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Using big data to map the relationship between time perspectives and economic outputs.

Christopher Y Olivola1,2, Helen Susannah Moat3,4, Tobias Preis3,4

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Population time perspectives, measured via search engine data, predict national economic output. This suggests a complex link between how societies view the future and their affluence, challenging simple assumptions.

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Area of Science:

  • Social Sciences
  • Economics
  • Psychology

Background:

  • Population-level time perspectives are increasingly studied.
  • Big data analytics offer novel ways to measure societal trends.
  • Previous research suggested a link between future orientation and economic prosperity.

Purpose of the Study:

  • To investigate the relationship between population time perspectives and economic indicators.
  • To determine if search engine query data can approximate societal time orientations.
  • To test Baumard's hypothesis on affluence and future-orientation.

Main Methods:

  • Utilizing large-scale "big data" from search engine queries.
  • Analyzing search query patterns to derive population time perspective indices.
  • Correlating these indices with per-capita Gross Domestic Product (GDP) data across countries.

Main Results:

  • Population time perspective indices derived from search data significantly predict per-capita GDP.
  • The findings support a connection between future-oriented societies and economic success.
  • A more complex, nuanced relationship between time perspectives and economic output was observed than previously suggested.

Conclusions:

  • Search engine data provides a viable proxy for population-level time perspectives.
  • Societal time orientation is a quantifiable factor influencing economic performance.
  • The interplay between affluence and future-time orientation is intricate and warrants further investigation.