteristics.

The returns to overeducation (as well as undereducation) can be found by estimating a Mincerian wage equation, where the attained education of individual i is decomposed into three parts — OE_i (duration of overeducation), RE_i (duration of required education), and UE_i (duration of undereducation) — and estimated separately (see Duncan and Hoffman 1981).¹² This is also referred to as the ORU model. For further details, see Supplemental Appendix D.

In this analysis, I use a related approach measuring education by different types t instead of length. The estimation includes the nine admission classes $(A_{i}^{c})$ described earlier and five education types: (1) basic ED, (2) VE, (3) short-CHE, (4) medium-CHE, and (5) long-CHE. Again, $X_{i}$ is a vector of personal characteristics:

\ln (w_{i}) = β_{0} + \sum_{c} β_{1 c} A_{i}^{c} + \sum_{t} β_{1 t} E_{i}^{t} + \sum_{t} β_{2 t} E_{i}^{t} O_{i} + \sum_{t} β_{3 t} E_{i}^{t} U_{i} + γ X_{i} + ϵ_{i}

(2)

Including interaction terms for being overeducated (if O_i = 1 and 0 otherwise) and undereducation (U_i = 1 and 0 otherwise) makes it possible to estimate the wage loss

(β_{2 t}

) due to being overeducated and the wage gain

(β_{3 t}

) due to being undereducated in one's current job compared to other individuals with similar education t and admission class c. Differences in wages by admission class are measured by including the variable

A_{i}^{c}

= 1 if individual i has admission class c and 0 otherwise.

In an extended analysis, the average returns $(β_{1 c t}$ ) to each education type t for each admission class c are estimated using the following equation:

\ln (w_{i}) = β_{0} + \sum_{t} \sum_{c} β_{1 ct} A_{i}^{t, c} + \sum_{t} \sum_{c} β_{2 c t} A_{i}^{t, c} O_{i} + \sum_{t} \sum_{c} β_{3 c t} A_{i}^{t, c} U_{i} + γ X_{i} + ϵ_{i}

(3)

where

A_{i}^{t, c}

is a vector of indicator variables.

A_{i}^{t, c}

= 1 if individual i has education type t and admission class c and 0 otherwise.

5. Results

In the following, the results regarding overeducation related to immigrants’ admission classes are presented. The results focusing on the probability of being overeducated are presented in Section 5.1, while estimates of the wage returns for overeducated individuals are presented in Section 5.2.

5.1 Incidence Rates of Overeducation by Admission Class

The share of overeducated individuals among immigrant employees aged 25–64 in 2017 is reported in Table 2 for each education type and admission class. With respect to education, the table shows that the share of overeducated individuals was highest for short-CHE and lowest for VE. This finding is consistent for natives and immigrants of all admission classes. Hence, a longer education does not automatically lead to a higher overeducation rate; it depends on the type of education.

Table 2.
Overeducation among Natives and Immigrants Aged 25–64 in 2017 by Education Type and Admission Class, %.

Education type: Refugees and family EU- new EU- old Family reuni. (natives) Family reuni. (others) Students Work Nordic Natives

Basic 0 0 0 0 0 0 0 0 0

Vocational 22 48 25 24 30 24 32 15 18

Short-cycle higher 75 95 79 80 83 83 88 78 59

Medium-cycle higher 60 89 53 64 63 71 57 41 24

Long-cycle higher 53 77 48 69 62 62 63 43 50

Mean all¹ 24 61 43 35 29 53 54 38 24

Mean educated ² 42 76 47 49 48 60 59 41 29

No. of obs. 9,887 11,506 12,298 8,608 5,805 7,788 7,564 5,853 1,590,957

Education type:	Refugees and family	EU- new	EU- old	Family reuni. (natives)	Family reuni. (others)	Students	Work	Nordic	Natives
Basic	0	0	0	0	0	0	0	0	0
Vocational	22	48	25	24	30	24	32	15	18
Short-cycle higher	75	95	79	80	83	83	88	78	59
Medium-cycle higher	60	89	53	64	63	71	57	41	24
Long-cycle higher	53	77	48	69	62	62	63	43	50
Mean all¹	24	61	43	35	29	53	54	38	24
Mean educated ²	42	76	47	49	48	60	59	41	29
No. of obs.	9,887	11,506	12,298	8,608	5,805	7,788	7,564	5,853	1,590,957

Note: Includes immigrants who arrived since 1997; initial admission class is noted.

Source: Administrative register information from Statistics Denmark. Weighted sample.

Includes basic educated. ² Excludes basic educated.

There is a larger share of overeducated people among immigrants than natives, and the rate varies substantially across admission classes. The highest overall share of overeducated employees (61%) is found among EU-new, followed by workers (54%) and students (53%). The lowest overall share (24%) is found among refugees. If only those refugees with educational skills above the basic level are considered, the rate is 42% and thereby higher than for a similar subsample of natives (29%).

