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Big Data for computing social well-being indices of the Russian population


  • Fantazzini, Dean

    (Moscow School of Economics — Moscow State University)

  • Shakleina, Marina

    (Moscow School of Economics — Moscow State University)

  • Yuras, Natalia

    (Moscow School of Economics — Moscow State University)


The article builds indices of social well-being based on Google Trends Data for predicting VCIOM indices. The Google indices were computed using a Google Trends dataset for 2006–2016 containing 512 search queries relative to housing conditions, income, education, etc., and applying factor analysis. Bayesian Model Averaging was then used to select the indexes of individual social well-being mostly associated with the VCIOM indices which measure the social well-being of the Russian population. Additional regression models and forecasting exercises confirmed the previous results. Based on the Google Trends Data, the indices of the subjective social well-being are statistically reliable, as evidenced by a strong correlation between the observed and predicted values of the VCIOM indices.

Suggested Citation

  • Fantazzini, Dean & Shakleina, Marina & Yuras, Natalia, 2018. "Big Data for computing social well-being indices of the Russian population," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 50, pages 43-66.
  • Handle: RePEc:ris:apltrx:0343

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    References listed on IDEAS

    1. Fernandez, Carmen & Ley, Eduardo & Steel, Mark F. J., 2001. "Benchmark priors for Bayesian model averaging," Journal of Econometrics, Elsevier, vol. 100(2), pages 381-427, February.
    2. Steven L. Scott & Hal R. Varian, 2015. "Bayesian Variable Selection for Nowcasting Economic Time Series," NBER Chapters, in: Economic Analysis of the Digital Economy, pages 119-135, National Bureau of Economic Research, Inc.
    3. Claeskens,Gerda & Hjort,Nils Lid, 2008. "Model Selection and Model Averaging," Cambridge Books, Cambridge University Press, number 9780521852258, December.
    4. Liang, Feng & Paulo, Rui & Molina, German & Clyde, Merlise A. & Berger, Jim O., 2008. "Mixtures of g Priors for Bayesian Variable Selection," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 410-423, March.
    5. Yann Algan & Elizabeth Beasley & Florian Guyot & Kazuhito Higa & Fabrice Murtin & Claudia Senik, 2016. "Big Data Measures of Well-Being: Evidence From a Google Well-Being Index in the United States," OECD Statistics Working Papers 2016/3, OECD Publishing.
    6. Nikolaos Askitas, 2015. "Google search activity data and breaking trends," IZA World of Labor, Institute of Labor Economics (IZA), pages 206-206, November.
    7. Mavragani, Amaryllis & Tsagarakis, Konstantinos P., 2016. "YES or NO: Predicting the 2015 GReferendum results using Google Trends," Technological Forecasting and Social Change, Elsevier, vol. 109(C), pages 1-5.
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    Cited by:

    1. Petrova, Diana & Trunin, Pavel, 2020. "Revealing the mood of economic agents based on search queries," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 59, pages 71-87.

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    More about this item


    social well-being indices; Google Trends Data; Factor analysis; Bayesian Model Averaging;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty


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