Keyword search

View results
Sort Help
entries

Results

All (75)

All (75) (0 to 10 of 75 results)

  • 1. Longitudinal Administrative Data Dictionary
    Surveys and statistical programs – Documentation: 12-585-X
    Description: This product is the dictionary for the Longitudinal Administrative Databank (LAD). The dictionary contains a complete description for each of the income and demographic variables in the LAD, including name, acronym, definition, source, historical availability and historical continuity.

    The following is a partial list of LAD variables: age, sex, marital status, family type, number and age of children, total income, wages and salaries, self-employment, Employment Insurance, Old Age Security, Canada and Quebec Pension Plans, social assistance, investment income, rental income, alimony, registered retirement savings plan (RRSP) income and contributions, low-income status, full-time education deduction, provincial refundable tax credits, goods and service tax (GST) credits, Canada Child Tax Benefits, selected immigration variables, Tax Free Savings (TFSA) information and Canadian Controlled Private Corporations (CCPC) information.

    Release date: 2023-11-10
  • 2. Municipal Financial and Socioeconomic Data Dashboard
    Data Visualization: 71-607-X2022004
    Description:

    This interactive dashboard presents key financial, economic and socio-economic data for individual municipalities and other local public administrations.

    Release date: 2022-07-26
  • 3. Small area estimation using Fay-Herriot area level model with sampling variance smoothing and modeling
    Articles and reports: 12-001-X202100200007
    Description:

    In this paper, we consider the Fay-Herriot model for small area estimation. In particular, we are interested in the impact of sampling variance smoothing and modeling on the model-based estimates. We present methods of smoothing and modeling for the sampling variances and apply the proposed models to a real data analysis. Our results indicate that sampling variance smoothing can improve the efficiency and accuracy of the model-based estimator. For sampling variance modeling, the HB models of You (2016) and Sugasawa, Tamae and Kubokawa (2017) perform equally well to improve the direct survey estimates.

    Release date: 2022-01-06
  • 4. Measuring uncertainty associated with model-based small area estimators Archived
    Articles and reports: 12-001-X201800254958
    Description:

    Domains (or subpopulations) with small sample sizes are called small areas. Traditional direct estimators for small areas do not provide adequate precision because the area-specific sample sizes are small. On the other hand, demand for reliable small area statistics has greatly increased. Model-based indirect estimators of small area means or totals are currently used to address difficulties with direct estimation. These estimators are based on linking models that borrow information across areas to increase the efficiency. In particular, empirical best (EB) estimators under area level and unit level linear regression models with random small area effects have received a lot of attention in the literature. Model mean squared error (MSE) of EB estimators is often used to measure the variability of the estimators. Linearization-based estimators of model MSE as well as jackknife and bootstrap estimators are widely used. On the other hand, National Statistical Agencies are often interested in estimating the design MSE of EB estimators in line with traditional design MSE estimators associated with direct estimators for large areas with adequate sample sizes. Estimators of design MSE of EB estimators can be obtained for area level models but they tend to be unstable when the area sample size is small. Composite MSE estimators are proposed in this paper and they are obtained by taking a weighted sum of the design MSE estimator and the model MSE estimator. Properties of the MSE estimators under the area level model are studied in terms of design bias, relative root mean squared error and coverage rate of confidence intervals. The case of a unit level model is also examined under simple random sampling within each area. Results of a simulation study show that the proposed composite MSE estimators provide a good compromise in estimating the design MSE.

    Release date: 2018-12-20
  • 5. Methodology of the Canadian Labour Force Survey
    Surveys and statistical programs – Documentation: 71-526-X
    Description:

    The Canadian Labour Force Survey (LFS) is the official source of monthly estimates of total employment and unemployment. Following the 2011 census, the LFS underwent a sample redesign to account for the evolution of the population and labour market characteristics, to adjust to changes in the information needs and to update the geographical information used to carry out the survey. The redesign program following the 2011 census culminated with the introduction of a new sample at the beginning of 2015. This report is a reference on the methodological aspects of the LFS, covering stratification, sampling, collection, processing, weighting, estimation, variance estimation and data quality.

