ICWSM-PromotionRelocation-GenderGaps

This repository contains the dataset and analysis code for the paper:

Title: "Gender Gaps in Online Social Connectivity, Promotion and Relocation Reports on LinkedIn"

Authors: Ghazal Kalhor, Hannah Gardner, Ingmar Weber, and Ridhi Kashyap

DOI: https://doi.org/10.1609/icwsm.v18i1.31353

If you use our dataset or analysis code in your work, please cite our paper:

Kalhor, G., Gardner, H., Weber, I., & Kashyap, R. (2024). Gender Gaps in Online Social Connectivity, Promotion and Relocation Reports on LinkedIn. Proceedings of the International AAAI Conference on Web and Social Media, 18(1), 800-812. https://doi.org/10.1609/icwsm.v18i1.31353

Dataset

We collected the following features, along with the audience estimate, for each combination of attributes:

• Country
• Gender
• Job Seniority
• Age Range
• Recent Status
• Social Connectivity
• Count

import pandas as pd

fullData = pd.read_csv('fullData.csv')
fullData.head(3)

Country	Gender	Job Seniority	Age Range	Recent Status	Social Connectivity	Count
US	Female	Director	55+	Any	Connected to big companies	8200
US	Female	Director	55+	Any	Any	19000
US	Female	Director	35 to 54	Relocated	Any	390

promotionData = pd.read_csv('promotionData.csv')
promotionData.head(3)

Country	Gender	Job Seniority	Age Range	Recent Status	Social Connectivity	Count
US	Female	Director	55+	Not promoted	Connected to big companies	8200
US	Female	Director	55+	Not promoted	Not connected	10800
US	Female	Director	35 to 54	Recently promoted	Connected to big companies	2600

relocationData = pd.read_csv('relocationData.csv')
relocationData.head(3)

Country	Gender	Job Seniority	Age Range	Recent Status	Social Connectivity	Count
US	Female	Director	55+	Not relocated	Connected to big companies	8200
US	Female	Director	55+	Not relocated	Not connected	10800
US	Female	Director	35 to 54	Recently relocated	Not connected	390