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In Search of Information: Use of Google Trends’ Data to Narrow Information Gaps for Low-income Developing Countries

Author

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  • Mr. Futoshi Narita
  • Rujun Yin

Abstract

Timely data availability is a long-standing challenge in policy-making and analysis for low-income developing countries. This paper explores the use of Google Trends’ data to narrow such information gaps and finds that online search frequencies about a country significantly correlate with macroeconomic variables (e.g., real GDP, inflation, capital flows), conditional on other covariates. The correlation with real GDP is stronger than that of nighttime lights, whereas the opposite is found for emerging market economies. The search frequencies also improve out-of-sample forecasting performance albeit slightly, demonstrating their potential to facilitate timely assessments of economic conditions in low-income developing countries.

Suggested Citation

  • Mr. Futoshi Narita & Rujun Yin, 2018. "In Search of Information: Use of Google Trends’ Data to Narrow Information Gaps for Low-income Developing Countries," IMF Working Papers 2018/286, International Monetary Fund.
    • Handle: RePEc:imf:imfwpa:2018/286
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    Citations

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    Cited by:

    1. Juan Camilo Anzoátegui-Zapata & Juan Camilo Galvis-Ciro, 2020. "Disagreements in Consumer Inflation Expectations: Empirical Evidence for a Latin American Economy," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 16(2), pages 99-122, November.
    2. Marcelo C. Medeiros & Henrique F. Pires, 2021. "The Proper Use of Google Trends in Forecasting Models," Papers 2104.03065, arXiv.org, revised Apr 2021.
    3. Vera Z. Eichenauer & Ronald Indergand & Isabel Z. Martínez & Christoph Sax, 2022. "Obtaining consistent time series from Google Trends," Economic Inquiry, Western Economic Association International, vol. 60(2), pages 694-705, April.
    4. García-Herrero, Alicia & Schindowski, Robin, 2023. "Global trends in countries' perceptions of the Belt and Road Initiative," BOFIT Policy Briefs 10/2023, Bank of Finland Institute for Emerging Economies (BOFIT).
    5. 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.
    6. Aaron Adalja & Jūra Liaukonytė & Emily Wang & Xinrong Zhu, 2023. "GMO and Non-GMO Labeling Effects: Evidence from a Quasi-Natural Experiment," Marketing Science, INFORMS, vol. 42(2), pages 233-250, March.
    7. Cebrián, Eduardo & Domenech, Josep, 2024. "Addressing Google Trends inconsistencies," Technological Forecasting and Social Change, Elsevier, vol. 202(C).
    8. Matheus Pereira Libório & Petr Iakovlevitch Ekel & Carlos Augusto Paiva Martins, 2023. "Economic analysis through alternative data and big data techniques: what do they tell about Brazil?," SN Business & Economics, Springer, vol. 3(1), pages 1-16, January.
    9. 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).
    10. Serhan Cevik, 2022. "Where should we go? Internet searches and tourist arrivals," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 4048-4057, October.
    11. Emna Mnif & Anis Jarboui & M. Kabir Hassan & Khaireddine Mouakhar, 2020. "Big data tools for Islamic financial analysis," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 27(1), pages 10-21, January.
    12. Alessio Ciarlone, 2023. "Remittances in times of crisis: evidence from Italian corridors," Temi di discussione (Economic working papers) 1402, Bank of Italy, Economic Research and International Relations Area.
    13. Abay,Kibrom A. & Hirfrfot,Kibrom Tafere & Woldemichael,Andinet, 2020. "Winners and Losers from COVID-19 : Global Evidence from Google Search," Policy Research Working Paper Series 9268, The World Bank.
    14. Nicolas Woloszko, 2020. "Tracking activity in real time with Google Trends," OECD Economics Department Working Papers 1634, OECD Publishing.
    15. Lolić, Ivana & Matošec, Marina & Sorić, Petar, 2024. "DIY google trends indicators in social sciences: A methodological note," Technology in Society, Elsevier, vol. 77(C).
    16. Iva Glišic, 2024. "A comparison of using MIDAS and LSTM models for GDP nowcasting," Working Papers Bulletin 22, National Bank of Serbia.

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