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Economic sentiment during the COVID pandemic: Evidence from search behaviour in the EU

Author

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  • van der Wielen, Wouter
  • Barrios, Salvador
Abstract
The COVID-19 pandemic has inflicted an economic hardship unprecedented for the modern age. In this paper, we show that the health crisis and ensuing lockdown, came with an unseen shift in households’ economic sentiment. First, using a European dataset of country-level and regional internet searches, we document a substantial increase in people's business cycle related searches in the months following the coronavirus outbreak. People's unemployment concerns jumped to levels well-above those during the Great Recession. Second, we observe a significant, coinciding slowdown in labour markets and consumption. Third, our analysis shows that the ensuing shift in sentiment was significantly more outspoken in those EU countries hit hardest in economic terms. Finally, we show that unprecedented fiscal policy actions, such as the short-time work schemes implemented or reformed at the onset of the COVID-crisis, however, have not eased economic sentiment.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:jebusi:v:115:y:2021:i:c:s0148619520304148
    DOI: 10.1016/j.jeconbus.2020.105970
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    1. Adam Sheridan & Asger Lau Andersen & Emil Toft Hansen & Niels Johannesen, 2020. "Social distancing laws cause only small losses of economic activity during the COVID-19 pandemic in Scandinavia," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 117(34), pages 20468-20473, August.
    2. Carola Binder, 2020. "Coronavirus Fears and Macroeconomic Expectations," The Review of Economics and Statistics, MIT Press, vol. 102(4), pages 721-730, October.
    3. Murillo Campello & Gaurav Kankanhalli & Pradeep Muthukrishnan, 2020. "Corporate Hiring under COVID-19: Labor Market Concentration, Downskilling, and Income Inequality," NBER Working Papers 27208, National Bureau of Economic Research, Inc.
    4. Graeme Chamberlin, 2010. "Googling the present," Economic & Labour Market Review, Palgrave Macmillan;Office for National Statistics, vol. 4(12), pages 59-95, December.
    5. Francisca Beer & Fabrice Hervé & Mohamed Zouaoui, 2013. "Is Big Brother Watching Us? Google, Investor Sentiment and the Stock Market," Economics Bulletin, AccessEcon, vol. 33(1), pages 454-466.
    6. Tito Boeri & Herbert Bruecker, 2011. "Short-time work benefits revisited: some lessons from the Great Recession [‘Reversed roles? Wage and employment effects of the current crisis’]," Economic Policy, CEPR, CESifo, Sciences Po;CES;MSH, vol. 26(68), pages 697-765.
    7. Reamonn Lydon & Thomas Y. Mathä & Stephen Millard, 2019. "Short-time work in the Great Recession: firm-level evidence from 20 EU countries," IZA Journal of Labor Policy, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 8(1), pages 1-29, December.
    8. Balleer, Almut & Gehrke, Britta & Lechthaler, Wolfgang & Merkl, Christian, 2016. "Does short-time work save jobs? A business cycle analysis," European Economic Review, Elsevier, vol. 84(C), pages 99-122.
    9. Karl BRENKE & Ulf RINNE & Klaus F. ZIMMERMANN, 2013. "Short-time work: The German answer to the Great Recession," International Labour Review, International Labour Organization, vol. 152(2), pages 287-305, June.
    10. Altig, Dave & Baker, Scott & Barrero, Jose Maria & Bloom, Nicholas & Bunn, Philip & Chen, Scarlet & Davis, Steven J. & Leather, Julia & Meyer, Brent & Mihaylov, Emil & Mizen, Paul & Parker, Nicholas &, 2020. "Economic uncertainty before and during the COVID-19 pandemic," Journal of Public Economics, Elsevier, vol. 191(C).
    11. Nikolaos Askitas & Klaus F. Zimmermann, 2009. "Google Econometrics and Unemployment Forecasting," Applied Economics Quarterly (formerly: Konjunkturpolitik), Duncker & Humblot, Berlin, vol. 55(2), pages 107-120.
    12. Konstantinos Efstathiou & Thomas Y. Mathä & Cindy Veiga & Ladislav Wintr, 2018. "Short-time work in Luxembourg: evidence from a firm survey," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 52(1), pages 1-20, December.
