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The aim of this paper is twofold. First, it is to answer the question of whether Russia is successful in attracting foreign direct investment (FDI). Second, it is to identify partner countries that “overinvest” and “underinvest” in the Russian economy. We do this by calculating potential FDI inflows to Russia and comparing them with actual values. This research is associated with the empirical estimation of factors explaining FDI flows between countries. The methodological foundation used for the research is the gravity model of foreign direct investment. In discussing the pros and cons of different econometric methods of the estimation gravity equation, we conclude that the Poisson pseudo maximum likelihood method with instrumental variables (IV PPML) is one of the best options in our case. Using a database covering about 70% of FDI flows for the period of 2001-2011, we discover the following factors that explain the variance of bilateral FDI flows in the world economy: GDP value of investing country, GDP value of recipient country, distance between countries, remoteness of investor country, remoteness of recipient country, level of institutions development in host country, wage level in host country, membership of two countries in a regional economic union, common official language, common border and colonial relationships between countries in the past. The potential values of FDI inflows are calculated using coefficients of regressors from the econometric model. We discover that the Russian economy performs very well in attracting FDI: the actual FDI inflows exceed potential values by 1.72 times. Large developed countries (France, Germany, UK, Italy) overinvest in the Russian economy, while smaller and less developed countries (Czech Republic, Belarus, Denmark, Ukraine) underinvest in Russia. Countries of Southeast Asia (China, South Korea, Japan) also underinvest in the Russian economy.

Relevance. The relevance of the study is determined by the growing importance of creative industries in the global economy, which necessitates the formation of common approaches to identifying and defining creative industries to make effective management decisions at the state level. The lack of a unified approach to defining the conceptual and methodological apparatus necessitates additional research on this topic. Purpose of the study. The purpose of this study is to conduct a comparative analysis of approaches to identifying creative industries that have developed in the international and domestic academic community. Data and methods. The study is based on the Scoping review method, which consists of a full analysis of the existing literature in the context of key concepts of a given area of research. The international bibliographic database Scopus was used to select publications for the review. To consider the national specifics of research, the sample was expanded to include articles from the Russian Science Citation Index (RSCI). Results. The article reviews and summarizes the existing scientific approaches to identifying creative industries, highlights the main debatable issues of terminology in the field of the creative economy. Based on a comprehensive review of the approaches of international and domestic researchers, the article presents a system of criteria for identifying creative industries, which are differentiated by types of sources, specifics, and results. The application of this system of criteria will allow us to determine the boundaries of creative industries and distinguish creative industries from the general array of economic sectors. Conclusion. Systematization of theoretical approaches to defining and identifying creative industries is a necessary condition for their further classification and evaluation. The proposed system of criteria is a synthesis of existing approaches, which makes it universal and suggests the possibility of its practical application for solving a wide range of tasks related to managerial decision-making in the field of creative economy development.

To foresee global economic trends, one needs to understand the present startup companies that soon may become new market leaders. In this paper, we explore textual descriptions of more than 250 thousand startups in the Crunchbase database. We analyze the 2009–2019 period by using topic modeling. We propose a novel classification of startup companies free from expert bias that contains 38 topics and quantifies the weight of each of these topics for all the startups. Taking the year of establishment and geographical location of the startups into account, we measure which topics were increasing or decreasing their share over time, and which of them were predominantly present in Europe, North America, or other regions. We find that the share of startups focused on data analytics, social platforms, and financial transfers, and time management has risen, while an opposite trend is observed for mobile gaming, online news, and online social networks as well as legal and professional services. We also identify strong regional differences in topic distribution, suggesting certain concentration of the startups. For example, sustainable agriculture is presented stronger in South America and Africa, while pharmaceutics, in North America and Europe. Furthermore, we explore which pairs of topics tend to co-occur more often together, quantify how multisectoral the startups are, and which startup classes attract more investments. Finally, we compare our classification to the one existing in the Crunchbase database, demonstrating how we improve it.

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