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01-08-2016

Peer effects of non-native students on natives’ educational outcomes: mechanisms and evidence

Author: Marco Tonello

Published in: Empirical Economics | Issue 1/2016

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Abstract

This paper analyzes whether the share of non-native students in a school determines externalities that affect natives’ educational outcomes. The identification strategy exploits the variation in the non-native school share between adjacent cohorts by using administrative data covering the census of Italian junior high schools. Our results show that the non-native school share has weak negative impact on the test scores of native peers: Increasing the non-native school share by 1 percentage point leads to a decrease of 0.043 % in native peers’ language school mean test scores, while no effect is detected for math. The effects are also highly nonlinear and marginally increasing with level of the non-native school share. Our findings are consistent with, though not direct evidence of, an ‘integration model’ of peer interactions between native and non-native students.

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Non-native students are those born from non-Italian parents. See following sections for details and sensitivity checks.

By non-native students’ disruption, we intend behaviors that might slow down the teaching activities, such as frequent interruptions, monopolizing classroom discussion or the need for additional help. See Sect. 4.

Manski (1993) identifies three main factors that are likely to influence social interactions: exogenous (or contextual) effects (i.e., when the propensity of an individual to behave in a particular way varies with the exogenous characteristics of the group), correlated effects (i.e., common shared group-level factors, such as the school environment) and endogenous social interactions (i.e., when the propensity of an individual to behave in a certain way varies with the behavior of the group).

Eighth-grade students attend the third year of junior high school and are 13 years old. The Italian ‘Junior High School Diploma’ corresponds to ISCED level 2. Further details on the Italian school system and the institutional background can be found in Sect. 3. Invalsi (Istituto Nazionale per la Valutazione del Sistema Educativo di Istruzione e di Formazione) is the independent public institute carrying out the evaluation of the Italian school system by testing students’ attainment levels.

The reference regulatory framework concerning non-native enrollment in Italian schools enforced in the years used in the empirical analysis was the D.P.R. No. 394/1999. The Decree did not contain a fixed cap or thresholds for the presence of non-native students in each school, but a general prescription to ‘[...] avoid the constitution of classes where their presence is predominant.’

Non-native students are defined as those students without Italian citizenship, consistently with the official definition provided by the Italian Ministry of Education Statistical Office and with the data used for the empirical analysis. The Italian citizenship follows the so-called ius sanguinis rule; that is, a child is entitled to Italian citizenship at birth if one of his or her two parents is an Italian citizen. In a later section, we also test an alternative definition (i.e., first-generation non-native students).

PISA is the acronym for the Programme for International Student Assessment, a survey conducted by the OECD on 15-year-olds every 3 years.

See also Dustmann and Frattini (2013), Table 1.

Only for the 2008/2009 school year, the Statistical Office provides data on non-native students born in Italy (i.e., second generations) and born abroad (i.e., first generations) (see Online Appendix Table A2, MIUR 2009).

The latter embeds the subculture model proposed by the US sociological literature (Fordham and Ogbu 1986; Steele and Aronson 1998). It is also used to ground the evidence of ‘acting white’ behavior in US schools, i.e., the existence of an oppositional culture among black youth that eschew behaviors traditionally seen as a prerogative for whites (Fordham and Ogbu 1986; Fryer and Torelli 2010; Hoxby and Weingarth 2006).

This is consistent with the institutional setting and data used, as in Italian schools—like the majority of European contexts—non-native students rarely constitute the majority of the school population (Eurydice 2012; MIUR 2010).

We assume that the average class size remains constant in the main analysis. In the robustness section, we extend the framework to address the point of heterogeneous class size.

Lazear (1999) presents a model of ‘cultural acquisition’ and shows that the incentives to be assimilated into the majority culture depend on the size of the relevant groups. The smaller is the minority relative to the majority, the greater is the incentive of a minority member to acquire the culture of the majority (Lazear 1999). This intuition is also present in Lazear (2001, p. 791), in which the assimilation process is thought to be mediated by social interactions between the majority and the minority types, as also illustrated in our conceptual framework.

