Keyword search

This paper highlights the Canadian Tourism Satellite Account (CTSA) developed by Statistics Canada. The CTSA provides an economic measure of the importance of tourism in terms of expenditures, Gross Domestic Product and employment for Canada. It permits a comparison of tourism with other industries within Canada since the concepts and methods used are based on the framework of the Canadian System of National Accounts. The study revealed that tourism is an important part of Canada's well diversified economy. This paper presents the results of the CTSA for reference year 2004.

This study was prepared by staff of the Research and Development Projects and Analysis Section, Income and Expenditure Accounts Division, Statistics Canada. The study was funded by the Canadian Tourism Commission.

Surveys are frequently required to produce estimates for subpopulations, sometimes for a single subpopulation and sometimes for several subpopulations in addition to the total population. When membership of a rare subpopulation (or domain) can be determined from the sampling frame, selecting the required domain sample size is relatively straightforward. In this case the main issue is the extent of oversampling to employ when survey estimates are required for several domains and for the total population. Sampling and oversampling rare domains whose members cannot be identified in advance present a major challenge. A variety of methods has been used in this situation. In addition to large-scale screening, these methods include disproportionate stratified sampling, two-phase sampling, the use of multiple frames, multiplicity sampling, panel surveys, and the use of multi-purpose surveys. This paper illustrates the application of these methods in a range of social surveys.

Randomized response strategies, which have originally been developed as statistical methods to reduce nonresponse as well as untruthful answering, can also be applied in the field of statistical disclosure control for public use microdata files. In this paper a standardization of randomized response techniques for the estimation of proportions of identifying or sensitive attributes is presented. The statistical properties of the standardized estimator are derived for general probability sampling. In order to analyse the effect of different choices of the method's implicit "design parameters" on the performance of the estimator we have to include measures of privacy protection in our considerations. These yield variance-optimum design parameters given a certain level of privacy protection. To this end the variables have to be classified into different categories of sensitivity. A real-data example applies the technique in a survey on academic cheating behaviour.

We examine overcoming the overestimation in using generalized weight share method (GWSM) caused by link nonresponse in indirect sampling. A few adjustment methods incorporating link nonresponse in using GWSM have been constructed for situations both with and without the availability of auxiliary variables. A simulation study on a longitudinal survey is presented using some of the adjustment methods we recommend. The simulation results show that these adjusted GWSMs perform well in reducing both estimation bias and variance. The advancement in bias reduction is significant.

Propensity weighting is a procedure to adjust for unit nonresponse in surveys. A form of implementing this procedure consists of dividing the sampling weights by estimates of the probabilities that the sampled units respond to the survey. Typically, these estimates are obtained by fitting parametric models, such as logistic regression. The resulting adjusted estimators may become biased when the specified parametric models are incorrect. To avoid misspecifying such a model, we consider nonparametric estimation of the response probabilities by local polynomial regression. We study the asymptotic properties of the resulting estimator under quasi-randomization. The practical behavior of the proposed nonresponse adjustment approach is evaluated on NHANES data.

In this paper a multivariate structural time series model is described that accounts for the panel design of the Dutch Labour Force Survey and is applied to estimate monthly unemployment rates. Compared to the generalized regression estimator, this approach results in a substantial increase of the accuracy due to a reduction of the standard error and the explicit modelling of the bias between the subsequent waves.

Estimation of small area (or domain) compositions may suffer from informative missing data, if the probability of missing varies across the categories of interest as well as the small areas. We develop a double mixed modeling approach that combines a random effects mixed model for the underlying complete data with a random effects mixed model of the differential missing-data mechanism. The effect of sampling design can be incorporated through a quasi-likelihood sampling model. The associated conditional mean squared error of prediction is approximated in terms of a three-part decomposition, corresponding to a naive prediction variance, a positive correction that accounts for the hypothetical parameter estimation uncertainty based on the latent complete data, and another positive correction for the extra variation due to the missing data. We illustrate our approach with an application to the estimation of Municipality household compositions based on the Norwegian register household data, which suffer from informative under-registration of the dwelling identity number.