    13. Christopher Roth & Johannes Wohlfart, 2020. "How Do Expectations about the Macroeconomy Affect Personal Expectations and Behavior?," The Review of Economics and Statistics, MIT Press, vol. 102(4), pages 731-748, October.
    14. repec:hal:wpspec:info:hdl:2441/6596a4s9af8lt872jnqvm5jg73 is not listed on IDEAS
    15. Idriss Fontaine, 2021. "Uncertainty and Labour Force Participation," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(2), pages 437-471, April.
    16. Pierre Cahuc & Francis Kramarz & Sandra Nevoux, 2018. "When Short-Time Work Works," Working papers 692, Banque de France.
    17. Marina Hoffmann & Stefan Schneck, 2011. "Short-Time Work in German Firms," Applied Economics Quarterly (formerly: Konjunkturpolitik), Duncker & Humblot, Berlin, vol. 57(4), pages 233-254.
    18. Coibion, Olivier & Gorodnichenko, Yuriy & Weber, Michael, 2020. "Labor Markets During the Covid-19 Crisis: A Preliminary View," Department of Economics, Working Paper Series qt7rx7t91p, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
    19. Altig, David & Barrero, Jose Maria & Bloom, Nicholas & Davis, Steven J. & Meyer, Brent & Parker, Nicholas, 2022. "Surveying business uncertainty," Journal of Econometrics, Elsevier, vol. 231(1), pages 282-303.
    20. Laura Nowzohour & Livio Stracca, 2020. "More Than A Feeling: Confidence, Uncertainty, And Macroeconomic Fluctuations," Journal of Economic Surveys, Wiley Blackwell, vol. 34(4), pages 691-726, September.
    21. Scott R. Baker & Nicholas Bloom & Steven J. Davis & Kyle J. Kost & Marco C. Sammon & Tasaneeya Viratyosin, 2020. "The Unprecedented Stock Market Impact of COVID-19," NBER Working Papers 26945, National Bureau of Economic Research, Inc.
    22. Jess Benhabib & Mark M Spiegel, 2019. "Sentiments and Economic Activity: Evidence from US States," The Economic Journal, Royal Economic Society, vol. 129(618), pages 715-733.
    23. Giulia Giupponi & Camille Landais, 2023. "Subsidizing Labour Hoarding in Recessions: The Employment and Welfare Effects of Short-time Work," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 90(4), pages 1963-2005.
    24. McLaren, Nick & Shanbhogue, Rachana, 2011. "Using internet search data as economic indicators," Bank of England Quarterly Bulletin, Bank of England, vol. 51(2), pages 134-140.
    25. Scott R. Baker & Nicholas Bloom & Steven J. Davis & Kyle J. Kost, 2019. "Policy News and Stock Market Volatility," NBER Working Papers 25720, National Bureau of Economic Research, Inc.
    26. Andreas Crimmann & Frank Wießner & Lutz Bellmann, 2012. "Resisting the crisis: short‐time work in Germany," International Journal of Manpower, Emerald Group Publishing Limited, vol. 33(8), pages 877-900, November.
    27. Ben-David, Itzhak & Fermand, Elyas & Kuhnen, Camelia M. & Li, Geng, 2018. "Expectations Uncertainty and Household Economic Behavior," Working Paper Series 2018-25, Ohio State University, Charles A. Dice Center for Research in Financial Economics.
    28. Jordi Galí, 2015. "Hysteresis and the European Unemployment Problem Revisited," NBER Working Papers 21430, National Bureau of Economic Research, Inc.
    29. Fondeur, Y. & Karamé, F., 2013. "Can Google data help predict French youth unemployment?," Economic Modelling, Elsevier, vol. 30(C), pages 117-125.
    30. Punzi, Maria Teresa, 2020. "The impact of uncertainty on the macro-financial linkage with international financial exposure," Journal of Economics and Business, Elsevier, vol. 110(C).
    31. Nicholas Bloom, 2009. "The Impact of Uncertainty Shocks," Econometrica, Econometric Society, vol. 77(3), pages 623-685, May.
    32. Nikolaos Askitas & Klaus F. Zimmermann, 2015. "The internet as a data source for advancement in social sciences," International Journal of Manpower, Emerald Group Publishing Limited, vol. 36(1), pages 2-12, April.
    33. Niesert, Robin F. & Oorschot, Jochem A. & Veldhuisen, Christian P. & Brons, Kester & Lange, Rutger-Jan, 2020. "Can Google search data help predict macroeconomic series?," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1163-1172.
    34. Jesus Fernandez-Villaverde & Pablo Guerron-Quintana, 2020. "Uncertainty Shocks and Business Cycle Research," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 37, pages 118-166, August.
