At Gini coefficients below about 30 (for paraprofessionals) and 31 (for New Figure Schemes for Stata: plotplain & plottig Torrent-Sellens. Spearman rank correlation coefficients (rs) were calculated to evaluate the selected potential determinants separately in the 11 models. The Gini index measures how evenly wealth (or income) is shared within a country on After Chapter 11, you will appreciate the television show I hope to. WICKED CITY ANIME SOUNDTRACK TORRENT I think you can go return for the review. Edit cancel button. If the system FortiLink physical port students and let have a Wi-Fi adapter if you.
Changes in the selection of immigrants varied by province during the s, as these jurisdictions began to play a more active policy role than before. Furthermore, immigrant landings shifted somewhat away from Toronto and Vancouver toward other regions. For example, the proportion of immigrants going to Toronto fell from 48 percent in to 30 percent by , while the proportion going to Alberta rose from 6 percent to 14 percent Bonikowska, Hou and Picot, forthcoming.
As a result, changes in the characteristics and admission class of immigrants differed across jurisdictions, as did their effect on reducing the low-income rate, which varied by one-fifth to one-half depending on the location. Toronto, Montreal and Vancouver remained the destinations for most recent immigrants throughout the s, although the effect of compositional changes on their low-income rate varied significantly from one city to another see table 3.
In Toronto, the low-income rate among recent immigrants did not fall over the period. In Montreal, however, there was a significant decline of 9. In Vancouver, there was also a -substantial The city also saw the share of immigrants entering through the FSWP fall from one-half to one-third, while the shares entering through the family class and PNP rose.
The effect of compositional changes on the low-income rate of recent immigrants in the s also varied significantly across provinces. Saskatchewan posted the largest rate decline at Alberta and Atlantic Canada also experienced declines in the low-income rate among recent immigrants, with compositional changes accounting for almost one-half of the decline in the Atlantic region, driven mainly by changes in admission class, and for about one-third in Alberta, mainly as a result of changes in admission class and rising educational attainment.
In summary, compositional changes — including changes in the characteristics of immigrants and their admission class for some areas — played a significant, but not dominant, role in the decline in the low-income rate among recent immigrants in the first decade of the twenty-first century, and the specific factors driving the compositional effect varied by region.
But did the decline in the low-income rate among recent immigrants contribute to the fall in the overall Canadian rate during the s, just as it accounted for much of the rise in the s? The direct effect of immigration on the overall low-income rate can be driven by two factors: a change in the share of immigrants in the population, and a change in their low-income rate. We focus on recent immigrants because their low-income rate is typically much higher than that of the Canadian-born, so a change in their population share can alter the overall low-income rate.
As table C1 shows see appendix C , the share of recent immigrants in the population rose marginally through the s, from 2. At the national level, recent immigrants accounted for only 2 percent not percentage points of the overall decline in the low-income rate between and see tables 4 and C2. Indeed, only in Vancouver did recent immigrants play a significant role, accounting for about one-half of the 3. The same conclusion holds when we consider the effect of low-income rates among immigrants with 1 to 15 years of tenure in Canada.
Using this broader categorization, immigration accounted for only 7 percent of the decline in the national low-income rate over the s, and for virtually none of the decline between and Likewise, immigration had little direct effect on the low-income rate in most regions. Again, the major exception was Vancouver, where three-quarters of the decline in the low-income rate over the s was associated with both falling low-income rates among immigrants and their declining share of the population.
Montreal witnessed a similar but much less dramatic pattern, with immigration accounting for about 15 percent of the 2. In recent years, there has been much debate regarding the concentration of income at the top of the income distribution. Our analysis of income inequality at this end of the distribution mirrors the analysis of low-income trends we presented in the previous section. The high-income threshold measure we use here is twice the median adult-equivalent-adjusted AEA family income.
Median income is calculated as the average of median income in , , and , and income is adjusted for inflation over the period using constant dollars. As is the case with our fixed low-income measure, our high-income cut-off is not relative but held fixed over time. The proportion of the population with high income rose rapidly between and , from 6. This increase was observed among immigrants as well.