    Release date: 2017-12-21
  • 6. Special Orders for Small Area Data
    61C9956
    Description:

    The Income Statistics Division offers custom tabulations designed to meet specific data requirements. From the income tax forms submitted each year by Canadians, a wealth of economic and demographic information is available, subject to confidentiality restrictions. The statistics are derived primarily from the annual tax file provided by the Canada Revenue Agency.

    Data are available starting in 1982 for some postal areas, some census regions, and for user-defined areas according to a postal code conversion file. Most current data are for the 2019 tax year.

    Release date: 2017-07-12
  • 7. Comparison of unit level and area level small area estimators Archived
    Articles and reports: 12-001-X201600114540
    Description:

    In this paper, we compare the EBLUP and pseudo-EBLUP estimators for small area estimation under the nested error regression model and three area level model-based estimators using the Fay-Herriot model. We conduct a design-based simulation study to compare the model-based estimators for unit level and area level models under informative and non-informative sampling. In particular, we are interested in the confidence interval coverage rate of the unit level and area level estimators. We also compare the estimators if the model has been misspecified. Our simulation results show that estimators based on the unit level model perform better than those based on the area level. The pseudo-EBLUP estimator is the best among unit level and area level estimators.

    Release date: 2016-06-22
  • 8. Model-assisted optimal allocation for planned domains using composite estimation Archived
    Articles and reports: 12-001-X201500214230
    Description:

    This paper develops allocation methods for stratified sample surveys where composite small area estimators are a priority, and areas are used as strata. Longford (2006) proposed an objective criterion for this situation, based on a weighted combination of the mean squared errors of small area means and a grand mean. Here, we redefine this approach within a model-assisted framework, allowing regressor variables and a more natural interpretation of results using an intra-class correlation parameter. We also consider several uses of power allocation, and allow the placing of other constraints such as maximum relative root mean squared errors for stratum estimators. We find that a simple power allocation can perform very nearly as well as the optimal design even when the objective is to minimize Longford’s (2006) criterion.

    Release date: 2015-12-17
  • 9. Dealing with small sample sizes, rotation group bias and discontinuities in a rotating panel design Archived
    Articles and reports: 12-001-X201500214231
    Description:

    Rotating panels are widely applied by national statistical institutes, for example, to produce official statistics about the labour force. Estimation procedures are generally based on traditional design-based procedures known from classical sampling theory. A major drawback of this class of estimators is that small sample sizes result in large standard errors and that they are not robust for measurement bias. Two examples showing the effects of measurement bias are rotation group bias in rotating panels, and systematic differences in the outcome of a survey due to a major redesign of the underlying process. In this paper we apply a multivariate structural time series model to the Dutch Labour Force Survey to produce model-based figures about the monthly labour force. The model reduces the standard errors of the estimates by taking advantage of sample information collected in previous periods, accounts for rotation group bias and autocorrelation induced by the rotating panel, and models discontinuities due to a survey redesign. Additionally, we discuss the use of correlated auxiliary series in the model to further improve the accuracy of the model estimates. The method is applied by Statistics Netherlands to produce accurate official monthly statistics about the labour force that are consistent over time, despite a redesign of the survey process.

    Release date: 2015-12-17
  • 10. Model-based small area estimation under informative sampling Archived
    Articles and reports: 12-001-X201500214248
    Description:

    Unit level population models are often used in model-based small area estimation of totals and means, but the models may not hold for the sample if the sampling design is informative for the model. As a result, standard methods, assuming that the model holds for the sample, can lead to biased estimators. We study alternative methods that use a suitable function of the unit selection probability as an additional auxiliary variable in the sample model. We report the results of a simulation study on the bias and mean squared error (MSE) of the proposed estimators of small area means and on the relative bias of the associated MSE estimators, using informative sampling schemes to generate the samples. Alternative methods, based on modeling the conditional expectation of the design weight as a function of the model covariates and the response, are also included in the simulation study.

    Release date: 2015-12-17
Data (1)

Data (1) ((1 result))

  • 1. Municipal Financial and Socioeconomic Data Dashboard
    Data Visualization: 71-607-X2022004
    Description:

    This interactive dashboard presents key financial, economic and socio-economic data for individual municipalities and other local public administrations.