This paper proposes an approach for small area prediction based on data obtained from periodic surveys and censuses. We apply our approach to obtain population predictions for the municipalities not sampled in the Brazilian annual Household Survey (PNAD), as well as to increase the precision of the design-based estimates obtained for the sampled municipalities. In addition to the data provided by the PNAD, we use census demographic data from 1991 and 2000, as well as a complete population count conducted in 1996. Hierarchically non-structured and spatially structured growth models that gain strength from all the sampled municipalities are proposed and compared.

Business surveys often use a one-stage stratified simple random sampling without replacement design with some certainty strata. Although weight adjustment is typically applied for unit nonresponse, the variability due to nonresponse may be omitted in practice when estimating variances. This is problematic especially when there are certainty strata. We derive some variance estimators that are consistent when the number of sampled units in each weighting cell is large, using the jackknife, linearization, and modified jackknife methods. The derived variance estimators are first applied to empirical data from the Annual Capital Expenditures Survey conducted by the U.S. Census Bureau and are then examined in a simulation study.

In large scaled sample surveys it is common practice to employ stratified multistage designs where units are selected using simple random sampling without replacement at each stage. Variance estimation for these types of designs can be quite cumbersome to implement, particularly for non-linear estimators. Various bootstrap methods for variance estimation have been proposed, but most of these are restricted to single-stage designs or two-stage cluster designs. An extension of the rescaled bootstrap method (Rao and Wu 1988) to stratified multistage designs is proposed which can easily be extended to any number of stages. The proposed method is suitable for a wide range of reweighting techniques, including the general class of calibration estimators. A Monte Carlo simulation study was conducted to examine the performance of the proposed multistage rescaled bootstrap variance estimator.

This product is a series of profiles for a number of census metropolitan areas, census agglomerations and communities across Canada with a large Aboriginal population, either in numbers or share of the area's total population. The series aims to present a demographic and socio-economic profile of the total Aboriginal population living in these areas. Demographic Data as well as information on living arrangements of children, education, labour, income, mobility, housing, and health are highlighted. While most of the focus is on adults, there is also limited information provided on children. Data showing comparisons between Aboriginal groups are provided for selected variables, as are comparisons with the non-Aboriginal population. Findings are based on the 2006 Census and the 2006 Aboriginal Peoples Survey.

This product is a series of profiles for a number of census metropolitan areas, census agglomerations and communities across Canada with a large Aboriginal population, either in numbers or share of the area's total population. The series aims to present a demographic and socio-economic profile of the total Aboriginal population living in these areas. Demographic Data as well as information on living arrangements of children, education, labour, income, mobility, housing, and health are highlighted. While most of the focus is on adults, there is also limited information provided on children. Data showing comparisons between Aboriginal groups are provided for selected variables, as are comparisons with the non-Aboriginal population. Findings are based on the 2006 Census and the 2006 Aboriginal Peoples Survey.

This product is a series of profiles for a number of census metropolitan areas, census agglomerations and communities across Canada with a large Aboriginal population, either in numbers or share of the area's total population. The series aims to present a demographic and socio-economic profile of the total Aboriginal population living in these areas. Demographic Data as well as information on living arrangements of children, education, labour, income, mobility, housing, and health are highlighted. While most of the focus is on adults, there is also limited information provided on children. Data showing comparisons between Aboriginal groups are provided for selected variables, as are comparisons with the non-Aboriginal population. Findings are based on the 2006 Census and the 2006 Aboriginal Peoples Survey.