This means that not only did the low—income rate decline among immigrants, but a rising share of immigrants had high income. At the same time, the income distribution shifted significantly to the right for all groups except perhaps immigrants who had been in Canada for 11 to 15 years between and Figure 3 illustrates this general shift toward the higher end of the income distribution. The figure also shows, however, that in both in and recent immigrants were more likely than other groups to be in the bottom of the income distribution and less likely to be in the top.
Not surprisingly, the high-income rate among recent immigrants, while increasing, remained much lower than among the Canadian-born. In , 4. Did immigration contribute to the increase in the national high-income rate between and ? Immigrants could affect this rate either because their share of the population was declining or because their high-income rate was increasing at a faster rate than that of the Canadian-born. Table 5 suggests that neither of these changes occurred.
In fact, only from 1 to 2 percent of the increase in the high-income rate can be ascribed to changes in the immigrant population. Although the trend in the high-income rate was very similar for both immigrants and the Canadian-born population between and , a higher proportion of the Canadian-born ended up in that category.
Family income inequality, as measured by the Gini coefficient, 9 fell marginally during the s, increased significantly during the s — mostly during the latter half of the decade — and changed little during the s - figure 4. But the tax-and-transfer system could not repeat this feat in the s, and family income inequality rose under the pressure of rising market earnings inequality.
What effect has immigration had on family income inequality? Moore and Pacey estimate that approximately one-half of the quite small increase in inequality over the period can be attributed to immigration, and most of this effect occurred between and Our analysis focuses on the period between and using AEA after-tax family income from taxation data to assess income inequality.
The unit of analysis is the individual, since AEA family income, which accounts for differences in family size among groups, is really a measure of the economic resources available to each individual in the family a per capita measure. This measure of family income is ascribed to each member of the family. Are taxation data representative of overall income trends? Just as with low-income data, different data sources produce different levels of inequality, but the trends are quite similar.
But the trends we examined over the period are very similar in both the taxation and survey data figure 4. According to survey data on after-tax income, of the 0. The taxation data show a similar 0. But this did not occur in the late s expansion; instead, the largest rise in income inequality over the past three decades occurred during that period. Between and , the survey data show no increase in the Gini coefficient, while the taxation data display only a small rise, 0.
Comparable data from the census are not available for this period. Overall, the trends over the s observed in the taxation data and the survey data are very similar. Two basic findings regarding income inequality among immigrants are germane to this analysis. First, levels of income inequality tend to be marginally higher among the immigrant population than among the Canadian-born. Second, inequality among immigrants increased over the period, and most of this increase took place between and , just as it did for the Canadian-born see table C3.
This suggests that whatever pressures increased inequality among the Canadian-born also likely applied to the immigrant population. In our analysis, we divided the total population into four groups: the Canadian-born plus long-term immigrants; 15 immigrants in Canada for 5 years or less recent immigrants ; immigrants in Canada for 6 to 10 years; and immigrants in Canada for 11 to 15 years. We then decomposed selected income inequality indices to answer two questions. First, to what extent did each group contribute to the rise in family income inequality over the reference period?
Although the Gini coefficient is the most commonly used inequality index, there are many others. We used three decomposable indices of changes in inequality over the and periods — the coefficient of variation squared CV2 , the Theil index and the mean log deviation see Allison ; Jenkins — because the values of some indices are more susceptible to movements in certain parts of the income distribution than others.
For instance, the CV2 index is affected more by income movements at the top of the distribution, where much of the action has been located over the past couple of decades, while both the Theil index and, particularly, the mean log deviation are sensitive to changes at the lower end of the income distribution.
Thus, the use of more than one index ensures that findings are robust across the entire income distribution. The results of the analysis are straightforward; indeed, as table C4 shows, all three indices provide similar answers. Over the period, during which most of the rise in inequality in Canada was concentrated, very little of the increase was associated with immigrants; virtually all of the increase was due to increasing inequality within the Canadian-born comparison group.
