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Big Data and Happiness

Author

Listed:
  • Rossouw, Stephanie
  • Greyling, Talita
Abstract
The pursuit of happiness. What does that mean? Perhaps a more prominent question to ask is, 'how does one know whether people have succeeded in their pursuit'? Survey data, thus far, has served us well in determining where people see themselves on their journey. However, in an everchanging world, one needs high-frequency data instead of data released with significant time-lags. High-frequency data, which stems from Big Data, allows policymakers access to virtually real-time information that can assist in effective decision-making to increase the quality of life for all. Additionally, Big Data collected from, for example, social media platforms give researchers unprecedented insight into human behaviour, allowing significant future predictive powers.

Suggested Citation

  • Rossouw, Stephanie & Greyling, Talita, 2020. "Big Data and Happiness," GLO Discussion Paper Series 634, Global Labor Organization (GLO).
    • Handle: RePEc:zbw:glodps:634
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    File URL: https://www.econstor.eu/bitstream/10419/223012/1/GLO-DP-0634.pdf
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    References listed on IDEAS

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    1. Thomas T. Hills & Eugenio Proto & Daniel Sgroi & Chanuki Illushka Seresinhe, 2019. "Historical analysis of national subjective wellbeing using millions of digitized books," Nature Human Behaviour, Nature, vol. 3(12), pages 1271-1275, December.
    2. Iacus Stefano M. & Salini Silvia & Siletti Elena & Porro Giuseppe, 2020. "Controlling for Selection Bias in Social Media Indicators through Official Statistics: a Proposal," Journal of Official Statistics, Sciendo, vol. 36(2), pages 315-338, June.
    3. Simon Kuznets, 1934. "National Income, 1929-1932," NBER Books, National Bureau of Economic Research, Inc, number kuzn34-1.
    4. Richardson, Adam & van Florenstein Mulder, Thomas & Vehbi, Tuğrul, 2021. "Nowcasting GDP using machine-learning algorithms: A real-time assessment," International Journal of Forecasting, Elsevier, vol. 37(2), pages 941-948.
    5. Renault, Thomas, 2017. "Intraday online investor sentiment and return patterns in the U.S. stock market," Journal of Banking & Finance, Elsevier, vol. 84(C), pages 25-40.
    6. Broadstock, David C. & Zhang, Dayong, 2019. "Social-media and intraday stock returns: The pricing power of sentiment," Finance Research Letters, Elsevier, vol. 30(C), pages 116-123.
    7. Yann Algan & Elizabeth Beasley & Florian Guyot & Kazuhito Higad & Fabrice Murtin & Claudia Senik, 2015. "Big Data Measures of Well-Being: Evidence from a Google Well-Being Index in the US," PSE Working Papers hal-03429943, HAL.
    8. Richardson, Adam & van Florenstein Mulder, Thomas & Vehbi, Tuğrul, 2021. "Nowcasting GDP using machine-learning algorithms: A real-time assessment," International Journal of Forecasting, Elsevier, vol. 37(2), pages 941-948.
    9. Rajagopal, 2014. "The Human Factors," Palgrave Macmillan Books, in: Architecting Enterprise, chapter 9, pages 225-249, Palgrave Macmillan.
    10. Ed Diener & Eunkook Suh, 1997. "Measuring Quality Of Life: Economic, Social, And Subjective Indicators," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 40(1), pages 189-216, January.
    11. Peter Dodds & Christopher Danforth, 2010. "Measuring the Happiness of Large-Scale Written Expression: Songs, Blogs, and Presidents," Journal of Happiness Studies, Springer, vol. 11(4), pages 441-456, August.
    12. Steyn, Dimitri H. W. & Greyling, Talita & Rossouw, Stephanie & Mwamba, John M., 2020. "Sentiment, emotions and stock market predictability in developed and emerging markets," GLO Discussion Paper Series 502, Global Labor Organization (GLO).
    13. Amitava Krishna Dutt & Benjamin Radcliff (ed.), 2009. "Happiness, Economics and Politics," Books, Edward Elgar Publishing, number 13280.
    14. repec:hal:spmain:info:hdl:2441/5k53daedc2827oa91tfpuscvbn is not listed on IDEAS
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    Citations

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    Cited by:

    1. Indy Wijngaards & Owen C. King & Martijn J. Burger & Job Exel, 2022. "Worker Well-Being: What it Is, and how it Should Be Measured," Applied Research in Quality of Life, Springer;International Society for Quality-of-Life Studies, vol. 17(2), pages 795-832, April.
    2. Tiziana CARPI & Airo HINO & Stefano Maria IACUS & Giuseppe PORRO, 2022. "A Japanese Subjective Well-Being Indicator Based on Twitter Data [‘Collective Smile: Measuring Societal Happiness from Geolocated Images’]," Social Science Japan Journal, University of Tokyo and Oxford University Press, vol. 25(2), pages 273-296.
    3. Tiziana Carpi & Airo Hino & Stefano Maria Iacus & Giuseppe Porro, 2021. "Twitter Subjective Well-Being Indicator During COVID-19 Pandemic: A Cross-Country Comparative Study," Papers 2101.07695, arXiv.org.
    4. Rossouw, Stephanie & Greyling, Talita & Adhikari, Tamanna, 2021. "New Zealand's happiness and COVID-19: a Markov Switching Dynamic Regression Model," GLO Discussion Paper Series 573 [rev.], Global Labor Organization (GLO).
    5. Philip S. Morrison & Stephanié Rossouw & Talita Greyling, 2022. "The impact of exogenous shocks on national wellbeing. New Zealanders’ reaction to COVID-19," Applied Research in Quality of Life, Springer;International Society for Quality-of-Life Studies, vol. 17(3), pages 1787-1812, June.

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    More about this item

    Keywords

    Happiness; Big Data; Sentiment analysis;
    All these keywords.

    JEL classification:

    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software
    • I31 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - General Welfare, Well-Being
    • I39 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Other
    • J18 - Labor and Demographic Economics - - Demographic Economics - - - Public Policy

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