This product is a series of profiles for a number of census metropolitan areas, census agglomerations and communities across Canada with a large Aboriginal population, either in numbers or share of the area's total population. The series aims to present a demographic and socio-economic profile of the total Aboriginal population living in these areas. Demographic Data as well as information on living arrangements of children, education, labour, income, mobility, housing, and health are highlighted. While most of the focus is on adults, there is also limited information provided on children. Data showing comparisons between Aboriginal groups are provided for selected variables, as are comparisons with the non-Aboriginal population. Findings are based on the 2006 Census and the 2006 Aboriginal Peoples Survey.

This product is a series of profiles for a number of census metropolitan areas, census agglomerations and communities across Canada with a large Aboriginal population, either in numbers or share of the area's total population. The series aims to present a demographic and socio-economic profile of the total Aboriginal population living in these areas. Demographic Data as well as information on living arrangements of children, education, labour, income, mobility, housing, and health are highlighted. While most of the focus is on adults, there is also limited information provided on children. Data showing comparisons between Aboriginal groups are provided for selected variables, as are comparisons with the non-Aboriginal population. Findings are based on the 2006 Census and the 2006 Aboriginal Peoples Survey.

The Lower Mainland ecoregion profile is the first in an upcoming series of ecoregion profiles. The information presented includes a brief description of the physical setting, a snapshot of land cover and use as well as selected statistics on the changing socio-economy in the region. This ecoregion will be the site for many of the activities associated with the 2010 Vancouver Winter Olympics.

This publication provides quarterly information on operational and financial variables related to the scheduled and charter operations of all Canadian-licensed air carriers classified into reporting levels I and II. Operational data (passengers, passenger-kilometres, goods carried, goods tonne-kilometres, etc.) and financial data (operating revenues, operating expenses, employment expenses) are presented. This publication also includes data highlights and some financial performance indicators and productivity measures.

The air fare data included in this publication relate to the scheduled operations of major Canadian air carriers. This publication provides information on average fares by sector and fare type group and average domestic fares for ten selected cities of enplanement. It also provides information on the distribution of passengers and passenger-kilometres by sector and fare type group. A series of air fare indexes by sector and fare type group is also presented. In addition to annual data, this publication also presents quarterly data. It also includes data highlights and a brief analysis.

This publication provides monthly information on key operational variables related to the scheduled and charter operations of Air Canada and WestJet. Operational data include passengers, passenger-kilometres, available seat-kilometres, goods carried, goods tonne-kilometres, hours flown and fuel. In addition to monthly data, this publication also presents annual data. Charts and data highlights supplement the tables.

This report provides trends on public school enrolments, educators and expenditures. It uses figures provided by provincial and territorial departments of education on public elementary and secondary schools.

This paper highlights the Canadian Tourism Satellite Account (CTSA) developed by Statistics Canada. The CTSA provides an economic measure of the importance of tourism in terms of expenditures, Gross Domestic Product and employment for Canada. It permits a comparison of tourism with other industries within Canada since the concepts and methods used are based on the framework of the Canadian System of National Accounts. The study revealed that tourism is an important part of Canada's well diversified economy. This paper presents the results of the CTSA for reference year 2004.

This study was prepared by staff of the Research and Development Projects and Analysis Section, Income and Expenditure Accounts Division, Statistics Canada. The study was funded by the Canadian Tourism Commission.

Surveys are frequently required to produce estimates for subpopulations, sometimes for a single subpopulation and sometimes for several subpopulations in addition to the total population. When membership of a rare subpopulation (or domain) can be determined from the sampling frame, selecting the required domain sample size is relatively straightforward. In this case the main issue is the extent of oversampling to employ when survey estimates are required for several domains and for the total population. Sampling and oversampling rare domains whose members cannot be identified in advance present a major challenge. A variety of methods has been used in this situation. In addition to large-scale screening, these methods include disproportionate stratified sampling, two-phase sampling, the use of multiple frames, multiplicity sampling, panel surveys, and the use of multi-purpose surveys. This paper illustrates the application of these methods in a range of social surveys.

