Matei, Alina

2023-09-19, Mihaela-Cătălina Anastasiade-Guinand, Matei, Alina, Tillé, Yves

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.

2018-4-16, Grafström, Anton, Matei, Alina

2015-6, Matei, Alina, Ranalli, Giovanna

2007-12-5, Matei, Alina, Tillé, Yves

2022-6-9, Ranalli, Giovanna, Matei, Alina, Neri, Andrea

2018-1-30, Matei, Alina

2012, Grafström, A., Qualité, Lionel, Tillé, Yves, Matei, Alina

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.

2018-12-21, Grafström, Anton, Matei, Alina

2015-12-1, Grafström, Anton, Matei, Alina

2009-1-27, Matei, Alina, Skinner, Chris