    Release date: 2022-07-26
Analysis (69)

Analysis (69) (50 to 60 of 69 results)

  • 51. Model-based unemployment rate estimation for the Canadian Labour Force Survey: A hierarchical Bayes approach Archived
    Articles and reports: 12-001-X20030016602
    Description:

    The Canadian Labour Force Survey (LFS) produces monthly direct estimates of the unemployment rate at national and provincial levels. The LFS also releases unemployment estimates for subprovincial areas such as census metropolitan areas (CMAs) and census agglomerations (CAs). However, for some subprovincial areas, the direct estimates are not very reliable since the sample size in some areas is quite small. In this paper, a cross-sectional and time-series model is used to borrow strength across areas and time periods to produce model-based unemployment rate estimates for CMAs and CAs. This model is a generalization of a widely used cross-sectional model in small area estimation and includes a random walk or AR(1) model for the random time component. Monthly Employment Insurance (EI) beneficiary data at the CMA or CA level are used as auxiliary covariates in the model. A hierarchical Bayes (HB) approach is employed and the Gibbs sampler is used to generate samples from the joint posterior distribution. Rao-Blackwellized estimators are obtained for the posterior means and posterior variances of the CMA/CA-level unemployment rates. The HB method smoothes the survey estimates and leads to a substantial reduction in standard errors. Base on posterior distributions, bayesian model fitting is also investigated in this paper.

    Release date: 2003-07-31
  • 52. The effect of model choice in estimation for domains, including small domains Archived
    Articles and reports: 12-001-X20030016605
    Description:

    In this paper, we examine the effects of model choice on different types of estimators for totals of domains (including small domains or small areas) for a sampled finite population. The paper asks how different estimator types compare for a common underlying model statement. We argue that estimator type - synthetic, generalized regression (GREG), composite, empirical best linear unbiased predicition (EBLUP), hierarchical Bayes, and so on - is one important aspect of domain estimation, and that the choice of the model, including its parameters and effects, is a second aspect, conceptually different from the first. Earlier work has not always made this distinction clear. For a given estimator type, one can derive different estimators, depending on the choice of model. In recent literature, a number of estimator types have been proposed, but there is relatively little impartial comparisons made among them. In this paper, we discuss three types: synthetic, GREG, and, to a limited extent, composite. We show that model improvement - the transition from a weaker to a stronger model - has very different effects on the different estimator types. We also show that the difference in accuracy between the different estimator types depends on the choice of model. For a well-specified model, the difference in accuracy between synthetic and GREG is negligible, but it can be substantial if the model is mis-specified. The synthetic type then tends to be highly inaccurate. We rely partly on theoretical results (for simple random sampling only) and partly on empirical results. The empirical results are based on simulations with repeated samples drawn from two finite populations, one artificially constructed, the other constructed from the real data of the Finnish Labour Force Survey.

    Release date: 2003-07-31
  • 53. Conditional and unconditional analysis of some small area estimators in complex sampling Archived
    Articles and reports: 12-001-X20030016608
    Description:

    This work deals with the unconditional and conditional properties of some well-known small area estimators: expansion, post-stratified ratio, synthetic, composite, sample size dependent and the empirical best linear unbiased predictor (EBLUP). A two-stage sampling design is considered as it is commonly used in household surveys conducted by the National Statistics Institute of Italy. An evaluation is carried out through a simulation based on 1991 Italian census data. The small areas considered are the local labour market areas, which are unplanned domains that cut across the boundaries of the design strata.

    Release date: 2003-07-31
  • 54. Strategies and approaches for small area statistics Archived
    Articles and reports: 12-001-X20020026418
    Description:

    National statistical offices are often called upon to produce statistics for small geographic areas, in addition to their primary responsibility for measuring the condition of the country as a whole and its major subdivisions. This task presents challenges that are different from those faced in statistical programs aiming primarily at national or provincial statistics. This paper examines these challenges and identifies strategies and approaches for the development of programs of small area statistics. The important foundation of a census of population, as well as the primary role of a consistent geographic infrastructure, are emphasized. Potential sources and methods for the production of small area data in the social, economic and environmental fields are examined. Some organizational and dissemination issues are also discussed.

