Throughout my coursework, internships, and my GA positions, I have seen a lot of wage data. Average weekly wages, median household income, inflation adjusted occupational wages- I thought I had seen it all prior to doing the background research for our upcoming quarterly workforce report. Filtering this data by gender yielded results I had never seen before.
Most of us are familiar with the gender wage gap. In my time in economic development and my studies in planning, I thought I was familiar with this concept. Despite years of hearing the same thing- women are paid less than men- I remained ignorant to the data backing this claim. I knew the inequality existed, but I had no clue how staggering the differences in compensation were for women and men for the same or similar occupations.
In many cases, it is common for a woman to make 65-70% of what their male counterparts are compensated. While women’s earnings have been on the rise for decades, this disparity is unacceptable for a number of apparent and not so obvious reasons. From the standpoint of equality this is wrong for a number of reasons that I am largely not qualified to comment on.
What I will say, however, is that this glaring discrepancy has a disastrous effect on income data. Measures of central tendency (averages, median, mode, etc.) are the basic analytic unit for this data. These measures work well with homogeneous sample sizes but are not robust when outliers infiltrate the sample size. Consider this, median earnings for an industry that has relatively low education requirements is listed at 31,000.
At first glance this is a livable wage, but upon further investigation we find that this industry is 80% male and 20% female; median earnings for men are $36,000 while median earnings for women are $14,000. In this case, our “median” is over double that of the median earnings for women in this industry. While this is misleading for those that work with this data, it is a reality for women working in many industries. For instance, the numbers above are rounded figures from the “production, transportation, and material moving” industry group for Tazewell County.
I have no solutions or answers to this longstanding problem, but I can say how important it is for those involved in economic development to take a deeper look into the data we report. Beyond being wrong for a number of equality reasons, income inequality distorts data and indirectly contributes to this issue.