The impact of immigration on school choice: Evidence from Australia Astghik Mavisakalyan∗ Abstract
If public schools respond more to immigrants by re-allocating budgets towards their needs than private schools do, increase in immigration will increase the public school attendance of immigrants and decrease that of natives. Using the 2001 Australian Census data, this paper confirms that possibility by showing that private school attendance among native-born Australians is higher in localities with higher share of immigrant population. In the meantime, immigrant private school attendance is lower where the share of their like-type immigrants is higher. These effects vary with “distance” between immigrants and natives and with socio-economic status of immigrants. Keywords: Immigration; Public Schools; Private Schools.
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Introduction
The effect of immigrants on outcomes of the native-born in host countries has been in the centre of recent immigration debate. This effect may manifest in a number of contexts, including the education systems. In particular, if an increase in immigrant share of population leads to an increase in immigrant propensity to attend public schools, natives who are concerned about negative influence of immigrants on the quality of the educational experience, may respond by opting out from public education. If so, divergent decisions on school choice will affect the student distributions across schools, potentially leading to segregation.1 A popular debate in major multicultural societies and more recently, in Australia, has suggested the possibility of a like scenario. A confidential survey of 163 high school principals in the state of New South Wales in 2006 raised serious concerns about the "white flight" - a "de facto apartheid" ∗ Tel:
+61 2 6125 1123; Fax: +61 2 6125 0182; E-mail address:
[email protected]. As demonstrated by the theoretical framework of Schelling (1969, 1971a, 1971b), weak preferences for being in a neighbourhood of “similar” types and the opportunity to move to a satisfactory position can in fact produce extreme segregation outcomes for neighbourhoods, while no agent strictly prefers it. 1
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as described by some teachers - undermining the public education system and threatening social cohesion (Patty, 2008). As Laurie Ferguson, the Australian parliamentary secretary for multicultural affairs and settlement services puts it, people withdrawing their children from public schools believe “there is an over-dominance of some cultures in schools, which is denigrating the quality of education” (Topsfield, 2008). Since immigration is the main source of transformation of the cultural profiles of localities, it may be an important source of the discussed “flight”. Supporting this possibility is the evidence reported in Figure 1 demonstrating that both private school rate and immigrant share of population in Australia have grown by around 10 percentage points in the period from 1970 to 2005. In addition, there has been a major change in the composition of sending countries of immigrants following the abandonment of the White Australia Policy in the 1960s.2 Fig. 1: Immigrant share of population and private school rates: 1971 to 2006
Source: Immigrant share data comes from Australian Historical Population Statistics 2008, Australian Bureau of Statistics; Private school rate data was requested from National Centre for Education and Training Statistics, Australian Bureau of Statistics.
Despite these observations, little is known on whether and how much the increase in immigrant share of population contributed to the increase in private school enrolments in Australia. While a number of studies have examined determinants of private/public school choice in the country (e.g., Williams, 1985; Vella, 1999; Le and Miller, 2003), none has investigated how it has been affected by the immigrant/minority share of population.3 In addition, while previous studies on “native flight” in the context of other countries hypothesize on possible school segregation as a result of 2 According to the Australian Historical Population Statistics 2008, in 1971, 47 percent of the foreign-born population came from the main English-speaking countries while by 2006, only 27 percent did. 3 The empirical literature on “native flight” from immigrants is new and has been restricted to the US (Betts and
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the “flight” (e.g., Van Hook and Snyder, 2007), surprisingly, none investigates how the immigrant school choice is affected - a question that needs to be addressed to infer on segregation (Lankford and Wyckoff, 2000).4 This study contributes to the understanding of these issues by providing a unified analysis of the consequences of immigration for determination of school choices of natives and immigrants across 63 Australian localities as identified in the 2001 Census of Population and Housing. The results suggest that native-born Australians respond to an inflow of immigrants by taking their children to private schools. The native “flight” is largely in response to immigrants that are culturally and linguistically distant from natives and it is the “third” generation natives who flee. In addition, for a given size of the immigrant share in the locality, the native “flight” to private schools decreases with the increase in relative socio-economic status of immigrants. In the meantime, an increase in immigrant’s like type in a locality is found to decrease their attendance of private schools. The decrease in probability of going to private schools is largely in response to like immigrants whose language at home is other than English, and is weaker on behalf of those immigrants whose language at home is English. Consistently with the results on natives, in places where immigrants have a higher socio-economic status, a higher share of immigrant’s like type is associated with a higher probability of private school attendance. These results are motivated in a simple model which assumes that immigrants have specific needs for education and schools respond to increase in immigrant enrolments by re-allocating a higher share of their budgets towards those needs. Since the schools offering immigrant-oriented education would find it difficult to recruit natives who are a majority in the population, profitmaximizing private schools are assumed to respond less to immigrants by adjusting the education to their needs. Given this setting, it is expected that immigration will increase the public school attendance of immigrants and decrease that of natives. The suggested intuition on native school choice behavior, while presented in a formal model Fairlie (2003) were the first to formally document a native (predominantly white) flight to private schools in response to immigrants based on 1980 and 1990 Census data from 132 metropolitan areas and Van Hook and Snyder (2007) documented “white flight” from Hispanic and limited English proficiency students using school-level administrative data from the California Department of Education from 1990 to 2000). In the meantime, there is a vast empirical literature on “white flight” from minorities from public to private schools in non-Austalian contexts providing mixed results. While some authors confirm the existence of “flight” (e.g., Sonstelie, 1979; James, 1987; Hamilton and Macauley, 1991; Fairlie and Resch, 2002; Li, 2005), others do not always find support for it (e.g., Lankford and Wyckoff, 1992; Buddin et al., 1998; Figlio and Stone, 2001). Some of the studies consider the flight from specific minority groups distinct in race or income (e.g., Conlon and Kimenyi, 1991; Fairlie and Resch, 2002; Li, 2005), while others look for existence of a threshold or a “tipping” point in white flight (e.g., Clotfelter, 1976; Lankford et al., 1995; Clotfelter, 2001; Li, 2005), despite of arbitrariness in creating thresholds when theoretical guidance is absent (Fairlie and Resch, 2002). 4 The few studies on minority school choice behaviour provide mixed results on their response to the presence of minority in the population. While some authors report lower aversion among minority than non-minority families to the presence of minorities at schools (e.g., Brunner et al., 2006; Kristen, 2008), others focus on the potential “flight” by minorities (e.g., Fairlie (2002) examines the “Latino-flight” while Katzman (1983) - “black flight”).
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here, is used to motivate the findings in Betts and Fairlie (2003). As they point out, immigrant children may affect the school resources due to the need to hire bilingual teachers and to devote additional resources to children who are yet to be ready for learning in English as well as due to their effect on teaching methods in general. Another explanation for native aversion to large numbers of migrants is the personal utility that comes with “maintaining common identity based on cultural values, language, ethnicity, history and religion” (Hansen, 2003). In addition, as Dottori and Shen (2008) suggest in their formal theoretical model, an increase in low-skilled immigration leads to a reduction of the average tax base, leading to a decrease in public expenditures per pupil. As a consequence, wealthier parents opt out in favour of private schools and vote for a lower tax rate for funding public education.5 As it comes to immigrant clustering, while existence of beneficial network externalities and “herd behaviour” are often used to explain it in other contexts, the extent and the nature of their applicability to the problem of school choice remains an open question.6 In addition, related studies that consider the impact of ethnic network size on educational attainment, provide mixed evidence.7 To examine the impact of immigration on the school choice behavior of natives and immigrants, a simple theoretical model is outlined in the following section. Section 3 presents the approach taken to empirically investigate the main predictions of the model and the data used. Sections 4 and 5 present the results on native and immigrant school choices respectively. The final section concludes.
2
Theoretical framework
There are two groups of individuals in the economy: the immigrants I and the natives N. In a given locality, there is one public school while there are a number of private schools around the region. Individuals (natives as well as immigrants) differ in terms of (i) a vector of observable characteristics. Such a vector will typically include family income, 5 Studies on “white flight” from minorities suggest a number of additional reasons that could be used to explain the native flight from immigrants too. Among those, Conlon and Kimenyi (1991) include “(1) irrational prejudice, (2) characteristics of poor black children which white parents fear or dislike, and (3) poor management of schools with poor black students, either because of the attitudes of administrators, or greater political passivity of low-income parents”. In addition, given the complexities associated with obtaining information on academic qualities of schools, their racial composition might be used as proxies by parents, who may not necessarily have racial preferences (e.g., Lankford and Wyckoff, 2000). Fairlie and Resch (2002) make a step further by formally documenting that racism against minority schoolchildren contributes to white flight. 6 See, for instance, Borjas, 1986; Bartel, 1989; Borjas and Hilton, 1996; Carrington et al., 1996; Bauer et al., 2002; Borjas, 2002; Epstein, 2002; Pedersen et al., 2004 and Chiswick et al., 2002; Le, 2008 for Australian evidence on network effects. 7 For instance, while Gang and Zimmermann (2000) and Cardak and McDonald (2004) identify positive impact of ethnic network size on educational attainment, Entorf and Lauk (2006) conclude that students with a migration background would benefit from higher heterogeneity within schools.
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educational attainment and socio-economic status of parents and other relevant information. Ω denotes the set of all such vectors of which ω represents a particular element. For technical simplicity, Ω is assumed to be a finite set and pi (ω), i = I, N, is the proportion of families that has characteristics ω. (ii) an unobservable characteristics a that is termed as "ability" and assumed to be distributed over the interval [0, a] ¯ with the distribution function F(a).8 A school is characterized by a pair (g, α) where g is the per capita student expenditure and proportion α of this expenditure is targeted towards immigrant students. For example, higher α may take the form of more resources directed for teaching English as a second language, which do not add value to native children. The following two assumptions are made about the nature of α. First, given a school, the proportion α depends negatively on the proportion of natives among students attending that school denoted as q. This is written as α = α(q). Second, other things the same, the proportion of this targeted expenditure is higher in a public school compared to that of a private school. One justification for this assumption is the fact that a private school, which provides too high a targeted expenditure for immigrants, will find it difficult to recruit native students. Since the natives constitute majority in the population, such a strategy of providing high targeted transfer will thus not be optimal for a profit maximizing owner(s) of a private school. Indeed, to simplify the presentation of the results, in what follows, it will be assumed that a private school chooses to provide a zero targeted transfer to the immigrant children. Next follows the description of payoffs to an individual with characteristic ω and ability a if he goes to a school with (g, α). The expected return of an immigrant in a public school is given by RI (ag, α; ω), where RI is increasing in both its arguments, ag and α. If the school is private, his payoff is RI (ag, 0; ω) −C, where C is the extra monetary costs required to attend a private school. By assumption, α in a private school is zero. To simplify notations, the net return to an immigrant of type ω and ability a of going to a private school that has a per capita student expenditure of g is denoted by R∗I (ag; ω) = RI (ag, 0; ω) −C. For a native in a public school, the expected return is given by RN (ag, α; ω), where RN is increasing in its first argument ag, but decreasing in α. While if the school is private, his payoff is RN (ag, 0; ω) −C. The net return to a native of type ω and ability a of going to a private school that has a per capita student expenditure of g is denoted as R∗N (ag; ω) = RN (ag, 0; ω) −C. 8
In general, one can allow F to depend on ω as well as on whether the individual is an immigrant or not. The qualitative results are unchanged.
