Differences in the labor market entry of second-generation immigrants and ethnic Danes

2.1 Existing literature on labor market performance on second & higher generation immigrants

Evidence from the ‘old’ immigrant-receiving countries (e.g. US, Canada, the UK, and Australia) in which the full age range of second-generation immigrants is observed shows that the second-generation overall appear to be doing almost as well in the labor market as their ethnic native counterparts (see e.g. Card ([2005]), Aydemir and Sweetman ([2007]), Miller and Chiswick ([2007]), Dustman et al. ([2004])). In part this is due to the relatively high level of education of second-generation immigrants compared to ethnic natives in these countries (i.e. positive selection), with the exception of certain ethnic groups.

In continental Europe earlier generations of relatively educated guest workers and immigrants from other European countries are being replaced by refugees and asylum-seekers, who tend to have lower educational attainment and employability. Not surprisingly, the evidence from these settings shows second generation immigrants lagging substantially behind their ethnic native counterparts in terms of educational achievement and, in particular, labor force participation, success in finding a job and wages (Nielsen et al. ([2003]), Rooth and Eckberg ([2003])).

A number of studies find that parental capital and family background are important for educational attainment and achievement of the second-generation immigrants (Ammermüller ([2007]), Rangvid ([2007])) and even more so for their waiting time until the first job and the duration of their first job spell. Besides parental capital, the ethnic concentration in a neighborhood matters for immigrant integration. As extensively discussed in the classic papers by Chiswick and Miller ([2005]) and Borjas ([1995]), ethnic neighborhood concentration reduces immigrants’ host country language skills, and thereby, educational achievement and earnings, and are detrimental also due to their independent negative effects on earnings. Enclaves could have positive effects, however, in terms of lowering the costs of ‘ethnic goods’ and serving as important information networks.

In an attempt to take such differences into account Behrenz et al. ([2007]) construct matched comparison samples of ethnic natives and second-generation immigrants according to parents’ socioeconomic background, age, occupational status, and county of residence. Results from probit employment models show that second-generation immigrant males and females face at least a 10 percentage point lower probability of having a job than do similar ethnic natives even after matching. The fact that native–second generation immigrant differences persist in Scandinavian countries even after taking parental and neighborhood networks into account have led some researchers to point instead to demand side factors and institutional explanations.

2.2 Existing literature on demand-side and institutional explanations

This literature includes economic models of ethnic discrimination in the labor market (see Becker ([1957])) arising from either employer prejudice against workers of foreign descent or from employee unwillingness to work alongside coworkers of foreign origin. In a different view, the statistical discrimination or screening model (see e.g. Arrow ([1972]) and Phelps ([1972])) explains the existence of labor market discrimination against ethnic minorities as cost-saving behavior on the part of employers, who use ethnicity as an effective sorting device when determining the (unknown) productive abilities of workers. The implication of both models is that individuals of foreign descent will be discriminated against in the labor market in terms of hiring, firing and remuneration. These individuals may even be given less training if employers perceive them to be less productive, which may reinforce statistical discrimination against them if, as a consequence of obtaining less training, their productivity suffers (Coate and Loury ([1993])). Empirical investigations from different countries using different econometric models have found that even after controlling for age, education and other background characteristics, immigrants earn lower wages than ethnic natives (see e.g. Altonji and Blank ([1999])). These unexplained differences are commonly attributed to selective screening by employers or discrimination.

In terms of institutions, a recent survey paper by Schröder ([2010]) focuses on the role of recruitment channels and other labor market institutions. Most jobs vacancies are filled through personal networks and informal recruitment channels, and there are differences in both the size and quality of the networks of ethnic natives and immigrants. Furthermore, institutions that pave the way from school to work, such as training or apprenticeship slots can foster and prolong labor market disadvantage. Colding ([2005]) finds a much higher dropout rate from vocational education in Denmark among children of immigrants (60% compared to 30% among ethnic natives), which is presumably due to the difficulties immigrant children face in finding an internship. Thus, if employers perceive immigrant children as being harder to train or more risky to employ in a setting where the wage for unskilled labor is given, this will result in higher unemployment for immigrant children compared to ethnic natives, even if their productivity is the same.

Given the evidence above on the importance of taking into account both individual and employer behavior, we describe below a methodological approach that can address these behaviors in a common framework.

