Educational Achievement Gaps Between Immigrantand Native Students in Three “New Immigration

Countries”

Davide Azzolinia, Philipp Schnellb,c, and John Palmerd

aUniversity of TrentobUniversity of Amsterdam

cAustrian Academy of SciencesdPrinceton University

Work in Progress: June 7, 2011

Abstract

We expand previous research on immigrant-native educational gaps by ex-ploring the patterns and the dynamics of the phenomenon in Ireland, Italyand Spain. Within the European context, these three countries represent newimmigration destinations and, as such, they have been covered by relativelylittle empirical research so far. However, over the past ten years these threecountries have experienced huge migratory inflows, and now host importantnumbers of school-age children of immigrants. By assessing and comparingimmigrants’ educational outcomes within and between these countries, we alsoaim to test the extent to which certain hypotheses circulating in the interna-tional literature apply to these new contexts. We analyze both mathematicsand reading skills using PISA data from 2003, 2006 and 2009. Our mainfindings are that first-generation students are the most disadvantaged groupand that their performances are only marginally and not linearly associatedwith age at arrival. At the same time, second-generation students seem toperform better than first-generation students but still not at the level of na-tives. These results vary both between countries and across time. Regardingbetween-country differences, immigrants in Ireland tend to perform much bet-ter than immigrants in the two Mediterranean countries, where their the gaprelative to natives persists even after accounting for both individual, family-and school-level factors. We argue that the higher socioeconomic status ofimmigrants in Ireland could be responsible for this relative advantage. Re-garding trends across time, we detected a pattern of convergence among thethree countries: the magnitude of the gap increased in all new countries, but itdid so more markedly in Ireland. Finally, the mechanisms underlying the ob-served immigrant-native gaps differ between the two Mediterranean countries

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and Ireland. While in the former, socioeconomic background seems to be themost relevant factor contributing to the gap, in Ireland the key-mechanism islanguage spoken at home, while socioeconomic background plays the oppositerole from that played in Italy and Spain.

1 Introduction

The great shift of migration flows from old to new destinations in the last three decadeshas been one of the most striking demographic developments in recent European his-tory. As a result of these novel migration patterns, a growing number of immigrantshave entered the educational systems and are now coming of age in countries that had,until recently, been known as net senders of migrants and have now become net receivers.The experiences of these new immigrant destinations are of great significance since thosecountries were caught ill prepared to manage the process of incorporation of new immi-grants and their children. The immigrant populations in these countries are still youngand education has become the key aspect in the process of social integration, in terms ofboth human capital formation and its later payoffs in the labour market.

This study compares the educational achievements of children of immigrants in“new” immigration countries, namely Italy, Spain and Ireland. Research in these coun-tries has been scarce so far, thus restricting the empirical basis of the theoretical ex-planations of the immigrant-native gaps mainly to European countries with long lastingimmigration histories. This paper aims at partially correcting this gap, by providinga sound descriptive inquiry on the patterns and dynamics of the phenomenon and byhighlighting similarities and differences between the three countries.

The key questions that we ask in this article are: How well do children of im-migrants perform in new immigration countries? Is there evidence of an educationaldisadvantage for immigrant children? What accounts for the observed differences withinand between the three new immigration countries? To what extent do the hypothesescirculating in the international literature also apply to these new immigration contexts?And how do the achievement gaps develop over time?

Our empirical analyses are based on standardized test scores in mathematics andreading derived from the PISA exams given in 2003, 2006 and 2009. These data allow usto investigate both cognitive and linguistic aspects of students’ achievement and examinetrends over time.

The paper begins with a short review of the literature on the immigrant-nativeeducational achievement gaps in countries with older immigration traditions (Part 2). Itthen considers the general patterns of immigration to the three new immigration coun-tries, the key features of their educational systems, and the existing country-specificliterature on their immigrants’ educational performance (Part 3). We summarize ourmain research questions and assumptions (Part 4) before proceeding with a descriptionof the data and the analytical strategy (Part 5). Our result section (Part 6) starts with adescriptive comparison of educational achievements of different immigrant generations aswell as natives at three points in time for each country. Next, we present multilevel regres-sion estimates of the immigrant-native gap and consider the extent to which the observeddifferences between the compared groups within countries can be explained by three mainsets of variables: language spoken at home, family background and school characteristics.Finally, we focus specifically on first-generations, examining the association between age

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at arrival and achievement and comparing first-generation performance across the threecountries. We conclude by discussing our empirical findings and providing suggestionsfor future research (Part 7).

2 Accounting for the immigrant-native educational

gap: an overview of the literature

The achievement gap between immigrants and natives is a well-established regularityin several western countries (Schnepf, 2004; Marks, 2005). However, immigrants’ perfor-mance and their relative positions compared to their native classmates differ substantially,both within and between countries.

Within countries there is higher dispersion of educational outcomes among immi-grants than among natives (Schnepf, 2008). In the first place, research indicates that im-migrant children’s generational status is associated with educational achievement. Ingeneral, second-generation children tend to outperform first-generation children (OECD,2006). This pattern is consistent with the “straight-line assimilation” theory, accordingto which the children of immigrants tend to adapt to the host society and, thus, to ex-perience upward mobility compared to their parental generation (Alba & Nee, 2003). Atthe same time, as noted above, it has been shown that immigrant students are a highlyheterogeneous group whose members might take differentiated paths of assimilation (i.e.segmented assimilation framework) (Haller, Portes, & Lynch, 2011; Portes & Zhou, 1993;Portes, Fernandez-Kelly, & Haller, 2009). Significant shares of second-generation chil-dren are at risk of experiencing downward mobility if the human capital of their parentsis low and the “mode of incorporation” into the host society is hostile. second-generationpupils can also experience upward assimilation and even outperform native-born students(Chiswick & DebBurman, 2004), which could be explained partially by the so called “im-migrant optimism” hypothesis, according to which immigrant parents conceive of theirmigration process as an upward mobility project and therefore invest substantial resourcesin the education of their children. For instance, this pattern holds true for many Asianstudents in the United States (Kao & Tienda, 1995; Portes et al., 2009) and has alsobeen found for Indians in the UK and Northern Europe (Heath, Rothon, & Kilpi, 2008;Heath & Brinbaum, 2007).

Along with generational status, another source of heterogeneity in educational out-comes is age at migration (Chiswick & DebBurman, 2004; Rumbaut, 2004; Myers, Gao,& Emeka, 2009). In general, the younger a student is when entering the host countryfor the first time, the higher the subsequent educational achievement. Two mechanismsappear to explain this trend: The first is that exposure to the host country culture andeducational system enhances children’s and parents’ acculturation and language acquisi-tion, which positively influence educational outcomes. The second mechanism relates tothe specific age at migration: Since cognitive and linguistic development is not constantover age, it is generally argued that the sooner a child enters a host country’s educationalsystem the better are his or her chances of achieving high education levels. Analogously,this also holds true for language acquisition which is quicker and more complete if thestudent arrives early. However, some studies cast doubts on the linearity of the rela-tionship, arguing that arriving at specific ages (adolescence period) could exert morenegative effects on education than arriving later (Chiswick & DebBurman, 2004; Myerset al., 2009).

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Achievement Gaps

Which other factors account for immigrants performances and their gap com-pared to natives? Most empirical findings point to the prominent contribution of familyand school level factors in accounting for substantial parts of these educational gaps.Regarding family level factors, research on inequality in educational opportunities haslargely documented the role played by family socioeconomic background - usually in-dexed through variables like parental occupation, income, and educational level (Breen& Jonsson, 2005). These factors partially account for the observed gap between childrenof immigrants and native born students, because immigrant families tend to be highlyconcentrated in the lower social strata (Heath et al., 2008). However, these “traditional”explanations do not always work in the same way for natives as for immigrants, and acrossnational-origin groups (Jackson, Kiernan, & McLanahan, 2010). For instance, quite oftenimmigrants have lower occupational returns to education compared to natives, especiallyif they hold foreign qualifications. This over-education determines a weakening of the ex-planatory power of parental education on children’s educational outcomes (Heath et al.,2008). This is why is often useful to use more direct measures of socioeconomic, educa-tional resources available in the home. This way it is possible to better explore the specificmechanisms through which socioeconomic background correlates with children learningoutcomes often remain elusive (Duncan & Magnuson, 2005). Socioeconomic backgroundis indeed associated with an ample array of other family characteristics, whose effects arenot easily distinguishable from one another. For instance, low socioeconomic resourcesare associated with weak family structures (i.e., unstable families, overcrowded houses)and with shortages of cognitively stimulating resources (Lahaie, 2008; McLanahan &Percheski, 2008).

Turning to explanations related to the school context, research points to a negativecorrelation between school immigrant composition and educational achievement: classeswith higher percentages of immigrant children display on average lower educational out-comes. However this correlation is often a reflection of the socioeconomic status of theschool, because immigrants are not randomly distributed across schools, and their fam-ilies are not randomly distributed across neighbourhoods. On the contrary, immigrantfamilies self-select into the most socioeconomically deprived neighbourhoods; as a result,their children are more likely to attend schools with lower average socioeconomic com-position and lower quality (Portes & Hao, 2004; Fekjær & Birkelund, 2007; Hanushek,Kain, & Rivkin, 2009; Cebolla-Boado & Medina, 2010).

Both family and school level factors are relevant determinants of between-countrydifferences in immigrants performances. In the first place, because of selection and self-selection of immigrants into the different countries (Feliciano, 2005; Schnepf, 2008; Lozano& Steinberger, 2010). Also the organization of the education systems has been frequentlyfound to be associated with the magnitude of the immigrant-native gaps. Countries with aschool-type tracked and less standardized educational systems tend to have higher levelsof inequality of educational opportunity by socioeconomic and immigrant background(Crul & Vermeulen, 2003; Werfhorst & Mijs, 2010).

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Achievement Gaps

3 Setting the scene: Ireland, Italy and Spain in com-

parison

3.1 From emigration to immigration countries

Within the European context, Ireland, Italy and Spain have traditionally been emigrationcountries marked by declining populations and high rates of out migration. In the secondhalf of the twentieth century, Italy and Spain became source countries of the “guestworker” recruitment needed by Northern European countries while the majority of Irishemigrants who left their home country in the early twentieth century went to the UnitedKingdom. During the last two decades this situation has reversed dramatically withinall three countries, making of them three new and important destinations of immigrationflows within Europe (Bauer, Lofstrom, & Zimmermann, 2000; Kreienbrink, 2008; OECD,2006; Barrett, 1999; Bover & Vellila, 1999; Colombo & Sciortino, 2004). The beginning ofthe 1990s marked the start of the new phase of immigration to Ireland, Italy, and Spain.The South-European countries experienced a rise in the number of migrants from theSouth-North migration trend originating primarily from Sub-Saharan and other Africanregions. After the fall of the “Iron Curtain”, also East-West migration from Centraland Eastern Europe accounted for a large part of the positive net immigration trend.Recently, the number of refugees seeking for asylum rose within Italy and Spain. Irelandexperienced economic growth from the mid 1990s onwards (known as the “Celtic Tiger”boom) which resulted in increasing immigration. From the mid 1990s to the early 2000s,the majority of immigrants were returning Irish nationals, asylum applicants dominatedfrom Nigerian and Romanian nationals, as well as non-EU nationals. After the 2004 EUenlargement, the inflow converted to EU flows primarily driven by nationals from thenew EU member states.

As a consequence of these movements, during the 2000s the presence of non-national immigrants in the three new immigration countries increased at much higherrates as compared to old immigration countries like Germany, the Netherlands, UnitedKingdom, Sweden or France (see Figure 1). On average, stocks of non-nationals resid-ing in Italy, Spain and Ireland increased by 4.6 times compared to only 0.2 times inthe remaining EU-15 countries. There are substantial differences among the “new im-migration” countries as well. Spain shows an increase of 7 times whereas the immigrantpopulation in Italy and Ireland increased by 3 times. The reduced but still significant netimmigration rate from 2008 onwards can largely be explained by economic contractionand associated decreased inflows.

In 2009, immigrants made up a sizable portion of the total population within allthree countries. Spain shows the highest percentage rate (12.3%), followed by Ireland(8.6%) and Italy (7.0%). Moreover, according to recent data, Ireland, Italy and Spainhost about one third of the total immigrant population among EU-15 countries.1

While sharing similar patterns of immigration, Ireland, Italy and Spain differwith regard to immigrant selectivity. The various phases of recent migration inflows ledto sensible differences in the national-origin composition of the immigrant populationresiding in the three countries. Differences are particularly marked between Ireland, onone side, and Italy and Spain, on the other. Ireland shows smaller incidences of non-nationals from outside EU-27 (around 18%) compared to Italy (71%) and Spain (60%).

1Official numbers are derived from Eurostat. “Immigrant population” is defined as foreign citizens

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Achievement Gaps

0

200

400

600

800

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Ireland Italy

Spain New Immigration Countries

Old European immigration countries

®

Figure 1 – Non-nationals living in Ireland, Italy, Spain and in the remaining EU-15 coun-tries (1999-2009). Source: Eurostat and OECD; base year: 1999. Note: Old immigrationcountries are Austria, France, Germany, Netherlands, Sweden and the United Kingdom.New immigration countries are Italy, Ireland and Spain.

More precisely, the main immigrant groups in Ireland are Eastern Europeans from Poland,Hungary, and the Czech Republic, followed by EU-15 nationals and immigrants from Asiaand the United States of America.2 Thus, Ireland is still quite an ethnically homogeneouscountry. Around 95 per cent indicated in the latest census that their ethnicity wasWhite, while Black, Asian and other ethnicities accounted for just 1 per cent (Populationstatistics , 2010; Quinn, 2010). On the contrary, since the early 2000s the main nationalitygroups residing in Italy have been Albanians, Moroccans, Romanians, Eastern Europeansand immigrants from Asia. In Spain the main nationalities are Romanians, Moroccans,Americans, citizens from Ecuador, Bolivia and Columbia as well as nationals from theUnited Kingdom (Estadıstica, 2008).

This different composition in terms of country of origin reflects, at least in part,a different selectivity of immigrants by human capital. Figure 2 shows that Irelandattracted higher educated immigrants compared to those who migrated to Italy andSpain. Moreover, even within the same broad nationality group, immigrants living inIreland display higher educational levels. These facts clearly point to a different selectionof immigrants by country of destination (Barrett, 1999).

Although the national and socioeconomic composition of the immigrant popula-tion varies across the compared countries, the vast majority of non-nationals in Italy,Spain and Ireland are first-generation and were born outside the host country. Second-generation immigrants only make up a small proportion of the foreign population, thisrepresenting a peculiarity of new immigration countries (Portes, Aparicio, Haller, & Vick-

2Unfortunately, official statistics in Ireland do not breakdown the Non-Irish population further intodifferentiated nationality groups

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Achievement Gaps

100%

80%

60%

40%

20%

0%

Africa Asia Europe North America Oceania Latin America

Ireland Italy

Spain

®

Figure 2 – Percentage of tertiary-educated immigrants by nationality group in Ireland,Italy and Spain (2001). Source: www.oecd.org.

strom, 2010).Driven by the increasing inflows of immigrants in the last decades, all three coun-

tries have constantly developed integration policies and regulation. According to thelatest results of the MIPEX index which measures the achievement of those policies interms of equal rights, responsibilities and opportunities for immigrants, differences be-tween the three compared countries remain small (Huddleston, Niessen, Ni Chaoimh, &White, 2011).3

In sum, the three compared countries share a number of commonalities in thehistorical experience of immigration. They turned recently from emigration to immigra-tion countries and reached an inflow peak in mid-2000. In general, the inflow of foreignnationals is much higher as compared to the remaining European countries with longlasting immigration experience. The vast majority of non-nationals are first-generationimmigrants, who are still relatively young. This is especially reflected in the foreign-bornschool aged population which is also overwhelmingly made up by first-generation immi-grants. Differences across the compared countries appear with respect to selectively ofparticular national groups.

3The MIPEX index measures policies and their implementation in 31 countries. In total, six policyareas are included: labor market mobility, family reunion, political participation, long-term residence,access to nationality and anti-discrimination. The index aims at measuring the achievement of equalrights, responsibilities and opportunities for all residents (Huddleston et al., 2011, p. 7).

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3.2 The educational systems in Italy, Spain and Ireland

The Italian education system is divided into five levels: non-compulsory pre-primary ed-ucation (from 3 to 6 years), primary education (from 6 to 11), lower secondary education(from 11 to 14), upper secondary education (from 14 up to 19 years), and tertiary ed-ucation. Pupils start compulsory education at the age of 6 in primary school (scuolaelementare), which lasts five years. Lower secondary education in Italy lasts three years.At the age of 14 regular students are supposed to attend upper secondary schools. Up-per secondary education includes academically oriented and generalist schools (licei andistituti d’arte), vocational schools (istituti professionali) and an intermediate and tech-nical type of school (istituti tecnici). At the end of secondary education, students takea final examination which is centrally regulated and therefore the same for each pupilwithin schools across Italy. Beside these three tracks, a further branch is representedby regional training courses (formazione professionale di base) which, contrary to theprevious ones, do not allow students to access tertiary education (Barone & Schizzerotto,2008). Compulsory education ends at the age of 16. In the Italian educational system,private schools only make up a small fraction of the total number of schools. In 2007/08,students enrolled in private schools (scuole paritarie) made up 5.5% of the entire schoolpopulation (Miur, 2009).