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Per capita expenditures in each type of schools are assumed to be fixed at g pri (resp. g pub ) for a private school (resp. public school). Moreover, the targeted transfer function α(q) is fixed. Let YI (a; ω) = RI (ag pub , α(q); ω)−R∗I (ag pri ; ω) and YN (a; ω) = RN (ag pub , α(q); ω)−R∗N (ag pri ; ω). The following assumption is made on the benefit functions: Assumption 1 Fix ω ∈ Ω, for i = I, N, Yi (a; ω) is non increasing in a. Remark 1: Assumption 1 ensures that if an individual of characteristic ω prefers to choose private school for ability level a, for ability level a0 > a, the private school continues to be the optimal choice for the individual. This is an extremely intuitive restriction. It is easy to check that it holds, for example, for the following specification of benefit functions: RI (ag pub , α; ω) = B(ω)ag pub [β α + (1 − α)], where β > 1 and B(ω) > 0; RN (ag pub , α; ω) = B(ω)ag pub (1 − α) and R∗I (ag pri ; ω) = R∗N (ag pri ; ω) = B(ω)ag pri −C. Taking (g pub , g pri , α) as given, individuals make their choice of schools. An immigrant of type ω and ability a will choose to opt for the public school if and only if RI (ag pub , α; ω) ≥ R∗I (ag pri ; ω)
(1)
Let aI (ω), denote the maximum ability of an immigrant with characteristics ω for which equation (1) holds.9 From Remark 1, it follows that all immigrants of type ω and of ability a ∈ [0, aI (ω)] will go to the public school. Thus, m pub (ω) = mpI (ω)F(aI (ω))
(2)
where m is the size of immigrants in the locality. Similarly, a native of type ω and ability a will opt for the public school if RN (ag pub , α; ω) ≥ R∗N (ag pri ; ω)
(3)
Let aN (ω) denote the maximum ability of a native with characteristics ω for which equation (3) holds. Because of Remark 1, the number of natives of characteristic ω that go to public school is given by n pub (ω) = npN (ω)F(aN (ω)) (4) where n is the size of natives in the locality. 9
Since at a = 0, an individual will strictly prefer to go to a public school, it follows that aI (ω) > 0. Moreover, if aI (ω) < a, ¯ then (1) holds as an equality.
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The total number of immigrants (resp. natives) that go to public school is thus given by m pub =
∑
m pub (ω)
n pub =
ω∈Omega
∑
n pub (ω)
(5)
ω∈Omega
The proportion of natives that go to the public school is then given by q pub =
n pub n pub + m pub
(6)
In what follows, it is assumed that given (n, m) and (g pub , g pri ), there exists a unique equilibrium. The following propositions help to illustrate how immigration affects the school choice in a host country. Proposition 1: If m increases, then n pub and q pub must decrease.10 The intuition for this result is straightforward. For any given value of q, an increase in m while keeping ai (ω) for i = I, N unchanged, increases the total amount of immigrant children in the public school. This increase in m pub decreases q and thus decreases aN (ω) while increasing aI (ω). The final equilibrium outcome then must correspond to a higher value of m pub , a lower value of n pub and a corresponding lower value of q pub . The formal proof follows. Proof of Proposition 1. For (m, n), let aI (ω) (resp. aN (ω)) denote the cut off maximum ability of an immigrant (resp. a native) who goes to public school and let n pub (resp. q pub ) denote the size (resp. proportion) of natives who go to public school in an equilibrium. Consider now (m∗ , n), where m∗ > m and let n∗pub (resp. q∗pub ) be the size (resp. proportion) of natives in public school in the new equilibrium. It will be shown that n∗pub < n pub and q∗pub < q pub . Consider the outcome when individuals make their school choices according to the following rules: For any q, an immigrant of type ω and ability a goes to the public school if a < aI (ω), however, if a > aI (ω), the immigrant goes to the public school if and only if equation (1) holds. For any q, a native of type ω and ability a goes to the private school if a > aN (ω), however, if a < aN (ω), the native goes to the public school if and only if equation (3) holds. Given these strategies, let n0pub (resp. q0pub ) denote the size (resp. proportion) of natives in the public school. 10
If there are multiple equilibria in this model, then the Proposition holds for the lowest value of q that is consistent with an equilibrium.
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Since m∗ > m, it follows from the strategies (outlined above) that n0pub < n pub and q0pub < q pub . Thus, α(q0pub ) > α(q pub ). Thus, from equation (1), it follows that i) an immigrant of type ω and ability a < aI (ω) will strictly prefer to go to the public school and ii) a native of type ω and ability a > aN (ω) will strictly prefer the private school. This implies that n0pub = n∗pub and q0pub = q∗pub and the result is proved. Of additional interest is how this result relates to the "distance" between natives and immigrants. In particular, the "distance" may be larger the poorer immigrants’ command of English, which will increase the requirement for targeting resources for teaching English that do not benefit the natives. To derive the implication of this possibility in the present theoretical structure, the targeted transfer function is modified as α(q; d) where d is a measure of distance between the two groups with α(q; d1 ) < α(q; d2 ) for d1 < d2 . Proposition 2: Given d1 < d2 such that α(q; d1 ) < α(q; d2 ), if q pub (di ) is the equilibrium proportion of natives in the public school when the distance between the groups is di , then q pub (d1 ) > q pub (d2 ). When the distance between the groups increases, the proportion of targeted transfer in the public school increases for every level of q. Consequently, at the equilibrium level of q pub (d1 ), the benefit to an immigrant is higher in a public school because of an increase in the targeted transfer while for the native, the benefit is decreased. This increases aI (ω) while reducing aN (ω) resulting in a decrease in the equilibrium value of q pub . The formal proof of this Proposition follows very similar lines to that of the 1st proposition. Next, the role of socio-economic status of immigrants in the locality is considered in the current structure. The set of observable characteristics Ω is assumed to consist of at least two vectors ωL and ωH , which are identical except only for one dimension, the one that captures the "socioeconomic status" with ωL representing an individual with a lower socio-economic status than ωH . Let pI (ωL ) and pI (ωH ) denote the proportion of immigrants across these two groups. To increase the average socio-economic status of the immigrants in a region, the proportion of immigrants with ωL is decreased to p0I (ωL ) = pI (ωL ) − 4 while increasing the proportion of immigrants with characteristics ωH to p0I (ωH ) = pI (ωH ) + 4. Let φ (a; ω) = RI (ag pub , α(q); ω) − R∗I (ag pri , ω). The following assumption is made: Assumption 2 If φ (a; ωL ) < 0, then φ (a; ωH ) < 0. This assumption implies that an immigrant of a given ability a is more likely to choose a private school if he is of higher socio-economic status. Under Assumption 2, the following result can be proved. 8
Proposition 3: Let pI (ω) (resp. p∗I (ω)) be the proportion of immigrants of characteristics ω in situation 1 (resp. situation 2) where p∗I (ωL ) = pI (ωL ) − 4, p∗I (ωH ) = pI (ωH ) + 4 and p∗I (ω) = pI (ω) for any other ω ∈ Ω. If q pub (resp. q∗pub ) is the equilibrium proportion of natives in the public school in situation 1 (resp. situation 2), then q pub < q∗pub . Proof of Proposition 3. Let aI (ω) (resp. aN (ω)) denote the cut off maximum ability of an immigrant (resp. a native) who goes to public school in situation 1 with q pub being the resulting equilibrium proportion of natives in the public school. In situation 2, consider the following strategy profile: For any q, an immigrant of type ω and ability a goes to the private school if a > aI (ω), however, if a < aI (ω), the immigrant goes to the public school if and only if equation (1) holds. For any q, a native of type ω and ability a goes to the public school if a < aN (ω), however, if a > aN (ω), the native goes to the public school if and only if equation (3) holds. Given these strategies, let q0pub denote the proportion of natives in the public school. By Assumption 2, aI (ωL ) > aI (ωH ). Since in situation 2, p∗I (ωL ) < pI (ωL ) and p∗I (ωH ) > pI (ωH ), it follows from the strategies outlined above that q0pub > q pub and therefore, α(q0pub ) < α(q pub ). Thus, i) an immigrant of type ω and ability a > aI (ω) will strictly prefer to go to the private school and ii) a native of type ω and ability a < aN (ω) will strictly prefer the public school. This implies that q0pub = q∗pub and the result is proved.
3
Empirical Approach and Data
To empirically investigate the main implications of the theory, econometric models of forms that have been used before in similar studies are estimated. Exploiting the fact that immigration is geographically concentrated, the level of immigration by locality is linked with school choices of natives and immigrants. The latent variable measuring the expected utility from going to a private school for individual i in locality l is modelled as Yil∗ = Xil β +Cl γ + Sl δ + εil
(7)
where Xil is a vector of individual characteristics, Cl is a vector of locality variables (including immigrant share), Sl is a dummy for the state where individual lives and εil is the disturbance term. Only the dichotomous variable Yil can be observed. It equals 1 if Yil∗ ≥ 0 (denoting private school attendance) and equals 0 otherwise (denoting public school attendance).11 If εil are assumed to be 11
The study is motivated by public versus private school choices in response to immigrants and is less
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normally distributed, the data can be described by a probit model. The probit model is estimated using individual level data and taking the statistical regions as per the Australian Standard Geographical Classification as locality dimension. Since the localitylevel characteristics do not vary across individuals based in the same locality, the standard errors are adjusted to account for clustering of the data by locality level. Separate models for natives and immigrants are estimated. Public and private schools may differ in many of their features and if families value some features more at one level of schooling than the other, the factors which influence the choice of schools may differ between primary and secondary levels (Long and Toma, 1988). Therefore, in each case separate equations for children enrolled in primary and secondary schools are estimated. The list of controls include a wide range of individual characteristics of natives and immigrants including age, gender, religion (Muslim, Buddhist, Catholic, other Christian, other religion), dummy for natural or adopted child, number of children in the family, dummy for single mother/father, mother’s/father’s education, government employee status, occupation, age. Measures of family income, home ownership and mobility history are also included. In addition, dummies for natives of Asian and other origin are specifically included in native models. Immigrantspecific individual characteristics on the other hand, include dummies for European, Asian and other immigrants, immigrants from developed countries and immigrants who speak English at home. The variable of central interest is the immigrant share of student population in native models and the like-type immigrant share of student population in immigrant models. In addition, a set of locality-level measures are added in benchmark models, including mean income, Gini coefficient, share of unemployed in local labour force, number of students, private school student/teacher ratios. In native models a minority share of population is also included to control for possibility of flight from native-born minorities. Finally, state fixed effects are included to capture the regional differences in private school attendance unexplained by included explanatory variables. The main data source of this study is the 1-percent household sample file of the 2001 Australian Census of Population and Housing. The estimations are based on the Expanded Confidentialized Unit Record Files (CURF) available through Remote Access Data Laboratory (RADL). The Census data is released in the form of a hierarchical file that allows to link the information of an individual with the information of other persons within the family as well as of the family and the household. Characteristics of the locality where respondents live are defined through aggregating concerned with the type of the private school that the students choose to attend. Therefore, following the previous studies (e.g., Fairlie and Resch, 2002), the estimations do not differentiate between Catholic and other private schools as both are alternatives to public schooling.
