Income Equity (90th-to-10th percentile wage ratio)

4.21

10th percentile hourly wage (2018)
$10.69
90th percentile hourly wage (2018)
$44.98
50th percentile hourly wage (2018)
18.87
What is this measure? This measure uses Bureau of Labor Statistics (BLS) wage data to compare the 90th percentile wage to the 10th percentile wage. It can be read as "The 90th percentile wage earner makes __ times more than the 10th percentile wage earner."

Why do we track this? Sharing average or median wages tell only a part of the prosperity story for an area. An important component of economic prosperity is whether or not prosperity is shared among workers. Questions to ask include: are wages increasing for all workers over time? Is this increase shared equally at different wage levels? do disparities in wage levels reflect changes in the nature of the types of jobs in a region? Income Equity was a recommended metric for the from the 2017 Talent 2.0 Regional Workforce Strategy.
The 90th to 10th percentile hourly earnings ratios are shown in the chart above along with components (90th to 50th and 50th to 10th) of that ratio relative to the median (50th percentile earnings). Many factors impact the range in wages, for example: less experienced workers make lower wages and jobs that require specialized training generally have higher wages. An increasing index value generally shows a rise in wages that is faster for higher paid workers. A decreasing index value would show rises in wages that were slower for the higher paid workers than the lowest paid workers. The components show the portion of wage change that is seen between the lower paid workers and median wage (50th to 10th) and the portion occurring within the higher paid workers and the median (90th to 50th).
What are some limitations of this metric's source? All measurements of income equity generally have some issues. This measure doesn't indicate why income equity may be decreasing (or increasing). Changes may be structural due to shifts in industry share in the economy, or they could be related to different rates of wage growth between various types of workers. This measurement only includes wage-related income.

Why did we use this source? Bureau of Labor Statistics Occupational Employment Survey data are available for all MSAs and states. "Earnings" estimated by this program are similar to "wages", but the earnings data comes from payroll records, so it incorporates shift differentials and regularly paid bonuses that some employees expect to typically find in their paycheck. The BLS program uses three years of data to create a single year estimate. They inflation adjust older data, but do not correct it for changes in minimum wages. This is why some Colorado estimates appear lower than the state legal minimum wage. We do not adjust these numbers to true Colorado minimums; we want the data to remain comparable with all regions covered in the OES programs. This measurement allows us to only look at earnings related income, as opposed to sources that incorporate other sources of income at the individual or household level.

Data Source

Related Dashboard Measures

Additional Information and Other Data Sources