2.3 The equilibrium search approach

Formally providing a structure for the workings of the demand side of the labor market and its institutions is the focus of equilibrium search models (ESMs) of the labor market.

We know of only a few attempts to apply an ESM to ethnic native-immigrant wage differentials and none to second generation immigrants. The most relevant are two unpublished paper by Bartolucci ([2008]) and Bowlus et al. ([2013]) both analyzing first generation immigrants. Bartolucci finds that immigrants are more productive than ethnic natives in similar jobs, are more mobile, but have lower bargaining power. Bowlus et al. conclude that immigrants take on average 13 years to acquire the native search parameters, using a version of the ESM used here allowing for different worker types in the same labor market.

3.1 The equilibrium search model (ESM)

Job search theory extends neoclassical theory by incorporating the notion that workers only have partial information about employment opportunities. Given this fact, employers have monopsony power in the sense that a higher wage relative to those offered by competing employers will attract more workers giving employers an incentive to differentiate wage policy, which implies wage dispersion. In the search model, differences in firm behavior are possible because of the presence of search friction, and this can explain the persistence of differences even over time, in contrast to the wage equation approach (Mortensen ([2003])).

The first attempt to use an ESM to interpret wage differentials was made by Bowlus ([1997]). In her paper, Bowlus focused on the effect of gender differences in friction patterns on wage differentials. We are similarly interested in applying the search model to try to identify if and how labor market dynamics and friction explain ethnic native–immigrant entry wage and early career differences in Denmark.⁴ We follow Bowlus et al. ([2001]) and base our analysis on the Burdett-Mortensen ESM (Burdett and Mortensen ([1998])).

showing that the optimal reservation wage depends on market opportunities as summarized in the wage-offer distribution G(w), the transition rates and supply-side factors. Lastly, h is the highest wage paid to the workers and b is unemployment benefit.⁵

where prices have been normalized to one, the firm productivity is P and the wage for the worker is w.

where P _j is the productivity, α _j is the fraction of firms with productivity P _j or less, r _j is the lowest wage offered by a firm of type j, and h _j is the highest wage paid by these firms. To estimate the model, we use the methodology developed by Bowlus et al. ([1995]).

Overall, the ESMs are good for exploring wage variation, which is difficult to correlate with observables. However, it is less well suited for understanding the effect of variables that correlate with wages. Thus, it is important to compare groups that are similar in terms of wage-enhancing characteristics. Since the sample we examine only contains young individuals, and we estimate the model separately for males and females as well as for ethnic natives, Western, and non-Western second-generation immigrants, and we divide the sample into three educational levels (high school, vocational school and primary school), we believe that the subsamples are homogenous.⁷ For the ethnic natives, we furthermore estimate separate models for a matched sample with characteristics matching second-generation immigrants.

After estimation, we compare estimates across subsamples. If all the parameters are similar across two subsamples (i.e. natives and immigrants), then it is reasonable to assume that all workers in the two subsamples are in the same market, behave similarly and are treated equally. On the other hand, if some of the parameters are different across two subsamples, then we technically assume that a separate market exists for each of the two subgroups, and the homogenous search model applies to each market, and the degree of search friction will influence the degree of wage dispersion. For example, Nielsen et al. ([2003]) found significant differences in the job-finding rate and the duration of the first employment spell between ethnic natives and second-generation immigrants, differences that were partially explained by parental capital and neighborhood effects. These differences in job arrival and separation across ethnic natives and second-generation immigrants could reflect potential differences in firm behavior, and, thus, cannot be handled in traditional human capital wage regressions. Although hazard based transition reduced-form models also are able to estimate job and unemployment durations, they cannot simultaneously estimate the wage offer distribution. Thus, the added value of the ESM approach is the joint modelling of the wage distribution and the unemployment and job durations, including in particular that the job arrival rate is conditional on wages, however, at the cost of assuming separate markets and thus ruling out any spillovers and externalities between competing groups.