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the individual/family level information of individuals living in that locality (Table 8 in the Appendix provides definitions of the main variables).12 The analysis focuses on 63 statistical regions identified in the RADL version of the Census. These localities are based on a minimum population size of 124,000 persons. 13 Summary statistics of the main variables for the samples of natives and immigrants is reported in Table 9 in the Appendix. Private school attendance rates are on average very high in Australia while there is hardly any difference between the percent of immigrants and natives attending private schools. Approximately 30 percent of both native and immigrant children go to private primary schools. Secondary private school rates for natives and immigrants are 38 and 36 percent respectively. 14 There is a substantial variation in immigrant share of population across localities where natives reside. An average native in the sample is based in a locality with around 12 percent of its student population born outside Australia. The variable ranges from around 3 percent to around 27 percent. The correlation between immigrant share of population and native private school attendance is positive and significant, as shown in the bottom panel of Table 9. On the other hand, an average immigrant in the sample is based in a location where around 4 percent of the population belongs to their type (around 3 percent in the case of more detailed division of types). The variable ranges from 0 to around 11 percent (9 percent in the case of detailed division). The correlation between like-type immigrant share of population and immigrant private school attendance is negative and significant, as shown in the bottom panel of Table 9. To obtain a further insight into the main variables of interest, Table 1 reports private school rates of immigrants and natives for a group of localities that together accommodate nearly half of the immigrant student population and only around 27 percent of the native population. The ranking 12
The data on private school student/teacher ratio comes from a different source, National Reporting and Data Services Unit of the Department of Education, Science and Training. There is no control for public school student/teacher ratio in the regressions assuming that it is constant within states, as suggested by departments of education of some of the states that did not provide locality-disaggregated data. 13 The Census data is the only source of national micro-data identified, which would allow comparisons of private school choice behaviour across a large number of localities. Usually existing micro-level survey data that include information on the type of school attended do not have geographical identifiers with the extent of detail the Census has, that would allow matching individual data with characteristics of localities. As a result, most studies on school choice in Australia hardly incorporate locality characteristics. The Census, on the other hand, lacks the degree of detail of information that would have been useful for the current analysis, but it nevertheless appears to be the most suitable dataset encompassing the information of central interest to this study. 14 To draw a comparison - for the US in 1990 Betts and Fairlie (2003) report private primary school attendance rates of 12.19 percent and 7.78 percent, and private secondary school attendance rates of 10.17 percent and 6.23 percent for natives and immigrants respectively.
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is based on the share of immigrants in the locality. In some of the localities with high shares of immigrants the private school rates for immigrants and natives appear quite different from those defined for the whole population. For example, in Central Western Sydney 17.46 percent of the student population are immigrants. Private primary and secondary school rates for natives in the locality are 39.73 and 49.21 respectively. This is well above the private school rates of natives at national level. In the case of immigrant private school rates, they are well below the private school rates at national level - 15.15 and 17.5 percent respectively. Tab. 1: Cross-locality differences in private school rates
Locality
Private school rate Primary school
Secondary school
Share of
Share of
Share of
immigrant
natives
immigrants
Native
Immigrant
Native
Immigrant
population
in locality
in locality
Central Northern Sydney
36.5169
34.6939
57.3477
38.8235
17.4252
2.3695
5.4098
South Eastern Melbourne
28.4281
20.5128
34.9282
23.3766
18.5897
1.8956
4.6831
Outer Western Melbourne
43.3735
26.8293
43.617
33.7838
14.1626
2.6008
4.6427
North Metrop. (Perth)
32.0872
30.4348
39.6313
38.4615
17.1032
2.0075
4.4812
Inner Eastern Melbourne
35.8696
38.2353
54.7112
46.5753
13.3085
2.6008
4.3197
Fairfield-Liverpool
24.4604
8.3333
35.5932
9.6774
17.7215
1.6978
3.9564
Brisbane City Outer Ring
32.6478
27.2727
45.9259
47.1698
12.8307
2.459
3.916
North Western Sydney
28.7645
31.4286
39.7906
45.614
9.2742
3.3583
3.7142
Central Western Sydney
39.726
15.1515
49.2063
17.5
17.4641
1.2874
2.9471
South and East Moreton
32.6772
32.1429
41.3333
43.1818
15.1261
1.5075
2.9067
South East Metrop. (Perth)
27.027
20.8333
34.1935
21.2766
14.6392
1.5448
2.8664
Canterbury-Bankstown
33.7778
20.8333
36.2205
19.1489
16.7849
1.3135
2.8664
South&East BSD Balance
26.2599
15.625
34.5622
38.8889
10.2719
2.2165
2.7453
There is quite a different picture in localities with relatively low shares of immigrants, such as North West Sydney. In this particular case with 9.27 percent of immigrant student population, private primary and secondary school rates for natives are 28.76 and 39.79 percent respectively. While the private school rates for immigrants are above the ones at national level - 31.42 and 45.61 percent. This evidence suggests that lower share of immigrants in a locality may be associated with lower private school rates for natives and higher private school rates of immigrants. Since this simple analysis of trends ignores the possibility that other factors have affected the school-choice decisions, OLS models of native and immigrant private school rates were estimated 12
for all school levels as well as primary and secondary levels separately, controlling for immigrant share and other locality-level characteristics, including measures of mean income, Gini coefficient, unemployment rate, number of student population, private school student/teacher ratio, nonEuropean native share of population (included only in native models) and state fixed effects. As expected, all the coefficients on immigrant share in native models had positive sign with only the one from secondary school level regressions being insignificant. Negative, yet again, not always significant, coefficients on immigrant share estimates in immigrant models were obtained.15 Overall, this simple analysis, while indicative of certain patterns, has obvious limitations. In particular, aggregate community characteristics are a poor proxy for important family characteristics that might affect the school choice (Buddin et al., 1998). In addition, it is difficult to infer on how school choice of individuals would be affected in response to immigration based on the locality-level results. Therefore, next a detailed regression analysis of immigration and individual school choices is provided.
4
Does “Native Flight” Into Private Schools Exist in Australia?
4.1
The role of individual characteristics
Table 10 in the Appendix displays the probit regression including only individual controls. Student and family characteristics affect school choice. The results suggest a decline in private school choice by older schoolchildren at secondary school level. There is some evidence that religiosity is positively associated with private school attendance. Representatives of most religions appear to have a higher probability of attending private schools compared to those with no religion (only Muslims have a lower probability of attending private schools at the secondary level). In addition, natives of Asian and other backgrounds are more likely to choose private schools than those of Anglo-European origin. Natural or adopted children are more likely to be sent to private schools than are step-children of one of the parents. In addition, the number of children in the family is positively associated with private school attendance at the secondary level. For a given education budget, a larger number of children per household implies a lower quality of public schools, thus increasing the demand for private schooling (Cohen-Zada, 2006). In addition, assuming that parents educate children through a combination of school and home investment, input of parental time per child may decrease with the increase in the number of children. This should lead families with more children to invest more in formal schooling. The same intuition can be applied to explain the result suggesting that single 15
Results available upon request.
13
parents send their children to private schools significantly more than traditional families.16 Years of education are a commonly used indicator of the value family places on education. Nevertheless, only a weak positive association between mother’s years of schooling and private primary school attendance of the child is found, while the coefficients on other parental education variables turn out to be insignificant. However, this is after controlling for occupational prestige of parents, which are positively associated with private school choice. Socio-economic status is identified to be one of the most important determinants of school choice with public schools typically attracting children with a working class background while private schools having those with upper or middle class backgrounds (Le and Miller, 2003). Parents working with the government send their children to private schools less than those working elsewhere. Finally, the probability of sending a child to a private school increases for older parents. As Buddin et al. (1998) interpret their similar result, “delayed parenthood might reflect an extra commitment to parenting and a more stable income flow”, thus leading higher investments in their children. Better off families have more resources to purchase private education. As the results indicate, the probability of attending private school is positively related to family income and home ownership. Finally, the history of residential mobility is positively associated with private secondary school choice. The rest of the individual controls are statistically not different from zero.17
4.2
Estimates with locality controls
In Table 2 the characteristics of the locality where the individual resides are included. First, results of regressions with locality level characteristics excluding immigrant share of population are reported. A number of locality characteristics are strongly associated with private school choice. Higher-income localities can support a wider and richer array of private schools. Therefore, as expected, natives based in higher-income localities have a higher probability of going to private schools. In addition, Gini coefficient has a positive sign and is statistically significant in primary school regressions. This is consistent with findings in Buddin et al. (1998), who argue that private choice may increase with greater variation in family income in a locality. The coefficient on the unemployed share of population is positive indicating the possibility that places with higher unemployment may be associated with a perception of poorer public education quality, thus higher likelihood of private school choice. 16
Similar finding is documented by Buddin et al. (1998). The coefficients on individual controls are stable and robust to subsequent inclusion of additional controls. Therefore, to economise on space, they are not reported in other estimations. 17
14
15 (0.1689)
(0.5231)
-5664.9115
11774
Yes
Yes
0.1806
-3959.0305
7260
Yes
Yes
(1.51)
-10.8991***
(0.3607)
1.3863***
(0.0345)
-0.0105
(0.0674)
0.0657
(1.6831)
3.5812**
(1.5412)
-2.2639
(0.2047)
1.1645***
Coefficient
Individual controls are all those reported in Table 10 in the Appendix.
Marginal effects are calculated at the means.
Standard errors are corrected for heteroskedasticity and clustering of the residuals at the level of localities.
Pseudo R2 0.2158 0.2161 *Denotes significance at 10 percent level; ** at 5 percent level; *** at 1 percent level.
