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Название: Data-driven definitions of gazelle companies that rule out chance: application for Russia and Spain
Авторы: Savin, I.
Novitskaya, M.
Дата публикации: 2023
Издатель: Springer Science and Business Media Deutschland GmbH
Библиографическое описание: Savin, I & Novitskaya, M 2023, 'Data-driven definitions of gazelle companies that rule out chance: application for Russia and Spain', Eurasian Business Review, Том. 13, № 3, стр. 507-542. https://doi.org/10.1007/s40821-023-00239-2
Savin, I., & Novitskaya, M. (2023). Data-driven definitions of gazelle companies that rule out chance: application for Russia and Spain. Eurasian Business Review, 13(3), 507-542. https://doi.org/10.1007/s40821-023-00239-2
Аннотация: The phenomenon of fast-growing companies exhibiting sustained growth and creating disproportionally many new jobs, so-called “gazelles”, has been widely analyzed in the literature. The criteria defining “gazelles”, however, lack a consensus, while it cannot be ruled out that superior performance of these companies is just good luck. We use large firm-level datasets for Russia and Spain and conduct a Monte Carlo experiment with first-order Markov chains to derive a definition of “gazelle” companies and ensure that their existence cannot be explained by chance only. Our results demonstrate that the definitions of “gazelle” companies differ between the two countries warning against using same definition for different countries. We find that the “gazelles” account for about 1–2% of the companies in our datasets and are responsible for approximately 14% of employment growth in Russia and 9% in Spain. These companies are concentrated in economic sectors like retail trade, real estate and construction. © 2023, The Author(s).
Ключевые слова: EMPLOYMENT
FAST-GROWING FIRM
MARKOV CHAIN MONTE CARLO
SME
SUSTAINED JOB CREATION
URI: http://elar.urfu.ru/handle/10995/130384
Условия доступа: info:eu-repo/semantics/openAccess
cc-by
Текст лицензии: https://creativecommons.org/licenses/by/4.0/
Идентификатор SCOPUS: 85152429357
Идентификатор WOS: 000965520100001
Идентификатор PURE: 43260519
ISSN: 1309-4297
DOI: 10.1007/s40821-023-00239-2
Сведения о поддержке: Russian Science Foundation, RSF: 19-18-00262
Ivan Savin acknowledges support from the Russian Science Foundation (RSF grant number 19-18-00262). We thank Vladimir Korzinov and Philipp Mundt for many helpful comments and suggestions on the earlier drafts of the paper. The usual disclaimer applies.
Карточка проекта РНФ: 19-18-00262
Располагается в коллекциях:Научные публикации ученых УрФУ, проиндексированные в SCOPUS и WoS CC

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