Writing Sample

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Research Project

Immigrant Wages: Alberta, Quebec, and the Rest of Canada

ECO 6904

Sam Louden6262028

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Introduction

In 2010, Serge Nadeau and Aylin Seckin decomposed the

immigrant wage gap in Canada using census data from the years

1981, 1991, and 2001. In their study, the country was divided

into two distinct labour markets, that of Quebec and that of the

rest of Canada (henceforth known as the ROC), and the immigrant

wage gaps of each region were decomposed by means of a customised

variant of the Blinder-Oaxaca method (Nadeau 266):

Equation 1:

In black are the terms of the standard Blinder-Oaxaca

decomposition. The difference in the mean log wages of two groups

(i.e. immigrants and non-immigrants) is decomposed into a

difference explained by the group’s respective labour market

characteristics (e.g. education, experience) and a difference

that cannot be explained by labour market characteristics and is

therefore attributed to labour market discrimination (Jann 2). It

is worth noting that in addition to discrimination, the

unexplained difference is likely to capture the effects of

factors either not specified in the decomposition (e.g. distance

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to work) or factors difficult to measure (e.g. cultural attitudes

towards work.)

In red is the element added to the decomposition by Nadeau

and Seckin. It is an additional term – thus making the model a

decomposition into three components in place of the normal two –

containing parameters unique to immigrants that are known to

affect their labour market potential (e.g. citizenship, age of

immigration.)

Reproduction of Study

A particularly interesting recent economic trend in Canada

is that of the oil boom in Alberta, which began in the early

2000s when the market price of petroleum products became

sufficiently elevated as to render profitable the development of

the Athabasca oil sands (National Energy Board 11). With the boom

has come a tremendous increase in provincial “GDP” and,

potentially, an increase in real wages and a decrease in the

immigrant wage gap. It is therefore that in creating a study

based on that of Nadeau and Seckin to explore such possibilities

that Alberta is posed as a third unique labour market within

Canada, in addition to those of Quebec and the ROC (which,

naturally, now does not include Alberta.) In order to capture the

effects of the oil boom, the data stem from the 2001 and 2006

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Canadian Censuses. The selection criteria for workers are the

same as in the original article: men (in order to isolate the

immigrant wage gap from a potential male-female wage gap), aged

between 20 and 64, not self-employed, and with a strong labour

force attachment, which is defined as working more than 20 hours

per week and more than 26 weeks per year (Nadeau 267). Because

Nadeau and Seckin chose 1981, 1991, and 2001 so as to have years

at similar stages in the business cycle (they are considered peak

years,) (Nadeau 282) it is fortunate that 2001 and 2006 share the

same traits.

While finding data in keeping with the criteria laid out by

Nadeau and Seckin is quite simple, reproducing the custom

Blinder-Oaxaca model of their original article is unfortunately

beyond the scope of this course. Consequently, the decomposition

method used in this 2001-2006 study is the standard Blinder-

Oaxaca decomposition and the third term for immigrant-specific

traits will not be included, precluding the analysis of the

impact of factors such as age of immigration.

Initial Analysis

Before proceeding to the Blinder-Oaxaca decomposition and

the OLS regression on which it is based, a preliminary analysis

of the data was undertaken (Table 1). Most notably one observes

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that the mean real wage (in 2001 Canadian dollars) increased by

approximately two dollars for all groups in the ROC and for non-

immigrants in Quebec. The fact that the mean real wage for

immigrants in Quebec is essentially unchanged from 2001 to 2006

plays into a greater narrative of the immigrant wage gap in

Quebec (more to come) and the capacity of the province to

integrate its immigrants. Alberta differs markedly from Quebec

and the ROC as both immigrants and non-immigrants saw a mean real

wage increase of $5.6 CAD.

Looking at factors other than mean real wages, one observes,

for instance, that immigrants in all three labour markets have a

higher level of education than their Canadian-born counterparts,

a trend in keeping with Canada’s policies of selected

immigration. Likewise, no surprises are found when looking at

‘languages spoken at home’ and ‘knowledge of official languages’:

the vast majority of non-immigrants speak English at home in

Alberta and the ROC and speak French at home in Quebec.

Immigrants are less likely to speak the dominant language of the

region at home. More people live in bilingual households in

Quebec than in Alberta and the ROC. Finally, and as is frequently

a subject of debate in Quebec, immigrants to the province are

less likely to speak French than immigrants to Alberta and the

ROC are to speak English, a fact which may point to an increased

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failure of immigrants to integrate in Quebec and which, as will

be seen in the Blinder-Oaxaca decomposition, causes a widening of

the immigrant wage gap. Finally, it is interesting to note that

in Alberta, Quebec, and the ROC, approximately 65% of non-

immigrants live in cities. Immigrants, on the other hand, are far

more likely to live in cities than non-immigrants with rates near

90% in Alberta and the ROC and near 96% in Quebec.

In looking at factors unique to immigrants, one observes

that immigrants to Quebec are far less likely to originate from

the United States and the United Kingdom than immigrants to

Alberta and the ROC and are more likely to originate from

‘other.’ As the U.S. and the U.K. share strong cultural ties to

Canada and typically provide the best-assimilating immigrants,

this may be amongst the root causes of Quebec’s integration

difficulties.