-5666.869
11774
Yes
States
Log pseudo-likelihood
0.351**
(0.1129)
0.0383
(0.0076)
-0.0048
(0.016)
-0.0086
(0.5588)
0.8471
(0.4214)
1.0881***
(0.0757)
0.0653
Marg. effect
1.0854**
(1.4762)
Yes
No. of observations
0.1185 (0.3488)
(1.6023)
Individual controls
Constant
0.1925* (0.1056)
-0.0148 (0.0235)
-5.872***
native share
-0.004 (0.0076)
(0.0493)
-0.0267
(1.716)
2.6192
(1.3107)
3.364***
(0.2335)
0.2018
Coefficient
-7.4014***
0.5952* (0.3267)
Non-European
Immigrant share
-0.0123 (0.0234)
(0.0168)
(0.0519)
student/teacher
-0.0051
(0.5713)
-0.0157
(1.7508)
(0.3903) 1.1824**
(1.2105) 3.6556**
0.7989**
(0.0701)
2.4699**
0.1554**
(0.2157)
Marg. effect
0.4803**
Private school
Students
Unemployment
Gini
Mean income
Coefficient
(0.135)
0.5179***
(0.0129)
-0.0039
(0.0252)
0.0245
(0.6285)
1.3378**
(0.5754)
-0.8457
(0.0762)
0.435***
Marg. effect
0.1812
-3955.7534
7260
Yes
Yes
(1.6285)
-8.5096***
(0.7818)
1.7274**
(0.4751)
0.6239
(0.0313)
-0.0139
(0.0602)
0.0478
(1.7707)
1.9048
(1.6649)
-0.7706
(0.264)
0.7257***
Coefficient
Secondary school
(0.2921)
0.6453**
(0.1774)
0.2331
(0.0117)
-0.0052
(0.0225)
0.0178
(0.6615)
0.7115
(0.6219)
-0.2879
(0.0984)
0.2711***
Marg. effect
Tab. 2: Locality characteristics and native school choice Primary school
The estimation results do not yield significant results on the relationship between private school quality measure and private school choice. This result, nevertheless, is consistent with a number of previous studies that often do not establish a relationship between school quality and attendance (e.g., Li, 2005). However, it is not excluded that other measures of school quality, were they available, would lead to different results. In particular, it has been argued that private schools in Australia have used increased government funding since mid-1970s to improve the quality of their services thus achieving increases in their enrolments (e.g., Williams, 1985; Ryan and Watson, 2004). Finally, the coefficient on the non-European native share of population is positive and significant, pointing out at native flight from minorities. However, excluding the immigrant share from the model can be problematic given that the location choice of immigrants may be motivated by the presence of minorities in a locality. In that case, if the immigrant share is not controlled for, the estimates of the impact of non-European native share on private school attendance may be capturing the impact of immigrant share of population. Indeed, once the immigrant share of population is included in the model, the non-European native share looses its significance. Therefore, it is not a “white flight” but rather a “native flight” that happens in Australia. Immigrant share has a positive and statistically significant impact on private school choice of natives. At the primary level, a 10 percent increase in the immigrant share increases the probability of private school attendance by 3.51 percentage points. The impact of immigration on school choice is stronger in secondary schools. A 10 percent increase in immigrant share increases the probability of private school attendance by 6.45 percentage points. Betts and Fairlie (2003) provide evidence of “native flight” from immigrants in the US at secondary school level only, while at primary level no statistically significant link emerges. The suggested reasons to explain this include the greater cross-group mixing at the secondary schools for which many primary schools act as feeders and the channelling of limited English proficiency students into classes with native speakers at secondary level. In addition, by the time native children reach secondary schools, their parents are more likely to have purchased houses, rendering private school enrolment more favourable than a change of residential locality. These may broadly apply to explaining the differences in the strength of impact of immigrant share of population on private school choice at primary and secondary levels in the results.
16
4.3
Robustness checks
Panel A of Table 12 in the Appendix reports the results of robustness checks across several dimensions.18 First, considering the potential effect of older and younger immigrants on the perceived quality of schooling experience, models are re-estimated with the immigrant share calculated as the proportion of the entire population who were born abroad. The coefficients on immigrant share while significant, are smaller in size than in the main estimation indicating that it is the immigrant share in the school population that matters more for the school choice of natives (part A of the Table). In addition, an alternative definition of immigrant share is applied by re-estimating the model with the immigrant share disaggregated by school types (part B of the Table). In this case, only the coefficients on immigrant share in public schools are significant, indicating, as expected, a positive association with the probability of going to a private school. The results may be biased because of the possible simultaneity of location decisions and school sector choices. In particular, if natives respond to immigration by changing their place of residence rather than enrolling their children to private schools in the same locality, the native private school attendance rate in the sending locality will rise while the native private school attendance rate in the receiving locality will fall. In that case the estimated effect of immigrant share on native private school attendance would be upwardly biased. This possibility may be of a particular concern for primary schools as families may have a lower opportunity cost of moving at that stage of life cycle.19 Simultaneity of location decisions and school sector choices may be less of a problem when the units of analysis are larger localities, as families may be more likely to move within them. First, the model is re-estimated for 48 localities as identified in the CD-ROM version of the Census (except for the Northern Territory, these localities are based on a minimum population size of 250,000 persons). The estimate on immigrant share at primary school level reported in part C of the Table, is smaller in size, indicating at the possible upward bias in the main regression, while it is no longer significant. The coefficient on the immigrant share at secondary school level is significant and of comparable size, indicating that simultaneity of location decisions and school sector choice would indeed have been less of a problem at secondary school level. In addition, the model is re-estimated based on 13 localities constituting the major statistical regions as per the Australian Standard Geographical Classification.20 The coefficients on immigrant share are 18
To economize space, only the coefficients on main variables are reported while the regressions controlled for all variables included in benchmark models. 19 As Table 9 in the Appendix indicates, 47.48 percent of primary- and 35.41 of secondary-level native students have moved during the five years preceding the Census year. 20 Each of the five larger States is divided into two geographical areas, the Capital City and the balance of
17
positive at both primary and secondary levels. However, only the coefficient at secondary level is statistically significant and larger in magnitude than the coefficient obtained using smaller localities. Overall, the estimation results appear to be quite robust to the alternative specifications with locality characteristics measured at different geographical levels. Another natural way of checking the extent to which the possibility of simultaneity of location decisions and school sector choices is of a concern would be by re-estimating the model including an interaction term of the immigrant share with the dummy indicating the change of the place of residence of the respondent during the five years preceding the Census year. Of most interest here are the interaction coefficients, which provide estimates of the difference between the effect of the immigrant share on the probability of private school attendance of movers and non-movers. The interaction coefficients reported in part D of the Table are statistically insignificant. Therefore, these estimates do not provide evidence suggesting that natives who have and have not moved in the past respond differently to concentrations of immigrant schoolchildren. While a long list of individual and family characteristics and locality-level controls are included in the models, there may be additional factors that are correlated with both the immigrant share of population and the school choice of natives. In particular, while the regressions include private school student/teacher ratios, the positive estimates on immigrant share might be partially due to natives avoiding low-quality school systems that immigrants attend, which remained uncaptured by the measure of student/teacher ratio. The importance of unobserved locality fixed effects in the benchmark regressions is evaluated by using the empirical technique suggested by Borjas and Sueyoshi (1994). In the first step a probit model is estimated where the school choice is a function of students’ individual and family characteristics and locality fixed effects. In the second stage the estimated fixed effects are regressed on observed locality characteristics, including immigrant share, using GLS. For comparison purposes, OLS estimation results of the second stage are reported as well (part E of the Table). The coefficient on immigrant share at primary school level is positive but statistically insignificant. In the case of secondary schools, the coefficient preserves its significance. It is positive and comparable in size with the estimated coefficient in the main model.21
4.4
Differences in “distance” between immigrants and natives
As per prediction of the proposed theoretical framework, natives may have a deeper aversion to immigrant groups who are more “distant” from them in their characteristics than to others. For the State. Due to population size limitations, Tasmania, Northern Territory, the Australian Capital Territory and Other Territories represent a major statistical region corresponding to the whole of the State/Territory. 21 The second-stage coefficients are in the same metric as probit coefficients.
18
example, the "distance" may be larger the poorer immigrants’ command of English, which will lead to more resources re-directed for teaching English as a second languages that do not add value to native children. To shed some light on this issue, additional sets of probit regressions are estimated for the probability of private school attendance that account for three dimensions of distance between immigrants and natives. Tab. 3: Immigrant English proficiency and native school choice Primary school
Secondary school
Coefficient
Marg. effect
Coefficient
Marg. effect
Non-European
-0.1956
-0.0633
0.6668
0.2491
native share
(0.4154)
(0.1344)
(0.5329)
(0.199)
English-speaking
-0.0744
-0.024
1.8737
0.6999
immigrants Non-English-speaking immigrants
(1.1803)
(0.3818)
(1.384)
(0.5168)
1.6458***
0.5323***
1.6495*
0.6162*
(0.6192)
(0.2004)
(0.8701)
(0.3253)
Individual controls
Yes
Yes
Locality controls
Yes
Yes
States
Yes
Yes
No. of observations 11774 7260 *Denotes significance at 10 percent level; ** at 5 percent level; *** at 1 percent level. Standard errors are corrected for heteroskedasticity and clustering of the residuals at the level of localities. Marginal effects are calculated at the means. Individual and other locality controls are all those included in the benchmark models.
In the first sets of regressions, the immigrant share is disaggregated into English-speaking/nonEnglish-speaking immigrant shares in total student population (Table 3). Language spoken at home is used to divide the immigrants into the two groups. An average native in the sample is based in a locality with around 6 percent of its student population comprising of immigrants who speak English at home and with around 5 percent of immigrants who speak a different language. As the estimates show, natives appear to be sensitive to the concentration of immigrants who do not speak English at home. More specifically, at the primary level, a 10 percent increase in the non-English-speaking immigrant share of student population leads to an increase in the probability of private school attendance of 5.32 percentage points. The impact is stronger at secondary level. A 10 percent increase in the non-English-speaking immigrant share of student population increases the probability of private school attendance by 6.16 percentage points. The natives’ choice of private school turns out to be insensitive to the share of English-speaking immigrants share. Similar findings have been documented in the US study by Betts and Fairlie (2003). Ethnic distance may also contribute to differences in tastes for education. If so, native parents would be more likely to switch their children into private schools the more ethnically distant the 19
20
-0.3652
(1.517)
1.3701**
(0.6113)
11774
native share
Like-type
immigrants
Other immigrants
Locality controls
No. of observations
Yes
Locality controls
0.0441
Yes
Yes
10279
(0.5749)
1.3437**
(0.4144)
(0.1803)
0.4234**
(0.1306)
0.0139
Marg. effect
3rd generation Coefficient
7260
(0.9021)
1.756*
(2.0579)
1.591
(0.5041)
0.6166
Coefficient
(0.3371)
0.6559*
(0.7687)
0.5943
(0.1883)
0.2303
Marg. effect
Yes
Yes
Yes
1002
(1.3284)
-3.277**
(0.6632)
3.3572***
Individual and other locality controls are all those included in the benchmark models.
