IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/115681.html

Высокочастотные Данные, Характеризующие Розничную Торговлю: Интересы Государства, Предприятий И Научных Организаций
[High-frequency retail data: the interests of the state, enterprises and scientific organizations]

Author

Listed:
  • Timiryanova, Venera

Abstract

Currently, there is a rapid development of technologies for collecting and analyzing big data, including those characterizing trade. This data, with a high degree of detail, takes into account the whole variety of consumer decisions, which allows to develop key management proposals on what, where and when to produce and sell. Banks, retail chains, and the state are actively interested in these data. At the same time, there is a weak use of big data in the activities of individual small and medium-sized enterprises. The purpose of this study is to highlight the problems and prospects for their application for management purposes, based on an analysis of the current practice of using high-frequency retail data. As a result of the study, the features of the available data of retail companies, payment systems and OFDs, which are manifested in their different structure and limitations for use. It is shown that big data characterizing retail trade is available to a narrow circle of people who, as a rule, have their own interests, which are not yet consistent with the idea of open publication of these data, even for scientific purposes. There are very few research publications based on high-frequency fiscal data. Сloseness of data determines the weak use of microdata for management purposes.

Suggested Citation

  • Timiryanova, Venera, 2022. "Высокочастотные Данные, Характеризующие Розничную Торговлю: Интересы Государства, Предприятий И Научных Организаций [High-frequency retail data: the interests of the state, enterprises and scientific organizations]," MPRA Paper 115681, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:115681
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/115681/2/MPRA_paper_115681.pdf
    File Function: original version
    Download Restriction: no
    File URL: https://mpra.ub.uni-muenchen.de/117540/2/MPRA_paper_115681.pdf
    File Function: revised version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Peter Casey & Patricio Castro, 2015. "Electronic Fiscal Devices (EFDs) An Empirical Study of their Impact on Taxpayer Compliance and Administrative Efficiency," IMF Working Papers 2015/073, International Monetary Fund.
    2. Trivedi, Minakshi, 2011. "Regional and Categorical Patterns in Consumer Behavior: Revealing Trends," Journal of Retailing, Elsevier, vol. 87(1), pages 18-30.
    3. Scott R. Baker & Robert A Farrokhnia & Steffen Meyer & Michaela Pagel & Constantine Yannelis, 2023. "Income, Liquidity, and the Consumption Response to the 2020 Economic Stimulus Payments," Review of Finance, European Finance Association, vol. 27(6), pages 2271-2304.
    4. Li‐Chun Zhang, 2021. "Proxy expenditure weights for Consumer Price Index: Audit sampling inference for big‐data statistics," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(2), pages 571-588, April.
    5. Martin O'Connell & Pierre Dubois & Rachel Griffith, 2022. "The Use of Scanner Data for Economics Research," Annual Review of Economics, Annual Reviews, vol. 14(1), pages 723-745, August.
    6. Angelov, Nikolay & Waldenström, Daniel, 2021. "The Impact of COVID-19 on Economic Activity: Evidence from Administrative Tax Registers," Working Paper Series 1397, Research Institute of Industrial Economics, revised 24 Apr 2023.
    7. Aditya Aladangady & Shifrah Aron-Dine & Wendy Dunn & Laura Feiveson & Paul Lengermann & Claudia Sahm, 2021. "From Transaction Data to Economic Statistics: Constructing Real-Time, High-Frequency, Geographic Measures of Consumer Spending," NBER Chapters, in: Big Data for Twenty-First-Century Economic Statistics, pages 115-145, National Bureau of Economic Research, Inc.
    8. Gábor Lovics & Katalin Szõke & Csaba G. Tóth & Bálint Ván, 2019. "The Effect of the Introduction of Online Cash Registers on Reported Turnover in Hungary," MNB Occasional Papers 2019/137, Magyar Nemzeti Bank (Central Bank of Hungary).
    9. Chacaltana Janampa, Juan. & Leung, Vicky. & Lee, Miso., 2018. "New technologies and the transition to formality the trend towards e–formality," ILO Working Papers 994998792502676, International Labour Organization.
    10. Asger Lau Andersen & Emil Toft Hansen & Niels Johannesen & Adam Sheridan, 2022. "Consumer responses to the COVID‐19 crisis: evidence from bank account transaction data," Scandinavian Journal of Economics, Wiley Blackwell, vol. 124(4), pages 905-929, October.
    11. David Bounie & Youssouf Camara & John Galbraith, 2020. "Consumers’ Mobility, Expenditure and Online-Offline Substitution Response to COVID-19: Evidence from French Transaction Data," Cahiers de recherche 14-2020, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    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. Martin O'Connell & Áureo de Paula & Kate Smith, 2021. "Preparing for a pandemic: spending dynamics and panic buying during the COVID‐19 first wave," Fiscal Studies, John Wiley & Sons, vol. 42(2), pages 249-264, June.
