2NA12E Intermediate Economics II including Degree Project (Bachelor) 2NA12E:3 Economics, Degree Project (Bachelor)
School of Business and Economics Department of Economics and Statistics Linnaeus University, Växjö, Sweden
Bachelor Thesis
The Assimilation of Turkish Immigrants in the German Labor
Market
Cross-national comparative study with the Austrian labor market and emphasis on differences in integration policies
Name of the candidate: Manon Spick Name of the supervisor: Abdulaziz Abrar Reshid
Name of the examiner: Mats Hammarstedt Subject: Economics of Migration Level and semester: Bachelor, Spring 2020
Abstract
The economic assimilation of immigrants is one of the main topics of the migration economic literature. The United States, the United Kingdom, or even Canada, are usually chosen to lead such studies. We have decided to observe the differences in immigrant’s economic assimilation between two host countries which are less studied in empirical papers and very similar in terms of geography and language: Germany and Austria. The country of origin for the immigrants observed in this study is Turkey because Turkish immigrants are highly represented among the immigrant population in the both host countries. We have found that both female and male Turkish immigrants assimilate faster in Germany than in Austria. This faster assimilation could be partly due to the implementation of less restrictive migratory policies in Germany compared to Austria.
Acknowledgment
I would like to express my thanks to my supervisor, Abdulaziz Abrar Reshid, who gave me precious advices, comments and remarks on my thesis throughout the weeks. Thanks to my examiner, Mats Hammarstedt, who takes the time to read and examine carefully my essay.
Keywords
immigration; assimilation; migratory policies; Turkey; Germany; Austria
Table of contents
Abstract 2
Acknowledgment 2
I. Introduction 4
II. Historical background
A. Review of the migration policies in Germany 7 B. Review of the migration policies in Austria 8 III. Literature review: previous relevant studies regarding 8
the research questions
IV. Theoretical framework 10
A. Human capital theory and the assimilation framework 11 B. The Context of Reception: institutional immigration framework 12 V. Data
A. Dataset 12
B. Strengths and weaknesses of our datasets 14
VI. Method 14
A. The Borjas extended assimilation model 15
B. The decomposition method: Blinder Oaxaca twofold decomposition 16 C. Dependent and independent variables: definitions 17 VII. Results
A. Descriptive overview 18
B. Main results 21
C. The decomposition output 24
VIII. Discussion 26
IX. Conclusion 28
References 29
Appendix 32
I. Introduction
Over the last decades, immigration has become one of the main topics debated in international and national media and more particularly in the Western societies. More than 1,8 million of immigrants have entered the Western economies (Germany, France, the Netherlands, Switzerland, Austria, Belgium, Luxembourg) within the year 2016 and have settled there in a permanent way (OECD, 2016). We can note that Germany represents more than half of the total permanent immigrants’ entries. Immigration is not a temporary issue. Indeed, years after years, the world becomes even more globalized, the inequalities between the Southern and Northern part of the world increase, free trade areas are larger and economic, social and political conflicts persist among the poorest countries. For all these reasons, the international population movements are expected to intensify in the upcoming decades (Castles et al., 2013). In the forthcoming half-century, more countries will be impacted by immigration and there will be even more heterogeneity in the migrant population (Van Tubergen, 2006). We can already notice this tendency through the most recent data (Migration Data Portal). European countries have adopted different kind of immigrant integration approaches over the years. Some states have a multiculturalism approach, as Great-Britain, whereas others try to implement policies which aims at fully assimilating the immigrants, as in France, into the whole society (labor market, education system). Some other countries, as the Netherlands and Germany are situated between these two aspects (Borooah and Mangan, 2009). Migration policies are crucial to determine the future and the integration of the migrating population. That’s why, assimilation of immigrants in their host country represents one of the main foundations of the economics of migration’s literature.
At the beginning of the 1920’s, the assimilation process was defined using a purely cultural and social integration approach. After the first Great Migration to the US, sociologists started to define the assimilation of immigrants as “a process of interpretation and fusion”
between the native’s cultural life and the immigrants (Park and Burgess, 1921). This quite vague manner to refer to the immigrant’s assimilation has been contested by other scientists, including sociologists (Alba and Nee, 1999, 2003) and economists (Chiswick, 1979) who argued that this assimilation process cannot not be fully measured and estimated if we do not consider the economic assimilation which assesses the immigrant’s economic progress in the
host societies and more particularly in the labor market. We will come back more accurately to this point in the theory part of the essay.
The labor market and the sphere of work is described by the economists as a “primary point of contact between immigrants and the receiving society” and is a key dimension of the immigrant’s integration and assimilation framework (Massey and Sanchez, 2010). That’s why I have decided to focus my essay on this aspect of assimilation.
As noted above, the integration policies seem to play a major role in determining the labor market outcome for immigrants, but is it necessarily the case everywhere? In the case of Turkish immigrants in Germany and Austria, do differences in labor market assimilation could be explained by differences in migration policy? If yes, to what extent do the migration policies influence the immigrant characteristics and their economic assimilation?
Little study evidences have taken the effects of policies into account and that is why it could be relevant to process to an analysis related to this subject matter.
We have decided to focus our analysis on the incorporation of immigrants from Turkey in two receiving countries, Germany and Austria, over the last two decades, by including in our analysis the notion of integration policies. This can be examined in a cross-national comparative analysis.
Turkey has been chosen as the origin country due to several elements regarding its historical background with these two countries and the availability of data Turkey is, and will continue to be, a country of emigration. According to a study led by a Turkish Univerisity (Haccettepe Universitesi, 2006) regarding the country’s demographic projections, the average age of the Turkish population tends to decrease over years and will be around 39 years old by 2050. As the propensity to migrate decreases from the age of 55 years old and Turkey having a younger population, the emigration will consequently increase over time and the upcoming decades (Akagül, 2008). The recent and particular political context of this country could be an added incentive for the population to migrate from the Eastern Europe to the Western Europe.