    Release date: 2003-01-29
  • 55. A hierarchical Bayesian nonignorable nonresponse model for multinomial data from small areas Archived
    Articles and reports: 12-001-X20020026428
    Description:

    The analysis of survey data from different geographical areas where the data from each area are polychotomous can be easily performed using hierarchical Bayesian models, even if there are small cell counts in some of these areas. However, there are difficulties when the survey data have missing information in the form of non-response, especially when the characteristics of the respondents differ from the non-respondents. We use the selection approach for estimation when there are non-respondents because it permits inference for all the parameters. Specifically, we describe a hierarchical Bayesian model to analyse multinomial non-ignorable non-response data from different geographical areas; some of them can be small. For the model, we use a Dirichlet prior density for the multinomial probabilities and a beta prior density for the response probabilities. This permits a 'borrowing of strength' of the data from larger areas to improve the reliability in the estimates of the model parameters corresponding to the smaller areas. Because the joint posterior density of all the parameters is complex, inference is sampling-based and Markov chain Monte Carlo methods are used. We apply our method to provide an analysis of body mass index (BMI) data from the third National Health and Nutrition Examination Survey (NHANES III). For simplicity, the BMI is categorized into 3 natural levels, and this is done for each of 8 age-race-sex domains and 34 counties. We assess the performance of our model using the NHANES III data and simulated examples, which show our model works reasonably well.

    Release date: 2003-01-29
  • 56. On measuring the quality of indirect small area estimates Archived
    Articles and reports: 11-522-X20010016246
    Description:

    This paper discusses in detail issues dealing with the technical aspects of designing and conducting surveys. It is intended for an audience of survey methodologists.

    Samples sizes in small population areas are typically very small. As a result, customary, area-specific, direct estimators of Small Area Means do not provide acceptable quality in terms of Mean Square Error (MSE). Indirect estimators that borrow strength from related areas by linking models based on similar auxiliary data are now widely used for small area estimation. Such linking models are either implicit (as in the case of synthetic estimators) or explicit (as in the case of model-based estimators). In the Frequentist approach, the quality of an indirect estimator is measured by its estimated MSE while the posterior variance of the Small Area Mean is used in the Bayesian approach. This paper reviews some recent work on estimating MSE and the evaluation of posterior variance.

    Release date: 2002-09-12
  • 57. Evaluation of small area estimation methods - An application to unemployment estimates from the UK LFS Archived
    Articles and reports: 11-522-X20010016247
    Description:

    This paper discusses in detail issues dealing with the technical aspects of designing and conducting surveys. It is intended for an audience of survey methodologists.

    This paper describes joint research by the Office for National Statistics (ONS) and Southampton University regarding the evaluation of several different approaches to the local estimation of International Labour Office (ILO) unemployment. The need to compare estimators with different underlying assumptions has led to a focus on evaluation methods that are (partly at least) model-independent. Model-fit diagnostics that have been considered include: various residual procedures, cross-validation, predictive validation, consistency with marginals, and consistency with direct estimates within single cells. These diagnostics have been used to compare different model-based estimators with each other and with direct estimators.

    Release date: 2002-09-12
  • 58. Producing small area estimates from national surveys: Methods for minimizing use of indirect estimators Archived
    Articles and reports: 12-001-X20010026094
    Description:

    This article reviews the methods that may be used to produce direct estimates for small areas, including stratification and oversampling, and forms of dual-frame estimation.

    Release date: 2002-02-28
  • 59. Hierarchical Bayes estimation of small area means using multi-level models Archived
    Articles and reports: 12-001-X20000025537
    Description:

    Standard multi-level models with random regression parameters are considered for small area estimation. We also extend the models by allowing unequal error variances or by assuming random effect models for both regression parameters and error variances.

    Release date: 2001-02-28
  • 60. Estimation of census adjustment factors Archived
    Articles and reports: 12-001-X20000015176
    Description:

    A components-of-variance approach and an estimated covariance error structure were used in constructing predictors of adjustment factors for the 1990 Decennial Census. The variability of the estimated covariance matrix is the suspected cause of certain anomalies that appeared in the regression estimation and in the estimated adjustment factors. We investigate alternative prediction methods and propose a procedure that is less influenced by variability in the estimated covariance matrix. The proposed methodology is applied to a data set composed of 336 adjustment factors from the 1990 Post Enumeration Survey.

    Release date: 2000-08-30
Reference (4)

Reference (4) ((4 results))

  • 1. Longitudinal Administrative Data Dictionary
    Surveys and statistical programs – Documentation: 12-585-X
    Description: This product is the dictionary for the Longitudinal Administrative Databank (LAD). The dictionary contains a complete description for each of the income and demographic variables in the LAD, including name, acronym, definition, source, historical availability and historical continuity.