Similar calculations to those shown in Table 2 regarding undereducation can be made, and the overall shares of undereducated immigrants by admission class are reported in Supplemental Appendix Table C.2. The results show that undereducation is a much less common phenomenon than overeducation, especially among immigrants. It was therefore decided that the current analysis would focus on overeducation.

The overall overeducation rate for men and women for each admission class is shown in Figure 2.A, which demonstrates that the overeducation rate is higher for men than women for all admission classes except EU citizens, where the pattern is the opposite for EU-new. The reason is that fewer women than men from EU-new are using their VE or long-CHE in a job of an appropriate level (see Supplemental Appendix Tables C.3 and C.4).

Figure 2.

Overeducation in 2017 among 25–64-year-old immigrants of different admission classes by gender and country where education was received, %.

Even among EU-new citizens that have received a Danish education, the overeducation rate is high (64%) compared to the majority with a foreign education (61%) (see Figure 2.B). This pattern is rather surprising, since Danish education could make it easier for employers to qualify immigrants’ skills. Under this hypothesis, overeducation rates among immigrants of other admission classes — for example, refugees, FR, workers, and students — would generally be lower if they have received a Danish education. The exception is immigrants from the Nordics, whose educational system is closely linked to the Danish educational system, including access to study within the Nordics.

An important question is whether overeducation is mainly a temporary phenomenon that immigrants experience in the first years of their stay in a new country or if it persists over time. To examine this, the overeducation shares for immigrants who have stayed for over five years and less than five years are illustrated in Figure 3.

Figure 3.

Overeducation among immigrants in 2017 by admission class and years since migration, %.

For all immigrant groups, the share of overeducated is smaller among those who have stayed longer compared to those who have stayed for shorter periods. Among EU-new, the overeducation shares are 58% for those with longer stays (more than five years) and 64% for those with shorter stays. For other groups, the differences between those with long and short stays are larger — especially for FR, refugees, and Nordic citizens. For FR and refugees, this difference could reflect the fact that these groups always intended their stay to be permanent and are therefore more likely to thrive and find a better job match, for example, through a network or by acquiring a Danish education, which can decrease the long-term overeducation rate in these groups.

So far, overeducation patterns have been described separately for different characteristics. To test the extent to which different characteristics explain why some immigrant admission classes are more often overeducated than others, I estimate the probability of being overeducated using a linear probability model as described in equation 1) of section 4. This analysis excludes employees with basic education, as they, by definition, cannot be overeducated. This sample includes 1,370,054 observations, and the main results are shown in Table 3. The full estimation result is found in Supplemental Appendix Table C.5.

Table 3.

The Probability of Being Overeducated among Employees by Admission Class.

	(1)	(2)	(3)
Refugees and family	0.137***	0.124***	0.139***
	(0.0068)	(0.0064)	(0.0056)
Work	0.306***	0.162***	0.050***
	(0.0062)	(0.0061)	(0.0064)
EU-new citizens	0.476***	0.389***	0.247***
	(0.0047)	(0.0047)	(0.0052)
EU-old citizens	0.189***	0.062***	−0.033***
	(0.0051)	(0.0051)	(0.0051)
Family reunified with natives	0.204***	0.212***	0.197***
	(0.0064)	(0.0060)	(0.0053)
Family reunified with others	0.194***	0.205***	0.210***
	(0.0086)	(0.0081)	(0.0070)
Students	0.311***	0.213***	0.157***
	(0.0062)	(0.0062)	(0.0057)
Nordic	0.125***	0.024***	−0.003
	(0.0073)	(0.0072)	(0.0063)
Natives (baseline)	-	-	-
Short-cycle higher education		0.409***	0.400***
		(0.0016)	(0.0016)
Medium-cycle higher education		0.085***	0.186***
		(0.0009)	(0.0011)
Long-cycle higher education		0.300***	0.313***
		(0.0012)	(0.0012)
Vocational education (baseline)		-	-
Controls:
Gender, experience, family type		X	X
Education field			X
No. of obs.	1,370,054	1,370,054	1,370,054
R-squared	0.025	0.127	0.171

Note: Includes immigrants who have arrived since 1997; initial admission class is noted.

Source: Administrative register information from Statistics Denmark. Weighted sample.

Robust std. errors in parentheses *** p < .01. ** p < .05. * p < .1.

The variable of interest is immigrants’ admission classes to reveal the gap in the probability of being overeducated between natives and each admission class. No additional controls are added in Column 1 of Table 3, and EU-new have a 48 percentage point (pp.) higher probability of being overeducated than natives, while students and workers have a 31 pp. higher probability, followed by FR and EU-old (19–20 pp.), and finally, refugees and Nordic citizens (13–14 pp.).¹³

Controls for gender, experience, family, and education type are included in Table 3, Column 2. Adding these controls substantially reduces the estimated effect of all admission classes except FR-O and refugees. It should also be noted that compared to VE, the probability of being overeducated is 41 pp., 9 pp., and 30 pp. higher for short-, medium-, and long-CHE, respectively. These estimates underline the conclusion drawn from Table 2 that overeducation does not necessarily rise with years of education but depends on education type. The full estimation results are available in Supplemental Appendix Table C.5 and show that women, parents, and the more experienced are less likely to be overeducated.