Therefore, Turkish emigration sounds interesting to study because it is an emigration which tend to last over the ages and even to intensify.
First of all, the choice of the destination countries has been made directly in link with the origin country. Indeed, Germany is well-known to welcome a lot of Turkish immigrants. In April 2017, 1 430 000 Turks were settled in Germany but still registered as a Turkish elector;
in Austria this number reached around 100 000 Turks (Turkey High Election Board). In
addition, it’s interesting to note that the both countries are in the same geographical areas. They are indeed neighbors. Both countries have the same official national language: they both speak German. The proportion of Turkish immigrants (relative to the whole native population), the distance from Turkey to these two countries and the language-speaking being similar, it can be interesting to do a cross-country analysis between Germany and Austria as regards the Turkish immigrant’s assimilation and the migratory policy influences. Moreover, Germany and Austria seem to have differences in integration policies by looking at the MIPEX (Migrant Integration Policy Index, 2015) regarding the labor mobility index. The MIPEX is constructed thanks to the aggregation of 167 policy indicators depicting eight policy area (as education, access to nationality, anti-discrimination measures and labor market mobility). The index goes from a score of 0 (no integration policies at all) to 100 (complete integration policies). German index is ranked fourth in the thirty-eight countries of the study with a score of 86, after Sweden (98), Portugal and Norway. Austria has a lower score (64) and is the sixteenth over thirty-eight.
In order to answer to our research question, we will process to earning regressions based on the assimilation model developed by the economist Borjas. Earnings will be then decomposed into explained and unexplained factors in order to support our analysis. According to the assimilation coefficients obtained through the regressions and earnings decompositions, we found out that Turkish immigrants assimilate economically faster in Germany where the migratory policies are less restrictive compared to Austria.
The structure of the essay will be as follows. First of all, we will do a short historical background of the migration policies in Germany and Austria in order to settle the context of the study. Secondly, we will make our literature review by describing, analyzing and assessing, if needed, previous relevant studies regarding the research question. Cross-national studies regarding immigrant’s attainment in the labor market and studies related to the assimilation and institutional approach will be mentioned. In a third part, we will present the theoretical framework of our study stressing on the human capital framework and context of reception theory. After that, we will present our data and variables in order to process to the method part.
The extended assimilation model (Borjas 1985) and a Blinder-Oaxaca decomposition method (Blinder and Oaxaca, 1973) linked to the DEMIG dataset and Migration Integration Policy index would help us to answer to the research question. Our main results will be discussed in an additional part.
II. Historical background
A. Review of the migration policies in Germany
Some years after the World War II, in 1950, about 500 000 foreigners lived in Germany, which represented 1 % of the whole population. Around 1955, the demand for unskilled and mid-skilled workers increased in the country and the domestic supply of labor were insufficient to meet the growing demand. That’s why a law has been implemented in order to allow the recruitment of workers from southern Europe and Turkey. The main vacancy positions were in the manufacturing, as the steel and metal working sectors. Due to the decision of East Germany (GDR) to close its borders and the construction of the Berlin Wall, the foreign recruitment stopped. Right after the reunification in 1989, German has seen a sudden increase of its migration inflows. This increase is mainly due to the growing democratization in Germany during this period of its history. It was easier for Eastern countries, and particularly Turkish population, to emigrate. This situation has been seen as an issue in Germany and the government decided to change its constitution in order to limit the number of asylum seekers and economic immigration in its territory (Foreigner’s law, 1990). The paradigm was clear:
“Germany is not a country of immigration”. This has noticeably changed in 2005 with a new law which emphasizes the need to develop a successful migration policy in the country. A pro- immigration policy slowly came up and high-skilled worker recruitment has been promoted (Butterwegge, Bundeszentrale für politische Bildung, 2007). Germany has taken time to provide both equal rights and greater support to its immigrant population. In 2011, the German’s integration policies have taken three steps forward on integration and a recognition act which aims to facilitate and support the recognition of foreign qualifications has been acted in 2012. Consequently, its total MIPEX (Migration Integration Policy Index) has been boosted.
What’s more, the country is seen as a leader in facilitating and supporting the recognition of foreign qualifications and skills. This is mainly to the implementation of the political act, entitled the 2012 Recognition Act. The labor market policy has raised from 75 to 86 from 2007 to 2014, 11 points more as a difference (Migration Integration Policy Index)
B. Review of the migration policies in Austria
As in Germany in the 1960’s, Austrian manufactures welcome mainly Turkish and Yugoslavia immigrants in order to compensate its need for low-skilled workers. Before 2007, the labor market mobility was not a major political priority for new integration policies. The 2010 National Action Plan and Integration/ Ministry Funds have launched new indicatives aiming to a better economic assimilation of immigrants among the Austrian labor market.
Austria has created as many obstacles than opportunities to provide to its immigrants a fuller participation in the Austrian society. In term of integration policy as a whole, Austria is situated, in index term, just above Switzerland but far below Germany and other destination country having a similar size. We can notice that the labor market policy has raised from 42 to 64 from 2007 to 2014, 22 points more as a difference. (Migration Integration Policy Index). This situation could be due to the National Action Plan and Integration Ministry funded acted in 2010 and which announced many new initiatives. But this final index remains consequently less than the German Labor Market mobility index in 2014. It reveals a more severe patterns regarding migration policy in Austria compared to Germany.
