A deep learning model identifies emphasis on hard work as an important predictor of income inequality

Abstract

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.

Using machine learning to analyze longitudinal data: A tutorial guide and best‐practice recommendations for social science researchers

“…Some recent studies (e.g. Sheetal et al, 2020Sheetal et al, , 2022 have set examples researchers can refer to when designing and implementing machine learning projects that involve casual inference.…”