3.2 The ESM and network contributions

Recent research has shown that an individual’s network contributes significantly to the job search process. In particular, individuals’ successes or failures in the labor market are, to a large extent, influenced by the characteristics of the social networks in the local neighborhood. For example, Calvo-Armengol and Jackson ([2004]) show that individuals living close to a large number of employed neighbors are more likely to have jobs than are individuals living in areas with fewer employed neighbors. Furthermore, Arrow and Borzekowski ([2004]) show by way of simulations that differences in the number of ties workers have with firms can lead to substantial inequality and explain roughly 15% of the unexplained gap in wages and a substantial part of the disparity between black and white incomes in the US.

Pinkster ([2009]) uses the ‘social isolation’ hypothesis according to which individuals living in disadvantaged neighborhoods are cut off from good job opportunities primarily because most of their contacts (their neighbors) are also out of work and, thereby, unable to provide them with important information about jobs openings and employers. High employment among one’s neighbors, on the other hand, can increase the chances of getting tips about openings, valuable information about employers and even recommendations. Furthermore, neighbors could directly assist with writing job applications or share their experiences with certain employers.

We believe that different search patterns among our groups might be a problem. This explanation has neither been captured in earlier wage regressions, employment regressions nor in duration models and this would not be captured in the ESM we use. If it is believed that second-generation immigrants behave differently in terms of the number of jobs applied for, how they write their applications or how they prepare for the job interview, this difference will in the search model transform into differences in the arrival rates of offers and give biased results. The solution is either to extend the existing search model or use matching to ensure homogeneity. We follow the second approach.

Because of the difficulty in obtaining a direct measure of contacts, a key challenge in the literature is how to construct a measure of an individual’s network. For our paper, this problem is even more complicated as we want to use the network variables in a matching analysis and, therefore, need to use variables that are comparable across ethnicity. We follow the previous literature and assume networks are geographic areas (see e.g. Topa ([2001])). The data includes information on the postal code area in which the individual is residing. Based on this information, we construct a variable which we call ‘postal code employment rate’ defined as the percentage of individuals employed in the postal code area for each of 1,019 postal code areas. We thus follow Andersson et al. ([2009]) and use small homogenous areas in terms of population characteristics, economic status and living conditions, for making the assumption of conditional independence of contacts more plausible. We expect that the ‘postal code employment rate’ captures the relevant network effect for finding jobs. We furthermore control for differences in labor market abilities through ten variables to proxy parental capital: four dummy variables for the education level of each of the parents and two dummy variables indicating whether the father and mother, respectively, are employed.⁸

4.1 Matching

The employment rate for ethnic Danes is different for Western second-generation mothers, and non-Western second-generation fathers and mothers. But when matched, the parental employment rate in the ethnic Danish twin sample is at the same level as that for immigrant parents. Finally, to find ethnic Danish twin counterparts, we have also matched on individuals’ own age within gender groups, and on the year their first spell starts.

To show the effect of matching on the wage distribution, we include Figure 1, showing the empirical distribution for males and females, respectively, before and after matching. The upper and lower left-hand side graphs present the empirical CDFs of wages from the first full-time job after graduation by ethnicity for females and males, respectively. For both genders, the CDF for ethnic Danes lies to the right of the CDF’s of second-generation immigrants. The graphs confirm the findings in Tables 1 and 2, that after matching, most of the wage difference disappears, however, second-generation non-Western immigrants, in particular males, still have a lower wage. For second-generation Western immigrants, on the other hand, the CDF is now to the right of that of the ethnic Dane CDF for most of the distribution for females and in the lowest part for males.

4.2 Education level

4.3 Main variables in the ESM

For males, for all three groups yearly income is higher for ethnic Danes. When examining the average spell length there seems to be a relation between the two for individuals with only either primary school education or vocational education – this is especially evident in terms of employment spells. However, since many of the differences are still present after matching, it indicates that more can be learned by estimating an ESM.

5.1 Heterogeneous ESM models

5.2 The importance of network in the analysis

In Denmark, especially in the largest cities, such as Copenhagen, Aarhus and Odense, a majority of immigrants are residentially segregated either through self-selection or because Danish immigrant housing policies, in place between 1986 and 1999, have placed immigrants in the same areas. Immigrants placed in the same city sections by the government often ended up in areas with a high unemployment rate among ethnic Danes Damm ([2009]). For this reason, we include the characteristics of the network in our matching procedure. As a robustness check, however, we exclude the network variables in the matching procedure and re-estimate the ESM for the different subsamples.