III. Literature review: previous relevant studies regarding the research question and how to proceed
We have searched and assessed some studies offering discussions around immigration and labor market-related integration policies. Most of the studies are not directly related to Turkish immigrant’s assimilation and even less to their integration on the Austrian and German labor market. However, the main articles studied provide us a global vision of the relation between immigration assimilation and institutional (as policies) approach. Please note that the well- known and more global literature review regarding the main theories and models will be announced and studied in the theoretical framework part.
A change in migratory policy restrictiveness: a Scandinavian case
By implementing a reform aiming to restrict the migration flows and using more strict migration policy in the early 2000’s, Denmark has increasingly deviated from its two neighbors, Norway and Sweden. Researchers have tried to assess the impact of such a reform in the labor
market outcomes (Jajobsen et al, 2018). Indeed, they have tracked the evolution of earning gaps and earnings gaps between 1993 and 2006 in order to envelop the year of the Danish restrictive migration reform. However, the researchers could not find any clear-cut effect of the immigrant’s earnings and employment evolutions. The main issue of this study seems to be the method part. An econometric regression model has not been proceeded and we can only refer ourselves to a descriptive statistics table with selected variables, including mean and standard deviation (SD) for each country and the two distinct period study. Some graphs showing the crude employment, earnings gaps and predicted earning gaps have been displayed but it cannot give us an accurate vision of results. Last remark, and not the less, the immigration population studied includes all the non-Western European immigrants. It would be preferable in that way to focus on one country of origin to avoid too much disparities between countries and years of the study.
Immigration policy and employment assimilation of immigrants
Using a pool-crossed section of the 1994-2004 Current Population Survey (CPS), two American researchers in economics have tried to examine what is the impact of an immigration in the assimilation of immigrants and the employment propensity (Pedace and DuBois, 2012).
They have performed a Blinder-Oaxaca (Blinder, 1973; Oaxaca, 1973) style decomposition.
The aim of this model is to estimate the employment gaps that we can attribute to the relative difference in native characteristics and the importance of the changes in the structure of the labor market. They have found that the employment assimilation is considerably influenced by the changes in policy and labor market conditions overtime. These latter factors impact employment propensities (and consequently economic assimilation) more than the cohort effects and differences in returns to skills.
Access to citizenship and the economic assimilation of immigrants
Accessing more easily to a citizenship can be seen as a less restrictive migratory policy, and vice-versa. An empirical study has been led to determine if a faster access to citizenship provides a better economic situation the immigrants (Gathmann, Keller; 2017). The two authors found out that it is indeed the case for immigrant women who tend to have a stronger attachment
to the labor market after such a less restrictive migratory policy. This stronger attachment is detected as an increase in hours works, a higher propensity to have a stable job and a higher employment rate for this immigrant population. Language and vocational-training consequently increase too. Access to citizenship can be seen as powerful political instruments which could even accelerate the assimilation of immigrants in the labor market.
Comparative study of immigrant’s assimilation
This point is relative to the way we will proceed for our research. Indeed, we have decided to do a cross-national comparative study between two receiving countries (here Austria and Germany) regarding the immigration from one single country (here Turkey). The aim of a study led by 4 researchers (Lewin-Epstein, Semyonov, Kogan, Wanner; 2003) was to examine to examine the socioeconomic attainment of immigrants from the Former Soviet Union who arrived in Canada and Israel between 1989 and 1995. To do so, they have proceeded for a comparative study of immigrants originating from the same society but reaching different host countries. The homogeneity of the group of immigrants permits to focus on the examination of the institutional and structural characteristics of receiving societies (here Canada and Israel) with concern the attainment of immigrants. However, cross-national differences might be due to some other reasons than the political system. The group members coming from a same origin can be treated in a different way, according to the reception country or it can be the results of strict immigrant policies (Inbar and Adler, 1977). The self-selection of the immigrants can lead to differences in cross-national analysis (Reitz, 1998). In that way, we should be careful with our final results and use a more step-by-step approach.
IV. Theoretical framework
In the following section, we will study relevant theoretical literature regarding the research question. First, the human capital theory will be covered. Then, the assimilation framework will be completed by the institutional framework with the context of reception theory.
A. Human capital theory in the assimilation framework
The human capital theory has been developed by several economists within the 50’s and 60’s. They agreed on two general assumptions: first, “the people’s life-chances depend on their human capital” and second, “people are aware of this relationship between individual skills”
(Mincer, 1958; Becker, 1964; Chiswick, 1966).
The first labor market assimilation hypothesis has been initially tested by Chiswick (1978).
A traditional human capital equation has been estimated and the variable “years since migration” has been used as an explanatory variable for the workers born abroad. The economist has found that it takes approximately 13 years for an immigrant worker to economically assimilate to a native worker. In other words, after 13 years, their wages (at least) converge. However, the changes skill and innate ability were not well-taken into account. Using cohort effect to control for these evolving characteristics, it has been proved that immigrants arriving in the country after 1975 cannot overcome their initial earnings disadvantages (Borjas;
1985, 1995).
During the first years in the host country, immigrants tend to be more often unemployed than natives, but this gap decreases with the increase of the “years since migration” variable, in other words the length of stay in the receiving country (Chiswick, 1982; Chiswick, Cohen and Zach, 1997; Chiswick and Hurst, 2000). Regarding the earnings, it has been found that immigrants progressively catch up with native’s wage over their life-course (Chiswick, 1978;
1979; Chiswick and Miller, 2002).
The three most commonly used measures for assessing the immigrant’s assimilation into the economic framework are the labor force participation which represents the fact to detain a secure job; the occupational attainment which shows if the job position matches with the skills;
and the earnings which is a “key indicator of economic well-being deriving from labor market activity” (Raijman and Tienda, 1999).
Even before entering the host country, the immigrants may be subject to selection.
