1. Presented by: Nazih Nasrallah & Ann Balasubramaniam

2. What is the Canadian Index for Measuring Integration (CIMI)?  The CIMI uses statistical methods to assess demographic changes and trends in immigrant integration across Canada.  It measures integration via an index of immigrant performance relative to population benchmarks across relevant indicators within the social, economic, health, civic and political dimensions.

3. The CIMI conceptual model While the CIMI endeavors to use best practices from existing (national/international) indices (Jedwab and Soroka, 2015), the successful creation of the CIMI relies on both the guidance and advice of our expert advisory committee (E.A.C.) and the quality and availability of Canadian datasets. CIMI E.A.C.Data

4. What are the broad steps in building an index?

5. Overview of our talk: Our talk will focus on the economic dimension of immigrant integration with emphasis on earned wages. We will share our methods and results from our preliminary analyses.

6. Indicators and data sources in Canada IndicatorsLG Immigr Database National Household Survey/ Census EDS/General Social Survey 27 Canadian Comm Health Survey Canada Election Study Labour Force Survey Giving, Volunteering & Participating Survey Civic & Democratic Participation X X Migration Internal/ Secondary XX Education X X Occupations and Labour Market Outcomes XX X Health XX Language Learning/Use XX Sense of Belonging X Social Network & Distance X X

7. According to the National Household Survey, 20.7 % of the Canadian population are immigrants*. 20.7% Who is an immigrant? * Based on NHS 2011 PUMF weighted counts.

8. Who are the wage earners in the sample population? Wage earners =  full-time workers (> 30 hours per week)  working population (ages 18 – 65 )  earners of at least minimum wage  non-self employed individuals

9. Now that we have identified our wage earners we must decide which characteristics influence immigrant wages.

10. Average Value INDICATORSImmigrantsNon-Immigrants GenderMale 54.8% AgeAge* 4442 EthnicityVisible Minority* 63%4% LanguageEnglish* 81%64% French* 3.0%12.4% Bilingual* 13.6%23.2% Neither* 2.40%0.04% EducationTrades, diplomas etc.* 33.4%42.7% Bachelor* 22.6%17.7% Advanced* 16.2%7.7% OccupationsManagement* 10.4%12.1% Business* 18.0%18.7% Natural and Applied Sciences* 13.0%8.40% Health Care Workers* 6.8%6.2% Social, Education and Law* 9.6%13.4% Art & Culture* 12.4%17.9% Services* 17.1%14.9% MobilityMigration within Prov (1yr)* 2.5%3.9% Migration between Prov (1yr)* 3.8%7.6% External Migration (1yr)* 6.9%2.8% Migration within Prov ( 5 yr)* 9.9%14.0% Migration between Prov ( 5 yr)* 2.7%3.5% External Migration ( 5 yr)* 8.4%1.3% *Denotes a significant mean/proportion difference at p<0.05 Examples of indicators that influence immigrant integration

11. Distribution of the immigrant population across Canada Regions Wage Earning Immigrants (Ages 18-65) Total Immigrant Population Atlantic (NS, PEI, NB,NFl) 1.2%1.3% Quebec 13.1%14.4% Ontario 54.6%53.3% Manitoba + Saskatchewan 3.9%3.70% Alberta 10.9%9.4% BC 16.2%17.6%

12. How do we build a model? Model Testing/ Validation Dependent Variable Data Quality Diagnostics Independent/ Control Variables Final Model

13. Analyzing wages: a multivariate approach Using OLS Regression we model wages using the following formula:  Wages = period of immigration + gender + age + visible minority status + knowledge of official languages + education + occupations + mobility (1yr, 5yr) + [geography]

14. Final model equation  Wages* = 33552 - 1144 (Pyrimm) + 12034 (Male) + 6 (Age 2 ) - 5364 (Minority) - 3395 (French) + 1407 (Bilingual) - 4034 (Neither) + 3969 (Secondary) + 10529(Nonuniv) + 21932 (Bachelor) + 28527(Advanced) +15398 (Management) - 1638 (Business) + 8453 (Natural) + 5087 (Health) + 2418 (Educlaw) -5930 (Artcultural) -9449 (Services) -2094 (Construction) -1820 (Manufact) -3320 (non-Mig 1) -2216 (ProvMig 1) -3866 (InterProvMig 1) -2361 (ExternMig 1) -1228 (NonMigrants 5) -752 (ProvMig 5) -1265 (InterProvMig 5) -6776 (ExternMig 5 ). *Per/Controlling for Canadian geographic locations (i.e., Provinces, CMAs); with the following model comparison groups : non- immigrants, female, not a visible minority, English speakers, no formal education, supervisors and technicians in fields such as natural resources and agriculture, and non-movers.)

