IDEAS home Printed from https://ideas.repec.org/a/spr/soinre/v164y2022i3d10.1007_s11205-022-02984-9.html
   My bibliography  Save this article

Forecasting National and Regional Youth Unemployment in Spain Using Google Trends

Author

Listed:
  • Mihaela Simionescu

    (Institute for Economic Forecasting of the Romanian Academy)

  • Javier Cifuentes-Faura

    (University of Murcia)

Abstract

In Spain, the youth unemployment rate is one of the highest in the European Union. With the pandemic caused by Covid-19, young people face high unemployment rates and are more vulnerable to a decrease in labour demand. This paper analyses and predicts youth unemployment using Google Trends indices in Spain for the period between the first quarter of 2004 and the second quarter of 2021, being the first work to carry out this study for Spain and the first to use the regional approach for the country. Vector autoregressive Bayesian models and vector error correction models have been used for national data, and Bayesian panel data models and fixed effects model for regional data. The results confirm that forecasts based on Google Trends data are more accurate in predicting the youth unemployment rate.

Suggested Citation

  • Mihaela Simionescu & Javier Cifuentes-Faura, 2022. "Forecasting National and Regional Youth Unemployment in Spain Using Google Trends," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 164(3), pages 1187-1216, December.
    • Handle: RePEc:spr:soinre:v:164:y:2022:i:3:d:10.1007_s11205-022-02984-9
    DOI: 10.1007/s11205-022-02984-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11205-022-02984-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11205-022-02984-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. Guglielmo Maria Caporale & Luis Gil-alana, 2014. "Youth Unemployment in Europe: Persistence and Macroeconomic Determinants," Comparative Economic Studies, Palgrave Macmillan;Association for Comparative Economic Studies, vol. 56(4), pages 581-591, December.
    3. Mousteri, Victoria & Daly, Michael & Delaney, Liam, 2020. "Underemployment and psychological distress: Propensity score and fixed effects estimates from two large UK samples," Social Science & Medicine, Elsevier, vol. 244(C).
    4. 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.
    5. Fondeur, Y. & Karamé, F., 2013. "Can Google data help predict French youth unemployment?," Economic Modelling, Elsevier, vol. 30(C), pages 117-125.
    6. 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.
    7. Giovanni Luca & Paolo Mazzocchi & Claudio Quintano & Antonella Rocca, 2020. "Going Behind the High Rates of NEETs in Italy and Spain: The Role of Early School Leavers," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 151(1), pages 345-363, August.
    8. repec:ilo:ilowps:488891 is not listed on IDEAS
    9. Laura Liu & Hyungsik Roger Moon & Frank Schorfheide, 2020. "Forecasting With Dynamic Panel Data Models," Econometrica, Econometric Society, vol. 88(1), pages 171-201, January.
    10. 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.
    11. James Forder, 2021. "Nine Historical Views Of The Phillips Curve: Eight Authentic And One Inauthentic," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 66(05), pages 1125-1140, September.
    12. 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.
    13. Marios Michaelides & Peter Mueser & Jeffrey Smith, 2019. "Youth Unemployment and U.S. Job Search Assistance Policy during the Great Recession," University of Cyprus Working Papers in Economics 13-2019, University of Cyprus Department of Economics.
    14. M. Hashem Pesaran & Aman Ullah & Takashi Yamagata, 2008. "A bias-adjusted LM test of error cross-section independence," Econometrics Journal, Royal Economic Society, vol. 11(1), pages 105-127, March.
    15. Jale Tosun & Oliver Treib & Fabrizio De Francesco, 2019. "The impact of the European Youth Guarantee on active labour market policies: A convergence analysis," International Journal of Social Welfare, John Wiley & Sons, vol. 28(4), pages 358-368, October.
    16. Faik Bilgili & Ilhan Ozturk & Emrah Kocak & Umit Bulut, 2017. "Energy Consumption-Youth Unemployment Nexus in Europe: Evidence from Panel Cointegration and Panel Causality Analyses," International Journal of Energy Economics and Policy, Econjournals, vol. 7(2), pages 193-201.
    17. Verónica Escudero & Elva López Mourelo, 2018. "‪La Garantie européenne pour la jeunesse‪. Bilan systématique des mises en œuvre dans les pays membres," Travail et Emploi, La DARES, vol. 0(1), pages 89-122.
    18. 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.
    19. Leopoldo Cabrera & Gustavo A. Marrero & Juan Gabriel Rodríguez & Pedro Salas-Rojo, 2021. "Inequality of Opportunity in Spain: New Insights from New Data," Hacienda Pública Española / Review of Public Economics, IEF, vol. 237(2), pages 153-185, June.
    20. Maryam Dilmaghani, 2019. "Workopolis or The Pirate Bay: what does Google Trends say about the unemployment rate?," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 46(2), pages 422-445, March.
    21. David N. F. Bell & David G. Blanchflower, 2011. "Young people and the Great Recession," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 27(2), pages 241-267.
    22. 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.
    23. 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.
    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. Mustafa Yurtsever, 2023. "Unemployment rate forecasting: LSTM-GRU hybrid approach," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 57(1), pages 1-9, December.
    2. Ted CT Fong & Paul SF Yip, 2023. "Prevalence of hikikomori and associations with suicidal ideation, suicide stigma, and help-seeking among 2,022 young adults in Hong Kong," International Journal of Social Psychiatry, , vol. 69(7), pages 1768-1780, November.

