Machine learning in the prediction of human wellbeing
Working Paper 2301 published by the Wellbeing Research Centre, University of Oxford. Subjective wellbeing data are increasingly used across the social sciences. Yet, our ability to model wellbeing is severely limited. In response, we here use tree-based Machine Learning (ML) algorithms to provide a...
| Main Authors: | , , , , , , |
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| Format: | Working paper |
| Language: | English |
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University of Oxford
2023
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| Subjects: |
| _version_ | 1826309667148529664 |
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| author | Oparina, E Kaiser, C Gentile, N Tkatchenko, A Clark, A E De Neve, J D'Ambrosio, C |
| author_facet | Oparina, E Kaiser, C Gentile, N Tkatchenko, A Clark, A E De Neve, J D'Ambrosio, C |
| author_sort | Oparina, E |
| collection | OXFORD |
| description | Working Paper 2301 published by the Wellbeing Research Centre, University of Oxford.
Subjective wellbeing data are increasingly used across the social sciences. Yet, our ability to model wellbeing is severely limited. In response, we here use tree-based Machine Learning (ML) algorithms to provide a better understanding of respondents’ self-reported wellbeing.
We analyse representative samples of more than one million respondents from Germany, the UK, and the United States, using the data between 2010 and 2018. In terms of predictive power, our ML approaches perform better than traditional ordinary least squares (OLS) regressions. We moreover find that drastically expanding the set of explanatory variables doubles the predictive power of both OLS and the ML approaches on unseen data. The variables identified as important by our ML algorithms – i.e. material conditions, health,
personality traits, and meaningful social relations – are similar to those that have already been identified in the literature. In that sense, our data-driven ML results validate the findings from conventional approaches.
Keywords: Subjective wellbeing, prediction methods, machine learning. |
| first_indexed | 2024-03-07T07:39:09Z |
| format | Working paper |
| id | oxford-uuid:4bad51c5-dc38-4d98-b02f-e2d9457a5e63 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T07:39:09Z |
| publishDate | 2023 |
| publisher | University of Oxford |
| record_format | dspace |
| spelling | oxford-uuid:4bad51c5-dc38-4d98-b02f-e2d9457a5e632023-04-13T12:08:42ZMachine learning in the prediction of human wellbeingWorking paperhttp://purl.org/coar/resource_type/c_8042uuid:4bad51c5-dc38-4d98-b02f-e2d9457a5e63Well-beingMachine learningEnglishHyrax DepositUniversity of Oxford2023Oparina, EKaiser, CGentile, NTkatchenko, AClark, A EDe Neve, JD'Ambrosio, CWorking Paper 2301 published by the Wellbeing Research Centre, University of Oxford. Subjective wellbeing data are increasingly used across the social sciences. Yet, our ability to model wellbeing is severely limited. In response, we here use tree-based Machine Learning (ML) algorithms to provide a better understanding of respondents’ self-reported wellbeing. We analyse representative samples of more than one million respondents from Germany, the UK, and the United States, using the data between 2010 and 2018. In terms of predictive power, our ML approaches perform better than traditional ordinary least squares (OLS) regressions. We moreover find that drastically expanding the set of explanatory variables doubles the predictive power of both OLS and the ML approaches on unseen data. The variables identified as important by our ML algorithms – i.e. material conditions, health, personality traits, and meaningful social relations – are similar to those that have already been identified in the literature. In that sense, our data-driven ML results validate the findings from conventional approaches. Keywords: Subjective wellbeing, prediction methods, machine learning. |
| spellingShingle | Well-being Machine learning Oparina, E Kaiser, C Gentile, N Tkatchenko, A Clark, A E De Neve, J D'Ambrosio, C Machine learning in the prediction of human wellbeing |
| title | Machine learning in the prediction of human wellbeing |
| title_full | Machine learning in the prediction of human wellbeing |
| title_fullStr | Machine learning in the prediction of human wellbeing |
| title_full_unstemmed | Machine learning in the prediction of human wellbeing |
| title_short | Machine learning in the prediction of human wellbeing |
| title_sort | machine learning in the prediction of human wellbeing |
| topic | Well-being Machine learning |
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