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Does Employment in Precarious Work Lead to Wage Disparities For Canadian Immigrants?


Labour force participation with an adequate income by all citizens of a country is fundamental to that country's economic success. With the increasing arrival of migrants from non-European countries, Canada's ethnic diversity appears to be expanding. According to a report by Statistics Canada (2013b), over 1 million foreign-born individuals arrived in Canada between 2006 and 2011 making up 17.2 percent of the total foreign-born population in this country (Statistics Canada 2013b). The majority of these individuals arrived from Asia and are some of the most educated immigrants in Canada's history. Given that higher education is assumed to lead to better jobs with higher earnings, it is reasonable to assume that educated immigrants' earnings would equal those of Canadian-born individuals with similar credentials. This does not appear to be the case, as immigrants continue to lag behind their Canadian-born counterparts in terms of economic success. Based on this, Morissette and Picot (2005) state that although the workforce has become more experienced and better educated over the two decades leading up to their report, the proportion of low-paid employees actually increased among certain groups such as recent immigrants. This assertion is also reflected in the work of Green and Worswick (2010) who found that sizeable reductions in returns to foreign work experience were paramount in declines in entry level earnings among Canadian immigrants.

Many studies have highlighted the steady increase of precarious work in Canada (Fudge and Vosko 2001; Shellenberg and Clarke 1996; Vosko, Zukewich and Cranford 2003). As Canadian companies adapt to changing global economies and a subsequent increase in the diversity of labour markets, work has become flexible and therefore precarious. Earning an adequate income is particularly problematic for immigrants engaged in precarious employment. For instance, earnings represent not only whether the newcomers can provide for themselves and their families, but also whether they earn enough to save for their retirement and to educate their offspring. The primary goal of this analysis, then, is to examine the role played by precarious work in making immigrant earnings lower than those of their Canadian-born counterparts. This analysis is especially important as there is no previous research that looks at the role of precarious employment in determining the earnings of Canadian immigrants.

Currently, a majority of research on economic outcomes among Canadian immigrants focuses on wages (Aydermir and Skuterud 2005; Bannerjee 2009; Frenette and Morrisette 2005). These researchers argue that lack of credential recognition and discriminatory hiring practices lead to low wages. I maintain that, in part at least, it is being employed in precarious jobs that create an immigrant earnings deficit. I also recognize that the combination of discriminatory hiring practice and discounting foreign education and work credentials may lead to precarious work, which in turn leads to low wages.

Discourse surrounding the economic integration disadvantages faced by Canadian newcomers identifies low immigrant wages as a probable outcome. In fact, a sizeable number of researchers recognize low income among Canadian newcomers as the key source of low economic integration among them. An extensive study using Canadian census data by Aydemir and Skuterud (2005) speaks to immigrant wage disparities across regions of Canada. Furthermore, Aydemir (2003) argues that not only have the earnings of newcomers been deteriorating, but so have their employment and labour force participation rates. As such, Aydemir states that, even though recent Canadian immigrants are more educated than previous cohorts, they are still not faring as well in Canada's labour markets (Aydemir 2003).

Immigrant wages are also examined by Bannerjee (2009), highlighting entry wage and ability to catch up to the wages of the Canadian-born. Bannerjee uses the Survey of Labour and Income Dynamics and finds that visible minority immigrants are unable to catch up to the earnings of Canadian-born respondents and therefore are disadvantaged economically. According to Bannerjee, white immigrants do not face similar challenges and are able to catch up to their Canadian-born counterparts. Frenette and Morrisette (2005) discuss immigrant wages and whether they will reach parity with those of the Canadian-born. They conclude that immigrant earnings would have to grow at an "abnormally high rate" in order to reach parity with their Canadian-born counterparts (Frenette and Morrisette 2005, 249).

