In the section | Articles |
Title of the article | Dynamics of Spatial Dependences of Prices for Vegetables and Fruits |
Pages | 94-125 |
Author 1 | Venera Maratovna Timiryanova Doctor of Economics, Associate Professor, deputy Head of the Laboratory for the Study of Socio-Economic Problems of the Regions Ufa University of Science and Technology 3/4 Karl Marx St., Ufa, 450076, Russian Federation This email address is being protected from spambots. You need JavaScript enabled to view it. ORCID: 0000-0002-1004-0722 |
Author 2 | Irina Aleksandrovna Lakman Candidate of Sciences (Technical), Head of the Laboratory for the Study of Socio-Economic Problems of the Regions Ufa University of Science and Technology 3/4 Karl Marx St., Ufa, 450076, Russian Federation This email address is being protected from spambots. You need JavaScript enabled to view it. ORCID: 0000-0001-9876-9202 |
Author 3 | Dina Khadimovna Krasnoselskaya Candidate of Sciences (Economics), Associate Professor of the Department of Innovative Economics Ufa University of Science and Technology 3/4 Karl Marx St., Ufa, 450076, Russian Federation This email address is being protected from spambots. You need JavaScript enabled to view it. ORCID: 0000-0002-1668-2937 |
Author 4 | Anna Viktorovna Stol Candidate of Sciences (Economics), Senior Researcher Institute for Strategic Studies, Academy of Sciences of the Republic of Bashkortostan 15 Kirova St., Ufa, 450008, Russian Federation This email address is being protected from spambots. You need JavaScript enabled to view it. ORCID: 0000-0002-9528-2893 |
Abstract | The paper addresses to a spatial analysis of daily prices for 6 types of fruits and vegetables in 81 regions of Russia from January 1, 2019 to March 31, 2022. The study set of information was formed by the Federal Tax Service with extraction data from fiscal registrars; it contains up to 95 332 observations for each type of fruit and vegetable products. Within the indicated data array, we explore the presence of a spatial correlation of prices and their growth rates by assessing the global Moran’s index and local Moran’s indices, in daily, weekly and monthly dynamics. To analyze the presence of cyclic changes in spatial dependencies, we use a correlogram, which was evaluated by various special tests. Our analysis showed that the level of data aggregation (in the form of daily, weekly or monthly values) affects the possibility of interpreting the results. The rate of spatial price change at the regional level usually exceeds one day. At the same time, in certain periods, it can be significantly registered within a week and it shows the synchronism of price changes on monthly data. Different Spatial dependence was revealed in the context of the considered goods types. We revealed the most stable spatial dependences for potatoes. Significant seasonal and weekly cycles of spatial dependence, determined by seasonal price fluctuations, were identified for potatoes. Our findings will be interesting to wide range of people dealing with issues of spatial measurements and problems of interregional interaction, as well as macroeconomic problems of pricing |
Code | 332.1+330.4 |
JEL | P22, R10, R15 |
DOI | https://dx.doi.org/10.14530/se.2023.2.094-125 |
Keywords | spatial autocorrelation, price, fruits and vegetables, seasonality of prices, rate of spatial price change, level of data aggregation, region, Russia |
Download | |
