The use of sample weights in multivariate multilevel models with an application to income data collected by using a rotating panel survey
Longitudinal data from labour force surveys permit the investigation of income dynamics at the individual level. However, the data often originate from surveys with a complex multistage sampling scheme. In addition, the hierarchical structure of the data that is imposed by the different stages of the sampling scheme often represents the natural grouping in the population. Motivated by how income dynamics differ between the formal and informal sectors of the Brazilian economy and the data structure of the Brazilian Labour Force Survey, we extend the probability‐weighted iterative generalized least squares estimation method. Our method is used to fit multivariate multilevel models to the Brazilian Labour Force Survey data where the covariance structure between occasions at the individual level is modelled. We conclude that there are significant income differentials and that incorporating the weights in the parameter estimation has some effect on the estimated coefficients and standard errors.
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