The determination of post
education and the economic returns to
pre- and post
among refugees in the Netherlands
Winny van Heijningen 10735860
Winnyvanheijningen@hotmail.com Master thesis in Sociology, Comparative organisation and labour studies
First reader: Mw. A.M. Kanas Second reader: Dhr. T. Bol 26-6- 2016
The determination of post-migration
and the economic returns to
and post-migration education
among refugees in the Netherlands
Winnyvanheijningen@hotmail.com Comparative
1
migration
and the economic returns to
migration education
among refugees in the Netherlands
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Abstract
This study examines migration education investments and the economic returns to pre- and post-migration education among five immigrant groups in the Netherlands. Large-scale survey data is used to examine the investments of immigrant groups who migrated for humanitarian reasons, which are: Afghanistan, Iraq, Iran, Somalia and former Yugoslavia. The hypotheses derived from the Human Capital Investment Model that argues that investments in education are an outcome of three main factors: settlement intentions, skills transferability and opportunity costs. The binary logistic
regression and the ordinary least square regression showed that the investments are higher when the immigrants have the intention to settle in the host country. Furthermore, evidence is found for the complimentarity between pre- and migration education, since the investments of the post-migration education level increases when the pre-post-migration education levels increases. Investments also tend to be higher when the immigrant is longer unemployed after migration, which confirms the hypothesis that smaller opportunity costs is an incentive to invest. Further, direct measures of the pre-and post-migration education are used to examine the economic returns, which is measured in employment status and earnings. It is found that the economic returns are greater when the nature of the education is more similar or when the risk and uncertainty about the pre-education is smaller. In that case the valuation of post-migration education is higher, and this translates into higher economic returns.
Keywords: Educational investment, Skills transferability, Immigrant Human Capital, Human Capital investment model, Refugee immigrant, Economic return
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Contents
Introduction ... 4
Theoretical framework ... 6
The human capital investment model ... 6
Settlement intentions ... 6
Skills transferability ... 8
Opportunity costs ... Error! Bookmark not defined. Economic returns to pre- and post-migration education ... 11
Methodology ... 14
Data ... 14
Method ... 14
Human capital investments... 14
Economic returns to pre- and post-migration education ... 16
Results ... 17
Human capital investments ... 19
Settlement intentions ... 19
Skills transferability ... 20
Opportunity costs ... Error! Bookmark not defined. Economic returns to pre- and post-migration education ... 23
Conclusion and discussion ... 26
4
Introduction
Lately, the current inflow of refugees is a hot topic in Europe and has even been called a refugee ‘crisis’ (OECD, 2015). However, inflows of refugees are not a phenomenon of the last years; rather it is something that has always been happening (Ibid, 2015). What is the difference between the current so-called ‘crisis’ and former inflows of refugees? And what can we learn from them to improve their assimilation? Generally speaking, one major difference between earlier and the current inflow is that the current refugee inflow is higher educated compared to former refugee inflows (OECD, 2015). Earlier research into immigrant’s labour market performance showed that the extent of assimilation partly depends on the investments in country specific skills of their host country (Chiswick & Miller, 1994), and formal education improves their labour market success. Which factors influence the decision to invest in host country education? Chiswick & Miller (1994) found that investments in pre-migration education are complimentair to investments in post-pre-migration education. However, what other factors determine the investments? From this question the two main questions derive: What are the determinations of investing in post-migration education for refugees in the Netherlands? And: what are the economic returns to pre- and post-migration investments? Through examining the former refugee inflow in the Netherlands, expectations about the current inflow can be provided.
Earlier research has been done in the United States (Akresh, 2007; Duleep & Regets, 1999) and Australia (Chiswick & Miller, 1994), but the schooling system in the Netherlands differs since there are multiple education tracks, which are differentiated by level and content (Van Tubergen & Van Der Werfhorst, 2007). In the Netherlands, Van Tubergen and Van De Werfhorst (2007) researched the immigrants and their participation in education and Kanas and Van Tubergen (2009) researched the economic returns to this education. However, those studies were targeted to the four larger groups of immigrants within the Netherlands, which are Surinamese, Dutch Antilleans, Turks, and Moroccans. These groups migrated for economic reasons and there is evidence that immigrants moving to the Netherlands for work are less likely to invest in post-migration education (Van Tubergen & Van De Werfhorst, 2007) and if they obtain a diploma, many of them obtain the lowest degree. Migrants can be divided into three major streams; economic migrants, family migrants and humanitarian migrants (Cobb-Clark et al., 2005).This research contributes by focusing on other immigrant groups who migrated to the Netherlands for mainly humanitarian reasons, namely: immigrants from Afghanistan, Iraq, Iran, Somalia and former Yugoslavia.
When immigrants arrive in their host country they can follow two strategies; eiter they rely on their pre-migration obtained skills and degrees, or they acquire post-migration education in order to obtain country specific skills (Arendt, Nielsen & Jakobsen, 2012). Immigrants tend to make greater investments when the expected economic returns outweigh the total cost, this means the direct and the opportunity costs (Akresh, 2007). The Immigrant Human Capital Investment model developed by Duleep and Regets (1999, 2002) is used to examine the determinations of investing in education
5 among the five immigrant groups within the Netherlands. In this model there are three main factors that have influence on the extent that immigrants invest in post-immigration education. These factors are: settlement intentions, skills transferability and opportunity costs.
The settlement intentions imply that immigrants are more likely to invest in post-migration education when there is a longer expected time period that they could use their obtained education in the host country. Several scholars measure this by various factors; Van Tubergen & Van De Werfhorst, (2007) measured this by the reason for migrating, Chiswick & Miller (1994) measured this by the status of their asylum and Cobb-Clark et al, (2005) measured this by the family situation. The scholars found that the greater the intention was to stay, the more likely the immigrants were to invest in education, because there is a longer expected time period that they can use their obtained education. The measurement, however, could be biased because of indirect measure. This research contributes by doing a direct measurement of the settlement intentions of refugees. Furthermore, the researchers acknowledge a negative correlation between age at arrival and the educational attainment, which has the same mechanism, namely the longer expected time period that they can make use of their obtained education (Chiswick & Miller, 1994; Van Tubergen & Van De Werfhorst; 2007).
Skills transferability implies that pre-migration education is often not fully valued in the host country (Duleep & Regets, 1999; Kanas & Van Tubergen, 2009; Van Tubergen & Van De Werfhorst, 2007). Pre-migration education is, however, not only valuable for earning, but also for learning (Duleep & Regets, 1999). Moreover, not only study specific skills are acquired, but also more general learning skills are obtained. Borjas (1982) & Hashmi (1987) estimated the post-migration education by calculating post-migration education as total education minus pre-migration education, and both scholars assume that individuals attend school continuously from the age of six. This can lead to systematic measurement errors (Van Tubergen & Van De Werfhorst, 2007). Another scholar measured the pre-migration education by years of schooling (van Tubergen & Van De Werfhorst, 2007). The scholars found that higher pre-migration education positively influences the post-migration education investments. However, another contribution is the direct measurement of the pre-migration obtained education, which is measured in a five point scale ranging from no education to higher education.