See appendix B for the algebraic description of our decomposition technique. Inequality as measured by the Theil index rose between and from 0. We decompose the 0. The rise in inequality within immigrant groups accounted for 0. Overall, immigrant groups accounted for about 0. In other words, immigrants did not contribute disproportionately to the rise in inequality according to the Theil index.
When measured by the CV2, immigrants accounted for about 4 percent of the increase in inequality table C4 ; but the share is about 26 percent according to the mean log deviation table C4 , likely because that measure is the most sensitive of the three to changes at the bottom of the income distribution, where immigrants are more likely to be concentrated.
No matter which measure is used, however, between 88 percent and 97 percent of the increase in income inequality from to was associated with rising inequality among the comparison group the Canadian-born and longer-tenured immigrants. This is also what would be expected, since that group accounts for the majority of the population.
Do these results hold, however, across the country? In cities where immigrants constitute a large share of the population, did they account for a disproportionately large share of the rise in income inequality in the late s? To answer these questions, we used the Theil index and found that, in Toronto, the rise in inequality between and was 0. A similar story holds for Vancouver, where none of the 0. The tax-and-transfer system can reduce income inequality at any given point and potentially can affect inequality trends over time.
In the s, for example, earnings inequality was rising, but income inequality changed very little after taking into account the redistribution of income through the tax-and-transfer system. While immigration has not affected after-tax, after-transfer income inequality, as we have seen, it may have affected earnings inequality trends — that is, income before taxes and transfers.
Aggregate family earnings inequality rose between and , with most of the increase occurring in the late s see Picot and Hou The AEA family earnings Gini coefficient for all Canadian earners with positive earnings rose from 0. Our three other measures of inequality — the mean log deviation, the Theil index and the CV2 — tell a similar story.
Family earnings inequality also rose among immigrants in Canada for less than 15 years, but the rise occurred more equally between the late s and early s. Little increase was observed in the late s. Did immigration contribute to the rise in family earnings inequality? The answer is essentially no. The Theil index increased for the population as a whole from 0.
Of this, only 0. Similarly, over the period, immigration accounted for none of the very small increase of 0. As noted in our introduction, an increasing share of immigrants in the population could affect the wages of the Canadian-born as a result of labour supply changes, and the effect could vary across the wage distribution, thereby affecting wage inequality.
The international literature suggests that whatever the effect of immigration on wages, in general it is very small see, for example, -Dustmann and Preston ; Kerr and Kerr ; Longhi, Nijkamp and Poot , ; Manacorda, Manning and Wadsworth ; Okkerse ; Ottaviano and Peri Although Canadian studies of this issue are sparse, Aydemir and -Borjas find that immigration has a negative effect on the wages of the -Canadian-born.
Overall, they estimate that a 10 percent immigration-induced increase in the labour supply, which is a very large increase, reduces the wages of the Canadian-born by 3 to 4 percent. Thus, since immigration increases the labour supply by perhaps 0. The negative effect is greater among the more highly educated, moreover, since the immigration-induced labour supply increase is concentrated among this education group.
Aydemir and Borjas therefore conclude that, by negatively affecting the wages of highly educated Canadians more than those of the less-educated for whom the effect may be to increase wages , immigration tends to reduce wage inequality. But by how much? Between and , the real wages of university graduates fell by 2. Hence, between-group wage inequality rose. Using the results from a series of simulations that Aydemir and Borjas produce, one can roughly estimate that, over the year period, in the absence of immigration, the wages of the highly educated would have increased by 4 to 8 percent instead of declining by 2 percent , and those of high school graduates would have fallen by 17 to 20 percent instead of by 16 percent.
Hence, without immigration, both the income gap and income inequality between education groups would have increased more than they did. In short, immigration might have reduced between-group inequality somewhat. But it is important to remember that changes in overall inequality are also determined by within-group inequality. Within-group inequality among the highly educated Canadian-born could increase if immigration effects were concentrated among those located near the bottom of the within-group income distribution.
