An Analysis of Employee Pay& Demographic Data

April 2024

AUTHORS
Data Team
Julia Fredenburg
Reese Hirota
Eric J. Koepcke
Rose Martinez
Melissa Nuñez
James Wu
Christopher Zawora

Legislative Division - Human Services
Elizabeth Arzt
Nicholas Connell
Smita Deshmukh
Sara Liss
Sahar Moazami
Rie Ogasawara

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TABLE OF CONTENTSEXECUTIVE SUMMARY............................................................................................................................................6
Summary of Findings (Data as of 2021)...................................................................................6
Recommendations......................................................................................................................................8
BACKGROUND...............................................................................................................................................................10
The Pay Equity Law and Related Legislation.......................................................................10
RACIAL DIVERSIFICATION OUTPACED GENDER DIVERSIFICATION................................ 12
Marginal Progress Since 2012............................................................................................................. 12
Benchmark Considerations...................................................................................................................14
Occupational Segregation Explains the Citywide Pay Gaps................................................... 16
Across and within agencies.................................................................................................................16
Citywide pay gaps..................................................................................................................................16
Uniformed Workforce..........................................................................................................................17
		
Department of Education................................................................................................................22
Pay Gaps Between Agencies............................................................................................................24
Pay Gaps Within Agencies.................................................................................................................30
Agencies with the largest racial/ethnic pay gap........................................................... 30
Agencies with the largest gender pay gap.........................................................................31
Case Studies: Examples of Uneven Gender Distribution and Pay......................32
Case Studies: Examples of Uneven Racial/Ethnic Distribution and Pay.......34
Case Study: The Asian, Black or African American, Hispanic/Latino,		and Other (ABHLO) Female Pay Gap in DOE....................................................................35

COMPOUND EFFECT: THE ABHLO FEMALE EMPLOYEE PAY GAP................................. 37
Citywide..............................................................................................................................................................37
		
ABHLO female employees saw the smallest improvements in the pay		gap over time.............................................................................................................................................37
Pay Gaps Are Largely Driven by ABHLO Female Employees................................38

Agency-Level...................................................................................................................................................40

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Agencies with many ABHLO female employees have salaries below
		the City’s median; those with few ABHLO female employees surpass		the City’s median.................................................................................................................................. 40

Title-Level........................................................................................................................................................41
As the Share of ABHLO Female Employees Increases, Median Salary		Decreases.....................................................................................................................................................41

		Female and ABHLO employees often fall below their salary band’s		

midpoint, raising questions about salary assignments.............................................42

TARGETING TITLES FOR INTERVENTION...............................................................................................44		Gender Diversification Push for Medium Income Titles............................................44		Gender Diversification Push for High-Income Titles...................................................45		Race/Ethnicity Diversification Push for Medium-Income Titles.........................46		Race/Ethnicity Diversification Push for High-Income Titles.................................47
		
Diversification Progress Will Take Decades with No Intervention...................48
RECOMMENDATIONS...............................................................................................................................................51
Strategies to Diversify High-Income Titles............................................................................51

ACKNOWLEDGMENTS.............................................................................................................................................53
APPENDIX......................................................................................................................................................................... 55
Terminology....................................................................................................................................................55
List of Figures................................................................................................................................................58
Agency Abbreviations.........................................................................................................................59
Civil Service Title and Associated Title Codes....................................................................60
Methodology..................................................................................................................................................61
Endnotes............................................................................................................................................................63

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EXECUTIVE
SUMMARYEXECUTIVE SUMMARY
In 2019, the New York City Council (Council) passed Local Law 18,1 also known as the “Pay Equity Law,”
to identify pay disparities among municipal employees of the City of New York (City) observed between
race, ethnicity, gender, and other protected classes. The Pay Equity Law requires the Mayor’s Office ofData Analytics (MODA) to report City municipal employee pay data publicly and to provide the Council withdirect access to the data to facilitate the Council’s own statistical analysis.
Since 2021, the Council’s Legislative Division and Data Team have issued reports2 analyzing the data provided
pursuant to Local Law 18 and have concluded that large pay gaps between male and female workers and whiteand Asian, Black, and Hispanic/Latino, and Other (ABHLO) workers were the result of occupational segregation.i While there is essentially equal pay for all individuals regardless of gender and/or race within the same civil
service title, occupational segregation and pay differences between occupations lead to large gender and racial
pay gaps within the municipal workforce. Building upon those findings, this report examines how segregation
both across and within City agencies contributes to the pay gaps, how data on large agencies skew the data
and mask pay gaps, and and how the low pay received by ABHLO female employees accounts for large portions of pay disparities within the municipal workforce. Although there have been small gains in narrowing pay
gaps over time, stagnant job segregation has prevented greater progress. To address these challenges, thisreport recommends diversifying well-paying career tracks that are currently predominantly held by male andwhite workers. Through simulations, the Data Team demonstrates that diversifying such titles would produce
substantial reductions in the municipal workforce‘s gender and racial pay gaps. Motivated by these findings, thereport suggests legislative and policy approaches to reduce municipal pay gaps.

