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Economic analysis through alternative data and big data techniques: what do they tell about Brazil?

Author

Listed:
  • Matheus Pereira Libório

    (Pontifical Catholic University of Minas Gerais)

  • Petr Iakovlevitch Ekel

    (Pontifical Catholic University of Minas Gerais
    Federal University of Minas Gerais)

  • Carlos Augusto Paiva Martins

    (Pontifical Catholic University of Minas Gerais)

Abstract

Alternative data are now widely used in economic analyses worldwide but still infrequent in studies on the Brazilian economy. This research demonstrates how alternative data extracted from Google Trends and Google Mobility contribute to innovative economic analysis. First, it demonstrates that the search for the future on the internet is correlated (R = 0.62) with the average household income in Brazilian states. The three Brazilian states with the most people looking for the future on the internet have an average household income 1.6 times higher than people from states that do not have this behavior. The search for the future represents 10.9% of the economic development potential of the states, while the proportion of people with university degrees, scientific publications, and researchers represents another 60.4%. The reduction in mobility in retail/recreation locations averaged 34.28% in Brazil, Ecuador, Paraguay, and Uruguay. This group of countries had COVID-19 infection and death rates 1.25 and 1.74 times higher than in countries that reduced their mobility in retail/recreation locations by 45.03%. The impact of reduced mobility in retail/recreation locations on the unemployment rate, gross domestic product degrowth, and inflation in countries such as Brazil was 1.1, 2.2, and 2.6 times lower than in countries that reduced mobility more of people. The research contributions are associated with identifying new indicators extracted from alternative data and their application to carry out innovative economic analyses.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:snbeco:v:3:y:2023:i:1:d:10.1007_s43546-022-00387-z
    DOI: 10.1007/s43546-022-00387-z
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    as
    1. Sultan Ayoub Meo & Abeer A Al Masri & Adnan Mahmood Usmani & Almas Naeem Memon & Syed Ziauddin Zaidi, 2013. "Impact of GDP, Spending on R&D, Number of Universities and Scientific Journals on Research Publications among Asian Countries," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-8, June.
    2. Jesus Fernandez-Villaverde & Charles I. Jones, 2020. "Macroeconomic Outcomes and COVID-19: A Progress Report," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 51(3 (Fall)), pages 111-166.
    3. Amélie Charles & Olivier Darné & Fabien Tripier, 2018. "Uncertainty and the macroeconomy: evidence from an uncertainty composite indicator," Applied Economics, Taylor & Francis Journals, vol. 50(10), pages 1093-1107, February.
    4. Roberto Gallardo & Brian Whitacre & Indraneel Kumar & Sreedhar Upendram, 2021. "Broadband metrics and job productivity: a look at county-level data," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 66(1), pages 161-184, February.
    5. Leonardo S. Alaimo & Filomena Maggino, 2020. "Sustainable Development Goals Indicators at Territorial Level: Conceptual and Methodological Issues—The Italian Perspective," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 147(2), pages 383-419, January.
    6. Nicholas W. Papageorge & Matthew V. Zahn & Michèle Belot & Eline Broek-Altenburg & Syngjoo Choi & Julian C. Jamison & Egon Tripodi, 2021. "Socio-demographic factors associated with self-protecting behavior during the Covid-19 pandemic," Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(2), pages 691-738, April.
    7. Abel Brodeur & Idaliya Grigoryeva & Lamis Kattan, 2021. "Stay-at-home orders, social distancing, and trust," Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(4), pages 1321-1354, October.
    8. Auld, M. Christopher & Toxvaerd, Flavio, 2021. "The Great Covid-19 Vaccine Rollout: Behavioural And Policy Responses," National Institute Economic Review, National Institute of Economic and Social Research, vol. 257, pages 14-35, August.
    9. Qadan, Mahmoud & Nama, Hazar, 2018. "Investor sentiment and the price of oil," Energy Economics, Elsevier, vol. 69(C), pages 42-58.
