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- 1. A model for estimating and imputing nonrespondent census households under sampling for nonresponse follow-up ArchivedArticles and reports: 12-001-X20060019264Description:
Sampling for nonresponse follow-up (NRFU) was an innovation for U.S. Decennial Census methodology considered for the year 2000. Sampling for NRFU involves sending field enumerators to only a sample of the housing units that did not respond to the initial mailed questionnaire, thereby reducing costs but creating a major small-area estimation problem. We propose a model to impute the characteristics of the housing units that did not respond to the mailed questionnaire, to benefit from the large cost savings of NRFU sampling while still attaining acceptable levels of accuracy for small areas. Our strategy is to model household characteristics using low-dimensional covariates at detailed levels of geography and more detailed covariates at larger levels of geography. To do this, households are first classified into a small number of types. A hierarchical loglinear model then estimates the distribution of household types among the nonsample nonrespondent households in each block. This distribution depends on the characteristics of mailback respondents in the same block and sampled nonrespondents in nearby blocks. Nonsample nonrespondent households can then be imputed according to this estimated household type distribution. We evaluate the performance of our loglinear model through simulation. Results show that, when compared to estimates from alternative models, our loglinear model produces estimates with much smaller MSE in many cases and estimates with approximately the same size MSE in most other cases. Although sampling for NRFU was not used in the 2000 census, our estimation and imputation strategy can be used in any census or survey using sampling for NRFU where units are clustered such that the characteristics of nonrespondents are related to the characteristics of respondents in the same area and also related to the characteristics of sampled nonrespondents in nearby areas.
Release date: 2006-07-20 - Articles and reports: 81-004-X20050059111Description:
This article presents an analysis of Census at School survey results that shows what students themselves had to say about their reading and associated daily habits. It was written by Statistics Canada analysts as an example to students of the type of detailed analysis that can be made using Canadian Census at School results. The article uses data that were collected from over 22,000 students across Canada during the 2004-2005 academic year. Census at School is an international classroom project that teaches students aged 8 to 18 about statistical enquiry and census-taking. Students anonymously fill in an online questionnaire about themselves - their height, time use, eating habits and much more - and then use their class results to learn statistical concepts, practice data analysis and explore social issues. Their responses also become part of national and international project databases that are used for teaching statistics.
The Census at School statistical literacy project is not an official Statistics Canada survey conducted under the Statistics Act. Schools; students participate on a voluntary basis and the data collected are not representative of Canada's student population. This is clearly stated with the summary Canadian results that are made available on the project website for the benefit of participating students.
Release date: 2006-02-28 - Articles and reports: 11F0019M2006274Geography: CanadaDescription:
We present new evidence on levels and trends in after-tax income inequality in Canada between 1980 and 2000. We argue that existing data sources may miss changes in the tails of the income distribution, and that much of the changes in the income distribution have been in the tails. Our data are constructed from Census files, which are augmented with predicted taxes based on information available from administrative tax data. After validating our approach in predicting taxes on the Census files, we document differences in the levels and trends in after-tax inequality between the newly constructed data source and the more commonly used survey data. We find that after-tax inequality levels are substantially higher based on the new data, primarily because income levels are lower at the bottom than in survey data. The new data show larger long-term increases in after-tax income inequality and far more variability over the economic cycle. This raises interesting questions about the role of the tax and transfer system in mitigating both trends and fluctuations in market income inequality.
Release date: 2006-02-27
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- 1. A model for estimating and imputing nonrespondent census households under sampling for nonresponse follow-up ArchivedArticles and reports: 12-001-X20060019264Description:
Sampling for nonresponse follow-up (NRFU) was an innovation for U.S. Decennial Census methodology considered for the year 2000. Sampling for NRFU involves sending field enumerators to only a sample of the housing units that did not respond to the initial mailed questionnaire, thereby reducing costs but creating a major small-area estimation problem. We propose a model to impute the characteristics of the housing units that did not respond to the mailed questionnaire, to benefit from the large cost savings of NRFU sampling while still attaining acceptable levels of accuracy for small areas. Our strategy is to model household characteristics using low-dimensional covariates at detailed levels of geography and more detailed covariates at larger levels of geography. To do this, households are first classified into a small number of types. A hierarchical loglinear model then estimates the distribution of household types among the nonsample nonrespondent households in each block. This distribution depends on the characteristics of mailback respondents in the same block and sampled nonrespondents in nearby blocks. Nonsample nonrespondent households can then be imputed according to this estimated household type distribution. We evaluate the performance of our loglinear model through simulation. Results show that, when compared to estimates from alternative models, our loglinear model produces estimates with much smaller MSE in many cases and estimates with approximately the same size MSE in most other cases. Although sampling for NRFU was not used in the 2000 census, our estimation and imputation strategy can be used in any census or survey using sampling for NRFU where units are clustered such that the characteristics of nonrespondents are related to the characteristics of respondents in the same area and also related to the characteristics of sampled nonrespondents in nearby areas.
Release date: 2006-07-20 - Articles and reports: 81-004-X20050059111Description:
This article presents an analysis of Census at School survey results that shows what students themselves had to say about their reading and associated daily habits. It was written by Statistics Canada analysts as an example to students of the type of detailed analysis that can be made using Canadian Census at School results. The article uses data that were collected from over 22,000 students across Canada during the 2004-2005 academic year. Census at School is an international classroom project that teaches students aged 8 to 18 about statistical enquiry and census-taking. Students anonymously fill in an online questionnaire about themselves - their height, time use, eating habits and much more - and then use their class results to learn statistical concepts, practice data analysis and explore social issues. Their responses also become part of national and international project databases that are used for teaching statistics.
The Census at School statistical literacy project is not an official Statistics Canada survey conducted under the Statistics Act. Schools; students participate on a voluntary basis and the data collected are not representative of Canada's student population. This is clearly stated with the summary Canadian results that are made available on the project website for the benefit of participating students.
Release date: 2006-02-28 - Articles and reports: 11F0019M2006274Geography: CanadaDescription:
We present new evidence on levels and trends in after-tax income inequality in Canada between 1980 and 2000. We argue that existing data sources may miss changes in the tails of the income distribution, and that much of the changes in the income distribution have been in the tails. Our data are constructed from Census files, which are augmented with predicted taxes based on information available from administrative tax data. After validating our approach in predicting taxes on the Census files, we document differences in the levels and trends in after-tax inequality between the newly constructed data source and the more commonly used survey data. We find that after-tax inequality levels are substantially higher based on the new data, primarily because income levels are lower at the bottom than in survey data. The new data show larger long-term increases in after-tax income inequality and far more variability over the economic cycle. This raises interesting questions about the role of the tax and transfer system in mitigating both trends and fluctuations in market income inequality.
Release date: 2006-02-27
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