    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. 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.
    2. Mihaela, Simionescu, 2020. "Improving unemployment rate forecasts at regional level in Romania using Google Trends," Technological Forecasting and Social Change, Elsevier, vol. 155(C).
    3. Rodrigo Mulero & Alfredo Garcia-Hiernaux, 2023. "Forecasting unemployment with Google Trends: age, gender and digital divide," Empirical Economics, Springer, vol. 65(2), pages 587-605, August.
    4. 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.
    5. Simionescu, Mihaela & Raišienė, Agota Giedrė, 2021. "A bridge between sentiment indicators: What does Google Trends tell us about COVID-19 pandemic and employment expectations in the EU new member states?," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    6. Tuhkuri, Joonas, 2016. "Forecasting Unemployment with Google Searches," ETLA Working Papers 35, The Research Institute of the Finnish Economy.
    7. Simionescu, Mihaela & Zimmermann, Klaus F., 2017. "Big Data and Unemployment Analysis," GLO Discussion Paper Series 81, Global Labor Organization (GLO).
    8. Nakamura, Nobuyuki & Suzuki, Aya, 2021. "COVID-19 and the intentions to migrate from developing countries: Evidence from online search activities in Southeast Asia," Journal of Asian Economics, Elsevier, vol. 76(C).
    9. Alessia Naccarato & Andrea Pierini & Stefano Falorsi, 2015. "Using Google Trend Data To Predict The Italian Unemployment Rate," Departmental Working Papers of Economics - University 'Roma Tre' 0203, Department of Economics - University Roma Tre.
    10. 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.
    11. Tuhkuri, Joonas, 2016. "ETLAnow: A Model for Forecasting with Big Data – Forecasting Unemployment with Google Searches in Europe," ETLA Reports 54, The Research Institute of the Finnish Economy.
    12. Perroni, Carlo & Scharf, Kimberley & Talavera, Oleksandr & Vi, Linh, 2022. "Does online salience predict charitable giving? Evidence from SMS text donations," Journal of Economic Behavior & Organization, Elsevier, vol. 197(C), pages 134-149.
    13. Ruggero Cefalo & Rosario Scandurra & Yuri Kazepov, 2020. "Youth Labor Market Integration in European Regions," Sustainability, MDPI, vol. 12(9), pages 1-18, May.
    14. Zhongchen Song & Tom Coupé, 2023. "Predicting Chinese consumption series with Baidu," Journal of Chinese Economic and Business Studies, Taylor & Francis Journals, vol. 21(3), pages 429-463, July.
    15. 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.
    16. Perroni, Carlo & Scharf, Kimberley & Talavera, Oleksandr & Vi, Linh, 2021. "Online Salience and Charitable Giving : Evidence from SMS Donations," The Warwick Economics Research Paper Series (TWERPS) 1325, University of Warwick, Department of Economics.
    17. David Coble & Pablo Pincheira, 2021. "Forecasting building permits with Google Trends," Empirical Economics, Springer, vol. 61(6), pages 3315-3345, December.
    18. Voraprapa Nakavachara & Nuarpear Lekfuangfu, 2017. "Predicting the Present Revisited: The Case of Thailand," PIER Discussion Papers 70, Puey Ungphakorn Institute for Economic Research.
    19. Agnese Carella & Federica Ciocchetta & Valentina Michelangeli & Federico Maria Signoretti, 2020. "What can we learn about mortgage supply from online data?," Questioni di Economia e Finanza (Occasional Papers) 583, Bank of Italy, Economic Research and International Relations Area.
    20. 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).

    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:spr:soinre:v:164:y:2022:i:3:d:10.1007_s11205-022-02984-9. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.