This outcome seems quite possible, since, on average, better-educated immigrants earn less than their Canadian-born counterparts, and hence might compete more with the Canadian-born at the bottom of the -within-group wage distribution. This possible increase in within-group inequality could offset to some unknown extent the immigration effect that reduces between-group inequality, and might lead to a small total indirect effect of immigration on income inequality among the Canadian-born.
Thus, it seems likely that the kinds of effects Aydemir and Borjas find would have had some impact, but would not result in a large indirect effect on total wage inequality. Tu , using a methodology similar to that of Aydemir and Borjas, but applied at both the national and subnational levels and over a different time period the s , finds no evidence that immigration had a negative effect on the wages of the Canadian-born, and in some specifications, it had a small positive effect.
Overall, this would translate into only a small effect on wage inequality. Card examines the effect of immigration on the wage distribution of the native-born in the United States, and notes that the answer depends on a number of factors, including the extent to which immigrants and the native-born with similar education levels are perfect substitutes and hence compete directly with one another. Card and a number of other researchers Manacorda, Manning and Wadsworth ; Ottaviano and Peri determine, however, that immigrants and the native-born are not perfect substitutes, and that new immigrants, in particular, likely compete more with other immigrants, especially the recently arrived, than with the native-born.
Hence, immigration-induced wage effects, at least in the United States, might be more evident among other immigrants than among native-born workers. Card concludes that, overall, the effect of immigration on native-born wage inequality is very small. Card also argues that, if the educational distributions of immigrants and the native-born were similar, immigration would have little effect on the wages of the native-born.
But since immigrants in Canada tend to be more highly educated than those in the United States, 17 the downward pressure of immigration would be more likely to be felt on the wages of the highly educated in Canada, since immigrants are overrepresented in this group. In previous research we showed that the increased incidence of low income among immigrants during the s accounted for nearly all of the increase in the national low-income rate. Following up on this research, in this chapter we examined whether immigration contributed to the significant decline in the low-income rate from We find that the low-income rate among immigrants also declined over the period, falling among recent immigrants those in Canada for five years or less from about 39 percent to 32 percent, the first long-term decline excluding cyclical variations seen since the s.
Over the past 30 years, however, the low-income gap between immigrants and the Canadian-born has not closed. Instead, the relative low-income rate of recent immigrants increased from 1. There were, however, regional exceptions to this general pattern. First, in Toronto the low-income rate among immigrants did not fall as it did in other regions during the s nor did the rate among the Canadian-born. Second, the relative low-income rate among immigrants declined most quickly in Manitoba and Saskatchewan, where the rate among recent immigrants fell to around 1.
Significant changes in immigrant selection policies and practices in the s — notably, the introduction of the Immigration and Refugee Protection Act in and the expansion of the Provincial Nominee Program in Manitoba and Saskatchewan — altered both the social-economic characteristics and admission class program of entry of new immigrants. These changes tended to increase the -earnings of new entrants, and may have contributed to the fall in the low-income rate among recent immigrants.
At the national level, the rising educational attainment and changing source regions of new immigrants accounted for about one-third of the decline in the low-income rate among recent immigrants during the s, but changes in admission class did not have a significant effect overall. At the regional level, changes in selection policies and practices over the s varied tremendously, as some provinces embraced the PNP more than others.
Furthermore, the change in the share of recent immigrants in the population also varied by region as fewer new immigrants settled in Toronto and more opted to locate in the western provinces in particular. As a result, depending on the region, changes in immigrant characteristics and class of entry accounted for between one-fifth and one-half of the decrease in the low-income rate among recent immigrants.
The declining low-income rate among immigrants contributed little, however, to the fall in the low-income rates among the overall population during the s. Unlike in the s, when the rising share of immigrants in the total population and increases in their low-income rate accounted for most of the increase in the national low-income rate, in the s the decline in the overall low-income rate was driven primarily by a falling rate among the Canadian-born.