See Online Appendix B for a formalization. For instance, the function \(p_{F}(\theta )\) can be defined according to an integration index \(I(\theta )=\theta /(1-\theta )\), representing the ratio between the number of non-native and native students.

CNEL (Consiglio Nazionale dell’Economia e del Lavoro) is the National Bureau for Economics and Labor Research; CENSIS (Centro Studi Investimenti Sociali) is a nonprofit research center for socioeconomic issues founded in 1964. The CENSIS (2008) survey interviewed a national representative sample of 414 teachers in schools with non-native students; the CNEL (2011) survey interviewed a national representative sample of 608 immigrant households.

In a later section, we discuss possible threats to our identification strategy. School districts broadly correspond to Italian provinces (NUTS 5). School district fixed effects are in the form of interaction terms between 103 provincial dummies and year dummies.

We thank an anonymous referee for pointing out this aspect. The log transformation leaves the mathematical properties of the stylized models unchanged.

Note that we cannot a priori exclude that different mechanisms—other than disruption—are at work. In a later section, we discuss some alternative explanations. Nevertheless, other mechanisms do not invalidate our empirical exercise and the underlying test of whether a simple bad apple model is enough to explain the interactions between native and non-native students.

See Table 2 for the complete list and description of the variables included in the \(X_{st}\) and \(S_{st}\) vectors. Notice also that these vectors of control variables and fixed effects are intended to proxy for native group propensity (not) to be disruptive (i.e., \(ln(p_{N})\) in Eq. 3).

The Invalsi First Cycle Examination data contain school-level information. OLS from individual-level regressions would be biased, as there is evidence that non-native students are not randomly allocated across classes (within schools) (Contini 2013; Ballatore et al. 2014).

From the original population of 6290 schools, almost 5 % are dropped because they appear in only one wave. The total number of observations (N) is 14,881 as some schools are observed only for 2 years. To be consistent with the theoretical framework, we also exclude 22 schools with the non-native school shares in the eighth grade greater than 0.5. Our results do not change including these schools.

Actually, non-native students can be enrolled during the school year or suffer higher-grade retention than natives, implying that the school composition could be subject to changes from grade 6 to grade 8. We conduct appropriate robustness checks that show that grade retention does not induce a relevant bias.

Notice, however, that findings in the existing literature on peer interactions between natives and non-natives are quite mixed in terms of which subject is the more affected. For example, Jansen and Rasmussen (2011) find that the negative peer effects are prevalently associated with math.

A higher share of female in the classroom is generally associated with positive effects on both male and female achievements, and these effects are mostly accruable to lower levels of classroom disruption and violence, and to improve relationship among students and between students and teachers, as males are characterized by a more disruptive behavior as compared to female peers (Lavy and Schlosser 2011).

Nonlinear effects are also detected using a spline specification, with two or three breaking points, or implementing a nonparametric approach, classifying each school into quartiles (or deciles) based on its average non-native share across all periods and then interacting the corresponding dummies. Although less precisely estimated, the results (available upon request) confirm the overall pattern of findings of the nonlinear analysis.

Because of privacy restrictions imposed on the Invalsi IC data, we cannot identify municipalities with only one school.

We are grateful to an anonymous referee for pointing out this aspect.

For example, a recent regulation act from the Italian Ministry of Education (Recommendation No. 2, 8/1/2010) imposes a threshold of 30 % on the non-native share in each school. The idea behind the implementation of such a threshold is to avoid social segregation in the schools and in the classes (within schools), especially in areas where immigrant population is particularly high, thus substituting the more general prescriptions contained in the previous regulatory framework (MIUR 2010).

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Title: Peer effects of non-native students on natives’ educational outcomes: mechanisms and evidence
Author: Marco Tonello
Publication date: 01-08-2016
Publisher: Springer Berlin Heidelberg
Published in: Empirical Economics / Issue 1/2016
Print ISSN: 0377-7332
Electronic ISSN: 1435-8921
DOI: https://doi.org/10.1007/s00181-015-0995-y

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