Immigration policies can play a major role in this selection, by applying quota systems or skill- based migration system which will respectively affect their number and characteristics (Clark et al, 2007). It would impact consequently the average period to run until perfect assimilation because high-skilled workers tend to economically assimilate faster than the low-skilled workers.
Human capital resources, as education, will mainly determine the immigrant’s labor market outcomes and his degree of economic assimilation. If an immigrant is better educated, the wages will be higher, the chance to be unemployed lower and the job position better.
Human capital can also refer to language-proficiency skills. When an immigrant can speak the host country’s language in a quite fluently way, his earnings will consequently increase.
This is the case in many countries, as the United Kingdom for example (Dustmann and Fabri, 2003). This language-proficiency variable cannot be considered in our analysis due to lack of data.
B. The Context of Reception: institutional immigration framework
However, the assimilation theory has faced contradictory findings that this theory was not able to explain by itself. In that way, we have concluded that both the socio-cultural and economic integration (regarding immigrants) depends not only on the individual characteristics, but also on the whole immigration group and the characteristics of the destination country.
There exist three modes for incorporating the labor market as an immigrant: the government policy, the labor market policy and the ethnic community characteristics (Portes and Rumbaut;
1996, 2001). Later, Model and Lin (2002) have elaborated on this subject. Indeed, some origin groups are warmly welcome whereas others are not really and can even be excluded from the receiving labor market. This idea was already suggested by Portes and Rumbaut (1996, 2001):
the economic integration of an immigrant belonging to a certain group will be better if there exist government policies towards this particular group. Other governmental policies can indirectly improve the assimilation of the immigrants in the labor market (Model and Lin, 2002). This is the case of anti-discrimination law which can reinforce the economic integration of immigrants by limiting the labor market discrimination related to an ethnicity, skin color or even foreign accent.
V. Data
A. Dataset
Ten datasets were used in order to evaluate the economic assimilation of Turkish immigrants in their two destination countries, Germany and Austria. These datasets have been
collected thanks to the LISSY remote-execution system created by the LIS Data Center (Luxembourg Income Study).
For Germany, we have used seven surveys from the German Socio-Economic Panel (GSEP). This kind of survey is conducted every year by the German Institute for Economic Research (Deutsches Institut für Wirschaftsforschung, DIW). This survey contains cross- sectional data. The seven years selected for our analysis are from the year 2010 to the year 2016. CAPI (Computer-assisted personal interviewing) has been used as a collection mode. It means that the interviewer is usually present and can guide the respondent.
For Austria, we have used three surveys from the Survey on Income and Living Conditions (SILC). This kind of survey is conducted every year, since 2004, by the Statistics Austria (Statistik Austria). This is a survey with integrated cross-sectional data and longitudinal design, through a rotating panel survey. The three years selected for our analysis are from 2010 to 2016 (specifically the surveys from year 2010; 2013; 2016). CAPI and CATI interviews have been used as collection modes. The main difference between CAPI and CATI is that CAPI interviews are proceed with the physical presence of the interviewer where the interview is conducted by call (phone) with the CATI mode.
We have proceeded to a merge of datasets (merge of 7 surveys for Germany, merge of 3 surveys for Austria) in order to get more relevant and significant results. Indeed, the low portion of Turkish immigrants in one single survey was not large enough to run a significant regression and analysis. In addition, female and male immigrants need to be studied independently. It means that we have to split the Turkish’s immigrants in two parts, men from a side and women from another side. The sample size would have been even lower. That’s why, we have preferred to merge multiple surveys together. What’s more, the period studied for both Germany and Austria is 2010-2016.
We have chosen the period 2010-2016 because this is the most recent period where data are available for our analysis.
Table A
Number of observations per year in Austria and Germany
Country /year AT10 AT13 AT16 DE10 DE11 DE12 DE13 DE14 DE15 DE16 Households 6187 5909 6090 16703 16397 17992 15946 15908 14426 15816 Individuals 13933 12982 12876 44134 42531 47816 41657 41245 36949 39758
AT= Austria; DE = Germany; AT10 = Austria for the year 2010; and so on…
B. Strengths and weaknesses of our datasets
Thanks to the use of cross-sectional dataset for both countries and both periods, we can access to a lot of useful variables for our study in order to answer to our research question.
Immigrant (dummy variable), sex, age, labor income, part-time employment, education and years since migration are for example variables available for the whole data sources. What’s more, using a computer-assisted personal or telephone interviewing can improve data quality.
However, some of the results of our regression may be insignificant. Even if we have merged samples together, Turkish immigrants represent a quite low percentage of the total sample size (around 1 % to 1,5 % for both countries). But we can note that Turkish immigrants are the immigrants who are one of the most represented in the sample in terms of total immigrants. cross-national comparative study based on different data sources can be biased due to mismatching between the definition of certain variables. Some variables can be defined in a certain way by the statistic institute in country A and can have a slightly or even different meaning in a country B. We will specifically come back a bit later on this point.
VI. Method
In order to study the case of economic assimilation of Turkish immigrants in Germany and Austria, we will first use the well-known assimilation model extended by the economist Borjas, then we will use a Blinder-Oaxaca specific decomposition method.
A. The Borjas extended assimilation model
After presenting a descriptive overview of our data, we will run our regression based on the assimilation model elaborated by Borjas in the 80’s. It is an extended version of the initial model presented by Chiswick in the 70’s. This time, the model considers the cohort effect (see IV.A). The initial estimation model is defined by two equations: one defined for the immigrants and another one for the natives. Regarding our research question which is to observe how immigrants are performing in the labor market compared to natives, we should have a model for both immigrants and natives.