For citation | Timiryanova V.M., Lakman I.A., Krasnoselskaya D.K., Stol A.V. Dynamics of Spatial Dependences of Prices for Vegetables and Fruits. Prostranstvennaya Ekonomika = Spatial Economics, 2023, vol. 19, no. 2, pp. 94–125. https://dx.doi.org/10.14530/se.2023.2.094-125 (In Russian) |
References | 1. Andriyanova 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: Collection of Scientific Papers Based on the Materials of the International Scientific and Practical Conference. Edited by E.P. Tkacheva. 2018, pp. 99–103. (In Russian). 2. Anselin L. Local Indicators of Spatial Association-LISA. Geographical Analysis, 1995, vol. 27, issue 2, pp. 93–115. 3. Benedetti I., Laureti T., Palumbo L., Rose B.M. Computation of High-Frequency Sub-National Spatial Consumer Price Indexes Using Web Scraping Techniques. Economies, 2022, vol. 10, issue 4, 95. 4. Cedrez C.B., Chamberlin J., Hijmans R.J. Seasonal, Annual, and Spatial Variation in Cereal Prices in Sub-Saharan Africa. Global Food Security, 2020, vol. 26. 100438. 5. Chacaltana J., Leung V., Lee M. New Technologies and the Transition to Formality: The Trend Towards E-Formality. Employment Policy Department. Working Paper No. 247, 2018, 40 p. 6. Demidova O.А. Spatial-Autoregressive Model for the Two Groups of Related Regions (Eastern And Western Parts of Russia). Prikladnaya Ekonometrika = Applied Econometrics, 2014, no. 2 (34), pp. 19–35. (In Russian). 7. Distefano T., Chiarotti G., Laio F., Ridolfi L. Spatial Distribution of the International Food Prices: Unexpected Heterogeneity and Randomness. Ecological Economics, 2019, vol. 159, pp. 122–132. 8. Girardin E., Sall Ch.A.T. Inflation Dynamics of Franc-Zone Countries Determinants, Co-Movements and Spatial Interactions. Open Economies Review, 2018, vol. 29, pp. 295–320. 9. Gluschenko K. Distribution Dynamics of Russian Regional Prices. Empirical Economics, 2016, vol. 51, pp. 1193–1213. 10. Gluschenko K. The Evolution of Cross-Region Price Distribution in Russia. Current Politics and Economics of Russia, Eastern and Central Europe, 2004, vol. 19, pp. 263–278. 11. Gluschenko K.P., Khimich A.Ye. Food Markets in Russia: Dynamics of their Integration. Region: Ekonomika i Sotsiologiya = Regional Research of Russia, 2007, no. 4, pp. 74–87. (In Russian). 12. Goodwin B.K., Grennes T.J., McCurdy Ch. Spatial Price Dynamics and Integration in Russian Food Markets. The Journal of Policy Reform, 1999, vol. 3, issue 2, pp. 157–193. 13. Gordievich T.I. Cyclical and Seasonal Fluctuations in Consumer Prices. Vestnik Chelyabinskogo Gosudarstvennogo Universiteta = Bulletin of Chelyabinsk State University, 2010, no. 26 (207), pp. 5–10. (In Russian). 14. Handbook of Spatial Analysis in the Social Sciences. Edited by S.J. Rey, R.S. Franklin. Cheltenham: Edward Elgar Publishing, 2022, 588 p. 15. Ivanova V.I. Convergence of Prices on the Grain Market: The Historical Aspect. Prostran-stvennaya Ekonomika = Spatial Economics, 2015, no. 3, pp. 34–56. 16. Kalinin A.M., Volin I.A. Data Sources for CPI: Big Data of the Internet and the Systems of the Federal Tax Service of Russia. Voprosy Statistiki [Statistical Issues], 2022, vol. 29, no. 1, pp. 44–51. 17. Kirillov A.M. A Study on Spatial Autocorrelation: Case of Russian Regional Inflation. Prikladnaya Ekonometrika = Applied Econometrics, 2021, no. 4 (64), pp. 5–22. 18. Kirillov A.M. Spatial Analysis of Food Inflation in Russian Regions. Prostranstvennaya Ekonomika = Spatial Economics, 2017, no. 4, pp. 41–58. 