Second model: Group II of provinces

Similar to the above presented interpretation, we focus on the composition of the provinces in the second group: Nova Scotia, Alberta, New Brunswick and Ontario. Ontario is the most populated territory in Canada, holding approximately 40% of the state. Alberta is the fourth largest province, and the others two have a low share of the total population (see Table 1). They all have English as the official language, with the exception of New Brunswick, which have both. Lastly, they all have in common a low number of private schools, which made us split the provinces. Because of this, we will be using the binary logit model, as explained insection 4.1.

As I highlighted previously, the setting of the benchmark family should not be an ar-bitrary decision. After analyzing the distribution of the explanatory variables, we set the benchmark values as follows:

(a) The family lives in Nova Scotia, as Quebec, Saskatchewan and British Columbia had a bigger differential effects when introduced as dummies.

(b) The home language is English as it is, once again, the most common outcome.

(c) The continuous variables are set to the mean: the family wealth, the occupational status, and the educational level for the parents.

(d) For the the other dummy variables, we have chosen the most common outcome, a household with with siblings and no grandparents.

(e) The reference level of the binary logit is Public English, in the same way as in the MNLM.

Figure 2 show the changes in probability for each variable. Referring to my previous argument in section section 3.2, for this group of provinces we only study the election of public schools. Due to this, the binary logit model is the right tool for our analysis.

Referring the continuous variables, very similar results rise from this binary logit model to the MNLM, as their influence on the outcome is rather lackluster - for most of them. Among these, the most prominent variable is Family Wealth. Its differential effects captures the diverse amalgam of the North American country. As a matter of fact, Ontario reveals the humongous importance of the family wealth when compared to the others provinces, across both models. In spite of the low percentage of French speakers my mother tongue, it has a remarkable share of public French schools - about 35% of the questioned students assisted to one - as presented inTable 3, much higher than the rest of the official English territories.

Besides, this high incidence is also captured by its dummy, as a consequence of that high number of French schools, in spite of the low number of households speaking it. The other provinces have little to none weight on the outcome. Moving to the other socio-economic dummy variables, we find that most of them excerpts no influence whatsoever, except maybe Cultural possessions and Grandparents.

Analogically to the obtained results of the first model, bilingualism is the main determi-nant of the school choice outcome. The differential effects vary considerably, even more than in the previous model. By considering this territorial discrimination, the probability of assisting a Public French school increases, approximately in 0.8, if the family is from New Brunswick and does not speak English at home. This confirms the high influence of having both languages as official.

Figure 1: Change in probability with respect to the benchmark family, Group I

28

Figure 2: Change in probability with respect to the benchmark family, Group II

29

6 Concluding Remarks & Further Research

This thesis analyses the social class segregation in upper-secondary school choice in Canada. This type of research had been done extensively before (Manski and Wise, 1983; H ´etu, 1991;Wyckoff et al, 1995, Calsamiglia et al, 2010, among many others), but the introduction of bilingualism on the model is fairly recent (Mariel et al Spanish study, 2015). The main findings of this previous literature are that there is an unremarkable evidence of social class segregation in the school choice, as wealthier households will select private and the best among the public schools, as they will be able to suffrage the indirect expenses (Bifulco and Ladd, 2007). The aim of this study is to analyse if the bilingualism affects the social class segregation in the same way as in the Mariel et al Spanish study (2015).

Hence, in order to answer this question, we selected the PISA 2012 questionnaires, as it is currently one of the most complete databases regarding students assessment. It contained enough information for us to be able to discriminate the observations by instruc-tional language, which is key due to the nature of our analysis. The surveyed sample is representative of the population, as the numbers match with the results from theStatistics Canada census (2016), with regards to the population by mother tongue and geography.

In spite of this, we were not able to study two provinces: New Foundland & Labrador, and Prince Edward Isle, as they did not stratified the schools by language. However, these provinces represent only 2% of the total population of Canada.

For our analysis, we carried out a discrete choice analysis based on a binary logit model and MNLM. The two methodologies were necessary as one half of the provinces show little to none private schooling, and the results were not sufficient. The first group of provinces include Quebec, Manitoba, Saskatchewan and British Columbia; while the second contains Nova Scotia, Alberta, New Brunswick and Ontario. Among these, we have one representative of French, the minority language in Canada, for each group:

Quebec and New Brunswick (see tableTable 1). The latter has both languages as official.