Our model can be defined as follow using the pooled cross-section data available:
𝐿𝑜𝑔(𝑤)' = 𝑋'𝜃 + 𝛼𝑦'+ / 𝛽1𝐶1'
1
+ / 𝜋4𝐶4'
4
+ 𝜀'
Where W can be defined as the annual labor earnings of individual i (individuals). We decide to take the logarithm of the wage because wages are generally not normally distributed.
In that way, we can create an outcome variable that is approximately normal and more suitable for regression modelling.
𝑋' controls the individual variables (as the experience, level of education, the marital status, and the number of own children in the household). Then we have the variable 𝑦6 which defines the year since migration (YSM)1. It measures the years since the immigrants arrived in the host country (tenure) and it shows us the assimilation rate. 𝐶1 is the dummy variable for cohorts, measuring the cohort effect, t represents the four cohorts. 𝜋6 measures the period effect, it’s a dummy variable for the year of observation (year of the survey). S represents our survey year. Finally, we have 𝜀' at the end of our equation. This is the error term.
1Here we take the choice to drop the variables YSM-squared from our econometric model. This variable is usually included in this kind of econometric model to allow for possible nonlinearities in the convergence path. After running multiple regressions, we have noticed that the variables YSM-squared is a factor of multicollinearity and lead to incoherent results
We will then run regressions using this model for natives and Turkish immigrants separately for each host country studied (Germany and Austria). It’s important to note that before regressing we have processed to a weighting of our data.
We will define and describe our variables more in details in the third part of this methodological section (VI.C.).
B.
The decomposition method: Blinder-Oaxaca twofold decomposition
2We will use a decomposition technic based on the Blinder-Oaxaca decomposition. The purpose of such a technique is to divide the wage gap between two groups, here immigrants and natives into a part that is explained by differences in determinants of wages (such as age, experience at work, level of education attainment, hours worked, …) and a part that cannot be explained by such group differences.
In our case of comparison between natives and immigrants, the Blinder-Oaxaca decomposition equation is defined by:
Characteristics effect (explained variation)
𝑊891':;4− 𝑊∗'>>'?@9814 = 𝛽891':;4(𝑆891':;4 − 𝑆'>>'?@9814)
Coefficients effects (unexplained variation)
𝑊∗'>>'?@9814− 𝑊'>>'?@9814
= B𝛼891':;4 − 𝛼'>>'?@9814C + B𝛽891':;4 − 𝛽'>>'?@9814C𝑆'>>'?@9814
We get the final decomposition equation with i for immigrants and n for natives
𝑊8− 𝑊𝑖 = B 𝛽8(𝑆8− 𝑆')C + ((𝛼8− 𝛼') + (𝛽8− 𝛽')𝑆'
We will run this Blinder Oaxaca decomposition thanks to the command available in the statistical package STATA. Here we used a twofold decomposition (including “pooled” to our Oaxaca command).
The unexplained variation could be partly due to the context of reception of the country (cf.
theoretical part at section IV paragraph B). This context of reception may rely on migratory policies for instance (anti-discrimination policy, social assistance, assistance to integrate the workforce, and so on…). Based on the impact of unexplained variation on the wage gap (in proportion), we could determine how big the wage gap between immigrants and natives is impacted by none-individual characteristics.
C. Dependent and independent variables: definitions
The labor income (pilabour) in our survey is defined as the sum of the wage income, the self-employment income and fringe benefits. Fringe benefits are benefits that an employee can get from his employer (as employee discount…). We have chosen to take in consideration these three factors because we study the complete assimilation of immigrants in the labor market and we consider these different dimensions of “income” as taking part of the labor market. The logarithm annual labor income is our outcome variable. The currency unit is the Euro for both countries (Austria, Germany).
The other variables of our assimilation model are experience, educational attainment, years since immigration, the marital status (married), the number of own children in the household, cohorts and years of observation.
We have made the choice to restrict our analysis to people aged from 25 to 65 years old at the time of the observation. Indeed, it’s important to focus our analysis on people who are in the working age group.
The different education attainment level selected are: compulsory education, secondary education and university education. The variable compulsory education is taken as a reference in the earning regressions. These annotations have been decided in accordance to the International Standard Classification of Education (ISCED, 2011). Compulsory education
corresponds to ISCED levels from 0 to 2, upper secondary from 3 to 4 and university education from level 5 to 7.
We have defined four cohorts in our model: before 1979, from 1980 to 1989, from 1990 to 1999, after 2000.
VII. Results
A. Descriptive overview
In the Table 1, we present the descriptive statistics of the observed characteristics for both gender (male and female) distinctly, and native and Turkish immigrants in their host countries, Germany and Austria.
We have decided to integrate some variables which are not present in the earning regressions in order to have a better understanding of the differences between immigrants and natives (men and women)3.
