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Published on: October 6, 2020
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Ranking occupations by their proximity to workers' profiles.
Mirjam Bächli1, Hélène Benghalem1, Doriana Tinello2
1Department of Economics, University of Lausanne, Lausanne, Switzerland.
Swiss Journal of Economics and Statistics
|July 29, 2024
Summary
Job seekers face information friction. This study introduces a novel method for personalized occupation recommendations, using skill and ability proximity to expand job search scope and reduce mismatch.
Area of Science:
- Labor Economics
- Occupational Science
- Human-Computer Interaction
Background:
- Information friction hinders effective job searching and career transitions.
- Existing methods for occupation recommendation may not sufficiently capture individual-specific needs and profiles.
- Understanding worker-occupation proximity is crucial for improving employment outcomes.
Purpose of the Study:
- To develop and validate a method for generating individual-specific occupation recommendations.
- To quantify the proximity between job seekers' profiles and occupational requirements.
- To assess the effectiveness of the proposed method in predicting job change intention and expanding search scope.
Main Methods:
- Identification of twelve key worker-oriented requirements (skills, abilities, work styles) applicable to all occupations.
- Measurement of these requirements through online questions and tasks to create individual worker profiles.
- Calculation of Euclidean distance between worker profiles and occupation requirements to determine proximity.
- Validation of the proximity measure by correlating it with job seekers' intention to change occupations.
Main Results:
- The proximity measure between a job seeker's profile and their previous occupation successfully predicted their intention to change jobs, indicating a meaningful capture of occupational mismatch.
- The proposed method generated occupation recommendations that differed from the previous roles of mismatched job seekers.
- This suggests the method can effectively identify suitable alternative career paths.
Conclusions:
- The developed method offers a data-driven approach to personalized occupation recommendations, addressing information friction in the job market.
- By quantifying worker-occupation proximity, the system can help job seekers discover relevant opportunities beyond their immediate experience.
- This approach has the potential to broaden job search strategies and improve employment matching outcomes.
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