Professional Equality Index

In compliance with the French law No. 2018-771 of September 5, 2018, Nintendo European R&D publishes its gender equality index. This index allows companies with more than 50 employees to assess aspects related to professional gender equality.

Women/men equality index: Not Calculable

Pay gap (écart de rémunération): Not Calculable

The reason why the pay gap is not calculable is because we don’t have 40% of employees covered by age groups that contain at least three women.

Individual pay increase gap (écart de taux d’augmentations individuelles): 35/35

100% of women’s and 100% of men’s salaries have been raised.

Pay increase upon returning from maternity leave (augmentation au retour de congé maternité): Not Calculable

100% of women have received a salary increase when returning from maternity leave, but no employee returned from maternity leave in 2024.

Highest salaries gap (hautes rémunérations): 0/10

One woman in the top ten salaries.

Progression goals (objectifs de progression):

We strive to improve the balance between genders among employees and actively search for female applicants in all positions.
We want to encourage women, both internally and externally, to apply to management positions. We have started promoting female employees to the executive committee to improve female presence in company leadership positions.

Note

NERD does not have a sufficient number of female employees in each pre-defined age group (<30, 30-40, 40-50, >50) which results in a “Not Calculable” status. In order to shed light on these categories and provide a more accurate description of the situation, we have conducted our own investigation and calculations.

We believe that experience is the main contributor to salary differences between employees.

To confirm this hypothesis, we implemented a linear regression on salary with respect to years of experience on NERD’s largest homogeneous population; R&D engineers (the group does not include managers, tech leads, project leads and has 5 female & 49 male), which turned out to show a strong statistical significance (p-value¹ of \(10^{-14}\)).

Once we subtract the contribution of experience to the salary, we can compare the residuals that don’t depend on experience but on individual performances. Our goal was to analyze if there is a statistical bias related to gender.

We implemented a linear regression on gender, which did not show statistical significance (p-value of 0.82).

As a complement, we also implemented a permutation test² on the residuals for which the women score ended-up slightly above 50% of the histogram. A score below 2.5% would mean women are disadvantaged, a score above 97.5% would mean men are disadvantaged.

This is not concrete proof that gender bias does not exist, and we will continue to address and improve any bias. However, we do believe our calculations can be a complement to the official gender equality index calculations required by law as it provides more insights than the “uncalculable” conclusion.