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Relationship of Property Structure and Performance of High-Tech Technology Companies

  • SCIENCE AND TECHNOLOGY
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Studies on Russian Economic Development Aims and scope

Abstract—

Based on data from high-tech companies in Eastern and Western Europe, the article analyzes the relationship between ownership structure parameters and some corporate governance factors and company performance, and examines the established patterns of ownership structure of successful companies. It is shown that at the initial stage of development of companies, the “Sole leader and like-minded people” is a more favorable model of ownership structure, and at the second stage of development, when the company becomes a large business, the “Leader Team” with a less concentrated level of ownership is a higher priority model.

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Notes

  1. The SME income boundary has been established in accordance with Decree of the Government of the Russian Federation of April 4, 2016 N 265.

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Authors and Affiliations

  1. Institute for Economic Forecasting, Russian Academy of Sciences, Moscow, Russia

    A. I. Rybalka

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  1. A. I. Rybalka
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Correspondence to A. I. Rybalka.

Appendix

Appendix

Results of step-by-step verification of hypotheses put forward

Hypothesis number

Description of the proxy metric by which hypothesis tested

SMEs

Large Companies

H1 (1)

Maximum ownership share among owners

0.001

(0.001)

–0.01

(0.005)

H1 (2)

Ownership share of the second largest owner

0.0001

(0.001)

0.01

(0.01)

H1 (3)

Low ownership concentration

–0.18*

(0.10)

–0.32

(0.78)

H1 (4)

Average ownership concentration

0.04

(0.04)

0.68*

(0.38)

H1 (5)

High concentration of ownership

–0.01

(0.04)

–0.54

(0.36)

H2 (1)

The total share of ownership of directors and managers of the company

0.001**

(0.0004)

0.01

(0.004)

H2 (2)

Maximum ownership share among management owners

0.002**

(0.001)

0.001

(0.01)

H2 (3)

Ownership share of the second largest management owner

0.01***

(0.003)

0.08*

(0.04)

H3 (1)

Number of directors

0.08**

(0.03)

–0.11

(0.13)

H3 (2)

Number of owners

–0.04

(0.03)

0.29*

(0.16)

H4 (1)

The share of foreign owners

-0.0001

(0.0005)

0.0004

(0.002)

H4 (2)

The share of state ownership (excluding state-owned companies)

–0.02***

(0.01)

–0.21

(8.17)

H5 (1)

Model No. 1 “Leader and like-minded people”

0.16*

(0.09)

–0.50

(1.20)

H5 (2)

Model No. 2 “Sole Leader”

–0.004

(0.04)

0.02

(0.42)

H5 (3)

Model No. 3 “Leader Team”

0.08

(0.09)

1.58*

(0.77)

Control financial variables

+

+

Dummy Country Variables

+

+

Industry Dummy Variables

+

+

Number of observations

20 013

865

Log likelihood

–10 609

–467

Akaike criterion

21 234

951

  1. * – significance at 10%; ** – significance at 5%; *** – significance at 1%. Proxy indicators for each hypothesis were separately included step by step in each of the logit models (No. 1 SMEs and No. 2 Large Companies), developed on the basis of control financial variables and dummy variables for countries and industries. Here, the average value of the Akaike criterion within each group of companies calculated for all iterations of hypothesis testing is shown for reference. Source: CMACP estimates.

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Rybalka, A.I. Relationship of Property Structure and Performance of High-Tech Technology Companies. Stud. Russ. Econ. Dev. 31, 264–270 (2020). https://doi.org/10.1134/S1075700720030144

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  • DOI: https://doi.org/10.1134/S1075700720030144

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