The Immigrant Wage Gap

The immigrant wage gap is found simply by calculating the

difference of mean log wages between immigrants and non-

immigrants. A negative value therefore indicates an advantage to

Canadian-born individuals. Results are displayed in Table 2.

Table 2: Wage Gaps, Immigrants vs. Those Born in Canada

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2001 2006

Province Gap |t| Province Gap |t| Δ

Alberta -0.050** 3.26 Alberta -0.038* 2.33 +0.012

Québec -0.129*** 10.9 Québec -0.168*** 14.32 -0.039

ROC -0.050*** 8.86 ROC -0.065*** 11.3 -0.015

In both 2001 and 2006, all results are found to be

statistically significant. All three regions are found to have

negative wage gaps over the period, indicating an advantage to

Canadian-born workers. One observes that in Quebec and the ROC,

the immigrant wage gap widens (i.e. becomes more negative) over

the period. Quebec, which has the widest wage gap in 2001, also

has the largest change in wage gap amongst the three labour

markets as it grows from -0.129 in 2001 to -0.168 in 2006. This

result is hinted at in Table 1 as one observes that while the

mean real wage for Canadian-born workers in the province

increases by approximately two dollars over the period, the mean

real wage for immigrants increases by only 0.3 dollars, the

smallest increase of all three labour markets. The wage gap in

the ROC, in contrast, widens by left than half that of Quebec

over the same period, a result also predicted by the data in

Table 1 as the mean real wage grows slightly slower (1.9 vs. 2.3)

for immigrants than for non-immigrants. Only in Alberta did the

wage gap shrink as it progressed from -0.050 in 2001 to -0.038 in

2006, a result not only expected due to the recent oil boom but

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also foreshadowed by Alberta’s relatively strong mean real wage

increase as seen in Table 1: exactly 5.6 dollars for both groups.

See Graph 1 for a visual representation of the wage gaps and

their evolution.

Regression Results

The standard Blinder-Oaxaca decomposition contains, as shown

in Equation 1, vectors βB and βI which contain the returns to

various labour market characteristics (e.g. education,

experience) as determined by an OLS regression for Canadian-born

and immigrant workers, respectively. It is worth noting that the

regression results are not merely an intermediate step of little

import but are in and of themselves an interesting point of

analysis allowing one to compare returns amongst the two groups

and across the labour markets. Note that the dependant variable

(wage) is logarithmic and the independent variables are level,

meaning that regression coefficients are interpreted as the

decimal expression of the percent change in the dependent

variable. E.g. a coefficient of 0.05 indicates each unit of the

factor is estimated to increase wage by 5%.

Alberta

Turning first to the Alberta regression (Table 3) and

looking only at statistically significant results, one observes

that education (educ) has a positive return amongst all groups

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and in both 2001 and 2006; the same is true for potential

experience (exp_poten.) The returns for immigrants, however, are

lower than those for Canadian-born workers, a result which holds

true for all three labour markets. The coefficients of potential

experience squared (exp_poten_sq) are all negative, indicating

(as expected) than potential experience has decreasing marginal

returns. Amongst linguistic factors, only the coefficients for

speaking a non-official language at home (other_home) are

statistically significant. As English is the reference language

and given the fact that the language spoken at home is a good

indicator of an individual’s fluency (Nadeau 267), it is not

surprising that the other_home coefficient is negative (i.e. it

is estimated to decrease one’s wage.) More interesting, however,

is the size of the coefficient which, at approximately -0.2 for

both groups and both years, is the largest single coefficient of

the regression. One can therefore conclude that there is a high

premium placed on fluency in English in Alberta. Another

interesting result is that of the return to living in a

metropolitan region (CMA). There is a premium of approximately

10% in 2001 but a premium of only approximately 4% in 2006, a

trend likely due to the fact that many jobs associated with the

oil boom, particularly those related to extraction and

transportation, are found outside of metropolitan areas.

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Amongst factors unique to immigrants, one observes that

there is a premium associated with becoming a Canadian citizen:

an 8.8% premium in 2001 and an 11% premium in 2006. This finding

is in keeping with other empirical studies of the Canadian labour

market (Nadeau 272); an explanation of the mechanism behind the

phenomenon is beyond the scope of this study. The other two

statistically significant coefficients, those of ‘other’

countries of origin (autre) and Asian foreign labour market

experience (exp_asie,) are both negative relative to the

reference countries the U.S. and the U.K., indicating that

immigrants from these regions may have a decreased

transferability of skills and/or work experience that employers

find less applicable to Canadian jobs.

Quebec

Turning to Quebec (Table 4), one observes a similar positive

return to education and potential experience for both groups and

a higher return for Canadian-born workers. Potential experience

is also found to have decreasing marginal returns. Amongst

linguistic factors, always a hot-button topic in the province,

there is a similar negative return to speaking a non-official

language at home, indicating that fluency in an official language

is as important in Quebec as it is in Alberta. A phenomenon not

seen in Alberta is that of a positive return on being bilingual.

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An increase in wage of 7.9% is predicted for Canadian-born

workers in 2001 and an increase of 13.0% is predicted for

immigrants in 2006. This finding is likely related to English’s

position as a global lingua franca and the fact that Quebec is,

as a province, more bilingual than either Alberta or the ROC

(Table 1). A final interesting characteristic of Quebec is that

relative to the U.S. and the U.K., all other countries of origin

have a negative return. As expected, ‘other’ countries have the

most negative coefficient, but interestingly, the coefficients

for countries in Europe and countries in Asia are of

approximately the same magnitude in both periods, a trend which

may indicate that cultural factors (other than language) are not

necessarily advantageous to Europeans despite sharing cultural

roots with Quebec.