19. Kitenge E.M., Morshed A.K.M.M. Price Convergence among Indian Cities: The Role of Linguistic Differences, Topography, and Aggregation. Journal of Asian Economics, 2019, vol. 61, pp. 34–50. 20. Korneychenko E.N., Novopashina A.N., Pikhteev Yu.N. Exchange Rate Pass-Through in Russian Regions: Searching for Spatial Dependencies. Izvestiya Saratovskogo Universiteta. Novaya Seriya. Seriya: Ekonomika. Upravlenie. Pravo = Izvestiya of Saratov University. Economics. Management. Law, 2021, vol. 21, issue 4, pp. 398–409. 21. Molinaro A., DeFalco F. Empirical Assessment of Alternative Methods for Identifying Seasonality in Observational Healthcare Data. BMC Medical Research Methodology, 2022, vol. 22, no. 182. 22. Montero J.-M., Laureti T., Minguez R., Fernandez-Aviles G. A Stochastic Model with Pena-lized Coefficients for Spatial Price Comparisons: An Application to Regional Price Indexes in Italy. Review of Income and Wealth, 2020, vol. 66, issue 3, pp. 512–533. 23. Ollech D., Webel K. A Random Forest-Based Approach to Identifying the Most Informative Seasonality Tests. Deutsche Bundesbank. Discussion Paper No. 55/2020, 2020, 36 p. http://dx.doi.org/10.2139/ssrn.3721055 24. Pivkin K.S. Modeling of Consumer Demand at Retail Enterprises Based on Machine Learning Methods: Diss. ... Candidate of Economic Sciences: 08.00.13. Izhevsk, 2018, 145 p. (In Russian). 25. Rokicki B., Hewings G.J.D. Regional Price Deflators in Poland: Evidence from NUTS-2 and NUTS-3 Regions. Spatial Economic Analysis, 2019, vol. 14, issue 1, pp. 88–105. 26. Sapova A.K., Porshakov A.S., Andreev A.V., Shatilo E. Yu. Peculiarities of the Consumer Price Index Seasonal Adjustment. Voprosy Statistiki [Statistical Issues], 2018, vol. 25, no. 5, pp. 42–54. (In Russian). 27. Sinelnikov-Murylev S.G., Perevyshin Yu.N., Trunin P.V. Inflation Differences in the Russian Regions: An Empirical Analysis. Ekonomika Regiona = Economy of Region, 2020, vol. 16, issue 2, pp. 479–493. 28. Stupnikova A.V. Spatial Behavior of Prices in the Russian Federation in 2003–2012. Ekonomicheskie i Sotsialnye Peremeny: Fakty, Tendentsii, Prognoz = Economic and Social Changes: Facts, Trends, Forecast, 2014, no. 3 (33), pp. 248–261. 29. Stupnikova A.V. Spatial Differentiation of Prices for Vegetables in Russia: The Impact of Foreign Markets. Proceedings of the International Scientific Conference ‘FarEastCon’ (ISCFEC 2020). Advances in Economics, Business and Management Research, Vladivostok, 2020, vol. 128, pp. 2522–2527. 30. Stupnikova A.V. Spatial Reaction of Prices in the Vegetable Markets to Restrictions on Foreign Trade. Prostranstvennaya Ekonomika = Spatial Economics, 2018, no. 1, pp. 117–137. 31. Svanidze M., Gotz L. Determinants of Spatial Market Efficiency of Grain Markets in Russia. Food Policy, 2019, vol. 89. 101769. 32. Timiryanova V.M., Zimin A.F., Zhilina E.V. The Spatial Change of the Indicators of Consumer Market. Ekonomika Regiona = Economy of Region, 2018, vol. 14, issue 1, pp. 164–175 33. Tsyplakov А.А. Statistical Analysis of the Dynamics of Regional Price Levels. Vestnik NGU. Seriya: Sotsialno-Ekonomicheskie Nauki = Vestnik NSU. Series: Social and Economic Sciences, 2000, vol. 1, issue 1, pp. 5–19. (In Russian). 34. Wu G., Zhang C., Liu P., Ren W., Zheng Y., Guo F., Chen X., Higgs R. Spatial Quantitative Analysis of Garlic Price Data Based on ArcGIS Technology. Computers, Materials & Continua, 2019, vol. 58, no. 1, pp. 183–195. |
Financing | |
Submitted | 30.09.2022 |
Approved after reviewing | 08.04.2023 |
Accepted for publication | 25.04.2023 |
Available online | 30.06.2023 |