The educational field is added to the controls in Table 3, Column 3. It turns out that the probability of being overeducated is higher among those educated within the social sciences than the natural sciences, while the probability is lower for those educated in health and welfare (see Supplemental Appendix Table C.5). Adding an educational field to the controls further reduces the gap in overeducation compared to natives. The results reveal that these control variables explain the entire gap in overeducation between Nordic citizens and natives and more than explain it (−3 pp.) for EU-old. However, a gap remains for the other admission classes, and the probability of being overeducated is 25 pp. higher for EU-new than natives, while FR have a 20–21 pp. higher probability, followed by students and refugees (14–16 pp.). For workers, the initial gap is reduced to 5 pp., suggesting that most of the gap compared to natives is explained by their having less experience as well as their education types and fields of study.¹⁴

As the estimates in Column 3 of Table 3 are considered the main results regarding differences in the probabilities of being overeducated by admission class, corresponding heterogeneity analyses concerning gender (Supplemental Appendix Table C.5), foreign education (Supplemental Appendix Table C.7), and years since migration (YSM) (Supplemental Appendix Table C.8) are provided in Supplemental Appendix C.

When the results are run separately by gender, I find that among women, EU-new are significantly more likely to be overeducated than FR. This is not the case for men; however, in general, the ranks are quite stable across gender with Nordic citizens and EU-old the least likely to be overeducated (see Supplemental Appendix Table C.5).

Lack of transferability of educational skills from the home to the host country is mentioned as a reason that immigrants cannot find a job that corresponds to their formal qualifications (Chiswick and Miller 2008). Therefore, having received a foreign (as opposed to a Danish) education is expected to increase the probability of being overeducated. Supplemental Appendix Table C.7 reveals that this is indeed the case, as immigrants of all admission classes (except Nordic citizens) have a considerably higher probability of being overeducated if their education was received abroad. These results underline that lack of transferability (or at least employers’ uncertainty about transferability) may play an important role in the phenomenon of immigrants’ overeducation. A Danish education may also signal better host-country language skills, as speaking Danish is a requirement for obtaining at least some education in Denmark.

Immigrants’ host country-specific capital is expected to accumulate with years of stay and may thereby improve their use of their education (Budría and Martínez de lbarreta 2020). The estimated effect of admission class varying by length of stay in Denmark shows that the probability of being overeducated is generally lower for all admission classes the longer they have stayed, except for students, workers, and EU-old (see Supplemental Appendix Table C.8). This result could be driven by selective attrition, as overeducated immigrants might be less likely to stay. Still, it is evident that for FR, students, and EU-new, the risk of being overeducated is much larger than for natives even after they have spent five to ten years in Denmark.

5.2 Returns to Overeducation

To understand the extent to which wage differences between different admission classes exist when controlling for other characteristics, I impose a regression analysis as defined in equation 2 but without taking over- and undereducation into account. The purpose is to show how much of the wage difference is left for each admission class when controlling for the general wage structure related to other characteristics, such as gender, age, and educational field. The outcome is the natural logarithm of hourly wages, and the OLS estimates can approximately be interpreted as wage returns in percentages. The main results are shown in Table 4.

Table 4.
Natural Logarithm of Hourly Wages Relative to Natives, %.

(1) (2) (3) (4)

Refugees and family −0.235* −0.168* −0.153* −0.136*

(0.0031) (0.0030) (0.0030) (0.0030)

Work 0.022* −0.088* −0.056* −0.052*

(0.0050) (0.0048) (0.0050) (0.0049)

EU-new −0.211* −0.165* −0.132* −0.109*

(0.0030) (0.0028) (0.0033) (0.0033)

EU-old 0.079* −0.003 0.027* 0.025*

(0.0045) (0.0039) (0.0039) (0.0038)

Family reunified with natives −0.207* −0.169* −0.156* −0.132*

(0.0032) (0.0030) (0.0030) (0.0030)

Family reunified with others −0.237* −0.165* −0.153* −0.119*

(0.0036) (0.0035) (0.0035) (0.0036)

Students −0.155* −0.143* −0.134* −0.125*

(0.0039) (0.0036) (0.0036) (0.0036)

Nordic 0.043* 0.026* 0.045* 0.040***

(0.0057) (0.0050) (0.0049) (0.0049)

Natives (baseline) - - - -

Controls:

