Elena G. Gureeva

This article investigates the potential of using aggregated accounting and tax reporting data, specifically revenue indicators, to profile business models and enhance regulatory policy in Russia. The study addresses a fundamental contradiction: while financial and tax filings contain invaluable information for macroeconomic analysis and state regulation, their utilization is severely restricted by the legal regime of tax secrecy.

The research demonstrates a practical methodology for profiling enterprises by analyzing the structure of their revenue as reported in corporate income tax returns. This approach enables the classification of companies into three distinct business models: product-based (revenue predominantly from own production), distribution-based (revenue mainly from resale), and hybrid. For each profile, the article identifies key economic characteristics, including value creation sources, margin stability, supply chain dependencies, and investment patterns.

A key finding is that data aggregation at the industry or regional level does not violate the provisions of Article 102 of the Russian Tax Code on tax secrecy, as it prevents the identification of individual taxpayers. The authors argue that leveraging such aggregated data can form the basis for targeted state support measures and a more flexible, evidence-based regulatory policy. The article concludes by advocating for the development of voluntary disclosure mechanisms, which would balance confidentiality with increased economic transparency, thereby improving the efficacy of public administration and fostering sustainable economic development.