Table 1
Means (SD) of variables – Turkish immigrants and natives, in Germany and Austria, men (male) and women (female)
Variables Germany Austria
Male Natives Immigrants Natives Immigrants
log(wage) 10.402 10.019 10.454 9.951
(0.906) (0.855) (1.007) (1.145)
age 45.815 42.9 46.133 43.935
(10.705) (8.077) (10.964) (9.888)
YSM . 25.493 . 19.775
. (11.712) . (8.565)
exp 22.393 19.531 27.686 24.157
(11.228) (8.951) (11.512) (9.817)
education years 13.615 10.82 12.89 10.54
(2.631) (0.289) (0.072) (2.333)
compulsory_ed 0.052 0.38 0.069 0.482
(0.221) (0.485) (0.253) (0.5)
secondary_ed 0.492 0.363 0.723 0.457
(0.500) (0.481) (0.447) (0.499)
university_ed 0.339 0.082 0.208 0.061
(0.474) (0.274) (0.406) (0.24)
married 0.595 0.738 0.576 0.849
(0.491) (0.44) (0.494) (0.359)
nchildren 1.120 1.983 0.76 1.539
(1.236) (1.352) (1.006) (1.362)
part-time 0.056 0.073 0.055 0.043
(0.231) (0.262) (0.23) (0.202)
Number of observations 47 792 1 481 7 473 208
Variables Germany Austria
Female Natives Immigrants Natives Immigrants
log(wage) 9.649 9.036 9.787 9.156
(1.062) (1.112) (1.200) (1.159)
age 45.196 42.082 46.44 42.049
(10.668) (8.572) (11.108) (10.393)
YSM . 24.641 . 17.637
. (11.44) . (8.87)
exp 17.885 9.389 22.93 14.878
(11.073) (8.115) (11.185) (9.342)
education years 13.247 10.248 12.586 9.547
(2.585) (2.828) (2.637) (2.7)
compulsary_ed 0.084 0.492 0.14 0.749
(0.278) (0.5) (0.347) (0.435)
secondary_ed 0.591 0.271 0.663 0.188
(0.492) (0.444) (0.473) (0.392)
university_ed 0.271 0.072 0.196 0.063
(0.444) (0.259) (0.40) (0.243)
married 0.567 0.728 0.568 0.852
(0.495) (0.444) (0.495) (0.356)
nchildren 1.174 2.1 0.814 1.69
(1.198) (1.377) (1.004) (1.325)
part-time 0.326 0.246 0.308 0.193
(0.468) (0.431) 0.462 (0.395)
Number of observations 49 376 809 7 129 125
Regarding the men group, we can observe that immigrants arriving in Germany and arriving in Austria have a similar profile. Indeed, they have studied on average 10 years and a half before migrating in these countries, around 3 years less than German natives and 2 years less than Austrian natives. We can notice that female immigrants are on average less educated than men immigrants whereas both women and men natives have on average a same level of education.
Around 33.9 % of native’s men in Germany are high-skilled (see through university education attainment) whereas Austrian natives (men) are less highly-educated: only 20.8 % of them have attained an university level. Interestingly, we can notice that Turkish immigrants with high-level of education (university) are equally represented in the two host countries for both men and women. Indeed, between 7 % and 8 % of male Turkish immigrants are highly- educated in Austria and Germany. Women Turkish immigrants are on average less highly- educated than men with a rate around 6 % in the two host countries.
Around 5.6 % of Austrian and German natives (men) are part-time employed. This rate is 2 percentage points higher in for immigrant’s men in Germany but one percentage point lower in Austria. However, this is only a slight difference. A more interesting mean can be observed in the women group. Whereas almost one third of the native women work part-time in Germany and Austria, only one fifth of the immigrant’s women have a part-time job in Austria and one quarter in Germany. We could think first this is due to a lower mean wage for native’s women, but they are on average older than immigrant’s women. In that way, we could interpret that difference due to the fact that immigrant’s women need to work more than natives in order to get a reasonable income. Even if immigrants have on average more children in their household than natives, immigrant’s women are less represented in the part-time employment.
It could show that immigrants’ women need to make additional efforts in order to get a decent salary.
Male Turkish immigrants have on average 3 years of experience less than natives’ men in the both countries studied whereas female Turkish immigrants have on average 8 years of experience less than native’s women. It could be easily explained by the fact that they stopped working due to more frequent pregnancy than natives.
We can conclude that Turkish immigrants (men and male) are negatively selected in the two host countries. Indeed, natives are on average way more skilled than the immigrants.
The tenure of immigrant is way higher in Germany than in Austria. Indeed, the mean tenure is around 25 years and a half in Germany against 19 years and a half in Austria. This difference is even larger regarding women. This could be due to less restrictive migration policy in Germany since the beginning of the 90’s. Many immigrants, noticeably Turkish immigrants, arrived after the reunification and lighter migratory policy.
We can observe in the Table 2 that on average the logarithm earning gap between male immigrants and male natives is lower in Germany compared to Austria. We can notice that the immigrant-to-natives earning gap is almost the same between Germany and Austria, regarding the women group.
Table 2
Turkish immigrant-to-native mean earnings (logarithm) ratios by country and gender
MEN WOMEN
Host country Germany Austria Germany Austria
Immigrant-to-natives earning gap (using ln(earnings)
differences) 0,38 0,5 0,61 0,63
The results are rounded to two digits after the point.
B. Main results
Table 3 (c.f. next page) presents earnings assimilation model for male and female in each country (Germany and Austria) based on the extended assimilation model (Borjas, 1985). The dependent variable in the model is the natural logarithm of annual earnings (period 2010-2016) from salaried work and self-employment.
We have decided to run our regression for both sexes separately. Some of our estimates in the regression tables are not statistically significant. It is easily understandable due to the fact that Turkish immigrants are underrepresented compared to natives.
The main purpose of this regression is to observe the rate of assimilation of the immigrants in respective country. This “assimilation rate” is captured by the YSM coefficient.
In the table below, we can see that the assimilation rate of male immigrants is quite low in Austria (around 0.7% and insignificant) whereas it is higher for Germany (around 2.1 % and significant level at 1 % level). It means that if Turkish immigrants (male) stay one year more in Germany, their wage will increase on average by 2.1 %, against only 0.7 % in Austria.
Immigrants catch up faster with natives in Germany than in Austria. It would mean that Turkish immigrants (men) who migrate to Germany have a better tendency to economically assimilate compared to Turkish immigrants who arrived in Austria.
Regarding women, we make similar findings. If Turkish immigrants (female) stay one year more in Germany, their wage increases on average by 1.4 % against 0.8 % in Austria. Once again, immigrants who migrate from Turkey to Germany assimilate faster than in Austria.
