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The anti-pandemic restrictions of recent years fueled an explosive growth of e-commerce in goods and services in Russia and worldwide. Therefore, the issue of macroeconomic consequences of the process of e-commerce growth is relevant. The subject of this research is economic transformation under the influence of the spread of e-commerce. The study aims to identify key theoretical mechanisms of how development of e-commerce influences main economic indicators, including inflation. It is based on the methods of generalization, systematization, descriptive and graphical analysis. According to the results of the study, e-commerce most notably influences aggregate factor productivity, the cost of production factors, consumer welfare, international trade volume and the cost of information for consumers. The study concludes that e-commerce causes predominantly downward pressure on prices and inflation, regardless of the mechanism of influence, because of a decrease in production and transport costs, reduction in the corporate monopoly power, and better product variety. This conclusion is also confirmed by some empirical countrywide studies. The scientific novelty of the research lies in systematization of the channels via which e-commerce influences various spheres of the economy. The study recommends considering the factor of downward pressure while conducting Russian monetary policy in the condition of continued e-commerce spread. A promising area of further research is the empirical verification of identified theoretical mechanisms of influence based on the data on e-commerce development in Russian regions. 

Abstract

The development of e-commerce in the context of digitalization of the economy contributes to the introduction and expansion of opportunities for the use of new macro and microeconomic indicators based on big data to study inflationary processes. The relevance of this work is determined by the fact that due to their availability and high frequency online prices of multi-channel retailers can be a more representative source of information for measuring and forecasting inflation levels than traditional data. Price indices based on online data made it possible to track price dynamics during the COVID-19 pandemic in real time. The main purpose of the study is to systematize the results of empirical work on the use of online trading data to analyze the features of inflationary processes (subject of the study). In addition, the work is aimed at exploring the possibilities of using online data and online price indices to predict offline prices. The achievement of the stated goal is facilitated by a review of academic literature devoted to the use of online price data for constructing high-frequency online indicators, measuring and forecasting the inflation rate, analyzing price dynamics in the online segment and comparing the rigidity of online prices and prices in traditional retail (tasks of research). The study was conducted using relevant academic literature and as the major source of information and methods such as descriptive, statistical, graphical analysis, a systematic approach, and comparative analysis. Based on the results of the study of the empirical experience of using online price data, it can be concluded that at present, online trade data are intensively used by foreign statistical agencies to build high-frequency price indices and can serve as a representative source of information about the level of inflation. Nevertheless, the review revealed that the official CPI was not replicated completely in any of the existing studies due to the high complexity of data collection and maintaining the database in working order (scientific novelty of the work). In this regard, the prospects for further research of the problem are presented in the maximum possible elimination of this shortcoming based on the previous world experience in the use of online price data and high frequency online indicators. The results of the review can be used in the interests of the monetary authorities of the Russian Federation to build forecast models of inflation, considering high-frequency online data on prices.