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Women on corporate boards, stated-owned enterprises and firm performance: Evidence from Vietnam and quantile regression

Author

Listed:
  • Jean-Michel Sahut

    (IDRAC Business School, France)

  • Rey Dang

    (ISTEC Paris, France)

  • L'Hocine Houanti

    (EM Normandie Business School, France)

  • Nhu Tuyen Le

    (Grenoble Ecole de Management, France)

Abstract

Using a unique sample of 498 non-financial companies listed on the two main Vietnamese stock exchanges between 2009 and 2014, we investigate the relationship between firm performance (FP) and women on corporate boards (WOCB), using the instrumental variable quantile regression panel data suggested by Powell (2016) to take into account heterogeneity and endogeneity issues. Compared to existing studies, we consider the Vietnamese institutional context through the weight and the role of the State. We find that, overall, WOCB have a negative influence on FP in State-owned enterprises (SOEs). This finding recognizes the importance of the State in a transactional economy, such as Vietnam, and the role of institutional context in the relationship between WOCB and FP.

Suggested Citation

  • Jean-Michel Sahut & Rey Dang & L'Hocine Houanti & Nhu Tuyen Le, 2020. "Women on corporate boards, stated-owned enterprises and firm performance: Evidence from Vietnam and quantile regression," Economics Bulletin, AccessEcon, vol. 40(3), pages 2538-2549.
    • Handle: RePEc:ebl:ecbull:eb-19-01136
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    References listed on IDEAS

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    2. Mohamed Khenissi & Amal Hamrouni & Nadia Ben Farhat Toumi, 2022. "Executive compensation indexed to corporate social responsibility and firm performance: empirical evidence from France," Post-Print hal-03771680, HAL.

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    More about this item

    Keywords

    Women on corporate boards; Governance; Board of directors; Firm performance; Quantile regression;
    All these keywords.

    JEL classification:

    • G3 - Financial Economics - - Corporate Finance and Governance
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics

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