The opportunity costs are the additional cost of educational investments. This does not mean the actual cost for education, but rather the income what the immigrant is missing out on when he is studying. When the skills transferability and the economic returns for the pre-migration education are both low, the opportunity costs are also lower (Duleep & Regrets, 1991). Van Tubergen & Van De Werfhorst (2007) found that greater investments are made, when the macro-unemployment levels are higher. The macro-level conditions, however, could affect some aspects harder than others. Another contribution is that the opportunity costs are measured on an individual level.
The last contribution for the first main question is that, in contrast to Van Tubergen & Van De Werfhorst (2007) the dependent variable is not only treated as a binary variable, but there is also
6 research done on the level of the investments. Thereby a more specific view about the influence of the different levels is provided.
Formal education improves labour market success (Chiswick & Miller, 1994) because, earnings are largely related to characteristics associated with the productivity (Chiswick, 1974) and education improves the capability of productive work (Kanas &Van Tubergen, 2009). Education provides productivity-enhancing skills to individuals, but the quality and nature of education can vary from country to country and therefore the pre-migration education can provide fewer skills and consequently the economic returns are lower. Another point of view is that the economic returns to education are positively correlated with the valuation of human capital, which is related to the risk and uncertainty for the employers. The risk and uncertainty increase when the native country differs more from the host country, and therefore the employers are reluctant in giving full credentials to pre-migration education (Chiswick, 1974). The last contribution is that the dependent variable is not only measured in employment status (Arendt, Nielsen & Jakobsen, 2012) or in occupational status (Kanas & Van Tubergen, 2009), but also measured in earnings.
Theoretical framework
The human capital investment model
When an immigrant arrives in the host country they can follow two strategies; either they rely on their pre-migration obtained skills and degrees, or they obtain post-migration education in order to obtain country specific skills (Arendt, Nielsen & Jakobsen, 2012). The exploration of the determinants which strategy is followed is based on the human capital investment model (Duleep & Regets, 1999). The model includes three mechanisms, which are: settlement intentions, skills transferability and opportunity costs, and from each mechanism hypotheses will derive. Key concept in this model is that the pre-migration obtained education may not be fully valued in the host country. Moreover, human capital according to Duleep & Regets (2002) contains both skills acquired from education and from professional experience, but in this research the focus is on human capital obtained from education.
Settlement intentions
The first mechanism discussed is the intention and the probability of staying in the host country (Van Tubergen & Van De Werfhorst, 2007), in other words; the settlement intentions. Individuals migrate for various reasons, which in the literature is often distinguished between humanitarian immigrants, family immigrants and economic immigrants (Cobb-Clark et al., 2005). The most important characteristic of an economic immigrant is that they are here voluntary and able to return to their native country (Cortes, 2004). In contrast, humanitarian immigrants are unable or unwilling to return to their native country for fear or threat of prosecution. They also have less social ties that attach them to their native country, because they have fewer social contacts in their native country, since they are
7 not able to return their origin country and visit their contacts. Through the absence of social ties and the intention to return, humanitarian refugee immigrants are more willing to build up a life in their host country and to achieve this they are more willing to receive education (Cortes, 2004). Evidence is found that immigrants moving to the Netherlands for work are less likely to invest in post-migration education (Van Tubergen & Van De Werfhorst, 2007) and if they obtain a diploma, many of them obtain the lowest degree. Furthermore, immigrants who migrate for family reasons invest significantly more, because they have more attachments to the host country and therefore are more likely to have high settlement intentions. The migrants who migrate for educational reasons are naturally most likely to invest (ibid). The longer an immigrant is intending to stay in the host country, the longer is the time period that they could use their post-migration obtained education in the host country and the higher their economic returns are. Therefore the intention to stay in the host country is an incentive to invest in education and evidence is found that permanent immigrants are more likely to invest in country specific skills (Borjas, 1982; Chiswick & Miller, 1994; Duleep & Regets, 1999). From this perspective the following hypothesis can be formulated: (H ) the intention to stay in the host country
positively affects the investments in post-migration education for humanitarian immigrants.
Furthermore, Van Tubergen & Van De Werfhorst (2007) found that the length of stay has a positive effect on educational investments, because commitments to the host country increase through the development of friendships and increasing participation in organizations and institutions. The settlement intentions increases and thus the investments are higher. This positive influence, however, has a decreasing rate, because of three main reasons (Chiswick & Miller, 1994; Chiswick & DebBurman, 2004). First, the economic returns to the education are higher when the investments are made in earlier stages, because of the longer time period that they can benefit of the obtained education. Second, the opportunity costs are lower in the first stage, due to the rising earnings with length of stay, assuming that the work experience and the knowledge of language increase. Lastly, the investments that can increase the transferability of the pre-migration obtained skills are more profitable when they are made in earlier stages.
Key finding from the literature is that the length of stay in combination with the age of arrival is a primary determinant for investing in education (Chiswick & DebBurman, 2004). Thus, another factor which determinates investments in post-migration education is the age of the immigrant when they migrate to the host country; since there is a longer time period of that they could use their obtained education. Also the opportunity costs are lower at a younger age of arrival, because of previous (pre-migration) investments (Chiswick& Miller, 1994). Investing in post-migration education is more beneficial for younger immigrants compared to their older ones, and therefore the investments will fall with age at migration and holding age constant, with the length of stay in the host country (Chiswick & DebBurman, 2004). Evidence is found that enrolling in education decreases with the age of migration (Chiswick & Miller, 1994; Van Tubergen & Van De Werfhorst, 2007). The authors did not find significant differences in the level of enrolled education. Even though this research focuses on
8 immigrants above the age of 18 and under 64, it is expected that within this range, the younger adult refugees invest more in post-migration education.(H )The younger the migrants are when they arrive
in the Netherlands, the higher the investments in education.
Skills transferability
A second mechanism is the skills transferability, which means that that the pre-migration obtained education may be not fully valued in the host country (Van Tubergen & Van De Werfhorst, 2007). Akresh (2007) argues that immigrants have two types of human capital: the general kind that is equally productive everywhere and the country-specific human capital that varies in the degree of transferability between countries. Education provides often both elements, but which of the two types is more dominant depends on the nature -level and type- of education. Higher education and vocational education, for example, may be more country specific than the basic skills learned in primary education (Friedberg, 2000). If this nature is not heavily biased towards country-specific human capital, than the skills are more transferable (Khan, 1997). Additionally, the reason for migration may also influence the extent of the immigrant’s skills transferability (Khan, 1997). Optimizing economic returns is the main factor to migrate for economic migrants, and they therefore can be self-selected for skills characterized by high skills transferability (Chiswick, 2000; Khan, 1997).