ROC

Unique to the regression for the ROC (Table 5) are variables

for the Prairie Provinces (taken here as Saskatchewan and

Manitoba) and British Columbia. The regression yields that living

in both the Prairies and B.C. reduces one’s income relative to

other parts of the ROC (essentially Ontario as the Territories

and Atlantic Provinces are excluded as they are home to

sufficiently few immigrants that confidentiality cannot be

assured.) Amongst education and potential experience one observes

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results comparable to those of the other two labour markets: a

positive return on education and potential experience, higher

returns for Canadian-born workers, and decreasing marginal

returns to potential experience. Amongst statistically

significant linguistic factors are speaking a non-official

language at home (other_home) and not having a knowledge of

either official language (none_know), both of which have, as

expected, negative returns.

Amongst factors unique to immigrants it is interesting to

note that the ROC regression has more statistically significant

coefficients than the previous two regressions. Relative to the

U.S. and U.K., for instance, all other countries of origin are

estimated to have a negative impact on an individual’s real wage.

The same pattern of negative returns is found when looking at

foreign work experience by country: work experience in all

regions, aside from the U.S. and the U.K., is expected to

diminish one’s real wage.

Blinder-Oaxaca Decomposition

In looking at the results of the decomposition (Table 6),

one first notices that all three labour markets have a positive

unexplained difference, signifying that it serves to widen the

immigrant wage gap. The existence of an unexplained difference is

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due not only to the presence of labour market discrimination, as

is most often attributed in literature, but also due to factors

either not specified in the decomposition (e.g. distance to work)

or factors difficult to measure (e.g. cultural attitudes towards

work, motivation.) Assuming that the authors of the original

study made the most of available Canadian Census data in

formulating their decomposition, the positive unexplained terms

imply that Canadian immigrants have difficulty integrating in the

labour market due to factors not easily measured by census-type

surveys.

Turning to the explained term of the decomposition, one

observes a negative overall coefficient for both Alberta and the

ROC, indicating that the traits of immigrants included in the

decomposition serve to shrink the immigrant wage gap. In Alberta,

Quebec, and the ROC, for instance, education and potential

experience have statistically significant negative coefficients

in all periods, indicating that they are two areas in which

immigrants perform well. Also in Alberta, Quebec, and the ROC,

speaking a non-official language at home has a positive

coefficient, signifying that it is estimated to widen the

immigrant wage gap, a logical conclusion due to the importance of

fluid communication in most forms of employment. Unique to Quebec

is the peculiar fact that speaking French at home is actually

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estimated at a statistically significant level (although only in

2006) to widen the immigrant wage gap. One reason for which this

could be the case is that foreign dialects of French are arguably

more varied than foreign dialects of English. A European

immigrant who speaks Occitan or a Caribbean immigrant who speaks

a French-based Creole could potentially indicate that they speak

French at home in completing the census yet have difficulty

communicating with speakers of Quebec French. Also unique to

Quebec is the advantage of bilingualism, as seen in the negative

coefficient of having as knowledge of both official languages

(both_know). Finally, living in a metropolitan area (cma) is

estimated at a statistically significant level to shrink the

immigrant wage gap in all three labour markets.

Conclusion

The immigrant wage gap is an important measure of the

capacity of Canadian immigration policies to identify foreign

workers that are able to successfully integrate into Canada’s

labour market. It is also, to a certain degree, a measure of

immigrants’ capacities to adapt to the realities of life in

Canada, be the factors cultural, political, or linguistic.

Historical analysis of the immigrant wage gap, as performed in

the study of Nadeau and Seckin, reveals that the gap has been

widening in all of Canada from 1981 to 2001 (Nadeau 269). This

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study found that the trend has continued in most regions of

Canada over the 2001-2006 period. While the manner in which the

wage gaps were decomposed varies between the two studies as

Nadeau and Seckin made use of a custom Blinder-Oaxaca method

which was not able to be reproduced in the current study, the

means of determining the wage gaps in both studies were the same.

A careful listing of the census criteria (men aged 20-64, etc.)

in the original study made possible a fidelitous selection of

data in this study. Additionally, many of the quantitative

methods of analysis (e.g. average wage, percent living in a

metropolitan area) were sufficiently standard as to also be

reliably reproduced. This includes the calculation of the wage

gap itself, defined simply as the difference of mean log wages. A

point of comparison between the studies in found in Quebec in the

year 2001 (the ROC may not be used due to the separation of

Alberta in this study.) As expected, one finds essentially the

same, although not exact figures. The 2001 Quebec wage gap was

found by Nadeau and Seckin to be -0.128 (Nadeau 269), whereas the

figure found in this study was a similar -0.129 (Table 1). The

same comparison was able to be made for the contents of Table 1,

for which a ‘Check’ column was added. One observes that all

figures vary from those of the original study by less than

abs(1), with the sole exception of the percentage of immigrants

who arrived in Canada before age 13, a figure which differs from

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that of the original by a remarkable 12.9 percentage points. It

is possible that this one large exception is due to a calculation

error on the part of myself or the original authors.

