TY  - RPRT
        AU  - Chaturvedi, Sugat
AU  - Mahajan, Kanika
AU  - Siddique, Zahra
        TI  - Using Domain-Specific Word Embeddings to Examine the Demand for Skills
        PY  - 2023/Nov/
        PB  - Institute of Labor Economics (IZA)
        CY  - Bonn
        T2  - IZA Discussion Paper
        IS  - 16593
        UR  - https://www.iza.org/publications/dp16593
        AB  - We study the demand for skills by using text analysis methods on job descriptions in a large volume of ads posted on an online Indian job portal. We make use of domain-specific unlabeled data to obtain word vector representations (i.e., word embeddings) and discuss how these can be leveraged for labor market research. We start by carrying out a data-driven categorization of required skill words and construct gender associations of different skill categories using word embeddings. Next, we examine how different required skill categories correlate with log posted wages as well as explore how skills demand varies with firm size. We find that female skills are associated with lower posted wages, potentially contributing to observed gender wage gaps. We also find that large firms require a more extensive range of skills, implying that complementarity between female and male skills is greater among these firms.
        KW  - machine learning
KW  - skills demand
KW  - gender
KW  - online job ads
KW  - text analysis
        ER  -