    The following is a partial list of LAD variables: age, sex, marital status, family type, number and age of children, total income, wages and salaries, self-employment, Employment Insurance, Old Age Security, Canada and Quebec Pension Plans, social assistance, investment income, rental income, alimony, registered retirement savings plan (RRSP) income and contributions, low-income status, full-time education deduction, provincial refundable tax credits, goods and service tax (GST) credits, Canada Child Tax Benefits, selected immigration variables, Tax Free Savings (TFSA) information and Canadian Controlled Private Corporations (CCPC) information.

    Release date: 2023-11-10
  • 2. Methodology of the Canadian Labour Force Survey
    Surveys and statistical programs – Documentation: 71-526-X
    Description:

    The Canadian Labour Force Survey (LFS) is the official source of monthly estimates of total employment and unemployment. Following the 2011 census, the LFS underwent a sample redesign to account for the evolution of the population and labour market characteristics, to adjust to changes in the information needs and to update the geographical information used to carry out the survey. The redesign program following the 2011 census culminated with the introduction of a new sample at the beginning of 2015. This report is a reference on the methodological aspects of the LFS, covering stratification, sampling, collection, processing, weighting, estimation, variance estimation and data quality.

    Release date: 2017-12-21
  • 3. Neighbourhood Insights - Your Guide to the Statistical Information Packages Available From Small Area and Administrative Data Division, Statistics Canada Archived
    Surveys and statistical programs – Documentation: 17-507-X
    Description:

    "Neighbourhood insights" is your guide to the statistical information packages available from the Small Area and Administrative Data Division. The guide provides descriptions of the various databanks, the geographic availability and the pricing structure. The guide also contains sample statistical tables showing data for Canada.

    Release date: 2006-05-04
  • 4. Useful Information for Construction Archived
    Surveys and statistical programs – Documentation: 64F0004X
    Description:

    This practical and informative guide for the construction industry will assist in navigating through numerous Statistics Canada products and services.

    Release date: 2002-12-13
Report a problem or mistake on this page
Date modified:

Search help

How do I use the filters and the search box?

  • You can initiate a search by entering keywords or selecting filters (e.g., under Subject, Geography, etc.) on the left side of the page.

  • Filters can be used together or in any combination. Each time you select a filter, the results on the page are updated.

  • To start a new search, click on the Clear all button above the search box or deselect each filter.

  • Specified keywords and filters appear above the search box. You can deselect any or all the tags to refine or clear your search.

How do I refine my search?

  • You can enter keywords in the search box. It is not necessary to use “+” or “, “ or “AND”.

  • You can delete some or all of the keywords from your search string.

  • Quotes around keywords limit the search to a specific phrase.

    • For example, “labour force survey” will limit results to products containing this phrase.

  • Use “or” between keywords to obtain results that contain one or more search terms.

    • For example, labour or­ force or survey will limit results to products containing any or all of these words.

How does the search work?

  • This search looks for results that have your query term in the title, description, subject, geography, product number or other information about the product.

    • For example, when searching the term ‘Diseases’, all results with the word ‘Diseases’ in the title, description or tagged the subject appear.

  • The text within an article or publication are not searched. For full text searching of articles, use the site search.

Sort help

How are the results ordered?

The Sort by feature provides three options:

  • By date – sorts results by product release date starting with the most recent.
  • By relevance is only needed or beneficial when you use the search box and submit a search query. Results are ordered by a combination of where the search term appears in the content, e.g., if it appears in the title, it likely means the product is more relevant to your search; how up to date or new the content in the product is; how popular the product is; and other factors.
  • By title – sorts results alphabetically.

Sort help

How are the results ordered?

The Sort by feature provides four options:

  • By date – sorts results by product release date starting with the most recent.
  • By most popular – gives priority to results that are most frequently selected by users, followed by the most recent.
  • By relevance is only needed or beneficial when you use the search box and submit a search query. Results are ordered by a combination of where the search term appears in the content, e.g., if it appears in the title, it likely means the product is more relevant to your search; how up to date or new the content in the product is; how popular the product is; and other factors.
  • By title – sorts results alphabetically.