Following other studies analyzing the performance of children in assessment tests such as PISA, in this paper we will use an educational production function of the form:

$$ scor{e_i}^t = {\alpha}^t + {\beta}^t{X_i}^t + {u_i}^t, $$ (1)

where score i t is the PISA reading score for student i in year t (t =2000, 2009), X is a vector of individual characteristics, and u i t is an error term with mean zero and variance σ t 2.

In order to choose which variables to include in the vector X of individual characteristics, we follow the international literature (Hanushek & Woessmann, 2011). The individual characteristics include parental education (e.g., Bauer and Riphahn, 2007), cultural capital, measured by the amount of books at home (e.g. Ammermueller & Pischke, 2009), parental occupation as a proxy for social status as well as for parental income. For the latter we use the International Socio-Economic Index of Occupational Status, which has a range between 16 and 90. Furthermore, we control for the language spoken at home, as most studies have found that not speaking the test language at home has a negative impact on the PISA scores (see, e.g., Ammermueller, 2007; Meunier, 2011). In addition, we include other variables that have been shown to affect PISA scores in previous studies such as family structure, whether the child has siblings, gender, age, parents’ country of origin, residential information (urban vs. rural areas) and language region of residence.

The purpose of this paper is to analyze whether the increase in the PISA scores for first-generation immigrants between 2000 and 2009 was due to an improvement in the individual characteristics of immigrants, in particular an improvement in the socio-economic backgrounds of new immigrant, and is therefore a consequence of the change in migration policy. Or if alternatively the increase cannot be explained by changes in the observable background characteristics of the students, this could indicate an improvement in the integration of migrants in the Swiss education system.

We do this by decomposing the score gap for first-generation immigrants between the years 2000 and 2009 into an explained and an unexplained component, using a Blinder-Oaxaca decomposition (Blinder, 1973; Oaxaca, 1973). For each year (2000 and 2009), we estimate the corresponding educational production function using only the sample of first-generation immigrants, and then following Blinder-Oaxaca, we decompose the mean score difference between 2000 and 2009 as follows:

$$ \overline{scor{e}_{09}}-\overline{scor{e}_{00}}=\left(\overline{X_{09}}-\overline{X_{00}}\right){\widehat{\beta}}_{09}+\overline{X_{00}}\left({\widehat{\beta}}_{09}-{\widehat{\beta}}_{00}\right) $$ (2)

Footnote 11

where the first summand shows how much of the change in PISA scores between 2000 and 2009 can be explained by differences in the predictors or characteristics. This part of the change tells us the extra number of PISA points immigrant students would have had in 2000 if they had had the same observable characteristics as the students in 2009 (the group differences in predictors are weighted by the coefficients from 2009). The second summand shows the contribution of the difference in the coefficients to the total score gap. This is known as the unexplained component, as it includes the part of the change that cannot be accounted for by the difference in endowments.Footnote 12 In our case, this component could be observed as an upper bound for the impact of improved immigrant integration, given that this proportion shows improved performance on the part of immigrants, regardless of the change in the observable endowments. It is an upper bound of the integration effect because it also includes any potential impact that could stem from a change in the unobservable characteristics of the students.

Panel A in Table 2 provides the estimation results from the OLS regressions for 2000 and 2009. We use the PISA reading score as a dependent variable, and we control for the demographic characteristics of the students such as gender, age and place of residence, family characteristics such as whether the child lives in a single-parent, mixed or nuclear household and whether the child has any siblings. The most important explanatory variables in our analyses are the socio-economic background characteristics of the parents, among which we include the socio-economic index, parents’ education, the number of books at home, the language spoken at home and parents’ nationality. The results are in line with previous findings in the literature. The only difference is that, depending on the year, we do not find a significant direct effect of parental education once the socio-economic index and number of books at home are controlled for. Footnote 13

Table 2 Oaxaca decomposition of the score gap between 2000 and 2009 for first-generation immigrants Full size table

Panel B of Table 2 shows the decomposition results. The total score gap between 2000 and 2009 is 40 points, of which approximately 22 points (55%) can be explained by differences in observable endowments between the two PISA tests.Footnote 14 Footnote 15

School characteristics and peers

Thus far we have analyzed the impact that the changes in the individual endowments of new immigrants had on increases in PISA test scores, but because it is likely that the new immigrants come from better socio-economic environments, the residential choices of the parents may also allow the students to attend better schools than the average first-generation immigrants in the 2000 PISA test. We therefore include school characteristics as explanatory variables in a second step of our education production function as well as information on peers as follows:

$$ scor{e_{is}}^t={\alpha}^t+\beta t{X_{is}}^t\kern0.5em +\kern0.75em \psi Sst+{u_{is}}^t $$ (3)

where score is t is the PISA reading score for student i in year t (t =2000, 2009) and school s, X is a vector for individual characteristics, S is a vector of school characteristics, and u is t is an error term with mean zero and variance σ t 2.