Marginal effects are calculated at the means.
7260
(0.9421)
1.8343*
(2.0886)
1.2811
(0.4973)
0.6075
Coefficient
(0.5233)
-1.2893**
(0.2607)
1.3209***
Marg. effect
(0.3519)
0.6852*
(0.7803)
0.4785
(0.1857)
0.2269
Marg. effect
Yes
Yes
Yes
6249
(0.9024)
2.672***
(0.5417)
-0.3631
Coefficient
(0.333)
0.9844***
(0.1997)
-0.1338
Marg. effect
3rd generation
Secondary school 2nd generation Coefficient
Standard errors are corrected for heteroskedasticity and clustering of the residuals at the level of localities.
States Yes Yes *Denotes significance at 10 percent level; ** at 5 percent level; *** at 1 percent level.
Yes
Individual controls
(0.4227)
1495
(1.1662)
No. of observations
-0.1712
-0.4728
Immigrant share
0.1164 (0.2186)
0.3215
(0.6031)
native share
Marg. effect
Non-European
Coefficient
(0.1959)
0.4402**
(0.4526)
-0.0145
(0.1199)
0.0249
Marg. effect
Aggregate grouping
Secondary school Detailed grouping
Panel B: School choice of the second and third generation natives
11774
(0.6045)
1.3612**
(1.3993)
-0.0448
(0.3706)
0.077
Coefficient
Aggregate grouping
Primary school
(0.1981)
0.4431**
(0.4909)
-0.1181
(0.1203)
0.018
Marg. effect
2nd generation
0.0557
(0.3721)
Non-European
Coefficient
Detailed grouping
Primary school
Panel A: Immigrant type and native school choice
Tab. 4: Other measures of immigrant-native “distance” and native school choice
immigrants in the locality are. An average native in the sample is based in a locality with around 2 percent of its student population comprising of immigrants of their like type and around 9 percent of immigrants of other types.22 Thus, in the second sets of regression, the immigrants are disaggregated into those belonging to the native’s ethnic type and to others. Both detailed and broad ethnic type groupings are employed. The estimates reported in Panel A of Table 4 show that it is the “distant” immigrant groups that are more likely to lead to native “flight”. At the primary level, a 10 percent increase in other (non-like type) immigrant share of student population leads to an increase in the probability of private school attendance of around 4 percentage points. The impact is stronger at the secondary school level. A 10 percent increase in the other immigrant share of student population increases the probability of private school attendance by around 7 percentage points. Likewise, like-type immigrant share has no statistically significant impact on the probability of attending private schools.23 Betts and Fairlie (2003) do their analysis separately for non-Hispanic, white and for all other native children concluding that almost all of the observed “flight” was accounted for by the sample of white natives. However, as the results here indicate, considering specific reaction to like-type and other immigrants sheds an additional light on the relationship by indicating that all natives, regardless of their ethnic type, are likely to flee public schools in response to immigrants who do not share their own type. Finally, natives are disaggregated into second and third generations and the model is re-estimated separately for each group. The second generation natives are defined to be the ones whose both parents were born overseas. All other natives are grouped under the third generation, including those of higher-order generations. As the results show, it is the third generation natives who flee immigrants (Panel B of Table 4). In the case of second generation natives, the coefficients on immigrant share appear with negative signs and the coefficients on non-European native share appear with positive signs, while they are statistically significant at secondary level only.24 When the model is re-estimated adopting a weaker definition of second generation natives to include those with only one parent born overseas, the results change. While third generation flight from immigrants to private schools is found, natives of second generation remain insensitive to immigrants at both 22
The definition of like/other types is the same as applied in calculation of those for immigrants detailed in Table 8 in the Appendix. 23 Differences in responsiveness to one’s like and other types across native ancestry groups were checked by including interaction terms in the model. Generally not much difference across ancestry groups in their response to like-type and other immigrants was found (only in the case of interaction term of other (nonAnglo-European or -Asian) ancestry with other immigrant share a significant coefficient with a negative sign was obtained). 24 This potentially resembles the hypothesized behavior of immigrants.
21
school levels (while the coefficients on immigrant share this time appear with positive signs).25 Tab. 5: Immigrant socio-economic status and school choice Panel A: Natives Primary school
Immigrant share Immigrant share*Occupation Occupation
No. of observations
Secondary school
Coefficient
Marg. effect
Coefficient
Marg. effect
3.642**
0.0813
2.0132
0.8009**
(1.494)
(0.2142)
(1.8016)
(0.3629)
-3.5257**
0.134
(1.6934)
(2.1425)
0.2581
-0.0495
0.1035
0.0445
(0.1651)
(0.0336)
(0.2227)
(0.0442)
11774
7260 Panel B: Immigrants
Primary school Coefficient Like-type immigrant
Marg. effect
Secondary school Coefficient
Marg. effect
-36.5633***
-1.1365
-9.3574
-1.9434**
share
(11.8399)
(0.8651)
(9.7315)
(0.8736)
Like-type immigrant
37.551***
4.6118
share*Occupation
(13.8375)
(11.5423)
Occupation
-0.9872*
0.1546
-0.1524
0.0137
(0.5415)
(0.1076)
(0.4028)
(0.1218)
No. of observations
763
914
Individual controls
Yes
Yes
Locality controls
Yes
Yes
States Yes Yes *Denotes significance at 10 percent level; ** at 5 percent level; *** at 1 percent level. Standard errors are corrected for heteroskedasticity and clustering of the residuals at the level of localities. Marginal effects are calculated at the means. Individual and other locality controls are all those included in the benchmark models.
25
Results available upon request.
22
4.5
Immigrant socio-economic status and native flight into private schools
Finally, the role of the relative socio-economic status of immigrants in the “native flight” to private schools is investigated. The immigrant socio-economic status is proxied by the average socioeconomic status of immigrants in a locality defined relative to average socio-economic status of natives in the locality. ANU3_2 status scale is used to assign socio-economic status scores to occupations in the dataset coded into the occupational classification by the Australian Bureau of Statistics (McMillan and Jones, 2000). 26 As the proposed theoretical model predicts, a higher immigrant socio-economic status in a locality should be associated with lower native “flight” to private schools. Panel A of Table 5 reports the results of estimation of the model for natives that includes immigrant socio-economic status term and its interaction with immigrant share. The results are statistically significant at primary school level only. The interaction term has a negative sign, implying that the impact of immigrant share on private school choice decreases with an increase in the relative socio-economic status of immigrants. As the socio-economic status of immigrants increases from zero (no occupation) to average, the magnitude of the impact of immigrant share on the probability of private school attendance decreases more than four times.
5
Immigrant Sorting into Public Schools
5.1
The role of individual characteristics
Table 11 in the Appendix displays the probit regression results for individual determinants of private school choice of immigrants. Student characteristics affect school choice albeit to a lesser degree than in the case of natives, which could be the result of smaller sample at hand. Catholics and other Christians have a higher probability of attending private schools compared to those with no religion while the relative probability of private school choice of representatives of other religions is hardly different from zero. In addition, immigrants from Asian and other countries are more likely to choose private schools than those from English-speaking countries. Finally, the results suggest an increase in private school attendance by schoolchildren from developed countries. This may be reflective of the possibility that people coming from developing countries may have a higher trust towards public institutions of the developed host country - arguably an important 26
The scale is based on a survey data on the perceived prestige, requirements (education) and income levels associated with occupations. It assigns a number between 0 and 100 to occupations. In the 1-percent sample only 44 occupational categories are identified.
23
determinant of their decision to migrate - compared to people from developed countries. Among family characteristics, the coefficients on single parent status are significant, but with different signs for each parent. Single mothers have a higher probability of choosing a private school for their child than traditional families, while for single fathers this probability is lower. For the case of natives, single status for both parents was associated with a higher probability of school choice, and the suggested explanation to that was decreased input of parental time in families with one parent as compared to traditional families that would lead to more investment in formal schooling (assuming parents educate children through combination of school and home investment). However, the validity of this explanation is indeed more likely to hold for the case of mothers, considering that at least in more traditional settings, home investment in children is often made by them. In addition, the results suggest a decline in private secondary school choice by children whose mothers are government employees (with the signs on other government employee status coefficients still negative, but insignificant) consistent with the underlying intuition. In this case again, parental occupational prestige is significantly positively associated with private school choice while years of education happen not to matter after controlling for it. Consistent with the results on natives, homeownership is positively related to private school choice. On the other hand, interestingly, immigrant families with higher income are less likely to send their children to private schools (the coefficient is significant at primary level only). This indicates a possible substitution of private schooling with other productive spending categories on behalf of the wealthier immigrant families.
5.2
Estimates with locality controls
In Table 6, the characteristics of the locality where the individual resides are included. Gini coefficient is significantly positively associated with immigrant private school choice, as it was the case in native regression results. The only other locality level characteristic that happens to matter for immigrant school choice is the presence of like-type immigrants in the locality. In the main specification a definition of like-type immigrant share by broad groups is adopted, to ensure that there is sufficient number of observations in each group. This definition distinguishes across immigrant groups from English-speaking, European, East Asian and other countries. Like-type immigrant share appears statistically significant with a negative sign supporting the underlying theoretical intuition. At the primary level, a 10 percent increase in the like-type immigrant share decreases the probability of private school attendance by 19.13 percentage points. For secondary schools, a 10 percent increase in the like-type immigrant share results in a decrease
24
of the probability of private school attendance by 20 percentage points. When the model is reestimated using a further disaggregated measure of like-type immigrant share across 9 types the estimated coefficients on like-type immigrant share preserve their negative sign, however only the coefficient at primary school level remains significant. 27 Tab. 6: Locality characteristics and immigrant school choice Primary school Coefficient Mean income
Gini
Unemployment
Students
Marg. effect
Secondary school Coefficient
Marg. effect
0.8607
0.2674
0.0551
0.0199
(0.741)
(0.2271)
(0.6586)
(0.2377)
9.3975**
2.9195**
9.4397***
3.4086***
(4.5439)
(1.4303)
(3.32)
(1.2085)
1.8987
0.5899
-7.4449
-2.6883
(6.3971)
(1.9811)
(5.2674)
(1.9031)
0.1835
0.057
0.1224
0.0442
(0.1922)
(0.0595)
(0.1408)
(0.0509)
Private school
0.0281
0.0087
0.0619
0.0223
student/teacher
(0.087)
(0.027)
(0.0757)
(0.0272)
-6.1563**
-1.9126**
-5.5398**
-2.0004**
(2.6274)
(0.8258)
(2.2327)
(0.8235)
Like-type immigrant share Constant
-11.606**
-5.7444
(5.2818)
(4.4559)
Individual controls
Yes
Yes
States
Yes
Yes
No. of observations Log pseudo-likelihood
763
914
-337.6515
-478.0953
Pseudo R2 0.2744 0.1994 *Denotes significance at 10 percent level; ** at 5 percent level; *** at 1 percent level. Standard errors are corrected for heteroskedasticity and clustering of the residuals at the level of localities. Marginal effects are calculated at the means. Individual controls are all those reported in Table 11 in the Appendix.
