The returns to school-quality-adjusted education of immigrants in Germany – Journal for Labour Market Research

In the aftermath of the financial crisis in 2009, Germany experienced a surge of immigration with an average annual net flow of approximately 300,000 people, a clear majority of whom came from new member states of the European Union (Dustmann et al. 2012). This considerably changed the composition of source countries, indicating a shift towards European immigrants, and a skill structure marked by higher skill levels of immigrants in Germany (Muysken et al. 2015). A major challenge of integrating immigrants into the host country are barriers to the transferability of human capital endowments and their educational skills (Chiswick and Miller 2008). It is well-known from empirical evidence that these barriers explain a substantial part of the native–immigrant wage gap (Friedberg 2000). In this paper, we show that one of the key factors characterizing the limitation of human capital transferability is the quality of schooling in immigrants’ source countries.

The majority of existing evidence on the native–immigrant wage gap considers education obtained abroad as a perfect substitute for the education in the host country (Basilio et al. 2017). However, one year of schooling in the host country might effectively be equal to more or less than a year of schooling in other countries. Therefore, it is crucial to consider not only the years of education but also the quality of these years of education (Rohrbach-Schmidt and Tiemann 2016; Wößmann 2003).

In this paper, we deviate from the conventional approach and provide an important measure that accounts for the differences in the quality of human capital endowments between the host and source countries of immigrants. We examine the wage assimilation of immigrants in Germany and the determinants of the native–immigrant wage gap. We adjust the educational level of immigrants by using the school quality index of Hanushek and Kimko (2000). The goal of this paper is to understand how the pattern of the wage growth changes when the school quality index is taken into account. In Fig. 1 we explore data from the German Socio-Economic Panel (wave: 1984–2016) to decompose the wage assimilation pattern of immigrants into low-quality and high-quality schooling groupsFootnote 1. As illustrated in this figure, upon arrival, both groups of immigrants have, on average, lower wages than natives. However, immigrants who obtained their education in countries with high schooling quality have higher wages than the other immigrant group. Moreover, the rate at which they catch up to natives is faster for the high-quality schooling group of immigrants. On average, their wages converge approximately 8 years faster to natives’ wage level than the low-quality schooling group of immigrants.

From this interesting observation, this paper attempts to improve the understanding of the role of schooling quality on the native–immigrant wage gap. Particularly, two key questions at the core of our analysis are (1) How does the school quality of immigrants’ source countries affect the returns to education? (2) How does school quality impact the native–immigrant wage gap?

Fig. 1

Sources: German Socio-Economic Panel (GSOEP) (wave: 1984–2016), version 33, released in 2018

Wage assimilation of foreign-educated immigrants (Total sample). Sample of individuals aged 18 to 64, having full-time or regular part-time employment. The (unadjusted) wage assimilation over the time since the migration of immigrants educated in foreign countries before arriving in Germany. The dotted lines represent the 95 percent confidence interval. The red horizontal line on top illustrates the mean wage of natives over time.

Full size image

Using a large, representative household dataset—the Germany Socio-Economic Panel (GSOEP) 1984–2016—we show that a substantial part of the unexplained wage gap decomposition is explained by the school quality in the source countries of immigrants. Further, these data provide detailed information about the pre- and post-migration educational activities of immigrants, which serves as a basis to understand the differences in the returns to education in Germany and the returns to education abroad. Unlike Coulombe et al. (2014), who use gross domestic product (GDP) as a proxy for school quality, or Basilio et al. (2017), who do not adjust the quality of foreign schooling in their wage estimations, we use a direct measure of school quality to calculate the effective years of schooling of immigrants in Germany. In particular, we first split the total years of education into years studying abroad and years studying in Germany. Next, we adjust years studying abroad by using the Hanushek and Kimko (2000) school quality index.

This approach is suited for the empirical analysis for a number of reasons. First, although GDP has a strong correlation with the amount of resources allocated to education, the question is whether this indicator measures the quantity rather than the quality of schooling. Furthermore, there could be a reverse causality where higher human capital leads to higher GDP (Hanushek 2005). Second, unlike contemporary school quality data such as PISA or PIACCFootnote 2 scores, the Hanushek and Kimko (2000) school quality index is a comprehensive indicator, which is constructed based on six voluntary international standardized tests in mathematics and science conducted between 1965 and 1991. Therefore, it can potentially better capture the time during which immigrants in the sample actually went to school in their home countriesFootnote 3.