Table 3
Earning regressions – Turkish immigrants and natives (male): Austria and Germany
Log (labor income) MALE FEMALE
Germany Austria Germany Austria
exp 0.073*** 0.075*** 0.060*** 0.119***
(0.01) (0.005) (0.001) (0.005) exp2 -0.001*** -0.001*** -0.001*** -0.002***
(0.001) (0.001) (0.001) (0.001) secondary_ed 0.349*** 0.449*** 0.383*** 0.424***
(0.016) (0.044) (0.017) (0.043) university_ed 0.840*** 0.867*** 0.975*** 1.044***
(0.017) (0.049) (0.018) (0.049) married 0.196*** 0.177*** -0.213*** -0.191***
(0.010) (0.028) (0.009) (0.028)
YSM 0.021*** 0.007 0.014 0.008
(0.006) (0.017) (0.009) (0.025)
nchildren 0.062*** 0.049*** -0.079*** -0.179***
(0.005) (0.012) (0.005) (0.014)
year_imm70 -0.946*** -0.067 -0.410 .
(0.262) (0.971) (0.362) .
year_imm80 -0.752*** -0.602 -0.410 0.120
(0.190) (0.506) (0.259) (0.780) year_imm90 -0.589*** -0.493 -0.521*** 0.062
(0.140) (0.349) (0.189) (0..501)
year imm00 -0.698*** -0.197 -0.206 0.385
(0.097) (0.162) (0.136) (0.300)
year 2011 0.026 . 0.042*** .
(0.014) (0.016)
year 2012 0.049*** . 0.079*** .
(0.014) (0.016)
year 2013 0.069*** 0.145*** 0.095*** 0.053 (0.014) (0.027) (0.016) (0.031)
year 2014 0.068*** . 0.101*** .
(0.014) (0.016)
year 2015 0.094*** . 0.160*** .
(0.015) (0.016)
year 2016 0.127*** 0.209*** 0.192*** 0.095***
(0.014) (0.027) (0.016) (0.031) constant 8.824*** 8.856*** 8.539*** 8.075***
(0.022) (0.068) (0.024) (0.068)
R-squared 0.196 0.113 0.164 0.175
R-squared ajusted 0.196 0.112 0.164 0.173
Number of observations for both natives and
immigrants 43 494 7 632 47 430 7168
Level of significance: p<0.01 *** p<0.02** p<0.05*
The results are rounded to three digits after the point.
In addition to assimilation coefficient, we can notice that in both country women’s wage (for natives and immigrants) is negatively impacted if they are married and having kids. It could be due to the fact that married women have a higher probability to become pregnant and ask for pregnancy leaves and longer parental leaves than men. This motherhood wage penalty can also be explained by the facts that women with kids work less hours, prefer taking care of their
family than working. Their human capital can consequently depreciate overtime. Women are also victim of discrimination from the employers.
The recognition for skills and education level are quite similar in the two host countries.
Here the reference education level is the compulsory education level (under secondary education). For example, men in Germany and Austria earn on average 85 %4 (at 1 % level of significance) more than low educated individuals, it’s even more for the women in both host countries. To give a clear example, it means that a low-educated individual earns on average 1000 euros, a high-educated individual will earn around 1850 euros. Secondary educated individuals are more recognized, in terms of earnings, in Austria than in Germany. As an example, men in Germany with a secondary education level earn around 45 % more than low educated men (at the 1 % level of significance) against 35 % (at the 1 % level of significance) in Germany. We can observe a similar situation for women.
We can also notice, through the coefficients for the years of observation, that the general level of earnings tends to increase overtime from 2010 to 2016. The economy has been slow down by the subprime crisis in 2008/2010 and this increase in wage symbolizes the economic recovery which occurs after this period.
C. The decomposition output
In order to decompose the wage gap between the natives and the Turkish immigrants, we have run the well-known Blinder-Oaxaca decomposition method.
We have decided to only process to a decomposition for male groups because female may decomposition may be biased due to sex discrimination and the results obtained were not relevant and coherent with the rest of the study.
4 This rate may seem a bit high regarding other countries as Sweden. Wage differentials between low and high skilled workers are way higher in the USA, Japan, Germany and Austria compared to Scandinavian countries which are ranked as having the lowest wage differential between low and high-skilled individuals in the world
We can see the results of that decomposition for each host country separately in the Table 4. We have decided to process to the decomposition only for the male group and did not consider the female’s one. Female immigrants are subject to additional discrimination compared to male immigrants, so our decomposition could be highly biased. That’s why we have decided to drop women from this last observation.
Table 4
Blinder-Oaxaca twofold decomposition (pooled) for male group
Logpilabour GERMANY AUSTRIA
Differential
Natives (group_1) 10.454*** 10.531***
(0.005) (0.008)
Immigrant (group_2) 10.217*** 10.164***
(0.027) (0.058)
Difference 0.236*** 0.366***
(0.028) (0.059)
Decomposition
Explained 0.183*** 0.22***
(0.015) (0.025)
Unexplained 0.052 0.1458**
(0.028) (0.061)
In % Ratio
Explained 78% 60 %
Unexplained 22 % 40 %
Observations
N observations 46502 9480
N natives 45259 9242
N immigrants 1243 238
Level of significance: p<0.01 *** p<0.02** p<0.05*
The results are rounded to three digits after the point.
Regarding the table obtained, we firstly notice that the logarithm earnings gap between natives and immigrants in Germany is explained at 78 % by individual characteristics. 22 % of
the wage gap is unexplained by the individual differences in characteristics between natives and immigrants.
In Austria, the case is a quite different. Indeed, 60 % of the earning gap is due to the difference in individual characteristics and 40 % are due to unexplained characteristics.