In summary, the immigrant wage gap from 2001 to 2006 was

found to have widened in both Quebec, where the wage gap has

historically been relatively large, and the ROC. Alberta, in

contrast, was found to have an immigrant wage gap that shrunk

over the same period. All three trends are likely due not only to

the decline of traditional sectors like manufacturing in the ROC

and Quebec and the rapid growth of the petroleum sector in

Alberta but also potentially to the increased tendency of

immigrants to originate from countries with larger cultural and

linguistic differences than past generations.

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-0,18

-0,16

-0,14-0,12

-0,1

-0,08

-0,06-0,04

-0,02

02001 2006

Alberta Québec ROC

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Table 3: Regression Results, Alberta

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Table 4: Regression Results, Quebec

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Table 5: Regression Results, ROC

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Appendix A: DataThe following data were found by means of the Canadian Census

Analyser (Cf. bibliography):

2001 Census:

Selection Filters (as outlined by Nadeau and Seckin)1

sexp(2), agep(20-64), hrswkp(20-100), wkswkp(26-52), selfip(0), totincp(-50000-200000)

+ Alta/Que, immigrants provp(48/24), yrimmig(1-6)+ Alta/Que, non-immigrants provp(48/24), yrimmig(9)

+ Rest of Canada, immigrants

provp(35,46,47,59)2, yrimmig(1-6)

+ Rest of Canada, non-immigrants

provp(35,46,47,59)2, yrimmig(9)

Variables Downloaded totincp, hrswkp, wkswkp, totschp, agep, hlnp, olnp, cmap, citizenp, immiagep, pobp

2006 Census:

Selection Filters 1 sex(2), agegrp(8-16), hrswrk(20-98), wkswrk(26-52), sempi(0), totinc(-50000-1285586)

+ Alta/Que, immigrants pr(48/24), yrimm(1-7, 1980-2006)+ Alta/Que, non-immigrants pr(48/24), yrimm(9999)

+ Rest of Canada, immigrants

pr(35,46,47,59)2, yrimm(1-7, 1980-2006)

+ Rest of Canada, non-immigrants

pr(35,46,47,59)2, yrimm(9999)

Variables Downloaded totinc, hrswrk, wkswrk, hdgree, agegrp, hlaen, hlafr, hlano, kol, cma, citizen, ageimm, pob

1: “men aged between 20 and 64, who work more than 20 hours per week and more than 26 weeks per year, and who are not self-employed” (Nadeau, 2010)2: The Atlantic Provinces are excluded for reasons of confidentiality (Nadeau, 2010)

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Appendix B: Variable Names

Variable in 2001

Meaning Equivalent in 2006

sexp Sex sexagep Age agegrphrswkp Hours worked per

weekhrswrk

wkswkp Weeks worked per year

wkswrk

selfip Self-employment income

sempi

totincp Total income totincprovp Province prtotschp Education hdgreehlnp Language.s

spoken at homehlaen (anglais), hlafr (français), hlano (autre)

olnp Knowledge of official languages

kol

cmap CMA (Canadian metropolitan area)

cma

citizenp Citizenship citizenimmiagep Age at

immigrationageimm

pobp Country of birth pob

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Appendix C: Do File, Construction of Initial Analysis Table, 2001 Census

/*Selection filters:Alberta/Quebec: sexp(2), agep(20-64), hrswkp(20-100), wkswkp(26-52), selfip(0), totincp(-50000-200000), provp(48/24)ROC (rest of Canada): sexp(2), agep(20-64), hrswkp(20-100), wkswkp(26-52), selfip(0), totincp(-50000-200000), provp(35,46,47,59)

yrimmig(1-6) for immigrants, yrimmig(9) for non-immigrants

Variables required:totincp, hrswkp, wkswkp, totschp, agep, hlnp, olnp, cmap, citizenp, immiagep, pobp*/

// Average wage:gen hour_wage = totincp/(hrswkp*wkswkp)summarize hour_wage// i.e. total income in 2001 divided by hours worked in 2001

// Median wage:// <see previous>

// Average education (years):gen educ = 0replace educ = 3 if(totschp==1)replace educ = 6.5 if(totschp==2)replace educ = 9 if(totschp==3)replace educ = 10 if(totschp==4)replace educ = 11 if(totschp==5)replace educ = 12 if(totschp==6)replace educ = 13 if(totschp==7)replace educ = 15.5 if(totschp==8)replace educ = 18 if(totschp==9)summarize educ

// Average age (years):summarize agep

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// Language.s spoken at home:// % English:gen en_home = 0replace en_home = 1 if(hlnp==1)// N.-B. one divides the number of “real changes made” by the sample size in order to calculate the percentage

// % French:gen fr_home = 0replace fr_home = 1 if(hlnp==2)

// % Both:gen both_home = 0replace both_home = 1 if(hlnp==3)

// % Other:gen other_home = 0replace other_home = 1 if(hlnp==4 | hlnp==5)// i.e. aboriginal languages (4), others (5)

// Knowledge of official languages// % English:gen en_work = 0replace en_work = 1 if(olnp==1)

// % French:gen fr_work = 0replace fr_work = 1 if(olnp==2)

// % Both:gen both_work = 0replace both_work = 1 if(olnp==3)

// % Neither:gen none_work= 0replace none_work = 1 if(olnp==4)