When the skills are perfectly transferable, pre- and post-migration education would be substitutes for each other (Chiswick & Miller, 1994), and therefore discourage post-migration education (Khan, 1997). When the skills are not perfectly transferable, pre- and post-migration education can be positive related to each other, in other words; they are complements from each other (Chiswick & Miller, 1994). For instance, post-migration education can increase the transferability of pre-migration obtained education (Chiswick & Miller, 1994).
Duleep & Regets (2002) argue that the valuations of the pre-migration education in the host country education influence the production of new human capital. The valuation of the human capital in the host country is always lower than the production factor for new host country specific skills and when the valuation falls the production factor falls less. Therefore, it is argued that for immigrants the pre-migration education is more valued in learning than in earning (Van Tubergen & Van De Werfhorst, 2007; Duleep & Regets, 2002). Pre-migration obtained education is still useful in the production of post-migration human capital (Duleep & Regets, 2002). Moreover, immigrants may have a desired total level of schooling attainment and immigrants with higher demand to education are tending to receive education in both countries. Earlier research showed a positive effect of pre-migration education on post-pre-migration education (Chiswick & Miller, 1994; Cobb-Clark et al., 2005; Van Tubergen & Van De Werfhorst, 2007). Akresh (2007) found that having more years of education are associated with higher probabilities of investing in post-migration education. However, there has been inconclusive evidence on the relationship between the obtained level of pre-migration education
9 and investments in post-migration education (Cobb-Clark et al., 2005). The scholars found that pre-migration obtained skills are still useful in attaining new skills. Also knowledge of their own abilities will influence the decision to invest in -and the level of- education (Duleep & Regets, 2002). Another reason to invest is that higher educated immigrants have experience in learning new knowledge and skills. Previous learned work and study habits influence the investment in human capital (Duleep & Regets, 2002). In addition, there is a negative correlation between effort of the individual and obtained education level (Spence, 2002). Thus, the more intelligent, the less effort it cost to obtain higher levels of education and the more likely he is to invest in higher education. From this perspective the third hypothesis can be derived: ( ) immigrants who are higher educated before migration are more likely
to invest in education in the host country.
Duleep & Regets (1999) found that the skills transferability was low for new U.S. immigrants from less-developed countries. The skill transferability for these groups is lower, since there is often less information about the obtained skills and the level of education. As a consequence, the employers have less information about the productivity of the immigrant and the valuation of the pre-migration education is lower (Chiswick, 1974). Given that international transferability of skills depends on the similarities between the native and the host country, Khan (1997) argues that the nature and quality of schooling is an important similarity. “Countries that have a language, culture, customs, and technology similar to that of the destination would also tend to exhibit similarities in the nature and quality of schooling”(Khan, 1997; 288). When the native country and the host country are more similar, valuations of the pre-migration obtained credentials are higher. Looking at the student-pupil ratio, it is argued that Yugoslavia is more similar to the Netherlands than Afghanistan, Iraq, Iran and Somalia. The numbers are obtained from the OECD database (OECD, 2015) and for the missing data averages are calculated and for former Yugoslavia the individual current country data is taken and the average of these countries is calculated. The numbers are over the years 1990-2000, because in these year’s 80.9% of the respondents migrated to the Netherlands. The outcome is as follows: Afghanistan has the highest pupil-teacher ratio (43.39), followed by Somalia (33.60), then Iran (30.58) and Iraq (22.55). There was data available of Croatia, Slovenia and Macedonia, with an average of 19.99. The Netherlands has during that time an average of 17.22, thus it can be argued that the former Yugoslavian schooling system was indeed quite similar.
One side note is that Van Tubergen & Van De Werfhorst (2007) found contradicting result for the four largest immigrant groups in the Netherlands, which are: Turks, Moroccan, Surinamese and Antilleans. The results showed that even though the skills for immigrants from former colonies are more transferable they tend to have higher investments in post-migration education. The scholars argue that in order to perform well at school in a new country, sufficient mastering of the host country language is necessary and the native country knowledge and skills needs to fit in the educational system. The immigrants from former Dutch colonies often speak the language to some extent and their schooling systems were more similar compared to the Turkish and Moroccan schoolings system. The
10 knowledge and skills of the Turkish and Moroccan did not fit smoothly into the educational system, but were easily transferable into the lower segments of the labour market, because no Dutch language skills and no appropriate educational qualifications were required. The five targeted immigrant groups in this research all lack the knowledge of the language, but the risk and uncertainly of the pre-migration obtained skills is lower, and the production factor could be higher for Yugoslavian immigrants through more similar and more qualitative schooling systems. The valuation of pre-migration education from the Yugoslavians could be higher and as consequence the Yugoslavians are less likely to invest in post-migration education. ( ): Immigrants from Yugoslavia have higher skills
transferability and are less likely to invest post-migration education than the Immigrants from Afghanistan, Iraq, Iran and Somalia.
Opportunity costs
The opportunity costs are defined as the additional cost of education investments. This does not mean the actual cost for education, but rather the income that the immigrant does not receive because he cannot work during the time that he is studying. This is why these costs are also called ‘alternative costs’. When the immigrants are not able to completely transfer their human capital, the opportunity costs are lower for them than for natives, and investment could be higher (Chiswick, 1978). In the most extreme case, the post-migration education is not valued at all in the host country. In this case the investments in education are more likely to be higher, because an additional unit of education adds nothing to the opportunity costs while is has enormous impact on the value of production (Akresh, 2007).
Immigrants who aimed to enter the labour market based on their pre-migration education may experience difficulties in finding a job that match the level of their pre-migration education and are more likely to obtain post-migration education. Arendt, Nielsen & Jakobsen (2012) emphasize the importance of the timing of education attainment and claim that difficulties in matching pre-migration education with the host country labour market will positively influence the decision to invest in post-migration education. “Enrolment of a destination-country education shortly after arrival, for example, is likely to send a strong signal to employers about an immigrant’s knowledge about the destination country, motivation and willingness to adapt, learning ability, and also about a limited impact of suffering prior to and during migration (Arendt, Nielsen & Jakobsen , 2012; 11). Moreover, the opportunity costs are lower in the first stage after migration, due to the rising earnings with length of stay (Chiswick & DebBurman, 2004). On the other hand, Arendt et al. (2012) discovered that in Denmark the timing of educational attainment may vary due to differences in immigrants’ knowledge (or perception) of the functioning of the destination-country labour market, including the transferability of their specific pre-migration education and labour market experience. Immigrants who initially aimed at entering the labour market based on their native country qualifications may find it
11 difficult to find jobs that match and becomes motivated for taking a host country education after some years in the destination country (cf. Nielsen 2011).
Van Tubergen and Van De Werfhorst (2007) found evidence supporting the opportunity costs mechanism and showed that higher national unemployment rates have a positive effect on educational investments. When the individual is unemployed, the opportunity costs are lower and the investments could be higher. Therefore it is argued that the longer the immigrant is unemployed, the higher the probability of investing in education. As explained before, length of stay in the host country has a positive non-linear impact on post-migration education (Hasmi, 1987; Chiswick& Miller, 1994) Thus, greater investments are made in the first period after immigration.( )The longer the immigrant is
unemployed in their first stage of immigration the more likely he is to invest in education because the opportunity costs are lower.
