One set of additional analyses was conducted to examine the association between gender and earnings. Using propensity score matching (Stata command teffects psmatch), staff within SHAs were matched on the basis of supervisory status, time in public health, educational attainment, position type, race/ethnicity, and whether they were paid as a salary or hourly wage; the “treatment effect” in this analysis was gender (female vs male) using a robust variance estimator. Of the 8572 full-time respondents used in this analysis, 8887 matches were established (minimum 1 match, maximum 9). After matching on seniority, experience, educational attainment, and other demographic characteristics within a state, a matched analysis suggests women earned approximately $2000 less than men on average (P < .001; 95% confidence interval [CI], −$3180 to −$891); the CI for this approach and the regressions overlap suggesting the impact of gender is robust to multiple approaches. On average, this represents women earning 90 to 95 cents on the dollar compared with men of similar experience and seniority. This gap grows considerably among women who have higher levels of supervisory status.
A similar analysis was conducted with treatment effect being a staff member who was a person of color. From full-time respondents with earnings information, 8572 matches were established (minimum 1 match, maximum 11). Staff who were people of color earned about $3500 less than their white counterparts (95% CI, −$2358 to −$4701). Overall, this also represents earning 90 to 95 cents on the dollar compared with their non-Hispanic white colleagues. This gap is not statistically significant among nonsupervisors but increases dramatically for staff with higher supervisory status.
PH WINS provides an opportunity to evaluate (and eliminate) many of the presumed explanations for differences in earnings, such as differences in educational attainment37–39 or the unequal distribution of employees in professional, executive, or managerial positions.38 , 39 After accounting for these characteristics, earnings gaps are still present by both gender and race/ethnicity. Further research is needed to explore factors associated with the persistence of gaps even in female-dominated workplaces, and in juxtaposition, factors in the governmental public health workplace that help explain those gaps that are significantly lower than in other sectors. This includes examinations of other potential factors that PH WINS was not equipped to measure, including systemic and institutional discrimination, the effect of educational prestige on earnings, differences in earnings within position types, and temporal associations between when a degree was received and changes in earnings.37 , 40 , 41 In addition, analyses similar to those conducted here could be conducted by each state health department using its own salary data to determine the presence of earnings gaps by demographic characteristics of interest; departments could then create and implement strategies that can contribute to improved earnings equity. Furthermore, state-specific studies may allow for collection of additional explanatory variables of interest that may explain some of the variation not captured in the current models.
This article has several limitations. The first relates to PH WINS itself; while the data are nationally representative of central office employees, it is possible that nonresponse bias exists. We attempted to address this through complex sampling methodology, a strong response rate (>46%), and poststratification adjustments. Another limitation is that the data for earnings came from scales rather than exact numbers. This was done to maximize item response with the thought that a large number of respondents would not give an exact answer to such a sensitive question, although the survey collected no personal identifiers. Because these scales are equidistant, and because PH WINS has a large sample size, interval estimates may be soundly converted to dollars for ease of interpretation. However, these estimates could be made more precise in future studies if exact salary/wage estimates were gathered from respondents. Although regional differences were broadly accounted for in the model, formal cost-of-living adjustments were not made, as significant intra- and interstate differences would have made such adjustments imprecise. Moreover, other adjustments might be warranted to generate more comparable estimates—for example, by marital status, number of dependents, and the like. However, these questions were not asked in PH WINS for confidentiality reasons.
For analysis purposes, dozens of specific position types were collapsed into 4 primary areas (eg, Epidemiology was subsumed by “Public Health Sciences,” Executive Assistants by “Administration”). It is worth noting that differences in roles, responsibilities, and required experience may exist within and across these jobs that affect earnings. These potential differences are not captured by PH WINS. Another significant consideration is overtime—PH WINS did not ask individuals to parse overtime from normal earnings. It may be the case that some (full-time) individuals work more than the estimated 2000 hours per year. For this reason sensitivity analysis was conducted removing hourly staff; coefficients from the regressions were similar. A final consideration is earnings versus total compensation. This analysis examines only earnings from salary or wages and not fringe benefits. There may be significant variations in benefits packages across SHAs that are different from the distribution of earnings across SHAs. For example, states with relatively higher salaries may or may not have relatively higher benefits packages. This warrants further research.
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* Salary options included less than $25 000; $25 001-$35 000; $35 001-$45 000; $45 001-$55 000; $55 001-$65 000; $65 001-$75 000; $75 001-$85 000; $85 001-$95 000; $95 001-$105 000; $105 001-$115 000; $115 001-$125 000; $125 001-$135 000; $135 001-$145 000; more than $145 000. Hourly equivalents were less than $12.50; $12.51-$17.50; $17.51-$22.50; $22.51-$27.50; $27.51-$32.50; $32.51-$37.50; $37.51-$42.50; $42.51-$47.50; $47.51-$52.50; $52.51-$57.50; $57.51-$62.50; $62.51-$67.50; $67.51-$72.50; more than $72.50. Among full-time employees, wages were annualized at 2000 paid hours worked per year.
† These items were collapsed from a list of job classifications respondents were asked to select as best representative of their position. This includes Administration & Business Support—Accountant Fiscal, Clerical Personnel (Administrative Assistant, Secretary), Custodian, Grant and Contracts Specialist, Health Officer, Human Resources Personnel, Information Technology Specialist, Other Facilities Operations worker, Public Health Agency Director, Public Information Specialist; Clinical and Laboratory & Behavioral Health Professional, Community Health Worker, Home Health Worker, Laboratory Aide Assistant, Laboratory Developmental Scientist, Laboratory Scientist (Manager, Supervisor), Laboratory Scientist Medical Technologist, Laboratory Technician, Licensed Practical Vocational Nurse, Medical Examiner, Nutritionist, Other Oral Health Professional, Other Physician, Other Registered Nurse—Clinical Services, Other Veterinarian, Physician Assistant, Public Health Dentist, Public Health Preventative Medicine Physician, Registered Nurse—Community Health Nurse, Registered Nurse—Unspecified; Public Health Science & Animal Control Registered Nurse—Unspecified; Public Health Science & Animal Control Worker, Behavioral Health Professional, Department Bureau Director, Deputy Director, Engineer, Environmentalist, Epidemiologist, Health Educator, Other Management and Leadership, Other Professional and Scientific, Program Director, Public Health Manager Program Manager, Public Health Veterinarian, Public Health Informatics Specialist, Sanitarian Inspector, Technician, Statistician, Student—Professional and Scientific; Social Services and All Other & Social Services Counselor, Social Worker, Other.
* These estimates are extrapolated from equidistant salary intervals and so should be viewed as approximations of the respective means.
† Nonsupervisors were defined as those who did not supervise other employees. All other employees were supervisory. Supervisory classifications includes team leaders (those who provide employees with day-to-day guidance in work projects but do not have official supervisory responsibility or conduct performance appraisals), supervisors (those who provide employees' performance appraisals and approval of leave, but do not supervise other supervisors), managers (those who supervise ≥1 supervisors), and executive (member of senior executive service or equivalent).