Having estimated the models, we test the hypothesis that network has a significant effect, and find because of the small samples and, hence, large standard errors, the hypothesis is most often rejected. However, if we examine the rates with and without network in the matching procedure, the differences are relatively large. For males, in most cases, network has a negative effect on the arrival rate of jobs, both while being unemployed and employed, meaning that the likelihood of finding a job is lower when you match on network. For females ethnic Danish twin network mostly affects those with a vocational education. For these workers, matching on network has a negative effect while they are unemployed. Comparing across ethnicity, the results are very clear when including network in the matching analysis. In particular, the arrival rate of jobs for the ethnic Danish twins is lower, indicating that living in an area with a high unemployment rate has negative effects.¹⁵

Endnotes

¹Second-generation immigrants: Individuals born in Denmark with both parents born outside Denmark. Ethnic Danes: Individuals with Danish-born parent(s).

²Observationally equivalent ethnic Danes (labeled Ethnic Danish ‘twin’): An ethnic Dane with similar background characteristics as a second-generation immigrant, found by using matching (see Section 4.2).

³In Denmark, the second generation is still rather young: 75% were under age 15 in 2006 (Leibig ([2007])).

⁴The search model has also been used to discuss race wage differentials in the US (see Bowlus and Seitz ([2000]) and Bowlus et al. ([2001])).

⁵For simplicity of exposition, workers and employers are assumed not to discount the future (see Burdett and Mortensen ([1998])).

⁶Several papers have shown that the inconsistency between the empirical and theoretical wage distributions can be reconciled if firms are heterogeneous in productivity. We follow Bowlus et al. ([1995]) where heterogeneity can be viewed as arising from a finite number of firm types i.e. a discrete distribution.

⁷Western second-generation immigrants: Children of immigrants from Western countries. The definition of Western and non-Western countries is taken from Statistic Denmark’s homepage: http://www.dst.dk/Vejviser/dokumentation/hvadbetyder.aspx?keyword=vestligelande. Western countries include EU, Andorra, Australia, Canada, Iceland, Liechtenstein, Monaco, New Zealand, Norway, San Marino, Switzerland, USA and the Vatican. All other countries are considered non-Western. In earlier work, we have estimated the ESM model for the non-Western second-generation group separately by Asian and African ethnicity (available on request).

⁸We include these parents in the basic group containing individuals with a primary education or lower. If information on the parents’ employment status is missing, which happens for 3% of parents, the dummy variable takes the value zero (i.e. not employed). Parental information is missing if either both parents are dead or they have emigrated from Denmark. We could have deleted the individuals for whom parental information is missing. However, since we only use this information for matching purposes, we retain them, rather than introduce potential non-randomness.

⁹Primary school, vocational school or high-school degrees make up nearly 60% of the highest educational degree obtained by age 30 in 2012 in Denmark (Statistics Denmark).

¹⁰The wage information reported needs to be handled with care: Some individuals have an hourly wage rate outside the acceptable range, which is set between 30 and 300 DKK—1% of the wages are above and 3% are below. About 4% of the wages are missing. These cases have been deleted from the sample. To check that this does not affect our results, we used regression imputation for the 8% of the observations using yearly wage, ethnicity, education and employment. Using the full sample does not change any of the results notably.

¹¹If an individual holds a high school degree and has also taken vocational education, the one obtained last is reported here.

¹²OLS estimates of the wage and employment equations show that for second-generation Western males, their wages would, in fact, exceed those of ethnic Danes by 1.7% if they had the same parental background, neighborhood conditions and educational profile as ethnic Danes. For the three other ethnic groups, there is still a negative penalty of 2.3% to 8.2%. These wage differences are economically significant in a welfare-state country such as Denmark where the income distribution tends to be relatively more compressed. Furthermore, if lower starting wages feedback into a lowered work effort or a shorter career, large differences in lifetime income can result. In terms of the probability of obtaining employment, second-generation immigrants have a 2.7% to 5.6% lower likelihood.

¹³These probabilities are calculated as F (t) =1 − exp(−λ _u t).

¹⁴These probabilities are calculated as F (t) = 1 − exp(−h _e t), where$h_e\left(t\right)=\left(\sigma +{\lambda }_e\right)\left(\sqrt{\frac{P-w}{P-r}}\right)-\sigma$.

¹⁵Results available on request.