An algorithm to reduce the occupational space in gender segregation studies

Abstract

This paper presents an algorithm based on the bootstrap to select an admissible aggregation level, that is, the minimum number of occupational categories which yield a gender segregation value which is not significantly smaller than that obtained from the large number of occupational categories usually available in any data set. The approach is illustrated using Labor Force Survey data for Spain for the comparison of gender segregation in 1977 and 1992, as well as 1994 and 2000. To measure gender segregation, an additively decomposable segregation index based in the entropy concept is used. Despite a substantial simplification in the size of the occupation's space, the decrease in the segregation index is very small and not significant, regardless of the year. Consequently, intertemporal changes in gender segregation can be studied using a greatly reduced classification of occupations that permits an easier interpretation of results.