Our empirical findings provide two novel insights. First, we show that the returns to quality-adjusted education are positive but significantly less than the returns to the unadjusted education of immigrants in Germany. This finding shows that lower school quality negates the endowment advantage that immigrants possess from their education. Second, there is a wage gap between natives and immigrants, and education plays an important role in explaining this earnings difference. Existing studies often use wage gap decomposition methods breaking down mean wage differentials into explanatory determinants and an unexplained part. The interpretation of the unexplained component is vulnerable because it captures a range of factors, such as discrimination effects and unobserved individual or institutional characteristics. Our analysis reveals that controlling for school quality allows to account for important institutional characteristics. This factor significantly reduces the unexplained wage gap. Ultimately, our results highlight the role of school quality in understanding the international transferability of human capital and the returns to education of immigrants in the host country.

Ever since the seminal research of Card and Krueger (1992) on the causal relationship between school quality and earnings in the United States, economists have used different measures of school quality to investigate its transmission mechanism to earnings. The quantity of schooling, which represents resources devoted to education such as the pupil–teacher ratio, expenditures per pupil, relative wages of teachers, and the length of school term, is easily measured, and data are widely available (Hanushek 2005). However, the central concern in contemporary education policy revolves much more around the quality of schooling. Compared to the quantity of schooling, the measurement of school quality is more challenging. This is because the question is whether we can assume international standardized test scores to be an appropriate measure of cognitive skills affected by the quality of schooling and whether students’ performance on these standardized tests has any correlation with economic outcomes such as their subsequent labour market performance (Hanushek 2005).

Nevertheless, Chiswick and Miller (2010) and Coulombe et al. (2014) still attempt to use different measures of school quality such as the PISA score and the national GDP to explain the relatively lower labour market performance of immigrants in host countries. Raaum (1998) and Friedberg (2000) estimate the return to foreign schooling of immigrants in Norway and Israel, respectively. They find a significantly lower return to foreign education than to host-country education. Betts and Lofstrom (2000), Bratsberg and Ragan Jr (2002), Bratsberg and Terrell (2002) and Chiswick and Miller (2010) investigate the payoff of schooling for immigrants in the United States, and Sweetman (2004) and Fortin et al. (2016) do so in Canada and find that the quality of the educational system obtained abroad accounts for a large fraction (from 30 to 80 percent) of the variation in the rates of return to education not only between immigrants and natives but also between immigrants who acquire education in the host countries and those who do not. In other words, the lower partial effect of schooling on earnings for immigrants from less developed countries is due to lower quality of foreign schooling, even after allowing for differences in working experience and other factors that might influence earnings.

Low international transferability of skills is a potential explanation for the poor labour market outcomes of immigrants in a host country due to large differences in human capital quality across countries (Friedberg 2000; Chiswick and Miller 2009, 2010; Aleksynska and Tritah 2013; Basilio et al. 2017). In particular, the differences in schooling systems, unrecognized qualifications, technological development, and other barriers to labour market entry could adversely affect international skill transferability (de Oliveira et al. 2000; Aleksynska and Tritah 2013).

This paper contributes to the literature in several important ways. First, we use an alternative measure of years of education obtained abroad that adjusts for the quality of schooling in the source country of migration. In this way, we are able to compute plausible and precise estimates of the returns to education of immigrants in the host country. Second, we use a large representative panel dataset, covering a long period until 2016, which allows us to include recent immigration flows to Germany, such as the refugee influx in 2015 (Eurostat 2018). Third, using a Oaxaca–Blinder decomposition model, we show that using a quality-adjusted measure of education substantially reduces the unexplained part of the native–immigrant wage gap by approximately 20 percent. Thus, our approach reveals that school quality appears to be a major factor in explaining the imperfect transferability of human capital and decreases the level of “ignorance” often attributed to “discrimination” in previous literature.

Nevertheless, another challenge in estimating the returns to education is the omitted variable bias that may arise due to unobservable variables, for example, family background, innate ability, motivation, and other non-cognitive skills. We assume that the direction of this bias is the same for both immigrants and natives. Under this assumption, we can compare the returns to education among natives, German-educated immigrants and foreign-educated immigrants.

The paper proceeds as follows. Section 2 presents our empirical strategies. Section 3 introduces the dataset and our key variables for analysis. Section 4 analyses the empirical findings and provides some insights of the results. Section 5 concludes the paper.