Gender, experience, education type X X X

Education field X X

Over/undereducation by education type X

No. of obs. 1,659,563 1,659,563 1,659,563 1,659,563

R-squared 0.014 0.284 0.295 0.328

	(1)	(2)	(3)	(4)
Refugees and family	−0.235***	−0.168***	−0.153***	−0.136***
	(0.0031)	(0.0030)	(0.0030)	(0.0030)
Work	0.022***	−0.088***	−0.056***	−0.052***
	(0.0050)	(0.0048)	(0.0050)	(0.0049)
EU-new	−0.211***	−0.165***	−0.132***	−0.109***
	(0.0030)	(0.0028)	(0.0033)	(0.0033)
EU-old	0.079***	−0.003	0.027***	0.025***
	(0.0045)	(0.0039)	(0.0039)	(0.0038)
Family reunified with natives	−0.207***	−0.169***	−0.156***	−0.132***
	(0.0032)	(0.0030)	(0.0030)	(0.0030)
Family reunified with others	−0.237***	−0.165***	−0.153***	−0.119***
	(0.0036)	(0.0035)	(0.0035)	(0.0036)
Students	−0.155***	−0.143***	−0.134***	−0.125***
	(0.0039)	(0.0036)	(0.0036)	(0.0036)
Nordic	0.043***	0.026***	0.045***	0.040***
	(0.0057)	(0.0050)	(0.0049)	(0.0049)
Natives (baseline)	-	-	-	-
Controls:
Gender, experience, education type		X	X	X
Education field			X	X
Over/undereducation by education type				X
No. of obs.	1,659,563	1,659,563	1,659,563	1,659,563
R-squared	0.014	0.284	0.295	0.328

Note: Includes immigrants who arrived since 1997; initial admission class is noted.

Source: Administrative register information from Statistics Denmark. Weighted sample.

Robust std. errors in parentheses *** p < .01. ** p < .05. * p < .1.

In Column 1 of Table 4, no controls are added, showing that the hourly wages of refugees, FR, and EU-new are, on average, 21–24% lower than those of natives. For students, the gap is smaller, as they earn 16% less than natives. Workers, on the other hand, earn 2% more than natives, while Nordic citizens earn 4% more, and EU-old earn 8% more.

Controls for differences in gender, experience, family type, and education type are added in the second column of Table 4, while educational field is added in the third column.¹⁵^,¹⁶ Adding these controls lowers the wage gap for refugees, EU-new, students, and FR to 13–16%. For workers, the hourly wage is 6% less than for comparable natives, while the wage gain for EU-old is reduced to 3%, but for Nordic citizens, it is still close to 4%.

An indicator of being under- or overeducated for each education type is added in Column 4 of Table 4, thereby controlling for the general wage loss/gain of over-/undereducated employees. The idea is to test the extent to which the higher probability that immigrants are overeducated (and the lower probability that they are undereducated) can explain the gap between their wages and those of natives. The results show that wage gaps are only slightly reduced for all admission classes when this control variable is added. Hence, immigrants do not seem to be rewarded like natives when they are overeducated.

Heterogeneity analyses corresponding to the specification in Table 4, Column 4 are provided in Supplemental Appendix C related to gender (Supplemental Appendix Table C.11), foreign education (Supplemental Appendix Table C.12), and YSM (Supplemental Appendix Table C.13).

When results are run separately by gender, the ranking of admission classes is rather similar across gender, but the estimated wage gaps between immigrants and natives are generally wider among men, presumably due to native men earning more than native women (see Supplemental Appendix Table C.11).

Distinguishing between immigrants who have received foreign or Danish education, the analysis shows that wages are higher for workers, EU-old, and Nordic citizens if they received a foreign education, while the opposite is the case for refugees and FR, and there is no difference for EU-new and students. The results suggest that for refugees and FR, in particular, foreign education is related to a wage loss due to the lack of transferability of foreign-obtained educational skills (see Supplemental Appendix Table C.12).

Distinguishing between short- and long-stay immigrants in Denmark, the results highlight that wage gaps are generally smaller for longer- than shorter-stay immigrants from each admission class. The only exceptions are Nordic citizens and EU-new, where the differences are small or insignificant (see Supplemental Appendix Table C.13). Apart from Nordic citizens, workers, and EU-old, all admission classes still experience considerable negative wage gaps after ten years of stay.

The analysis has so far concentrated on the average wage gap ( $β_{1 c}$ ) between natives and group members of each admission class, including a set of controls. Below, the model is extended, as described in equation 3, by adding an interaction term allowing wage gaps for over-/undereducated individuals to differ by admission class. The focus in this analysis is on the wage penalty for being overeducated ( $β_{2 c t}$ ). The model includes gender, experience, family type, education type, and field as controls as in the previous analysis shown in Table 4, Column 4.¹⁷ The corresponding estimated wage gaps between overeducated individuals in each admission class and natives are shown in Figure 4. The estimates are grouped by education type. In each of the four graphs, the full line designates the estimate for natives, while the dashed line designates a value of zero.¹⁸

Figure 4.