According to Green and Worswick (2010), recent immigrants to Canada are finding it increasingly difficult to economically integrate into Canadian society. These researchers assert that low-income rates among immigrants are evident shortly after these newcomers arrive in Canada (Green and Worswick 2010). In fact, immigrants arriving in the 1980s saw their average earnings fall by more than 20 percent in the first year after arrival, and this number declined further among those arriving in the 1990s and in the early 2000s (Green and Worswick 2010). Immigrant earnings are additionally examined by Bannerjee (2009), who highlights newcomers' earnings upon entering Canada and their subsequent inability to catch up to the earnings of the Canadian-born. Bannerjee cites the "entry effect" of immigrants as they enter the Canadian labour market and the "assimilation effect" as two possible factors hindering Canadian newcomers and therefore rendering them economically disadvantaged. Frenette and Morrisette (2005) conclude that immigrant earnings would have to grow at an extremely high rate in order to be equivalent to the earnings of their Canadian-born counterparts (Frenette and Morrisette 2005, 249). Therefore, while there is a sizeable amount of Canadian research addressing deteriorating immigrant earnings, little is known about the type of employment these newcomers are engaged in which subsequently leads to immigrant wage disparities with the Canadian-born. The purpose of this analysis, then, is to examine immigrant earnings from the perspective of precarious employment.


Whether it is referred to as precarious, non-standard, or contingent, employment in the secondary labour market evokes images of economic struggle among members of the labour force. Work not only locates the worker within the country's stratification system, but also links individuals to each other and, as Kalleberg (2008) argues, is "central to individual identity." Kalleberg defines "precarious work," as employment that is uncertain, unpredictable, and risky from the point of view of the worker (Kalleberg 2009). Some Canadian scholars refer to work that differs from that of standard employment as non-standard work (1) (Krahn 1992). However, Vosko (2003) argues that definitions of 'nonstandard work' seldom include direct indicators of insecure or precarious employment. Vosko further states that the focus is instead on all employment types or arrangements that differ from the standard employment relationship (Vosko 2003). Therefore, non-standard work classifications fail to address all aspects of precarious work. Vosko suggests linking notions of non-standard work with that of precarious work using mutually exclusive employment forms (Vosko 2003). Precarious employment in this analysis is defined as individuals employed in temporary jobs, multiple jobs and, finally, involuntary part-time work. According to a recent study, today's immigrants are much more likely to encounter labour market difficulties in Canada's "new" economy, regardless of their education, pre-migration work experience and language skills (Goldring and Landolt 2009). In addition, I highlight the increasing earnings gap between immigrants and native-born workers "with comparable human capital", along with "the racialization of income disparities and of poverty" (Goldring and Landolt 2009).

The financial consequences of precarious work are a major concern for many Canadians employed in such positions. Kapsalis and Tourigny (2004) state that not only do those employed in precarious work engage in fewer hours of work, they earn considerably lower hourly wages, and are employed for shorter durations than permanent employees. Therefore, the economic instability not only arises from lower wages and fewer hours worked, but also from the inability to access employment insurance because of short employment durations (Kapsalis and Tourigny 2004). Moreover, financial uncertainty is further exacerbated if precarious workers go from one temporary job to another and never have the chance to build up retirement funds (Kapsalis and Tourigny 2004). Given that newcomers to Canada face challenges of not only language and cultural barriers but also lack of credential recognition by Canadian employers, it is vital that immigrants' earnings be examined with respect to precarious employment.

As reported in a Globe and Mail article dated May 5, 2013, "Canada's Shift to a Nation of Temporary Workers," there were 2 million temporary workers in Canada in 2012 and this type of precarious employment has grown at triple the rate of permanent positions (Grant 2013). This is also true for individuals employed in more than one position. Multiple job holders are also among many who hold employment out of necessity rather than choice. According to Gilmore (2008), almost 65 percent of Canadian multiple-job holders were engaged in such employment to supplement their income, assist with debt repayment, or save for the future. Kimmel and Powell (1999) concur with Gilmore on the increase in this form of employment in both Canada and the United States. Kimmel and Powell (1999) assert that the number of Canadians engaged in multiple jobs has increased by 50 percent over a decade and continues to climb steadily. A great number of immigrants face a mismatch between their educational credentials and available employment, leading many to be employed in involuntary part-time work in the hopes of one day finding permanent positions reflecting their qualifications. Precarious employment inevitably leads to economic instability as newcomers struggle to obtain viable employment for successful economic integration and social cohesion in their host country.


Data and Variables

In this analysis I use data from the Canadian Labour Force Survey (LFS) master file from 2006 to 2012. The LFS is a monthly household survey carried out by Statistics Canada involving around 56,000 Canadian households. Statistics Canada added five questions to the LFS to identify the Canadian immigrant population. Therefore, prior LFS waves cannot be used to study the types of immigrant labour outcomes being examined in this analysis. The five Canadian immigrant questions in the 2006 LFS included: country of birth of the respondent, whether or not the respondent was a "landed immigrant", the month and year the respondent became a landed immigrant, and the country where the respondent received his or her highest level of education (Statistics Canada 2013a).

