Идентификация локальных центров экономической активности с использованием цифровых данных о хозяйствующих субъектах
Identification of Local Economic Activity Centres Using Digital Data on Business Entities: Evidence from Vologda Oblast, Russia

Summary/Abstract: Local economic activity forms the foundation of regional and national economies by generating employment, producing goods and services, and supporting local development processes. In Russia, however, research on this topic remains limited due to the scarcity of municipal-level statistical data, which constrains the analysis of small towns and rural settlements and underscores the need to develop more robust analytical tools for studying local economies. The aim of this article is to develop and test a methodological framework for obtaining reliable data on local economic activity. The proposed framework is based on web scraping techniques and automated information extraction via APIs. Its application enabled the creation of a dataset on the activities of legal entities and individual entrepreneurs in Vologda Oblast for 2025 (n = 49,766 records across more than 200 settlements). The main selection criteria included taxpayer identification numbers, current operational status, and affiliation with specific municipal units. The analysis of legal entity data reveals significant sectoral differences between two small towns: Sokol, an industrial centre dominated by the woodworking and pulp-and-paper industries, and Veliky Ustyug, a peripheral town with a predominance of agriculture, handicrafts, and tourism services. Financial indicators by type of economic activity demonstrate the substantial fiscal importance of individual large enterprises. In contrast, data on individual entrepreneurs are considerably less informative, being largely limited to sectoral classification and taxation regime. The findings may support regional and municipal policy planning, contribute to the development of small and medium-sized enterprises, and improve understanding of local economic structures. Key limitations of the study include the labour-intensive nature of data collection and the difficulty of constructing dynamic time series, which points to the need for further integration with digital platforms and the expansion of periodic monitoring.

• Details
• Contents