15. Partial OLS regression results predicting earned wages, in dollars (NHS, 2011 ) *Significant at P<0.001. Unstandardized coefficients, standard errors in parenthesis N= 248128 (normalized weights)

16. Meet Anita. She is a visible minority woman in her mid- 30 s who immigrated to Canada in 2001. She is bilingual (English and French) with an advanced business degree and currently works as a senior manager in a non-governmental organization. She has been living in the same home in Ottawa for the past 5 years. Based on this information, we can estimate her wage to be around $67,000. Anita’s higher education, knowledge of both official languages, type of job, and stability within a city has increased her earning potential. She earns about $6,800 less than a non-immigrant woman with the same characteristics and qualifications. Personal profiles from the data: examples

17. Meet Moe. Similar to Anita, he is a visible minority, in his mid- 30 s, who immigrated to Canada in 1992. Moe also has an advanced degree, is working as a senior manager at a computer technology company, and has been living in the same home in Ottawa since he arrived. But unlike Anita, he only speaks English. Based on this information, his estimated wage is around $80,000. While Moe’s education, knowledge of English, type of job, and stability within a city has increased his earning potential, he makes about $4,600 less than a non-immigrant man with the same characteristics and qualifications. Moe’s wage is much higher than Anita’s despite relatively similar characteristics and her bilingual ability – which increases her earning potential- primarily because immigrant men on average tend to make $12,000 more than immigrant women but also because Moe immigrated 9 years earlier than Anita.

18. Key results : 1. There is a wage gap between immigrants and non-immigrants. This wage gap decreases as immigrants spend more time working in country. 2. Education is the single most powerful predictor of wages. Immigrants earn approximately $29,000 more on average than immigrants without formal schooling. However, they earn less than their non-immigrant counterparts despite their markedly higher proportion of advanced degrees. 3. Mobility is also a determinant of wages. For example, immigrants who changed residence within a metropolitan area, town, or village on average make around $3,300 less than immigrants that do not move.

19. How do we rank or score regions based on wages? Region Standardized Scores Std. Rank Alberta +13.0%1 British Columbia +11.9%2 Manitoba + Saskatchewan +11.7%3 Ontario 04 Atlantic Provinces -5.1%5 Quebec -8.0%6 Note: the standardized scores for the Northern Territories is +3.36% Based on immigrant wages, preliminary analysis suggests that Alberta ranks #1 and Quebec ranks last, when accounting for gender, age, language, education, occupation and mobility (as well as geographic locations).

20. Other analyses and future directions ► Similar analyses for wages were conducted using census 2006 data and similar results were attained. Future analyses will span previous census cycles. ► We also tested other economic indicators within census and NHS (e.g. LICO and labour force status). ► We will also conduct analyses of economic indicators using other data sets including labour force survey and potentially other administrative data (e.g. pre-landing vs. post-landing indicators) across comparable geographies. ► Future analyses will focus on indicators of the Social, Health, Civic and Democratic participation dimensions of CIMI across comparable geographies. ► CIMI scores and rankings will be further evaluated by the expert advisory committee.

21. Thank-y0u! Please feel free to contact our Ottawa team: Nazih Nasrallah, M.A., Ph.D.(c) Project Manager Email: nazih.nasrallah@acs-aec.ca Tel: (613) 656 - 8123 Ann Balasubramaniam, BSc., Ph.D.(c) Data Manager Email: ann.balasubramaniam@acs-aec.ca Tel: (613) 656 - 8123 Canadian Institute for Identities and Migration 230 - 44 Byward Market Square Ottawa, Ontario K1N 742