This report seeks to:
1. Compare the demographics of the municipal workforce to those of NYC’s working population
as a whole and discuss how municipal employee demographics have changed over time, both
citywide and at the agency level.
2. Illustrate how occupational segregation and pay disparities across and within agencies
contribute significantly to pay gaps.
3. Demonstrate how pay disparities are most attributable to the low salaries paid to ABHLO
female employees in the municipal workforce.
4. Identify and recommend possible avenues to close pay gaps in the municipal workforce.

Summary of Findings (Data as of 2021)
Racial/ethnic diversification has outpaced that of gender in the municipal workforce. Of agencies with over 100 employees, 13 agencies saw a significant increase in the percentage of ABHLO employees
from Fiscal Year 2012 (FY 2012) to Fiscal Year 2021 (FY 2021). Only three agencies saw a significant increasein the percentage of female employees. Of those three agencies with female employee increases, only one
agency had an under-representation of female employees.ii

i Occupational segregation is the uneven distribution of workers by race and gender across occupations.
ii Information on employee demographics and pay change from FY 2012-2021 is drawn from the Department of Citywide Administrative
Services’ (DCAS) Workforce Profile Report. See Id., at p. 10.

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Several agencies’ racial/ethnic or gender compositions differ greatly from the demographics
of NYC. The Department of Homeless Services (DHS), Department of Probation (DOP), Administration forChildren’s Services (ACS), Department of Correction (DOC), and the Fire Department (FDNY) are the least
racially diverse agencies. The Department of Sanitation (DSNY) and FDNY lack racial diversity and gender
diversity, with DSNY being 9% female and 51% white, and FDNY being 12% female and 62% white.iii

Pay disparities are largely attributable to the low pay received by ABHLO female employees
in the municipal workforce. ABHLO female employees are concentrated in particularly low-paying roles,

even relative to white female employees and ABHLO male employees, and this explains large portions of themunicipal pay gaps. For every dollar earned by white male employees, all other workers earn on average $0.82.
Of this pay gap, white female employees account for 13.1%, ABHLO male employees 31.5%, and ABHLO
female employees 55.4%. ABHLO workers earn $0.84 for every dollar earned by white workers, with ABHLO
male employees accounting for 32.4% and ABHLO female employees accounting for 67.6% of thisgap. Female employees earn $0.84 for every dollar earned by male employees, with white female employees
accounting for 15% and ABHLO female employees 85% of this pay gap.

Across the municipal workforce, ABHLO female employees saw the smallest improvement in their pay
gap between 2018 and 2021. Across agencies, those that are predominantly ABHLO and female tend tohave below-median salaries, while those with few ABHLO female employees tend to surpass the municipal
workforce’s median pay. Across titles, as the share of ABHLO female employees increases in a job title,
median salary decreases and salaries fall below the title’s salary band midpoint (the midpoint of each job
title’s fixed minimum and maximum salary).

High concentrations of certain employees in particular municipal positions mask pay
disparities across and within agencies. Exclusion of data on the uniformed workforce (employees whowear uniforms to identify themselves and whose roles involve public safety, law enforcement, emergency
response, and similar functions) or the inclusion of data on the Department of Education’s (DOE) pedagogicalemployeesiv significantly reduces the overall municipal gender and race/ethnicity pay gaps, even as significant
disparities remain. The gender pay gap shrinks by 16% when uniformed workers are removed from theanalysis. Given the number of well-paid female employees in DOE, including data on DOE pedagogicalemployees would reduce the gender pay gap by around 90%. While difficult to estimate, including DOE data
would also reduce the racial pay gap by, at most, 40-50 percent. However, despite these pay gap reductions,the fact would remain that ABHLO female employees in DOE continue to earn significantly less than theircounterparts and that female employees across the remaining municipal workforce earn significantly lessthan male employees on average.

