6 research outputs found
Topic-based classification and identification of global trends for startup companies
Altres ajuts: Acord transformatiu CRUE-CSICUnidad de excelencia MarΓa de Maeztu CEX2019-000940-MTo 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
Theoretical approaches to identifying creative industries
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
ΠΠ»ΠΈΡΠ½ΠΈΠ΅ ΡΡΡΠ΅ΠΊΡΠΎΠ² ΠΏΠ΅ΡΠ΅ΡΠ΅ΠΊΠ°Π½ΠΈΡ Π²ΠΎΠ»Π°ΡΠΈΠ»ΡΠ½ΠΎΡΡΠΈ Π½Π° ΠΏΠΎΠ»ΠΈΡΠΈΡΠ΅ΡΠΊΡΡ Π½Π΅ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΠΎΡΡΡ, ΡΠ΅Π½Ρ Π½Π° Π½Π΅ΡΡΡ, Π±ΠΈΡΠΆΡ ΠΈ ΡΡΠ½ΠΊΠΈ Π΄ΡΠ°Π³ΠΎΡΠ΅Π½Π½ΡΡ ΠΌΠ΅ΡΠ°Π»Π»ΠΎΠ² Π² ΡΠΎΡΡΠΈΠΉΡΠΊΠΎΠΉ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠ΅
Π ΠΎΡΡΠΈΠΉΡΠΊΠ°Ρ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠ° β ΡΡΠΎ ΡΠ°Π·Π²ΠΈΠ²Π°ΡΡΠ°ΡΡΡ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠ°, ΠΏΡΠΈΡΠΎΠ΄Π½ΡΠ΅ ΡΠ΅ΡΡΡΡΡ ΠΈΠ³ΡΠ°ΡΡ Π΄ΠΎΠΌΠΈΠ½ΠΈΡΡΡΡΡΡ ΡΠΎΠ»Ρ Π² ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΎΠΌ ΡΠ°Π·Π²ΠΈΡΠΈΠΈ ΡΡΡΠ°Π½Ρ. Π‘Π»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΠΎ, Π½Π° Π½Π°ΡΠΈΠΎΠ½Π°Π»ΡΠ½ΡΡ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΡ Π²Π»ΠΈΡΠ΅Ρ Π·Π½Π°ΡΠΈΡΠ΅Π»ΡΠ½Π°Ρ Π²ΠΎΠ»Π°ΡΠΈΠ»ΡΠ½ΠΎΡΡΡ ΡΠ΅Π½ Π½Π° ΡΠ΅ΡΡΡΡΡ. Π ΡΡΠ°ΡΡΠ΅ ΠΈΡΡΠ»Π΅Π΄ΡΠ΅ΡΡΡ Π²Π»ΠΈΡΠ½ΠΈΠ΅ ΡΡΡΠ΅ΠΊΡΠΎΠ² ΠΏΠ΅ΡΠ΅ΡΠ΅ΠΊΠ°Π½ΠΈΡ Π²ΠΎΠ»Π°ΡΠΈΠ»ΡΠ½ΠΎΡΡΠΈ Π½Π° ΠΏΠΎΠ»ΠΈΡΠΈΡΠ΅ΡΠΊΡΡ Π½Π΅ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΠΎΡΡΡ, ΠΌΠΈΡΠΎΠ²ΡΠ΅ ΡΠ΅Π½Ρ Π½Π° Π½Π΅ΡΡΡ, ΠΎΠ±ΠΌΠ΅Π½Π½ΡΠΉ ΠΊΡΡΡ, ΡΠΎΠ½Π΄ΠΎΠ²ΡΠ΅ ΠΈΠ½Π΄Π΅ΠΊΡΡ ΠΈ ΡΠ΅Π½Ρ Π½Π° ΠΌΠ΅ΡΠ°Π»Π»Ρ Π² ΡΠΎΡΡΠΈΠΉΡΠΊΠΎΠΉ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠ΅ Π·Π° ΠΏΠ΅ΡΠΈΠΎΠ΄ ΡΠΎ 2 ΠΈΡΠ»Ρ 2008 Π³. ΠΏΠΎ 15 ΠΌΠ°Ρ 2020 Π³. ΠΠ»Ρ Π°Π½Π°Π»ΠΈΠ·Π° ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½Π° ΠΌΠΎΠ΄Π΅Π»Ρ Π²Π΅ΠΊΡΠΎΡΠ½ΠΎΠΉ Π°Π²ΡΠΎΡΠ΅Π³ΡΠ΅ΡΡΠΈΠΈ Ρ ΠΈΠ·ΠΌΠ΅Π½ΡΡΡΠΈΠΌΠΈΡΡ Π²ΠΎ Π²ΡΠ΅ΠΌΠ΅Π½ΠΈ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠ°ΠΌΠΈ (TVP-VAR). ΠΡΠΎΠ²Π΅Π΄Π΅Π½Π½ΠΎΠ΅ ΡΠΌΠΏΠΈΡΠΈΡΠ΅ΡΠΊΠΎΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΏΠΎΠΊΠ°Π·ΡΠ²Π°Π΅Ρ, ΡΡΠΎ ΡΠ΅Π½Π° Π½Π° Π·ΠΎΠ»ΠΎΡΠΎ, ΠΏΠΎΠ»ΠΈΡΠΈΡΠ΅ΡΠΊΠ°Ρ Π½Π΅ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΠΎΡΡΡ, ΡΠ΅Π½Π° Π½Π° Π½Π΅ΡΡΡ ΠΈ ΡΠΎΠ½Π΄ΠΎΠ²ΡΠΉ ΠΈΠ½Π΄Π΅ΠΊΡ ΡΠ²Π»ΡΡΡΡΡ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠ°ΠΌΠΈ Π²ΠΎΠ»Π°ΡΠΈΠ»ΡΠ½ΠΎΡΡΠΈ. Π ΡΠΎ ΠΆΠ΅ Π²ΡΠ΅ΠΌΡ, Π²ΠΎΠ»Π°ΡΠΈΠ»ΡΠ½ΠΎΡΡΡ Π²Π»ΠΈΡΠ΅Ρ Π½Π° ΡΠ°ΠΊΠΈΠ΅ ΡΠ°ΠΊΡΠΎΡΡ, ΠΊΠ°ΠΊ ΠΏΠ°Π»Π»Π°Π΄ΠΈΠΉ, ΠΏΠ»Π°ΡΠΈΠ½Π°, ΡΠ΅ΡΠ΅Π±ΡΠΎ ΠΈ ΠΎΠ±ΠΌΠ΅Π½Π½ΡΠΉ ΠΊΡΡΡ ΡΡΠ±Π»Ρ. Π ΡΠ½ΠΎΡΠ½Π°Ρ ΠΊΠ°ΠΏΠΈΡΠ°Π»ΠΈΠ·Π°ΡΠΈΡ ΡΠ²Π»ΡΠ΅ΡΡΡ ΡΠΈΡΡΡΠΌ Π΄ΠΎΠ½ΠΎΡΠΎΠΌ, ΡΡΠ½ΠΎΠΊ ΡΠ΅ΡΠ΅Π±ΡΠ° β ΡΠΈΡΡΡΠΌ ΠΏΠΎΠ»ΡΡΠ°ΡΠ΅Π»Π΅ΠΌ. ΠΠ°Π»Π»Π°Π΄ΠΈΠΉ ΡΡΠ°Π» ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΎΠΌ ΡΠΈΡΡΠΎΠΉ Π²ΠΎΠ»Π°ΡΠΈΠ»ΡΠ½ΠΎΡΡΠΈ ΠΏΠΎΡΠ»Π΅ ΠΌΠΈΡΠΎΠ²ΠΎΠ³ΠΎ ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΠΎΠ³ΠΎ ΠΊΡΠΈΠ·ΠΈΡΠ°. ΠΠ΅ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΠΎΡΡΡ ΡΠΎΡΡΠΈΠΉΡΠΊΠΎΠΉ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΏΠΎΠ»ΠΈΡΠΈΠΊΠΈ Π±ΡΠ»Π° ΠΎΡΠ½ΠΎΠ²Π½ΡΠΌ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΎΠΌ Π²ΠΎΠ»Π°ΡΠΈΠ»ΡΠ½ΠΎΡΡΠΈ Ρ 2008 ΠΏΠΎ 2014 Π³Π³., ΠΎΠ΄Π½Π°ΠΊΠΎ Π²ΠΏΠΎΡΠ»Π΅Π΄ΡΡΠ²ΠΈΠΈ Π²ΠΎΠ»Π°ΡΠΈΠ»ΡΠ½ΠΎΡΡΡ Π΄ΡΡΠ³ΠΈΡ
