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Can unemployment forecasts based on Google Trends help government design better policies? An investigation based on Spain and Portugal

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  • Simionescu, Mihaela
  • Cifuentes-Faura, Javier
Abstract
The Covid-19 pandemic has increased the unemployment issue and accelerated the digital transformation. Real-time data specific to ongoing revolution in applied economic analysis are increasingly demanded to anticipate changes in unemployment to improve decision-making. The aim of this paper is to test whether unemployment rate forecasts based on Google Trends data improve the predictions based only on macroeconomic indicators published with a longer time lag. The research has been carried out at the national level for Spain and Portugal, and the main novelty is the analysis of unemployment rate forecasts at the regional level for Spain using dynamic panel data models to implement the best policies to reduce unemployment. The keywords unemployment and job offers have been used in each language. The results obtained demonstrate the capacity of Google Trends data associated with unemployment to improve the predictions of unemployment rates in Spanish regions. Moreover, predictions based on Google Trends data at national level in Spain and Portugal are significantly more accurate than those based on autoregressive models for both countries.

Suggested Citation

  • Simionescu, Mihaela & Cifuentes-Faura, Javier, 2022. "Can unemployment forecasts based on Google Trends help government design better policies? An investigation based on Spain and Portugal," Journal of Policy Modeling, Elsevier, vol. 44(1), pages 1-21.
    • Handle: RePEc:eee:jpolmo:v:44:y:2022:i:1:p:1-21
    DOI: 10.1016/j.jpolmod.2021.09.011
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    as
    1. Fondeur, Y. & Karamé, F., 2013. "Can Google data help predict French youth unemployment?," Economic Modelling, Elsevier, vol. 30(C), pages 117-125.
    2. 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.
    3. Zhi Su, 2014. "Chinese Online Unemployment-Related Searches and Macroeconomic Indicators," Frontiers of Economics in China-Selected Publications from Chinese Universities, Higher Education Press, vol. 9(4), pages 573-605, December.
    4. Wieland, Volker & Wolters, Maik, 2013. "Forecasting and Policy Making," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 239-325, Elsevier.
    5. 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.
    6. Andrea Brandolini & Piero Cipollone & Eliana Viviano, 2006. "Does The Ilo Definition Capture All Unemployment?," Journal of the European Economic Association, MIT Press, vol. 4(1), pages 153-179, March.
    7. Vera Eichenauer & Ronald Indergand & Isabel Z. Martínez & Christoph Sax, 2020. "Constructing Daily Economic Sentiment Indices Based on Google Trends," KOF Working papers 20-484, KOF Swiss Economic Institute, ETH Zurich.
    8. Meltem Gulenay Chadwick & Gonul Sengul, 2015. "Nowcasting the Unemployment Rate in Turkey : Let's ask Google," Central Bank Review, Research and Monetary Policy Department, Central Bank of the Republic of Turkey, vol. 15(3), pages 15-40.
    9. Rodrigo Mulero & Alfredo García-Hiernaux, 2021. "Forecasting Spanish unemployment with Google Trends and dimension reduction techniques," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 12(3), pages 329-349, September.
    10. Hakan Yilmazkuday, 2020. "Unequal unemployment effects of COVID-19 and monetary policy across U.S. States," Journal of Behavioral Economics for Policy, Society for the Advancement of Behavioral Economics (SABE), vol. 4(S3), pages 45-53, December.
    11. repec:eee:labchp:v:1:y:1986:i:c:p:641-692 is not listed on IDEAS
    12. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    13. Fabio Milani, 2021. "COVID-19 outbreak, social response, and early economic effects: a global VAR analysis of cross-country interdependencies," Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(1), pages 223-252, January.
    14. Caperna, Giulio & Colagrossi, Marco & Geraci, Andrea & Mazzarella, Gianluca, 2020. "Googling Unemployment During the Pandemic: Inference and Nowcast Using Search Data," JRC Working Papers in Economics and Finance 2020-04, Joint Research Centre, European Commission.
    15. Smeekes, Stephan & Wijler, Etienne, 2021. "An automated approach towards sparse single-equation cointegration modelling," Journal of Econometrics, Elsevier, vol. 221(1), pages 247-276.
    16. Nagao, Shintaro & Takeda, Fumiko & Tanaka, Riku, 2019. "Nowcasting of the U.S. unemployment rate using Google Trends," Finance Research Letters, Elsevier, vol. 30(C), pages 103-109.
    17. Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
    18. 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.
    19. Mihaela Simionescu & Dalia Streimikiene & Wadim Strielkowski, 2020. "What Does Google Trends Tell Us about the Impact of Brexit on the Unemployment Rate in the UK?," Sustainability, MDPI, vol. 12(3), pages 1-10, January.
    20. Nikolaos Dritsakis & Paraskevi Klazoglou, 2018. "Forecasting Unemployment Rates in USA using Box-Jenkins Methodology," International Journal of Economics and Financial Issues, Econjournals, vol. 8(1), pages 9-20.
    21. repec:eee:labchp:v:1:y:1986:i:c:p:525-602 is not listed on IDEAS
    22. Benedikt Maas, 2020. "Short‐term forecasting of the US unemployment rate," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(3), pages 394-411, April.
    23. 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).
    24. Nuno Barreira & Pedro Godinho & Paulo Melo, 2013. "Nowcasting unemployment rate and new car sales in south-western Europe with Google Trends," Netnomics, Springer, vol. 14(3), pages 129-165, November.
    25. Paul Smith, 2016. "Google's MIDAS Touch: Predicting UK Unemployment with Internet Search Data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(3), pages 263-284, April.
    26. Granger, Clive W.J. & Machina, Mark J., 2006. "Forecasting and Decision Theory," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 2, pages 81-98, Elsevier.
    27. Vicente, María Rosalía & López-Menéndez, Ana J. & Pérez, Rigoberto, 2015. "Forecasting unemployment with internet search data: Does it help to improve predictions when job destruction is skyrocketing?," Technological Forecasting and Social Change, Elsevier, vol. 92(C), pages 132-139.
    28. Fajar, Muhammad & Prasetyo, Octavia Rizky & Nonalisa, Septiarida & Wahyudi, Wahyudi, 2020. "Forecasting unemployment rate in the time of COVID-19 pandemic using Google trends data (case of Indonesia)," MPRA Paper 105042, University Library of Munich, Germany, revised 30 Nov 2020.
    29. Dingdong Yi & Shaoyang Ning & Chia-Jung Chang & S. C. Kou, 2021. "Forecasting Unemployment Using Internet Search Data via PRISM," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 1662-1673, October.
    30. 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.
    31. 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.
    32. Simionescu, Mihaela & Zimmermann, Klaus F., 2017. "Big Data and Unemployment Analysis," GLO Discussion Paper Series 81, Global Labor Organization (GLO).
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