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  • Articles and reports: 12-001-X201600114544
    Description:

    In the Netherlands, statistical information about income and wealth is based on two large scale household panels that are completely derived from administrative data. A problem with using households as sampling units in the sample design of panels is the instability of these units over time. Changes in the household composition affect the inclusion probabilities required for design-based and model-assisted inference procedures. Such problems are circumvented in the two aforementioned household panels by sampling persons, who are followed over time. At each period the household members of these sampled persons are included in the sample. This is equivalent to sampling with probabilities proportional to household size where households can be selected more than once but with a maximum equal to the number of household members. In this paper properties of this sample design are described and contrasted with the Generalized Weight Share method for indirect sampling (Lavallée 1995, 2007). Methods are illustrated with an application to the Dutch Regional Income Survey.

    Release date: 2016-06-22

  • Stats in brief: 11-630-X2015008
    Description:

    In this edition of Canadian Megatrends, we look at at changes in household size from 1941 to 2011.

    Release date: 2015-11-23

  • Articles and reports: 75F0002M2010004
    Description:

    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: 12-001-X20050029047
    Description:

    This paper considers the problem of estimating, in the presence of considerable nonignorable nonresponse, the number of private households of various sizes and the total number of households in Norway. The approach is model-based with a population model for household size given registered family size. We account for possible nonresponse biases by modeling the response mechanism conditional on household size. Various models are evaluated together with a maximum likelihood estimator and imputation-based poststratification. Comparisons are made with pure poststratification using registered family size as stratifier and estimation methods used in official statistics for The Norwegian Consumer Expenditure Survey. The study indicates that a modeling approach, including response modeling, poststratification and imputation are important ingredients for a satisfactory approach.

    Release date: 2006-02-17
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  • Surveys and statistical programs – Documentation: 62F0026M2001002
    Description:

    This report describes the quality indicators produced for the 1999 Survey of Household Spending. It covers the usual quality indicators that help users interpret data, such as coefficients of variation, nonresponse rates, imputation rates and the impact of imputed data on the estimates. Added to these are various less often used indicators such as slippage rates and measures of the representativity of the sample for particular characteristics that are useful for evaluating the survey methodology.

    Release date: 2001-10-15
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