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/10.48550/arXiv.2308.13296

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. Accepted for publication in the Proceedings of the International AAAI Conference on Web and Social Media (ICWSM) 2024.

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