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Matei, Alina
Nom
Matei, Alina
Affiliation principale
Fonction
prof. titulaire
Email
alina.matei@unine.ch
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Voici les éléments 1 - 10 sur 28
- PublicationAccès libreGender wage difference estimation at quantile levels using sample survey data(2023-09-19)
; This paper is motivated by the growing interest in estimating gender wage differences in official statistics. The wage of an employee is hypothetically a reflection of her or his characteristics, such as education level or work experience. It is possible that men and women with the same characteristics earn different wages. Our goal is to estimate the differences between wages at different quantiles, using sample survey data within a superpopulation framework. To do this, we use a parametric approach based on conditional distributions of the wages in function of some auxiliary information, as well as a counterfactual distribution. We show in our simulation studies that the use of auxiliary information well correlated with the wages reduces the variance of the counterfactual quantile estimates compared to those of the competitors. Since, in general, wage distributions are heavy-tailed, the interest is to model wages by using heavy-tailed distributions like the GB2 distribution. We illustrate the approach using this distribution and the wages for men and women using simulated and real data from the Swiss Federal Statistical Office. - PublicationAccès libreEstimating a counterfactual wage heavy-tailed distribution using survey data(2021-7-9)
; - PublicationMétadonnées seulementUsing a GB2 distribution to estimate gender wage discrimination(2017-6-22)
; ; The generalized beta of the second kind (GB2) distribution was used in the literature to model wages. It is a distribution characterized by three shape parameters and a scale parameter. McDonald (1984) showed that it performs better in terms of adjusting wages than other distributions, such as Singh-Maddala or generalized gamma. We investigate a slightly changed version of the GB2 by introducing covariates in one of the parameters. The scale parameter takes the form of an exponential function which accounts for the characteristics of the observed individuals. Conditional to their characteristics, each individual wage has its own distribution. The assumption that all wages follow the same distribution GB2 is thus relaxed. The proposed method is used to model the wages of Swiss men and women in 2010. A counterfactual wage distribution is then built, by using the characteristics of men but the estimated parameters of women. This artificial wage distribution represents the distribution of wages of women, if they had the same characteristics of men. The purpose is to quantify to what extent gender discrimination occurs. - PublicationMétadonnées seulementSize constrained unequal probability sampling with a non-integer sum of inclusion probabilities(2012)
; ; More than 50 methods have been developed to draw unequal probability samples with fixed sample size. All these methods require the sum of the inclusion probabilities to be an integer number. There are cases, however, where the sum of desired inclusion probabilities is not an integer. Then, classical algorithms for drawing samples cannot be directly applied. We present two methods to overcome the problem of sample selection with unequal inclusion probabilities when their sum is not an integer and the sample size cannot be fixed. The first one consists in splitting the inclusion probability vector. The second method is based on extending the population with a phantom unit. For both methods the sample size is almost fixed, and equal to the integer part of the sum of the inclusion probabilities or this integer plus one. - PublicationMétadonnées seulement
- PublicationMétadonnées seulement
- PublicationMétadonnées seulement