// CMA (Canadian metropolitan area):gen cma = 0replace cma = 1 if(cmap!=999)// if countryside == 999, then in town != 999

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Unique to immigrants:

// % Canadian citizen:gen citizen = 0replace citizen = 1 if(citizenp==1 | citizenp==2)// i.e. by birth, by naturalisation

// % Immigrated before age 13:gen young = 0replace young = 1 if(immiagep==1 | immiagep==2)// i.e. 0-4 + 5-12 for "under 13"

// Foreign work experience (years):gen age_immigration = 0replace age_immigration = 2 if(immiagep==1)replace age_immigration = 8.5 if(immiagep==2)replace age_immigration = 16 if(immiagep==3)replace age_immigration = 22 if(immiagep==4)replace age_immigration = 27 if(immiagep==5)replace age_immigration = 32 if(immiagep==6)replace age_immigration = 37 if(immiagep==7)replace age_immigration = 42 if(immiagep==8)replace age_immigration = 47 if(immiagep==9)replace age_immigration = 52 if(immiagep==10)replace age_immigration = 57 if(immiagep==11)replace age_immigration = 60 if(immiagep==12)replace age_immigration = 0 if(age_immigration<0)

// gen years_since_immigration = agep - age_immigration// gen pre_immig_exp = agep - educ - 6 - years_since_immigration// which simplifies to:

gen pre_immig_exp = age_immigration - educ – 6replace pre_immig_exp = 0 if(pre_immig_exp<0)summarize pre_immig_exp

// Country of origin:// % U.S. and U.K.:gen us_uk = 0replace us_uk = 1 if(pobp==6 | pobp==7)

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// % Other European:gen rest_europe = 0replace rest_europe = 1 if(pobp==8 | pobp==9 | pobp==10)

// % Asia:gen asia = 0replace asia = 1 if(pobp==11)

// % Others:gen other = 0replace other = 1 if(pobp==12)

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Appendix D: Appendix C: Do File, Construction of Initial Analysis Table, 2006 Census

/*Selection filters:Alberta/Quebec: sex(2), agegrp(8-16), hrswrk(20-98), wkswrk(26-52), sempi(0), totinc(-50000-1285586), pr(48/24)ROC: Quebec: sex(2), agegrp(8-16), hrswrk(20-98), wkswrk(26-52), sempi(0), totinc(-50000-1285586), pr(35,46,47,59)

yrimm(1-7, 1980-2006) for immigrants, yrimm(9999) for non-immigrants

Variables required:totinc, hrswrk, wkswrk, hdgree, agegrp, hlaen, hlafr, hlano, kol, cma, citizen, ageimm, pob*/

// Average wage:gen hour_wage = (totinc/(hrswrk*wkswrk))*0.9summarize hour_wage// // i.e. total income in 2006 divided by hours worked in 2006// CPI base year = 2001, therefore *0.9 as recommended by the Bank of Canada

// Median wage:// <see previous>

// Average education (years)gen educ = 0replace educ = 8 if(hdgree==1)replace educ = 12 if(hdgree==2)replace educ = 13 if(hdgree==3 | hdgree==4 | hdgree==5)replace educ = 14 if(hdgree==6 | hdgree==7)replace educ = 15 if(hdgree==8)replace educ = 16 if(hdgree==9)replace educ = 17 if(hdgree==10)replace educ = 18 if(hdgree==12)replace educ = 22 if(hdgree==11 | hdgree==13)summarize educ

// Average age (years):gen age = 0

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replace age = 2 if(agegrp==1)replace age = 5.5 if(agegrp==2)replace age = 8 if(agegrp==3)replace age = 10.5 if(agegrp==4)replace age = 13 if(agegrp==5)replace age = 16 if(agegrp==6)replace age = 18.5 if(agegrp==7)replace age = 22 if(agegrp==8)replace age = 27 if(agegrp==9)replace age = 32 if(agegrp==10)replace age = 37 if(agegrp==11)replace age = 42 if(agegrp==12)replace age = 47 if(agegrp==13)replace age = 52 if(agegrp==14)replace age = 57 if(agegrp==15)replace age = 62 if(agegrp==16)replace age = 67 if(agegrp==17)replace age = 72 if(agegrp==18)replace age = 77 if(agegrp==19)replace age = 82 if(agegrp==20)replace age = 85 if(agegrp==21)summarize age

// Language.s spoken at home// % English:gen en_home = 0replace en_home = 1 if(hlaen==1)

// % French:gen fr_home = 0replace fr_home = 1 if(hlafr==1)

// % Both:gen both_home = 0replace both_home = 1 if(hlaen==1 & hlafr==1)

// % Other:gen other_home = 0replace other_home = 1 if(hlano!=1)

// Knowledge of official languages:// % English:gen en_work = 0replace en_work = 1 if(kol==1)

// % French:gen fr_work = 0

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replace fr_work = 1 if(kol==2)// % Both:gen both_work = 0replace both_work = 1 if(kol==3)

// % Other:gen none_work= 0replace none_work = 1 if(kol==4)

// CMA (Canadian metropolitan area):gen metro_area = 0replace metro_area = 1 if(cma!=999)// if countryside == 999, then in town != 999

Unique to immigrants:

// % Canadian citizen:gen can_citizen = 0replace can_citizen = 1 if(citizen==1 | citizen==2)