Cobb-Clark et al. (2005) found that humanitarian migrants spend a higher proportion of their time unemployed and this time also varies between native countries. The scholars found that U.S. immigrants with English speaking background are unemployed shorter in Australia than non-English speaking background immigrants due to higher skills transferability. Since the opportunity costs are lower when the migrant is unemployed, this is consistent with previous evidence that investments in post-migration education are higher for migrants with less transferable skills. However, the authors acknowledge that the high unemployment rate is due to less effective job search and also discrimination may be a reason why immigrants with foreign-acquired qualifications experience difficulties in the destination-country labour market (Jacobsen 2004).When the valuations of the credentials are higher, the opportunity costs are higher. Thus, when the skills transferability is higher for refugees from Yugoslavia, their opportunity costs are also higher.( )Refugees from Yugoslavia
have higher opportunity costs and are less likely to invest in post-migration education than the refugees from Afghanistan, Iraq, Iran and Somalia.
Economic returns to pre- and post-migration education
Human capital is conceptualized as embodying a set of skills, and refers therefore to the capability of productive work, which can be used and is demanded by employers (Kanas &Van Tubergen, 2009). The economic returns are largely related to characteristics associated with the productivity (Chiswick, 1974), and the economic returns to the human capital can differ among individuals. This difference especially appears among immigrants and natives, because origin country obtained degrees are less valued than skills obtained in the host country (Borjas 1994; Duleep and Regets 1999; Friedberg 2000).
Formal education improves labour market success (Chiswick & Miller, 1994), however, there are three perspectives about the exact function of education, which are: the human capital theory, the positional good perspective and the social closure perspective (Van De Werfhorst, 2011). There are
12 two main reasons for the difference in economic returns to pre- and post-migration education, which both derive from a different perspective.
The first reason for the differentiation in economic returns for pre- and post-migration education derived from the human capital theory. This theory refers to the human capital as the capability of performing productive work (Kanas & Van Tubergen, 2009) and education provides productivity-enhancing skills to individuals (Van De Werfhorst, 2011). Investing in education is a way to increase the human capital and thus the productivity, assuming that knowledge and skills are obtained during education that have direct influence on this productivity. Hence, education provides individuals with skills, this increase their productivity and they have more opportunities in the labour market (Kanas &Van Tubergen, 2009). Since the individuals with more education are more productive, they are more demanded and higher valued by the employers and therefore the economic returns to education are positively correlated with the obtained human capital (Chiswick, 1974). The reason for the difference in earnings for pre-and post migration education is then that the quality and nature of education can vary between countries. Migrants from developed countries receive higher economic returns to their education because schooling can be of lower quality in less developed countries. In addition, human capital includes general and country specific skills, and the latter may be difficult to transfer (Akresh, 2007). Friedberg (2000) discovered that migrants with pre-migration secondary education receive higher returns than migrants with higher education. The scholar argues that this is due to the skills specificity of the education, which varies between the levels of education. Higher education, for example, may be more country specific than basic skills learned in primary education (Friedberg, 2000). The result shows that native country obtained primary education from various countries is equally valued in Israel, and therefore seemed to be quite portable across different countries. This in contrast to the higher education obtained human capital. Like said before, the extent of the skills transferability depends on similarities between the native and host country (Khan, 1997). Countries that share the same language, culture, customs and technology also tend to exhibit similarities in the nature and quality of schooling (Khan, 1997), and then the skills acquired in education are more transferable, higher valued in the host country and the immigrants receive therefore higher economic returns.
Another view on education is the signaling perspective. This approach argues that education provides signals to the employers about the productivity of the individual. The signaling theory deals with information asymmetry between two parties, which means there is uncertainty about the productivity of potential employees (Spence, 1973; Van De Werfhorst, 2011). Consequently, rather than intensively investigating the qualities of individuals, employers rely on signals that can provide expectations about the productivity (Van De Werfhorst, 2011). Employers try to estimate this by characteristics that are associated to groups of applicants like, among others, age, gender, work experience and education (Spence, 1973). Education itself is a sorting device that sorts the individuals on characteristics before they obtained any education, like intelligence and perseverance, and on
13 characteristics obtained during education, like learning skills. For this reason employers use it as a screening mechanism to sort the potential employees, because it provides broad information about the possible productivity (Ehrenberg & Smith, 1994; Van De Werfhorst, 2011). Moreover, employers try to estimate the training cost when hiring a worker and earlier provided education reduces the training costs. When the training costs are lower, the employers are more likely to hire that individual. In short, education provides signals to the employers about the characteristics of the individual, and with this they obtain a certain position within the labour market relative to the individuals with lower levels of education (Van De Werfhorst, 2011). From this perspective it can be argued that difficulties in transferring the pre-migration education to the host country is only due to lower production; it may also be a consequence of risk and uncertainty for the employer. The skill transferability for migrants is lower, since there is often less information about the obtained skills and level of education. As a consequence, the employers have less information about the productivity of the immigrant and the valuation of the pre-migration education is lower (Chiswick, 1974). The employers are therefore reluctant in giving full credentials to pre-migration education. On the other hand, employers are familiar with host country obtained degrees and these credentials are fully recognized.
Kanas & Van Tubergen (2009) found that the economic returns to post-migration for each level of education are significant higher than the economic returns to pre-migration education. The scholars investigated this by the odds of being employed and by the obtained occupational status and found that migrants who obtained degrees in the Netherlands have higher odds of being employed and that it has a positive effect on the status of the jobs that migrants occupy. In addition, Friedberg (2000) argues that formal education after arrival provides country specific skills that will enable better application of the pre-migration obtained skills in the host country labour market. ( ) Economic
returns are higher to post-migration education than to pre-migration obtained education.
Some immigrant groups receive higher economic returns than others, which can be due to similarities between countries (Khan, 1997). When native country and the host country are more similar, the education systems are often quite similar in nature and quality, there is less risk and uncertainty for the employer and the valuations of the credentials will be higher. Friedberg (2000) studied the transferability mechanism within Israel and found that immigrants from Western countries receive higher economic returns than immigrants from Asia and Africa. Kanas & Van Tubergen (2009) found that in the Netherlands for each higher level of pre-migration education obtained in more similar education systems (i.e., Surinamese, Antilleans), immigrants obtain higher occupational status compared to the less similar educational systems (i.e., Turks, Moroccans). As described before the Yugoslavian schooling system is more similar compared to the schooling system of the other countries. Thereby Yugoslavia belongs to the Western world in contrast to the other countries. From this perspective the last hypothesis derived. ( ) Refugees from Yugoslavia receive higher economic
14
Methodology
Data
The used dataset is the SOCIALE POSITIE EN VOORZIENINGENGEBRUIK VAN ALLOCHTONEN 2003 – (SPVA), obtained from the database of the institute of sociologic – economic research (ISEO, 2003). It is a survey among groups origination from Afghanistan, Iraq, Iran, Somalia and former Yugoslavia. This research is performed in the context of the reports about ‘Accessibility and Proportionality’. These reports are maintained since 1988 among immigrants and natives households. It contains fifteen groups of question about, among others: language, education, labour, income, health, social contacts. The research is conducted in twelve municipalities, which are Amsterdam, Rotterdam, Den Haag, Utrecht, Eindhoven, Enschede, Almere, Nijmegen, Delft, Leeuwarden, Tilburg and Groningen and in total there are 3500 interviews conducted with the head of the households equally divided over the five groups.