The unexplained characteristics may be split for instance into market structure and discrimination. The market structure openness for immigrants and the discrimination towards immigrants are two factors which are regulated by migratory policies (anti-discrimination policy, quota of immigrants, working visa…).
We know that Austria has applied more restrictive measures towards immigrates over the 1990 and 2000 decades. We can also observe this tendency through the Migratory Policy Index by comparing Austria and Germany indexes.
What’s more, thanks to the earnings regression of the extended assimilation model, we can observe that Turkish immigrants economically assimilate in a faster way in Germany compared to Austria.
All these observations may lead us to think that migratory policy have quite an important role to play in the economic assimilation of the immigrants, here the male Turkish immigrants.
VIII. Discussion
Our main results are partly relevant to previous studies and theories. Indeed, according to the descriptive statistics overview, Turkish immigrants in Germany are less educated than the immigrants in Austria. According to the human capital theory, immigrants in Austria should consequently earn a higher wage in this host country compared to Germany. But this is not the case here. But why is it so? It is then explained by the context of reception theory. As we have explained in our conclusion part, this context of reception is directly linked to migratory policies. In this specific case, it would mean that the context of reception of a host country influences more on the economic assimilation than the human capital of the immigrants. It reinforces the fact that before migrating, individual should highly take care of the characteristics of the host country beforehand.
Throughout the earnings regression, we have seen that Turkish immigrants (here men) assimilate faster in Germany compared to Austria. This could be partly due to the migratory policies implemented in Germany which are less restrictive than in Austria and support the economic assimilation of immigration. Indeed, if we report these results to the MIPEX (Migratory Policy Index) data tables, we can outline some elements.
First of all, all immigrants have an immediate access to the labor market in Germany, whereas this access is more limited in Austria and was even highly restricted before the law of the 1st July 2011. Indeed, we can read in the MIPEX database that before 2011 “the settlement permit key worker or employment permit holders are issued for a certain employer and certain position after a labor market test”. The fact to restrict the direct access to the labor market for all the immigration may lead to a lower economic assimilation rate. Indeed, immigrant may wait a longer period of time before entering the labor market and they will consequently wait a longer period of time in order to assimilate. The access to self-employment is also restricted in Austria where the access to trade license is privileged for those who acquired their education in Austria.
Academic and professional skills acquired abroad are well recognized in the both host countries. Indeed, they have implemented bilateral agreements on recognition of professional and academic qualifications. In Germany, they have even introduced a law which states that the recognition of credentials is independent from the country of origin for these credentials.
We have seen in a study mentioned in the literature review of our essay that access to nationality/citizenship can be a powerful political instrument which could accelerate the assimilation of immigrants in the labor market. In Austria (where the assimilation rate is low), immigrants need to wait at least 10 years to ask for a naturalization. What’s more, they need to cumulate five years with a permanent resident permit immediately before the application in order to be eligible. In Germany, immigrants have to wait a maximum of 7-8 years and possessing a permanent or long-term resident permit is not required. Consequently, this could accelerate the immigrant economic assimilation.
What’s more, Austria and Germany have a political difference in their history. Indeed, Germany opened its frontier to a wide number of immigrants after the German Reunification in the beginning of the 90’s. Many immigrants came and found a job easily because workforce was necessary to revitalize the country’s economy. We can see through the mean years since migration that many Turkish immigrants came during this period of time and could get a job more easily than nowadays or another country which didn’t need additional workforce. Austria didn’t go through such an economic and political challenge.
IX. Conclusion
Throughout this empirical study, we tried to understand if, in the case of Turkish immigrants in Germany and Austria, the differences in labor market assimilation could be explained by differences in migration policy. By running earning regressions based on the Borjas assimilation model, we have found that Turkish immigrants, both males and females, assimilate faster in German labor market than in the Austrian’s one. By comparing our regression results with the Migratory Policy Database (Appendix B), we noticed that a better and faster assimilation may be linked with less restrictive policies regarding the access to the labor market, access to nationality and recognition of human capital (education, experience, skills).
This specific case reinforces the fact that more restrictive policies will decrease the assimilation rate of immigrants in the labor market. It is essential for policy makers to support and encourage the economic integration of immigrants through laws, agreements and public assistance. The host country would mostly benefit from a faster economic assimilation: its economy would be even more boosted, dynamized and strengthened. However, some political parties believe that the policy makers should implement stricter rules in order to limit the number of upcoming immigrants in their own country. Their main argument is to claim that immigrants take the jobs which were supposed to be dedicated for natives. According to them, the average level of wage of natives would consequently decrease. But does immigration impact negatively the economic situation of natives in the labor market? We could try to answer this research question in further studies by analyzing the impact of immigration on natives’
employment and natives’ wage by running earning regressions.
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Appendix
A. Key of readings – abbreviations table
Variable name Variable meaning
Year_imm70 Cohort 1970: immigrants arrived before 1979
Year_imm80 Cohort 1980: immigrants arrived between 1980
and 1989
Year_imm90 Cohort 1990: immigrants arrived between 1990
and 1999
Year_imm00 Cohort 2000: immigrants arrived after 2000
Exp Experience: number of years worked during the
entire career
Exp2 Experience-squared
nchildren Number of own children living in the household
YSM years since migration in the host country (tenure)
Married Refers to the de jure unions
Compulsory_ed Compulsory education: low, less than upper
secondary; primary; lower secondary
Secondary_ed Secondary education: upper secondary; post-
secondary non-tertiary
University_ed University education: short-cycle tertiary;
bachelor; master; doctorate B. Migratory Policies Database
Migrant Integration Policy Index, http://www.mipex.eu/
Policy indicators score available at: http://www.mipex.eu/download-pdf (excel folders)
