Determinants of receiving government support by small and medium-sized enterprises in Russia

Government support measures help companies overcome various restrictions impeding their growth and develop ment, which is of special relevance for small and medium-sized enterprises (SMEs). These measures, therefore, must reach the target companies and ensure efficient allocation of resources. The article reveals key characteristics of SMEs in Russia that increase the probability of them receiving government support. The research methodology is based on public interest theory, public choice theory and the concept of economic freedom. The research method is a two-step regression analysis, in which the binary choice model is first evaluated, and then the LASSO model is used to assess the impact of the textual content of the companies’ websites. The dataset of the study is represented by data on 2,176 Russian metallurgical enterprises for 2020–2023, of which 57% were receiving government support. The results of the first step of evaluation showed that the organization’s age and size meas ured by revenue were the key factors associated with the probability of receiving government support. At the second step, we found that the mention of certain goods or materials on the websites, the enterprise’s location and orientation towards foreign stakeholders were significantly related to the likelihood of getting state support. The study offers tools for analyzing the determi nants of government support for SMEs and highlights the importance of this analysis for more efficient allocation of government resources.

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Publication Details

Journal
Upravlenets
Published
2024-11-06
DOI
https://doi.org/10.29141/2218-5003-2024-15-5-2
Citations
2
Primary Topic
Innovation Policy and R&D
Type
article
Field-Weighted Citation Impact
2.26

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article

Determinants of receiving government support by small and medium-sized enterprises in Russia

А. И. Смирнов, Iuliia Naidenova, Petr Parshakov
2 citations
Upravlenets
Innovation Policy and R&D
2.26
article

Determinants of receiving government support by small and medium-sized enterprises in Russia

А. И. Смирнов, Iuliia Naidenova, Petr Parshakov
article en
2 citations

Abstract

Government support measures help companies overcome various restrictions impeding their growth and develop ment, which is of special relevance for small and medium-sized enterprises (SMEs). These measures, therefore, must reach the target companies and ensure efficient allocation of resources. The article reveals key characteristics of SMEs in Russia that increase the probability of them receiving government support. The research methodology is based on public interest theory, public choice theory and the concept of economic freedom. The research method is a two-step regression analysis, in which the binary choice model is first evaluated, and then the LASSO model is used to assess the impact of the textual content of the companies’ websites. The dataset of the study is represented by data on 2,176 Russian metallurgical enterprises for 2020–2023, of which 57% were receiving government support. The results of the first step of evaluation showed that the organization’s age and size meas ured by revenue were the key factors associated with the probability of receiving government support. At the second step, we found that the mention of certain goods or materials on the websites, the enterprise’s location and orientation towards foreign stakeholders were significantly related to the likelihood of getting state support. The study offers tools for analyzing the determi nants of government support for SMEs and highlights the importance of this analysis for more efficient allocation of government resources.

UpravlenetsVol. 15(5)
National Research University Higher School of Economics (RU)
Russian Science Foundation
Peace, Justice and strong institutions
Openalex Percentile: Top 8%
Innovation Policy and R&D
2.26
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Determinants of receiving government support by small and medium-sized enterprises in Russia — А. И. Смирнов, Iuliia Naidenova, et al. · Upravlenets (2024) | TGRS Research Map | TGRS