A deep learning model identifies emphasis on hard work as an important predictor of income inequality
High levels of income inequality can persist in society only if people accept the inequality as justified. To identify psychological predictors of people's tendency to justify inequality, we retrained a pre-existing deep learning model to predict the extent to which World Values Survey responde...
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sg-ntu-dr.10356-1712942023-10-26T15:35:46Z A deep learning model identifies emphasis on hard work as an important predictor of income inequality Sheetal, Abhishek Chaudhury, Srinwanti H. Savani, Krishna Nanyang Business School Business::General Correlational Study Income Inequality High levels of income inequality can persist in society only if people accept the inequality as justified. To identify psychological predictors of people's tendency to justify inequality, we retrained a pre-existing deep learning model to predict the extent to which World Values Survey respondents believed that income inequality is necessary. A feature importance analysis revealed multiple items associated with the importance of hard work as top predictors. As an emphasis on hard work is a key component of the Protestant Work Ethic, we formulated the hypothesis that the PWE increases acceptance of inequality. A correlational study found that the more people endorsed PWE, the less disturbed they were about factual statistics about wealth equality in the US. Two experiments found that exposing people to PWE items decreased their disturbance with income inequality. The findings indicate that machine learning models can be reused to generate viable hypotheses. Nanyang Technological University Published version This research was supported by a Nanyang Assistant Professorship grant awarded by Nanyang Technological University to Krishna Savani. 2023-10-23T06:50:14Z 2023-10-23T06:50:14Z 2022 Journal Article Sheetal, A., Chaudhury, S. H. & Savani, K. (2022). A deep learning model identifies emphasis on hard work as an important predictor of income inequality. Scientific Reports, 12(1), 9845-. https://dx.doi.org/10.1038/s41598-022-13902-x 2045-2322 https://hdl.handle.net/10356/171294 10.1038/s41598-022-13902-x 35701456 2-s2.0-85131877087 1 12 9845 en Scientific Reports © 2022 The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. application/pdf |
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Business::General Correlational Study Income Inequality Sheetal, Abhishek Chaudhury, Srinwanti H. Savani, Krishna A deep learning model identifies emphasis on hard work as an important predictor of income inequality |
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High levels of income inequality can persist in society only if people accept the inequality as justified. To identify psychological predictors of people's tendency to justify inequality, we retrained a pre-existing deep learning model to predict the extent to which World Values Survey respondents believed that income inequality is necessary. A feature importance analysis revealed multiple items associated with the importance of hard work as top predictors. As an emphasis on hard work is a key component of the Protestant Work Ethic, we formulated the hypothesis that the PWE increases acceptance of inequality. A correlational study found that the more people endorsed PWE, the less disturbed they were about factual statistics about wealth equality in the US. Two experiments found that exposing people to PWE items decreased their disturbance with income inequality. The findings indicate that machine learning models can be reused to generate viable hypotheses. |
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Nanyang Business School |
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Nanyang Business School Sheetal, Abhishek Chaudhury, Srinwanti H. Savani, Krishna |
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Article |
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Sheetal, Abhishek Chaudhury, Srinwanti H. Savani, Krishna |
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Sheetal, Abhishek |
title |
A deep learning model identifies emphasis on hard work as an important predictor of income inequality |
title_short |
A deep learning model identifies emphasis on hard work as an important predictor of income inequality |
title_full |
A deep learning model identifies emphasis on hard work as an important predictor of income inequality |
title_fullStr |
A deep learning model identifies emphasis on hard work as an important predictor of income inequality |
title_full_unstemmed |
A deep learning model identifies emphasis on hard work as an important predictor of income inequality |
title_sort |
deep learning model identifies emphasis on hard work as an important predictor of income inequality |
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2023 |
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https://hdl.handle.net/10356/171294 |
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