    35. Laurent Ferrara & Anna Simoni, 2023. "When are Google Data Useful to Nowcast GDP? An Approach via Preselection and Shrinkage," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(4), pages 1188-1202, October.
    36. Ghosal, Vivek & Ye, Yang, 2019. "The impact of uncertainty on the number of businesses," Journal of Economics and Business, Elsevier, vol. 105(C).
    37. 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.
    38. Yu, Lean & Zhao, Yaqing & Tang, Ling & Yang, Zebin, 2019. "Online big data-driven oil consumption forecasting with Google trends," International Journal of Forecasting, Elsevier, vol. 35(1), pages 213-223.
    39. Thiemo Fetzer & Lukas Hensel & Johannes Hermle & Christopher Roth, 2021. "Coronavirus Perceptions and Economic Anxiety," The Review of Economics and Statistics, MIT Press, vol. 103(5), pages 968–978-9, December.
    40. Simeon Vosen & Torsten Schmidt, 2011. "Forecasting private consumption: survey‐based indicators vs. Google trends," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(6), pages 565-578, September.
    41. Scott R Baker & Robert A Farrokhnia & Steffen Meyer & Michaela Pagel & Constantine Yannelis & Jeffrey Pontiff, 0. "How Does Household Spending Respond to an Epidemic? Consumption during the 2020 COVID-19 Pandemic," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 10(4), pages 834-862.
    42. Forsythe, Eliza & Kahn, Lisa B. & Lange, Fabian & Wiczer, David, 2020. "Labor demand in the time of COVID-19: Evidence from vacancy postings and UI claims," Journal of Public Economics, Elsevier, vol. 189(C).
    43. Shelby R. Buckman & Adam Hale Shapiro & Moritz Sudhof & Daniel J. Wilson, 2020. "News Sentiment in the Time of COVID-19," FRBSF Economic Letter, Federal Reserve Bank of San Francisco, vol. 2020(08), pages 1-05, April.
    44. Mao, Huina & Counts, Scott & Bollen, Johan, 2015. "Quantifying the effects of online bullishness on international financial markets," Statistics Paper Series 09, European Central Bank.
    45. Daniel Aaronson & Scott A. Brave & R. Andrew Butters & Daniel Sacks & Boyoung Seo, 2020. "Using the Eye of the Storm to Predict the Wave of Covid-19 UI Claims," Working Paper Series WP-2020-10, Federal Reserve Bank of Chicago, revised 16 Apr 2020.
    46. Wendy E. Dunn, 1998. "Unemployment risk, precautionary saving, and durable goods purchase decisions," Finance and Economics Discussion Series 1998-49, Board of Governors of the Federal Reserve System (U.S.).
    47. George A. Akerlof & Robert J. Shiller, 2010. "Animal Spirits: How Human Psychology Drives the Economy, and Why It Matters for Global Capitalism," Economics Books, Princeton University Press, edition 1, number 9163.
    48. Daniel J. Lewis & Karel Mertens & James H. Stock & Mihir Trivedi, 2022. "Measuring real activity using a weekly economic index," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(4), pages 667-687, June.
    49. Asger Lau Andersen & Emil Toft Hansen & Niels Johannesen & Adam Sheridan, 2022. "Consumer responses to the COVID‐19 crisis: evidence from bank account transaction data," Scandinavian Journal of Economics, Wiley Blackwell, vol. 124(4), pages 905-929, October.
    50. Konstantinos Efstathiou & Thomas Y. Mathä & Cindy Veiga & Ladislav Wintr, 2018. "Short-time work in Luxembourg: evidence from a firm survey," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 52(1), pages 1-20, December.
    51. Mao, Huina & Counts, Scott & Bollen, Johan, 2015. "Quantifying the effects of online bullishness on international financial markets," Statistics Paper Series 9, European Central Bank.
    52. Landais, Camille & Giupponi, Giulia, 2018. "Subsidizing Labor Hoarding in Recessions: The Employment & Welfare Effects of Short Time Work," CEPR Discussion Papers 13310, C.E.P.R. Discussion Papers.
    53. Andres Algaba & David Ardia & Keven Bluteau & Samuel Borms & Kris Boudt, 2020. "Econometrics Meets Sentiment: An Overview Of Methodology And Applications," Journal of Economic Surveys, Wiley Blackwell, vol. 34(3), pages 512-547, July.
    54. D’Amuri, Francesco & Marcucci, Juri, 2017. "The predictive power of Google searches in forecasting US unemployment," International Journal of Forecasting, Elsevier, vol. 33(4), pages 801-816.
