T1 - kijouneli/EAR-MP-2025 GitHub Wiki
Title
Data-driven exploration of the job landscape for Sustainable Development Goals
Summary
While Sustainable Development Goals (SDGs) provide reference points for societal and economic progress, labor transformation aligned with SDGs remains largely unexplored. Using datasets of job postings, job information, labor statistics, and natural language processing techniques, this project seeks to link jobs to specific SDGs and survey the current landscape of SDG-related employment. This project may include the following specific questions but is not limited to:
- Which SDGs are most related to each occupation and skill?
- What is the level of actual job demand for each SDG observed in job postings data?
- Do SDGs explain employment size and wage variation?
- Which regions demonstrate comparative strengths or deficiencies in SDG-specific employment opportunities?
The findings will inform strategic labor market interventions necessary to advance SDG achievement
Deliverables
Analysis report and preprocessed data
Expected number of team members
Ideally 3 students (2-4 students)
Expected duration in month
Ideally 4 months (3-5 months)
Data sets
- LinkedIn Job Postings (2023-2024) Link(https://www.kaggle.com/datasets/arshkon/linkedin-job-postings)
- O*NET: Job information Link(https://www.onetcenter.org/database.html#individual-files)
- US Bureau of Labor Statistics: US occupation statistics Link(https://www.bls.gov/oes/)
Additional Information
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Tools
- Text2sdg Link(https://www.text2sdg.io/)
- LinkedIn Job Scraper Link(https://github.com/ArshKA/LinkedIn-Job-Scraper)
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References:
- Park et al., Nature Communications, 10(1), 3449 (2019).
- Ma et al., Nature Communications, 16(1), 1107 (2025).
- Alabdulkareem et al., Science Advances, 4(7), eaao6030 (2018).