In Spain, pupils enter compulsory (general) education when they turn age 6. TheSpanish compulsory period is divided into Primary Education (Educacion Primaria) andSecondary Compulsory Education (Educaion Secundaria Obligatoria). Primary educa-tion is the first compulsory stage and covers six years of education, divided into threetwo-year cycles. It is normally completed with the age of 12. Afterwards, students con-tinue on to compulsory secondary education which lasts until the age of 16. This stageis divided into four courses and is ordinarily completed after four years. The compulsoryperiod in Spain is a completely comprehensive system. However, the Spanish school mar-ket is significantly segmented (Cebolla-Boado & Medina, 2010). Private schools (colegiosconcertados) make up a considerable portion of all secondary schools (26.3% of the stu-dents enrolled in compulsory education in 2007-2008). Admission criteria to these schoolsassign priority to those pupils who live in the same district where the school is located.Students who achieve the Graduado en Educacion Secundaria degree after lower sec-ondary education are allowed to pass on to post compulsory secondary education (Ortiz,2008). Upper secondary education in Spain is currently branched into an academic anda vocational training track. The former lasts two years, from 16 to 18, and gives accessto university after an exam while the latter was established in 1990 during the educa-tional reforms of the Spanish educational system as an alternative track to the academicorientated one.

In Ireland, school attendance is compulsory from the age of six onwards withpupils first entering primary school.4 The majority of primary schools are run by religiousorders and are generally state aided. At the age of 12 students enter the second stageof compulsory education. Participation in full-time education in Ireland is compulsoryuntil the age of 16 leading up to the national Junior Certificate examination. This testexamination is nationally standardized and besides individually chosen subjects all pupilsstudy English and Mathematics. The grades achieved in the Junior Certificate determine

4Many children in Ireland, however, enter full-time education at the age of four or five by enrolling inreception classes within primary schools. In 2005, almost every second four or five-year-old was enrolledin reception classes (Smyth, 2008, p. 301)

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Achievement Gaps

the access to particular subjects and levels within the next educational stage. Studentseither continue in a “Transition year” or may proceed directly into a two-year uppersecondary school which leads to the Leaving certificate. There are three different typesof leaving certificates to be obtained in upper secondary education. The “EstablishedLeaving Certificate” (LCE) is academic orientated in focus, while the “Leaving CertificateVocational” (LCVP) trains students in vocational and technological skills. The lasttype of school provides the “Leaving Certificate Applied (LCA)” which is intended tocatch less academic-orientated students who are potentially at risk of dropping out ofschool. Students are awarded points on the basis on their Leaving Certificate grades whichare taken into consideration in the allocation process for places in tertiary educationalinstitutions.

Table 1 summarizes the key characteristics of the three compared educationalsystems, highlighting similarities and differences in terms of vertical progression andhorizontal differentiation. In short, all three countries have the same starting age andthe same age of compulsory schooling. However, the three countries differ with regardto the degree of differentiation of the system. In Italy, students are sorted into fourdifferent tracks at the age of 14. This choice represents in Italy a topical moment for theeducational careers, since depending on the school track attended future educational andprofessional outcomes change dramatically. The Spanish educational system is instead acomprehensive one, since students are not tracked into separate schools before the end ofcompulsory education while Ireland can be situated between the two other countries.

Table 1 – Comparison of key features of secondary education in Ireland, Italy and Spain.

Ireland Italy Spain

Age at entering compulsory education 6 6 6Years spent in school until first tracking 9 8 10Age when first selected 15 14 16Number of tracks at secondary level 3 4 1Age when leaving compulsory education 16 16 16

3.3 Children of Immigrants at school

Over the past 10 years the presence of children of immigrants in Italian schools has in-creased by almost six times, making up around 7% of the whole student body today.However, their presence is significantly lower at upper secondary education compared tothe lower educational levels (Miur, 2009, 2010). This is partially due to demographicfactors, but also due to the fact that immigrants display lower school attendance ratesand higher risk of dropping out of school as well as experiencing delay. Focusing onupper secondary education only, immigrants display higher probability of enrolling in vo-cational schools and lower propensity to choose general and pre-academic schools (Barban& White, 2009; Azzolini & Barone, 2011). In addition to these aspects, research findingsconverge in pointing out that immigrants lag behind in marks and achievement devel-opment, especially in subjects like writing and reading (Mantovani, 2008). This gap isa consequence of a double disadvantage faced by children of immigrants. On one hand,immigrants are more likely than natives to grow up in socioeconomically deprived families

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Achievement Gaps

and this accounts for a substantial part of their educational disadvantage. On the otherhand, immigrants face the specific hurdles given by their migration background. High het-erogeneity is found with regard to country of origin, e.g., students from Northern Africa,China and Indian Subcontinent display particularly low educational attainment whilestudents from Eastern European countries perform almost similar as natives. Moreover,as far as immigrant generational status is concerned, most empirical findings indicate thatsecond-generation students outperform first-generations. However, second-generation inItaly is still a very young population which are in large part in primary education years,therefore new studies will be needed in the future (Miur, 2009).

Also in Spain, the share of foreigners in schools has increased massively over thepast decade and now it has set at similar levels as in Italy. In general, immigrants displaylower school attendance at upper secondary education and higher propensity to enrol inpublic schools compared to natives. Analogously to Italy, there is compelling evidence ofa pronounced achievement gap of children of immigrants at both primary and secondarylevel (Zinovyeva, Felgueroso, & Vazquez, 2008; Cebolla-Boado & Medina, 2010; Calero& Waisgrais, 2009). Previous studies on PISA data indicate that first-generation aremore disadvantaged than second-generations (Zinovyeva et al., 2008; Calero & Waisgrais,2009). Moreover, there is multiple evidence that the immigrant-native gap has been in-creasing over the past years (Zinovyeva et al., 2008). Indeed, previous studies (carried outon 1990s data) found no significant gap (Buchmann & Parrado, 2006). African studentsare found to be the lowest performing group in Catalonian schools (Gutierrez-Domenech& Adsera, 2009). Since the beginning of 2000 the total number of immigrant origin stu-dents has increased at a higher rate in the public sector compared to the private one,indicating difficulties to access access private and publicly funded private schools. More-over, there is also evidence of a negative association between immigrant concentrationand achievement (Zinovyeva et al., 2008; Cebolla-Boado & Medina, 2010; Calero & Wais-grais, 2009). Portes and colleagues (2010) also point to low aspirations and expectationsof children of immigrants in Spanish secondary schools.

In Ireland, the percentage of children of immigrants within secondary schools isrelatively low while in contrast, the pattern among primary schools is quite different in-dicating a growing generation of children of immigrants which are still in the beginningof their educational career. This pattern is comparable to Italy and Spain confirming apeculiarity of new immigration countries. By 2008, children of immigrants made up morethan 20 per cent of the student body in every tenth primary school (Smyth, Darmody,McGinnity, & D., 2009). Following residential patterns, children of immigrants are highlyrepresented in urban schools and those already catering for more disadvantaged popu-lations. According to recent PISA data, differences in educational achievements weremost pronounced between the native student population and first-generation students in2009. They are likely to have most difficulty in terms of school performance, as they havedirectly experienced the challenges of immigration while their second-generation coun-terparts score almost equally high in achievement tests as the Irish student population(OECD, 2010b). In Ireland, the great majority of the recent immigration waves do nothave English or Irish as their first language. Several studies have identified that lan-guage issues among students for whom English is a foreign language is one of the biggestchallenges. While there is no observed difference in the socioeconomic background of stu-dents by immigrant status in Ireland, low levels of proficiency in the language of the hostcountry is likely to affect immigrant students’ academic achievement (Devine, Kenny, &Macneeka, 2004; Keogh & Whyte, 2003; Smyth et al., 2009; Vekic, 2003). Difficulties may

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Achievement Gaps

already arise during primary school as children are introduced into the main subjects.Achievement problems even increase in the course of the educational career (especially insenior classes) when the reading level of texts and the emphasis on written work enhance.Immigrant students with lower test scores in the early cycles of the educational careerare more often found to be reliant on family assistance in upper secondary education(Byrne & Smyth, 2010) which sets immigrants parents under pressure given their minorlanguage abilities and generally low knowledge of the Irish educational system.

4 Research questions

In this paper we make three kinds of comparisons: We compare immigrant and nativeperformance within the three countries, we compare immigrant performance across thecountries, and finally we examine the trends over time. We ask five sets of questions.

In the first place, we are interested in assessing whether average scores are sensitiveto some key features of individuals’ migration backgrounds. In line with the above-citedliterature, we break down our sample by immigrant generational status. In addition, wealso investigate the association between achievement test scores and language spoken athome.

Secondly, we ask what part of the immigrant-native gap is accounted for by thedifferent family socioeconomic backgrounds of immigrants and native households. Sincesocioeconomic resources are likely to be unevenly distributed between natives and immi-grants, we expect to find that a substantial portion of the gap is indeed accounted for byfamily socioeconomic background.

Next, we shift our focus to the school level in order to investigate which features ofthe national education systems (i.e., tracking, standardization, and school composition)account for the gaps. We expect the estimated gap to decrease in highly tracked and lessstandardized systems as well along with the segmentation of the school system in termsof socioeconomic and immigrant family backgrounds.

Given the nature of the data that we use, we compare the above results acrossthe three countries, aiming to highlight similarities and differences. Moreover, we com-pare results across the three available time-points in order to understand trends in thephenomena.

Finally, given the specificity of new immigration countries, we focus our analyseson first-generation children. We analyze the paths along which these children assimilate,looking at the association between age at arrival and achievement, and we compare theirperformances across the three countries.

5 Data, Variables and Analytical Strategy

5.1 Data

Our empirical analyses are based on data from the Programme for International StudentAssessment (PISA) collected in Ireland, Italy and Spain in the years 2003, 2006 and 2009.This is an examination and survey administered to representative samples of 15-year-oldstudents enrolled in any educational institution in selected countries around the world.PISA assesses students’ learning abilities in three domains: reading, mathematics andscience. Apart from assessing students’ skills, individual, family and school background

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Achievement Gaps

information are collected through questionnaires administered to students and school offi-cials. PISA samples are derived from a complex, two-stage stratified sampling procedure,with schools containing 15-year-old students selected in the first stage and individualstudents selected in the second. The data include a set of probability weights and 80 setsof replicate probability weights at each level.5

The samples used in this article contain some missing values in our main dependentand independent variables of interest. We applied a listwise deletion method, deleting allcases with any missing values on the variables used. Table 2 provides an overview on thethe total sample size of the final subsamples for each wave per country after the listwisedeletion.

Table 2 – Sample sizes used in regression analysis (total N per country and year).

Ireland Italy Spain

2003 3,285 10,993 9,8642006 4,253 20,800 19,0302009 3,017 29,573 24,216

5.2 Dependent Variables: Mathematics and Reading compe-tencies

As dependent variables we use students’ scores in mathematics and reading literacy tests.Reading literacy is defined as an individuals capacity to understand, use, reflect on andengage with written texts. More precisely, reading literacy assessment focuses on the skillsof students in understanding different text formats (e.g., continuous and non-continuoustexts, different prose forms); accessing and retrieving information, forming a broad gen-eral understanding of the text, interpreting it, reflecting on its contents and reflecting onits form and features. The tests are aimed at measuring students’ capability apply theirreading literacy to different situations (e.g., a novel, personal letters, official documents,etc.). Mathematical literacy is concerned with the ability of students to analyse, reason,and communicate ideas effectively as to pose, formulate, solve, and interpret solutionsto mathematical problems in a variety of situations (e.g., personal, educational, occupa-tional, public and scientific). The tests cover concepts like quantity, space and shape,change and relationships, and uncertainty numbers, algebra and geometry. Test scoresare measured on a common scale allowing us to compare student achievement acrosscountries and in time.6 However, students are not administered the entire set of itemsof each test. On the contrary, they are assigned only in a sub-group of them. As aconsequence, student performance estimates are imputed as five plausible values whichrepresent the range of abilities that a student reasonably has. In order to capture therange we carried out our analysis on all five plausible values simultaneously.

5These weights are based on the probability of units at each level being selected, adjusted for non-response. Replicate weights are generated using the Balanced Repeated Replication (BRR) method(OECD, 2009). PISA reports the school level weights and the weights of both levels combined; studentweights may be derived simply by dividing the latter by the former.

6OECD mean is set to 500 and standard deviation to 100 in the year when the domain was the majordomain.

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5.3 Independent variables

5.3.1 Immigrant Generational Status

The sampled student body has been classified by immigrant generational status. In-formation on the place of birth (abroad vs. host country) of the students has beencombined with that of their parents. We used a ‘strict’ definition of immigrant genera-tional status by identifying first- and second-generation immigrants as those individualswith both parents born abroad. More precisely, the sample has been broken down intofollowing categories: natives (defined as native-born children whose parents are bothnative-born); first-generations (foreign-born children with both parents born abroad),second-generations (native-born children whose parents are both foreign-born). In addi-tion, children of mixed parentage are considered as a distinct category because of theirquantitative relevance in the three countries and their specificity compared to childrenof exclusively immigrant parentage (Brinbaum & Cebolla-Boado, 2007).7 Thus, immi-grant generational status enters our analysis as a categorical variable with four distinctcategories.

The distribution of the different groups is roughly similar across countries revealingan already mentioned common trait of new immigration countries. Overall, the amount ofschool aged children of immigrants is small, especially when looking at second-generationimmigrants. Nevertheless, the the all three groups of children of immigrants increasesover time. Whereas first-generation students accounted for only 2% in Italy and Irelandand 3% in Spain in 2003, six years later (2009) their share climbed to 7-8% and to 4%in Ireland, Spain and Italy respectively. The share of second-generation immigrants alsoincreased, but at a much lower rate (around 1% per wave and country). As far as the sizeof mixed-parentage children, it has been increasing in all three countries, and it representsa particular weight in Ireland where it reaches 16% in 2009, compared with 6% in Italyand Spain.

5.3.2 Language Spoken At Home

We include language spoken at home as a dummy variable, which takes on the value0 if the student declares he usually speaks the host-country language (or a nationaldialect) and on the value 1 if he usually speaks a test language.8 As expected, second-generation immigrants show higher rates speaking the host language at home comparedto first-generations in all three countries. However, great differences across countriesexist as well. Ireland and Spain have much higher rates of immigrants talking a foreignlanguage at home compared to Italy. Moreover, it is also evident that the percentageof immigrants who declare to speak the host language in Ireland has been continuouslydecreasing over time, confirming the above described pattern of increased presence ofnon-return immigrants.

7Children of mixed parentage are put in a unique category regardless of their place of birth, becausethose born abroad represent only a small fraction and do not differ significantly from those born in thereceiving country and thus do not alter our estimates.

8Due to high percentage of missing values on language information in Italy, instead of deleting missingvalues, we define a category for missing values.

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5.3.3 Family Socioeconomic Background

We measure family socioeconomic status and the availability of cultural and educationalresources at home through four variables: In the first place we use parental education levelin order to capture the human capital possessed by the family. This variable has beencoded in a categorical way following the ISCED classification ranging from ISCED 1 toISCED 5a-6. Secondly, we use the highest occupational status of parents by including theinternational socioeconomic index of occupational status (ISEI) (Ganzeboom & Treiman,1996). Third, to further capture the availability of cognitively stimulating resources athome, we include an index which summarizes cultural and educational resources (e.g.,availability of a place to study, a personal computer, books at home), possessions andindicators of wealth at home. Finally, we add a binary variable which allows us to adjustfor variations in the family structure. The variable takes on the value 1 for nuclearfamilies and on the value 0 for non-nuclear family types.

Differences between the three compared countries are most pronounced when look-ing at the socioeconomic status of the parental generation. On average, Ireland and Spainhave higher rates of highly educated parents (ISCED 5a-6). However, what is more rel-evant is the educational level of immigrant parents and their comparison with nativeparents. Whereas in Spain and in Italy immigrant households display roughly similareducational attainment as natives, this does not hold true for Ireland where parents offirst-generation immigrants have significantly higher educational attainment comparedwith native parents. The same patterns observed for parental education apply to highestparental occupational status: In Ireland immigrant parents display higher labor marketoutcomes and relevant resources compared to natives, while the exact opposite occurs inthe two Mediterranean countries, where parents of first-generations display particularlylow occupational attainment.

5.3.4 School Characteristics

Drawing on the above cited literature on education systems and equality of educationalopportunity and on the descriptions of the three national systems, we include a set ofvariables at the school level. In the first place, we include information in order to accountfor the degree of school-type differentiation and to adjust for the different likelihood ofstudents to enroll in one school over another. More precisely, we include a categorical vari-able which indicates the specific programme at secondary education in which the studentis enrolled. This variable is not included in Spain because it is a comprehensive system.9

In Italy this variable is coded as following: Academic schools, Technical schools, Voca-tional schools, Vocational training, Lower secondary schools. The distribution of studentsamong these different tracks indicate the existence of a very pronounced “segregation” ofimmigrants into the shorter and less prestigious tracks (vocational schools and trainingcourses) over the most academically oriented ones (licei). Such a segregation based onimmigrant status is not to be found in Ireland since differentials between natives andchildren of immigrants with regard to school type are rather small.