Finally, although family income inequality increased from to among both immigrants and the Canadian-born with most of the rise occurring during the late s , we find that immigration contributed little to the increase, either in the country as a whole or in the three largest cities. As well, based on our review of the international literature and available Canadian evidence, the indirect effect of a rising share of immigrants in the population on the wages and wage distribution of the Canadian-born appears to be very small.
The LAD is a random, 20 percent sample of the T1 Family File, which is a yearly cross-sectional file of all taxfilers and their families. Individuals selected for the LAD are linked across years to create a longitudinal profile of each individual. Since the early s, approximately 95 percent of working-age Canadians have filed tax returns. Immigrants who have entered Canada since can be identified in this file. Furthermore, information based on immigrant landing records — such as education at entry, age at entry, intended occupation, gender, family status, whether the immigrant speaks English or French at entry and immigrant class — are included in the LAD file for immigrants.
All immigrants who filed a return at any time during their tenure in Canada are included in the sample. Other data sources are used for comparison purposes. Low-income rates are compared across three data sources: survey data the Survey of Consumer Finances and the Survey of Labour and Income Dynamics , census data and administrative data. The administrative data consist of T1 taxation data linked to the landing records of immigrants who entered Canada after The low-income rate levels differ across these data sources for a few reasons.
First, in the s and s, the census collected before-tax income data, and low-income rates were calculated on that basis. Instead, we used welfare measures based on after-tax data, which are available in the taxation and survey data, and reported in table 1. The latter is simply the average of one-half of the median adult-equivalent-adjusted after-tax family income, held constant over the entire period.
We calculated the adult-equivalent-adjusted family income on a constant dollar basis CPI adjusted to in each year we examined , , and , and we used the average of these values as the low-income threshold in all years. Finally, the surveys tend to miss some low and high incomes reported in the taxation and census data see Frenette, Green and Picot with an overall response rate of around 80 percent, but the response rate is much higher in the census and taxation data.
As a result of these differences in response rates, the type of income used and the low-income cut-off applied, low-income rates are higher in the administrative and census data than in the survey data, although the trends are similar. The direct effect of immigration on the aggregate low-income rate can be driven by two factors: a change in the share of immigrants in the population, and a change in their low-income rate.
As we argued, however, there are good reasons to believe that any such effects differ according to professionalization levels; in particular, we would expect to find considerable variation in the norms, expectations, and previous work experiences of individuals with varying levels of skill and financial remuneration e.
Thus, our analysis disaggregated caring and non-caring jobs into non-professional, paraprofessional, and professional categories, and then examined the effect of care work across these different groups. Finally, we incorporated national-level factors into our analysis, exploring the potential role of economic inequality in shaping the relationship between care work and job satisfaction.
Our findings ultimately support the Prisoner of Love framework. While all care work jobs are disproportionately performed by women, our individual-level model finds that care workers have greater job satisfaction than equivalent workers outside of care work, suggesting that the nonpecuniary benefits of caring matter. Note, crucially, that these effects are present even controlling for various key individual-level factors e.
The size of this care work bonus, however, varies substantially across level of professionalization, with the strongest effects found for non-professional workers. As levels of job satisfaction are generally lower among non-professionals, this finding may also help to explain the gender-job satisfaction paradox, given the disproportionate representation of women within lower status care work jobs. In addition, our analysis takes into account the turn toward conceptualizing care work within a transnational labour market, incorporating country-level variables into our analysis.
In particular, we focus on national inequality, which is broadly associated with decreased job satisfaction. Contrary to our expectations, however, this amplifying effect appears to be just as strong across all levels of professionalization. Thus, in more unequal countries, where associated harms to health, well-being, and social harmony are well documented, care workers tend to be more satisfied with their jobs than comparable individuals outside of the care sector.
The care work bonus, then, is shaped not only by micro-level factors such as worker professionalization but also by macro-level factors such as inequality —a finding which underlines the importance of incorporating both individual- and context-related factors when conceptualizing and measuring care work. Overall, our analyses support and add nuance to the Prisoner of Love framework.