Among the school characteristics, we include the proportion of foreign language speakers in the school. Previous studies (for Switzerland, see Coradi Vellacott et al., 2003 Footnote 16) have shown that the effect of the this is not linear and that the negative effect of a bigger fraction of students who do not speak the test language on the performance is almost exponential once a threshold of 20% of these students is crossed. We therefore use three dummy variables: less than 20%, between 20 and 40% and more than 40% of students in a school who do not speak the test language.

The proportion of migrant children (first and second generation) who attend schools with more than 40% foreign language speakers decreased significantly between 2000 and 2009, and the fraction of students attending schools with less than 20% foreign language speakers increased (see Table 3). The share of foreign language speakers in school also changed for Swiss natives. The improvement of the schools’ composition for natives is most probably not due to a “native flight” from less favorable schools. In Switzerland there is not only a lack of free school choice, but also very low geographical mobility of people, mainly due to differences in housing prices and locally highly diverse tax levels. In this context, Swiss native students with low-skilled and low-earning parents probably live in the same neighborhoods as first-generation immigrants that had entered Switzerland before 1994, and highly educated Swiss natives live in neighborhoods where most of the new better-off immigrants locate. Therefore, children from lower-income households attend the same schools as children from lower–income immigrant families, and better-off Swiss children share schools with better-off immigrant children. As the share of low skilled and foreign language speaking immigrants decreased dramatically over time, also “immobile” native Swiss students were affected by the changes in the composition of schools. Contrary to foreign language speaking immigrant students, however, the change in the composition of schools did not led to a sizeable improvement of schooling results for native students as it is mainly the foreign language speaking students themselves that suffer first from high levels of concentrations of foreign language speaking students in a school (see e.g. Coradi Vellacott et al., 2003).

Table 3 School composition: Proportion of foreign language speakers at school Full size table

The model in Table 4 includes school characteristics such as total enrollment, school location, whether the school is financed by public or private funds and the proportion of foreign language speakers in the school. The results show that it is indeed the higher proportion of other students in school who do not speak the test language that is the primary contribution to low test scores both in 2000 and in 2009. Because the proportion of students not speaking the test language is much smaller in 2009, this variable explains a great deal of the change in the test scores of first-generation immigrants between 2000 and 2009. The part of the difference explained by observables increases from 55% to 75%.Footnote 17 With respect to other school characteristics, attending a private schoolFootnote 18 has a positive influence on the PISA scores, and a larger number of students has a negative influence, but these variables are not significantly explaining the score differences between the two years.

Table 4 Oaxaca decomposition of the score gap between 2000 and 2009 for first-generation immigrants including controls for school characteristics Full size table

In contrast, for immigrants that were born in Switzerland of two foreign-born parents, the increase in PISA scores between 2000 and 2009 is not only much smaller but also cannot be explained by changes in observable endowments (See Table 5).Footnote 19 When we include school characteristics, and especially the proportion of foreign language speakers, the explained component of the score difference over time increases also for these children (see Table 5).Footnote 20 This is because the percentage of students who attend schools with more than 40% of foreign language speakers was also reduced in this group, and the percentage of students who attend schools with less than 20% foreign language speakers increased inversely. Although all immigrants benefit from lower shares of students who do not speak the test language in Swiss schools, the first-generation immigrants benefited the most. This is because first-generation immigrants, despite having much better socio-economic backgrounds on average in 2009, still have the largest share of low qualified parents. Because it is predominantly the pupils of these parents who benefited from a lower share of foreign language speaking pupils in schools, the first-generation immigrants themselves are also those that benefited the most from the improved composition of new immigrants.Footnote 21 Additionally, the lower effect of the policy for the second-generation can also be explained by the fact that they are more likely to speak or have at least better knowledge of the test language, and therefore, just like for natives, their reading literacy is less likely to be affected by the presence of foreign language speaking pupils. First-generation pupils not speaking the test language are by contrast the ones who have the most to gain from an improvement of the peer group.