A second indicator of horizontal differentiation of the education systems is schoolownership. We include school ownership as a dummy variable (public vs private school).Substantial differences exits between natives and children of immigrants. However, these

9In Spain, individuals enrolled in higher secondary school (Bachilerado) account for less than 0.05%and so they are deleted. Moreover, we did not include grade because of collinearity with school pro-gramme.

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are not consistent across countries: while immigrants in Spain have much lower probabil-ity to enrol in a private institution, no differences are found in Ireland while immigrantsin Italy are more often found in private schools.

Third, we include a measure of socioeconomic school composition by taking theweighted average of the highest parental occupational status. There is a similar patternacross countries in 2009, since immigrants tend to attend schools with lower socioeconomicstatus compared to natives, this is especially true in Italy and Spain, even though in Ire-land a pronounced decline in socioeconomic status of schools attended by first-generationimmigrants is found across waves.

Fourth, we control for the proportion of first-generation immigrants enrolled ineach class. On average, school immigrant concentration is higher in Spain and Irelandcompared with Italy.

5.3.5 Additional Control Variables

Finally, we further control for gender and age as well as for the size of area of residence(rural area, small town, town, city, large city).10

5.4 Analytical strategies

In what follows, we first analyse the mean reading and mathematics scores of natives andthe three groups of immigrants (first-generations, second-generations, and children ofmixed-parentage) (6.1). We estimate mean scores following the approach recommendedin the PISA documentation, using the five plausible values, final sampling weights, and 80replicate sampling weights provided with the data. The use of plausible values is aimed atcapturing an unbiased and continuous measure of student proficiency from discrete examscores, while the weights account for the sampling structure of the survey and providedesign-based measures of uncertainty (OECD, 2009). By using this approach, we alsoensure that our estimates are comparable with the official OECD figures. As a secondstep, we regress reading and mathematics scores on immigrant generational status usinghierarchical linear models with levels for individual students and their schools (6.2). Inthese models, we treat student performance as a normally distributed random variabledrawn from a distribution with mean µ for student i in school j modeled as:

µij = αj + XTλ (5.1)

where αj is a random intercept estimated for each school, and XTλ is a trans-posed matrix of covariates and their estimated coefficients. We fit a series of modelspecifications, progressively adding covariates to assess how variations in language spo-ken at home, family socioeconomic background and school characteristics account forimmigrant-native achievement gaps. We fit these models using the student and schoollevel probability weights adjusted according to the approach suggested in Pfefferman,Skinner, Homes, Goldstein, and Rasbash (1998).11 We fit every model five times, usingeach of the plausible values as the dependent variable, and then averaged the resultingparameter estimates. We did not use the replicate weights to estimate uncertainty in theregression models, opting instead to use model-based uncertainty estimates. Next, we

10A descriptive overview of all variables and their distribution are summarized in Appendix B11Specifically, we calculated the Pfefferman et al. (1998)adjusted weights using software described in

Chantala, Blanchette, and Suchindran (2006).

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restrict our analysis to first-generation students and investigate the relationship betweentest scores and age at migration. Results are displayed by plotting the marginal effectsof age at migration, adjusted for the full set of variables described in the previous section(6.3).

Finally, we explore cross-national differences in the achievements of first-generationimmigrants (6.4). We proceeded as following. As a first step, we took the predicted scoresof four profiles of first-generation students obtained from the full model (thus includingall the above described variables) within the three countries. We defined the four ideal-type profiles of immigrants by crossing their socioeconomic status (high vs. low) and theschool track attended (high- vs. low-performing). More precisely, we defined socioeco-nomic status as low for households with no highly educated parents, and placed at the25th percentile in both the parental ISEI and the home possession indices. Accordingly,we labeled as high-socioeconomic status those families with at least a tertiary educatedparent and placed at the 75th percentiles in the two indices mentioned above. Regardingschool type, we empirically defined vocational tracks as low-performing tracks, and forhigh-performing we picked academic tracks. Of course, in Spain we did not distinguishbetween tracks, since the education system is a completely comprehensive one. All othercharacteristics were held at constant values, meaning that we picked boys who live innuclear families which reside in towns (15,000 to 100,000) and where the most frequentlyspoken language is not the official language of the host country. The remaining con-tinuous variables were set to their mean values. As a second step, we re-estimated thepredicted scores for the just described profiles of immigrants in Italy and Spain by apply-ing to them the values of the two socioeconomic variables (ISEI and home possessions)of their counterparts in Ireland. This way we obtain new predicted scores, adjusted forthe different socioeconomic resources between the countries.12

6 Empirical Results

6.1 Average competencies of natives and children of immigrants

Figure 3 displays average scores in reading and mathematics of each immigrant group ofstudents as defined by their immigrant generational status in 2003, 2006 and 2009.

Overall, natives tended to perform better than first-generation immigrants, andto some extent better than second-generation immigrants, although there are substan-tial variations between the three countries. Irish native students display higher averagecompetencies in both mathematics and reading compared to their Italian and Spanishcounterparts. However, Ireland natives’ average score in both subjects sharply decreasedbetween 2006 and 2009, while in Italy it increased and in Spain it remained substantiallyunchanged. These developments led to an increasing convergence between the threecountries.

The achievement outcomes of children of immigrants show even more strikingvariations across countries. However, the uncertainty of the estimates is also much higherthan that of the native estimates. This is largely due to the small sizes of the immigrantsub-samples, although it may also result, in part, from higher educational dispersion

12This statistical exercise resembles the Blinder-Oaxaca technique for decomposing continuous depen-dent variables. In order to take into account the multilevel structure of the data and to compare preciseprofiles of immigrants across countries, we did not use the Stata routine oaxaca

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Figure 3 – Estimated mean mathematics and reading scores on PISA 2003, 2006 and2009 exams for native, first generation, second generation, and mixed parentage students inIreland, Italy and Spain. Squares show point estimates calculated using sampling weightsand all five plausible values; lines show 95% confidence intervals calculated using all 80replicate sampling weights and all five plausible values.

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among immigrants (Schnepf, 2008).13

On average, first-generation immigrant students mostly under-performed nativesin Italy and Spain in both mathematics and reading. The same does not hold true forIreland, where the mean score for first-generation immigrant students is statistically indis-tinguishable from that of native-born students until 2009, when it becomes clearly lowerin both subjects. Moreover, it is also evident that first-generation immigrants in Ire-land performed significantly better than immigrants in the two Mediterranean countriesduring all of the observed years.

By comparing the performances of first-generation immigrants across the threewaves considered, we can observe a clear decreasing pattern in Ireland, since their meansin 2009 are significantly lower than in 2003. A more stable—even though slightly decreasing—pattern seems to have taken place in the two South-European countries. It is surely diffi-cult to infer from only three points in time a general trend, but other studies, carried outon PISA data on partially different waves, seem to point to the same direction (Zinovyevaet al., 2008; OECD, 2010a). We will return to this point in the following section whenwe analyze the gap between natives and first-generations (6.2).

Turning our attention to second-generations, the picture becomes fuzzier. Esti-mated means have even higher uncertainty than first-generations. The small samples areresponsible for the large confidence intervals. However, along with the larger samples (inSpain and Italy), the incidence of second-generation in schools steadily increased overtime in all three countries, leading to a noticeable reduction of the estimates’ uncertaintyacross waves. Indeed, in 2009 we can observe statistically significant differences betweensecond-generations and natives in Italy and Spain, indicating that second-generation per-form slightly worse. In Ireland, the differences between second-generations and nativesmean scores are in general smaller - compared to the the two South-European countries- and never significant at the 5% level.

Finally, children of mixed-parentage display roughly similar—if not even higher—competencies as natives in all three countries.

6.2 Stepwise analysis of the immigrant-native gap within newimmigration countries

After showing how achievements vary across groups, in this section we test, first, whetherthese results hold even after accounting for school random effects and, second, to whatextent existing explanatory hypotheses account for the observed differences. In Figures?? and ?? we present the parameter estimates from of a sequence of four multilevel modelsestimated separately for each country and wave. The squares in Figure ?? and ?? showour point estimates from each model of the coefficients on the dummy variables for eachimmigrant generation status, with native students used as the reference category. Linesshow the 95% confidence intervals around each estimate. (Appendix C provides tableswith all estimates from these models.)

The first model (M1) allows for school random intercepts and incorporates im-migrant generational status, age, sex and area of residence as covariates. The results ofthis model’s specification confirm some of the regularities found by looking at averagesin the previous section. In general, first-generation immigrants perform systematically

13We have estimated the population standard deviations of each sub-group’s test scores and they arenot significantly different from one another. However, our inability to detect a significant difference heremay itself result from the small subsample sizes.

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Figure 4 – Achievement gaps in mathematics and reading competences between nativesand first-generation immigrants in Ireland, Italy, and Spain: Point estimates of the coeffi-cients first generation (with native students as reference category) in models 1-4 (coloredsquares) with 95% confidence intervals (lines) using pooled PISA data for 2003, 2006, and2009. M1 includes immigrant generational status, age, sex and area of residence as covari-ates. M2 incorporates language spoken at home. M3 adds the highest parental occupationand education, home possessions, and family structure. M4 includes school track, schoolownership, and immigrant and socioeconomic composition of the school.

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Figure 5 – Achievement gaps in mathematics and reading competences between nativesand children of second-generation immigrants in Ireland, Italy, and Spain: Point estimatesfrom models 1-4 (colored squares) with 95% confidence intervals (lines) using pooled PISAdata for 2003, 2006, and 2009. M1 includes immigrant generational status, age, sex andarea of residence as covariates. M2 incorporates language spoken at home. M3 adds thehighest parental occupation and education, home possessions, and family structure. M4includes school track, school ownership, and immigrant and socioeconomic composition ofthe school.

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worse than natives while second-generation students display a smaller gap (Figure 4).This pattern holds true for the two South-European countries and for Ireland in 2009.On the contrary, in Ireland before 2009 it is second-generation students who displayed adisadvantage compared to natives (Figure 5), while first-generation did not differ fromnatives.

Comparing the magnitude of the gap between first-generation and natives, thethree countries place themselves in different positions. The size of the gap reaches itsmaximum in Spain, where it ranges between 66 points in mathematics and 56 points inreading. In Italy the gap is slightly smaller and, in any case, smaller in mathematics (36points) than in reading (44 points). In Ireland, as already seen in the mean estimatesabove, first-generation immigrants did not display an educational disadvantaged until2009, when the magnitude of the gap amounted to 20 points in mathematics and 29points in reading.

Focusing on the trends over time, we find that our point estimates of the immigrant-native achievement gaps increase over time, although we cannot distinguish this trendwith confidence in most cases because of large standard errors. This is in line with ourunivariate mean estimates and with other studies using PISA data for the same countries(Zinovyeva et al., 2008; OECD, 2010a).14 Our data indicate that in Ireland the magni-tude of the gap between first-generation and natives increased between 2003 and 2009even though not significantly. Roughly similar patterns are observable in Italy and Spainas well, although the uncertainty of the estimates and the inconsistency across subjects(e.g., reading literacy gap in Italy) do not allow us to derive strong generalizations.

However, in the two South-European countries we detect significant gaps betweensecond-generations and natives only in 2009, and in mathematics only in 2006, while in2003 the gaps were both smaller and not significant. This could be seen as a clue of theexistence of an overall increase of the gap of children of immigrants, even though theinsignificant gaps in 2003 could be mostly due to the small sample sizes.

As far as children of mixed parentage is concerned, they perform essentially asgood as natives in all three countries and across the three waves. Moreover, the differentmodels’ specifications presented below leave substantially unchanged their estimated dif-ferences to natives. For this reason we do not show these parameters (these are availablein the full models in Appendix C).

How will these patterns change after including other predictors at the family andat the school level? Which factors will matter more in accounting for the immigrant-native gaps within the countries? And will these factors play the same contribution inall of three countries?

Model 2 incorporates language spoken at home. This reduces model deviance, andexplains some of the immigrant-native achievement gap that is attributed to immigrationgeneration in Model 1. Although the differences between the M1 and M2 gap estimatesare not significantly different from 0, if we look just at our point estimates the inclusionof the language variable is largely consistent in improving the estimated performanceof immigrant students relative to natives. As expected, this effect is more apparent forreading than for math scores, and, language reduces the deviance in models of readingscores more than in models of math scores. For first generation students in Ireland, the

14Zynovyeva and colleagues (2008) show increases in the gap in Spain in all domains between 2000and 2006 using point estimates. OECD (2010a) shows increases in the reading literacy gap in Ireland,Italy, and Spain, although these increases are statistically significant at the 95% confidence level only inIreland and Italy.

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contribution of language spoken at home is most pronounced in 2009 (when it reduces thegap by 50% in mathematics and by 66% in reading), up to the point that the gap becomesstatistically insignificant. In the two Mediterranean countries, language spoken at homeis less relevant than in Ireland in accounting for immigrant-native gaps. Especially inSpain, language spoken at home explains up to 15% of the immigrants-native gaps inreading (in 2006) while it does not affect the gap in mathematics. The same patternis found for both first and second-generation immigrant students. In Italy, languageaccounts for slightly more of the reading gap (around 25% in 2006) than in Spain.

Model 3 adds the four indicators of family socioeconomic background describedabove (parental education and occupation, home possessions, and family structure). Thismodel reveals one of the most striking differences between the two Mediterranean coun-tries, on one side, and Ireland, on the other. Indeed, socioeconomic background exertsopposite effects on the immigrant-native differentials: while it contributes in reducingthe performance gap in Spain and Italy, socioeconomic background amplifies the gapbetween first-generation and natives in Ireland. The explanatory power of family back-ground is especially strong in Spain, where it represent by far the most important factorin contributing to the gap, since in 2009 it significantly reduces the gap by about 20%for first-generations. Also in Italy, a similar portion of the disadvantaged position of chil-dren of immigrants seems to be due to the socioeconomic deprivation of their families.These contrasting findings are not surprising, on the contrary they could be in large partexplained by the marked differences in the socioeconomic status of immigrant families inIreland compared to those in the two South-European countries. This point representsan interesting insight of our study, on which we follow up with additional investigationsin section 6.4.

The last model (Model 4) incorporates four additional variables at the school level(programme, school ownership, school socioeconomic and immigrant background). Thismodel does not contribute to the immigrant-native gaps in any of the three countries. Atfirst, this finding could be taken as an unexpected result, given the different patterns ofschool participation of immigrants and natives within the three countries (e.g., immigrantsin Italy have a much higher risk of enrolling in lower quality school tracks, and immigrantsin Spain have less chances to access private schools). However, these different schoolparticipation modes between immigrants and natives are in large part due to their lowersocioeconomic resources: once controlled for these factors, it is not that surprising tofind that schools factor play a minor role. Besides, some country studies came up withsimilar results (Mantovani, 2008; Zinovyeva et al., 2008). Nonetheless, what just saiddoes not mean that the school factors are not relevant at all in explaining the variancein the educational achievement. On the contrary, likelihood-ratio tests indicate that thislast model specification fits the data significantly better than previous models. This isparticularly true in Italy, where the highly school-type differentiated system is responsiblefor more than half of the total variance in test scores at the school level.

To conclude, large differences exists in the magnitude of the immigrant-nativegaps between the three countries. In the two Mediterranean countries a sizable andhighly significant gap between first-generation and native students persists even aftercontrolling for the above mentioned set of explanatory variables. On the contrary, inIreland a significant immigrant-native gap exists in 2009 only, and it disappears afterholding language spoken at home constant.

In 2009 (when the between-country differences are lowest) the performance gapin mathematics between natives and first-generations in Ireland is about three times as

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smaller as in Spain and approximately half of that observed in Italy. This pattern holdsfor reading as well: the gap in Ireland is about half of that in Spain and roughly twothirds of the gap in Italy.

During the time span considered, the performance gap of second-generation stu-dents proved to be significant in the two Mediterranean countries while the oppositepattern was observed in Ireland. However, these findings call for more research becauseof the high uncertainty of the estimates due to the small sample sizes.

The factors that account for these differentials also vary across countries: whileit is found that the key factor in Ireland is the language spoken at home, in Spain andItaly the causes of the immigrants’ disadvantage are to be searched also in the socioe-conomic deprivation of their families. Indeed, within the two South-European countriessocioeconomic background accounts for a substantial part of the gaps, while in Irelandthe disadvantage of immigrants increases once controlled for family background. Thisfinding seems to suggest that the different selectivity of immigrant workers might be animportant determinant of the educational outcomes of their children.

6.3 The association between age at arrival and educational achieve-ments

Within new immigration countries, first-generation immigrants are the largest immi-grant group aged 15 while their second-generation immigrant age mates are still smallin numbers. Moreover, when comparing the educational achievements between first- andsecond-generation immigrants, the former tend to be more disadvantaged compared tothe latter. Thus, first-generation immigrants are the most relevant generational groupin new immigration countries, at least in secondary education. They were not born inthe host countries and entered Italy, Spain or Ireland together with their parents at anearly age. Some might have arrived before starting compulsory school while some othersentered the educational system as lateral entrants. In this section we investigate theeffect of age at arrival on first-generation immigrants’ educational achievements withinthe three compared new immigration countries.

