by John Mulholland

This legislative session Senator Anderegg created a bill to try to address a pay gap that exists between men and women. This seems like a popular cause to get behind, though. Who could be against equal pay? But we me must make sure we are taking not only a data-driven approach but that we are gathering the right data. If we look at the wrong data we will come up will come up with the wrong approach that will have unintended consequences.

We also need to be careful to listen to all voices, not just the loudest. My friend and former state representative, Holly Richardson, told me that the best bills are created when people collaborate and consider other viewpoints. This can be extremely difficult when it is a sensitive topic and a lot of emotion is involved.

I have to admit that at first I was excited to see this topic addressed. I have a strong utilitarian philosophy and see discrimination as a huge waste and inefficiency. I had heard about the pay gap several times. And then somebody pointed me to the methodology as to how it was calculated. He claimed that all that was done was divide the total women’s salary by the number of women working compared to men.

I doubted this was true. I knew that there must be a better methodology used. I searched and searched and came up with nothing. I asked my friends who strongly believed in this cause and didn’t see any better methodology used. Apparently, some people had taken a number and had greatly exaggerated what it meant.

Somebody finally pointed me to a Freakonomics episode where it was discussed. I had really enjoyed previous things done by them and found them to be pretty accurate.

After listening I had to admit two things. First, the wage gap was a lot smaller than had been previously advertised when you used controls for the same job. It was between 2 – 5%, not the 25% that had been quoted by politicians. Secondly, I thought that industry segment had a much bigger part in the 25%. The professor on the podcast said that only accounted for about 1/4. Instead, she claimed that the data showed that the biggest factor was a career choice. She explained that women choose different and more flexible career tracks as they are often primary caregivers of children and elderly adults. It was great to finally have some much more thorough data.

With a new perspective, I noticed this in my own life. I work in software, which is very heavily male. I work to help remove barriers for women so they can work in software if they wish. Soon after learning more about the gender wage gap I attended a women’s tech meeting with my wife and daughter. My coworker was presenting on how to do well in tech interviews and I was there to help.

After a great presentation, one of the chapter leaders got up and explained how to get flexibility from an employer. She explained how to clearly state that she wanted a specific day off every week and would only work certain hours. I thought to myself that of course she was going to get paid less than a full-time employee who will be there during business hours to work. It would be much harder to integrate somebody who works different hours into projects. I thought about explaining this but then I realized that this was her choice and if she could find an employer willing to work with those restrictions then that was between her and her employer.

I spoke with my wife after as she is thinking of doing software after the kids have all moved out. She would want a job with more flexibility. She wasn’t looking for a career but wanted something to do so she wouldn’t get bored. Should she be able to do this?

I suggest you read the actual bill. It isn’t very long. Please note what data is to be gathered and what data isn’t. If you notice, job flexibility is not one of those things that are measured. Again, the biggest factor that the Harvard Professor listed, job flexibility, is NOT being measured.

If we look at the wrong data then it is easy to make the wrong conclusions. Making the wrong conclusions means that we make the wrong laws. In this case, it would be very easy to make an equal pay law that doesn’t consider job flexibility. Would an employer be able to offer greatly increased flexibility if they had to pay the same wages?

If we are going to address this issue then we need to do it with an open mind and understand what research has already been done on this issue. We need to put aside our agendas and be willing to dive into the data. We need to take the time to understand what the data is telling us and what it isn’t.

Even the 2-5% gap doesn’t mean there is discrimination going on. It just means that we haven’t accounted for what that represents. It could very possibly represent bias but it might represent something else that we haven’t accounted for yet.

I propose that we take an open-minded look at the data, keeping previous research in mind. Then we can find both the real issues and what, if any, solutions should be implemented. Data gathering is an essential step but we must gather the right data.

The bill can be found at