// % Immigrated before age 13:gen young = 0replace young = 1 if(ageimm==1 | ageimm==2 | ageimm==3)// i.e. 0-4 + 5-9 + 9-14 to approximate "under 13"

// Foreign work experience (years):gen age_immigration = 0replace age_immigration = 2 if(ageimm==1)replace age_immigration = 7 if(ageimm==2)replace age_immigration = 12 if(ageimm==3)replace age_immigration = 17 if(ageimm==4)replace age_immigration = 22 if(ageimm==5)replace age_immigration = 27 if(ageimm==6)replace age_immigration = 32 if(ageimm==7)replace age_immigration = 37 if(ageimm==8)replace age_immigration = 42 if(ageimm==9)replace age_immigration = 47 if(ageimm==10)replace age_immigration = 52 if(ageimm==11)replace age_immigration = 57 if(ageimm==12)replace age_immigration = 60 if(ageimm==13)

gen pre_immig_exp = age_immigration - educ - 6replace pre_immig_exp = 0 if(pre_immig_exp<0)summarize pre_immig_exp

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// Country of origin:// % U.S. and U.K.:gen us_uk = 0replace us_uk = 1 if(pob==2 | pob==7)

// % Other European:gen rest_europe = 0replace rest_europe = 1 if(pob==8 | pob==9 | pob==10 | pob==11 | pob==12 | pob==13 | pob==14)

// % Asia:gen asia = 0replace asia = 1 if(pob==18 | pob==19 | pob==20 | pob==21 | pob==22 | pob==23 | pob==24 | pob==25 | pob==26)

// % Others:gen other = 0replace other = 1 if(pob==3 | pob==4 | pob==5 | pob==6 | pob==15 | pob==16 | pob==17 | pob==27)

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Appendix E: Do File, Oaxaca Decomposition, 2001 Census

/*- one must first execute the command "ssc install oaxaca" in order to install the plug-in

Selection filters:Alberta: sexp(2), agep(20-64), hrswkp(20-100), wkswkp(26-52), selfip(0), wagesp(0-200000), provp(48)

ROC: sexp(2), agep(20-64), hrswkp(20-100), wkswkp(26-52), selfip(0), wagesp(0-200000), provp(35,46,47,59)

yrimmig(1-6) for immigrants, yrimmig(9) for non-immigrants

Variables required:totincp, hrswkp, wkswkp, totschp, agep, hlnp, olnp, cmap, citizenp, immiagep, pobp*/

// Dependant variable:gen hour_wage = totincp/(hrswkp*wkswkp)gen log_hour_wage = log(hour_wage)replace log_hour_wage = 0 if(log_hour_wage<0)

// Variable by (what distinguishes the two groups):gen immig = 0replace immig = 1 if(yrimmig!=9)

// Prairies:gen prairies = 0replace prairies = 1 if(provp==46 | provp==47)

// Quebec:gen quebec = 0replace quebec = 1 if(provp==24)

// B.C.:gen bc = 0replace bc = 1 if(provp==59)

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// Education:gen educ = 0replace educ = 3 if(totschp==1)replace educ = 6.5 if(totschp==2)replace educ = 9 if(totschp==3)replace educ = 10 if(totschp==4)replace educ = 11 if(totschp==5)replace educ = 12 if(totschp==6)replace educ = 13 if(totschp==7)replace educ = 15.5 if(totschp==8)replace educ = 18 if(totschp==9)

// Potential experience:gen poten_exp = agep - educ - 6replace poten_exp = 0 if(poten_exp<0)// negative values are removed as they have no practical interpretation

// Potential experience, squared, over 100:gen poten_exp_sq = (poten_exp^2)/100

// Language.s spoken at home:// Reference: English

// French:gen fr_home = 0replace fr_home = 1 if(hlnp==2)

// Both:gen both_home = 0replace both_home = 1 if(hlnp==3)

// Other:gen other_home = 0replace other_home = 1 if(hlnp==4 | hlnp==5)

// Knowledge of official languages// Reference: English

// French:gen fr_work = 0replace fr_work = 1 if(olnp==2)

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// Both:gen both_work = 0replace both_work = 1 if(olnp==3)

// Neither:gen other_work = 0replace other_work = 1 if(olnp==4)

// CMA (Canadian metropolitan area):gen cma = 0replace cma = 1 if(cmap!=999)// en campagne == 999, donc en ville != 999

// Canadian citizen:gen citizen = 0replace citizen = 1 if(citizenp==1 | citizenp==2)

// Immigrated before age 13:gen young = 0replace young = 1 if(immiagep==1 | immiagep==2)

// Immigrated before age 13, education:gen young_educ = young*educ

// Country of origin:// Reference: U.S. and U.K.