The analysis is restricted to migrants from Afghanistan, Iraq, Iran Somalia and former Yugoslavia, migrated to the Netherlands. The analysis will be limited to individuals who migrated between the age of 18 and 64, because children have qualitatively different educational investments than adults (Chiswick & DebBurman, 2006). Further, the selection is filtered to immigrants originating from the five countries and thus respondents with the answers ‘the Netherlands’ or ‘Other’ are not taken into account. The remaining sample contains 2318 respondents, 450 from Afghanistan, 485 from Iraq, 488 from Iran, 410 from Yugoslavia and 349 from Somalia. The response rates were for each group around 50% (groups based on native country). Unfortunately the data is a bit outdated, but there was not more recent data about these groups within the Netherlands.
Method
Human capital investments
The investments in education are analyzed in two different ways. First, a binary logistic regression model is made to analyze the influence of the three mechanisms on the determinations whether the immigrants chose to rely on their pre-migration obtained skills or decide to invest in post-migration education. Second, an ordinary linear regression is contrived, because there is not only researched if they obtained education, but also the level of education is measured.
The first dependent variable is the obtained degrees in post-migration education. First, the variable is treated as a binary variable with a dummy variable set equal to one if the respondent obtained a degree in post-migration education, and a dummy variable set equal to zero if the respondent did not obtain a degree post-migration education. Second, this variable is treated as a continuous variable ranging from zero to seven and is measured in no education, lower vocational education, lower secondary education, upper secondary education, vocational education, tertiary
15 vocational education and tertiary education. Only the successful obtained degrees are measured because Van Der Meer (2007) proved that the valuation of credentials is higher than the years of education in the Netherlands and because the available data is constrained to measure the years of obtained post-migration education without making estimations.
The settlement intentions will be measured by the intention to return to their native country and the age at migration. The intention to return to their native country is measured by the following question: would you like to return to your origin country? Dummy variables are made, which is set equal to one if the respondent is intended to stay and is set equal to zero if the respondent did not know or would like to return to their native country. Further, direct measure will be used for the age at migration.
The skills transferability will be measured by the country of origin and the pre-migration obtained education. Four dummy variables are added in order to analyze the effect for each immigrant group, which is set equal to one for the matching country of origin and set equal to zero if otherwise. Thus, each respondent has one dummy variable set equal to one and the rest equal to zero. The pre-migration obtained education is measured by the question if they obtained education and if yes, the highest degree of the obtained education. The degree of education is distinguished in five categories: no educations, primary education, lower secondary education, upper secondary education and higher education, and is treated as a continuous variable ranging from zero to four. In the second analysis, for each level a dummy variable is added and is set equal to one if the respondent obtained that certain degree and set zero if otherwise. Thus, each respondent has set one dummy variable equal to one for his obtained degree; with the other dummy variables set equal to zero.
The opportunity costs are measured by the individual years of unemployment after arrival in the host country. Direct measure is used to examine the individual years of unemployment after arrival in the Netherlands. This is calculated by the year they first obtained work, minus the year of arrival. This measurement has some advantages and disadvantages, which are deliberated in the discussion. However, the influence of the national unemployment rate in the year of migration is also analyzed. The national unemployment rate is based on the official data retrieved from the national databank CBS (Statistics Netherlands, 2014). The used data is about the registered unemployed at the UWV WERKbedrijf (heretofore CWI), and is a percentage of the working population (16 to 65 years) who work less than 12 hours a week and are available for work more than 12 hours or already accepted a job for more than 12 hours a week, but did not start yet. The data was available from the years 1988 to 2014. There is no data for immigrants who arrived before 1988, but this is only 6.8% of the sample. For each respondent a new variable is made with the national unemployment rate at the individual year of arrival, which is added in the analysis instead of the individual weeks of unemployment. The results showed that the overall F-score was higher with the direct individual measurement, and therefore in this research only the years of unemployment are included.
16 The analysis will be controlled for gender, the marital status at the moment of arrival and the current length of stay at the moment of the questionnaire. To control for gender a dummy variable is added, which is set equal to one if the respondent is a woman en set equal to zero is the respondent is a man. Van Tubergen and Van De Werfhorst (2007) theorized that migrants who married after migration have more attachment to their host country, which could influence the determination to invest in education. Therefore a dummy variable is made which is set equal to one if the respondent is married before migration and a dummy variable set equal to zero, if the respondent was not married during migration. To retrain this information there is calculated if the wedding year was before or after migration. Lastly, the analysis will be controlled for the current length of stay at the moment of the questionnaire, since it takes a few years to obtain a degree.
Economic returns to pre- and post-migration education
The economic returns to native or host country obtained education are measured in two different analyses. First, a binary logistic regression model is contrived to analyze the odds of being employed versus being unemployed, and an ordinary least square linear regression model is used to analyze the economic returns in earnings to pre-migration & post-migration education.
To measure the employment status a dummy variable set equal to one is made if the respondent is employed versus zero if the respondent is unemployed. The earnings are measured by their net monthly obtained income from labour. If they rather don’t like to answer this question or when they don’t know it precisely, the respondent is asked to give an indication to which income group they belong. For these respondents the class averages are merged with the continue variable. At this manner the sample grew to 938 out of 2318 respondents instead of the former 851.
The economic returns to education are measured by pre-migration education, post-migration education and country of origin. The country of origin will be measured by the same approach as above and four dummy variables are added in order to analyze the effect for each immigrant group, which is set equal to one for the matching country of origin and set equal to zero if otherwise. Thus, each respondent has one dummy variable set equal to one and the rest is set equal to zero. The pre-migration obtained education is measured by the obtained degrees and this variable is treated as a continuous variable ranging from zero to four, which is distinguished in five categories: no education, primary education, lower secondary education, upper secondary education and higher education. The post-migration obtained education is measured by the question if they obtained education in the Netherlands, and if yes the highest obtained education. This variable is measured in no education, lower vocational education, lower secondary education, upper secondary education, vocational education, tertiary vocational education and tertiary education, and dummy variables are made classified in the same five categories as above. For each degree a separate dummy variable is made and set equal to one if that certain post-migration degree is obtained and set equal to zero if otherwise.
