Produção de indicadores do mercado de trabalho com modelos de estimação em pequenas áreas para dados composicionais

Abstract

The demand for increasingly disaggregated indicators is rising, mainly as a result of the 2030 Agenda. Indicators can describe important characteristics of the population and provide a basis for public policies, allocation of resources, among other uses. Therefore, ensuring that their estimates are representative and reliable is essential. However, when an indicator is calculated based on data from sample surveys, its precision may sometimes fall below the threshold suitable for dissemination to certain population groups. This is the case for the proportion of unemployed people and the unemployment rate for municipal strata, both calculated using data from the Continuous National Household Sample Survey (PNADC). In the third quarter of 2023, for both indicators, some strata had estimates that were either not very precise or imprecise, according to IBGE classification. In this context, Small Area Estimation can be used, which consists of employing models that allow the inclusion of auxiliary data about population groups to assist in the estimation process. Thus, Dirichlet and Generalized Dirichlet models, which are standard models for compositional data, were proposed as alternatives for estimating labor market indicators, such as the unemployment rate, the proportion of unemployed, employed, and people outside the labor force, with the aim of improving the precision of the unemployment rate and the proportion of unemployed people. It was expected that, by jointly estimating the proportions of interest, there would be a significant increase in the precision of the three proportions and the unemployment rate, which can be derived from the proportions.