Uniformed agencies and those agencies related to science and technology (such as the
Office of Technology and Innovation (OTI), Department of Environmental Protection (DEP),
and Department of Transportation (DOT)) have large within-agency gender pay gaps. While
there are titles exhibiting gender parity within these agencies, most employees are concentrated in either
higher-paying, predominantly male positions, or lower-paying, predominantly female titles. Agencies withthe highest percentage of female employees consistently have median salaries below the City workforce’s

iii Diversity indices, or similarity/dissimilarity scores, were calculated using FY 2012-2021 data from the DCAS Workforce Profile Report(see Id., at p.10) and Census Bureau Estimates from the 5-Year American Community Survey (see Id., at p.11). More information on themethodology is available in the Appendix.
iv Local Law X/2023 amended the original Pay Equity Law to require DOE pedagogues to be included in the data provided to theCouncil beginning the year following its enactment. For this analysis, DOE’s impact on the City’s observed pay gaps is estimated using the
DCAS Workforce Profile Report, which includes the entire DOE employee population.

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median, while those with the lowest percentage of female employees have salaries above the median. The
same pattern holds for agencies with the highest and lowest percentages of ABHLO employees.

Overall headcount reductions during COVID-19 disproportionately affected agencies that werepredominantly female or ABHLO. Agencies that predominantly employ female and/or ABHLO employees,
such as DHS, HRA/DSS DOP, and DOE, experienced substantial reductions in headcount after COVID-19. Thisis especially the case for DHS, which experienced an almost 19% headcount decrease. In contrast, agencies
with predominantly male and/or white employees, such as the NYPD, DSNY, DOT, FDNY, and OTI, maintained
relatively stable headcounts, with decreases below 5% or increases no greater than 4%.

The City can achieve substantial reductions in both the gender and racial pay gaps by
diversifying well-paying career tracks that are currently predominantly male and white. The
Data Team’s simulation to align the gender composition of 18 well-paying, populousv, predominantly male titles
with the gender composition of the labor force of NYC overall resulted in a 37% reduction in the gender pay gap.
Similarly, the Team’s simulation to align the racial composition of 18 well-paying, populous, predominantly white
titles with the racial composition of the labor force of NYC overall resulted in a 24% reduction in the racial pay
gap. Without interventions to diversify high-paying, promotional roles, or increase pay for undervalued titles, itwill take decades to significantly reduce the gender and racial pay gaps in the municipal workforce.

Recommendations
1. Continue oversight and implementation over existing Pay Equity Laws. The Council willcontinue to conduct oversight on the implementation of laws intended to bring about pay equity,with a particular focus on Local Law 27 of 2023, which required the City to conduct a comparable
worth analysis to examine whether titles are being valued appropriately.
2. Pursue legislation to establish amunicipal career counselor within the Department ofCitywide Administrative Services (DCAS) to advise municipal employees of professional
growth and promotional opportunities, and to provide guidance regarding the process of taking
promotional exams and agency training courses.
3. Pursue legislation to require the distribution of workplace culture surveys to municipal
employees. Surveys would offer employees the opportunity to provide feedback on perceived barriers
to promotion, adverse working conditions that may motivate employees to leave the civil service, andideas for workplace improvement.
4. Pursue a resolution to call for the State to reexamine requirements regarding the ranking of
eligible candidates for promotions to better recognize highly skilled and motivated employees.

The term “Asian, Black or African American, Hispanic or Latino, or Some Other Race” which may be abbreviatedas “ABHLO” is a designation representing individuals who are “Asian”, “Black or African American”, “SomeOther Race”, or “Hispanic or Latino”. Alternatively this designation can be described as including all races
known not to be “White” in addition to individuals who are known to be “Hispanic or Latino”.

v

As used in section entitled “Targeting Titles for Intervention”, the term “populous” means titles that have more than 450 employees.

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BACKGROUNDBACKGROUND
The Pay Equity Law and Related Legislation
Local Law 18 of 2019, also known as The Pay Equity Law, expanded access to the City’s data to uncover pay
disparities among the municipal workforce, and enabled the City to develop and implement solutions to achieve
pay equity. Under the law, MODA must publicly report aggregated City pay data annually and assess it for
disparities based on gender, race, and ethnicity. The Pay Equity Law also entitles the Council to directly access
NYC employee pay data via an Application Programming Interface, or API, to facilitate independent verificationof MODA’s findings. The Pay Equity Law is the first ever local law that grants the Council direct access toindividualized data in this way, making this analysis possible. In the years since Local Law 18 was passed, theCouncil has passed additional legislation and introduced new initiatives to improve upon existing data collection
and diversify recruitment efforts across the City’s workforce.
•