17 Both models will be controlled for a few factors. The first control variable is the knowledge of the Dutch language, since evidence is found that immigrants who speak the Dutch language fluently achieved higher occupational status (Kanas &Van Tubergen, 2009). The respondents were asked if they master the language and if the answer was ‘good’ than the dummy variable is set equal to one and if the respondent answered with ‘moderate’ or ‘bad’ the dummy variable is set equal to zero. Further, the analysis will be controlled for gender, and a dummy variable is added, which is set equal to one if the respondent is a woman en a set equal to zero if the respondent is a man. Lastly, the analysis will be controlled for the years of work experience in the Netherlands.
Results
To examine the determinations for investing in education, first a binary logistic regression is done, followed by an ordinary least square regression (OLS). In this chapter the results will be presented, starting with the mechanism behind the determinations for investing in education and then the economic returns to pre-and post migration education First the descriptive statics will be presented in table 1.
Table 1; descriptive statics of the independent and dependent variable
Independent variables Minimum Maximum Mean or % SD N
Settlement intentions Migration reason Economic 6.3 147 Political 86.7 2009 Family 5.2 121 Other 1.8 41 Total 100 2318 Intention to stay Yes 56.8 1316 No 43.2 1002 Total 100 2318
Current length of stay (in years) 1 60 10.27 5.545 2318
18
Skills transferability Minimum Maximum Mean or % SD N
Country of origin Afghanistan 21.2 491 Iraq 21.9 508 Iran 22.7 527 Yugoslavia 18.3 425 Somalia 15.8 367 Total 100 2318 Pre-migration education No education 19.9 461 Primary education 1.8 41
Lower secondary education 14.5 337
Upper secondary education or
vocational education 31.3 726
Higher education 24.1 559
Total 100 2318
Opportunity costs Minimum Maximum Mean or % SD N
years of unemployment 0 20 4.012 2.623 1373
Unemployment rate at year of
arrival 27 52 40.409 5.8231 2160
Dependent variable Min Max Mean or % SD N
Post-migration education
No degree 75.6 1650
Primary education 7.6 166
Lower secondary education (VBO)
3.1 68
Lower secondary education (Mavo)
1.3 29
Upper secondary education (mbo)
5.6 122
Upper secondary (Havo/Vwo) 0.6 13
Higher education (Hbo) 4.2 92
Higher education (Wo) 1.9 42
Total 100 2182
Employment status Employed 42.7 1329
Unemployed 57.3 989
Total 100 2318
19
Human capital investments
Table 2 gives an overview of the investments in education divided in groups based on their native country. Of the 2.318 respondents in our sample, approximately 25% invested in education. Migrants from Iran invest most, whereas immigrants from Afghanistan and Somalia invest least in education. Further, the investments are divided in the original measurement of post-migration education and in the main used international classifications. The table shows that approximately 50% of the immigrants only obtain a degree in pre-migration education and approximately 25% obtained a degree in migration education. This means that approximately 75% obtained a degree in or pre- or post-migration education. The smaller investments in lower secondary education can be explained by the Dutch schooling system, whereas students from lower secondary education tend to flow into upper secondary education. The same accounts for investments in Havo/Vwo/Gymnasium where students flow into higher education, but because vocational education is also included the upper secondary education classification these percentages are higher.
Table 2; Crosstab investments in education in the original measurement and classifications by native country
Classifications Orignial measurement Native country
Afghanistan Iraq Iran Yugoslavia Somalië Total Diploma in the Netherlands Yes 15.3% 22.1% 42.8% 23.7% 14.3% 24.4% No 84.7% 77.9% 57.2% 76.3% 85.7% 75.6% no education No diploma 30.2% 27.0% 10.2% 18.0% 45.0% 25.1% Only in native country 54.4% 50.9% 46.9% 58.3% 40.7% 50.5% Total 84.7% 77.9% 57.2% 76.3% 85.7% 75.6% Primary education 7.6% 7.0% 11.1% 4.6% 7.2% 7.6% Lower secondary education Vbo 3.3% 3.3% 3.9% 2.2% 2.6% 3.1% Mavo 0.7% 1.2% 2.0% 2.2% 0.3% 1.3% Total 4.0% 4.5% 5.9% 8.3% 3.7% 4.4% Upper secondary education or vocational education Mbo 2.7% 3.9% 10.5% 7.3% 2.9% 5.6% Havo/Vwo/Gym - - 1.2% 1.0% 0.9% 0.6% Total 2.7% 3.9% 11.7% 8.3% 3.7% 6.2% Higher education Hbo 0.9% 4.1% 9.6% 4.6% 0.6% 4.2% Wo 0.2% 2.5% 4.5% 1.7% - 1.9% Total 1.1% 6.6% 14.1% 6.3% 0.6% 6.1% N=2182 Settlement intentions
The first hypothesis stated that the intention to stay in the host country positively affects the investments in post-migration education. There is a positive significant influence found, with the odds of investing in education increasing with 1.339 (e . , p-value<0.05) when the respondent is intend to stay in the host country. In addition, table 3, model 3 shows that it also has a significant positive influence on the obtained level of post-migration education. Thus, (higher level) post-migration degrees are obtained when the immigrants tend to stay in the host country and the hypothesis can be confirmed.
20 In line with the second hypothesis and similar to previous findings (Chiswick & Miller, 1994; Van Tubergen & Van De Werfhorst, 2007) there is found that the age of migration has a significant negative influence on the investments in post-migration education. The odds for obtaining a degree decrease with a factor of 0.943 (e . , p-value<0.01) for each year that the migration age increases. This implies that younger immigrants are more likely to invest than older immigrants. Additionally, as model 3 shows, younger immigrants are also more likely to achieve higher levels of education. Thus, evident evidence is found to confirm the hypothesis that the increasing age of migration decreases the investments in post-migration education.
Noteworthy, the length of stay has a significant positive influence on the investments in education. This is also due to the time it takes to obtain a degree, since the measurement is only in obtained diplomas instead of years of education. In addition, also the levels of the investments are higher when the length of stay in the host country increases, which is in line with the result of Chiswick and Miller (1994).
In summary, younger immigrants who have the intention to stay in the host country are more likely to invest in post-migration education and obtain higher levels of post-migration education degrees and both hypotheses can be confirmed.
Skills transferability
The third hypothesis stated that immigrants who are higher educated before migration are more likely to invest in education in the host country. In line with the hypothesis there is found that pre-migration obtained degrees positively influence the investments in the Netherlands, and the odds of investing in education increase with a factor of 1.393 (e . , p-value<0.01) for each increasing level of obtained pre-migration education. In addition, immigrants with higher pre-migration education are not only more likely to invest in post-migration education, but they also achieve higher levels of education (model 3). This provides strong support for the argument that pre-migration obtained skills are useful in gaining new skills, because the knowledge of their own abilities will influence the decision of invest in -and the level of- education and that those higher educated immigrants have experience in learning new knowledge and skills (Duleep & Regets, 2002).
