Low income and inequality
Key indicators
Selected geographical area: Canada
-
$73,000
More low income and inequality indicators
Selected geographical area: Canada
-
9.8%
-
-0.040
-
11.1%
-
-3.3
-
$70,336
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14.2%
-
17.0%
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32.0%
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Proportion of households contributing to TFSA, RRP or RRSP in 2015 - Canada
(2016 Census of Population)65.2%
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All (5) ((5 results))
- 1. Comparison of survey regression techniques in the context of small area estimation of poverty ArchivedArticles and reports: 12-001-X201000211378Description:
One key to poverty alleviation or eradication in the third world is reliable information on the poor and their location, so that interventions and assistance can be effectively targeted to the neediest people. Small area estimation is one statistical technique that is used to monitor poverty and to decide on aid allocation in pursuit of the Millennium Development Goals. Elbers, Lanjouw and Lanjouw (ELL) (2003) proposed a small area estimation methodology for income-based or expenditure-based poverty measures, which is implemented by the World Bank in its poverty mapping projects via the involvement of the central statistical agencies in many third world countries, including Cambodia, Lao PDR, the Philippines, Thailand and Vietnam, and is incorporated into the World Bank software program PovMap. In this paper, the ELL methodology which consists of first modeling survey data and then applying that model to census information is presented and discussed with strong emphasis on the first phase, i.e., the fitting of regression models and on the estimated standard errors at the second phase. Other regression model fitting procedures such as the General Survey Regression (GSR) (as described in Lohr (1999) Chapter 11) and those used in existing small area estimation techniques: Pseudo-Empirical Best Linear Unbiased Prediction (Pseudo-EBLUP) approach (You and Rao 2002) and Iterative Weighted Estimating Equation (IWEE) method (You, Rao and Kovacevic 2003) are presented and compared with the ELL modeling strategy. The most significant difference between the ELL method and the other techniques is in the theoretical underpinning of the ELL model fitting procedure. An example based on the Philippines Family Income and Expenditure Survey is presented to show the differences in both the parameter estimates and their corresponding standard errors, and in the variance components generated from the different methods and the discussion is extended to the effect of these on the estimated accuracy of the final small area estimates themselves. The need for sound estimation of variance components, as well as regression estimates and estimates of their standard errors for small area estimation of poverty is emphasized.
Release date: 2010-12-21 - 2. Low Income Lines, 2008-2009 ArchivedArticles and reports: 75F0002M2010005Description:
In order to provide a holographic or complete picture of low income, Statistics Canada uses three complementary low income lines: the Low Income Cut-offs (LICOs), the Low Income Measures (LIMs) and the Market Basket Measure (MBM). While the first two lines were developed by Statistics Canada, the MBM is based on concepts developed by Human Resources and Skill Development Canada. Though these measures differ from one another, they give a generally consistent picture of low income status over time. None of these measures is the best. Each contributes its own perspective and its own strengths to the study of low income, so that cumulatively, the three provide a better understanding of the phenomenon of low income as a whole. These measures are not measures of poverty, but strictly measures of low income.
Release date: 2010-06-17 - Articles and reports: 75F0002M2010004Description:
Statistics Canada introduced its Low Income Measure (LIM) in 1991 as a complement to its Low Income Cut-Offs (LICOs). The Low Income Measure (LIM) is a dollar threshold that delineates low-income in relation to the median income and different versions of this measure are in wide use internationally. Over the intervening 25 years there have been a number of useful methodological and conceptual developments in the area of low income measurement. To make the Canadian LIM methodology consistent with international norms and practices, a revision of the Statistics Canada LIM methodology appears desirable.
This paper describes three modifications to the LIM that Statistics Canada plans to introduce in 2010: replacing the economic family by household; replacing the current LIM equivalence scale by the square root of household size; and taking household size into consideration in determining the low-income thresholds. The paper explains the rationale behind each modification and demonstrates the impacts the revisions will have on low-income statistics in comparison with those under the existing LIM. Overall the revisions do not have any significant effect on broad historic trends in low-income statistics in Canada. However, compared to the existing LIM the revised LIM produces lower estimates of low-income incidence for certain groups of individuals such as unattached non-elderly individuals.
Release date: 2010-06-07 - Articles and reports: 75F0002M2010003Description:
This study assesses the existing LICO, LIM, and MBM lines, together with a fixed LIM, by using several distribution sensitive indexes. We found that the low income lines tracked each other well in the long-run. But, in the short-run, they often behaved differently. The same was observed when examining different indexes under the same line. In the long-run, the low income rate, gap, and severity indexes all moved in the same direction. However in the short-run, they sometimes varied in opposite directions, or in the same direction with different magnitudes, suggesting that a single line or index can be misleading in some circumstances.
Release date: 2010-05-26 - 5. Cities and Growth: Earnings Levels Across Urban and Rural Areas: The Role of Human Capital ArchivedArticles and reports: 11-622-M2010020Geography: CanadaDescription:
Using 2001 Census data, this paper investigates the extent to which the urban-rural gap in the earnings of employed workers is associated with human capital composition and agglomeration economies. Both factors have been theoretically and empirically linked to urban-rural earnings differences. Agglomeration economies-the productivity enhancing effects of the geographic concentration of workers and firms-may underlie these differences as they may be stronger in larger urban centres. But human capital composition may also drive the urban-rural earnings gap if workers with higher levels of education and/or experience are more prevalent in cities. The analysis finds that up to one-half of urban-rural earnings differences are related to human capital composition. It also demonstrates that agglomeration economies related to city size are associated with earnings levels, but their influence is significantly reduced by the inclusion of controls for human capital.