Figure 6 displays the results of this analysis graphically. It plots the estimatedmarginal effect of age at arrival on test scores as age at arrival moves across the rangeof values observed in the data. The estimates are those from the full quadratic model,which includes all covariates from Model 4 explained above. The central black line in eachgraph is calculated from the point estimates and the grey lines are calculated from eachof 1,000 simulations of these estimates.15 The dark dotted lines indicate 95% confidenceintervals. To reduce the uncertainty in the estimates, results are examined after poolingall the three waves together.16

Figure 6 provides mixed evidence on the relationship between age at arrival and

15The equation of each line is the derivative of the regression equation with respect to age at arrival.Since the regression equation is yij = αj + β1arrival + β2arrival2 +XTλ (with yij as predicted test scoreof student i in school j, αj as the random intercept of school j, β1 and β2 as the estimated coefficients on,

respectively age of arrival and age of arrival squared, and XTλ as a transposed matrix of all other modelcovariates and their estimated coefficients), then δy/δarrival = β1 + β2 · 2 · arrival. Whereas the darkcentral line uses the point estimates of β1 and β2 in this equation, each grey line uses random numberdrawn from normal distributions with means equal to the point estimates and standard deviations equalto the standard errors of these estimates.

16We also ran the same models without pooling the three waves obtaining fairly consistent resultsacross the three waves.

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educational achievement within the three new immigration countries. In the two Mediter-ranean countries the middle black line, which shows the marginal effects, has a cleardownward slope indicating the instantaneous effect of one additional year of age at ar-rival increasingly and negatively affecting students achievement. More precisely, it showsthat the underlying relationship between age at arrival and achievement is a curvilinearone, ascending at younger and decreasing with higher ages.

The grey “simulation lines” as well as the confidence intervals plotted in Figure 6indicate that our results do not come without uncertainty. The marginal effects in Italyare statistically significant at the 5% level only with regard to the bottom and uppervalues of age at migration. The marginal effect of age at arrival is positive at early ages(between 0 and 3), turning insignificant until around the age of 12 before a negativeeffect is detected once first-generation students arrive one or two years before the testage. A similar pattern as that observed in Italy is observable in Spain, even though theuncertainty of the estimates is even higher with regard to early and older ages.

Turning to the Irish case, a clear pattern is more difficult to detect—as shown inthe graphs. Overall, the point estimates of the marginal effects seem to follow a similar(even though flatter) pattern as those of the other two countries with early arrivalscatching up while the late arrivals face a rather disadvantaged position. But as with theestimates for Italy and Spain, there is a large amount of uncertainty due to small samplesizes—and this problem is most accute for Ireland, as it has the smallest sample sizes ofthe three. This makes it impossible to draw further conclusions from the estimates.

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Figure 6 – Marginal effects of age at arrival on mathematics and reading achievement inIreland, Italy, and Spain (using PISA data pooled across 2003, 2006, and 2009). Blackcentral lines show estimated marginal effects based on point estimates; grey lines showthe same based on 1,000 simulated estimates; dotted black lines indicate 95% confidenceintervals. The red line at y = 0 shows the null hypothesis that a change in age at arrivalhas no effect on achievement test scores.

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6.4 Accounting for differences across countries

The evidence presented in the previous sections points to marked differences in the educa-tional performances of first-generation immigrants residing in Ireland compared to theircounterparts living in the two Mediterranean countries. More precisely, the immigrant-native gaps proved to be much larger in Italy and Spain than in Ireland. The multilevelestimates also indicated that while in the South-European countries socioeconomic back-ground contributes to reducing the immigrant-native gap, an opposite effect is found inIreland. These findings lead us to hypothesise that the socioeconomic resources possessedby immigrant families might play a pivotal role in explaining differentials in immigrantsachievement, not only within—but also between-countries, and especially between Irelandand the two South-European countries.

To examine between-countries differences we perform a statistical exercise aimedto compare first-generation performances across countries and to simulate how the rela-tively disadvantaged position of first-generation immigrants in the two South-Europeancountries would look if they had the same socioeconomic resources as their counterpartsin Ireland. As already described in section 5.3, we estimate the educational achievementseparately for students with low and high socioeconomic status and who are placed eitherin lower or higher academic tracks in secondary education. This leads to a fourfold com-parison: low ability track-high SES; low ability track -low SES; high ability track-highSES and high ability track-low SES.

The results of this statistical exercise are displayed in Figure 6, separately formathematics (left side) and reading (right side) achievements in the three countries in2009.17 The filled symbols are the mean achievement scores predicted after accountingfor the full set of independent variables (as in Model 4), while the empty symbols arethe predicted scores for immigrants in Italy and Spain adjusted for the socioeconomicbackground of their counterparts in Ireland.

At a first glance, the unadjusted estimates (filled symbols) confirm the relativeadvantaged position of first-generation immigrants in Ireland. However, between coun-try differences are not consistent across the four profiles. Indeed, students with highersocioeconomic status and those attending academic oriented tracks display on averagehigher competences.

More precisely, within academic tracks Irish immigrant students with high so-cioeconomic background outperform their counterparts in Italy and Spain, while, amongstudents with lower socioeconomic status, immigrants in Ireland and Italy show simi-lar outcomes, and those in Spain still lag significantly behind. Moving on to within-vocational-schools comparisons (Spain is excluded due to its comprehensive system),we see that Irish immigrant students always outperform immigrants in Italy. This isespecially true among immigrants originating from high-socioeconomic-status families,indicating that in Italy high socioeconomic resources do not prevent students from lowperformances if they attend vocational schools.

But “what if” first-generation immigrants in Italy and Spain would posses thesame socioeconomic resources as their counterparts in Ireland? We can try answering thisquestion by looking again at Figure 6, which shows that the adjusted estimates for Italyand Spain (empty symbols) are always higher than the unadjusted ones. These patternssuggest that if immigrants in the two South-European countries had the same economic

17We replicated the analyses in 2003 and 2006 as well without finding substantially different results.These results are available upon request

26

Achievement Gaps

400

450

500

550

Mat

h sc

ore

(20

09)

Low SES−High schoolHigh SES−High school

Low SES−Low schoolHigh SES−Low school

Ireland Italy

Spain Italy adj.

Spain adj.

400

450

500

550

Rea

ding

sco

re (

2009

)

Low SES−High schoolHigh SES−High school

Low SES−Low schoolHigh SES−Low school

Ireland Italy

Spain Italy adj.

Spain adj.

®

Figure 7 – Adjusted and unadjusted test scores of first-generation immigrants in Ireland,Italy, and Spain (pooled PISA file 2003-2009). These estimates are based only on the firstplausible values of mathematics and reading scores.

resources as their “Irish” counterparts, their performances would slightly increase and,consequently, their disadvantage compared to immigrants in Ireland would decrease. Thegap between Spain and Ireland would significantly drop from approximately 70 to 40points among high-socioeconomic status families; and immigrants in Italy would catch-up with their Irish counterparts, with the exception of high socioeconomic students invocational tracts.

However, we should once again underscore the uncertainty of the estimates: thedifferences between the unadjusted and adjusted mean scores are often not significant. Weargue that this is in part due to small sample sizes, but we should also remember that ourdata do not contain information on the country of origin of students, and this unobservedvariable could be responsible for a substantial part of the between-country differences.Nonetheless, these analyses seem to indicate that at least part of cross-country variationsin immigrants outcomes are actually accounted for by the different socioeconomic re-sources of immigrants in Ireland and in the two South-European countries. Moreover ouranalysis also suggests that the largest between-country differences are among the moreaffluent first-generation immigrants. This could be a consequence of the higher propen-sity of Ireland to attract high-skilled labor force from other countries. Finally, also thedegree of school-type differentiation plays a role. This is especially evident in the coun-try with the highest differentiated system, Italy, where immigrant students, regardless oftheir socioeconomic status, are among the highest achievers of the three countries, if theyattend academic tracks, and among the lowest, if they attend vocational tracks.

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7 Summary and Conclusion

This study investigated the patterns of the educational achievements of immigrant andnative 15-year-old students in Ireland, Italy and Spain. These three countries represent“new immigration countries” and have not been adequately covered by empirical researchyet. With this paper we attempted to redress this research gap. Although our empiricalinvestigations are primarily descriptive, they present one of the first attempts to comparethe educational performances of children of immigrants within and across new immigra-tion countries. Our study also represents an opportunity to explore, in new immigrantcontexts, the validity of various hypotheses about immigrant-native achievement differ-ences that have been widely tested in countries with older migration traditions. Finally,by exploring three different points in time, we tried to describe the temporal dynamicsof the gaps in the three countries.

Overall, our results indicate that within all three new immigration countriesachievement differences exist between 15-year-old immigrants and natives. We found anassociation between educational achievement and immigrant generational status. How-ever different patterns are observable within the three countries.

While in the two South-European countries first-generation immigrant studentsare by far the most disadvantaged group, in Ireland this holds true only in 2009. Evenin this last wave—when between-country differences were lowest—the magnitude of thegap was found to be still much larger in Italy and Spain (where it sets at around 40 and60 points respectively) compared to Ireland (where it ranges between 20 and 30 points).

Second-generation immigrants were found to perform better than first-generationimmigrants but slightly worse than natives, although this does not apply to the Irish casebefore 2009. On the specific condition of the second-generation, however, more researchis still needed, since in new immigration countries second-generation students at this agerepresent a very small population and the large majority of them is still attending lowereducational levels.

Regarding children of mixed-parentage, they display roughly similar—if not evenhigher—competencies as natives in all the three countries.

Overall, between 2003 and 2009, our results point to a slight increase in theimmigrant-native gaps in new immigration countries. In Ireland we found robust evi-dence that the performances of first-generation immigrants declined. A similar—althoughweaker and not significant—pattern was found in the two Mediterranean countries aswell. We found that the gap between second-generation and natives became significantin the two Mediterranean countries only after 2006. Although the empirical evidence isnot always very strong on this point—because of the high degree of uncertainty of ourestimates—such an increasing pattern has been detected in previous (national) studies aswell. Therefore, there is cumulative evidence that an increasing pattern in the immigrant-native gap took indeed place in the three new immigration countries. We argue that thispattern might be due to compositional effects. In the time span considered, immigrantscoming from poor countries have progressively increased while immigrants from moreaffluent countries and return migrants have decreased. Indeed, we detected a slight de-terioration of the average socioeconomic status of immigrants, which might have led to aconsequent worsening of their children’s educational outcomes.

Given the quantitative and substantive relevance of first-generations in new im-migration countries, we examined this group of immigrants more closely by trying toestablish the rate at which they assimilate to natives. We did this by exploring the as-

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Achievement Gaps

sociation between age at arrival and student achievement. Although the strength of thisassociation is weak, especially in Ireland, we provided evidence that in Italy (and to alesser extent also in Spain) age at arrival exerts positive marginal effects when immi-grants migrate at very early ages, and has increasingly negative marginal effects whenstudents arrive one or two years before the test age. Thus, like several other research,our study seems to point out a nonlinearity of the relationship between age at arrival andeducational outcomes.

When turning to the explanations of achievement differences between natives andimmigrants, our findings highlighted the significant role of language spoken at home,especially for reading skills. This result holds true especially for Ireland, where it accountsfor up to two thirds of the gap in 2009, while it seems less relevant in Italy and Spain.

Much of the research conducted in “old immigration countries” stresses the roleplayed by socioeconomic family background as the major determinant of the immigrant-native achievement gaps. However, we found mixed evidence for this hypothesis in newimmigration countries. In Spain and Italy, socioeconomic family background is a strongpredictor of the gap, since it reduces the immigrant-native gaps by about one third andone fifth respectively. On the contrary, we observed a reverse effect in Ireland. Whenwe controlled for family background in Ireland, the immigrant-native gap was amplified.This represents additional evidence for the positive selection of immigrants in Irelandwho posses oftentimes higher socioeconomic resources compared to natives. Anotherstriking difference is that in the two Mediterranean countries the immigrant-native gapstill persists significantly for first generations even after controlling for individual andschool level variables while the significant difference disappears in Ireland after controllingfor language spoken at home.

Although the decline in immigrants’ outcomes has been particularly marked inIreland, immigrant students living in this country still outperform immigrants in the twoSouth-European countries. We argued that once again the higher selection of highlyskilled immigrants migrating to Ireland is likely to be responsible for a large part of thesebetween-countries differentials. However, between-country differences in immigrants’ ed-ucation are also shaped by the organization of the education systems. In Italy, which isthe country characterized by the most differentiated system, immigrant students, regard-less of their socioeconomic status, are among the highest achievers of the three countriesif they attend academic tracks, and among the lowest if they attend vocational tracks.

To conclude, both similarities and differences were found within the three consid-ered new immigration countries. In particular, the different selectivity of immigrants inIreland compared to the two Mediterranean countries represents the strongest differenceacross the new immigration countries. However, we have also demonstrated the existenceof a trend of convergence among the three countries. The novelty of immigration to thesethree countries implies that the phenomenon of the educational performances of immi-grants is not settled yet, as it could be in countries with older traditions of immigration.On the contrary, in the three new immigration countries there seems to be a problem of“maturation”: the magnitude of the gap is slightly changing over time, and a convergencebetween the three countries is observable. However, it is important that future researchwill keep monitoring these developments in coming years.

Moreover, future research should further investigate some aspects which—due todata constrains—have not been adequately covered in our study. In the first place,research should assign special attention to second-generations, which is a still small butrapidly growing population in new immigration countries, and whose sample sizes were

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Achievement Gaps

sometimes too small in our data to yield reliable estimates. Secondly, research shouldinvestigate the existence of country of origin variations in the educational outcomes ofimmigrants: This is an important aspect, given the great diversity within the immigrantpopulations in new immigration countries, but which we could not investigate because ofthe unavailability of such information in our data.

A Additional Analyses and Robustness Checks

To partially check for biases due to missing values on some of the independent variablesused in the analyses, we replicated the above presented models with both a stepwisedeletion method and by imputing missing values as their own “missing” category for whichwe obtained estimates. These additional analyses did not yield substantially differentresults from those presented in the paper.

We also ran additional analyses using slightly different model specifications byadding the following school-level variables: streaming between classes, schools’ autonomyin resources and assessment allocation, and student-teacher ratio. These variables didnot alter our estimates. This is essentially due to the fact that Ireland, Italy and Spainhave similar (low) levels of streaming, and similar degree of schools’ autonomy in bothcurriculum, assessment and resources allocation (OECD, 2010a). Moreover, given thewide regional dispersion of educational performances within Italy and Spain (Zinovyevaet al., 2008; Checchi & Peragine, 2005) we estimated additional models including regionsas fixed effects to test for further influences on student performance. Since the resultsdid not change substantially we decided to not include these variables in our final modelspecifications.

Finally, all of the multilevel estimates presented in this paper are produced us-ing Sophia Rabe-Hesketh’s gllamm package for Stata. This allowed us to incorporateprobability weights at each level as well as to make empirical Bayes estimates of theschool random effects. We fit the models using gllamm’s adaptive quadrature approachcombined with maximum likelihood estimation. As a robustness check, however, we repli-cated all analyses without probability weights using maximum likelihood estimation andrestricted maximum likelihood estimation implemented by Stata’s xtmixed function aswell as the lmer function in R’s lme4 package. The results were consistent across all ofthe different estimation methods.

