Imputation of Income Data with Generalized Calibration Procedure and GB2 distribution: Illustration with SILC data
| Résumé |
In sample surveys of households and persons, questions about income
are important variables and often sensitive and thus subject to a
higher nonresponse rate. The distribution of such collected incomes
is neither normal, nor log-normal. Hypotheses of classical
regression models to explain the income (or their log) are not
satisfied. Imputations using such models modify the original and
true distribution of the data which is not. Empirical studies have
shown that the generalized beta distribution of the second kind
(GB2) it fits income data very well. We present a parametric method
of imputation relying on weights obtained by generalized
calibration. A GB2 distribution is fitted on the income
distribution in order to assess that these weights can compensate
for nonignorable nonresponse that affects the variable of interest.
The success of the operation greatly depends on the choice of
auxiliary and instrumental variables used for calibration, which we
discuss. We validate our imputation system on data from the Swiss
Survey on Income and Living Conditions (SILC) and compare it to
imputations performed through the use of IVEware software running
on SAS. We have made great efforts to estimate variances through
linearization, taking all the steps of our procedure into
account. |
| Mots-clés |
GB2, generalized calibration, inequality measures, Laeken indicators, nonignorable nonresponse, SILC, IVEware |
| Citation | Graf, E. (2014). Imputation of Income Data with Generalized Calibration Procedure and GB2 distribution: Illustration with SILC data. Unpublished Submitted article. Université de Neuchâtel. |
| Type | Working paper (Anglais) |
| Année | 2014 |
| Type de travail | Submitted article |
| Département | Statistical Institute |
| Institution | Université de Neuchâtel (Neuchâtel) |
| Nombre de pages | 36 |
| Liée au projet | Convention Université de Neuchâtel/Office fédéral de la s... |