ΡΠ°ΠΊΡΠΎΡΠΎΠ² ΠΎΠΊΠ°Π·ΡΠ²Π°Π»Π° Π½Π° Π½Π΅Π΅ Π±ΠΎΠ»ΡΡΠ΅Π΅ Π²Π»ΠΈΡΠ½ΠΈΠ΅. Π 2014 Π³., ΠΊΠΎΠ³Π΄Π° ΡΠ΅Π½Π° Π½Π° Π½Π΅ΡΡΡ Π·Π½Π°ΡΠΈΡΠ΅Π»ΡΠ½ΠΎ ΡΠ½ΠΈΠ·ΠΈΠ»Π°ΡΡ, ΡΠ΅Π½Π° Π½Π° Π·ΠΎΠ»ΠΎΡΠΎ Π±ΡΠ»Π° ΠΎΡΠ½ΠΎΠ²Π½ΡΠΌ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΎΠΌ Π²ΠΎΠ»Π°ΡΠΈΠ»ΡΠ½ΠΎΡΡΠΈ Π΄Π»Ρ Π΄ΡΡΠ³ΠΈΡ
ΡΡΠ½ΠΊΠΎΠ². ΠΠΎΠ»Π½Π°Ρ ΡΠ²ΡΠ·Π°Π½Π½ΠΎΡΡΡ ΡΡΠ½ΠΊΠΎΠ² Π² Π·Π½Π°ΡΠΈΡΠ΅Π»ΡΠ½ΠΎΠΉ ΡΡΠ΅ΠΏΠ΅Π½ΠΈ Π·Π°Π²ΠΈΡΠΈΡ ΠΎΡ ΡΡΠ΄Π° ΡΠΊΠ·ΠΎΠ³Π΅Π½Π½ΡΡ
ΠΏΠΎΡΡΡΡΠ΅Π½ΠΈΠΉ, ΡΠ°ΠΊΠΈΡ
ΠΊΠ°ΠΊ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΡΠ°Π½ΠΊΡΠΈΠΈ, Π²Π²Π΅Π΄Π΅Π½ΠΈΠ΅ ΡΠ΅ΠΆΠΈΠΌΠ° ΠΏΠ»Π°Π²Π°ΡΡΠ΅Π³ΠΎ ΠΎΠ±ΠΌΠ΅Π½Π½ΠΎΠ³ΠΎ ΠΊΡΡΡΠ°, ΠΏΠ°Π΄Π΅Π½ΠΈΠ΅ ΡΠ΅Π½ Π½Π° Π½Π΅ΡΡΡ. ΠΡΡ
ΠΎΠ΄Ρ ΠΈΠ· ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Π½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π°, ΡΡΠΎΡΠΌΡΠ»ΠΈΡΠΎΠ²Π°Π½ΠΎ Π½Π΅ΡΠΊΠΎΠ»ΡΠΊΠΎ ΡΠ΅ΠΊΠΎΠΌΠ΅Π½Π΄Π°ΡΠΈΠΉ Π΄Π»Ρ ΠΏΠΎΡΡΡΠ΅Π»ΡΠ½ΡΡ
ΠΈΠ½Π²Π΅ΡΡΠΎΡΠΎΠ² ΠΈ ΡΡΠ΅ΠΉΠΊΡ
ΠΎΠ»Π΄Π΅ΡΠΎΠ² Π² ΡΠΎΡΡΠΈΠΉΡΠΊΠΈΡ
ΡΠ΅Π³ΠΈΠΎΠ½Π°Ρ
ΠΠ»ΠΈΡΠ½ΠΈΠ΅ ΡΡΡΠ΅ΠΊΡΠΎΠ² ΠΏΠ΅ΡΠ΅ΡΠ΅ΠΊΠ°Π½ΠΈΡ Π²ΠΎΠ»Π°ΡΠΈΠ»ΡΠ½ΠΎΡΡΠΈ Π½Π° ΠΏΠΎΠ»ΠΈΡΠΈΡΠ΅ΡΠΊΡΡ Π½Π΅ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΠΎΡΡΡ, ΡΠ΅Π½Ρ Π½Π° Π½Π΅ΡΡΡ, Π±ΠΈΡΠΆΡ ΠΈ ΡΡΠ½ΠΊΠΈ Π΄ΡΠ°Π³ΠΎΡΠ΅Π½Π½ΡΡ ΠΌΠ΅ΡΠ°Π»Π»ΠΎΠ² Π² ΡΠΎΡΡΠΈΠΉΡΠΊΠΎΠΉ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠ΅
Π ΠΎΡΡΠΈΠΉΡΠΊΠ°Ρ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠ° β ΡΡΠΎ ΡΠ°Π·Π²ΠΈΠ²Π°ΡΡΠ°ΡΡΡ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠ°, ΠΏΡΠΈΡΠΎΠ΄Π½ΡΠ΅ ΡΠ΅ΡΡΡΡΡ ΠΈΠ³ΡΠ°ΡΡ Π΄ΠΎΠΌΠΈΠ½ΠΈΡΡΡΡΡΡ ΡΠΎΠ»Ρ Π² ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΎΠΌ ΡΠ°Π·Π²ΠΈΡΠΈΠΈ ΡΡΡΠ°Π½Ρ. Π‘Π»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΠΎ, Π½Π° Π½Π°ΡΠΈΠΎΠ½Π°Π»ΡΠ½ΡΡ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΡ Π²Π»ΠΈΡΠ΅Ρ Π·Π½Π°ΡΠΈΡΠ΅Π»ΡΠ½Π°Ρ Π²ΠΎΠ»Π°ΡΠΈΠ»ΡΠ½ΠΎΡΡΡ ΡΠ΅Π½ Π½Π° ΡΠ΅ΡΡΡΡΡ. Π ΡΡΠ°ΡΡΠ΅ ΠΈΡΡΠ»Π΅Π΄ΡΠ΅ΡΡΡ Π²Π»ΠΈΡΠ½ΠΈΠ΅ ΡΡΡΠ΅ΠΊΡΠΎΠ² ΠΏΠ΅ΡΠ΅ΡΠ΅ΠΊΠ°Π½ΠΈΡ Π²ΠΎΠ»Π°ΡΠΈΠ»ΡΠ½ΠΎΡΡΠΈ Π½Π° ΠΏΠΎΠ»ΠΈΡΠΈΡΠ΅ΡΠΊΡΡ Π½Π΅ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΠΎΡΡΡ, ΠΌΠΈΡΠΎΠ²ΡΠ΅ ΡΠ΅Π½Ρ Π½Π° Π½Π΅ΡΡΡ, ΠΎΠ±ΠΌΠ΅Π½Π½ΡΠΉ ΠΊΡΡΡ, ΡΠΎΠ½Π΄ΠΎΠ²ΡΠ΅ ΠΈΠ½Π΄Π΅ΠΊΡΡ ΠΈ ΡΠ΅Π½Ρ Π½Π° ΠΌΠ΅ΡΠ°Π»Π»Ρ Π² ΡΠΎΡΡΠΈΠΉΡΠΊΠΎΠΉ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠ΅ Π·Π° ΠΏΠ΅ΡΠΈΠΎΠ΄ ΡΠΎ 2 ΠΈΡΠ»Ρ 2008 Π³. ΠΏΠΎ 15 ΠΌΠ°Ρ 2020 Π³. ΠΠ»Ρ Π°Π½Π°Π»ΠΈΠ·Π° ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½Π° ΠΌΠΎΠ΄Π΅Π»Ρ Π²Π΅ΠΊΡΠΎΡΠ½ΠΎΠΉ Π°Π²ΡΠΎΡΠ΅Π³ΡΠ΅ΡΡΠΈΠΈ Ρ ΠΈΠ·ΠΌΠ΅Π½ΡΡΡΠΈΠΌΠΈΡΡ Π²ΠΎ Π²ΡΠ΅ΠΌΠ΅Π½ΠΈ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠ°ΠΌΠΈ (TVP-VAR). ΠΡΠΎΠ²Π΅Π΄Π΅Π½Π½ΠΎΠ΅ ΡΠΌΠΏΠΈΡΠΈΡΠ΅ΡΠΊΠΎΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΏΠΎΠΊΠ°Π·ΡΠ²Π°Π΅Ρ, ΡΡΠΎ ΡΠ΅Π½Π° Π½Π° Π·ΠΎΠ»ΠΎΡΠΎ, ΠΏΠΎΠ»ΠΈΡΠΈΡΠ΅ΡΠΊΠ°Ρ Π½Π΅ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΠΎΡΡΡ, ΡΠ΅Π½Π° Π½Π° Π½Π΅ΡΡΡ ΠΈ ΡΠΎΠ½Π΄ΠΎΠ²ΡΠΉ ΠΈΠ½Π΄Π΅ΠΊΡ ΡΠ²Π»ΡΡΡΡΡ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠ°ΠΌΠΈ Π²ΠΎΠ»Π°ΡΠΈΠ»ΡΠ½ΠΎΡΡΠΈ. Π ΡΠΎ ΠΆΠ΅ Π²ΡΠ΅ΠΌΡ, Π²ΠΎΠ»Π°ΡΠΈΠ»ΡΠ½ΠΎΡΡΡ Π²Π»ΠΈΡΠ΅Ρ Π½Π° ΡΠ°ΠΊΠΈΠ΅ ΡΠ°ΠΊΡΠΎΡΡ, ΠΊΠ°ΠΊ ΠΏΠ°Π»Π»Π°Π΄ΠΈΠΉ, ΠΏΠ»Π°ΡΠΈΠ½Π°, ΡΠ΅ΡΠ΅Π±ΡΠΎ ΠΈ ΠΎΠ±ΠΌΠ΅Π½Π½ΡΠΉ ΠΊΡΡΡ ΡΡΠ±Π»Ρ. Π ΡΠ½ΠΎΡΠ½Π°Ρ ΠΊΠ°ΠΏΠΈΡΠ°Π»ΠΈΠ·Π°ΡΠΈΡ ΡΠ²Π»ΡΠ΅ΡΡΡ ΡΠΈΡΡΡΠΌ Π΄ΠΎΠ½ΠΎΡΠΎΠΌ, ΡΡΠ½ΠΎΠΊ ΡΠ΅ΡΠ΅Π±ΡΠ° β ΡΠΈΡΡΡΠΌ ΠΏΠΎΠ»ΡΡΠ°ΡΠ΅Π»Π΅ΠΌ. ΠΠ°Π»Π»Π°Π΄ΠΈΠΉ ΡΡΠ°Π» ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΎΠΌ ΡΠΈΡΡΠΎΠΉ Π²ΠΎΠ»Π°ΡΠΈΠ»ΡΠ½ΠΎΡΡΠΈ ΠΏΠΎΡΠ»Π΅ ΠΌΠΈΡΠΎΠ²ΠΎΠ³ΠΎ ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΠΎΠ³ΠΎ ΠΊΡΠΈΠ·ΠΈΡΠ°. ΠΠ΅ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΠΎΡΡΡ ΡΠΎΡΡΠΈΠΉΡΠΊΠΎΠΉ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΏΠΎΠ»ΠΈΡΠΈΠΊΠΈ Π±ΡΠ»Π° ΠΎΡΠ½ΠΎΠ²Π½ΡΠΌ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΎΠΌ Π²ΠΎΠ»Π°ΡΠΈΠ»ΡΠ½ΠΎΡΡΠΈ Ρ 2008 ΠΏΠΎ 2014 Π³Π³., ΠΎΠ΄Π½Π°ΠΊΠΎ Π²ΠΏΠΎΡΠ»Π΅Π΄ΡΡΠ²ΠΈΠΈ Π²ΠΎΠ»Π°ΡΠΈΠ»ΡΠ½ΠΎΡΡΡ Π΄ΡΡΠ³ΠΈΡ
ΡΠ°ΠΊΡΠΎΡΠΎΠ² ΠΎΠΊΠ°Π·ΡΠ²Π°Π»Π° Π½Π° Π½Π΅Π΅ Π±ΠΎΠ»ΡΡΠ΅Π΅ Π²Π»ΠΈΡΠ½ΠΈΠ΅. Π 2014 Π³., ΠΊΠΎΠ³Π΄Π° ΡΠ΅Π½Π° Π½Π° Π½Π΅ΡΡΡ Π·Π½Π°ΡΠΈΡΠ΅Π»ΡΠ½ΠΎ ΡΠ½ΠΈΠ·ΠΈΠ»Π°ΡΡ, ΡΠ΅Π½Π° Π½Π° Π·ΠΎΠ»ΠΎΡΠΎ Π±ΡΠ»Π° ΠΎΡΠ½ΠΎΠ²Π½ΡΠΌ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΎΠΌ Π²ΠΎΠ»Π°ΡΠΈΠ»ΡΠ½ΠΎΡΡΠΈ Π΄Π»Ρ Π΄ΡΡΠ³ΠΈΡ
ΡΡΠ½ΠΊΠΎΠ². ΠΠΎΠ»Π½Π°Ρ ΡΠ²ΡΠ·Π°Π½Π½ΠΎΡΡΡ ΡΡΠ½ΠΊΠΎΠ² Π² Π·Π½Π°ΡΠΈΡΠ΅Π»ΡΠ½ΠΎΠΉ ΡΡΠ΅ΠΏΠ΅Π½ΠΈ Π·Π°Π²ΠΈΡΠΈΡ ΠΎΡ ΡΡΠ΄Π° ΡΠΊΠ·ΠΎΠ³Π΅Π½Π½ΡΡ
ΠΏΠΎΡΡΡΡΠ΅Π½ΠΈΠΉ, ΡΠ°ΠΊΠΈΡ
ΠΊΠ°ΠΊ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΡΠ°Π½ΠΊΡΠΈΠΈ, Π²Π²Π΅Π΄Π΅Π½ΠΈΠ΅ ΡΠ΅ΠΆΠΈΠΌΠ° ΠΏΠ»Π°Π²Π°ΡΡΠ΅Π³ΠΎ ΠΎΠ±ΠΌΠ΅Π½Π½ΠΎΠ³ΠΎ ΠΊΡΡΡΠ°, ΠΏΠ°Π΄Π΅Π½ΠΈΠ΅ ΡΠ΅Π½ Π½Π° Π½Π΅ΡΡΡ. ΠΡΡ
ΠΎΠ΄Ρ ΠΈΠ· ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Π½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π°, ΡΡΠΎΡΠΌΡΠ»ΠΈΡΠΎΠ²Π°Π½ΠΎ Π½Π΅ΡΠΊΠΎΠ»ΡΠΊΠΎ ΡΠ΅ΠΊΠΎΠΌΠ΅Π½Π΄Π°ΡΠΈΠΉ Π΄Π»Ρ ΠΏΠΎΡΡΡΠ΅Π»ΡΠ½ΡΡ
ΠΈΠ½Π²Π΅ΡΡΠΎΡΠΎΠ² ΠΈ ΡΡΠ΅ΠΉΠΊΡ
ΠΎΠ»Π΄Π΅ΡΠΎΠ² Π² ΡΠΎΡΡΠΈΠΉΡΠΊΠΈΡ
ΡΠ΅Π³ΠΈΠΎΠ½Π°Ρ
Is Russia successful in attracting foreign direct investment? Evidence based on gravity model estimation
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 βoverinvestβ and βunderinvestβ 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
DETERMINANTS OF FDI INFLOWS: THE CASE OF RUSSIAN REGIONS
This paper empirically analyses the determinants of foreign direct investment inflows into the Russian regions.
This problem has become highly relevant for the necessary modernization of the Russian economy after
the recent economic slowdown and sharp decrease in budget revenues. The authors model foreign direct
investment flows with the use of the gravity approach according to which investment flows are positively correlated
with the size of the investorβs country as well as the size of the recipient region and are negatively correlated
with the distance between investor and recipient. The empirical analysis is based on a constructed
database consisting of the foreign direct investment flows from 179 investor countries into 78 Russian regions
for the period 2006β2013. The authors apply the Poisson Pseudo Maximum Likelihood method and identify
the following factors determining foreign direct investment inflows into the Russian economy: the gross domestic
product of the investorβs country, the gross domestic product per capita in the recipient region, the
distance from the investor to Moscow, the openness of the region, the economic situation in the region, the
innovative capacity of the region and the foreign direct investment of the previous period. Interestingly, the
distance from the recipient region to Moscow matters for the regions in the western part of Russia (relatively
close to Moscow) but is not significant for the regions in the eastern part (remote regions)