IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2101.07695.html
   My bibliography  Save this paper

Twitter Subjective Well-Being Indicator During COVID-19 Pandemic: A Cross-Country Comparative Study

Author

Listed:
  • Tiziana Carpi
  • Airo Hino
  • Stefano Maria Iacus
  • Giuseppe Porro

Abstract

This study analyzes the impact of the COVID-19 pandemic on the subjective well-being as measured through Twitter data indicators for Japan and Italy. It turns out that, overall, the subjective well-being dropped by 11.7% for Italy and 8.3% for Japan in the first nine months of 2020 compared to the last two months of 2019 and even more compared to the historical mean of the indexes. Through a data science approach we try to identify the possible causes of this drop down by considering several explanatory variables including, climate and air quality data, number of COVID-19 cases and deaths, Facebook Covid and flu symptoms global survey, Google Trends data and coronavirus-related searches, Google mobility data, policy intervention measures, economic variables and their Google Trends proxies, as well as health and stress proxy variables based on big data. We show that a simple static regression model is not able to capture the complexity of well-being and therefore we propose a dynamic elastic net approach to show how different group of factors may impact the well-being in different periods, even over a short time length, and showing further country-specific aspects. Finally, a structural equation modeling analysis tries to address the causal relationships among the COVID-19 factors and subjective well-being showing that, overall, prolonged mobility restrictions,flu and Covid-like symptoms, economic uncertainty, social distancing and news about the pandemic have negative effects on the subjective well-being.

