This retrospective, cross-sectional study evaluated data from the AAN Compensation and Productivity Survey for differences in neurologist compensation by gender.

Women neurologists have been found to earn the least amount annually among men and women specialty physicians, with men neurologists paid substantially more, despite adjustment for multiple factors. The AAN's Compensation and Productivity Survey of its members provides benchmarking data for salaries, benefits and workload. 

Of 3268 completed surveys submitted, 1466 were from neurologists and had sufficient data for analysis (551 women, 951 men). We calculated an hourly wage from full time equivalent (FTE) status and weeks worked per year. We evaluated differences in men and women neurologist compensation with multivariable generalized linear models adjusting for race, ethnicity, geographic region, practice setting, years in practice, call status, leadership role, and subspecialty. 

Baseline characteristics were similar except for subspecialty distribution. More men practiced in higher-wage subspecialties (p < 0.05). Mean FTE equivalent annual salary for all neurologists was $280,315 and mean standardized hourly compensation was $131. Estimated annual salary for women was 10.7% less (p ≤ 0.001, 95% CI -4% - 16%), after controlling for the above variables. FTE equivalent annual salary for women neurologists in high compensation specialties ($281,838) was lower than the mean annual salary for men neurologists in high ($365,751) and low ($282,813) compensation subspecialties. The highest earning women neurologists’ mean hourly wage (11-20 years of practice, $128/hour) was less than all men neurologists except those with 0-5 years of practice ($125/hour).

This study adds to the evidence demonstrating gender disparities in compensation persist despite efforts by professional societies including the AAN to address systemic issues and barriers to equal pay. Further research into underlying causes and mitigation strategies is recommended; use of probability sampling methods in future research will be important to decrease potential bias and increase generalizability.