    10. Marcelo S. Perlin & João F. Caldeira & André A. P. Santos & Martin Pontuschka, 2017. "Can We Predict the Financial Markets Based on Google's Search Queries?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(4), pages 454-467, July.
    11. 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.
    12. Corbi, Raphael & Picchetti, Pedro, 2020. "The cost of gendered attitudes on a female candidate: Evidence from Google Trends," Economics Letters, Elsevier, vol. 196(C).
    13. 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.
    14. 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.
    15. Chernozhukov, Victor & Kasahara, Hiroyuki & Schrimpf, Paul, 2021. "Causal impact of masks, policies, behavior on early covid-19 pandemic in the U.S," Journal of Econometrics, Elsevier, vol. 220(1), pages 23-62.
    16. Petr Ekel & Patrícia Bernardes & Gláucia Maria Vasconcellos Vale & Matheus Pereira Libório, 2022. "South American business environment cost index: reforms for Brazil," International Journal of Business Environment, Inderscience Enterprises Ltd, vol. 13(2), pages 212-233.
    17. Vlastakis, Nikolaos & Markellos, Raphael N., 2012. "Information demand and stock market volatility," Journal of Banking & Finance, Elsevier, vol. 36(6), pages 1808-1821.
    18. 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.
    19. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    20. Samira El Gibari & Trinidad Gómez & Francisco Ruiz, 2019. "Building composite indicators using multicriteria methods: a review," Journal of Business Economics, Springer, vol. 89(1), pages 1-24, February.
    21. Piccoli, Pedro & de Castro, Jessica, 2021. "Attention-return relation in the gold market and market states," Resources Policy, Elsevier, vol. 74(C).
    22. 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.
    23. Yan‐ran Ma & Qiang Ji & Jiaofeng Pan, 2019. "Oil financialization and volatility forecast: Evidence from multidimensional predictors," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(6), pages 564-581, September.
    24. Ding, Rong & Hou, Wenxuan, 2015. "Retail investor attention and stock liquidity," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 37(C), pages 12-26.
    25. José M. Pastor & Carlos Peraita & Lorenzo Serrano & Ángel Soler, 2018. "Higher education institutions, economic growth and GDP per capita in European Union countries," European Planning Studies, Taylor & Francis Journals, vol. 26(8), pages 1616-1637, August.
    26. Melissa S. Kearney & Phillip B. Levine, 2015. "Media Influences on Social Outcomes: The Impact of MTV's 16 and Pregnant on Teen Childbearing," American Economic Review, American Economic Association, vol. 105(12), pages 3597-3632, December.
    27. Amélie Charles & Olivier Darné & Fabien Tripier, 2018. "Uncertainty and the Macroeconomy: Evidence from an uncertainty composite indicator," Post-Print hal-01757042, HAL.
    28. Brett H. Day, 2020. "The Value of Greenspace Under Pandemic Lockdown," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 76(4), pages 1161-1185, August.
    29. Matteo Mazziotta & Adriano Pareto, 2016. "On a Generalized Non-compensatory Composite Index for Measuring Socio-economic Phenomena," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 127(3), pages 983-1003, July.
    30. Henry Kaiser, 1974. "An index of factorial simplicity," Psychometrika, Springer;The Psychometric Society, vol. 39(1), pages 31-36, March.
    31. Blazquez, Desamparados & Domenech, Josep, 2018. "Big Data sources and methods for social and economic analyses," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 99-113.
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    More about this item

    Keywords

    Alternative data; Google Trends; Google Mobility; Big data; Economic analysis;
    All these keywords.

    JEL classification:

    • A12 - General Economics and Teaching - - General Economics - - - Relation of Economics to Other Disciplines
    • C00 - Mathematical and Quantitative Methods - - General - - - General
    • E01 - Macroeconomics and Monetary Economics - - General - - - Measurement and Data on National Income and Product Accounts and Wealth; Environmental Accounts
    • J1 - Labor and Demographic Economics - - Demographic Economics

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