Methodology
Our investigative approach merges data analysis with scholarly inquiry to gauge GenAI's workforce impact. Utilizing the AI Occupational Exposure Index, we've quantified AI's integration across professions, emphasizing Language Modeling's influence. Based on the correlation between AI application scores and O*NET's occupational abilities3, we sourced job posting trends for emerging patterns, and linked AI exposure to salary trajectories.
Additionally, we conducted longitudinal studies to identify trends in job skills, drawing correlations between AI exposure and salary levels. The analysis was performed with programming in Python and using ChatGPT for code generation and additional context. By integrating these diverse methods, we aim to paint a detailed picture of GenAI's burgeoning role in shaping the future of employment.
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Data Description
Job postings for entry and junior level positions
Total number of data: Total of 44,222 job postings from 8,969 employers.
Timeframe: Jan 2014 – Sept 2023
Minimum Experience Required: 0 – 3 years
Included: business analytics, data analytics (general), financial analytics, data analytics (other), data science (general), data science (other), data visualization
Education Level: Any
Job Type: include internships
Posting Type: Newly Posted
Parameters: Executive Summary, Advertised Salary, Advertised Wage Trend, Regional, Minimum Education, Top Companies, Top cities, Top Posted Occupations (SOC), Top Posted Occupations (O*NET), Top Specialized Skills, Top Software Skills
Job Market Trends - Demand Growth Overview (2014 - 2023)
Phase 1:
Initial Buildup of Demand (2014 - 2017)
The growth of demand from 6K to over 11K.
The accelerating annual growth rate (from 9% to 44%)
Breaking Point:
Periods of Sharp Increases (2018 / 2021)
The sharp increases in demand (67% in 2018 and 57% in 2021).
Phase 2:
Two 3-Year Segments
2018-2020 / 2021-2023