High-frequency retail data: the interests of the state, enterprises and scientific organizations
https://doi.org/10.22394/1726-1139-2023-3-34-45
Abstract
The rapid development of technologies for collecting and analyzing big data, including those characterizing trade, is currently taking place. This data, with a high degree of detail, takes into account the whole variety of consumer decisions, which allows them 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, individual small and medium-sized enterprises weak use of big data in their activities. 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 in the development of management decisions, are highlighted. 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. Such closeness of data does not create prerequisites for the active development of skills in working with them in most enterprises and organizations, which determines the weak use of microdata for management purposes.
About the Author
V. M. Timiryanova
Ufa University of Science and Technology
Russian Federation
Russian Federation
Venera M. Timiryanova, Deputy Head Laboratory for the Study of Socio-Economic Problems of the Regions of the Bashkir State University, Doctor of Science (Economics), Associate Professor
Ufa
References
1. Andrianova I. D., Ryabinina E. V. Tax control during the period of digital transformation in Russia and foreign countries // Key problems of social sciences and humanities in modern Russia: a collection of scientific papers based on the materials of the International Scientific and Practical Conference. Ed. E. P. Tkacheva. Belgorod : LLC Agency for Advanced Scientific Research (APNI), 2018. P. 99–103 (in Rus).
2. Zhabin D. V., Turkov M. M., Volkov D. V. Potential of using the information of the operator of fiscal data // Social policy and sociology [Sotsial’naya politika i sotsiologiya]. 2017. Vol. 16. N 5 (124). P. 25–33 (in Rus). DOI: 10.17922/2071-3665-2017-16-5-25-33
3. Use of Big Data in the Financial Sector and Risks to Financial Stability: Public Consultation Report. Moscow: Central Bank of the Russian Federation, 2021 (in Rus).
4. Korolev V. Yu., Chertok A. V., Korchagin A. Yu., Gorshenin A.K. Probability and statistical modeling of information flows in complex financial systems based on high-frequency data // Informatics and its Applications [Informatika i Ee Primeneniya]. 2013. Vol. 7, N 1. P. 12–21 (in Rus).
5. Kudashkin A. V., Mokhov A.S. Clustering of bank clients based on their personal data and bank transactions // Information systems and technologies IST-2020. Collection of materials of the XXVI International Scientific and Technical Conference. Nizhny Novgorod State Technical University. 2020. P. 780–785 (in Rus).
6. Labusov M. V. Modeling high-frequency financial time series using artificial intelligence methods: dis. ... candidate of economic sciences. M., 2022 (in Rus).
7. Nikitin A. Transaction data analysis and determination of quantitative criteria of customer’s loyalty // Economy. Taxes. Right [Ekonomika. Nalogi. Pravo]. 2012. N 2. P. 113–124 (in Rus).
8. Timiryanova V. M., Lakman I. A., Baymurzina G.R. Big data research: should aggregation be used for modeling? // Big data and problems of society. Collection of articles following the results of the International scientific conference. Tomsk, 2022. P. 144–149 (in Rus).
9. Tkachev I., Skobelev V., Starostina Yu., Ageeva O. Control-cash inflation // RBC Newspaper. 2020. N 102 (3269) (in Rus).
10. Turdyeva N., Tsvetkova A., Movsesyan L., Porshakov A., Chernyadyev D. Data of Sectoral Financial Flows as a High-Frequency Indicator of Economic Activity // Russian Journal of Money and Finance [Den’gi i kredit]. 2021. N 80 (2). P. 28–49 (in Rus). DOI: 10.31477/rjmf.202102.28.
11. Shigilcheva S. A., Khristoforova A. V., Smirnova E. N. Features of business model of Tinkoff bank JSC as a subject of electronic business // Vestnik of the Russian University of Cooperation [Vestnik Rossiiskogo universiteta kooperatsii]. 2020. N 2 (40). P. 113–117 (in Rus).
12. Aladangady A., Aron-Dine Sh., Dunn W., Feiveson L., Lengermann P., Sahm C. From Transactions Data to Economic Statistics: Constructing Real-time, High-frequency, Geographic Measures of Consumer Spending // Finance and Economics Discussion Ser. 2019-057. Washington : Board of Governors of the Federal Reserve System, 2019. 37 p. DOI:10.17016/FEDS.2019.057.
13. Andersen A. L., Hansen E. T., Johannesen N., Sheridan A. Consumer Responses to the COVID-19 Crisis: Evidence from Bank Account Transaction Data // CEPR Discussion Paper. 2020. N DP14809. DOI: 10.2139/ssrn.3609814.
14. Baker S. R., Farrokhnia R. A., Meyer S., Pagel M., Yannelis C. Income, Liquidity, and the Consumption Response to the 2020 Economic Stimulus Payments // NBER Working Paper. 2020. N 27097. DOI: 10.3386/w27097.