Given that the existing LFS files in the Research Data Centres (RDCs) do not provide the necessary tools to compute variances of the estimators from month to month, I can only use the month of March annual surveys (Wanner 2013). Essentially, this means that it is not possible for me to analyze the monthly data correctly as the required software for combining monthly data was not provided by Statistics Canada. However, monthly data is unnecessary for my study, as I am not interested in pursuing my research question using the rotating sample design. This type of sample design enables each household to remain in the sample for six months, and each month one-sixth of the households is rotated out of the sample and replaced by a new subsample (Statistics Canada 2013a). However, for this and similar studies, a time series of surveys six months or a year apart is sufficient (Wanner 2013). Hence, I merged the March surveys for each year from 2006 and 2012. The analysis is restricted to the month of March for each survey year. The month of March is significant as this is the time of year when very few workers are on holidays or in the process of changing jobs. The present study's sample is restricted to respondents who are 20-59 years of age and either Canadian-born or landed immigrants. In order to further contrast hourly wage outcomes between newcomers and their Canadian-born counterparts, this analysis further divides immigrants into recent immigrants (immigrants who have resided in Canada for five years or less) and established immigrants (immigrants who have lived in Canada for more than five years). Subdividing immigrant status allows for a detailed examination of Canadian newcomers with respect to earnings. Although many studies show that the wage gaps between immigrants and their Canadian-born counterparts are reduced over time, immigrants tend to earn significantly less upon arrival (Gilmore 2008).

The outcome variable in this analysis is the natural log of hourly earnings and is measured as a continuous variable in the models. The explanatory variables in the present study include immigrant status, work experience (total and foreign), sex, age, education, number of children, year of survey, and marital status. Immigrant status, as mentioned above, is measured by two dummy variables, 1 equalling 1 if an immigrant has been in Canada five years or less, the other equalling 1 if an immigrant has been in the country for more than five years. Canadian-born is the reference category. Total work experience is calculated using "age--years of schooling--6" and is a continuous variable. This is only an approximation of actual experience, since many individuals will discontinue their education and enrol again later in life. Foreign work experience is calculated by subtracting Canadian work experience from total work experience. (2) Education is divided into six categories: less than high school (used as reference category), high school graduate, some post-secondary education, trades, bachelor's degree, and graduate degree. Marital status is coded as: married (married and common-law), single, never married, and other (widowed, separated or divorced) with other as the reference category. Also, the number of children is coded as: no children (reference), 1-2 children, and 3 or more children. Year of the survey, measured continuously, is included to assess the possible linear trend in wages over the study period. Finally, the models include three precarious employment variables: involuntary part-time work, multiple job holders and temporary job holders. Involuntary part-time work is defined as those seeking full-time employment but who are relegated to work part-time. The second precarious work variable, multiple job holders, includes respondents who are employed in more than one job. The final precarious employment variable identifies those respondents who are employed in temporary jobs and includes seasonal, temporary, term or contract employment, including work done via a temporary help agency, a casual job, and other temporary work. All three variables are coded as dummy (indicator) variables with employment in any of these types of precarious work as equal to one, if not they equal zero.


This analysis examines how several factors add to the disparities in immigrant and Canadian-born hourly earnings by combining the data for both immigrant and Canadian-born respondents from the 2006-2012 survey periods. The earnings in this analysis are not adjusted for inflation as I am interested in comparing actual wage trajectories between Canadian immigrants and their Canadian-born counterparts. I begin the analysis by constructing a baseline ordinary least squares regression model using the background explanatory variables with the log of hourly wages as the outcome (creating a semi-log model). Then I add the dummy variables measuring the three indicators of precarious work to the baseline model, followed by interaction terms between each immigrant dummy variable and types of precarious employment. These interactions will show if there are differences in the effects of precarious work between recent immigrants, established immigrants, and the Canadian-born. All models in this analysis are run separately for males and females, given the different labour force experiences they encounter. Data from the pooled survey years from the LFS for the month of March are used to estimate the following models:

(Model 1) In Y = [alpha] + [summation][[beta].sub.k][X.sub.k] + [[beta].sub.1][X.sub.1] + [[beta].sub.2][X.sub.2] + [[beta].sub.3][X.sub.3] + [[beta].sub.4][X.sub.4] + [[beta].sub.5][X.sub.5]

Model 1 includes:

lnY = the natural logarithm of a respondent's hourly earnings

(Model 2) lnY = [alpha] + [summation][[beta].sub.k][X.sub.k] + [[beta].sub.1][X.sub.1] + [[beta].sub.2][X.sub.2] + [[beta].sub.3][X.sub.3] + [[beta].sub.4][X.sub.4] + [[beta].sub.5][X.sub.5]

Model 2 include all predictors in model 1 plus:

[X.sub.3]= Involuntary part-time job holder

[X.sub.4]= Multiple job holder

[X.sub.5]= Temporary job holder

(Model 3) lnY = [alpha] [summation][[beta].sub.k][X.sub.k] + [[beta].sub.1][X.sub.1] + [[beta].sub.2][X.sub.2] + [[beta].sub.3][X.sub.3] + [[beta].sub.4][X.sub.4] + [[beta].sub.5][X.sub.5] + [beta]k[X.sub.3]*[X.sub.1] + [beta]k[X.sub.3]*[X.sub.2] + [beta]k[X.sub.4]*[X.sub.1] + [beta]k[X.sub.4]*[X.sub.2] + [beta]k[X.sub.5]*[X.sub.1] + [beta]k[X.sub.5]*[X.sub.2]

To produce Model 3, interaction terms are added to Model 2 between each immigrant dummy variable and the precarious employment variables which will measure the differences in effects of precarious work between the two types of immigrant and the Canadian-born. The coefficients of these semi-log models, after one is subtracted from their anti-logs, can be interpreted as proportional effects on log earnings, or as percentage effects after they are multiplied by 100. In addition, I show the indirect effects of the background variables on earnings transmitted by the precarious work variables (involuntary part-time work, multiple job holder and temporary job holder). The indirect effects are calculated by subtracting the coefficients for the background variables in model 2 from corresponding coefficients in model 1. The precarious work variables act as mediators and transmit indirect effects from the background variables to log hourly earnings. I also report the Bayesian Information Criterion (BIC) which assesses the overall fit of a model and compares nested and non-nested models. Accordingly, the model exhibiting the smaller BIC is the preferred model.

Weighting the data and adjusting for the complex sample design in the Labour Force Survey proved to be challenging, as Statistics Canada failed to provide either bootstrap weights or sufficient information regarding adjustment for sample design. As argued by Wanner (2013), I do not use the regression composite method only available to LFS researchers at Statistics Canada. However, as I do not use monthly data points but rather 1 month (March) per each year of the survey, the regression composite method is not necessary (Wanner 2013).

In order to identify primary sampling units, strata, clusters, and sample weights, I use information provided in the Labour Force Survey Guide (Statistics Canada 2013a) and Methodology documents. One assumption made here is that the primary sampling unit (PSU) is a cluster (Wanner 2013). The clusters are sampled before sampling households and then are aggregated into strata (Wanner 2013). My analysis uses the survey system in Stata to adjust for weighting and sample design. As such, the design is represented in the svyset command (StataCorp 2013). This command identifies the data as complex survey data. Hence, this includes variables representing the PSU, sample weight, clustering and stratification (Wanner 2013).

Probability weights are used to account for the fact that, even though a random sampling method was used, cases may have unequal probabilities of being selected (Lohr 2010). According to Lohr (2010) these weights normally equal the inverse of the probability of selection and are calculated to provide sample estimates that more closely correspond to census population values for certain variables. Furthermore, they adjust for nonresponse and coverage error. The final weight (finalwt) is further adjusted using the calibration method. Calibration is unwarranted in this analysis as it uses surveys six months or more apart. Therefore, I will use subwt. (3) For purposes of statistical inference, these weights must be normalized so that their mean equals 1 (Wanner 2013). The frequency counts will then take on sample rather than population values. This is accomplished by dividing the weights by their mean.


Figure 1 illustrates the sizeable disparity in hourly earnings between recent Canadian immigrants and more established immigrants and the Canadian-born. The figure shows that both male and female recent immigrants are consistently earning less per hour with respect to both the established immigrants and Canadian-born males and females during the study period. It is worth noting that while the earnings of the Canadian-born and established immigrants increase steadily during the period of the present study, the gap between these groups and recent immigrants appears to widen. Furthermore, the women's earnings are much lower than the men's, regardless of immigrant status.