These results on immigrant responsiveness to like-type are consistent with the findings on natives in the context of the predictions of the theoretical model. In addition, the combined results on diverging school choices of natives and immigrants may imply a potential segregation across schools, while investigating it falls beyond the scope of this paper. 27
Results available upon request.
25
5.3
Robustness checks
The robustness checks of the results across several dimensions are reported in Panel B of Table 12 in the Appendix. First, is immigrants’ preference for like immigrants or for like natives? In addition, if the shares of like-type natives and immigrants are correlated, the consistency of the estimates could be threatened. Therefore, the main specification is augmented with measures of like-type natives. Insignificant results on like-type natives are obtained, while the coefficients on like-type immigrants preserve their signs and remain statistically significant (part F of the Table). It may be relevant to include the share of like-type immigrants in the entire population in the calculation of like-type immigrant shares. Estimates with this measure are reported in part G of the Table. The coefficients on like-type immigrant share are smaller in size than in the main estimation, and they are marginally significant only at the secondary school level indicating that it is the liketype immigrant share in the school population that matters more for school choice of immigrants. In addition, the model is re-estimated disaggregating the like-type immigrant shares by school types. As expected, the coefficients on like-type immigrant share at public schools appear with negative signs while those for immigrants at private schools have positive signs (part H of the Table). In all cases they are highly statistically significant. In addition, is it the total immigrant share or just the like-type immigrant share that matters? Or, if it is the like-type immigrant share that matters, do other (non-like-type) immigrants affect the school choice of immigrants too? Estimation results where like-type immigrant share is replaced with a measure of total immigrant share in the student population are reported in part I of the Table. Private school choice appears insensitive to total immigrant share of population. On the other hand, augmenting the main specification with a measure of other immigrants share, again, does not yield significant results, while it leaves the results on the effect of like-type immigrant share on school choice unchanged (part J of the Table). Given that members of the same family share the same origin, including them in the calculation of like-type immigrant share may be driving the results. Therefore, the share of schoolchildren in each individual’s family (including the individual) in the student population is deducted from the main measure of like-type immigrant share and this new measure is used to re-estimate the model. The results reported in part K of the Table are largely similar to benchmark results thus indicating that the main definition of like-type immigrant share has not been problematic. Differences in responsiveness to one’s like type across broadly defined origins of immigrants have been also checked by including interaction terms of like-type immigrant share with European, Asian and other origin dummies. None of them were significant at secondary school level indicating absence of differences across immigrant origin groups in responding to their like type in the locality when making school choice decisions. At primary school level, the interaction terms 26
with European and Other origin dummies were significant with positive signs, indicating lower responsiveness to their like type when leaning towards public schools, in comparison to children from English-speaking countries.28 To minimize the possibility of simultaneity of location decisions and school sector choices, the model is re-estimated for the 48 localities identified in the CD-ROM version of the Census. The estimate on like-type immigrant share at primary level is larger in size indicating at the possible downward bias in the main regression (part L of the Table). Estimates are significant at both primary and secondary levels. In addition, the model is re-estimated based on 13 localities constituting the major statistical regions. The coefficients on like-type immigrant shares preserve their signs while only those at secondary school level remain significant in this case. In addition, the model is re-estimated including an interaction term of the like-type immigrant share with the dummy indicating the change of place of residence of the respondent during the five years preceding the Census year. The results reported in part M of the Table show that the interaction term is insignificant at both school levels indicating that the impact of like-type immigrant share on private school choice is not different across people who have and have not moved in the past.29 Next, to control for unobserved characteristics that may potentially affect both the immigrant share and the school choice of students living in the locality, the model is re-estimated with locality fixed-effects that replace all the locality-level variables except for the like-type immigrant share that varies within the localities. Doing this, however, has the cost of capturing a part of variation in like-type immigrant share variable. The results reported in part N of the Table show significance only on like-type immigrant share (detailed definition) at primary school level, while it preserves the negative sign in all specifications.
5.4
Language and school choice of immigrants
Next, the nature of the relationship between school choice and one’s like type in the locality is considered. The “distance” from natives may matter here too. In particular, it should be the more “distant” immigrants who result in the public schools getting closer to immigrants’ needs. On the other hand, for immigrants who are less “distant” from natives, the potential of public schools to adapt to immigrant-specific needs should be less attractive. To test if this is the case, two approaches are taken. 28
Results available upon request. However, the coefficient on like-type immigrant share looses its significance under this specification, which may be related to adding an irrelevant variable in the model that leads to lower efficiency. 29
27
First, do immigrants exhibit stronger response to English-speaking immigrants than to nonEnglish-speaking immigrants? Like-type immigrant share is disaggregated into those who speak English at home and others. It turns out to be that it is the share of other immigrants that leads to a decrease in immigrant’s probability of going to private school (Panel A of Table 7). Next, immigrants’ English proficiency is considered by re-estimating the model augmenting it with an interaction term of like-type immigrant share with the dummy variable indicating whether the respondent speaks English at home. The results are reported in Panel B of Table 7. The signs on the interaction term are positive, indicating lower responsiveness to their like type in school choice on behalf of immigrants who speak English at home. However they are significant only at primary school level.30 Tab. 7: Language and school choice of immigrants Panel A: Like-type immigrant English proficiency and school choice Primary school
Secondary school
Coefficient
Marg. effect
Coefficient
Marg. effect
Engllish-speaking like-
-4.9998
-1.5528
-3.8541
-1.3917
-type immigrant share
(5.123)
(1.6003)
(4.7945)
(1.741)
Non-Engllish-speaking
-6.7183*
-2.0865*
-6.164**
-2.2259**
like-type immigrant share
(3.5144)
(1.0937)
(3.1215)
(1.1378)
Panel B: Immigrant English proficiency and school choice Primary school
Like-type immigrant share Like-type immigrant share*English-speaking
Secondary school
Coefficient
Marg. effect
Coefficient
Marg. effect
-12.7875***
-1.4898
-8.1205***
-1.8597**
(3.6291)
(0.9256)
(2.8071)
(0.855)
14.9773***
7.0938
(5.7028)
(4.7645)
Individual controls
Yes
Yes
Locality controls
Yes
Yes
States
Yes
Yes
No. of observations 763 914 *Denotes significance at 10 percent level; ** at 5 percent level; *** at 1 percent level. Standard errors are corrected for heteroskedasticity and clustering of the residuals at the level of localities. Marginal effects are calculated at the means. Individual and other locality controls are all those included in the benchmark models.
30
Spoken English at home may of course capture the effect of other factors correlated with it and that are likely to affect the degree of attachment to one’s like type. As Chiswick and Miller (1999) show, for example, positive attitudes and commitment among immigrants towards Australia are likely to lead to their greater proficiency in English.
28
5.5
Immigrant socio-economic status and school choice
The impact of immigrant share of population on “native flight” was shown to decrease with the increase in the relative socio-economic status of immigrants. As predicted by the theoretical framework, a higher socio-economic status of immigrants in a locality should be associated with a smaller presence of immigrants in the public school system thus weakening natives’ motivation to flee public schools. The impact of immigrant socio-economic status within the locality on immigrant school choice is investigated by re-estimating the benchmark model for immigrants, augmented with an interaction term of like-type immigrant share with the immigrant socio-economic status measure. The results are reported in Panel B of Table 5. The like-type immigrant share terms appear with negative signs, while the interaction terms have positive signs, confirming the above intuition. However, the results are statistically significant at primary school level only. The magnitude of the negative impact of like-type immigrant share on the probability of private primary school attendance resulting from changing the socio-economic status from zero (no occupation) to average is more than twice as small. 31
6
Summary
The study is the first to document “native flight” from public schools to private schools in response to inflow of immigrants to Australia. In particular, the native flight appears to be sensitive to the concentration of “distant” immigrants. Moreover, the impact of immigration on private school attendance by natives is suggested to decrease with immigrant socio-economic status in locality. Divergent school choice decisions on behalf of the immigrants are found. The study demonstrates that an increase in a given immigrant type in a locality is associated with a decrease in its representatives’ likelihood of enrolling into private schools. Variation of this effect with “distance” between immigrants and natives and with socio-economic status of immigrants is documented. Overall, the results are consistent with the predictions of the proposed theoretical model which is based on the assumption that public schools respond more to presence of immigrants by re-allocating budgets towards their needs than private schools do. The findings in this paper are significant for a number of reasons. In particular, social integration in schools can be threatened if native-born children flee from public schools into private schools in response to the inflow of immigrants. This may have a number of implications related 31
The interaction terms of like-type immigrant share with both immigrant socio-economic status and the dummy indicating whether the immigrant speaks English at home preserve their signs and significance, once entered jointly into the model.
29
to the quality of educational experience and thus, subsequent labour market outcomes. Given that it is often the most able students, typically from families with high socio-economic status, that shift to private schools, those at public schools remain with fewer opportunities to gain from peer groups. Indeed, as Anderson et al. (2005) point out, the public school system in Australia is under the danger of becoming an inadequate “safety net” system as a result of growing enrolments in private schools and evidence that students in private schools on average outperform students in public schools. This is particularly problematic for immigrants as separation from natives has the cost of giving up on opportunities to improve language proficiency as well as develop other socio-cultural skills necessary for their successful career at school and beyond. This in turn may threaten the success of immigration policies as immigrants who do not achieve their potential and have problems in adapting to the local setting may cost more than they may contribute to the economy.
Acknowledgements This paper is based on my doctoral thesis submitted to the University of Sydney. I am deeply indebted to my supervisor Professor Kunal Sengupta for his great support and invaluable guidance, in particular, in shaping the theoretical approach of the paper. I thank my associate supervisor Dr. Hajime Katayama for his close guidance on my empirical work and for his numerous observations and insightful comments. My special gratitude goes to Elisabeth Huynh for her great support, encouragements and many suggestions on various problems encountered in the process of writing the paper. Financial support for my postgraduate studies from the Government of Australia and from the University of Sydney is very gratefully acknowledged.