Wage loss due to overeducation by admission class and education type, %.

Figure 4.A shows that among overeducated natives with vocational backgrounds, the wage penalty for being overeducated, as opposed to being correctly matched, is quite large (−0.185%). For most admission classes, the wage gap compared to natives is rather small ( < 3%) or simply insignificant, which means that overeducated immigrants in these classes experience the same large wage penalty as natives. However, for EU-new students and Nordic citizens, the wage gap is positive (2–4%), which means that overeducated immigrants in these classes do indeed experience a wage penalty (e.g., students: −0.185 + 0.035 = −0.150) but a lower one than natives. In sum, the overeducated vocational-educated face wage penalties ranging from 17% to 24%.

In accordance with Skaksen (2016), Figure 4.B reports a small average wage premium (1.7%) for overeducated natives with short-CHE. In contrast, a considerable wage gap for overeducated immigrants with short-CHE is present for all admission classes (−24 to −17%), although it is lower than that experienced by Nordic citizens (−7%).

Natives with medium-CHE are paid the same, overeducated or not (see Figure 4.C). However, immigrants with medium-CHE experience wage gaps, although these are once more lower (−3%) for Nordic citizens, followed by EU-old (−8%), workers (−17%), students, EU-new (−26 to −25%), and FR and refugees (−34 to −33%).

Finally, overeducated natives with long-CHE experience a wage loss of 1.9% (see Figure 4.D). EU-old and Nordic citizens experience a similar wage loss to that of natives. All other admission classes experience huge wage gaps, ranging from −25% for students and workers to −36% for FR and −41% for refugees.

In sum, wage returns vary substantially by education type and immigrants’ admission classes. Overeducated vocational-educated immigrants experience a considerable wage loss, but so do natives, and some admission classes (citizens from the Nordics, students, and EU-new) even earn more than natives, while others earn less. Overeducated higher-educated immigrants generally earn less than natives. Except for citizens from the Nordics and old-EU countries, most overeducated immigrants with short-CHE and long-CHE earn 17–24% and 25–36% less, respectively, than if they had a job at a suitable level. For highly educated refugees, the wage loss is even larger. These results emphasize the cost of being overeducated that many immigrants face.

A robustness check was performed to analyze overeducation in the standard ORU model (see Supplemental Appendix D), where education is measured in years instead of education types. The result confirms that returns by years of education for natives (9%) are in line with the previous literature (Leuven and Oosterbeek 2011), while returns to overeducation are to the larger side (6%) (see Supplemental Appendix Table D.1). For Nordic citizens and EU-old, the returns to overeducation are also quite high (5–6%), but as expected, based on Table 4, they are lower for immigrants from all other admission classes. The robustness check thereby confirms the overall results of the wage returns found in the main analysis based on education types.

6. Conclusion

This analysis introduces a new angle to the overeducation literature by focusing on how overeducation among immigrants differs by admission class, the wage losses overeducated immigrants face, and why these losses vary across admission classes. To the best of my knowledge, immigrants’ overeducation has not previously been measured by admission class; however, admission classes are important for at least two reasons. First, admission classes are closely linked to the reasons that people migrate and can provide new insight into why overeducation among immigrants occurs. Second, granting residence permits through different admission classes is a policy tool for politicians to manage migration flows and set the conditions under which immigration takes place.

The analysis is based on administrative data and covers all native and immigrant employees aged 25–64 who were resident in Denmark on 1 January 2017. The data include information regarding demography, education, labor market attachment, hourly wages, and immigration history, including admission class. Information regarding immigrants’ admission classes is available from 1997, and the analysis therefore concentrates on immigrants who have arrived in Denmark since that date. Immigrants are classified according to the admission classes of their initial residence permits.

The results show that the overall share of overeducated individuals is highest among citizens from new-EU countries (61%), followed by immigrants with a work permit (54%), students (53%), immigrants from old-EU countries (43%), the Nordics (38%), family reunified with natives (35%), and others (29%). The lowest share is found among refugees and natives (24%). The low share of overeducated refugees is due to the fact that few refugees have skills above the basic level. If low-skilled people are disregarded, the overeducation rate is higher for refugees than natives.

In Denmark, the share of overeducation varies by type of education but not unambiguously by length of education. The share of overeducated individuals is lowest among the vocational-educated (i.e., those who completed 14 years of education) and highest among short-cycle higher-educated (i.e., those who completed 15–16 years of education), with medium- and long-cycle higher-educated in between. It turns out that this pattern is consistent for natives and immigrants of all admission classes.