Although the focus of interest in this paper is on the role of precarious work in the earnings attainment process, Model 1, including only the background variables, provides some interesting results. Each one-year increase in work experience for females results in a 1 percent increase in hourly earnings. However, for each one-year increase in foreign work experience, females earn 1.3 percent less in hourly earnings. The outcomes for Model 1 also show that as education increases so do hourly earnings. For instance, high school graduates can earn approximately 20 percent more in hourly earnings than non high school graduates whereas respondents with a graduate degree can earn almost 120 percent more than their counterparts who did not finish high school. Female recent immigrants earn 18 percent less than their Canadian-born counterparts and established immigrant females earn almost 6 percent less than the Canadian-born. It is also interesting to note that married females earn about 3 percent more than women who are widowed, separated or divorced while single women earn 4 percent less than this reference group. Finally, women with one to two children earn a little over 5 percent more than those without children and women with three or more children earn approximately 3.5 percent less. Finally, Model 1 shows that women who belong to labour unions earn about 23 percent more than those who are not union members.

The results in Model 2 of Table 1 illustrate the hourly wage outcomes once the three precarious employment variables are added to Model 1. Females engaged in involuntary part-time work earn almost 20 percent less while multiple job holders earn 4 percent less and temporary job holders earn 9.5 percent less in hourly earnings. The final model depicted in Table 1 shows the results of the interaction effects between immigrants and precarious employment. Specifically, the table shows that recently arrived females who are also employed in multiple jobs earn approximately 10 percent more in hourly earnings, whereas established female immigrants earn approximately 6 percent more. None of the other interaction terms is significant.

Table 2 shows results for male respondents with respect to hourly earnings. For males, Canadian work experience translates into 1 percent higher earnings per year. However, males with foreign work experience earn approximately 1.5 percent less per year of experience. Similar to females in this analysis, as education increases so do male earnings. For example, males with a high school diploma earn 15 percent more than those who have not graduated from high school. The earning potential for males with graduate degrees is 85 percent higher in comparison to those without high school diplomas. Also based on Model 1, recently arrived males earn about 16 percent less than their Canadian-born counterparts, while established immigrant males earn 6.5 percent less. In contrast to how well females fared with respect to earnings and union memberships, males belonging to unions earn about 11 percent more than non-union males.

Table 2 also reports the outcomes of Model 2 after the precarious employment variables are added to Model 1. Accordingly, male involuntary part-time workers earn almost 25 percent less, followed by multiple job holders earning approximately 8 percent less and temporary job holders earning 13 percent less than those not employed in these types of jobs. Finally, Table 2 (Model 3) shows the outcomes of adding interaction terms to Model 2 and there is only one significant result which shows established immigrants employed in temporary positions tend to earn almost 4% less. Model 2 is the preferred model, supported by the larger negative BIC' value. This supports my contention that involvement in precarious work is an important predictor of hourly earnings, regardless of immigrant status.

Table 3 reports the indirect effects of the background variables on earnings transmitted by precarious work variables for females in the sample. For instance, 0.1 percent of the effect of foreign work experience on hourly earnings is transmitted by the precarious employment variables. Additionally, the indirect effect transmitted by precarious employment variables among recent immigrant females on hourly earnings is almost 2 percent. This effect is slightly lower for established immigrants at 0.6 percent. Individuals with graduate degrees show an indirect effect of 1.1 percent on hourly earnings transmitted by precarious work variables. Furthermore, Table 3 also depicts the total effects of each variable. For example, 12 percent of the total effect of being a recent immigrant female is transmitted by precarious employment variables and approximately 9 percent of the total effect of being an established female immigrant is transmitted by these variables. The largest direct effect transmitted by the precarious employment variables is 27 percent for respondents with three or more children.

Table 4 shows the indirect effects of the background variables on earnings transmitted by precarious work variables for males in the sample. According to Table 4, the indirect effect transmitted by precarious employment variables among recent immigrant males on hourly earnings is substantially lower than females at approximately 3 percent. Again, percent total effect is reported in Table 4 for males in the sample. For example, 15 percent of the total effect of being a recent immigrant male is transmitted by precarious employment variables and approximately 12 percent of the total effect of being an established male immigrant is transmitted by these variables. Again, respondents with three or more children show a large 47 percent total direct effect as exhibited by precarious employment variables.