    2. Horvath, Akos & Kay, Benjamin & Wix, Carlo, 2023. "The COVID-19 shock and consumer credit: Evidence from credit card data," Journal of Banking & Finance, Elsevier, vol. 152(C).
    3. Hackethal, Andreas & Weber, Annika, 2020. "Fiscal policies and household consumption during the COVID-19 pandemic: A review of early evidence," SAFE White Paper Series 76, Leibniz Institute for Financial Research SAFE.
    4. Carvalho, V & Garcia, Juan R. & Hansen, S. & Ortiz, A. & Rodrigo, T. & More, J. V. R., 2020. "Tracking the COVID-19 Crisis with High-Resolution Transaction Data," Cambridge Working Papers in Economics 2030, Faculty of Economics, University of Cambridge.
    5. John Gathergood & Fabian Gunzinger & Benedict Guttman-Kenney & Edika Quispe-Torreblanca & Neil Stewart, 2020. "Levelling Down and the COVID-19 Lockdowns: Uneven Regional Recovery in UK Consumer Spending," Papers 2012.09336, arXiv.org, revised Dec 2020.
    6. Xiong, Yanyan & Cui, Xue & Yu, Liuming, 2024. "Impact of COVID-19 pandemic on online consumption share: Evidence from China's mobile payment data," Journal of Retailing and Consumer Services, Elsevier, vol. 81(C).
    7. Hacıoğlu-Hoke, Sinem & Känzig, Diego R. & Surico, Paolo, 2021. "The distributional impact of the pandemic," European Economic Review, Elsevier, vol. 134(C).
    8. Ali B. Barlas & Seda Guler Mert & Berk Orkun Isa & Alvaro Ortiz & Tomasa Rodrigo & Baris Soybilgen & Ege Yazgan, 2021. "Big Data Information and Nowcasting: Consumption and Investment from Bank Transactions in Turkey," Papers 2107.03299, arXiv.org.
    9. Maximiliano Gómez Aguirre & Ariel David Krysa, 2023. "Consumer Loans Dynamics in 2020 in Argentina: An Approach Using Error Correction Models," Ensayos Económicos, Central Bank of Argentina, Economic Research Department, vol. 1(81), pages 111-158, May.
    10. Scott R. Baker & Robert A Farrokhnia & Steffen Meyer & Michaela Pagel & Constantine Yannelis, 2023. "Income, Liquidity, and the Consumption Response to the 2020 Economic Stimulus Payments," Review of Finance, European Finance Association, vol. 27(6), pages 2271-2304.
    11. Akos Horvath & Benjamin S. Kay & Carlo Wix, 2021. "The COVID-19 Shock and Consumer Credit: Evidence from Credit Card Data," Finance and Economics Discussion Series 2021-008, Board of Governors of the Federal Reserve System (U.S.).
    12. Kanat Abdulla & Balzhan Serikbayeva, 2022. "Adoption of fiscal devices and tax compliance. New evidence from Kazakhstan," Economics Bulletin, AccessEcon, vol. 42(2), pages 757-770.
    13. Donato Masciandaro, 2020. "Ecb Helicopter Money: Economic And Political Economy Arithmetics," BAFFI CAREFIN Working Papers 20138, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    14. Bruno P. Carvalho & Susana Peralta & João Pereira dos Santos, 2022. "Regional and sectorial impacts of the Covid‐19 crisis: Evidence from electronic payments," Journal of Regional Science, Wiley Blackwell, vol. 62(3), pages 757-798, June.
    15. Martin Brown & Matthias R. Fengler & Jonas Huwyler & Winfried Koeniger & Rafael Lalive & Robert Rohrkemper, 2023. "Monitoring consumption Switzerland: data, background, and use cases," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-16, December.
    16. Anson T. Y. Ho & Lealand Morin & Harry J. Paarsch & Kim P. Huynh, 2022. "Consumer credit usage in Canada during the coronavirus pandemic," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 55(S1), pages 88-114, February.
    17. Hakan Yilmazkuday, 2021. "Changes in Consumption in the Early COVID-19 Era: Zip-Code Level Evidence from the U.S," JRFM, MDPI, vol. 14(10), pages 1-10, October.
    18. Anete Brinke & Ludmila Fadejeva & Boriss Siliverstovs & Kārlis Vilerts, 2023. "Assessing the informational content of card transactions for nowcasting retail trade: Evidence for Latvia," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(3), pages 566-577, April.
    19. Ali Batuhan Barlas & Seda Guler Mert & Berk Orkun Isa & Alvaro Ortiz & Tomasa Rodrigo & Baris Soybilgen & Ege Yazgan, 2021. "Turquía | Big Data y Nowcasting: consumo e inversión de transacciones bancarias [Turkey | Big Data and Nowcasting: Consumption and Investment from Bank Transactions]," Working Papers 21/07, BBVA Bank, Economic Research Department.
    20. Christelis, Dimitris & Georgarakos, Dimitris & Jappelli, Tullio & Kenny, Geoff, 2021. "How has the COVID-19 crisis affected different households’ consumption in the euro area?," Research Bulletin, European Central Bank, vol. 84.

    More about this item

    Keywords

    ;
    ;
    ;

    JEL classification:

    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • D29 - Microeconomics - - Production and Organizations - - - Other
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty

    Statistics

    Access and download statistics

    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:pra:mprapa:115681. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    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.