IDEAS home Printed from https://ideas.repec.org/a/gam/jadmsc/v13y2023i11p229-d1267156.html
   My bibliography  Save this article

The Impact of the COVID-19 Pandemic on the Use of the Menor Preço Brasil Application

Author

Listed:
  • Jorge Luis Tonetto

    (Business School, Pontifical Catholic University of Rio Grande do Sul, Porto Alegre 90619-900, Brazil)

  • Adelar Fochezatto

    (Business School, Pontifical Catholic University of Rio Grande do Sul, Porto Alegre 90619-900, Brazil)

  • Josep Miquel Pique

    (La Salle, Universitat Ramon Llull, 08022 Barcelona, Spain)

Abstract

The Menor Preço Brasil application, based on a version developed in the state of Rio Grande do Sul, was launched in 2019 with the aim of expanding digital services to citizens. This application provides information on the nearest establishments and their product prices based on issued invoices. With the advent of the COVID-19 pandemic, this application adapted its service to facilitate access to prevention products. We are not aware of any other similar government application that uses individual invoice data to support citizens in finding products closer to them at better prices. This study aims to verify the impact of the COVID-19 pandemic on the use of the Menor Preço Brasil app service. To this end, it investigates both the correlation between confirmed cases of COVID-19 in Brazil and the changes in the application’s functionalities with the variation in citizens’ queries to the application. It is a quantitative approach. For this purpose, Bai and Perron’s method of identifying multiple structural breaks and regression models are employed. The results indicate five structural breaks in the number of queries to the application, and that a 1% increase in COVID-19 cases led to a 0.2% increase in queries. These results confirm that user behavior related to the Menor Preço Brasil application was influenced not only by changes in the number of confirmed COVID-19 cases but also by those in the app’s features and inflation rates. The literature also tends to consider the relevance of the relative effects of risk aversion on behavior, especially in the relationship with the tax authorities. This study reinforces the position of the initial relevance of risk aversion and when trust gradually strengthening the aversion to losses diminishes. The public sector has progressively increased the availability of digital services over time, and the results of this study underscore their significance in coping with extreme situations, such as pandemics, natural disasters, and other challenges to accessing goods and services.

Suggested Citation

  • Jorge Luis Tonetto & Adelar Fochezatto & Josep Miquel Pique, 2023. "The Impact of the COVID-19 Pandemic on the Use of the Menor Preço Brasil Application," Administrative Sciences, MDPI, vol. 13(11), pages 1-19, October.
    • Handle: RePEc:gam:jadmsc:v:13:y:2023:i:11:p:229-:d:1267156
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2076-3387/13/11/229/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2076-3387/13/11/229/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jushan Bai & Pierre Perron, 1998. "Estimating and Testing Linear Models with Multiple Structural Changes," Econometrica, Econometric Society, vol. 66(1), pages 47-78, January.
    2. Mariana Mazzucato & Rainer Kattel, 2020. "COVID-19 and public-sector capacity," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 36(Supplemen), pages 256-269.
    3. Clements, Michael P & Hendry, David F, 1996. "Intercept Corrections and Structural Change," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(5), pages 475-494, Sept.-Oct.
    Full references (including those not matched with items on IDEAS)

    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. Aaron Smith, 2005. "Forecasting in the presence of level shifts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(8), pages 557-574.
    2. Rossi, Barbara, 2013. "Advances in Forecasting under Instability," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1203-1324, Elsevier.
    3. Jan J. J. Groen & George Kapetanios & Simon Price, 2013. "Multivariate Methods For Monitoring Structural Change," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(2), pages 250-274, March.
    4. Huang, Tao & Fildes, Robert & Soopramanien, Didier, 2019. "Forecasting retailer product sales in the presence of structural change," European Journal of Operational Research, Elsevier, vol. 279(2), pages 459-470.
    5. Todd E. Clark & Michael W. McCracken, 2006. "Forecasting of small macroeconomic VARs in the presence of instabilities," Research Working Paper RWP 06-09, Federal Reserve Bank of Kansas City.
    6. Ericsson, Neil R., 2017. "How biased are U.S. government forecasts of the federal debt?," International Journal of Forecasting, Elsevier, vol. 33(2), pages 543-559.
    7. Giraitis, Liudas & Kapetanios, George & Price, Simon, 2013. "Adaptive forecasting in the presence of recent and ongoing structural change," Journal of Econometrics, Elsevier, vol. 177(2), pages 153-170.
    8. Inoue, Atsushi & Jin, Lu & Rossi, Barbara, 2017. "Rolling window selection for out-of-sample forecasting with time-varying parameters," Journal of Econometrics, Elsevier, vol. 196(1), pages 55-67.
    9. Ericsson, Neil R., 2017. "Economic forecasting in theory and practice: An interview with David F. Hendry," International Journal of Forecasting, Elsevier, vol. 33(2), pages 523-542.
    10. Dellas, Harris & Gibson, Heather D. & Hall, Stephen G. & Tavlas, George S., 2018. "The macroeconomic and fiscal implications of inflation forecast errors," Journal of Economic Dynamics and Control, Elsevier, vol. 93(C), pages 203-217.
    11. Brian Goff, 2005. "Supreme Court consensus and dissent: Estimating the role of the selection screen," Public Choice, Springer, vol. 122(3), pages 483-499, March.
    12. Brian Goff, 2006. "Supreme Court consensus and dissent: Estimating the role of the selection screen," Public Choice, Springer, vol. 127(3), pages 367-383, June.
    13. David Hendry & Michael P. Clements, 2010. "Forecasting from Mis-specified Models in the Presence of Unanticipated Location Shifts," Economics Series Working Papers 484, University of Oxford, Department of Economics.
    14. Banerjee, Anindya & Urga, Giovanni, 2005. "Modelling structural breaks, long memory and stock market volatility: an overview," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 1-34.
    15. Akosah, Nana Kwame & Alagidede, Imhotep Paul & Schaling, Eric, 2020. "Testing for asymmetry in monetary policy rule for small-open developing economies: Multiscale Bayesian quantile evidence from Ghana," The Journal of Economic Asymmetries, Elsevier, vol. 22(C).
    16. Kelly Burns & Imad Moosa, 2017. "Demystifying the Meese–Rogoff puzzle: structural breaks or measures of forecasting accuracy?," Applied Economics, Taylor & Francis Journals, vol. 49(48), pages 4897-4910, October.
    17. Matteo Mogliani, 2010. "Residual-based tests for cointegration and multiple deterministic structural breaks: A Monte Carlo study," Working Papers halshs-00564897, HAL.
    18. J. Cuñado & L. Gil-Alana & F. Gracia, 2009. "US stock market volatility persistence: evidence before and after the burst of the IT bubble," Review of Quantitative Finance and Accounting, Springer, vol. 33(3), pages 233-252, October.
    19. Vincent Dekker & Karsten Schweikert, 2021. "A Comparison of Different Data-driven Procedures to Determine the Bunching Window," Public Finance Review, , vol. 49(2), pages 262-293, March.
    20. Kar, Sabyasachi & Pritchett, Lant & Raihan, Selim & Sen, Kunal, 2013. "Looking for a break: Identifying transitions in growth regimes," Journal of Macroeconomics, Elsevier, vol. 38(PB), pages 151-166.

    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:gam:jadmsc:v:13:y:2023:i:11:p:229-:d:1267156. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.