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

Author

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  • Fabrice Murtin
  • Max Salomon-Ermel

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.

Suggested Citation

  • Fabrice Murtin & Max Salomon-Ermel, 2024. "Nowcasting subjective well-being with Google Trends: A meta-learning approach," OECD Papers on Well-being and Inequalities 27, OECD Publishing.
    • Handle: RePEc:oec:wiseaa:27-en
    DOI: 10.1787/cbdfb5d9-en
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    More about this item

    Keywords

    poverty; spatial inequality; well-being;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • D60 - Microeconomics - - Welfare Economics - - - General
    • I31 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - General Welfare, Well-Being

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