Randomized response strategies, which have originally been developed as statistical methods to reduce nonresponse as well as untruthful answering, can also be applied in the field of statistical disclosure control for public use microdata files. In this paper a standardization of randomized response techniques for the estimation of proportions of identifying or sensitive attributes is presented. The statistical properties of the standardized estimator are derived for general probability sampling. In order to analyse the effect of different choices of the method's implicit "design parameters" on the performance of the estimator we have to include measures of privacy protection in our considerations. These yield variance-optimum design parameters given a certain level of privacy protection. To this end the variables have to be classified into different categories of sensitivity. A real-data example applies the technique in a survey on academic cheating behaviour.

We examine overcoming the overestimation in using generalized weight share method (GWSM) caused by link nonresponse in indirect sampling. A few adjustment methods incorporating link nonresponse in using GWSM have been constructed for situations both with and without the availability of auxiliary variables. A simulation study on a longitudinal survey is presented using some of the adjustment methods we recommend. The simulation results show that these adjusted GWSMs perform well in reducing both estimation bias and variance. The advancement in bias reduction is significant.

Propensity weighting is a procedure to adjust for unit nonresponse in surveys. A form of implementing this procedure consists of dividing the sampling weights by estimates of the probabilities that the sampled units respond to the survey. Typically, these estimates are obtained by fitting parametric models, such as logistic regression. The resulting adjusted estimators may become biased when the specified parametric models are incorrect. To avoid misspecifying such a model, we consider nonparametric estimation of the response probabilities by local polynomial regression. We study the asymptotic properties of the resulting estimator under quasi-randomization. The practical behavior of the proposed nonresponse adjustment approach is evaluated on NHANES data.

In this paper a multivariate structural time series model is described that accounts for the panel design of the Dutch Labour Force Survey and is applied to estimate monthly unemployment rates. Compared to the generalized regression estimator, this approach results in a substantial increase of the accuracy due to a reduction of the standard error and the explicit modelling of the bias between the subsequent waves.

Estimation of small area (or domain) compositions may suffer from informative missing data, if the probability of missing varies across the categories of interest as well as the small areas. We develop a double mixed modeling approach that combines a random effects mixed model for the underlying complete data with a random effects mixed model of the differential missing-data mechanism. The effect of sampling design can be incorporated through a quasi-likelihood sampling model. The associated conditional mean squared error of prediction is approximated in terms of a three-part decomposition, corresponding to a naive prediction variance, a positive correction that accounts for the hypothetical parameter estimation uncertainty based on the latent complete data, and another positive correction for the extra variation due to the missing data. We illustrate our approach with an application to the estimation of Municipality household compositions based on the Norwegian register household data, which suffer from informative under-registration of the dwelling identity number.

This paper proposes an approach for small area prediction based on data obtained from periodic surveys and censuses. We apply our approach to obtain population predictions for the municipalities not sampled in the Brazilian annual Household Survey (PNAD), as well as to increase the precision of the design-based estimates obtained for the sampled municipalities. In addition to the data provided by the PNAD, we use census demographic data from 1991 and 2000, as well as a complete population count conducted in 1996. Hierarchically non-structured and spatially structured growth models that gain strength from all the sampled municipalities are proposed and compared.

Business surveys often use a one-stage stratified simple random sampling without replacement design with some certainty strata. Although weight adjustment is typically applied for unit nonresponse, the variability due to nonresponse may be omitted in practice when estimating variances. This is problematic especially when there are certainty strata. We derive some variance estimators that are consistent when the number of sampled units in each weighting cell is large, using the jackknife, linearization, and modified jackknife methods. The derived variance estimators are first applied to empirical data from the Annual Capital Expenditures Survey conducted by the U.S. Census Bureau and are then examined in a simulation study.