B Full description of variables

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Table 3 – 2003 Estimated Means and Variances of Covariates Using Sampling Weights

variable ESP Mean ESP Var IRE Mean IRE Var ITA Mean ITA Varage 15.85 0.08 15.71 0.08 15.70 0.08famstruc 1. Single parent family 0.17 0.14famstruc 1. Single parent or mixed family 0.14 0.12 0.15 0.13famstruc 2. Nuclear family 0.81 0.15 0.80 0.16 0.80 0.16famstruc 3. Mixed family 0.03 0.03 0.03 0.03famstruc 4. Other 0.02 0.02 0.02 0.02 0.03 0.03fmb 0. natives 0.92 0.07 0.84 0.13 0.94 0.06fmb 1. 1st generation 0.03 0.03 0.02 0.02 0.02 0.02fmb 2. 2nd generation 0.01 0.01 0.01 0.01 0.00 0.00fmb 3. mixed-couples 0.04 0.04 0.12 0.11 0.04 0.04hisced 1. isced 1 0.04 0.03hisced 2. isced 2 0.07 0.06 0.25 0.19hisced 3. isced 3b, c 0.02 0.02 0.04 0.04hisced 4. isced 3a, isced 4 0.27 0.20 0.32 0.22hisced 5. isced 5b 0.12 0.10 0.20 0.16 0.14 0.12hisced 6. isced 5a, 6 0.26 0.19 0.20 0.16 0.21 0.16hisced isced None, 1 0.26 0.19hisced isced None, 1, 2, 3a, 4 0.60 0.24hisei 44.29 265.67 48.34 251.67 46.99 279.74homepos 0.26 0.72 0.12 0.79 0.15 0.80language 1. <Test language> 0.84 0.13 0.96 0.04language 2. <Other national language> 0.14 0.12 0.02 0.02language 4. <Other languages> 0.02 0.02 0.01 0.01language 99 0.00 0.00 0.02 0.02language Foreign language 0.01 0.01language Missing/invalid 0.07 0.07language Test language 0.91 0.08male 1. Female 0.51 0.25 0.50 0.25 0.53 0.25male 2. Male 0.49 0.25 0.50 0.25 0.47 0.25meanhisei 44.25 68.68 48.31 46.78 46.92 73.04pfirst 0.03 0.00 0.02 0.00 0.02 0.00programme Academic schools 0.40 0.24programme Applied/Voc 0.19 0.15programme Baccalaureat 0.00 0.00programme Compulsory sec. ed. 1.00 0.00programme Established 0.01 0.01programme Junior certificate(2a) 0.64 0.23programme Lower sec schools 0.01 0.01programme Technical schools 0.34 0.23programme Transition year (2s) 0.17 0.14programme Vocational schools 0.24 0.18schcontext 1 0.01 0.01schcontext 1. Village (less 3 000) 0.04 0.04 0.23 0.18schcontext 2 0.12 0.11schcontext 2. Small town (3 000 to 15 000) 0.25 0.19 0.37 0.23schcontext 3 0.55 0.25schcontext 3. Town (15 000 to 100 000) 0.28 0.20 0.09 0.08schcontext 4 0.24 0.18schcontext 4. City (100 000 to 1 000 000) 0.33 0.22 0.11 0.09schcontext 5 0.07 0.07schcontext 5. Large city (more 1 000 000) 0.09 0.09 0.20 0.16schcontext 99 0.00 0.00schtype 1 0.95 0.04schtype 1. Public 0.62 0.24 0.39 0.24schtype 2 0.05 0.04schtype 2. Private 0.38 0.24 0.61 0.24schtype 99 0.00 0.00

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Table 4 – 2006 Estimated Means and Variances of Covariates Using Sampling Weights

variable ESP Mean ESP Var IRE Mean IRE Var ITA Mean ITA Varage 15.83 0.08 15.70 0.08 15.75 0.08fmb 0. natives 0.88 0.10 0.79 0.16 0.91 0.08fmb 1. 1st generation 0.06 0.06 0.05 0.04 0.03 0.03fmb 2. 2nd generation 0.01 0.01 0.01 0.01 0.01 0.01fmb 3. mixed-couples 0.05 0.04 0.15 0.13 0.06 0.05hisced 1. isced 1 0.02 0.02hisced 2. isced 2 0.21 0.17 0.26 0.19hisced 3. isced 3b, c 0.06 0.05 0.06 0.06hisced 4. isced 3a, isced 4 0.24 0.18 0.42 0.24 0.40 0.24hisced 5. isced 5b 0.10 0.09 0.22 0.17 0.08 0.07hisced 6. isced 5a, 6 0.27 0.20 0.25 0.19 0.18 0.15hisced isced None, 1 0.13 0.11hisced isced None, 1, 2 0.12 0.11hisei 44.81 288.33 48.98 268.46 46.45 266.69homepos 0.11 0.72 -0.03 0.72 0.19 0.73language 1. language of test 0.81 0.15 0.94 0.06language 2. other national language 0.13 0.11 0.02 0.02language 3. other language 0.03 0.02 0.02 0.02language 99 0.03 0.03 0.02 0.02language Foreign language 0.03 0.02language Missing/invalid 0.12 0.11language Test language 0.85 0.12male 1. female 0.49 0.25 0.51 0.25 0.51 0.25male 2. male 0.51 0.25 0.49 0.25 0.49 0.25meanhisei 44.77 78.01 48.84 53.49 46.44 61.27pfirst 0.06 0.01 0.04 0.00 0.03 0.00programme Academic schools 0.43 0.25programme Applied/Voc 0.05 0.05programme Baccalaureat 0.00 0.00programme Compulsory sec. ed. 1.00 0.00programme Established 0.13 0.11programme Junior certificate(2a) 0.61 0.24programme Lower sec schools 0.01 0.01programme Technical schools 0.31 0.21programme Transition year (2s) 0.21 0.17programme Vocational schools 0.22 0.17programme Vocational training 0.02 0.02schcontext 1 0.01 0.01schcontext 1. village 0.04 0.04 0.27 0.20schcontext 2 0.19 0.16schcontext 2. small town 0.26 0.19 0.27 0.20schcontext 3 0.49 0.25schcontext 3. town 0.31 0.22 0.19 0.15schcontext 4 0.19 0.16schcontext 4. city 0.30 0.21 0.09 0.08schcontext 5 0.09 0.08schcontext 5. large city 0.09 0.08 0.18 0.15schcontext 99 0.02 0.02schtype 1 0.96 0.04schtype 1. public 0.65 0.23 0.40 0.24schtype 2 0.04 0.04schtype 2. private 0.35 0.23 0.60 0.24schtype 99 0.00 0.00

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Table 5 – 2009 Estimated Means and Variances of Covariates Using Sampling Weights

variable ESP Mean ESP Var IRE Mean IRE Var ITA Mean ITA Varage 15.85 0.08 15.71 0.08 15.73 0.08famstruc 1. Single parent family 0.12 0.10famstruc 1. Single parent or mixed family 0.15 0.13 0.17 0.14famstruc 2. Nuclear family 0.85 0.13 0.83 0.14 0.88 0.10fmb 0. natives 0.85 0.13 0.75 0.19 0.88 0.10fmb 1. 1st generation 0.08 0.08 0.07 0.06 0.04 0.04fmb 2. 2nd generation 0.01 0.01 0.01 0.01 0.01 0.01fmb 3. mixed-couples 0.06 0.06 0.16 0.14 0.06 0.06hisced 1. isced 1 0.02 0.02hisced 2. isced 2 0.20 0.16 0.22 0.17hisced 3. isced 3b, c 0.03 0.02 0.06 0.06hisced 4. isced 3a, isced 4 0.24 0.18 0.37 0.23 0.37 0.23hisced 5. isced 5b 0.12 0.11 0.17 0.14 0.06 0.06hisced 6. isced 5a, 6 0.32 0.22 0.34 0.22 0.27 0.20hisced isced None, 1 0.09 0.08hisced isced None, 1, 2 0.12 0.11hisei 45.85 293.27 49.92 266.26 47.00 268.76homepos -0.11 0.70 0.08 0.63 0.01 0.70language 1. Language of test 0.81 0.16 0.91 0.08language 2. Another language 0.18 0.15 0.06 0.05language 99 0.02 0.02 0.03 0.03language Foreign language 0.03 0.03language Missing/invalid 0.11 0.10language Test language 0.86 0.12male 1. Female 0.49 0.25 0.49 0.25 0.49 0.25male 2. Male 0.51 0.25 0.51 0.25 0.51 0.25meanhisei 45.78 82.37 49.72 57.55 46.97 71.48pfirst 0.08 0.01 0.07 0.00 0.04 0.01programme Academic schools 0.45 0.25programme Applied/Voc 0.04 0.04programme Baccalaureat 0.00 0.00programme Compulsory sec. ed. 1.00 0.00programme Established 0.11 0.10programme Junior certificate(2a) 0.62 0.24programme Lower sec schools 0.01 0.01programme Technical schools 0.30 0.21programme Transition year (2s) 0.24 0.18programme Vocational schools 0.21 0.17programme Vocational training 0.03 0.03schcontext 1 0.01 0.01schcontext 1. Village 0.04 0.04 0.22 0.17schcontext 2 0.16 0.13schcontext 2. Small Town 0.26 0.19 0.36 0.23schcontext 3 0.51 0.25schcontext 3. Town 0.34 0.23 0.17 0.14schcontext 4 0.23 0.18schcontext 4. City 0.26 0.19 0.14 0.12schcontext 5 0.08 0.08schcontext 5. Large City 0.09 0.08 0.12 0.10schcontext 99 0.01 0.01schtype 1 0.93 0.07schtype 1. Public 0.69 0.21 0.43 0.25schtype 2 0.02 0.02schtype 2. Private government-dependent 0.26 0.19 0.50 0.25schtype 3 0.03 0.03schtype 3. Private independent 0.05 0.05 0.07 0.07schtype 99 0.02 0.02

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References

Alba, R. D., & Nee, V. (2003). Remaking the american mainstream: Assimilation andcontemporary immigration.3

Azzolini, D., & Barone, C. (2011). The ethnic gap in italian upper secondary educationand its interplay with social class. (working paper presented at Migrations, socialstratification and educational inequality: Italy in comparative perspective, January27th, Bologna, Italy) 9

Barban, N., & White, M. (2009). The transition to secondary school of the second gen-eration of immigrants in italy. XXVI IUSSP International Population Conference.9

Barone, C., & Schizzerotto, A. (2008). The application of the ISCED-97 to Italy, ed-itor=Silke L. Schneider, booktitle=The International Standard Classification ofEducation (ISCED-97). An Evaluation of Content and Criterion Validity for 15European Countries. address=Mannheim,.8

Barrett, A. (1999). Irish migration: Characteristics, causes and consequences. (IZADiscussion Paper No. 97, IZA, Bonn) 5, 6

Bauer, T., Lofstrom, M., & Zimmermann, K. (2000). Immigration policy, assimilation ofimmigrants and natives’ sentiments towards immigrants: Evidence from 12 oecd-countries. IZA Discussion Paper Series . 5

Bover, O., & Vellila, P. (1999). Migration in spain: Historical background and currenttrends. IZA Discussion Paper Series . 5

Breen, R., & Jonsson, J. O. (2005). Inequality of opportunity in comparative perspective:Recent research on educational attainment and social mobility. Annual Review ofSociology , 31 . 4

Brinbaum, Y., & Cebolla-Boado, H. (2007). The school careers of ethnic minority youthin france. success or disillusion? Ethnicities , 7 . 13

Buchmann, C., & Parrado, E. A. (2006). Educational achievement of immigrant-originand native students: A comparative analysis informed by institutional theory. In-ternational Perspectives on Education and Society , 7 . 10

Byrne, D., & Smyth, E. (2010). Behind the scenes? a study of parental involvement inpost-primary education. Dublin: The Liffey Press in association with The Economicand Social Research Institute. 11

Calero, C. A., J., & Waisgrais, S. (2009). Determinantes del rendimiento educativo delalumnado de origen nacional e inmigrante en pisa-2006. Cuadernos EconomicosICe, 78 . 10

Cebolla-Boado, H., & Medina, L. G. (2010). The impact of immigrant concentrationin Spanish schools: School, class, and composition effects. European SociologicalReview . 4, 8, 10

Chantala, K., Blanchette, D., & Suchindran, C. (2006). Software to compute samplingweights for multilevel analysis. Carolina Population Center . 15

Checchi, D., & Peragine, V. (2005). Regional disparities and inequality of opportunity:The case of italy. IZA Discussion Paper Series , 1874 . 30

Chiswick, B. R., & DebBurman, N. (2004). Educational attainment: Analysis by immi-grant generation. Economics of Education Review , 23 , 361-379. 3

Colombo, A., & Sciortino, G. (2004). Italian immigration: The origins, nature and

34

Achievement Gaps

evolution of italy’s migratory systems.5

Crul, M., & Vermeulen, H. (2003). The second generation in europe.4

Devine, D., Kenny, M., & Macneeka, E. (2004). Experiencing racism in the primaryschool: Children’s perspectives. In J. Deegan, D. Devine, & A. Lodge (Eds.),Primary voices - equality, diversity and childhood in irish primary schools. Dublin:Institute of Public Administration. 10

Duncan, G. J., & Magnuson, K. A. (2005). Can family socioeconomic resources accountfor racial and ethnic test score gaps? The future of Children, 15 . 4

Estadıstica, I. N. de. (2008). Padron municipal. Available from http://www.ine.es/ 6Fekjær, S. N., & Birkelund, G. E. (2007). Does the ethnic composition of upper secondary

schools influence educational achievement and attainment? a multilevel analysis ofthe norwegian case. European Sociological Review , 23 . 4

Feliciano, C. (2005). Does selective migration matter? explaining ethnic disparitiesin educational attainment among immigrants’ children. International MigrationReview . 4

Ganzeboom, H., & Treiman, D. (1996). Internationally comparable measures of occupa-tional status for the 1988 international standard classification of occupations. SocialScience Research, 25 . 14

Gutierrez-Domenech, M., & Adsera, A. (2009). What matters for education? evidencefor catalonia. La Caixa Working Paper Series , 1 . 10

Haller, W., Portes, A., & Lynch, S. (2011). Dreams fulfilled, dreams shattered: De-terminants of segmented assimilation in the second generation. Social Forces , 89 .3

Hanushek, E. A., Kain, J. F., & Rivkin, S. G. (2009). New evidence about brown v. boardof education: The complex effects of school racial composition on achievement,.Journal of Labor Economics , 27 . 4

Heath, A., & Brinbaum, Y. (2007). Explaining ethnic inequalities in educational attain-ment. Ethnicities , 7 . 3

Heath, A., Rothon, C., & Kilpi, E. (2008). The second generation in western europe: Ed-ucation, unemployment and occupational attainment. Annual Review of Sociology ,34 . 3, 4

Huddleston, T., Niessen, J., Ni Chaoimh, E., & White, E. (2011). Migrant integrationpolicy index iii. Brussels: MPG and British Council. 7

Jackson, M., Kiernan, K., & McLanahan, S. (2010). Nativity differences in child de-velopment across diverse populations, settings and outcomes: Do socioeconomicresources narrow or widen the gap?4

Kao, G., & Tienda, M. (1995). Optimism and achievement: The educational performanceof immigrant youth. Social Science Quarterly , 76 (1), 1-19. 3

Keogh, & Whyte. (2003). Getting on, the experiences of ethnic minority second-levelstudents. Dublin: Trinity College. 10

Kreienbrink, A. (2008). Spain. FOCUS Migration Migration Country profile(8). 5Lahaie, C. (2008). School readiness of children of immigrants: Does parental involvement

play a role? Social Science Quarterly , 89 . 4Lozano, F. A., & Steinberger, M. D. (2010). Empirical methods in the economics of

international immigration. IZA Discussion Paper Series, 5328 . 4

35

Achievement Gaps

Mantovani, D. (2008). Gli studenti stranieri sui banchi di scuola in emilia-romagna. InLe competenze degli studenti in emilia-romagna. i risultati di pisa 2006. Bologna:Il Mulino. 9, 22

Marks, G. N. (2005). Accounting for immigrant non-immigrant differences in readingand mathematics in twenty countries. Ethnic and Racial Studies , 28 . 3

McLanahan, S., & Percheski, C. (2008). Family structure and the reproduction ofinequalities. Annual Review of Sociology , 34 . 4

Miur. (2009). Alunni con cittadinanza non italiana scuole statali e non statali. AnnoScolastico 2007/2008 . 8, 9, 10

Miur. (2010). Focus in breve sulla scuola. la presenza degli alunni stranieri nelle scuolestatali.9

Myers, D., Gao, X., & Emeka, A. (2009). The gradient of immigrant age-at-arrival effectson socioeconomic outcomes in the u.s.,. International Migration Review , 43 . 3

OECD. (2006). Where immigrant students succeeds. a comparative review of performanceand engagement in pisa 2003. Paris: OECD Publishing. 3, 5

OECD. (2009). PISA data analysis manual : SAS (2nd ed.). 12, 15OECD. (2010a). Pisa 2009 results: Learning trends: Changes in student performance

since 2000. OECD Publishing. 18, 21, 30OECD. (2010b). Pisa 2009 results: Overcoming social background. equity in learning

opportunities and outcomes (Vol. II). OECD Publishing. 10Ortiz, L. (2008). Evaluation of the isced-97 for the spanish system of education.

In S. L. Schneider (Ed.), The international standard classification of education(ISCED-97). an evaluation of content and criterion validity for 15 European coun-tries. 8

Pfefferman, D., Skinner, C., Homes, D., Goldstein, H., & Rasbash, J. (1998). Weightingfor unequal selection models in multilevel models. Journal of the Royal StatisticalSociety, Series B , 60 , 23-40. 15

Population statistics. (2010). Available from http://www.cso.ie/ 6Portes, A., Aparicio, R., Haller, W., & Vickstrom, E. (2010). Moving ahead in madrid:

Aspirations and expectations in the spanish second generation. International Mi-gration Review , 44 . 6, 7

Portes, A., Fernandez-Kelly, P., & Haller, W. (2009). The adaptation of the immigrantsecond generation in america: A theoretical overview and recent evidence. Journalof Ethnic and Migration Studies , 35 . 3

Portes, A., & Hao, L. (2004). The schooling of children of immigrants: Contextual effectson the educational attainment of the second generation. Proceedings of the NationalAcademy of Sciences , 101 . 4

Portes, A., & Zhou, M. (1993). The new second generation: Segmented assimilation andits variants. Annals of the American Academy of Political and Social Science, 530 .3

Quinn, E. (2010). Ireland. FOCUS Migration Migration Country profile(19). 6Rumbaut, R. G. (2004). Ages, life stages, and generational cohorts: Decomposing the

immigant first and second generations in the united states. International MigrationReview , 38 . 3

Schnepf, S. V. (2004). How different are immigrants? a cross-country and cross-surveyanalysis of educational achievement. IZA working paper, 1398 . 3

Schnepf, S. V. (2008). Inequality of learning amongst immigrant children in industrialised

36

Achievement Gaps

countries.3, 4, 18

Smyth, E. (2008). The irish educational system: a note on classification. In S. L. Schnei-der (Ed.), The international standard classification of education (ISCED-97), anevaluation of content and criterion validity for 15 European countries. Mannheim.8

Smyth, E., Darmody, M., McGinnity, F., & D., B. (2009). Adapting to diversity: Irishschools and newcomer students. ESRI Research Series . 10

Vekic, K. (2003). Unsettled hope: Unaccompanied minors in ireland, from understandingto response.10

Werfhorst, H. G. Van de, & Mijs, J. J. B. (2010). Achievement inequality and theinstitutional structure of educational systems: A comparative perspective. AnnualReview of Sociology , 36 . 4

Zinovyeva, N., Felgueroso, F., & Vazquez, P. (2008). Immigration and students’ achieve-ment in spain. Serie Capital Humano y Empleo - FEDEA, 37 . 10, 18, 21, 22,30

37

Educational Achievement Gaps Between Immigrantand Native Students in Three “New Immigration

Countries”Appendix of Model Estimates

Davide Azzolinia, Philipp Schnellb,c, and John Palmerd

aUniversity of TrentobUniversity of Amsterdam

cAustrian Academy of SciencesdPrinceton University

Work in Progress: June 7, 2011

The following tables present estimates, for each country, year, and subject, fromthe multilevel linear models discussed in the main text.