    55. Gary Koop & Luca Onorante, 2019. "Macroeconomic Nowcasting Using Google Probabilities☆," Advances in Econometrics, in: Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part A, volume 40, pages 17-40, Emerald Group Publishing Limited.
    56. Coleman, Winnie & Nautz, Dieter, 2020. "The credibility of the ECB's inflation target in times of Corona: New evidence from an online survey," Discussion Papers 2020/11, Free University Berlin, School of Business & Economics.
    57. Zhi Da & Joseph Engelberg & Pengjie Gao, 2015. "Editor's Choice The Sum of All FEARS Investor Sentiment and Asset Prices," The Review of Financial Studies, Society for Financial Studies, vol. 28(1), pages 1-32.
    58. Fantazzini, Dean & Toktamysova, Zhamal, 2015. "Forecasting German car sales using Google data and multivariate models," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 97-135.
    59. repec:hal:spmain:info:hdl:2441/6596a4s9af8lt872jnqvm5jg73 is not listed on IDEAS
    60. Sebastian Doerr & Leonardo Gambacorta, 2020. "Covid-19 and regional employment in Europe," BIS Bulletins 16, Bank for International Settlements.
    61. Müller, Henrik & Hornig, Nico, 2020. "Expecting the Unexpected: A new Uncertainty Perception Indicator (UPI) – concept and first results," DoCMA Working Papers 1-2020, TU Dortmund University, Dortmund Center for Data-based Media Analysis (DoCMA).
    62. Carvalho, V & Garcia, Juan R. & Hansen, S. & Ortiz, A. & Rodrigo, T. & More, J. V. R., 2020. "Tracking the COVID-19 Crisis with High-Resolution Transaction Data," Cambridge Working Papers in Economics 2030, Faculty of Economics, University of Cambridge.
    63. Caperna, Giulio & Colagrossi, Marco & Geraci, Andrea & Mazzarella, Gianluca, 2020. "Googling Unemployment During the Pandemic: Inference and Nowcast Using Search Data," Working Papers 2020-04, Joint Research Centre, European Commission.
    64. Bruno Carvalho & Susana Peralta & Joao Pereira dos Santos, 2020. "What and how did people buy during the Great Lockdown? Evidence from electronic payments," Working Papers ECARES 2020-20, ULB -- Universite Libre de Bruxelles.
    65. Jeremy Ginsberg & Matthew H. Mohebbi & Rajan S. Patel & Lynnette Brammer & Mark S. Smolinski & Larry Brilliant, 2009. "Detecting influenza epidemics using search engine query data," Nature, Nature, vol. 457(7232), pages 1012-1014, February.
    66. Maximo Camacho & Matías José Pacce, 2018. "Forecasting travellers in Spain with Google’s search volume indices," Tourism Economics, , vol. 24(4), pages 434-448, June.
    67. Goolsbee, Austan & Syverson, Chad, 2021. "Fear, lockdown, and diversion: Comparing drivers of pandemic economic decline 2020," Journal of Public Economics, Elsevier, vol. 193(C).
    68. Boeri, Tito & Jimeno, Juan F., 2016. "Learning from the Great Divergence in unemployment in Europe during the crisis," Labour Economics, Elsevier, vol. 41(C), pages 32-46.
    69. repec:iab:iabjlr:v:52:i:1:p:art.14 is not listed on IDEAS
    70. Casalis, André & Krustev, Georgi, 2020. "Consumption of durable goods in the euro area," Economic Bulletin Articles, European Central Bank, vol. 5.
    71. Guzman, Giselle C., 2011. "The case for higher frequency inflation expectations," MPRA Paper 36656, University Library of Munich, Germany.
    72. Naccarato, Alessia & Falorsi, Stefano & Loriga, Silvia & Pierini, Andrea, 2018. "Combining official and Google Trends data to forecast the Italian youth unemployment rate," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 114-122.
    73. Jun, Seung-Pyo & Yoo, Hyoung Sun & Choi, San, 2018. "Ten years of research change using Google Trends: From the perspective of big data utilizations and applications," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 69-87.
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    More about this item

    Keywords

    COVID-19; Economic sentiment; Employment; Consumption; Google Trends;
    All these keywords.

    JEL classification:

    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • J60 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - General

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