Evidence on the determinants of corporate ownership concentration: Economic development and inequality in OECD countries

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Type of the article: Research Article

Abstract
Ownership concentration remains a defining feature of global corporate governance, with significant implications for economic development and inequality. This study aims to analyze the determinants of corporate ownership concentration in OECD and partner countries, focusing on the interplay between macroeconomic factors and distributional dynamics. The analysis relies on a short-balanced panel of 48 countries for 2020 and 2022, using OECD’s newly published indicator of concentrated ownership and applying OLS, Fixed Effects, and Random Effects models in R Studio. The results show that GDP per capita is negatively and significantly associated with ownership concentration (RE model, β = –1.73, p < 0.01), confirming that higher development levels are linked to more dispersed ownership structures. Wealth inequality exerts a dual effect: the top 10% wealth share is negatively related to ownership concentration (β = –135.4, p < 0.05), while the top 1% wealth share is positively related (β = 58.1, p < 0.05), underscoring the reinforcing effect of elite concentration. Income inequality measures were less robust, though the FE model indicated a positive association with the top 10% income share (β = 281.1, p = 0.021) and a negative association with the top 1% income share (β = –125.5, p = 0.006). Country-specific effects revealed persistent structural deviations, with Luxembourg (+43.5), the Slovak Republic (+29.5), and Indonesia (+27.0) exhibiting systematically higher ownership concentration. At the same time, India (–32.5), South Africa (–30.4), and Japan (–29.6) consistently displayed lower levels than predicted.

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    • Figure 1. Diagnostic plots for the OLS regression model for ownership concentration
    • Table 1. Variables and their sources
    • Table 2. Descriptive statistics
    • Table 3. OLS regression results for ownership concentration
    • Table 4. OLS regression results (t-test of coefficients)
    • Table 5. FE and RE regression results
    • Table 6. RE regression with robust standard errors
    • Table 7. Country-specific random effects
    • Table A1. Sample of countries
    • Conceptualization
      Mehriban Aliyeva, Leyla Huseynova
    • Formal Analysis
      Mehriban Aliyeva, Leyla Huseynova
    • Funding acquisition
      Mehriban Aliyeva
    • Methodology
      Mehriban Aliyeva
    • Resources
      Mehriban Aliyeva, Leyla Huseynova
    • Software
      Mehriban Aliyeva, Leyla Huseynova
    • Supervision
      Mehriban Aliyeva
    • Validation
      Mehriban Aliyeva
    • Writing – original draft
      Mehriban Aliyeva, Leyla Huseynova
    • Writing – review & editing
      Mehriban Aliyeva, Leyla Huseynova
    • Data curation
      Leyla Huseynova
    • Investigation
      Leyla Huseynova
    • Project administration
      Leyla Huseynova
    • Visualization
      Leyla Huseynova