Clustering Russian Regions by Cash Circulation
Bibliographic Data
| ID | 21864495 |
|---|---|
| Authors | A I Shaidullin (0000-0002-2653-1745, National Research University Higher School of Economics), Snezhana V Sedipkova (0000-0001-7591-8340, Siberian Main Branch of the Bank of Russia), С В Седипкова (Central Bank of the Russian Federation) |
| Year | 2026 |
| Volume | 21 |
| Issue | 2 |
| Pages | 32-65 |
| Publication date | 2026-04-20 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Economic Policy (JOURNAL) |
| Journal identifiers | ISSN: 1994-5124 • E-ISSN: 2411-2658 |
| Publisher | Economic Policy (PUBLISHER) |
| DOI | 10.18288/1994-5124-2026-2-32-65 |
| OpenAlex | W7155047580 |
| Language | EN |
| References cited | 19 |
Demand for cash increases during crises and thus places additional strain on the payment infrastructure. Clustering the regions of the Russian Federation will facilitate differentiated response scenarios to shocks and improve forecasting tools that are responsive to specific regional characteristics. This article clusters Russian regions on the basis of the dynamics of their cash inflows and outflows. By applying correlation clustering analysis, Ward s method, and the K-Means algorithm, the authors identified groups of Russia s constituent regions with similar cash circulation patterns. For most denominations, the Elbow Method resulted in an optimal division into three clusters, whose boundaries varied with the volatility of operations. Most Russian regions exhibit similar dynamics in cash transactions for the most common denominations in circulation (5,000, 1,000, and 500 rubles). Major exogenous shocks tend to make regional patterns in the use of various cash denominations uniform. The formation of dominant clusters indicates that nationwide factors (such as changes in consumer behavior, adaptation to uncertainty, and unified economic policies) prevail over regional specifics in the dynamics of cash circulation. The results of the study have practical applications for the Bank of Russia and other financial institutions in their effort to transition to territorially oriented forecasting of cash demand with consequent opportunities to optimize logistical processes and minimize costs. The methodology proposed by the authors potentially has a broader scope, as it can be adapted to analyze other macroeconomic indicators
Cash · Cash flow · Cash flow forecasting · Cluster analysis · Payment · Russian federation · Economic and Technological Developments in Russia · Economic and Technological Innovation · Socioeconomic and Demographic Analysis
| Highly cited | No |
|---|