In addition, the individual influence for each pre-migration obtained degree is analyzed (table 4, model 5 & 6) and there is found a positive non-linear influence. When degrees in upper secondary education or in higher education are obtained, the odds for investing in post-migration education increase with a factor of 1.967 for upper secondary education (e . , p-value<0.01) and with a factor of 3.145(e , , p-value<0.01) for a higher degree. In addition, immigrants with upper secondary or higher level of pre-migration education are also more likely to obtain higher level degrees of post-migration education (model 6).
The fourth hypothesis stated that migrants from Yugoslavia invest less in education since they can rely more on their native country obtained skills. These skills are better transferable since the
21 schooling systems are more similar and/of higher quality or there is less risk and uncertainty for the employers. The analysis does not show significant results for this argument (Model 1 & 3) and the hypothesis is rejected. Holding the other factors constant, Yugoslavian migrants indeed invest 2.548 (e . , p-value<0.01) less than migrants from Iran, but migrants from Somalia make even lower investments than the Yugoslavian immigrants.
Also in contrast to the hypothesis, the interaction effects (model 2 & 4) showed that the influence of the pre-migration obtained education has a greater positive effect on the post-migration investments for immigrants from Yugoslavia, with significant differences compared to immigrants from Somalia and Afghanistan. Thus, higher educated immigrants from Yugoslavia are more likely to invest in post-migration education than higher educated immigrants from Somalia and Afghanistan. This is in contrast to the expectations, but in line with Van Tubergen & Van De Werfhorst (2007), who found similar results and argue that in order to perform well in post-migration education, the pre-migration education obtained skills need to fit in the host country’s schooling system.
In summary, although less evidence is found for the skills transferability argument, evident conformation is found for the complimentarity of pre- and post-migration investments, which is even stronger for immigrants from Yugoslavia compared to the other immigrant groups. The outcomes are in line with the findings of Van Tubergen & Van De Werfhorst (2007) who also found contrasting result between different immigrant groups in the Netherlands and Chiswick and Miller (1994) who found strong evidence for the complimentarity.
Opportunity costs
In contrast to Van Tubergen & Van De Werfhorst (2007) who measured the opportunity costs at a macro-level, the individual opportunity costs are measured. The hypothesis stated that the longer the immigrant is unemployed in their first stage of immigration the more likely he is to invest in education because the opportunity costs are lower. In line with the hypothesis it is found that the years of unemployment after migration does have a positive influence (table 3, model 1 and 3), with the odds of obtaining a degree in the Netherlands increasing with a factor of 1.102 (e . , p-value<0.01) for
each year. Also significant results are found for the level of the investments, and higher level degrees are obtained as the opportunity costs are lower and the hypothesis can be confirmed.
The 6th hypothesis stated that migrants from Yugoslavia have higher opportunity costs because they have higher skills transferability and therefore invest less compared to other countries. There are found significant differences for the immigrants from Somalia and Iraq compared to Yugoslavians, with a greater effect of the years of unemployment on the decision to invest. This could imply that the lower opportunity costs is an incentive for immigrants from Somalia and Iraq to invest in post-migration education, but it lacks strong support for the skills transferability argument and the hypothesis is therefore rejected.
22
Table 3; Binominal logistic and OLS regression of immigrants’ post-migration education investments in the Netherlands
Variable N=2318
Model 1
Binary regression model for investments in education
Model 2
Binary regression model for investments in education with interactions
Model 3
Ordinary least squares
Model 4
Ordinary least squares with interactions
Constant -0.543 (0.424) -0.839 (0.607) 0.684 (0.348)* 0.154 (0.481)
intention to stay (relative to no intention to stay)
0.292 (0.142)* 0.285 (0.144)* 0.415 (0.125)** 0.384 (0.125)**
Age of migration (in years) -0.058 (0.012)** -0.060 (0.012)** -0.044 (0.009)** -0.043 (0.009)**
Pre- migration education 0.332 (0.058)** 0.563 (0.156)** 0.355 (0.047)** 0.539 (0.116)**
Country (relative to Yugoslavia) Afghanistan -0.312 (0.252) 0.710 (0.663) -0.326 (0.208) 0.996 (0.512)
Iraq 0.317 (0.216) -0.356 (0.681) 0.269 (0.194) -0.116 (0.517)
Iran 0.935 (0.199)** 1.777 (0.633)** 0.956 (0.182)** 1.365 (0.533)*
Somalia -0.357 (0.286) -0.620 (0.934) -0.357 (0.240) 0.434 (0.667)
Years unemployed 0.098 (0.028)** 0.143 (0.024)** 0.057 (0.025)* 0.063 (0.043)
Current length of stay 0.006 (0.012) 0.008 (0.012) 0.017 (0.011) 0.021 (0.011)
Married after migration (relative to married before migration)
-0.230 (0.148) -0.241 (0.150) 0.017 (0.052) 0.008 (0.052)
Gender (Man relative to Woman) -0.135 (0.202) -0.157 (0.202) 0.279 (0.184) 0.252 (0.184)
Country of origin * pre-migration education (relative to Yugoslavia)
Afghanistan *Pre-migration education
-0.406 (0.191)* -0.424 (0.143)**
Iraq * pre-migration education -0.068 (0.193) 0,00003221 (0,146)
Iran * pre-migration education -0.344 (0.193) -0.084 (0.157)
Somalia * pre-migration education
-0.247 (0.246) -0.409 (0.181)*
Country of origin * Years of unemployment (relative to Yugoslavia)
Afghanistan * Years of unemployment
0.030 (0.086) -0.082 (0.081)
Iraq * Years of unemployment 0.227 (0.069)** 0.093 (0.073)
Iran * Years of unemployment 0.059 (0.047) -0.047 (0.063)
Somalia * Years of unemployment 0.246 (0.123)* 0.048 (0.108) Notes: *p<.05; ** p<0.01 x (11) 148.925 ** Nagelkerke R=0.178 x (18) 161.389 ** Nagelkerke R=0.192 F(11)17.319** R=0.390 F(19)11.435** R=0.413
23
Table 4; Binary logistic regression and OLS regression of immigrants’ post-migration education investments in the Netherlands specified for each level of education
Economic returns to pre- and post-migration education
The 7th hypothesis stated that that the economic returns are higher to post-migration education than to pre-migration obtained education. The binary logistic regression analyzed the chance of being employed as return to the pre- and post-migration education. The results in table 5, model 7 shows that pre-migration education does not have a significant influence on the odds of being employed. In contrast, post-migration obtained education does have a positive influence and migrants who obtained a degree higher than primary education have significant higher odds of being employed relative to migrants without a degree. Especially migrants who obtained a degree in higher education have the greatest chances of being employed, and the odds increase with a factor of 2.527 (e . , p-value<0.01), relative to the ones without a degree, but also for migrants with lower secondary or upper secondary education the odds to be employed increase for both approximately with a factor of 2, relative with the ones without a degree. Further, the OLS regression (table, 5, model 9) showed that pre-migration education has a positive significant influence on the earnings and for each higher level obtained degree, the earnings rise with 45.527. The model also showed that migrants with a degree in higher migration education receive significant more compared to migrants without a post-migration education degree and the hypothesis can therefore be confirmed.