1

Table 1 – Weighted MLM for 2003 IRE math

variable M1 M2 M3 M4interceptBeta 214.192* 214.203* 110.625 731.557*

(98.677) (98.507) (87.754) (114.657)lns1consBeta 4.348* 4.348* 4.292* 4.262*

(0.015) (0.015) (0.015) (0.014)sch1consBeta 28.715* 28.739* 3.727 -11.311*

(2.956) (2.961) (2.502) (2.136)fmb2Beta 8.771 10.969 0.952 3.020

(10.909) (11.169) (10.649) (10.651)fmb3Beta -24.032 -23.253 -23.375 -29.994*

(15.650) (15.722) (16.002) (14.510)fmb4Beta -1.409 -1.374 -3.416 -4.588

(5.013) (5.045) (4.395) (4.418)ageBeta 17.321* 17.340* 19.764* -25.852*

(6.247) (6.240) (5.567) (7.148)male2Beta 16.743* 16.752* 17.272* 19.147*

(4.346) (4.343) (3.878) (3.762)schcontext2Beta 15.194* 14.914* 7.953 -2.627

(6.298) (6.267) (4.933) (5.736)schcontext3Beta 6.114 5.786 -0.975 -12.094

(9.047) (9.025) (6.214) (8.276)schcontext4Beta 30.337* 30.159* 16.111 -10.075

(11.566) (11.554) (8.475) (8.425)schcontext5Beta 12.079 11.886 4.474 -19.652*

(9.069) (9.071) (6.080) (6.687)lang2Beta -7.852 -4.906 -6.034

(12.665) (12.330) (12.439)lang3Beta -19.086 -18.137 -20.876

(32.000) (32.456) (28.920)hisced2Beta 6.247 3.107

(4.488) (4.152)hisced3Beta 8.082 5.900

(5.316) (4.964)hisced4Beta 9.131 7.456

(6.335) (5.991)hiseiBeta 0.946* 0.729*

(0.119) (0.112)homeposBeta 20.082* 18.474*

(2.041) (2.006)famstruc2Beta 20.813* 17.853*

(3.950) (3.809)schtype2Beta 6.883

(4.227)programme2Beta 43.059*

(4.684)programme3Beta 46.541*

(4.409)programme4Beta -24.521*

(11.272)pfirstBeta 15.815

(54.855)meanhiseiBeta 2.126*

(0.325)SchV1 718.789 719.759 185.741 53.161N1 3285.000 3285.000 3285.000 3285.000Deviance 141789.219 141786.062 140128.141 139207.875

2

Table 2 – Weighted MLM for 2006 IRE math

variable M1 M2 M3 M4interceptBeta 90.626 93.511 30.150 518.822*

(71.797) (71.530) (71.201) (86.389)lns1consBeta 4.285* 4.283* 4.244* 4.223*

(0.012) (0.012) (0.011) (0.012)sch1consBeta 29.162* 29.170* 21.319* 10.018*

(2.306) (2.313) (1.860) (1.596)fmb2Beta -7.340 1.292 -6.840 -6.988

(8.225) (8.198) (7.966) (7.668)fmb3Beta -19.372 -17.526 -16.375 -19.566*

(10.645) (10.501) (9.424) (8.773)fmb4Beta 5.360 5.343 4.042 3.356

(3.653) (3.649) (3.488) (3.306)ageBeta 25.811* 25.647* 26.618* -10.382

(4.570) (4.554) (4.485) (5.442)male2Beta 11.510* 11.748* 11.383* 14.405*

(3.405) (3.389) (2.998) (2.884)schcontext2Beta 10.728 10.496 5.366 -2.416

(5.765) (5.727) (5.024) (5.234)schcontext3Beta -1.088 -0.741 -9.199 -25.839*

(6.992) (6.957) (6.048) (5.846)schcontext4Beta 10.678 10.571 -2.268 -21.781*

(11.545) (11.661) (9.300) (7.418)schcontext5Beta 14.419 15.254 3.376 -22.319*

(9.815) (9.854) (7.176) (6.131)lang2Beta -23.244* -17.456 -20.443*

(10.588) (9.783) (9.165)lang3Beta -35.728* -39.903* -40.208*

(17.340) (16.867) (16.833)hisced2Beta -41.683* -38.320*

(11.748) (11.342)hisced3Beta 12.503* 11.804*

(4.401) (4.457)hisced4Beta 16.883* 15.867*

(4.736) (4.783)hisced5Beta 23.886* 21.734*

(5.328) (5.332)hiseiBeta 0.808* 0.676*

(0.096) (0.090)homeposBeta 12.259* 11.091*

(1.687) (1.636)schtype2Beta 11.638*

(4.450)programme2Beta 36.651*

(3.491)programme3Beta 6.997

(7.367)programme4Beta 37.728*

(4.380)pfirstBeta 8.446

(37.677)meanhiseiBeta 1.818*

(0.272)SchV1 801.906 797.023 373.134 204.614N1 4253.000 4253.000 4253.000 4253.000Deviance 173317.922 173271.375 171833.641 171051.203

3

Table 3 – Weighted MLM for 2009 IRE math

variable M1 M2 M3 M4interceptBeta 230.099* 229.746* 142.386* 555.050*

(76.603) (76.003) (71.665) (85.083)lns1consBeta 4.292* 4.291* 4.243* 4.230*

(0.019) (0.019) (0.019) (0.019)sch1consBeta 32.063* 31.804* 23.807* 21.794*

(2.824) (2.837) (2.367) (2.365)fmb2Beta -20.887* -10.353 -14.585 -13.692

(6.717) (7.496) (7.718) (7.802)fmb3Beta -5.641 -4.901 -7.523 -9.223

(11.841) (11.839) (13.168) (13.391)fmb4Beta 7.797* 7.864* 5.389 5.049

(3.802) (3.819) (3.490) (3.445)ageBeta 15.334* 15.373* 16.665* -15.534*

(4.829) (4.791) (4.502) (5.228)male2Beta 18.905* 19.165* 16.777* 19.270*

(4.127) (4.044) (3.719) (3.501)schcontext2Beta 14.383 14.540 14.296 12.192

(9.373) (9.314) (8.344) (8.054)schcontext3Beta 16.858 16.451 10.513 -0.066

(10.944) (10.908) (9.267) (9.673)schcontext4Beta 29.226* 29.506* 18.179* -0.044

(10.929) (10.885) (9.003) (9.287)schcontext5Beta 8.951 10.010 0.302 -17.237

(18.498) (18.343) (12.675) (10.277)lang2Beta -19.071* -11.345 -10.971

(9.293) (8.741) (9.050)lang3Beta -26.897 -34.612* -31.121*

(15.615) (16.511) (15.759)hisced2Beta -7.167 -1.803

(10.567) (10.363)hisced3Beta 11.569* 12.145*

(4.559) (4.655)hisced4Beta 25.487* 27.328*

(5.483) (5.449)hisced5Beta 12.876* 14.599*

(5.551) (5.526)hiseiBeta 1.006* 0.902*

(0.094) (0.095)homeposBeta 16.079* 14.671*

(2.374) (2.400)famstruc2Beta 8.131 7.480

(4.198) (4.038)schtype2Beta 4.931

(5.049)schtype3Beta -1.992

(11.564)programme2Beta 29.669*

(4.690)programme3Beta 14.703

(10.139)programme4Beta 32.478*

(5.486)pfirstBeta 12.845

(43.510)meanhiseiBeta 1.777*

(0.491)SchV1 915.529 897.193 469.629 401.185N1 3017.000 3017.000 3017.000 3017.000Deviance 121127.367 121099.727 119933.742 119597.992

4

Table 4 – Weighted MLM for 2003 IRE read

variable M1 M2 M3 M4interceptBeta 125.131 126.270 35.808 574.285*

(87.197) (86.843) (78.003) (105.511)lns1consBeta 4.304* 4.302* 4.243* 4.216*

(0.016) (0.016) (0.016) (0.016)sch1consBeta 32.512* 32.566* 22.060* 14.786*

(3.155) (3.158) (2.719) (2.195)fmb2Beta -1.504 7.950 -1.792 -0.088

(11.281) (11.292) (11.184) (11.057)fmb3Beta -30.844* -30.463* -30.481* -36.719*

(14.274) (14.375) (15.391) (14.779)fmb4Beta 1.585 1.387 -0.620 -1.755

(4.625) (4.642) (4.079) (4.128)ageBeta 25.000* 25.015* 26.902* -14.590*

(5.541) (5.521) (4.911) (6.643)male2Beta -27.535* -27.508* -26.280* -24.439*

(4.247) (4.233) (3.781) (3.610)schcontext2Beta 18.577* 17.354* 10.063 -2.154

(7.095) (7.131) (5.753) (6.331)schcontext3Beta 8.749 7.385 0.393 -12.950

(10.461) (10.433) (7.162) (7.476)schcontext4Beta 37.344* 36.613* 22.012* -7.033

(12.846) (12.808) (10.004) (9.446)schcontext5Beta 19.186* 18.264 10.024 -16.221*

(9.729) (9.789) (6.997) (7.307)lang2Beta -33.911* -31.038* -31.817*

(13.278) (12.701) (13.001)lang3Beta -23.418 -19.439 -21.410

(27.278) (29.595) (22.023)hisced2Beta 5.659 3.088

(4.161) (3.890)hisced3Beta 0.251 -1.353

(5.183) (4.911)hisced4Beta 1.467 0.333

(5.585) (5.245)hiseiBeta 1.010* 0.823*

(0.114) (0.109)homeposBeta 20.606* 19.250*

(1.994) (1.994)famstruc2Beta 15.242* 12.666*

(3.945) (3.767)schtype2Beta 8.285

(4.806)programme2Beta 37.891*

(4.389)programme3Beta 43.667*

(4.190)programme4Beta -25.100*

(10.735)pfirstBeta 22.480

(58.706)meanhiseiBeta 2.491*

(0.348)SchV1 984.710 986.252 379.915 127.413N1 3285.000 3285.000 3285.000 3285.000Deviance 140835.812 140794.250 139107.000 138217.141

5

Table 5 – Weighted MLM for 2006 IRE read

variable M1 M2 M3 M4interceptBeta 132.396 136.901 74.828 597.975*

(76.137) (75.354) (74.829) (87.253)lns1consBeta 4.367* 4.363* 4.329* 4.311*

(0.012) (0.012) (0.012) (0.012)sch1consBeta 34.632* 34.953* 27.932* 24.077*

(2.582) (2.571) (2.225) (2.112)fmb2Beta 1.409 14.432 6.069 6.299

(8.408) (8.514) (8.193) (8.106)fmb3Beta -23.622* -20.691 -19.555 -22.091*

(11.775) (11.476) (10.994) (10.292)fmb4Beta 6.148 6.115 4.714 4.038

(4.124) (4.120) (3.949) (3.830)ageBeta 25.150* 24.893* 25.893* -14.268*

(4.802) (4.756) (4.680) (5.481)male2Beta -30.400* -30.027* -31.024* -28.417*

(3.797) (3.763) (3.355) (3.279)schcontext2Beta 13.583 13.221 8.257 0.072

(7.715) (7.684) (6.937) (6.467)schcontext3Beta 11.105 11.616 3.104 -13.360

(8.546) (8.538) (7.526) (7.502)schcontext4Beta 16.369 16.171 3.106 -16.052

(12.021) (12.065) (10.894) (10.294)schcontext5Beta 19.767 21.016* 9.043 -17.884*

(10.527) (10.626) (8.779) (8.390)lang2Beta -34.733* -28.135* -30.661*

(10.964) (10.173) (10.035)lang3Beta -59.570* -63.064* -64.215*

(14.554) (14.250) (15.341)hisced2Beta -40.222* -36.519*

(13.270) (12.783)hisced3Beta 10.823* 10.573*

(5.125) (5.172)hisced4Beta 16.481* 16.165*

(5.363) (5.447)hisced5Beta 21.681* 20.386*

(5.779) (5.796)hiseiBeta 0.806* 0.680*

(0.106) (0.103)homeposBeta 12.693* 11.746*

(1.873) (1.830)schtype2Beta 9.324

(5.143)programme2Beta 35.686*

(3.805)programme3Beta 25.976*

(8.176)programme4Beta 40.602*

(4.773)pfirstBeta -74.547

(54.420)meanhiseiBeta 2.194*

(0.411)SchV1 1202.531 1230.934 718.241 465.674N1 4253.000 4253.000 4253.000 4253.000Deviance 175869.891 175774.375 174570.000 173919.016

6

Table 6 – Weighted MLM for 2009 IRE read

variable M1 M2 M3 M4interceptBeta 233.666* 233.410* 132.123 477.502*

(89.025) (88.233) (83.361) (101.463)lns1consBeta 4.367* 4.363* 4.321* 4.310*

(0.018) (0.018) (0.018) (0.018)sch1consBeta 36.399* 36.416* 28.459* 24.618*

(3.213) (3.229) (3.079) (2.805)fmb2Beta -29.048* -9.577 -16.712* -16.922*

(7.492) (8.109) (8.154) (8.151)fmb3Beta -7.183 -5.905 -8.379 -9.878

(12.340) (12.319) (13.249) (13.194)fmb4Beta 10.412* 10.529* 6.893 6.740

(4.307) (4.354) (3.925) (3.898)ageBeta 17.151* 17.195* 18.378* -10.996

(5.629) (5.571) (5.207) (6.171)male2Beta -27.636* -27.121* -30.524* -28.491*

(4.393) (4.279) (3.914) (3.544)schcontext2Beta 7.922 8.217 7.778 3.943

(11.307) (11.296) (10.450) (9.832)schcontext3Beta 17.226 16.456 10.257 -2.546

(13.414) (13.411) (11.978) (11.486)schcontext4Beta 38.557* 39.076* 26.775* 2.667

(13.242) (13.262) (11.500) (11.147)schcontext5Beta 26.881 28.823 17.779 -5.230

(21.409) (21.486) (16.042) (12.511)lang2Beta -35.036* -27.491* -26.683*

(10.395) (9.832) (10.015)lang3Beta -51.871* -58.586* -54.581*

(19.904) (20.495) (19.827)hisced2Beta 1.216 5.152

(11.017) (10.800)hisced3Beta 25.414* 25.340*

(5.145) (5.272)hisced4Beta 35.819* 36.876*

(6.083) (6.009)hisced5Beta 31.610* 32.436*

(6.283) (6.245)hiseiBeta 1.073* 0.958*

(0.103) (0.105)homeposBeta 9.877* 8.460*

(2.427) (2.434)famstruc2Beta 9.194* 8.670

(4.594) (4.450)schtype2Beta 7.270

(5.506)schtype3Beta -2.532

(13.637)programme2Beta 31.282*

(5.336)programme3Beta 5.270

(11.103)programme4Beta 24.775*

(5.715)pfirstBeta 84.516

(51.081)meanhiseiBeta 2.215*

(0.561)SchV1 1319.039 1329.013 751.240 531.206N1 3017.000 3017.000 3017.000 3017.000Deviance 122741.992 122660.719 121627.602 121316.398

7

Table 7 – Weighted MLM for 2003 ESP math

variable M1 M2 M3 M4interceptBeta 214.294* 214.250* 204.6003* 138.3023*

(52.880) (52.744) (48.2931) (49.9891)lns1consBeta 4.331* 4.331* 4.2898* 4.2895*

(0.009) (0.009) (0.0088) (0.0088)sch1consBeta 37.140* 37.106* 28.4589* 23.8145*

(1.813) (1.815) (1.6364) (1.4767)fmb2Beta -53.851* -56.463* -40.9050* -39.0705*

(7.710) (8.319) (7.6051) (7.8118)fmb3Beta -19.599 -21.216 -14.0008 -14.7775

(14.390) (14.774) (13.4932) (13.4508)fmb4Beta -8.298 -8.930 -8.7053 -8.8138*

(4.658) (4.651) (4.4874) (4.4875)ageBeta 16.653* 16.659* 15.2640* 15.2266*

(3.249) (3.241) (2.9954) (3.0162)male2Beta 13.064* 13.021* 13.1934* 13.0685*

(1.852) (1.855) (1.8234) (1.8128)schcontext2Beta 5.208 5.079 3.9716 -1.5560

(8.007) (8.041) (6.0179) (5.5847)schcontext3Beta 8.501 8.475 4.2248 -6.3556

(8.120) (8.150) (6.2368) (6.1909)schcontext4Beta 19.288* 19.223* 11.5006* -4.3726