15. Bounie D., Camara Y., Galbraith J. W. Consumers’ Mobility, Expenditure and Online-Offline Substitution Response to COVID-19: Evidence from French Transaction Data // SSRN Working Paper. 2020. N 3588373. DOI: 10.2139/ssrn.3588373.
16. Carvalho V. M., Garcia J.R., Hansen S., Ortiz Á., Rodrigo T., Rodríguez Mora J. V., Ruiz P. Tracking the COVID-19 crisis with high-resolution transaction data // Royal Society Open Science. 2021. N 8: 210218. DOI: 10.1098/rsos.210218.
17. Casey P., Castro P. Electronic Fiscal Devices (EFDs) An Empirical Study of their Impact on Taxpayer Compliance and Administrative Efficiency // IMF Working Papers. 2015. N 073. DOI: 10.5089/9781475521023.001.
18. Chacaltana J., Leung V., Lee M. New technologies and the transition to formality: The trend towards e–formality // Employment: Working Paper. International Labour Organization. 2018. N 247.
19. Chen H., Qian W., Wen W. The Impact of the COVID-19 Pandemic on Consumption: Learning from High Frequency Transaction Data // SSRN Working Paper. 2020. N 3568574. DOI: 10.2139/ssrn.3568574.
20. Clark S. D., Shute B., Jenneson V., Rains T., Birkin M., Morris M. A. Dietary Patterns Derived from UK Supermarket Transaction Data with Nutrient and Socioeconomic Profiles // Nutrients. 2021. N 13: 1481. DOI: 10.3390/nu13051481.
21. Cotti Ch.D., Courtemanche Ch.J., Maclean J. C., Nesson E. T., Pesko M. F., Tefft N. The effects of e-cigarette taxes on e-cigarette prices and tobacco product sales: evidence from retail panel data // NBER working paper series. 2020. N 26724. DOI: 10.3386/w26724.
22. Dacorogna M. An Introduction to High-Frequency Finance / M. Dacorogna, R. Gencay, U.A. Muller [etc.]; 1st ed. California : Academic Press, 2001. 383 p.
23. Dubois P., Griffith R., O’Connell M. The Use of Scanner Data for Economics Research // CEPR Discussion Paper. 2022. N DP16954.
24. Gelman M., Kariv S., Shapiro M. D., Silverman D., Tadelis S. Harnessing naturally occurring data to measure the response of spending to income // Science. 2014. Vol. 345 (6193). P. 212–215. DOI:10.1126/science.1247727.
25. Implementing Online Cash Registers: Benefits, Considerations and Guidance. Paris : OECD, 2019.
26. Kokh L. V., Kovaleva Ju.V., Ivanova O. P. Big Data in Public Administration // Proceedings of International Scientific and Practical Conference “Russia 2020 — a new reality: economy and society” (ISPCR 2020). 2021. P. 250–254. DOI: 10.2991/aebmr.k.210222.049.
27. Linden G., Smith B., York J. Amazon.com recommendations: item-to-item collaborative filtering // IEEE Internet Computing. 2003. Vol. 7, N 1. P. 76–80. DOI: 10.1109/MIC.2003.1167344.
28. Lovics G., Szőke K., Tóth C. G., Ván B. The effect of the introduction of online cash registers on reported turnover in Hungary // MNB (Magyar Nemzeti Bank) Occasional papers, 2019. N 137. 24 p.
29. Muth M.K., Okrent A., Zhen C., Karns Sh. Using Scanner Data for Food Policy Research. Elsevier Academic Press. 2020. DOI: 10.1016/C2017-0-01027-3.
30. Rogoff K., Scazzero J. Covid Cash // Cato Journal. 2021. Vol. 41. N 3. P. 571–592. DOI:10.36009/CJ.41.3.6.
31. Trivedi M. Regional and Categorical Patterns in Consumer Behavior: Revealing Trends // Journal of Retailing. 2011. N 87. P. 18–30. DOI: 10.1016/J.JRETAI.2010.11.002.
32. Waldenström D., Angelov N. The Impact of COVID-19 on Economic Activity: Evidence from Administrative Tax Registers // CEPR Discussion Paper. 2021. N DP16332.
33. Zhang L.-Ch. Proxy expenditure weights for Consumer Price Index: Audit sampling inference for big-data statistics // Journal of the Royal Statistical Society Series A. 2021. N 184 (2). P. 571–588. DOI: 10.1111/rssa.12632.
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For citations:
Timiryanova V.M. High-frequency retail data: the interests of the state, enterprises and scientific organizations. Administrative Consulting. 2023;(3):34-45. (In Russ.) https://doi.org/10.22394/1726-1139-2023-3-34-45
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