Variable N=2318
Model 5
Binary regression model for investments in education
Model 6
Ordinary least squares
Constant -1.222 (0.719) 1.276 (0.368)**
intention to stay (relative to no intention to stay)
0.261 (0.143) 0.376 (0.124)** Age of migration (in years) -0.061 (0.012)** -0.049 (0.009)** Pre- migration education
(relative to no education)
Primary -0.577 (0.689) -0.599 (0.483) Lower secondary -0.088 (0.270) -0.117 (0.217) Upper secondary 0.677 (0.222)** 0.603 (0.184)* Higher 1.146 (0.236)** 1.335 (0.196)** Country (relative to Yugoslavia) Afghanistan -0.466 (0.259) -0.546 (0.211)**
Iraq 0.216 (0.221) 0.113 (0.196)
Iran 0.857 (0.202)** 0.851 (0.182)** Somalia -0.468 (0.290) -0.487 (0.240)*
Years unemployed 0.100 (0.028)** 0.060 (0.025)*
Duration of stay (in years) 0.008 (0.012) 0.020 (0.011) Married after migration (relative
to married before migration)
-0.238 (0.149) 0.002 (0.052)* Gender (Man relative to
Woman) -0.134 (0.454) 0.259 (0.184) Notes: *p<.05; ** p<0.01 x (14) 158.950** Nagelkerke R=0.189 F(14)15.444** R=0.412
24
Table 5; Binary logistic regression and OLS regression of immigrants’ economic returns to pre-and post migration education
Variable N=2318 Model 7 Binary logistic regression Model 8
Binary logistic regression with interactions
Model 9
Ordinary least squares
Model 10
Ordinary least squares with interactions
Constant -0.863 (0.210)** -1.585 (0.372)** 1108.016 (73.318)** 689.504 (114.266)**
Pre- migration education 0.073 (0.045) 0.364 (0.127)** 45.527 (15.287)** 200.175 (35.272)**
Post- migration education (relative to no education) Primary education 0.155 (0.217) 0.155 (0.218) -146.366 (56.863)** -141.283 (56.422)* Lower secondary education 0.819 (0.290)** 0.838 (0.292)** -69.192 (58.862) -58.260 (58.160) Upper secondary education 0.639 (0.240)** 0.589 (0.241)* 15.746 (53.251) 9.028 (52.558) Higher education 0.927 (0.274)** 0.486 (0.476) 252.906 (52.684)** 240.206 (52.290)** Country (relative to Yugoslavia) Afghanistan 0.517 (0.205)* 1.620 (0.428)** -199.750 (64.967)** 373.363 (126.596)** Iraq -0.079 (0.200) 0.508 (0.442) -105.217 (63.494) 268.784 (145.852) Iran -0.077 (0.192) 0.553 (0.846) -27.588 (55.599) 473.001 (148.659)** Somalia -0.383 (0.228) 0.486 (0.476) -95.979 (74.912) 302.735 (160.022)
Work experience (a year) 0.292 (0.015)** 0.292 (0.015)** 19.242 (3.560)** 22.701 (3.584)**
Gender (woman relative to man)
-0.260 (0.159) -0.281 (0.159) -167.457 (53.123)** -170.517 (52.481)**
Language (relative to no knowledge)
0.241 (0.140) 0.221 (0.142) 38.140 (48.153) 27.083 (47.966)
Country of origin * pre-migration education
Afghanistan *Pre-migration education
-0.449 (0.149)** -228.039 (43.606)**
(relative to Yugoslavia) Iraq * pre-migration education -0.229 (0.153) -140.856 (48.068)** Iran * pre-migration education -0.245 (0.166) -184.558 (48.860)** Somalia * pre-migration education -0.349 (0.176)* -150.037 (57.185)** Notes: *p<.05; ** p<0.01 x (12) 1074.922** Nagelkerke R=0.556 x (15) 1085.281** Nagelkerke R=0.560 F(12)11.283** R=0.365 F(16)10.470** R=0.400
25 Because there is shown that each level of post-migration education has a different effect on the economic returns, the pre-migration education degrees are also analyzed separately. The results in table 6, model 11 shows that pre-migration education does not have a significant influence in increasing the odds of being employed, except for upper secondary education what increase the odds of being employed with a factor of 1.431 (e . , p-value<0.05). The OLS regression (model 12) showed a non-linear regression with only significant higher earnings for higher education relative to no education. Thus, the hypothesis can be confirmed.
The last hypothesis stated that immigrants from Yugoslavia receive higher economic returns to their pre-migration education than the immigrants from Afghanistan, Iraq, Iran and Somalia. There is found that immigrants from Afghanistan are most likely to be employed (e . , p-value<0.05), relative to migrants from Yugoslavia. On the other hand, the OLS regression (table 5, model 9) shows that, holding the other factors constant, immigrants from Yugoslavia earn significant more compared to migrants from Iraq and Afghanistan. Thus, immigrants from Afghanistan are more likely to be employed, but they earn significant less. Although there was no significant influence found of the pre-migration education and only a significant difference for immigrants from Afghanistan on the employment status, the interaction effects (table 5, model 8 & 10) showed significant differences in the influence of the pre-migration obtained education and the native country for immigrants from Afghanistan (e . , p-value<0.01) and Somalia (e . , p-value<0.05) compared to Yugoslavians. This implies that the pre-migration obtained education for immigrants from these two countries has less influence to increase the odds of being employed compared to immigrants from Yugoslavia and the hypothesis can be confirmed. The OLS regression showed that there was a significant positive effect for pre-migration education and a significant difference for some native countries. The interaction effects in the regression model, however, showed that there is significant influence of the country where the pre-migration is obtained on the earnings, and all immigrant groups earn significant less compared to Yugoslavians. This implies that the valuations of the pre-migration obtained degrees are higher for Yugoslavians and thus that the skills transferability is higher for migrants from Yugoslavia. This is in line with the result from Friedberg (2000), who found that in Israel the economic returns are higher to Western degrees compared to African and Asian degrees.
The analysis is controlled for work experience in the Netherlands, gender and the knowledge of the Dutch language. There is found a significant positive correlation with an increasing amount of 19.242 for each year of post-migration work experience and the odds of being employed increasing with a factor of 1.339 (e . , p-value<0.01) for each year. The combination of the higher valuation of post-migration education and the significant increasing economic returns on work experience in the Netherlands shows the emphasis on country specific human capital (Akresh, 2007). There is a significant influence of gender, with women earning 167.457 (p<0.01) less than men. Even though the literature extensively acknowledges the role of the language(Akresh, 2007; Chiswick & Miller, 1994; Duleep & Regets, 1999), there is no significant influence found for this claim. This literature is mostly