The gap between Nordic citizens and natives in the probability of being overeducated is explained when demographics and educational characteristics are controlled for. For citizens from old-EU countries, the gap is more than explained (−3 pp.), but a gap remains for all other admission classes. Hence, the risk of being overeducated is 25 pp. higher for citizens from new-EU countries than natives, while family reunified with natives and others have a 20–21 pp. higher risk, followed by students and refugees (14–16 pp.) and workers (5 pp.). In general, the risk of being overeducated is lower among immigrants with longer stays. However, citizens from new EU countries, reunified family, and students, in particular, have much higher risks of being overeducated than natives, even after five to ten years’ residence in Denmark.

Turning to wages, I find that citizens from the Nordics and old-EU countries earn, on average, 3–4% more than natives when controlling for differences in education and demographics. In contrast, immigrants with work permits earn 6% less and all other admission classes 13–16% less than natives. Furthermore, the wage penalties tend to persist among immigrants with longer periods of residence and are a result not only of immigrants more often being overeducated than natives, but also, and importantly, of overeducated immigrants being paid less. However, the wage returns vary substantially by education types and immigrants’ admission classes.

Overeducated vocational-educated individuals (immigrants as well as natives) experience considerable wage losses (14–25%) compared to having work appropriate to their educational level. Some admission classes (students and citizens from the Nordics and new EU countries) earn more than overeducated natives, while all others earn less.

Higher-educated natives face low or no wage losses when they are overeducated for their work. In contrast, overeducated immigrants with short-, medium-, or long-cycle higher education generally earn 17–36% less when they are overeducated. Only citizens from the Nordics and old-EU countries experience low or no wage losses, whereas workers, students, and new-EU citizens experience substantial wage penalties. The largest wage penalties (up to 41%) are found among refugees and family reunified with natives and others.

In general, there is a low risk that immigrants from the Nordics and old-EU countries are overeducated and a high chance one that they earn the same or even higher wages than natives, which is most likely because their educational skills are more transferable and they have better outside options than other immigrant groups. Citizens of new-EU countries, on the other hand, face the same (liberal) possibilities to obtain residence in Denmark, but outside options (e.g., wages in the home country) are worse, and they may therefore be more willing to work in Denmark even if they are not rewarded for their educational skills.

Immigrants with work permits typically come from less wealthy countries, and their access to Denmark is restricted by job and wage requirements. Their (long) education is not rewarded as it is for natives but is presumably rewarded better than it would be at home. Students earn less than workers, but overeducated students exhibit many of the same features regarding overeducation levels and wage gaps.

The share of overeducated individuals is lower among refugees, family reunified with natives, and others, but the wage gaps for overeducated individuals in these groups are high given their education types. Refugees generally have poor outside options; these options are better for reunified family, but their migration decisions are based on the utility of the whole family, which may explain why they choose to stay despite being overeducated.

In sum, there may be rational reasons behind immigrants’ overeducation, and information regarding admission classes seems to help us understand why overeducation is more common among immigrants than natives and why it varies among groups. Still, for countries experiencing an ageing population and increasing lack of skilled labor, it is crucial to lower the barriers that prevent immigrants from using their education in appropriate positions. The Danish case shows that more attention could be given to the transferability and use of the qualifications of short-cycle and higher-cycle immigrants, in general, and immigrants from new-EU countries, in particular. These results are relevant for old-EU countries receiving immigrants from new member states, but also more broadly speaking for highly developed countries with a need for skilled labor to assist their ageing populations.

Supplemental Material

sj-docx-1-mrx-10.1177_01979183241264991 - Supplemental material for How Does Overeducation Depend on Immigrants’ Admission Class?

Supplemental material, sj-docx-1-mrx-10.1177_01979183241264991 for How Does Overeducation Depend on Immigrants’ Admission Class? by Marie Louise Schultz-Nielsen in International Migration Review

Footnotes

Acknowledgments

I would like to thank the three anonymous referees for their constructive remarks and suggestions that improved the manuscript. A special thank goes to Drilon Helshani and Andreas Eklundh Sørensen for computational assistance and to Francesca Truffa, Bodil Wullum Nielsen, Claus Larsen, Vibeke Jakobsen, and participants in the Midwest Economics Association's 2023 meeting for their valuable comments and suggestions on the manuscript.

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Rockwool Foundation (grant number 1187).

ORCID iD

Marie Louise Schultz-Nielsen

Supplemental Material

Supplemental material for this article is available online.

Notes

References

Adda

Dustmann

Görlach

J. S.

. 2022. “The Dynamics of Return Migration, Human Capital Accumulation, and Wage Assimilation.” The Review of Economic Studies 89 (6): 2841–71. doi:10.1093/restud/rdac003.

Aleksynska

Tritah

. 2013. “Occupation-Education Mismatch of Immigrant Workers in Europe: Context and Policies.” Economics of Education Review 36: 229–44. doi:10.1016/j.econedurev.2013.06.001.

Becker

G. S.

1962. “Investment in Human Capital: A Theoretical Analysis.” Journal of Political Economy 70 (5): 9–49. doi:10.1086/258724.

