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  • Publication
    Accès libre
    Imputation of income variables in a survey context and estimation of variance for indicators of poverty and social exclusion
    (2014-11-25)
    We present a method of imputation for income variables allowing direct analysis of the distribution of such data, particularly the estimation of complex statistics such as indicators for poverty and social exclusion as well as the estimation of their precision.
  • Publication
    Métadonnées seulement
    Variance Estimation for Regression Imputed Quantiles, A first Step towards Variance Estimation for Inequality Indicators
    (2014-8-20)
    In a sample survey only a sub-part of the selected sample has answered (total non-response, treated by re-weighting). Moreover, some respondents did not answer all questions (partial non-response, treated through imputation). One is interested in income type variables. One further supposes here that the imputation is carried out by a regression. The idea presented by Deville and Särndal in 1994 is resumed, which consists in constructing an unbiased estimator of the variance of a total based solely on the known information (on the selected sample and the subset of respondents). While these authors dealt with a conventional total of an interest variable y, a similar development is reproduced in the case where the considered total is one of the linearized variable of quantiles or of inequality indicators, and that, furthermore, it is computed from the imputed variable y. By means of simulations on real survey data, one shows that regression imputation can have an important impact on the bias and variance estimations of inequality indicators. This leads to a method capable of taking into account the variance due to imputation in addition to the one due to the sampling design in the cases of quantiles.
  • Publication
    Métadonnées seulement
    Traitement de la non-réponse dans l’enquête SILC suisse
    (2013-11-28)
    On a discuté les traitements actuellement en production à l'Office Fédéral Suisse de la Statistique dans les enquêtes auprès des ménages et des personnes. Le sujet a été illustré à l'aide du pan suisse de l'enquête européenne sur le revenu et les condition de vie où le redressement de la non-réponse est approché par la méthode de segmentation et des calages sur marge. On a mis en évidence un exemple réel de variable où le mécanisme de non-réponse est non-ignorable. On a vu qu'un traitement par calage généralisé est possible et convainquant. On a également illustré l'impact de la non-réponse sur la distribution observée des revenus des personnes et ménages ainsi que sur des statistiques complexes telles que les indices de pauvreté et d'exclusion sociale.
  • Publication
    Métadonnées seulement
    Imputation of income data with generalized calibration procedure and GB2 law: illustration with SILC data
    In sample surveys of households and persons, questions about income are often sensitive and thus subject to a higher non-response rate. Nevertheless, the household or personal incomes are among the important variables in surveys of this type. The distribution of such collected incomes is not normal, neither log-normal. Hypotheses of classical regression models to explain the income (or their log) are not fulfilled. Imputations using such models modify the original and true distribution of the data. This is not suitable and may conduct the user to wrong interpretations of results computed from data imputed in this way. The generalized beta distribution of the second kind (GB2) is a four parameters distribution. Empirical studies have shown that it adapts very well to income data. The advantage of a parametric income distribution is that there exist explicit formulae for the inequality measures like the Laeken indicators as functions of the parameters. We present a parametric method of imputation, based on the fit of a GB2 law on the income distribution by the use of suitably adjusted weights obtained by generalized calibration. These weights can compensate for non ignorable non-response that affects the variable of interest. We validate our imputation system on data from the Swiss Survey on Income and Living Conditions (SILC).