Appendix Tab. 8: Variable definition Category
Type
Variable
Definition
Dependent
Dummy
School type
Respondent attends a private school
Individual/
Cont
Age
Respondent’s age in years
family controls
Dummy
Male
Respondent is a male
Dummy
Muslim
Respondent is a Muslim
Dummy
Buddhist
Respondent is a Buddhist
variable
30
Dummy
Catholic
Respondent is a Catholic
Dummy
Other Christian
Respondent is a Christian other than Catholic
Dummy
Other religion
Respondent has a different religion than above
Dummy
Own child
Respondent is the natural or adopted child of both parents or of lone parent
Cont
Children
Number of children in respondent’s family1
Dummy
Single mother (father)
Respondent comes from a family with only one parent2
Cont
Mother’s (father’s) education
Equivalent years of education completed by respondent’s mother (father) based on the educational categories defined in the Census3
Dummy
Mother (father) government
Respondent’s mother (father) works in the government sector
employee Cont
Mother’s (father’s) occupation
Socio-economic status of the job4
Cont
Mother’s (father’s) age
Age of respondent’s mother (father) in years
Cont
Family income
Logarithm of the value of respondent’s family income plus 15
Dummy
Home ownership
Respondent lives in a house that is owned or being purchased6
Dummy
Mobility
Respondent had a different address 5 years before the Census year.
Native-specific
Dummy
Asian native
Respondent is of Asian ancestry
controls
Dummy
Other native
Respondent is of any ancestry other than European and Asian
Immigrant-
Dummy
European immigrant
Respondent was born in a European country
Dummy
Asian immigrant
Respondent was born in an East-Asian country
Dummy
Other immigrant
Respondent was born in a non-English-speaking country, outside
-specific controls
Europe and East Asia Dummy
Developed country origin
Respondent was born in a developed country
Dummy
English-speaking
Respondent speaks English at home
Locality
Cont
Mean income
Logarithm of mean income per capita in locality
Controls
Cont
Gini
Gini coefficient calculated based on income per capita in locality
Cont
Unemployment
Share of unemployed in the labour force of the locality 7
Cont
Students
Logarithm of the number of students residing in the locality
Cont
Private school student/teacher
Student to teacher ratio for private schools in the locality
Cont
Non-European native share
Share of native-born of non-European ancestry in the population enrolled in schools in the locality
Cont
Immigrant share
Share of the overseas-born population in the population enrolled in schools in the locality
31
Cont
Like-type (other) immigrants
Share of immigrants belonging to the same ethnic background as (other ethnic background than) the respondent in the population enrolled in schools in the locality 9
Cont
Immigrant occupation
Average socio-economic status of immigrants in the locality defined relative to average socio-economic status of natives in the locality. The average socio-economic status is obtained through aggregating family socio-economic status that is defined as the highest of the mother’s and father’s occupations (as per definition above).
1
Since the Census reports the number of one or more children temporarily absent from family in one category, the number of non-zero absent children added to the total number of children is taken to be 1. 2 Variables characterizing parents are defined to be equal to zero in the case of a missing parent. 3 Individuals who possess a postgraduate degree have been assigned 19 years of education, individuals who possess a graduate diploma or graduate certificate 17 years, individuals who possess a bachelor degree 16 years, individuals who possess advanced diploma or diploma 15 and individuals who possess a certificate have been assigned 12 years of education. Categories defining 9, 10, 11, 12 years of schooling have been directly recoded into numbers. Number of years of education is recoded to 6 for the single category defining respondents who didn’t go to school or who completed year 8 or below. For people still at school the number of years of education is recoded as 6 for those in primary school and 10 for those in secondary school. 4 Coded to the Australian National University3_2 (ANU3_2) scale (McMillan and Jones, 2000). The higher is the score the higher the occupational prestige. 5 The weekly earning data were collected in the following categories: (1) Negative income; (2) Nil income; (3) $1-$119; (4) $120-$159; (5) $160-$199; (6) $200-$299; (7) $300-$399; (8) $400-$499; (9) $500-$599; (10) $600-$699; (11) $700-$799; (12) $800-$999; (13) $1000-$1199; (14) $1200-$1499; (15) $1500-$1999; (16) $2000 or more. Value of $0 is used for negative and nil income categories and $3000 for the top category. All other intervals are recoded by their midpoint values. 6 For families that are coded as secondary and unrelated to the primary family in owned houses where two or more families reside, the dummy is defined to equal to zero. 7 As unemployed are counted the respondents who do not have a job and who are looking for full time/part time jobs. 9 Four backgrounds are defined by aggregating the following categories in the Census: (1) English-speaking countries (New Zealand, North America, The United Kingdom, Ireland); (2) Europe (excluding English-speaking countries); (3) East Asia; (4) Others. Alternatively, ethnic backgrounds are further disaggregated into nine groups as follows (1) English-speaking countries; (2) Oceania and Antarctica (excluding New Zealand and Australia); (3) North-West Europe (excluding the United Kingdom and Ireland); (4) Southern and Eastern Europe; (5) North Africa and the Middle East; (6) South-East Asia; (7) North-East Asia; (8) Southern and Central Asia; (9) Other (Sub-Saharan Africa, South and Central America, Caribbean).
Tab. 9: Descriptive statistics
Panel A: Summary statistics Natives Primary school
Immigrants
Secondary school
Primary school
Secondary school
Mean
Std. dev.
Mean
Std. dev.
Mean
Std. dev.
Mean
Std. dev.
School type
0.3035
0.4598
0.3829
0.4861
0.2988
0.458
0.36
0.4803
Age
9.5062
2.1473
15.5237
1.6697
9.9174
2.1541
15.8753
1.7797
Male
0.5109
0.4999
0.4912
0.5
0.4915
0.5003
0.4956
0.5003
32
Muslim
0.0193
0.1375
0.0185
0.1346
0.0983
0.2979
0.0755
0.2643
Buddhist
0.0148
0.1207
0.012
0.1088
0.0315
0.1747
0.093
0.2906
Catholic
0.3346
0.4719
0.3369
0.4727
0.2516
0.4342
0.2626
0.4403
Other Christian
0.4184
0.4933
0.4406
0.4965
0.3552
0.4789
0.3074
0.4617
Other religion
0.0092
0.0953
0.0094
0.0963
0.0708
0.2566
0.0908
0.2875
Own child
0.935
0.2465
0.9143
0.2799
0.9345
0.2476
0.9322
0.2516
Children
2.5476
0.9939
2.3229
1.025
2.3761
0.944
2.2462
0.9703
Single mother
0.1807
0.3847
0.1824
0.3862
0.1324
0.3391
0.151
0.3582
Single father
0.0208
0.1427
0.0315
0.1748
0.0183
0.1343
0.0317
0.1754
Mother’s education
9.9156
3.2955
9.6058
3.4867
10.2307
3.5949
9.7681
3.9828
Father’s education
8.3878
4.639
8.2977
4.6282
9.3512
4.6629
8.7243
4.9042
Mother government employee
0.1383
0.3452
0.1661
0.3722
0.097
0.2961
0.116
0.3204
Father government employee
0.114
0.3178
0.1384
0.3454
0.0852
0.2793
0.0952
0.2936
Mother’s occupation
21.3562
22.5458
24.1727
22.6612
18.8906
23.3009
19.3501
22.6334
Father’s occupation
26.003
23.4776
26.4055
23.9012
27.9992
25.8978
25.7657
25.0456
Mother’s age
37.502
7.8487
42.1147
9.2615
37.637
7.4588
42.2845
9.4204
Father’s age
33.7197
16.8579
37.7934
18.7192
36.0878
15.2487
39.8829
17.8616
Family income
6.8583
0.7604
6.9828
0.7347
6.7419
1.2707
6.7788
1.2247
Home ownership
0.7251
0.4465
0.7941
0.4044
0.4967
0.5003
0.6521
0.4766
Mobility
0.4748
0.4994
0.3541
0.4783
0.8218
0.383
0.6214
0.4853
Asian native
0.0339
0.1809
0.0333
0.1795
Other native
0.045
0.2073
0.0388
0.1932
European immigrant
0.1206
0.3258
0.1182
0.323
Asian immigrant
0.1887
0.3915
0.3282
0.4698
Other immigrant
0.2883
0.4533
0.2954
0.4565
Developed country origin
0.4797
0.4999
0.3271
0.4694
English-speaking
0.5374
0.4989
0.4245
0.4945
Mean income
5.9098
0.1763
5.9135
0.1786
5.9839
0.1981
5.9788
0.1967
0.26
0.0169
0.26
0.0171
0.26
0.0196
0.259
0.0199
Unemployment
0.0764
0.0203
0.076
0.0208
0.0715
0.0205
0.0721
0.0202
Students
6.2554
0.373
6.2683
0.3742
6.261
0.4067
6.2952
0.4024
Private school student/teacher
15.3453
1.0377
15.3236
1.0588
15.1522
1.2429
15.1859
1.2525
Non-European native share
0.1046
0.0741
0.1035
0.0708
Immigrant share
0.1173
0.053
0.1175
0.053
0.152
0.0477
0.1581
0.0485
0.0391
0.0248
0.0412
0.0257
Gini
Like-type immigrant share
33
Like-type immigrant share (disaggr) Immigrant occupation
0.9606
0.2972
0.9702
0.3032
0.0281
0.02
0.0276
0.0207
0.8794
0.2121
0.8664
0.2146
Panel B: Correlations Natives
Immigrants
Primary school type
Secondary school type
Primary school type
Secondary school type
0.1036***
0.1543***
Like-type immigrant share
-0.1025***
-0.1099***
Like-type immigrant share (disaggr)
-0.1405***
-0.1359***
Immigrant share
***Denotes significance at 1 percent level
Tab. 10: Individual determinants of schools choice: natives
Primary school
Age
Male
Muslim
Buddhist
Catholic
Other Christian
Other religion
Asian native
Other native
Own child
Children
Secondary school
Coefficient
Marg. effect
Coefficient
Marg. effect
-0.0113
-0.0036
-0.0421***
-0.0158***
(0.0074)
(0.0024)
(0.0084)
(0.0031)
-0.0212
-0.0069
-0.0383
-0.0143
(0.0311)
(0.0101)
(0.0308)
(0.0115)
0.5656***
0.208***
-0.3082*
-0.1078*
(0.1548)
(0.0614)
(0.1748)
(0.0563)
0.4585***
0.1661**
0.0483
0.0182
(0.1747)
(0.0684)
(0.2662)
(0.1012)
1.6513***
0.5594***
1.1519***
0.4297***
(0.0577)
(0.0175)
(0.0663)
(0.0233)
0.5436***
0.1796***
0.4317***
0.1618***
(0.0555)
(0.0181)
(0.0571)
(0.0213)
1.0054***
0.3808***
0.7573***
0.295***
(0.2239)
(0.0839)
(0.2493)
(0.0921)
0.364***
0.1291***
0.4356***
0.1704***
(0.1077)
(0.0413)
(0.1123)
(0.0444)
0.4408***
0.1583***
0.5257***
0.206***
(0.1009)
(0.0392)
(0.1618)
(0.0637)
0.3419***
0.0993***
0.3029***
0.1072***
(0.0879)
(0.0224)
(0.061)
(0.0202)
0.007
0.0023
0.0891***
0.0333***
34
Single mother
Single father
Mother’s education
Father’s education
Mother government employee Father government employee Mother’s occupation
Father’s occupation
Mother’s age
Father’s age
Family income
Home ownership
Mobility
Constant
States No. of observations Log pseudo-likelihood Pseudo
R2
(0.0202)
(0.0065)
(0.0206)
(0.0077)
0.4842***
0.17***
0.7083**
0.2744***
(0.1734)
(0.0646)
(0.2789)
(0.1071)
-0.0565
-0.018
1.0965***
0.412***
(0.1965)
(0.0614)
(0.2569)
(0.0814)
0.0108*
0.0035*
0.0107
0.004
(0.0065)
(0.0021)
(0.0066)
(0.0025)
0.0114
0.0037
0.0135
0.005
(0.0071)
(0.0023)
(0.0082)
(0.0031)
-0.2319***
-0.0708***
-0.2073***
-0.0754***
(0.0523)
(0.015)
(0.0496)
(0.0172)
-0.1626***
-0.0504***
-0.2861***
-0.1023***
(0.0569)
(0.017)
(0.0705)
(0.0239)
0.0028***
0.0009***
0.0077***
0.0029***
(0.001)
(0.0003)
(0.0012)
(0.0004)
0.0046***
0.0015***
0.0075***
0.0028***
(0.0011)
(0.0003)
(0.0012)
(0.0005)
0.0019
0.0006
0.0213***
0.008***
(0.0038)
(0.0012)
(0.0055)
(0.002)
0.0077**
0.0025**
0.0093*
0.0035*
(0.0037)
(0.0012)
(0.0055)
(0.0021)
0.1973***
0.0639***
0.2243***
0.0839***
(0.0418)
(0.0134)
(0.0511)
(0.019)
0.2463***
0.0768***
0.2869***
0.1035***
(0.0496)
(0.015)
(0.064)
(0.0224)
0.0584
0.0189
0.1214***
0.0457***
(0.0402)
(0.013)
(0.0446)
(0.0169)
-4.0491***
-4.5814***
(0.3523)
(0.4624)
Yes
Yes
11774
7260
-5695.7886
-4021.1843
0.2118
0.1677
*Denotes significance at 10 percent level; ** at 5 percent level; *** at 1 percent level. Standard errors are corrected for heteroskedasticity and clustering of the residuals at the level of localities.
