Discovering Philippine's Occupational Gender Segregation through Data Analytics

A Filipino value where women nurture, and men provide certainly exhibits gender stereotyping. This paper aims to explore a textual analysis of multi-domain articles through big data analytics to establish and validate the Philippines’ occupational gender segregation along economic participation and opportunity dimension of the Global Gender Gap Index (GGGI). The collected dataset featured multi-domain, multi-source English articles from 1901 to 2021. Data preprocessing included cleaning, filtering, stemming, and standardization using Natural Language ToolKit (NLTK) library. For feature extraction, the adopted occupational index was cross-matched against the pre-processed dataset to extract the body of paragraphs, including their adjacent nouns and pronouns. Extracted feature sets are then analyzed and classified if male or female dominant to determine the degree of occupational gender segregation using Duncans’ Segregation Index. Findings showed that occupational gender segregation in the Philippines has indeed decreased over time but remains persistent, with unequal distribution of female employees favoring males across all the occupational categories. The result of this textual analysis validates the country’s current position on occupational gender segregation, which may be utilized to improve institutional transformation and policy formulation for equalizing economic participation and opportunity for women.