Overall, the estimation of the parameters from both models are robust. The Wald test confirms that all variables are relevant across both models. The probability of choosing schools where French is the instructional language is affected the most by those variables related to the home language. This proves that bilingualism is the main driver of school choice. Among the provinces, Quebec and New Brunswick uncover the greater influence of French when compared to others. Besides, the second most important group of pa-rameters are those related to family wealth. Once again, Quebec and Ontario showcase bigger differential effects, which is logical, given that they are among the richest territories in Canada (Statistics Canada, 2016). Having analyzed all of the variables, a few stand out for their lack of relevance, such as siblings and the educational level of both parents, which are relevant in other papers, such as in the Mariel et al study of Japan (2019).

In conclusion, similar to some studies in Basque Country (Mariel et al Spanish study, 2015), we have gathered for Canada compelling evidence of bilingualism diluting the socio-economic segregation previously found in the school choice. It would be interest-ing to analyze other bilinterest-ingual or multilinterest-ingual countries. Nonetheless, we need to take into account that not many countries boast from the unremarkable situation of Canada or the Basque Country. In order to properly understand the bilingualism effect on the school choice, the instructional language must not be restricted by territory (e.g. Belgium, where the language depends on the area in which you study).

There are 55 officially bilingual countries in the world (University of Ottawa, 2020), so there is plenty of potential research ahead. Regarding Canada, it would also be alluring to use other databases to test our findings. Statistics Canada, is the official institute of statistics of the state, and it contains enough studies to carry a similar analysis to the one we have conducted, such as the Classification of Instructional Programs (Statistics Canada, 2016). Studying school choice from another point of view could be interesting:

as a compromise between fulfilling the preferences of the households (people will tend to segregate on their own), or making an effort to eradicate the existing segregation on a social, racial, or cultural level.

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Appendix I

Table 8: Multinomial logit: first model estimation

Dependent variable:

Private English Public French Private French

(1) (2) (3)

Mother not working 0.439^∗∗∗ −0.292^∗∗∗ 0.226^∗∗

(0.100) (0.083) (0.109)

Two-parent family 0.268^∗ 0.108 0.617^∗∗∗

(0.155) (0.107) (0.179)

Siblings −0.471^∗∗∗ 0.133 0.738^∗∗∗

(0.135) (0.108) (0.190)

Grandparents 0.187 −0.560^∗∗∗ −0.589^∗∗

(0.207) (0.170) (0.278)

Cultural Possessions 0.483^∗∗∗ −0.217^∗∗∗ 0.229^∗∗∗

(0.057) (0.043) (0.059)

Mother Educ. level 0.219^∗∗∗ 0.035 0.127^∗∗

(0.052) (0.033) (0.051)

Father Educ. level 0.149^∗∗∗ −0.029 0.055

(0.042) (0.027) (0.042)

Family Wealth 0.522^∗∗∗ 0.330^∗∗∗ −0.104

(0.113) (0.087) (0.148)

Occup. Status 0.031^∗∗∗ 0.010^∗∗∗ 0.016^∗∗∗

(0.003) (0.002) (0.003)

Home Language not English 0.231 2.413^∗∗∗ 0.203

(0.338) (0.175) (0.383)

Quebec 1.453^∗∗∗ 0.266 1.142^∗∗∗

(_0.204) (_0.201) (_0.208)

Sasketchwan −0.223 −1.678^∗∗∗ −0.099

(0.267) (0.331) (0.263)

B. Columbia −0.435 0.109 −0.439

(0.267) (0.202) (0.278)

Home Language not English: Quebec −0.668^∗ 1.886^∗∗∗ 1.093^∗∗∗

(0.377) (0.239) (0.411)

Home Language not English: Saskatchewan 0.323 0.145 −0.628

(0.575) (0.432) (0.830)

Home Language not English: B. Columbia 0.289 −1.356^∗∗∗ 0.265

(0.444) (0.287) (0.509)

Family Wealth: Quebec −0.064 −0.752^∗∗∗ −0.173

(0.132) (0.102) (0.167)

Family Wealth: Saskatchewan −0.153 −0.129 0.070

(0.165) (0.198) (0.206)

Family Wealth: B.Columbia −0.160 −0.709^∗∗∗ 0.417^∗∗

(0.166) (0.147) (0.200)

Appendix II

Table 9: Binary logit: second model estimation

Dependent variable: Home Language not English: Alberta −0.778^∗∗∗

(0.286) Home Language not English: N. Brunswick 2.225^∗∗∗

(0.295) Home Language not English: Ontario −1.408^∗∗∗

(_0.241)

Family Wealth: Alberta −0.299^∗∗

(0.142)

Family Wealth: N. Brunswick −0.407^∗∗∗

(0.152)