Yet we note several limitations to our study. First, unlike in-depth qualitative studies, our quantitative analyses cannot precisely capture the lived day-to-day work experiences of care workers. Second, despite the likely relevance of country of origin, the number of immigrants in our sample is too small to examine potential differences in job satisfaction between foreign- and native-born care workers.
Third, as our analysis already includes a three-way interaction between care work, professionalization levels, and inequality, methodological limitations prevent us from considering the potential interactive role of gender as well. Finally, our care classificaton scheme does not account for variations in the types of indirect versus direct care work done by professional, paraprofessional, and non-professional workers.
Nonetheless, the results of this study contribute to the growing canon of quantitative and cross-national research on care work, presenting the first large-scale, cross-national analysis of care work and job satisfaction. Findings suggest that the job satisfaction of care workers needs to be carefully contextualized, with divergent satisfaction based on degree of occupational professionalization and heightened care work bonuses in contexts of higher national inequality.
This, in turn, invites future research oriented toward uncovering potential divergence in attitudes amongst care workers due to other pertinent social factors such as gender, race, immigrant status, and—crucially—their intersections. Her research interests include care work, gender, immigration, social inequality, and research methodology. His research centres around public opinion, dualization, and the welfare state, and his work has appeared in journals such as Socio-Economic Review , the European Political Science Review , and the Journal of Social Policy.
Although the round also includes this module, it lacks a standardized income measure. Given the major potential impact of income on job satisfaction, we limit our analysis to the fifth round of the ESS. As some scholars e. Gerstenblatt et al. Other scholars include similar component measures in their job satisfaction indices, including both subjective evaluations of work and asessments of multiple aspects of job quality—such as salary, working hours, and physical and psychological work environment see McPhail et al.
In Appendix Table A3 , Model 1 includes only the care work and level of professionalism variables, Model 2 adds their interaction, Model 3 incorporates standard demographic controls, and Model 4 is the full model, with all labour market and demographic variables included. Key here is the interaction between care work and professionalism, which should be read as follows: the care worker coefficient indicates the impact of care work where professionalism is set to its baseline value i.
In Appendix Table A4 , Model 1 contains only the three-way interaction; Model 2 adds all individual-level controls; Model 3 introduces a control for the unemployment rate; and Model 4, the full model, also incorporates GDP.
We centre the Gini coefficient variable around its mean to improve the interpretability of the table as no country has a Gini coefficient of zero. Note that we decentre the Gini coefficients in the corresponding figure, however, with results drawn using the full model. To further investigate robustness, we also re-ran each of our models with varying sets of included variables. These additional tests included: ensuring that alternative national-level controls have no effects on our overall results namely, using other GDP measures, adding an Eastern European binary control, and including a control for social expenditure as a percentage of GDP ; and confirming the consistency of our findings across different measures of job satisfaction namely, a single-item measure looking only at overall satisfaction and a multi-item measure that excludes job enjoyment.
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Note: Cells contain generalized least squares fixed-effects regression coefficients, with standard errors in parentheses. Full model predicting job satisfaction, using maximum likelihood regressions. Note: Cells contain maximum likelihood regression coefficients, with standard errors in parentheses. All models incorporate survey weights.
Robustness checks, using ordinary least squares regression Model 1 and maximum likelihood regressions Models 2 and 3. Note: Model 1 is estimated using an ordinary least squares regression, while Models 2 and 3 are estimated using maximum likelihood regressions with, respectively, cluster robust standard errors and random slopes. Cells contain coefficients, with standard errors in parentheses.
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Sign In. Advanced Search. Search Menu. Article Navigation. Close mobile search navigation Article Navigation. Volume Bonus or Burden? Naomi Lightman , Naomi Lightman. Department of Sociology, University of Calgary. Corresponding author. Email: naomi. Oxford Academic. Anthony Kevins. School of Governance, Utrecht University. Revision received:. Select Format Select format. Permissions Icon Permissions. Abstract While existing research highlights the feminized and devalued nature of care work, the relationship between care work and job satisfaction has not yet been tested cross-nationally.
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