Nowcasting subjective well-being with Google Trends: A meta-learning approach

Fabrice Murtin and Max Salomon-Ermel

No 27, OECD Papers on Well-being and Inequalities from OECD Publishing

Abstract: 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.

Keywords: poverty; spatial inequality; well-being (search for similar items in EconPapers)
JEL-codes: C1 C45 C53 D60 I31 (search for similar items in EconPapers)
Date: 2024-06-28
New Economics Papers: this item is included in nep-big, nep-cmp, nep-hap and nep-inv
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Persistent link: https://EconPapers.repec.org/RePEc:oec:wiseaa:27-en

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