Release date: 2010-01-25
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- 1. Comparison of survey regression techniques in the context of small area estimation of poverty ArchivedArticles and reports: 12-001-X201000211378Description:
One key to poverty alleviation or eradication in the third world is reliable information on the poor and their location, so that interventions and assistance can be effectively targeted to the neediest people. Small area estimation is one statistical technique that is used to monitor poverty and to decide on aid allocation in pursuit of the Millennium Development Goals. Elbers, Lanjouw and Lanjouw (ELL) (2003) proposed a small area estimation methodology for income-based or expenditure-based poverty measures, which is implemented by the World Bank in its poverty mapping projects via the involvement of the central statistical agencies in many third world countries, including Cambodia, Lao PDR, the Philippines, Thailand and Vietnam, and is incorporated into the World Bank software program PovMap. In this paper, the ELL methodology which consists of first modeling survey data and then applying that model to census information is presented and discussed with strong emphasis on the first phase, i.e., the fitting of regression models and on the estimated standard errors at the second phase. Other regression model fitting procedures such as the General Survey Regression (GSR) (as described in Lohr (1999) Chapter 11) and those used in existing small area estimation techniques: Pseudo-Empirical Best Linear Unbiased Prediction (Pseudo-EBLUP) approach (You and Rao 2002) and Iterative Weighted Estimating Equation (IWEE) method (You, Rao and Kovacevic 2003) are presented and compared with the ELL modeling strategy. The most significant difference between the ELL method and the other techniques is in the theoretical underpinning of the ELL model fitting procedure. An example based on the Philippines Family Income and Expenditure Survey is presented to show the differences in both the parameter estimates and their corresponding standard errors, and in the variance components generated from the different methods and the discussion is extended to the effect of these on the estimated accuracy of the final small area estimates themselves. The need for sound estimation of variance components, as well as regression estimates and estimates of their standard errors for small area estimation of poverty is emphasized.
Release date: 2010-12-21 - 2. Low Income Lines, 2008-2009 ArchivedArticles and reports: 75F0002M2010005Description:
In order to provide a holographic or complete picture of low income, Statistics Canada uses three complementary low income lines: the Low Income Cut-offs (LICOs), the Low Income Measures (LIMs) and the Market Basket Measure (MBM). While the first two lines were developed by Statistics Canada, the MBM is based on concepts developed by Human Resources and Skill Development Canada. Though these measures differ from one another, they give a generally consistent picture of low income status over time. None of these measures is the best. Each contributes its own perspective and its own strengths to the study of low income, so that cumulatively, the three provide a better understanding of the phenomenon of low income as a whole. These measures are not measures of poverty, but strictly measures of low income.
Release date: 2010-06-17 - Articles and reports: 75F0002M2010004Description:
Statistics Canada introduced its Low Income Measure (LIM) in 1991 as a complement to its Low Income Cut-Offs (LICOs). The Low Income Measure (LIM) is a dollar threshold that delineates low-income in relation to the median income and different versions of this measure are in wide use internationally. Over the intervening 25 years there have been a number of useful methodological and conceptual developments in the area of low income measurement. To make the Canadian LIM methodology consistent with international norms and practices, a revision of the Statistics Canada LIM methodology appears desirable.
This paper describes three modifications to the LIM that Statistics Canada plans to introduce in 2010: replacing the economic family by household; replacing the current LIM equivalence scale by the square root of household size; and taking household size into consideration in determining the low-income thresholds. The paper explains the rationale behind each modification and demonstrates the impacts the revisions will have on low-income statistics in comparison with those under the existing LIM. Overall the revisions do not have any significant effect on broad historic trends in low-income statistics in Canada. However, compared to the existing LIM the revised LIM produces lower estimates of low-income incidence for certain groups of individuals such as unattached non-elderly individuals.
Release date: 2010-06-07 - Articles and reports: 75F0002M2010003Description:
This study assesses the existing LICO, LIM, and MBM lines, together with a fixed LIM, by using several distribution sensitive indexes. We found that the low income lines tracked each other well in the long-run. But, in the short-run, they often behaved differently. The same was observed when examining different indexes under the same line. In the long-run, the low income rate, gap, and severity indexes all moved in the same direction. However in the short-run, they sometimes varied in opposite directions, or in the same direction with different magnitudes, suggesting that a single line or index can be misleading in some circumstances.
Release date: 2010-05-26 - 5. Cities and Growth: Earnings Levels Across Urban and Rural Areas: The Role of Human Capital ArchivedArticles and reports: 11-622-M2010020Geography: CanadaDescription:
Using 2001 Census data, this paper investigates the extent to which the urban-rural gap in the earnings of employed workers is associated with human capital composition and agglomeration economies. Both factors have been theoretically and empirically linked to urban-rural earnings differences. Agglomeration economies-the productivity enhancing effects of the geographic concentration of workers and firms-may underlie these differences as they may be stronger in larger urban centres. But human capital composition may also drive the urban-rural earnings gap if workers with higher levels of education and/or experience are more prevalent in cities. The analysis finds that up to one-half of urban-rural earnings differences are related to human capital composition. It also demonstrates that agglomeration economies related to city size are associated with earnings levels, but their influence is significantly reduced by the inclusion of controls for human capital.
Release date: 2010-01-25
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