In large scaled sample surveys it is common practice to employ stratified multistage designs where units are selected using simple random sampling without replacement at each stage. Variance estimation for these types of designs can be quite cumbersome to implement, particularly for non-linear estimators. Various bootstrap methods for variance estimation have been proposed, but most of these are restricted to single-stage designs or two-stage cluster designs. An extension of the rescaled bootstrap method (Rao and Wu 1988) to stratified multistage designs is proposed which can easily be extended to any number of stages. The proposed method is suitable for a wide range of reweighting techniques, including the general class of calibration estimators. A Monte Carlo simulation study was conducted to examine the performance of the proposed multistage rescaled bootstrap variance estimator.

The 2006 Census Technical Report on Aboriginal Peoples deals with: (i) Aboriginal ancestry, (ii) Aboriginal identity, (iii) registered Indian status, and (iv) First Nation or Band membership. The report aims to inform users about the complexity of the data and any difficulties that could affect their use. It explains the conceptual framework and definitions used to gather the data, and it discusses factors that could affect data quality. The historical comparability of the data is also discussed.

Since 2007/2008, Statistics Canada has centred analysis of data holdings related to health as well as our program of dissemination of health research within the new Health Information and Research Division (HIRD).

The new division has launched a comprehensive approach to analytical planning including environmental scanning and consultation; establishment of strategic multi-year priorities for health research at Statistics Canada; a process of project selection and review that ensures that analytical effort addresses our priorities; metrics to measure our adherence to priorities and the impact of our analytical effort; and communication and dissemination of analytical plans.

This multi-year analytical plan identifies the key high-level priority areas for Statistics Canada's investment in health research for 2008/2009 to 2010/2011, and serves as a blueprint for subsequent operational research planning.

This document outlines the definitions and the typology now used by Statistics Canada's Centre for Education Statistics to identify, classify and delineate the universities, colleges and other providers of postsecondary and adult education in Canada for which basic enrolments, graduates, professors and finance statistics are produced.

These new rigorous definitions were needed to capture the growing complexity of postsecondary education in Canada. They differentiate the various types of postsecondary institutions, address the blurring distinction between colleges and universities and handle the various forms of possible relationships between institutions.

The document brings closure to the extensive consultation that took place between January 2003 and the spring of 2007 as it summarizes the changes made following the 2004 paper entitled "A New Understanding of Post-secondary Education in Canada: A Discussion Paper".

Such an extensive consultation was deemed necessary to ensure that the typology is useful to the whole sector and that it allows comparisons between provinces and territories despite the significant differences of their respective postsecondary education systems.

This guide is intended to help data users understand the concepts and methods used in the 2006 Aboriginal Peoples Survey (APS), which was conducted from October 2006 to March 2007.

Technical details on sampling, processing and data quality are included in this guide. Further, the guide explains the relationship between the APS and the 2006 Census and cautions users as to important differences in the data produced from these two sources. Appendix 1 provides a list of communities for which data are available while Appendix 2 contains a glossary of terms that relate to the APS. Answers to some frequently asked questions are provided in Appendix 3. Links to the 2006 APS questionnaires are found in Appendix 4.

This series provides detailed documentation on income developments, including survey design issues, data quality evaluation and exploratory research for the Survey of Labour and Income Dynamics in 2006.

The objective of this study is to examine the feasibility of using provincial and territorial health care files of new registrants as an independent measure of preliminary inter-provincial and inter-territorial migration. The study aims at measuring the conceptual and quantifiable differences between this data source and our present source of the Canada Revenue Agency's Canadian Child Tax Benefit.

Criteria were established to assess the quality and appropriateness of these provincial/territorial health care records as a proxy for our migration estimates: coverage, consistency, timeliness, reliability, level of detail, uniformity and accuracy.

Based on the present analysis, the paper finds that these data do not ameliorate the estimates and would not be suitable at this time as a measure of inter-provincial/territorial migration. These Medicare data though are an important independent data source that can be used for quality evaluation.

The Canadian Export Classification, incorporates amendments to the Nomenclature of the Harmonized Commodity and Coding System.