As shown above, interactions with immigrant status suggest that, in the case of women, holding multiple jobs enhances the earnings of established immigrants, while for men, holding a temporary job reduces the earnings of established immigrants. Therefore, precarious work seems to operate uniformly to reduce earnings, regardless of immigrant status. The results also indicate that recent immigrants continue to struggle financially as they try to integrate themselves and their families into Canadian society. For example, immigrant females earn substantially less in hourly earnings than their Canadian-born counterparts. Specifically, recently arrived females earn 18 percent less than Canadian-born females and established immigrant females earn 6 percent less. These findings coincide with those of Bannerjee (2009) as well as Green and Worswick (2010) who exposed the financial struggles experienced by Canadian immigrants shortly after arriving in this country. Another interesting outcome of this analysis is the earning power of individuals belonging to unions. Female union members earn 23 percent more than non-union females.

The disparity of hourly wage earnings of recently arrived males is somewhat lower than that of comparable females. I found that recently arrived males earn about 15 percent less than their Canadian-born counterparts, while established immigrant males earn 6 percent less. Furthermore, in contrast to how well females fared with respect to earnings and union memberships, males belonging to unions earn less than half that of females belonging to unions; about 11 percent more than non union males.

Precarious work variables act as mediating variables and transmit indirect effects from the background variables to hourly earnings. A notable finding in the present study is the indirect effects transmitted by precarious work variables from background variables such as foreign work experience, immigrant status and education credentials to hourly earnings. As shown above, the indirect effect of foreign work experience on hourly earnings is slightly higher for males than females as transmitted by the precarious employment variables. Additionally, the indirect effect is also evident among recent immigrants as well as established immigrants. Again it appears to be substantially higher for males than females and this pattern continues for higher education.

These are interesting outcomes as they augment current research on diminishing immigrant earnings. Aydemir and Skuterud (2005) found a marked decrease in economic returns based on foreign labour market experience. They assert that this finding was most prevalent among males originating in non-European countries. Researchers continue to stress wage disparities between immigrants and the Canadian-born, however they have yet to link jobs with low earning potential with immigrant economic outcomes. Policy makers should note that attracting educated immigrants is not enough to boost the host country's GDP. Hence, the host country's government must also recognize credentials of newcomers and ensure gainful employment for all sexes of incoming migrants. It is this underutilization of educational and work experience credentials that is likely leading to newcomers' dismal economic plight. As shown above, although females with foreign work experience see an increase in hourly earnings, males with foreign work experience actually earn less. This earnings disadvantage faced by recent Canadian immigrants and less so by established immigrants warrants further exploration using longer time periods. The outcomes of this analysis may be even more pronounced if data were available for longer time periods allowing for a comparison across decades coupled with measures of precarious employment.


(1.) Standard employment is by and large defined as a circumstance where the employee has 1 employer, works full-time, year-round on the employer's premises, takes pleasure in extensive statutory benefits and entitlements and is employed indefinitely (Fudge 1997; Schellenberg and Clark 1996; Vosko 1997).

(2.) Total Work Experience = Age - Years of Schooling - 6; Canadian Potential Work Experience for Immigrants = Survey Year - Year of Landing (Year they landed in Canada); Foreign Work Experience = Total Work Experience - Canadian Potential Work Experience for Immigrants.

(3.) According to Wanner (2013), in the March 2007 EES, the correlation between subwt and finalwt is approximately 0.97 and regression coefficients estimated using the two weights are equal to the fifth decimal point. Therefore, they are virtually interchangeable.


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PARVINDER HIRA-FRIESEN is a Post-doctoral Fellow on the Closing the Enforcement Gap Project at York University. She received her PhD from the Department of Sociology at the University of Calgary. Using the Canadian Labour Force Survey (2006-2012), she examined the prevalence of and trends in precarious employment, comparing recent and established immigrants to their Canadian-born counterparts for her dissertation. Her research interests include social inequality, race, immigration and labour. Her current research focuses on employment standards using administrative data from the Ontario Ministry of Labour and the Canadian Labour Force Survey and General Social Survey from Statistics Canada.
TABLE 1. OLS Regression Models Predicting Log of Respondents' Hourly
Earnings for Females, 2006-2012

