Predicting Attitudes toward UBI in EU using Machine Learning Techniques
- Performed logistic regression, Decision Tree,Random Forest, and AdaBoost on the Dataset to identifying the key influential factor on the attitude of EU citizen towards UBI
This study seeks to explore the factors that influence citizens' attitudes towards Universal Basic Income (UBI) – a social welfare policy that provides a fixed, unconditional income to all citizens regardless of their employment status or income level (Parijs & Vanderborght, 2017). Drawing on data from the European Social Survey (ESS) round 8, Choi (2021) investigates the impact of socio-demographic characteristics, self-interest, values or beliefs, specific attitudes, and socioeconomic structures on people's attitudes towards UBI. In particular, Choi (2021) tests the hypothesis that human basic values serve as the most determinant predictor among all factors in developed welfare states. The present study aims to examine the robustness of Choi's (2021) conclusions regarding the primary determinants of attitudes towards UBI by employing logistic regression, propensity score matching, and random forest techniques. Through this comprehensive analysis, we seek to validate and extend the findings of Choi (2021) and contribute to a deeper understanding of the factors shaping public opinion on UBI in developed welfare states.