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Gender discrimination and firm survival: a multilevel approach for EU textile companies

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Abstract

Recent economic literature highlighting the influence of gender discrimination on firm performance suggests that promoting gender diversity is key for boosting a company’s efficiency. This paper analyses the channels through which gender discrimination affects a specific performance indicator: the probability of a firm’s survival. The available evidence is controversial. We argue that a complex set of information at various levels (firm, sector, country, time, etc.) is required to correctly address these issues, i.e., that the data are likely to have a hierachical structure. Against this background, we generalize the standard business demography approach and propose the application of a non-linear, multilevel Cox model. For the empirical application, we focus on the survival probability of European firms in the textile and garment sector, which is characterized by a high percentage of women employees, a wide gender wage gap, and high discrimination. In line with standard business demography studies, we show that size and internationalization modes positively affect firms’ survival probability, while gender-related variables have a significant and negative impact.

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Notes

  1. ec.europa.eu/eurostat/statistics-explained/index.php/Gender_pay_gap_statistics#Gender_pay_gap_levels_vary_significantly_across_EU.

  2. See OECD, https://data.oecd.org/earnwage/gender-wage-gap.htm#indicator-chart

  3. For instance, for UK Data, see Office for National Statistics (2018), and for Egypt, see El Haddad (2016).

  4. For instance, Ryan and Haslam (2005) claim that firms are more likely to promote women after negative performance shocks.

  5. According to several studies, about three quarters of garment workers worldwide are female (Salloum et al 2016). See, for instance, the independent project on 'Garment Industry Supply Chains' (Women Working Worldwide, http://www.womenww.org/documents/www_education_pack.pdf).

  6. This approach is based on the Mundlak (1978) technique developed for panel data.

  7. We assume a non-homogeneous and not constant correlation structure at higher level. This means that in a standard regression analysis.

    $${\mathrm{y}}_{\mathrm{ijt}}=\mathrm{\alpha }+\upgamma {\mathrm{x}}_{\mathrm{ijt}}+{\mathrm{u}}_{\mathrm{i}}+{\mathrm{e}}_{\mathrm{j}}+{\mathrm{v}}_{\mathrm{t}},$$

    where i:1,..., n units are clustered in j:1,..., k groups on t:1,…T years, the correlation between any two units i and i’ conditional on time t is.

    $$\mathrm{corr}\left({\mathrm{y}}_{\mathrm{ij}},{\mathrm{y}}_{{\mathrm{i}}^{\mathrm{^{\prime}}}\mathrm{j}}|\mathrm{t}\right)=\frac{{\upsigma }_{\mathrm{u}}^{2}}{{\upsigma }_{\mathrm{u}}^{2}+{\upsigma }_{\mathrm{e}}^{2}},$$

    allowing to correctly estimate the variance of the system.

  8. Due to the presence of censored data in survival models, the R-square index cannot be used, so to identify the best fit, we used the criterion suggested by Schemper (1990). It consists in identifying the minimum absolute distance or the mean squared distance between the survival curves of a null model obtained through Kaplan–Meier estimation and a covariate including Cox model. Results obtained by models with alternative distributions are available from authors upon request.

  9. See, for instance, Tyrowicz et al. (2020). They attribute gender to board members based on an heuristic procedure, exogenously defined.

  10. According to this definition, firms are classified as follows: small (0–2,085,311), medium-small (2,085,312–4,179,623), medium-large (4,179,624–6,255,935), large (6,255,936–8,341,248).

  11. Notice that, although with small magnitude, the regressors are statistically significant so their impact on firms survival is evident and strong.

  12. Ahern and Dittmar (2012), who looked at similar issues from a slightly different perspective, show that the introduction of mandatory board-member gender quotas led to an increase in acquisitions but also performance deterioration in publicly traded Norwegian firms. We concentrated instead on acquisition by women investors and found that the probability of survival is higher.

  13. We used other proxies for wage discrimination like a categorical variable (low, medium, high) and several other specifications to test non-linear effects on firms’ survival probability and the results were consistent. These results are available from the authors upon request.

  14. Note that in our database, there are only very few countries having a non-zero percentage of CEO women (UK, Ireland and Latvia) and this may affect the estimation result To check the robustness of the results, we run several other specifications (available from authors upon request), controlling for the role of women executives and representatives in the company and the estimates are in line with Model 4 results. This may be the effect of a small presence of CEO/executives/representatives women in EU countries.

  15. Country-level gender effects are taken under control via the error structure specified in the multilevel approach and the ratio of the between-cluster variance to the total variance is represented by the Intraclass Correlation. Notice that it represents the proportion of the dependent variable’s total variance that is accounted for by the clustering. It can also be interpreted as the correlation among observations within the same cluster.

  16. The Norwegian law required all publicly listed companies to increase female representation on their boards of directors to 40 percent within two years. See Dale-Oelsen et al. (2013) for an analysis of the impact on firm performance.

  17. According to Catalyst (https://www.catalyst.org/knowledge/womens-earnings-wage-gap), “Ontario passed a law that required part-time casual workers, who are often women or new Canadians, to be paid at the same rate as full-time workers; Iceland passed a law that by January 2022, companies must prove they are paying equal wages for equal work or pay fines. Northern Ireland has reversed the gender pay gap: since 2010, women have earned on average 3.4% more than men. Germany passed a law that went into effect in January 2018 which gives women and men who feel disadvantaged the right to learn the salary of co-workers in the same job. In 2017, five US states—California, Colorado, Delaware, Nevada, and Oregon—as well as Puerto Rico passed pay equality laws”.

  18. The gender pay gap is the defined as the “difference between the average earnings of men and women, expressed relative to men’s earnings. For example, ‘women earn 15% less than men per hour’”. See, https://www.gov.uk/guidance/gender-pay-gap-reporting-overview

  19. The other two being Kaltex Textiles and Libra textiles.

  20. See INVISTA-gender-pay-gap-report.pdf.

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Acknowledgements

The authors wish to thank Francesca Di Iorio and Wendy Harcourt for their helpful comments on previous versions of this paper. All errors remain ours.

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Authors and Affiliations

  1. Dipartimento di Economia, Università di Firenze, and European University Institute, San Domenico di Fiesole, Fiesole, Italy

    Giorgia Giovannetti

  2. Dipartimento di Scienze Umane, Università Europea di Roma, Rome, Italy

    Margherita Velucchi

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  1. Giorgia Giovannetti
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Correspondence to Margherita Velucchi.

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Giovannetti, G., Velucchi, M. Gender discrimination and firm survival: a multilevel approach for EU textile companies. SN Bus Econ 2, 142 (2022). https://doi.org/10.1007/s43546-022-00315-1

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