                      Model 1        Model 2        Model 3

Work Experience       0.009 (***)    0.009 (***)    0.009 (***)
                     (0.000)        (0.000)        (0.000)
Foreign Work
Experience           -0.011 (***)   -0.011 (***)   -0.011 (***)
                     (0.000)        (0.000)        (0.000)
High School
Graduate              0.204 (***)    0.202 (***)    0.201 (***)
                    (-0.005)       (-0.005)       (-0.005)
Trades Training       0.252 (***)    0.251 (***)    0.250 (***)
                    (-0.006)       (-0.006)       (-0.006)
Some Post-Secondar
Education             0.442 (***)    0.436 (***)    0.438 (***)
                    (-0.005)       (-0.005)       (-0.005)
Bachelor's Degree     0.855 (***)    0.844 (***)    0.852 (***)
                    (-0.006)       (-0.006)       (-0.006)
Graduate Degree       1.175 (***)    1.164 (***)    1.175 (***)
                    (-0.008)       (-0.008)       (-0.008)
Survey Year           0.029 (***)    0.031 (***)    0.030 (***)
                    (-0.001)       (-0.001)       (-0.001)
Immigrant 5 years
or less              -0.180 (***)   -0.161 (***)   -0.185 (***)
                    (-0.013)       (-0.012)       (-0.014)
Immigrant more
than 5 years         -0.058 (***)   -0.052 (***)   -0.062 (***)
                    (-0.005)       (-0.005)       (-0.005)
Married               0.030 (***)    0.028 (***)    0.027 (***)
                    (-0.004)       (-0.004)       (-0.004)
Single               -0.040 (***)   -0.044 (***)   -0.036 (***)
                    (-0.006)       (-0.005)       (-0.005)
1-2 Children          0.054 (***)    0.051 (***)    0.050 (***)
                    (-0.004)       (-0.004)       (-0.003)
3 or more Children   -0.036 (***)   -0.023 (***)   -0.036 (***)
                    (-0.007)       (-0.007)       (-0.006)
Union Member          0.230 (***)    0.239 (***)   -0.232 (***)
                    (-0.003)       (-0.003)       (-0.003)
Part-time Worker                    -0.187 (***)   -0.193 (***)
                                   (-0.008)       (-0.009)
Job Holder                          -0.110 (***)   -0.107 (***)
                                   (-0.004)       (-0.004)
Multiple Job
Holder                              -0.033 (***)   -0.046 (***)
                                   (-0.005)       (-0.006)
Invol P/T (*)
Recent Immgr                                        0.074
Invol P/T (*)
Established Immgr                                   0.011
Multijob (*)
Recent Immgr                                        0.095 (*)
Multijob (*)
Established Immgr                                   0.061 (***)
Tempjob (*)
Recent Immgr                                        0.034
Tempjob (*)
Established Immgr                                   0.006
Number of Cases       348299      334004       348299
BIC'                  -59987.96   -48857.867   -53083.728
[R.sup.2]                  0.313       0.252        0.324

Coefficients for semi-log wage models equal ([e.sup.[beta]] - 1) which
produce proportional effects. These are multiplied by 100 and reported
as percentages in the text.
Exponentiated coefficients; standard errors in parentheses
(*) p<0.05, (**) p<0.01, (***) p<0.001

TABLE 2. OLS Regression Models Predicting Log of Respondents' Hourly
Earnings for Males, 2006-2012

