The Gender Pay Gap in the Netherlands

Hey everyone!

Today I’m going to analyze the “Earnings Gap between Men and Women in Various Sectors in the Netherlands” dataset that I found on Wobby.

As a policymaker, journalist, advocate, or anyone interested in workplace equity, you may want to examine the latest statistics on the gender pay gap between men and women across different industries in the Netherlands. This critical measure shows the average hourly wage for women as a percentage of men’s average hourly wage.

Key insights

The pay gap is calculated by dividing the average gross hourly wage for women by the average gross hourly wage for men within each industry subgroup and time period. A higher percentage indicates a smaller gap, while a lower percentage indicates a bigger inequality in pay.

The 2022 visualization revealed some noteworthy findings:

  • Public administration (Openbaar bestuur en overheidsdiensten) had the smallest gender pay gap at 1.1%.
  • Waterbedrijven en afvalbeheer showed no gender pay gap, with women earning 100% of what men earn on average.
  • Finance and insurance and trade (handel) demonstrated the widest pay gaps at 22.8% and 23% percent respectively.

Other Analysis Ideas

Even though my insight is focused on the gender pay gap in 2022, the CBS dataset provides 14 years (2008-2022) of historical data.

You can do way more with the data. For example, you could:

  • Track how the gender pay gap has changed over the years for each industry.
  • Analyze if certain sectors have made more progress in closing the gap.
  • Look for particular years when the gap widened or narrowed significantly.
Other datasets you might find interesting.


Recommended datasets

In addition, you could incorporate related data sources on factors like workforce participation, hourly wages, and seniority levels to provide context and additional insights into the gender pay divide. At Wobby, we have a feature that will show you related datasets to that.

The ability to quickly generate visualizations with Wobby is just the starting point – deeper analysis can uncover richer insights. 

I wonder what kind of insights you can get out of this dataset! Let me know if you do, you can always mail to me at :-)

Happy Wobby’ing,


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