(7.676) (7.714) (5.7939) (5.7680)schcontext5Beta 37.569* 37.614* 24.8683* 0.9589

(12.782) (12.800) (9.4443) (8.1165)lang2Beta 11.017 8.4095 8.3701

(11.383) (10.0878) (10.0362)lang3Beta -15.302 -2.4630 -4.0102

(17.993) (15.5244) (15.4768)hisced2Beta 22.7338* 22.3580*

(3.9790) (3.9685)hisced3Beta 18.8889* 18.0443*

(5.9129) (5.9288)hisced4Beta 10.4524* 9.4984*

(2.9787) (2.9496)hisced5Beta 7.7367* 6.8451*

(3.0746) (3.0765)hisced6Beta 24.2162* 21.9051*

(3.1127) (3.1066)hiseiBeta 0.1927* 0.1388*

(0.0545) (0.0540)homeposBeta 23.5581* 22.8551*

(1.2808) (1.2703)famstruc2Beta 7.8171* 8.2021*

(2.2370) (2.2397)schtype2Beta 6.6977

(4.0023)pfirstBeta -58.6935

(40.9039)meanhiseiBeta 1.7957*

(0.2246)SchV1 1151.070 1148.196 635.3011 412.3784N1 9864.000 9864.000 9864.0000 9864.0000Deviance 242399.531 242392.312 240374.7969 240152.2031

8

Table 8 – Weighted MLM for 2006 ESP math

variable M1 M2 M3 M4interceptBeta 146.7665* 145.9919* 117.030* 63.575

(36.9676) (37.0232) (35.075) (35.522)lns1consBeta 4.3577* 4.3564* 4.327* 4.326*

(0.0072) (0.0073) (0.007) (0.007)sch1consBeta 34.4416* 34.2646* 28.611* 26.238*

(1.2391) (1.2287) (1.225) (1.191)fmb2Beta -55.9467* -55.5838* -46.170* -44.048*

(4.0675) (4.2871) (4.010) (4.164)fmb3Beta -27.7084* -24.1902* -19.364* -18.569*

(8.6308) (8.2703) (7.540) (7.516)fmb4Beta -4.5446 -3.9158 -6.155 -6.025

(3.2677) (3.2545) (3.165) (3.140)ageBeta 21.7192* 21.7864* 20.415* 20.364*

(2.2849) (2.2892) (2.189) (2.185)male2Beta 12.7395* 12.9388* 11.928* 11.986*

(1.3856) (1.3786) (1.316) (1.314)schcontext2Beta -6.1194 -5.9322 -2.707 -0.259

(6.9046) (6.8767) (5.620) (5.028)schcontext3Beta -4.5133 -4.2936 -4.993 -8.349

(6.9344) (6.8981) (5.503) (4.879)schcontext4Beta 17.3059* 17.5285* 10.183 1.666

(6.9541) (6.9177) (5.532) (4.955)schcontext5Beta 5.8068 6.1820 1.065 -2.897

(10.4552) (10.4260) (7.895) (7.126)lang2Beta 1.0197 1.212 1.203

(6.5436) (6.155) (6.169)lang3Beta -28.2828* -29.502* -29.433*

(4.1009) (4.230) (4.205)hisced2Beta 26.905* 26.755*

(2.481) (2.487)hisced3Beta 34.145* 33.684*

(3.163) (3.169)hisced4Beta 33.705* 32.843*

(2.456) (2.462)hisced5Beta 30.364* 29.576*

(2.924) (2.924)hisced6Beta 43.845* 42.041*

(2.823) (2.826)hiseiBeta 0.435* 0.396*

(0.048) (0.048)homeposBeta 12.025* 11.609*

(0.950) (0.945)schtype2Beta 0.535

(2.875)pfirstBeta -44.166*

(19.593)meanhiseiBeta 1.365*

(0.174)SchV1 1005.3295 994.7455 657.659 527.513N1 19030.0000 19030.0000 19030.000 19030.000Deviance 474316.2812 474198.0938 471413.594 471207.156

9

Table 9 – Weighted MLM for 2009 ESP math

variable M1 M2 M3 M4interceptBeta 230.018* 231.973* 185.6817* 118.7916*

(33.192) (33.224) (31.4770) (32.2917)lns1consBeta 4.366* 4.366* 4.3323* 4.3321*

(0.006) (0.006) (0.0059) (0.0059)sch1consBeta 36.754* 36.669* 31.3918* 28.8728*

(1.310) (1.311) (1.2481) (1.1954)fmb2Beta -65.069* -64.190* -51.3772* -50.2815*

(2.741) (2.913) (2.8389) (2.9627)fmb3Beta -31.904* -30.801* -24.5454* -24.5203*

(6.112) (6.221) (6.0068) (5.9617)fmb4Beta -8.489* -8.328* -6.8068* -6.8423*

(2.463) (2.456) (2.3888) (2.3830)ageBeta 15.725* 15.620* 15.7738* 15.6079*

(2.049) (2.051) (1.9571) (1.9534)male2Beta 17.969* 17.987* 16.9093* 16.8750*

(1.181) (1.183) (1.1504) (1.1498)schcontext2Beta -2.158 -2.154 -2.2090 -1.6298

(7.167) (7.152) (5.9480) (5.0932)schcontext3Beta 2.508 2.474 -0.9645 -4.6043

(6.981) (6.966) (5.7611) (5.0079)schcontext4Beta 26.403* 26.300* 17.6744* 6.5695

(7.049) (7.035) (5.8544) (5.3173)schcontext5Beta 30.222* 30.019* 20.6730* 7.8745

(9.567) (9.557) (7.6098) (7.3314)lang2Beta -3.817 -3.1210 -3.1487

(4.215) (3.9964) (3.9952)lang3Beta -24.602* -22.8171* -21.6920*

(5.596) (5.1851) (5.1705)hisced2Beta 9.4288* 9.1922*

(2.7075) (2.6971)hisced3Beta -2.6270 -3.1009

(4.2373) (4.2244)hisced4Beta 22.5329* 21.8776*

(2.5589) (2.5462)hisced5Beta 13.6806* 13.0279*

(2.7777) (2.7643)hisced6Beta 33.8968* 32.1338*

(2.7849) (2.7608)hiseiBeta 0.3838* 0.3498*

(0.0388) (0.0389)homeposBeta 14.5925* 14.1968*

(0.8974) (0.9011)famstruc2Beta 12.3624* 12.5023*

(1.8551) (1.8472)schtype2Beta -4.8552

(2.7970)pfirstBeta -10.7926

(13.4458)meanhiseiBeta 1.7209*

(0.1854)SchV1 1255.159 1248.912 890.6317 729.2849N1 24216.000 24216.000 24216.0000 24216.0000Deviance 620457.562 620411.438 616437.0625 616164.2500

10

Table 10 – Weighted MLM for 2003 ESP read

variable M1 M2 M3 M4interceptBeta 313.546* 313.395* 299.742* 235.958*

(52.097) (52.018) (49.387) (51.417)lns1consBeta 4.385* 4.385* 4.351* 4.351*

(0.010) (0.010) (0.010) (0.010)sch1consBeta 37.928* 37.937* 30.342* 25.332*

(2.001) (2.004) (1.763) (1.560)fmb2Beta -52.991* -52.033* -36.541* -34.311*

(8.720) (9.626) (9.090) (9.253)fmb3Beta -27.261 -26.665 -19.157 -19.969

(16.246) (16.452) (14.773) (14.722)fmb4Beta -8.305 -8.078 -7.286 -7.243

(4.607) (4.671) (4.779) (4.727)ageBeta 11.114* 11.123* 9.893* 9.937*

(3.216) (3.212) (3.081) (3.087)male2Beta -35.155* -35.134* -34.944* -35.064*

(2.085) (2.086) (1.987) (1.966)schcontext2Beta 11.983 12.024 10.959 4.857

(8.068) (8.076) (6.264) (5.667)schcontext3Beta 16.467* 16.476* 12.636* 1.200

(8.014) (8.011) (6.298) (6.211)schcontext4Beta 28.496* 28.511* 21.445* 4.189

(7.743) (7.749) (5.982) (5.777)schcontext5Beta 45.690* 45.669* 34.008* 8.940

(12.409) (12.418) (9.552) (9.193)lang2Beta -4.074 -6.648 -6.567

(14.522) (14.031) (13.903)lang3Beta 1.633 13.612 12.241

(36.729) (32.746) (32.917)hisced2Beta 24.260* 23.568*

(4.149) (4.145)hisced3Beta 19.137* 18.060*

(6.314) (6.400)hisced4Beta 12.625* 11.527*

(3.211) (3.189)hisced5Beta 10.878* 9.752*

(3.533) (3.538)hisced6Beta 23.431* 21.004*

(3.459) (3.456)hiseiBeta 0.157* 0.103

(0.066) (0.065)homeposBeta 22.366* 21.597*

(1.316) (1.299)famstruc2Beta 10.392* 10.751*

(2.460) (2.455)schtype2Beta 11.806*

(4.116)pfirstBeta -72.650

(41.016)meanhiseiBeta 1.697*

(0.240)SchV1 1226.911 1227.733 749.077 487.270N1 9864.000 9864.000 9864.000 9864.000Deviance 244592.047 244591.109 242953.641 242732.859

11

Table 11 – Weighted MLM for 2006 ESP read

variable M1 M2 M3 M4interceptBeta 194.4603* 194.5815* 163.3341* 121.0220*

(34.2182) (34.1878) (32.3835) (33.0089)lns1consBeta 4.3040* 4.3005* 4.2767* 4.2767*

(0.0076) (0.0076) (0.0075) (0.0075)sch1consBeta 34.4868* 34.1904* 30.0267* 27.6772*

(1.2043) (1.2065) (1.1743) (1.1322)fmb2Beta -45.9505* -39.1740* -32.4165* -30.8209*

(3.7199) (3.8089) (3.7290) (3.7465)fmb3Beta -11.3735 -3.4133 -0.1534 0.4691

(9.5591) (9.2928) (8.8161) (8.8581)fmb4Beta 0.0348 1.4484 -0.4928 -0.2850

(3.0184) (2.9672) (2.8827) (2.8736)ageBeta 18.4092* 18.4307* 17.3109* 17.2403*

(2.1083) (2.1079) (2.0133) (2.0115)male2Beta -31.3411* -31.0208* -31.9305* -31.8885*

(1.2910) (1.2865) (1.2341) (1.2331)schcontext2Beta -1.1892 -0.7246 1.8648 4.4173

(7.1890) (7.1496) (6.3715) (5.9550)schcontext3Beta 2.0371 2.4692 1.8534 -1.2725

(7.4272) (7.3772) (6.5174) (6.0736)schcontext4Beta 19.7896* 19.9981* 13.7600* 5.4741

(7.3095) (7.2657) (6.4446) (6.1632)schcontext5Beta 16.1223 16.3680 11.9159 5.9568

(11.2178) (11.1799) (9.2940) (8.3146)lang2Beta -20.3546* -20.0226* -19.9860*

(6.4941) (5.9930) (5.9977)lang3Beta -39.3787* -39.9524* -39.7866*

(3.9636) (4.0591) (4.0420)hisced2Beta 28.8550* 28.6746*

(2.4136) (2.4140)hisced3Beta 36.1550* 35.6469*

(3.4086) (3.4167)hisced4Beta 34.3103* 33.5330*

(2.3527) (2.3549)hisced5Beta 34.4986* 33.7373*

(2.7173) (2.7184)hisced6Beta 40.9875* 39.4547*

(2.6936) (2.6939)hiseiBeta 0.4003* 0.3706*

(0.0464) (0.0464)homeposBeta 7.6674* 7.2866*

(0.9128) (0.9095)schtype2Beta 9.6520*

(3.1730)pfirstBeta -30.7161

(21.0606)meanhiseiBeta 1.0297*

(0.1874)SchV1 1040.2247 1022.1990 772.5527 636.2803N1 19030.0000 19030.0000 19030.0000 19030.0000Deviance 470058.9062 469766.8125 467567.4062 467380.1875

12

Table 12 – Weighted MLM for 2009 ESP read

variable M1 M2 M3 M4interceptBeta 203.7765* 206.2666* 169.1201* 102.3199*

(31.2295) (31.2121) (29.8779) (30.6590)lns1consBeta 4.3090* 4.3081* 4.2801* 4.2799*

(0.0062) (0.0062) (0.0062) (0.0062)sch1consBeta 34.1195* 33.9705* 28.8089* 26.0953*

(1.3471) (1.3435) (1.2205) (1.0798)fmb2Beta -55.5037* -51.6655* -40.9686* -40.2356*

(2.4382) (2.5830) (2.5561) (2.6556)fmb3Beta -26.2491* -22.7246* -17.5316* -17.5578*

(5.6385) (5.6551) (5.5500) (5.4959)fmb4Beta -4.0838 -3.4971 -2.4981 -2.5481

(2.2884) (2.2824) (2.2315) (2.2218)ageBeta 18.1638* 18.0346* 18.1150* 17.9210*

(1.9269) (1.9264) (1.8669) (1.8611)male2Beta -27.9484* -27.9430* -28.8943* -28.9525*

(1.1284) (1.1334) (1.1097) (1.1059)schcontext2Beta -0.7071 -0.6918 -0.7499 -0.0710

(6.7604) (6.7384) (5.4173) (4.1314)schcontext3Beta 9.6861 9.5961 6.1649 1.8728

(6.7016) (6.6802) (5.3628) (4.1908)schcontext4Beta 31.6480* 31.4009* 23.2088* 10.5663*

(6.6991) (6.6807) (5.3406) (4.3947)schcontext5Beta 44.2986* 43.7978* 35.0283* 19.7367*

(9.4859) (9.4833) (7.1786) (5.9707)lang2Beta -14.5687* -13.9944* -14.0896*

(3.9215) (3.8470) (3.8453)lang3Beta -26.1990* -24.8560* -23.6479*

(5.4421) (5.1590) (5.1381)hisced2Beta 5.2909* 5.0050

(2.6599) (2.6504)hisced3Beta -3.7093 -4.3107

(3.9178) (3.9075)hisced4Beta 20.2276* 19.4603*

(2.5110) (2.4995)hisced5Beta 16.9025* 16.1235*

(2.6723) (2.6610)hisced6Beta 30.6627* 28.6954*

(2.6841) (2.6672)hiseiBeta 0.3383* 0.3020*

(0.0395) (0.0394)homeposBeta 12.4039* 11.9833*

(0.8372) (0.8422)famstruc2Beta 7.5331* 7.6832*

(1.7398) (1.7364)schtype2Beta 1.2468

(2.3880)pfirstBeta 9.1758

(11.4287)meanhiseiBeta 1.6706*

(0.1669)SchV1 1087.2416 1076.1163 757.3630 603.1678N1 24216.0000 24216.0000 24216.0000 24216.0000Deviance 614296.0625 614191.4375 610770.9375 610453.6875

13

Table 13 – Weighted MLM for 2003 ITA math

variable M1 M2 M3 M4interceptBeta 388.9467* 387.0779* 356.1703* 147.9084*

(47.9060) (47.6125) (47.7123) (56.0534)lns1consBeta 4.1987* 4.1969* 4.1839* 4.1834*

(0.0082) (0.0082) (0.0084) (0.0084)sch1consBeta 63.4309* 62.8018* 57.9102* 39.0487*

(2.3539) (2.3429) (2.1569) (1.7365)fmb2Beta -24.9114* -26.7775* -19.1146 -18.7453

(7.6218) (10.1627) (9.8181) (9.8503)fmb3Beta -10.6062 -10.8161 -7.6173 -7.8897

(11.0478) (11.0608) (11.5777) (11.4757)fmb4Beta 2.7119 2.8674 3.2835 3.6300

(3.1948) (3.1538) (3.2013) (3.2123)ageBeta 5.6534* 5.9007* 6.9035* 6.6034*

(2.6575) (2.6375) (2.6759) (2.6574)male2Beta 24.8933* 25.2935* 24.7873* 26.2439*

(1.7890) (1.7693) (1.8184) (1.8150)schcontext2Beta 15.1776 14.4180 13.0847 -19.5833

(25.5121) (25.4069) (23.6800) (26.5478)schcontext3Beta 4.2920 3.1110 1.6359 -38.0267

(24.5145) (24.4063) (22.7145) (26.0630)schcontext4Beta 8.2445 7.0397 4.7036 -41.2631

(25.6891) (25.5542) (23.7630) (26.6168)schcontext5Beta -33.0567 -33.5702 -33.7098 -81.0543*

