Women remain underrepresented in the tech sector. According to the Equal Employment Opportunity Commission, women make up “nearly half of the total U.S. workforce … [but] just 22.6% of the high tech workforce.” Opportunities for women in tech can vary considerably by location, and these differences shape access to jobs, career advancement and earning potential. In some places, women account for a larger share of tech workers, face narrower pay gaps and enjoy a stronger local labor market. In others, they may encounter a more challenging career environment.
SmartAsset evaluated data for 50 of the largest U.S. cities, assigning each a composite score based on four metrics: the pay gap between women and men in tech; women’s share of all tech workers; tech employment concentration, or tech workers as a share of the local workforce; and median earnings for women tech workers, adjusted for regional price differences. (The earnings adjustment accounts for local purchasing power, making salaries in higher- and lower-cost cities more comparable.)
Bakersfield has the largest pay disparity. Bakersfield, California, is not widely known as a tech hub, and just 1.9% of its workforce is employed in tech. Those jobs are concentrated in local IT firms and in technology roles at non-tech companies. Women tech workers there earn 65.5% as much as men, the largest pay disparity in the study. In Bakersfield, the sample of women in tech is relatively small, so pay comparisons may be less precise than in other markets.
1. San Jose, California
2. Seattle, Washington
3. San Francisco, California
4. Atlanta, Georgia
5. Baltimore, Maryland
6. Austin, Texas
7. Oakland, California
8. Portland, Oregon
9. Boston, Massachusetts
10. Minneapolis, Minnesota
11. San Diego, California
12. Chicago, Illinois
13. Charlotte, North Carolina
14. Memphis, Tennessee
15. Houston, Texas
16. Denver, Colorado
17. Albuquerque, New Mexico
18. New York, New York
19. Columbus, Ohio
20. Louisville, Kentucky
21. Raleigh, North Carolina
22. Miami, Florida
23. Tampa, Florida
24. Long Beach, California
25. Colorado Springs, Colorado
26. Fort Worth, Texas
27. Indianapolis, Indiana
28. Virginia Beach, Virginia
29. Kansas City, Kansas
30. Sacramento, California
31. Tucson, Arizona
32. Dallas, Texas
33. Nashville, Tennessee
34. Los Angeles, California
35. Jacksonville, Florida
36. Phoenix, Arizona
37. Philadelphia, Pennsylvania
38. Arlington, Texas
39. Wichita, Kansas
40. Oklahoma City, Oklahoma
41. Omaha, Nebraska
42. San Antonio, Texas
43. Milwaukee, Wisconsin
44. Detroit, Michigan
45. Tulsa, Oklahoma
46. Fresno, California
47. Las Vegas, Nevada
48. Mesa, Arizona
49. El Paso, Texas
50. Bakersfield, California
U.S. Census Bureau 2024 American Community Survey five-year estimates, representing survey data collected from 2020 through 2024, were analyzed for 50 of the largest U.S. cities by population. The analysis defined tech workers as city residents employed in computer and mathematical occupations. Each city received a composite score based on four weighted metrics: women’s median earnings as a percentage of men’s median earnings in tech; women’s share of all tech workers; tech employment concentration, calculated as tech workers’ share of the local population aged 16 and older employed or looking for work; and women tech workers’ median earnings, updated with a national wage index and adjusted for regional price differences.
For purposes of this study, workforce is synonymous with labor force. ACS earnings are reported in 2024 dollars. For comparability, each city’s median was updated to June 2026 using the national Bureau of Labor Statistics Employment Cost Index for private-industry wages and salaries. The same factor was applied to every city. Those figures were then adjusted using the Bureau of Economic Analysis’ all-items regional price parity for the metropolitan area containing each city. The updated amounts are not observed 2026 salaries, and ACS estimates may be subject to sampling and nonsampling error, particularly where occupational sample sizes are small. Rank differences should therefore be interpreted as directional rather than as evidence that similarly ranked cities are statistically distinguishable from one another. Source data providers are not affiliated with, and do not endorse or sponsor, this study or its findings.
This story was produced by SmartAsset and reviewed and distributed by Stacker.