                          Model 1        Model 2        Model 3

Work Experience          0.010 (***)    0.010 (***)    0.010 (***)
                        (0.000)        (0.000)        (0.000)
Foreign Work
Experience              -0.014 (***)   -0.014 (***)   -0.013 (***)
                       (-0.001)       (-0.001)       (-0.001)
High School
Graduate                 0.147 (***)    0.142 (***)    0.141 (***)
                       (-0.005)       (-0.005)       (-0.005)
Trades Training          0.283 (***)    0.280 (***)    0.278 (***)
                       (-0.005)       (-0.005)       (-0.005)
Some Post-Secondary
Education                0.292 (***)    0.288 (***)    0.289 (***)
                       (-0.005)       (-0.005)       (-0.005)
Bachelor's Degree        0.657 (***)    0.644 (***)    0.650 (***)
                       (-0.006)       (-0.006)       (-0.006)
Graduate Degree          0.850 (***)    0.840 (***)    0.850 (***)
                       (-0.008)       (-0.008)       (-0.008)
Survey Year              0.027 (**)     0.028 (***)    0.027 (***)
                       (-0.001)       (-0.001)       (-0.001)
Immigrant 5
years or less           -0.161 (***)   -0.133 (***)   -0.149 (***)
                       (-0.013)       (-0.013)       (-0.014)
Immigrant more
than 5 years           -0.065 (***)   -0.056 (***)   -0.060 (***)
                       (-0.005)       (-0.006)       (-0.006)
Married                  0.038 (***)    0.041 (***)    0.035 (***)
                       (-0.006)       (-0.006)       (-0.006)
Single                  -0.095 (***)   -0.096 (***)   -0.089 (***)
                       (-0.007)       (-0.007)       (-0.007)
1-2 Children             0.080 (***)    0.083 (***)    0.076 (***)
                       (-0.004)       (-0.004)       (-0.004)
3 or more Children      -0.090 (***)   -0.010 (***)   -0.088 (***)
                       (-0.006)       (-0.007)       (-0.006)
Union Member             0.108 (***)    0.115 (***)    0.112 (***)
                       (-0.003)       (-0.003)        (0.003)
Part-time Work                         -0.249 (***)   -0.251 (***)
                                      (-0.014)       (-0.016)
Temporary Job Holder                   -0.140 (***)   -0.128 (***)
                                      (-0.005)       (-0.005)
Multiple Job Holder                    -0.078 (***)   -0.074 (***)
                                      (-0.007)       (-0.008)
Invol P/T (*)
Recent Immgr                                          -0.003
Invol P/T (*)
Established Immgr                                      0.022
Multijob (*)
Recent Immgr                                          -0.030
Multijob (*)
Established Immgr                                     -0.023
Tempjob (*)
Recent Immgr                                          -0.016
Tempjob (*)
Established Immgr                                     -0.038 (*)
Number of Cases         334004       334004       334004
BIC'                    -43807.177   -48857.867   -48576.128
Adjusted [R.sup.2]           0.275        0.324        0.275

Coefficients for semi-log wage models equal ([e.sup.[beta]] - 1) which
produce proportional effects. These are multiplied by 100 and reported
as percentages.
Exponentiated coefficients; standard errors in parentheses
(*) p<0.05, (**) p<0.01, (***) p<0.001

TABLE 3. Indirect Effects of the Background Variables on Earnings
Transmitted by Precarious Work for Females

                     [([e.sup.[beta]]-1).sub.Model 1] -   % Total
                     [([e.sup.[beta]]- 1).sub.Model 2]     Effect

Work Experience              0                                0%
Foreign Work
Experience                   0                                0%
High School
Graduate                     0.002                            1%
Trades Training              0.001                            0%
Some Post-Secondary
Education                    0.006                            1%
Bachelor's Degree            0.011                            1%
Graduate Degree              0.011                            1%
Survey Year                 -0.002                            7%
Immigrant 5 years
or less                     -0.019                           10%
Immigrant more than
5 years                     -0.006                            9%
Married                      0.002                            6%
Single                       0.004                           11%
1-2 Children                 0.003                            5%
3 or more Children          -0.013                           27%
Union Member                -0.009                            4%

TABLE 4. Indirect Effects of the Background Variables on Earnings
Transmitted by Precarious Work for Males

                      [([e.sup.[beta]]-1).sub.Model 1] -   % Total
                      [([e.sup.[beta]]- 1).sub.Model 2]    Effect

Work Experience                 0                            0%
Foreign Work
Experience                      0                            0%
High School Graduate            0.005                        3%
Trades Training                 0.003                        1%
Some Post-Secondary
Education                       0.004                        1%
Bachelor's Degree               0.013                        2%
Graduate Degree                 0.01                         1%
Survey Year                    -0.001                        4%
Immigrant 5 years
or less                        -0.028                       15%
Immigrant more than
5 years                        -0.009                       12%
Married                        -0.003                        9%
Single                          0.001                        1%
1-2 Children                   -0.003                        4%
3 or more Children             -0.08                        47%
Union Member                   -0.007                        7%
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Author:Hira-Friesen, Parvinder
Publication:Canadian Ethnic Studies Journal
Article Type:Report
Geographic Code:1CANA
Date:Mar 22, 2018
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