Becker

G. S.

. 1964. Human Capital. New York: Columbia University Press.

Bertrand

Mullainathan

. 2004. “Are Emily and Greg More Employable Than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination.” American Economic Review 94 (4): 991–1013. doi:10.1257/0002828042002561.

Bodvarsson

Ö. B.

Simpson

N. B.

Sparber

. 2013. “Chapter 1: Migration Theory.” In In Economics of International Migration Vol. 1A, edited by Chiswick

B. R.

Miller

P. W.

, 3–52. Amsterdam: North-Holland.

Bodvarsson

Ö. B.

Van den Berg

. 2009. The Economics of Immigration. Heidelberg: Springer.

Bratsberg

Terrell

. 2002. “School Quality and Returns to Education of U.S. Immigrants.” Economic Inquiry 40 (2): 177–98. doi:10.1093/ei/40.2.177.

Brell

Dustmann

Preston

. 2020. “The Labor Market Integration of Refugee Migrants in High-Income Countries.” Journal of Economic Perspectives 34 (1): 94–121. doi:10.1257/jep.34.1.94.

10.

Budría

Martínez de lbarreta

. 2020. “Educational and Skills Mismatches among Immigrants: The Impact of Host Language Proficiency.” IZA Discussion Papers, 13030.

11.

Burdett

1978. “A Theory of Employee Job Search and Quit Rates.” American Economic Review 68 (1): 212–20. http://www.jstor.org/stable/1809701.

12.

Chiswick

B. R.

Miller

P. W.

. 2008. “Why is Payoff to Schooling Smaller for Immigrants?” Labour Economics 15 (6): 1317–40. doi:10.1016/j.labeco.2008.01.001.

13.

Chiswick

B. R.

Miller

P. W.

. 2010. “The Effects of Educational-Occupational Mismatch on Immigrant Earnings in Australia, with International Comparisons.” International Migration Review 44 (4): 869–98. doi:10.1111/j.1747-7379.2010.00829.x.

14.

Christensen

L. B.

Hallberg

Homann

Kjær

K. U.

Koch

I. E.

Lassen

Mikkelsen

P. B.

Thomassen

Vedsted-Hansen

. 2006. Udlændingeret (3. Edition). Copenhagen: Jurist- og Økonomforbundets Forlag.

15.

Clausen

B. K.

Skaksen

J. R.

. 2018. “Underudnyttelse af uddannelsesmæssige kompetencer.” ROCKWOOL Foundation Research Unit, Study Paper 52.

16.

Damm

A. P.

2014. “Neighbourhood Quality and Labour Market Outcomes; Evidence from Quasi-Random Neighborhood Assignment of Immigrants.” Journal of Urban Economics 79: 139–66. doi:10.1016/j.jue.2013.08.004.

17.

Dell’Aringa

Pagani

. 2011. “Labour Market Assimilation and Over-Education: The Case of Immigrant Workers in Italy.” Economica Politica 28 (2): 219–40. doi:10.1428/35095.

18.

Duleep

H. O.

Regets

M. C.

. 1999. “Immigrants and Human-Capital Investment.” American Economic Review 89 (2): 186–91. doi:10.1257/aer.89.2.186.

19.

Duncan

G. J.

Hoffman

S. D.

. 1981. “The Incidence and Wage Effects of Overeducation.” Economics of Education Review 1 (1): 75–86. doi:10.1016/0272-7757(81)90028-5.

20.

Dustmann

1993. “Earnings Adjustment of Temporary Migrants.” Journal of Population Economics 6 (2): 153–68. doi:10.1007/BF00178559.

21.

Dustmann

Görlach

J. S.

. 2016. “The Economics of Temporary Migrations.” Journal of Economics Literature 54 (1): 98–136. doi:10.1257/jel.54.1.98.

22.

Friedberg

2000. “You Can’t Take It With You? Immigrant Assimilation and Portability of Human Capital.” Journal of Labor Economics 18 (2): 221–51. doi:10.1086/209957.

23.

Green

Kler

Leeves

. 2007. “Immigrant Overeducation: Evidence from Recent Arrivals to Australia.” Economics of Education Review 26: 420–32. doi:10.1016/j.econedurev.2006.02.005.

24.

Hansen

M. F.

Schultz-Nielsen

M. L.

Tranæs

. 2017. “The Fiscal Impact of Immigration to Welfare States of the Scandinavian Type.” Journal of Population Economics 30: 925–52. doi:10.1007/s00148-017-0636-1.

25.

Hartog

2000. “Overeducation and Earnings: Where Are We, Where Should We Go?” Economics of Education Review 19: 131–47. doi:10.1016/S0272-7757(99)00050-3.

26.

Hatton

T. J.

2009. “The Rise and Fall of Asylum: What Happened and Why?” The Economics Journal 119: 183–213. doi:10.1111/j.1468-0297.2008.02228.x.

