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Ethnic Variations in Immigrant Poverty Exit and Female Employment: The Missing Link

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Demography

Abstract

Despite widespread interest in poverty among recent immigrants and female immigrant employment, research on the link between the two is limited. This study evaluates the effect of recently arrived immigrant women’s employment on the exit from family poverty and considers the implications for ethnic differences in poverty exit. It uses the bivariate probit model and the Fairlie decomposition technique to analyze data from the Longitudinal Survey of Immigrants to Canada (LSIC), a nationally representative survey of immigrants arriving in Canada, 2000–2001. Results show that the employment of recently arrived immigrant women makes a notable contribution to lifting families out of poverty. Moreover, the wide ethnic variations in the probability of exit from poverty between European and non-European groups are partially explained by the lower employment rates among non-European women. The results suggest that the equal earner/female breadwinner model applies to low-income recent immigrant families in general, but the male breadwinner model explains the low probability of poverty exit among select non-European groups whose female employment rates are notably low.

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Notes

  1. The statistics are based on calculations using data from the 2001 Census Public Use Microdata File.

  2. The percentages are based on calculations using data from the LSIC.

  3. Admittedly, some of the cited works study the labor force participation of immigrant women and categorize employed and unemployed individuals (but actively seeking paid work) in the same group. For brevity, I consistently use the term employment even when it refers to such works.

  4. Focusing on specific ethnic groups (e.g., Chinese, Ghanaian, Korean, Vietnamese), Canadian qualitative research generally agrees that immigrant women choose paid work as a solution to their families’ economic challenges (Giles 1997; Giles and Preston 1996; Ng 1999; Wong 2000).

  5. Admittedly, a wide variation exists in ethnocultural and religious characteristics between Arab and West Asian groups. However, I aggregate these two groups into one in the Fairlie decomposition analysis due to the small unweighted sample size of each group. Moreover, this aggregation can be justified, given that the majority of West Asian subgroups are Afghan and Iranian, and these two groups can stand as a broad pan–Middle Eastern and West Asian group, sharing “ethnic heritage greatly influenced by Islamic values, especially those regarding gender roles and family relations” (Moghadam 2004; Read 2003:210). Combining Arab and West Asian groups is also a common practice in Canadian quantitative research on the ethnic variations in behaviors and attitudes using Statistics Canada’s data (Dogra et al. 2010; Kobayashi et al. 2008; Silver et al. 2004).

  6. See Simard et al. (2003) and Statistics Canada (2007) for details on how the weight is computed.

  7. Although an examination of poverty exit between Waves 1 and 2 would allow the analysis of a larger sample, I did not consider the Wave 1 poverty status for two reasons. First, the Wave 1 interview was conducted six months after immigrants’ arrival, which is too soon to assess their low-income status. Second, one’s poverty status is commonly based on one’s annual family income in Canada; therefore, the income for six months makes it difficult to determine an immigrant’s poverty status.

  8. I do not consider education obtained in Canada before establishing permanent residency because of the small number of applicable cases.

  9. Ideally, the incomes of other family members (e.g., children, relatives) would be controlled, as they may constitute important income sources for poor families who pool each member’s income to survive as a family unit. As the LSIC does not contain income data for coresiding children and relatives, I am unable to control for these factors.

  10. This percentage is based on my calculation using the LSIC data.

  11. This is speculative, as the data on the spouse’s education and training activities after arrival are unavailable in the LSIC. However, my analysis of the sample of low-income men (whose education/training activity information is available) shows that 45 % of those not employed in Wave 2 were enrolled in education/training programs, which is 10 percentage points higher than their employed counterparts.

  12. Propensity score analysis (PSA) is now used more frequently to handle selection bias (Brand and Davis 2011; Kuhn et al. 2011). This method allows adjusting for differences in observable characteristics between treatment and control groups by creating a counterfactual of the treatment group using the control group (Rosenbaum and Rubin 1983; Rubin 1974). Although PSA is favored over a standard regression “as a promising procedure for estimating causal effects,” there are limitations (Morgan and Harding 2006:4). For example, selection associated with unobserved heterogeneity cannot be controlled (DiPrete and Gangl 2004). As this is a crucial methodological challenge in this study, I use the bivariate probit model.

  13. For more details of the Fairlie decomposition technique, see Do and Paley (2007) and Van Hook et al. (2004).

  14. As Eq. (2) indicates, the effects of compositional differences in X 1 and X 2 depend on the overall compositional differences in other variables, indicating that the order of variables in a probit regression equation influences the results. To consider this influence, I randomize the ordering of variables (Fairlie 2006).

  15. As in the standard logit model, the coefficients of the bivariate probit model allow the assessment of the direction and statistical significance of the covariates. Unlike logits, probit coefficients are not suited to the assessment of the impact of a variable because they estimate “the difference a one-unit increase in […] the variable will have on the cumulative normal probability of the dependent variable, expressed in Z-scores” (Miller and van der Meulen Rodgers 2008:139). Given such complexity, I use marginal effects to interpret results. For details on the calculation of marginal effects, see Kaida (2013).

  16. As shown in Table S1 (Online Resource 1), I calculate the marginal effects of all covariates, assuming that neither the female nor male spouse worked in Wave 2. Therefore, their Wave 2 weekly earnings are set at $0. The changes in male spouses’ weekly earnings from Wave 2 to 3 are based on the means for those not employed in Wave 2. The other dummy variables (e.g., city of residence in Wave 2) are set at 0; the continuous variables (e.g., age at Wave 2) are set at the sample means.

  17. For the city of residence variable, Toronto, Montreal, and Vancouver are categorized into one group because unweighted numbers of cases for some cities are too small to produce reliable estimates.

  18. Admittedly, there could be unobserved heterogeneity among immigrants’ origin countries within each ethnic group (e.g., gender socialization, cultural norms). This may influence female spouses’ employment and should ideally be taken into account in the Fairlie decomposition models, as in the bivariate probit models discussed previously. However, there is no established consensus to correct for such selection into employment associated with unobserved heterogeneity in the Fairlie decomposition technique to date. When this methodological limitation is addressed, the results of this section could be made more robust.

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Acknowledgments

Earlier versions of this article were presented at the annual meetings of the Canadian Sociological Association in Ottawa, May 31–June 2, 2009, and at the annual meetings of the Population Association of America in Washington, DC, April 30–May 2, 2011. Thanks go to Monica Boyd, Cynthia Cranford, John Myles, Jeffrey Reitz, Michael Haan, Anessa Kimball, Taylor Hui, and Demography’s anonymous referees for helpful comments. Thanks also to Elizabeth Thompson for her editorial support. The analysis presented in this article was conducted at the Toronto and Memorial University Research Data Centres (RDCs), which are part of the Canadian Research Data Centre Network (CRDCN). The services and activities provided by the Toronto and Memorial RDCs are made possible by the financial or in-kind support of the Social Sciences and Humanities Research Council (SSHRC), the Canadian Institutes of Health Research (CIHR), the Canada Foundation for Innovation (CFI), Statistics Canada, the University of Toronto, and Memorial University of Newfoundland. The views expressed in this article do not necessarily represent those of the CRDCN’s or its partners.

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  1. Department of Sociology, Memorial University of Newfoundland, St. John’s, NL, A1C 5S7, Canada

    Lisa Kaida

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Kaida, L. Ethnic Variations in Immigrant Poverty Exit and Female Employment: The Missing Link. Demography 52, 485–511 (2015). https://doi.org/10.1007/s13524-015-0371-8

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