(29.8775) (29.6148) (27.4613) (28.1027)schcontext6Beta 33.4555 31.3649 24.8123 -36.8282

(24.0860) (23.9975) (22.3301) (26.1277)lang2Beta 4.3039 6.4611 6.8686

(9.3735) (8.8905) (8.7727)lang3Beta -18.9350* -16.1355* -15.7819*

(3.3470) (3.3470) (3.3273)hisced2Beta -0.3894 -0.6668

(5.1192) (5.1563)hisced3Beta 14.6557* 13.8818*

(5.7933) (5.8023)hisced4Beta 6.8392 6.2013

(5.0592) (5.0854)hisced5Beta -12.9422* -13.9941*

(5.5743) (5.5885)hisced6Beta -2.2987 -3.9122

(5.5730) (5.5697)hiseiBeta 0.1892* 0.1431*

(0.0607) (0.0605)homeposBeta 11.2645* 10.7780*

(0.9364) (0.9363)famstruc2Beta 5.5718* 5.4945*

(1.9363) (1.9241)schtype2Beta -37.4956*

(11.4686)schtype3Beta -7.0051

(5.8475)programme2Beta 113.8978*

(24.9997)programme3Beta 64.8070*

(24.4984)programme4Beta 122.1719*

(26.0076)pfirstBeta 109.5324*

(45.7517)meanhiseiBeta 3.1843*

(0.4768)SchV1 4115.9702 4034.7874 3406.5691 1416.6455N1 10993.0000 10993.0000 10993.0000 10993.0000Deviance 497723.4375 497545.3125 496205.0625 495045.7500

14

Table 14 – Weighted MLM for 2006 ITA math

variable M1 M2 M3 M4interceptBeta 311.5041* 318.6704* 295.8882* 165.8931*

(34.9090) (34.5094) (35.4131) (39.3076)lns1consBeta 4.1839* 4.1771* 4.1728* 4.1728*

(0.0069) (0.0068) (0.0067) (0.0067)sch1consBeta 66.1941* 64.7648* 62.5028* 44.1245*

(2.0966) (2.1000) (2.1321) (1.8873)fmb2Beta -40.9493* -42.1019* -38.3563* -37.9957*

(4.0912) (5.5815) (5.6516) (5.6520)fmb3Beta -26.8650* -25.6328* -23.1299* -23.2602*

(7.0692) (7.1125) (7.2044) (7.1730)fmb4Beta -1.7699 -1.9592 -1.9592 -1.7562

(2.4460) (2.4509) (2.4511) (2.4444)ageBeta 6.0470* 5.9325* 5.9062* 5.6098*

(2.0666) (2.0366) (2.0574) (2.0536)male2Beta 24.2529* 24.8966* 24.0423* 24.8579*

(1.4385) (1.4438) (1.4407) (1.4325)schcontext2Beta 39.3165* 37.6702* 35.2685* -9.6905

(13.4965) (13.6882) (14.4388) (10.9027)schcontext3Beta 59.1467* 56.4398* 52.8470* -11.5255

(12.8009) (13.0235) (13.8501) (10.5806)schcontext4Beta 72.8391* 69.9289* 65.2540* -15.4978

(13.5278) (13.6937) (14.4606) (11.3541)schcontext5Beta 72.1362* 69.8440* 65.3006* -23.5577

(21.9915) (21.7903) (21.9163) (16.4720)schcontext6Beta 23.2000 21.9441 19.6224 -21.5353

(21.8340) (21.6679) (20.9895) (15.4617)lang2Beta 0.3668 2.2863 1.7248

(5.9965) (5.9727) (5.9791)lang3Beta -26.6493* -25.8282* -25.1446*

(1.8136) (1.8158) (1.8213)hisced2Beta 15.6781* 15.2543*

(4.4689) (4.4816)hisced3Beta 20.1335* 19.4972*

(4.7905) (4.8007)hisced4Beta 17.6965* 16.7750*

(4.4590) (4.4687)hisced5Beta -1.4138 -2.2369

(4.9032) (4.9080)hisced6Beta 13.6307* 12.3349*

(4.7582) (4.7756)hiseiBeta 0.2465* 0.2077*

(0.0509) (0.0507)homeposBeta 3.1751* 2.6952*

(0.7931) (0.7888)schtype2Beta -24.6622*

(7.2753)schtype3Beta -42.6638*

(14.3582)programme2Beta 6.9039

(5.9835)programme3Beta -41.4065*

(7.3210)programme4Beta -89.9696*

(16.9987)programme5Beta -14.2976

(9.5304)pfirstBeta 142.3010*

(28.2078)meanhiseiBeta 4.4016*

(0.4263)SchV1 4560.2837 4348.9146 4038.9170 1876.2185N1 20800.0000 20800.0000 20800.0000 20800.0000Deviance 903678.5000 902511.5000 901657.3750 899822.0000

15

Table 15 – Weighted MLM for 2009 ITA math

variable M1 M2 M3 M4interceptBeta 339.1272* 342.7721* 321.4677* 258.1559*

(26.3349) (26.1975) (25.9300) (30.4195)lns1consBeta 4.1471* 4.1414* 4.1339* 4.1328*

(0.0054) (0.0054) (0.0054) (0.0054)sch1consBeta 62.4047* 60.9988* 58.0382* 40.0662*

(1.9625) (1.9403) (1.8936) (1.3992)fmb2Beta -36.3161* -33.7972* -26.1499* -26.1618*

(2.5163) (3.3892) (3.5001) (3.5109)fmb3Beta -25.3714* -23.3734* -17.6060* -17.8068*

(4.5912) (4.6888) (4.6175) (4.6130)fmb4Beta -0.9747 -1.0597 -0.1224 -0.1062

(1.6595) (1.6568) (1.6344) (1.6367)ageBeta 6.6251* 6.6388* 6.5468* 6.3991*

(1.6020) (1.5960) (1.5653) (1.5553)male2Beta 26.8256* 27.3719* 26.2416* 27.3732*

(1.0751) (1.0714) (1.0578) (1.0702)schcontext2Beta 23.8481* 22.9772* 21.0963* -20.3552*

(9.3541) (9.1393) (8.4830) (7.0149)schcontext3Beta 43.9705* 42.3928* 38.7927* -20.2299*

(8.5145) (8.3032) (7.6353) (6.8723)schcontext4Beta 44.6011* 42.8692* 38.0380* -31.7978*

(9.5584) (9.3242) (8.5984) (7.2202)schcontext5Beta 16.4289 14.5606 10.7459 -54.3033*

(20.5412) (20.1438) (19.1156) (12.4235)schcontext6Beta 25.1676* 22.9944 20.6730 0.9136

(12.7026) (12.4197) (11.4996) (8.6931)lang2Beta -3.9646 -2.2337 -1.7240

(3.6722) (3.7070) (3.7052)lang3Beta -25.1015* -24.0119* -23.5137*

(1.5628) (1.5447) (1.5387)hisced2Beta 11.1756* 10.3624*

(4.3335) (4.3312)hisced3Beta 15.0242* 13.8727*

(4.4252) (4.4373)hisced4Beta 11.7727* 10.3504*

(4.2551) (4.2550)hisced5Beta -10.2350* -11.3857*

(4.6569) (4.6624)hisced6Beta 5.4042 3.6984

(4.4366) (4.4397)hiseiBeta 0.3635* 0.3214*

(0.0365) (0.0366)homeposBeta 5.8461* 5.3482*

(0.6491) (0.6485)famstruc2Beta 0.2798 0.1867

(1.4507) (1.4455)schtype2Beta -25.2358*

(6.4861)schtype3Beta -44.3059*

(6.6994)programme2Beta -16.6023*

(6.0777)programme3Beta -65.8196*

(6.4622)programme4Beta -127.4788*

(15.1933)programme5Beta -37.8083*

(7.3956)pfirstBeta 86.4445*

(20.5271)meanhiseiBeta 3.0202*

(0.3398)SchV1 3851.5981 3673.5388 3323.6631 1517.4233N1 29573.0000 29573.0000 29573.0000 29573.0000Deviance 1248186.1250 1246801.3750 1244814.5000 1241926.6250

16

Table 16 – Weighted MLM for 2003 ITA read

variable M1 M2 M3 M4interceptBeta 305.99* 305.68* 264.670* 92.638

(50.20) (50.00) (49.690) (58.645)lns1consBeta 4.23* 4.23* 4.224* 4.223*

(0.01) (0.01) (0.010) (0.010)sch1consBeta 61.10* 60.58* 54.919* 35.909*

(2.33) (2.32) (2.093) (1.706)fmb2Beta -53.58* -45.39* -39.521* -38.867*

(8.28) (11.20) (10.807) (10.963)fmb3Beta -16.28 -14.60 -13.005 -13.310

(14.94) (15.12) (15.410) (15.313)fmb4Beta 3.35 3.99 2.905 3.344

(3.21) (3.20) (3.148) (3.154)ageBeta 11.98* 12.10* 13.601* 13.216*

(3.01) (2.98) (2.960) (2.943)male2Beta -27.19* -26.84* -28.765* -27.368*

(1.97) (1.96) (1.985) (1.943)schcontext2Beta 23.94 23.37 26.130 -7.316

(19.43) (20.01) (17.122) (19.213)schcontext3Beta 18.31 17.40 19.279 -20.461

(18.55) (19.15) (16.235) (18.808)schcontext4Beta 26.82 25.89 26.106 -19.565

(19.70) (20.23) (17.294) (19.492)schcontext5Beta -4.39 -4.83 -3.264 -50.085*

(25.24) (25.54) (22.189) (21.477)schcontext6Beta 49.34* 47.66* 44.709* -21.037

(17.97) (18.62) (15.745) (18.806)lang2Beta -13.02 -11.580 -11.358

(14.04) (13.081) (13.115)lang3Beta -14.68* -12.954* -12.677*

(3.65) (3.623) (3.627)hisced2Beta -5.094 -5.280

(5.429) (5.443)hisced3Beta 3.333 2.504

(6.355) (6.331)hisced4Beta 1.315 0.684

(5.387) (5.395)hisced5Beta -9.224 -10.296

(5.767) (5.749)hisced6Beta 0.255 -1.544

(5.838) (5.824)hiseiBeta 0.333* 0.279*

(0.069) (0.068)homeposBeta 9.674* 9.092*

(1.056) (1.051)famstruc2Beta 0.675 0.599

(2.144) (2.122)schtype2Beta -27.667*

(9.691)schtype3Beta -18.976*

(5.189)programme2Beta 91.000*

(30.479)programme3Beta 43.960

(30.115)programme4Beta 105.173*

(31.296)pfirstBeta 77.942

(48.217)meanhiseiBeta 2.887*

(0.423)SchV1 3892.16 3825.79 3123.368 1192.378N1 10993.00 10993.00 10993.000 10993.000Deviance 500631.31 500524.00 499446.094 498202.125

17

Table 17 – Weighted MLM for 2006 ITA read

variable M1 M2 M3 M4interceptBeta 398.7610* 405.2023* 375.7484* 279.8343*

(34.1141) (34.1582) (34.9801) (39.5943)lns1consBeta 4.2653* 4.2619* 4.2572* 4.2571*

(0.0074) (0.0074) (0.0074) (0.0074)sch1consBeta 70.2866* 69.1095* 66.3307* 44.3219*

(2.1891) (2.1657) (2.1484) (1.5913)fmb2Beta -56.1485* -42.4827* -40.3133* -39.6604*

(4.5961) (5.9693) (5.9773) (5.9725)fmb3Beta 11.1329 16.5225 18.7308* 18.5099*

(9.1269) (9.0746) (9.1531) (9.0877)fmb4Beta -1.4631 -0.8042 -1.3272 -1.0598

(2.6025) (2.6059) (2.6000) (2.5948)ageBeta 1.2677 1.1776 1.1474 0.7074

(2.1496) (2.1519) (2.1648) (2.1444)male2Beta -23.9086* -23.3975* -24.3831* -23.7297*

(1.6567) (1.6491) (1.6379) (1.6369)schcontext2Beta 49.8714* 47.6649* 42.0236* 5.5252

(8.6243) (8.5449) (8.1393) (12.0101)schcontext3Beta 79.5438* 76.5990* 69.7130* 11.2619

(6.5125) (6.4623) (6.0476) (11.6132)schcontext4Beta 94.4137* 91.3524* 83.0178* 7.4539

(8.0934) (7.9647) (7.5912) (12.3315)schcontext5Beta 85.8145* 83.2047* 74.9602* -7.1752

(14.4494) (14.0575) (13.3698) (14.5166)schcontext6Beta 39.5203* 37.7726* 32.9527 -0.7854

(19.4835) (19.1267) (17.2612) (17.9203)lang2Beta -20.7421* -19.6953* -20.5366*

(6.3327) (6.2806) (6.2860)lang3Beta -20.2882* -19.5089* -18.9119*

(2.1349) (2.0923) (2.0949)hisced2Beta 19.8253* 19.2876*

(4.7767) (4.7708)hisced3Beta 23.7482* 22.9367*

(5.1698) (5.1554)hisced4Beta 28.2078* 26.9881*

(4.7560) (4.7358)hisced5Beta 7.2443 6.0740

(5.1729) (5.1483)hisced6Beta 22.9636* 21.2751*

(5.1061) (5.0883)hiseiBeta 0.3084* 0.2649*

(0.0535) (0.0533)homeposBeta 0.1095 -0.3782

(0.9066) (0.9076)schtype2Beta -20.5337*

(6.7993)schtype3Beta -31.5998*

(13.4306)programme2Beta -15.0267*

(5.6392)programme3Beta -64.6601*

(7.9223)programme4Beta -137.1100*

(14.4308)programme5Beta -49.8015*

(9.0564)pfirstBeta 177.1255*

(31.1668)meanhiseiBeta 3.8527*

(0.3823)SchV1 5324.8960 5136.9624 4726.7290 1988.0874N1 20800.0000 20800.0000 20800.0000 20800.0000Deviance 916547.3750 915931.8125 914990.6250 912871.0625

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Table 18 – Weighted MLM for 2009 ITA read

variable M1 M2 M3 M4interceptBeta 314.2623* 318.4191* 291.2761* 245.2793*

(26.1861) (26.1545) (26.0309) (29.1091)lns1consBeta 4.1196* 4.1137* 4.1064* 4.1050*

(0.0062) (0.0062) (0.0062) (0.0062)sch1consBeta 63.3624* 61.9653* 58.3997* 33.6431*

(1.8947) (1.8621) (1.8153) (1.0054)fmb2Beta -43.6541* -35.8185* -30.1293* -30.1820*

(2.5394) (3.2571) (3.3435) (3.3490)fmb3Beta -26.5155* -22.4708* -17.5066* -17.7637*

(4.5695) (4.6226) (4.5577) (4.5333)fmb4Beta -1.0904 -0.9211 -0.4850 -0.4255

(1.6242) (1.6363) (1.6176) (1.6126)ageBeta 8.8250* 8.8016* 8.9182* 8.7206*

(1.5557) (1.5568) (1.5293) (1.5162)male2Beta -22.2248* -21.7085* -23.0149* -22.0486*

(1.0341) (1.0361) (1.0385) (1.0310)schcontext2Beta 36.8853* 36.0761* 33.9512* -13.7224

(10.8935) (10.7682) (10.0768) (9.0342)schcontext3Beta 61.0904* 59.5734* 55.5241* -10.9723

(10.3259) (10.2109) (9.5473) (8.9146)schcontext4Beta 69.7422* 68.0750* 62.6719* -15.9280

(11.1117) (10.9668) (10.2398) (9.1212)schcontext5Beta 47.0505* 45.2788* 40.6940* -33.0445*

(21.1805) (20.8528) (19.6294) (12.9683)schcontext6Beta 46.0958* 43.9224* 40.7694* 13.1100

(16.0063) (15.6808) (14.6919) (12.7312)lang2Beta -12.4443* -10.7751* -10.1074*

(3.7421) (3.7416) (3.7373)lang3Beta -24.6875* -23.6806* -23.3269*

(1.4581) (1.4356) (1.4273)hisced2Beta 10.7307* 10.1422*

(4.4443) (4.4192)hisced3Beta 18.7489* 17.8586*

(4.5063) (4.4927)hisced4Beta 18.7641* 17.5704*

(4.3507) (4.3315)hisced5Beta -3.0870 -4.1787

(4.6789) (4.6602)hisced6Beta 12.6733* 11.1377*

(4.5259) (4.5062)hiseiBeta 0.3299* 0.2850*

(0.0355) (0.0354)homeposBeta 4.2225* 3.6896*

(0.6326) (0.6301)famstruc2Beta -0.1789 -0.2874

(1.3713) (1.3675)schtype2Beta -23.2520*

(6.5359)schtype3Beta -46.1508*

(10.0041)programme2Beta -32.2950*

(3.8665)programme3Beta -78.2410*

(5.1647)programme4Beta -131.2803*

(14.4756)programme5Beta -67.2088*

(6.7994)pfirstBeta 83.6928*

(20.2623)meanhiseiBeta 2.9955*

(0.2506)SchV1 4099.4136 3917.8376 3480.7644 1088.7808N1 29573.0000 29573.0000 29573.0000 29573.0000Deviance 1242398.6250 1240969.0000 1238975.5000 1234699.3750

19