This paper applies Machine learning techniques to Google Trends data to provide real-time estimates of national average subjective well-being among 38 OECD countries since 2010. We make extensive usage of large custom micro databases to enhance the training of models on carefully pre-processed Google Trends data. We find that the best one-year-ahead prediction is obtained from a meta-learner that combines the predictions drawn from an Elastic Net with and without interactions, from a Gradient-Boosted Tree and from a Multi-layer Perceptron. As a result, across 38 countries over the 2010-2020 period, the out-of-sample prediction of average subjective well-being reaches an R2 of 0.830.
Nowcasting subjective well-being with Google Trends
A meta-learning approach
Working paper
OECD Papers on Well-being and Inequalities
Edited by OCDE
![](/https/www.oecd.org/adobe/dynamicmedia/deliver/dm-aid--65270e2a-cd9b-4c09-8124-47f5c3b6575c/01.png?quality=80&preferwebp=true)
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