Suggested Citation

  • 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.
  • Handle: RePEc:arx:papers:2101.07695
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2101.07695
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Warwick McKibbin & Roshen Fernando, 2021. "The Global Macroeconomic Impacts of COVID-19: Seven Scenarios," Asian Economic Papers, MIT Press, vol. 20(2), pages 1-30, Summer.
    2. Chan, K C, et al, 1992. "An Empirical Comparison of Alternative Models of the Short-Term Interest Rate," Journal of Finance, American Finance Association, vol. 47(3), pages 1209-1227, July.
    3. Vasicek, Oldrich, 1977. "An equilibrium characterization of the term structure," Journal of Financial Economics, Elsevier, vol. 5(2), pages 177-188, November.
    4. 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.
    5. Luigi Curini & Stefano Iacus & Luciano Canova, 2015. "Measuring Idiosyncratic Happiness Through the Analysis of Twitter: An Application to the Italian Case," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 121(2), pages 525-542, April.
    6. van der Wielen, Wouter & Barrios, Salvador, 2021. "Economic sentiment during the COVID pandemic: Evidence from search behaviour in the EU," Journal of Economics and Business, Elsevier, vol. 115(C).
    7. Vasicek, Oldrich Alfonso, 1977. "Abstract: An Equilibrium Characterization of the Term Structure," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 12(4), pages 627-627, November.
    8. Rossouw, Stephanie & Greyling, Talita, 2020. "Big Data and Happiness," GLO Discussion Paper Series 634, Global Labor Organization (GLO).
    9. Grewenig, Elisabeth & Lergetporer, Philipp & Simon, Lisa & Werner, Katharina & Woessmann, Ludger, 2018. "Can Online Surveys Represent the Entire Population?," Rationality and Competition Discussion Paper Series 117, CRC TRR 190 Rationality and Competition.
    10. Pragyan Deb & Davide Furceri & Jonathan D. Ostry & Nour Tawk, 2022. "The Economic Effects of COVID-19 Containment Measures," Open Economies Review, Springer, vol. 33(1), pages 1-32, February.
    11. John C. Cox & Jonathan E. Ingersoll Jr. & Stephen A. Ross, 2005. "A Theory Of The Term Structure Of Interest Rates," World Scientific Book Chapters, in: Sudipto Bhattacharya & George M Constantinides (ed.), Theory Of Valuation, chapter 5, pages 129-164, World Scientific Publishing Co. Pte. Ltd..
    12. Robert C. Merton, 2005. "Theory of rational option pricing," World Scientific Book Chapters, in: Sudipto Bhattacharya & George M Constantinides (ed.), Theory Of Valuation, chapter 8, pages 229-288, World Scientific Publishing Co. Pte. Ltd..
    13. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    14. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    15. Greyling, Talita & Rossouw, Stephanie & Adhikari, Tamanna, 2020. "Happiness-lost: Did Governments make the right decisions to combat Covid-19?," GLO Discussion Paper Series 556, Global Labor Organization (GLO).
    16. Brouste, Alexandre & Fukasawa, Masaaki & Hino, Hideitsu & Iacus, Stefano & Kamatani, Kengo & Koike, Yuta & Masuda, Hiroki & Nomura, Ryosuke & Ogihara, Teppei & Shimuzu, Yasutaka & Uchida, Masayuki & Y, 2014. "The YUIMA Project: A Computational Framework for Simulation and Inference of Stochastic Differential Equations," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 57(i04).
    17. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    18. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    19. S. M. Iacus & G. Porro & S. Salini & E. Siletti, 2022. "An Italian Composite Subjective Well-Being Index: The Voice of Twitter Users from 2012 to 2017," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 161(2), pages 471-489, June.
    20. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    21. Rosseel, Yves, 2012. "lavaan: An R Package for Structural Equation Modeling," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i02).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Francesca Marazzi & Andrea Piano Mortari & Federico Belotti & Giuseppe Carrà & Ciro Cattuto & Joanna Kopinska & Daniela Paolotti & Vincenzo Atella, 2022. "Staying Strong, But For How Long? Mental Health During COVID-19 in Italy," CEIS Research Paper 541, Tor Vergata University, CEIS, revised 26 Apr 2022.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Antonio Mele, 2003. "Fundamental Properties of Bond Prices in Models of the Short-Term Rate," The Review of Financial Studies, Society for Financial Studies, vol. 16(3), pages 679-716, July.
    3. Yu, Jun, 2014. "Econometric Analysis Of Continuous Time Models: A Survey Of Peter Phillips’S Work And Some New Results," Econometric Theory, Cambridge University Press, vol. 30(4), pages 737-774, August.
    4. Fergusson, Kevin, 2020. "Less-Expensive Valuation And Reserving Of Long-Dated Variable Annuities When Interest Rates And Mortality Rates Are Stochastic," ASTIN Bulletin, Cambridge University Press, vol. 50(2), pages 381-417, May.
    5. Jianqing Fan, 2004. "A selective overview of nonparametric methods in financial econometrics," Papers math/0411034, arXiv.org.
    6. repec:uts:finphd:40 is not listed on IDEAS
    7. Suresh M. Sundaresan, 2000. "Continuous‐Time Methods in Finance: A Review and an Assessment," Journal of Finance, American Finance Association, vol. 55(4), pages 1569-1622, August.
    8. Moreno, Manuel, 1995. "On the term structure of Interbank interest rates: jump-diffusion processes and option pricing," DES - Working Papers. Statistics and Econometrics. WS 7074, Universidad Carlos III de Madrid. Departamento de Estadística.
    9. Boero, G. & Torricelli, C., 1996. "A comparative evaluation of alternative models of the term structure of interest rates," European Journal of Operational Research, Elsevier, vol. 93(1), pages 205-223, August.
    10. Koo, Bonsoo & Linton, Oliver, 2012. "Estimation of semiparametric locally stationary diffusion models," Journal of Econometrics, Elsevier, vol. 170(1), pages 210-233.
    11. repec:wyi:journl:002108 is not listed on IDEAS
    12. Al-Zoubi, Haitham A., 2009. "Short-term spot rate models with nonparametric deterministic drift," The Quarterly Review of Economics and Finance, Elsevier, vol. 49(3), pages 731-747, August.
    13. Broze, Laurence & Scaillet, Olivier & Zakoian, Jean-Michel, 1995. "Testing for continuous-time models of the short-term interest rate," Journal of Empirical Finance, Elsevier, vol. 2(3), pages 199-223, September.
    14. Feng, Ling & Huang, Zhigang & Mao, Xuerong, 2016. "Mean percentage of returns for stock market linked savings accounts," Applied Mathematics and Computation, Elsevier, vol. 273(C), pages 1130-1147.
    15. Dette, Holger & Podolskij, Mark, 2008. "Testing the parametric form of the volatility in continuous time diffusion models--a stochastic process approach," Journal of Econometrics, Elsevier, vol. 143(1), pages 56-73, March.
    16. Kevin John Fergusson, 2018. "Less-Expensive Pricing and Hedging of Extreme-Maturity Interest Rate Derivatives and Equity Index Options Under the Real-World Measure," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 3-2018, January-A.
    17. Dennis Kristensen, 2004. "A Semiparametric Single-Factor Model of the Term Structure," FMG Discussion Papers dp501, Financial Markets Group.
    18. Byers, S. L. & Nowman, K. B., 1998. "Forecasting U.K. and U.S. interest rates using continuous time term structure models," International Review of Financial Analysis, Elsevier, vol. 7(3), pages 191-206.
    19. T. J. Brailsford & K. Maheswaran, 1998. "The Dynamics of the Australian Short†Term Interest Rate," Australian Journal of Management, Australian School of Business, vol. 23(2), pages 213-234, December.
    20. Jonathan A. Batten & Karren Lee-Hwei Khaw & Martin R. Young, 2014. "Convertible Bond Pricing Models," Journal of Economic Surveys, Wiley Blackwell, vol. 28(5), pages 775-803, December.
    21. Kristensen, Dennis, 2004. "Estimation in two classes of semiparametric diffusion models," LSE Research Online Documents on Economics 24739, London School of Economics and Political Science, LSE Library.
    22. Jun Yu, 2009. "Econometric Analysis of Continuous Time Models : A Survey of Peter Phillips’ Work and Some New Results," Microeconomics Working Papers 23046, East Asian Bureau of Economic Research.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2101.07695. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.