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The Impact of Language Skills on Immigrants’ Labor Market Integration: A Brief Revision With a New Approach

Eric Schuss


I examine the impact of language skills on immigrants’ labor market performance by applying a new approach, which allows to estimate wage benefits attributed to initial language skills at arrival. By exploiting unique data, I isolate the endogenous part of current German skills and instrument current command by German proficiency measured retrospectively at the point in time of migration. This approach tackles the problem that labor market effects from current language skills are at risk to reflect merely the sum of a successful residence in Germany and only display growth effects. I find that a good command of German increases labor market income by 47.0% for males, while no significant language effects are detected for females. Further analyses illustrate that differences in language effects by gender can be attributed to selection into different occupations and part-time employments and that language operates complementary and enables cross-border transferability of human capital.

JEL Classification: C26; J24; J31; J61



Table 7:

Different disciplines of German language command over 2013–2015 (in %).

Very good31.833.213.028.332.
Very poor0.
Very good31.832.414.728.434.634.917.631.4
Very poor2.
Very good27.826.711.424.028.930.213.826.4
Very poor4.

  1. Source: IAB–SOEP Migration Sample, own illustration.

Table 8:

Sample composition by labor market performance and countries of origin.

GoodNoGoodNoMean diff.
commandcommandMean diff.commandcommandMean sex
Labor market performance:
Intensive margin:
Labor market income (in € per year)23,621.819,869.43,752.424,649.319,476.35,178.41,000.5
Hours weekly worked40.840.3-0.529.725.64.111.7
Employed (in %)79.667.412.263.644.719.017.3
Full-time employed66.
Part-time employed8.
Not employed20.432.612.236.455.319.017.3
Occupational sectors (in %):
Blue collar42.565.523.027.460.032.614.7
White collar44.627.816.860.030.129.914.0
Public sector8.04.63.418.610.38.39.9
Countries of origin (in %):
(Former) Yugoslavia13.06.911.
Countries of EU11.414.712.415.314.715.12.7
enlargement 2004
Countries of EU9.212.410.111.712.411.91.8
enlargement 2007
Russia and (former) USSR29.024.727.734.819.430.83.1
East, Southeast and2.
remaining Asia
Northern Africa2.
Arabic countries6.

  1. Notes: Two-sided t tests: significant at 10%, significant at 5%, significant at 1%

    Source: IAB–SOEP Migration Sample, own calculations.

Table 9:

Baseline estimations for females: The effect of German language skills on logarithmized labor market income (conditional on employment).

Language skills0.12840.05470.04870.75140.38920.2465
Socioeconomic variables:
Years of residence0.00450.00990.00100.0077
German citizenship0.03470.0307-0.01870.0008
Informal way of job search0.0048-0.00660.0042-0.0070
Member of EU0.27130.13850.25230.1291
High education, ISCED 5-60.38500.34710.34490.3256
Low education, ISCED 1-20.22240.19400.18460.1740
Graduation in Germany-0.0918-0.0210-0.1110-0.0319
First stage:
Language skills at arrival0.23210.18880.1916
F statistic65.251.452.1
Additional control variables:
Country of originYesYesYesYes
Current residency statusYesYesYesYes
Pre-migration characteristicsYesYes
Residency status at arrivalYesYes
Number of observations2,3592,3592,3592,3592,3592,359

  1. Notes: p<10%, p<5%, p<1%; standard errors in parentheses. Table 2 considers females and uses logarithmized labor market income per year conditional on employment as the dependent variable. Models (1)–(3) apply random effects GLS and Models (4)–(6) apply two-stage least-squares instrumental variable (2SLS IV) estimations, where current command of the German language is instrumented by German language skills at arrival. The models are extended stepwise with socioeconomic and educational covariates, country of origin-fixed effects, current residency status, pre-migration characteristics and initial residency status at arrival. A list on used covariates can be found in Table 1.

    Source: IAB–SOEP Migration Sample, own illustration.

Table 10:

Minor robustness checks.

Ethnic GermansAge at arrivalEthnic GermansAge at arrival
excluded≥ 18excluded≥ 18
Language skills0.47230.53610.52100.4484
First stage:
Language skills at arrival0.26990.31120.17270.2127
F statistic63.569.029.948.7
Additional controlsYesYesYesYes
Number of observations2,1812,0301,9191,925

  1. Notes: p<10%, p<5%, p<1%; standard errors in parentheses. Table 10 presents 2SLS IV estimations for males and females conditional on employment with logarithmized labor market income per year as the dependent variable. Each estimation controls on the full set of covariates summarized in Table 1. Model (1) and (3) excludes ethnic Germans from estimations and Model (2) and (4) excludes childhood immigrants, which arrived before the age of 18 in Germany.

    Source: IAB–SOEP Migration Sample, own illustration.


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Published Online: 2018-08-17

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