Template-Type: ReDIF-Paper 1.0
Author-Name: Sugat Chaturvedi
Author-Name-First: Sugat
Author-Name-Last: Chaturvedi
Author-Email: sugat.chaturvedi@ahduni.edu.in
Author-Workplace-Name: Ahmedabad University
Author-Name: Kanika Mahajan
Author-Name-First: Kanika
Author-Name-Last: Mahajan
Author-Email: kanika.mahajan@ashoka.edu.in
Author-Workplace-Name: Ashoka University
Author-Name: Zahra Siddique
Author-Name-First: Zahra
Author-Name-Last: Siddique
Author-Email: zahra.siddique@bristol.ac.uk
Author-Workplace-Name: University of Bristol
Title: Using Domain-Specific Word Embeddings to Examine the Demand for Skills
Abstract: 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.
length: 58
Creation-Date: 20231110
Revision-Date: 
Publication-Status: 
File-URL:https://dp.ashoka.edu.in/ash/wpaper/paper107_0.pdf
File-Format: Application/pdf 
Number: 107
Keywords: Gender
Keywords: Machine learning
Keywords: online job ads
Keywords: Skills demand
Keywords: Text analysis
Handle: RePEc:ash:wpaper:107