The County of San Mateo is dedicated to advancing equity. The publication of this interactive pay equity dashboard is one of many initiatives the County is undertaking to advance equity within its workforce and the community. Now more than ever, equity and pay transparency have become a critical priority for organizations and an essential tool for building trust within the workforce, which leads to engagement, retention, and performance. 
This dashboard displays our workforce's pay data relative to age, race, gender, and job category. Key pay equity data is highlighted in specific sections below. Click on each of the graphs/charts in the summaries to access the interactive dashboard, or click HERE to jump directly to the dashboard. 
Data Parameters:
  1. Unless otherwise noted, headcount in each of the metrics provided is based on filled, regular positions (excludes extra help and limited-term employees).
  2. Pay rate is comprised of hourly salary rate + percentage allowances such as differential pay, experience pay, etc., and excludes flat rate allowances such as uniform, transportation, and bilingual pay.
  3. Race/Ethnicity and Gender data are based on employee-disclosed information only.
  4. Data and analysis will be refreshed quarterly.

HIGHLIGHTS (FY 2023-2024 Q3)

Pay by Gender
Based on data available as of 3/31/2024, the County’s female workforce earned on average 6% less than its male counterpart. While this pay gap is well below the 2022 National Gender Pay Gap of 16% as reported by the Bureau of Labor and Statistics, the County is currently reviewing its compensation data in order to identify potential reasons for the 6% gap and ways to narrow it.
The County's female workforce is primarily in Professional and Administrative Support jobs which have the highest and lowest average pay rates respectively, of all job categories. The pay gap could be attributed to fact that far more women are in the job categories that are among those with the lowest average rate (Administrative Support and Technicians), thereby impacting averages as it relates to headcount.  
Pay by Race/Ethnicity
The County’s Non-White workforce which makes up 75% of the total workforce earned on average 23% less than the White workforce, keeping in mind however, that 23% of employees declined to identify race/ethnicity.
Native Hawaiian or Other Pacific Islander which comprise 2% of the workforce has the lowest pay average at $51.88 per hour. Of the three largest race/ethnicity group that comprise the total workforce, the average pay of the Hispanic workforce is the lowest. Majority of the Native Hawaiian or Other Pacific Islander workforce is in Administrative Support job category (27%) which is also the job category that has the lowest pay average. Majority of the Hispanic workforce is in the Professional job category (38%) but a combined 51% are in job categories with the lowest average pay rate: Administrative Support, Technician, and Service Maintenance.
Pay by Age Group
The youngest age groups (16-24 and 25-34) which make up 23% of the workforce has the lowest average pay rate, while the 65 or older group has the highest average pay rate even though they comprise only 3% of the total workforce. The most significant factor contributing to the gap is years of service which is compensated via experience pay. The younger the worker, the newer to the organization and therefore not yet eligible for experience pay. Between the 45-54 and 35-44 age groups which comprise a majority of the workforce, the pay gap is 5%. Both age groups are primarily in the Professional job category which has the second highest average pay rate. 


Use the interactive graphs and charts below to dive deeper into the data. To view this section on to a new browser page, click HERE. Click inside the graph to expand view and filter data where possible. Hover over the charts and bars to see specific details behind the data. Be sure to click the right arrow below to see page two. 
This dashboard is in its beta stage and could expand to include other pay-related data and cross-referencing. Exercise caution when interpreting any of the data. Remember that certain variables such as years of service, job-specific duties, market rate of classifications, licensure/certification requirements among many others, may need to be considered when interpreting the observed differences in pay.