35
Marginal effects are calculated at the means.
Tab. 11: Individual determinants of schools choice: immigrants
Primary school
Secondary school
Coefficient
Marg. effect
Coefficient
Marg. effect
-0.0038
-0.0012
-0.0168
-0.0061
(0.0327)
(0.0103)
(0.0268)
(0.0097)
0.0046
0.0015
-0.0641
-0.0233
(0.1355)
(0.0427)
(0.1064)
(0.0387)
0.5414*
0.1916
0.3983
0.1525
(0.3135)
(0.1188)
(0.2435)
(0.0961)
0.4161
0.1462
0.1868
0.0698
(0.3021)
(0.1148)
(0.2622)
(0.1007)
1.7557***
0.6051***
0.9539***
0.3594***
(0.216)
(0.0643)
(0.1666)
(0.0606)
0.6866***
0.2275***
0.4189**
0.156**
(0.1948)
(0.0657)
(0.1761)
(0.0664)
0.534
0.19
0.4218*
0.1615*
(0.3659)
(0.1414)
(0.2323)
(0.0918)
0.2556
0.0855
0.1319
0.0489
(0.2842)
(0.1)
(0.27)
(0.1018)
0.734***
0.2589**
0.6516**
0.2429**
(0.2828)
(0.1062)
(0.2935)
(0.11)
0.8467***
0.2904***
0.9468***
0.3544***
(0.3138)
(0.1133)
(0.3172)
(0.116)
Developed country
0.5974**
0.1882**
0.5359*
0.1996*
origin
(0.2683)
(0.0846)
(0.3055)
(0.1148)
0.2852
0.089
0.1267
0.0462
(0.1939)
(0.0597)
(0.1459)
(0.0531)
0.1473
0.0443
0.1901
0.0663
(0.2825)
(0.0809)
(0.2544)
(0.085)
-0.0982
-0.0309
-0.0501
-0.0182
(0.0667)
(0.0208)
(0.0557)
(0.0203)
Age
Male
Muslim
Buddhist
Catholic
Other Christian
Other religion
European immigrant
Asian immigrant
Other immigrant
English-speaking
Own child
Children
36
Single mother
1.5525**
0.5593***
0.7364
0.283
(0.6982)
(0.2171)
(0.6852)
(0.2625)
-1.5573*
-0.2416***
0.0708
0.026
(0.8212)
(0.0333)
(0.8912)
(0.3324)
-0.0102
-0.0032
0.016
0.0058
(0.0205)
(0.0065)
(0.0193)
(0.007)
0.0175
0.0055
0.0005
0.0002
(0.0264)
(0.0083)
(0.0227)
(0.0083)
Mother government
-0.0581
-0.018
-0.6199***
-0.1956***
employee
(0.2219)
(0.0677)
(0.1661)
(0.042)
Father government
-0.2658
-0.077
-0.2276
-0.0789
employee
(0.2449)
(0.0644)
(0.2166)
(0.0708)
0.003
0.0009
0.0098***
0.0036***
(0.0032)
(0.001)
(0.0028)
(0.001)
0.0142***
0.0045***
0.0148***
0.0054***
(0.0023)
(0.0007)
(0.0032)
(0.0011)
-0.0102
-0.0032
0.0043
0.0016
(0.0153)
(0.0048)
(0.0164)
(0.006)
0.0156
0.0049
0.0062
0.0022
(0.0137)
(0.0043)
(0.0127)
(0.0046)
-0.1073**
-0.0338**
-0.0389
-0.0141
(0.0529)
(0.0163)
(0.0482)
(0.0175)
0.4303***
0.1351***
0.3066**
0.1086**
(0.1418)
(0.0434)
(0.1387)
(0.048)
0.0001
0
-0.3046**
-0.1119**
(0.1559)
(0.0491)
(0.1335)
(0.0484)
Single father
Mother’s education
Father’s education
Mother’s occupation
Father’s occupation
Mother’s age
Father’s age
Family income
Home ownership
Mobility
Constant
-2.6651***
-1.9283**
(0.9182)
(0.9372)
States
Yes
Yes
No. of observations
763
914
-346.9050
-488.46
0.2545
0.1821
Log pseudo-likelihood Pseudo R2
*Denotes significance at 10 percent level; ** at 5 percent level; *** at 1 percent level. Standard errors are corrected for heteroskedasticity and clustering of the residuals at the level of localities. Marginal effects are calculated at the means.
37
Tab. 12: Robustness checks Panel A: Natives Primary school
A
B
Coefficient
Marg. effect
Coefficient
Marg. effect
0.8662***
0.2802***
1.1212**
0.4188**
in population
(0.3323)
(0.108)
(0.4453)
(0.167)
Public school
1.0153**
0.3284**
0.9939**
0.3713**
immigrant share
(0.4132)
(0.1338)
(0.482)
(0.1802)
0.5507
0.1781
0.7048
0.2633
immigrant share
(0.4951)
(0.1593)
(0.8112)
(0.3029)
Immigrant share
0.8474
0.2748
1.8545**
0.6929**
(1.0314)
(0.3337)
(0.8937)
(0.3341)
0.2235
0.0725
4.9208*
1.8381*
13 localities
(0.8729)
(0.2836)
(2.6098)
(0.9801)
Immigrant share
1.4445**
0.349**
1.8639**
0.647**
(0.6139)
(0.1706)
(0.8524)
(0.2933)
Immigrant share
Private school
C
48 localities Immigrant share
D
E
Secondary school
Immigrant share*
-0.7686
-0.3721
*Mobility
(0.701)
(0.8529)
Immigrant share
GLS
OLS
GLS
OLS
0.3135
0.6114
1.8971**
1.6342*
(0.9069)
(0.8927)
(0.8658)
(0.951)
Panel B: Immigrants Primary school
F
Coefficient
Marg. effect
Coefficient
Marg. effect
-7.6705***
-2.3789***
-6.4107***
-2.3133***
(2.7168)
(0.8481)
(2.3567)
(0.8684)
1.0251
0.3179
0.6444
0.2325
share
(1.0877)
(0.3351)
(0.9846)
(0.3551)
Like-type immigrant
-1.3762
-0.429
-2.3802*
-0.8599*
share in population
(1.7767)
(0.5569)
(1.2858)
(0.47)
Public school like-
-14.0264***
-4.0884***
-11.3415***
-3.9918***
(1.8232)
(0.5255)
(1.5104)
(0.517)
28.2791***
8.2428***
21.9186***
7.7146***
(4.6832)
(1.3295)
(4.3875)
(1.4905)
Like-type immigrant share Like-type native
G
H
Secondary school
-type immigrant share Private school like-type immigrant share
38
I
J
Total immigrant share
-1.502
-0.4677
-0.7358
-0.2661
(1.4836)
(0.4665)
(1.2813)
(0.4654)
-6.1677**
-1.9173**
-5.7588**
-2.0797**
(2.5786)
(0.8119)
(2.2835)
(0.8433)
1.1825
0.3676
2.131
0.7696
(2.2301)
(0.6907)
(1.9528)
(0.7039)
-5.8861**
-1.8294**
-5.7173**
-2.0641**
(2.6273)
(0.8266)
(2.2595)
(0.8333)
-5.8257**
-1.8263*
-6.3052***
-2.2807***
share 48 localities
(2.9456)
(0.9375)
(2.4327)
(0.8905)
Like-type immigrant
-2.7518
-0.8706
-8.4316*
-3.0625*
share 13 localities
(3.6653)
(1.16)
(4.8844)
(1.7867)
Like-type immigrant
-6.3436
-1.9112**
-1.2567
-2.1971***
share
(5.2991)
(0.8173)
(3.6639)
(0.7918)
Like-type immigrant share Other immigrant share
K
Like-type immigrant share alternatively defined
L
M
Like-type immigrant
Like-type immigrant
N
0.2331
-7.7738
share*Mobility
(5.7579)
(4.8744)
Like-type immigrant
-6.7609
-2.1528
-6.6711
-2.3589
share; aggregate grouping
(4.6484)
(1.4733)
(4.3939)
(1.5557)
-11.3117**
-3.6766**
-4.1557
-1.4686
(5.6327)
(1.8191)
(4.5081)
(1.5946)
Like-type immigrant share; detailed grouping
*Denotes significance at 10 percent level; ** at 5 percent level; *** at 1 percent level. Standard errors are corrected for heteroskedasticity and clustering of the residuals at the level of localities. Marginal effects are calculated at the means. Individual and other locality controls are all those included in the benchmark models.
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