// Other European:gen rest_europe = 0replace rest_europe = 1 if(pobp==8 | pobp==9 | pobp==10)

// Asia:gen asia = 0replace asia = 1 if(pobp==11)

// Others:gen other = 0replace other = 1 if(pobp==12)

// Foreign work experiencegen age_immigration = 0

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replace age_immigration = 2 if(immiagep==1)replace age_immigration = 8.5 if(immiagep==2)replace age_immigration = 16 if(immiagep==3)replace age_immigration = 22 if(immiagep==4)replace age_immigration = 27 if(immiagep==5)replace age_immigration = 32 if(immiagep==6)replace age_immigration = 37 if(immiagep==7)replace age_immigration = 42 if(immiagep==8)replace age_immigration = 47 if(immiagep==9)replace age_immigration = 52 if(immiagep==10)replace age_immigration = 57 if(immiagep==11)replace age_immigration = 60 if(immiagep==12)

// gen years_since_immig = agep - age_immigration// gen pre_immig_exp = poten_exp - years_since_immig// which simplifies to:

gen pre_immig_exp = age_immigration - educ - 6replace pre_immig_exp = 0 if(pre_immig_exp<0)

// U.S. and U.K.gen us_uk = 0replace us_uk = 1 if(pobp==6 | pobp==7)gen us_uk_exp = us_uk*pre_immig_exp

// Other European:gen rest_europe_exp = rest_europe*pre_immig_exp

// Asia:gen asia_exp = asia*pre_immig_exp

// Others:gen other_exp = other*pre_immig_exp

// Foreign work experience, squared, over 100:// U.S. and U.K.gen us_uk_exp_sq = (us_uk_exp^2)/100

// Other European:gen rest_europe_exp_sq = (rest_europe_exp^2)/100

// Asia:gen asia_exp_sq = (asia_exp^2)/100

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// Others:gen other_exp_sq = (other_exp^2)/100

// Foreign work experience * experience in Canada, over 100:gen dom_exp = agep - educ - 6 - pre_immig_expreplace dom_exp = 0 if(dom_exp<0)

// U.S. and U.K.:gen us_uk_exp_dom = (us_uk_exp*dom_exp)/100

// Other European:gen rest_europe_exp_dom = (rest_europe_exp*dom_exp)/100

// Asia:gen asia_exp_dom = (asia_exp*dom_exp)/100

// Others:gen other_exp_dom = (other_exp*dom_exp)/100

OAXACA:

Regression for Immigrants:regress log_hour_wage prairies bc educ poten_exp poten_exp_sq fr_home both_home other_home fr_work both_work other_work cma citizen young young_educ rest_europe asia other us_uk_exp rest_europe_exp asia_exp other_exp us_uk_exp_sq rest_europe_exp_sq asia_exp_sq other_exp_sq us_uk_exp_dom rest_europe_exp_dom asia_exp_dom other_exp_dom if(immig==1), vce(robust)

Regression for Non-Immigrants:regress log_hour_wage prairies bc educ poten_exp poten_exp_sq fr_home both_home other_home fr_work both_work other_work cma if(immig==0), vce(robust)

Oaxaca Decomposition, Immigrant Coefficients as Reference:oaxaca log_hour_wage prairies bc educ poten_exp poten_exp_sq fr_home both_home other_home fr_work both_work other_work cma, by(immig) weight(0) detail

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Oaxaca Decomposition, Non-Immigrant Coefficients as Reference:oaxaca log_hour_wage prairies bc educ poten_exp poten_exp_sq fr_home both_home other_home fr_work both_work other_work cma, by(immig) weight(1) detail

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Appendix F: Do File, Oaxaca Decomposition, 2006 Census

/*Selection filters:Alberta: sex(2), agegrp(8-16), hrswrk(20-98), wkswrk(26-52), sempi(0), wages(0-1226490), pr(48)

ROC: sex(2), agegrp(8-16), hrswrk(20-98), wkswrk(26-52), sempi(0), wages(0-1226490), pr(35,46,47,59)

yrimm(1-7, 1980-2006) for immigrants, yrimm(9999) for non-immigrants

Variables required:totinc, hrswrk, wkswrk, hdgree, agegrp, hlaen, hlafr, hlano, kol, cma, citizen, ageimm, pob*/

// Dependant variable:gen hour_wage = (totinc/(hrswrk*wkswrk))*0.9gen log_hour_wage = log(hour_wage)replace log_hour_wage = 0 if(log_hour_wage<0)

// Variable by (what distinguishes the two groups):gen immig = 0replace immig = 1 if(yrimm!=9999)

// Prairies:gen prairies = 0replace prairies = 1 if(pr==46 | pr==47)

// Quebec:gen quebec = 0replace quebec = 1 if(pr==24)

// B.C.:gen bc = 0replace bc = 1 if(pr==59)

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// Education:gen educ = 0replace educ = 8 if(hdgree==1)replace educ = 12 if(hdgree==2)replace educ = 13 if(hdgree==3 | hdgree==4 | hdgree==5)replace educ = 14 if(hdgree==6 | hdgree==7)replace educ = 15 if(hdgree==8)replace educ = 16 if(hdgree==9)replace educ = 17 if(hdgree==10)replace educ = 18 if(hdgree==12)replace educ = 22 if(hdgree==11 | hdgree==13)

// Potential experience:gen age = 0replace age = 2 if(agegrp==1)replace age = 5.5 if(agegrp==2)replace age = 8 if(agegrp==3)replace age = 10.5 if(agegrp==4)replace age = 13 if(agegrp==5)replace age = 16 if(agegrp==6)replace age = 18.5 if(agegrp==7)replace age = 22 if(agegrp==8)replace age = 27 if(agegrp==9)replace age = 32 if(agegrp==10)replace age = 37 if(agegrp==11)replace age = 42 if(agegrp==12)replace age = 47 if(agegrp==13)replace age = 52 if(agegrp==14)replace age = 57 if(agegrp==15)replace age = 62 if(agegrp==16)replace age = 67 if(agegrp==17)replace age = 72 if(agegrp==18)replace age = 77 if(agegrp==19)replace age = 82 if(agegrp==20)replace age = 85 if(agegrp==21)gen poten_exp = age - educ - 6replace poten_exp = 0 if(poten_exp<0)// negative values are removed as they have no practical interpretation