27.

Joona

P. A.

Gupta

N. D.

Wadensjö

. 2014. “Overeducation Among Immigrants in Sweden: Incidence, Wage Effects and State Dependence.” IZA Journal of Migration 9 (3). doi:10.1186/2193-9039-3-9.

28.

Kalfa

Piracha

. 2018. “Social Networks and the Labour Market Mismatch.” Journal of Population Economics 31: 877–914. doi:10.1007/s00148-017-0677-5.

29.

Kler

2007. “A Panel Data Investigation into Overeducation Among Tertiary Educated Australian Immigrants.” Journal of Economic Studies 34 (3): 179–93. doi:10.1108/01443580710772759.

30.

Kreiner

C. T.

Svarer

. 2022. “Danish Flexicurity: Rights and Duties.” Journal of Economic Perspectives 36 (4): 81–102. doi:10.1257/jep.36.4.81.

31.

Leuven

Oosterbeek

. 2011. “Overeducation and Mismatch in the Labor Market.” In In Handbook of the Economics of Education, edited by Hanushek

Machin

Woessmann

. Vol. 4, 283–325. Amsterdam: North-Holland.

32.

Sweetman

. 2014. “The Quality of Immigrant Source Country Educational Outcomes: Do They Matter in the Receiving Country?” Labour Economics 26: 81–93. doi:10.1016/j.labeco.2013.12.003.

33.

Lindley

2009. “The Overeducation of UK Immigrants and Minority Ethnic Groups: Evidence from the Labour Force Survey.” Economics of Education Review 28: 80–9. doi:10.1016/j.econedurev.2007.11.003.

34.

Maani

S. A.

Wen

. 2021. “Over-Education and Immigrant Earnings: A Penalized Quantile Panel Regression Analysis.” Applied Economics 53 (24): 2771–90. doi:10.1080/00036846.2020.1869169.

35.

Malchow-Møller

Munch

J. R.

Skaksen

J. R.

. 2007. “Udenlandsk arbejdskraft i Danmark – konsekvenser for løn og beskæftigelse.” ROCKWOOL Foundation Research Unit, Study Paper 16.

36.

Mincer

1974. Schooling, Experience, and Earnings. New York: Columbia University Press.

37.

Mincer

. 1978. “Family Migration Decisions.” Journal of Political Economy 86 (5): 749–73. doi:10.1086/260710.

38.

Mortensen

D. T.

1988. “Wages, Separations, and Job Tenure: On-the-Job Specific Training or Matching?” Journal of Labor Economics 6 (4): 445–71. doi:10.1086/298191.

39.

Nielsen

C. P.

2011. “Immigrant Overeducation: Evidence from Denmark.” Journal of Population Economics 24 (2): 499–520. doi:10.1007/s00148-009-0293-0.

40.

Nilas

2021. Migrationsjura – udenlandsk arbejdskraft og EU-borgere. Copenhagen: Jurist- og Økonomforbundets Forlag.

41.

OECD. 2023. International Migration Outlook. Paris: OECD.

42.

Schultz-Nielsen

M. L.

2017. “Labour Market Integration of Refugees in Denmark.” Nordic Economic Policy Review 7 (2017): 45–72. doi:10.6027/TN2017-520.

43.

Schultz-Nielsen

M. L.

Skaksen

J. R.

. 2017. “Indvandreres uddannelse.” ROCKWOOL Foundation Research Unit, Study Paper 48.

44.

Sjaastad

L. A.

1962. “The Costs and Returns of Human Migration.” Journal of Political Economics 70 (5): 80–93. doi:10.1086/258726.

45.

Skaksen

J. R.

2016. Indvandreres uddannelse”. ROCKWOOL Foundation Research Unit, Study Paper 46.

46.

Skaksen

J. R.

. 2018. Afkast af uddannelse. Det samfundsmæssige og individuelle rationale. Odense: University Press of Southern Denmark.

47.

Statistics Denmark. 2023. Indvandrere i Danmark 2023. Copenhagen: Statistics Denmark.

48.

Thurow

L. C.

1975. Generating Inequality. Mechanisms of Distribution in the U.S. Economy. New York: Basic Books.

49.

Wald

Fang

. 2008. “Overeducated Immigrants in the Canadian Labour Market: Evidence from the Workplace and Employee Survey.” Canadian Public Policy 34 (4): 457–79. doi:10.3138/cpp.34.4.457.

50.

Wen

Maani

S. A.

. 2018. “A Panel Study of Immigrants’ Overeducation and Earnings in Australia.” International Migration 56 (2): 1–24. doi:10.1111/imig.12425.

51.

Zwysen

2019. “Different Patterns of Labor Market Integration by Migration Motivation in Europe: The Role of Host Country Human Capital.” International Migration Review 53 (1): 59–89. doi:10.1177/0197918318767929.

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