// Potential experience, squared, over 100:gen poten_exp_sq = (poten_exp^2)/100

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// Language.s spoken at home:// Reference: English

// French:gen fr_home = 0replace fr_home = 1 if(hlafr==1)

// Both:gen both_home = 0replace both_home = 1 if(hlaen==1 & hlafr==1)

// Other:gen other_home = 0replace other_home = 1 if(hlano!=1)

// Knowledge of official languages// Reference: English

// French:gen fr_work = 0replace fr_work = 1 if(kol==2)

// Both:gen both_work = 0replace both_work = 1 if(kol==3)

// Neither:gen none_work= 0replace none_work = 1 if(kol==4)

// CMA (Canadian metropolitan area):gen metro_area = 0replace metro_area = 1 if(cma!=999)

// Canadian citizen:gen can_citizen = 0replace can_citizen = 1 if(citizen==1 | citizen==2)

// Immigrated under age 13:gen young = 0replace young = 1 if(ageimm==1 | ageimm==2 | ageimm==3)

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// Immigrated before age 13, education:gen young_educ = young*educ

// Country of origin:// Reference: U.S. and U.K.

// Other European:gen rest_europe = 0replace rest_europe = 1 if(pob==8 | pob==9 | pob==10 | pob==11 | pob==12 | pob==13 | pob==14)

// Asia:gen asia = 0replace asia = 1 if(pob==18 | pob==19 | pob==20 | pob==21 | pob==22 | pob==23 | pob==24 | pob==25 | pob==26)

// Others:gen other = 0replace other = 1 if(pob==3 | pob==4 | pob==5 | pob==6 | pob==15 | pob==16 | pob==17 | pob==27)

// Foreign work experience:gen age_immigration = 0replace age_immigration = 2 if(ageimm==1)replace age_immigration = 7 if(ageimm==2)replace age_immigration = 12 if(ageimm==3)replace age_immigration = 17 if(ageimm==4)replace age_immigration = 22 if(ageimm==5)replace age_immigration = 27 if(ageimm==6)replace age_immigration = 32 if(ageimm==7)replace age_immigration = 37 if(ageimm==8)replace age_immigration = 42 if(ageimm==9)replace age_immigration = 47 if(ageimm==10)replace age_immigration = 52 if(ageimm==11)replace age_immigration = 57 if(ageimm==12)replace age_immigration = 60 if(ageimm==13)

gen pre_immig_exp = age_immigration - educ - 6replace pre_immig_exp = 0 if(pre_immig_exp<0)

// U.S. and U.K.:gen us_uk = 0replace us_uk = 1 if(pob==2 | pob==7)gen us_uk_exp = us_uk*pre_immig_exp

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// Other European:gen rest_europe_exp = rest_europe*pre_immig_exp

// Asia:gen asia_exp = asia*pre_immig_exp

// Others:gen other_exp = other*pre_immig_exp

// Foreign work experience, squared, over 100:// U.S. and U.K.:gen us_uk_exp_sq = (us_uk_exp^2)/100

// Other European:gen rest_europe_exp_sq = (rest_europe_exp^2)/100

// Asia:gen asia_exp_sq = (asia_exp^2)/100

// Others:gen other_exp_sq = (other_exp^2)/100

// Foreign work experience * experience in Canada, over 100:gen dom_exp = age - educ - 6 - pre_immig_expreplace dom_exp = 0 if(dom_exp<0)

// U.S. and U.K.:gen us_uk_exp_dom = (us_uk_exp*dom_exp)/100

// Other European:gen rest_europe_exp_dom = (rest_europe_exp*dom_exp)/100

// Asia:gen asia_exp_dom = (asia_exp*dom_exp)/100

// Others:gen other_exp_dom = (other_exp*dom_exp)/100

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OAXACA:

Regression for Immigrants:regress log_hour_wage prairies bc educ poten_exp poten_exp_sq fr_home both_home other_home fr_work both_work none_work metro_area can_citizen young young_educ rest_europe asia other us_uk_exp rest_europe_exp asia_exp other_exp us_uk_exp_sq rest_europe_exp_sq asia_exp_sq other_exp_sq us_uk_exp_dom rest_europe_exp_dom asia_exp_dom other_exp_dom if(immig==1), vce(robust)

Regression for Non-Immigrants:regress log_hour_wage prairies bc educ poten_exp poten_exp_sq fr_home both_home other_home fr_work both_work none_work metro_area if(immig==0), vce(robust)

Oaxaca Decomposition, Immigrant Coefficients as Reference : oaxaca log_hour_wage prairies bc educ poten_exp poten_exp_sq fr_home both_home other_home fr_work both_work none_work metro_area, by(immig) weight(0) detail

Oaxaca Decomposition, Non-Immigrant Coefficients as Reference:oaxaca log_hour_wage prairies bc educ poten_exp poten_exp_sq fr_home both_home other_home fr_work both_work none_work metro_area, by(immig) weight(1) detail

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