•

•

•

Local Law 27 of 2023 requires the City
to contract with an outside expert to conduct
a comparable worth analysis. This local
law also requires agency heads to adopt
and implement annual plans to provide
equal employment opportunities. As of
February 2024, DCAS has not yet selected acontractor to conduct this comparable worth
analysis.
Local Law 28 of 2023 requires DCAS
to report data on applicants for civil service
examinations and agency training programs.
Additionally, the Office of Diversity andInclusion is required to conduct training
sessions for City agencies to evaluate
job postings and recruitment practices forunconscious biases, which may alienate
diverse applicants.
Local Law 29 of 2023 amended the Pay
Equity Law by requiring DCAS to collectand provide additional employment data,such as DOE pedagogical data, a job
title’s status as a uniform or non-uniform
position, amounts of leave or overtime
pay accrued by employees, and otherinformation. Furthermore, starting in
2024, the data that the City provides to
the Council will be required to have anassigned unique identifier number foreach employee whose employment data
is analyzed; this will allow the Data Teamto track employees through time, andgarner a better understanding of career
trajectories.
Local Laws 116, 117, 118, 119, and120 of 2022 address equity in the FDNY,
and require FDNY to take measures toimplement recruitment and retention of

populations that are under-represented
in the firefighter rank. FDNY is required
to submit reports on their recruitment andretention efforts, their diversity improvement
strategies, and the complaints filed with the
FDNY’s Equal Employment Opportunity
Office.
•Local Law 67 of 2017 prohibits
employers from inquiring about job
applicants’ past salary history and prohibits
the use of an applicant’s prior salaries todetermine their future wages.

•

Local Law 2 of 2023 created the Civil
Service Ambassador Program and requires
DCAS to conduct a citywide educational
outreach program to provide information
about the various pathways into the civil
service.

•

Local Law 130 of 2023 requires DCAS
to design comprehensive exit surveys,
which agency heads are required
to distribute to departing employees(employees who are resigning or retiring),
to complete voluntarily.

•

The Civil Service Pathways
Fellowship (CSP) is a collaborative effort
between DCAS and City University of
New York (CUNY) schools. This fellowship
program connects recent graduates withcareers in the municipal workforce andhas been a meaningful initiative to recruit
diverse applicants. Over two-thirds of
CUNY Fellows are ABHLO employees,
and over half of the individuals who
participated in the CSP program are stillworking for the City.

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RACIAL
DIVERSIFICATIONOUTPACEDGENDER
DIVERSIFICATIONRACIAL DIVERSIFICATION OUTPACED
GENDER DIVERSIFICATION
Marginal Progress Since 2012
Utilizing data from the DCAS Workforce Profile Report, the Data Team monitored diversification from FY 2012
onward. The Team created an index to measure the similarity or dissimilarity of agency demographic compositions compared to the broader NYC working population.vi In Figure 1 below, the racial/ethnic and gender diversity indices are assessed along the two axes. Agencies aligning most closely with the racial/ethnic and gendercompositions of NYC’s entire labor force appear in the bottom left quadrant. Label size reflects how many workers the agency employs. Blue indicates better while red indicates worse diversity performance, meaning anagency scored over 80 on either racial/ethnic or gender axis. The brighter, orange-red indicates that the agency
moved from blue to red from FY 2012 to FY 2021.
Figure 1. Agency Similarity or Dissimilarity Scores: 2021 Agency Demographics Compared to NYC’s Working
Population

An agency with a lower score (0) is closer to, while a higher score (100) is farther away from NYC’s
Gender & Racial breakdown.

vi There are different ways to measure diversity and decisions on the appropriate cutoffs when evaluating diversity. The Team decided touse NYC’s gender and racial/ethnic demographic breakdowns as the cutoffs and a proximity or distance measure to evaluate diversity. Moreinformation on the methodology is available in the Appendix.

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Most agencies (78%) are doing well in terms of diversity. These agencies include the Department of Investigation (DOI), Department of Small Business Services (SBS), and Department of Consumer and Worker Protection (DCWP), among others. Conversely, FDNY, in the top right quadrant, stands out as the poorest performing, with both high gender dissimilarity (12% female) and race/ethnicity dissimilarity (62% white). As agency
demographics skew farther from the racial composition of the overall NYC labor force, they are indicatedhigher on this graph. As agency demographics skew farther from the gender composition of the overall NYC
labor force, they are indicated farther to the right. Agencies in the top left and bottom right are low-performing
in terms of race/ethnicity diversity and gender diversity, respectively. DHS, DOP, ACS, and DOC lack racial
diversity, whereas DSNY and FDNY (in addition to lacking racial diversity) lack gender diversity. For example,
the percentage of female workers at DSNY only increased by one percentage point, from 8% to 9%, from FY
2012 to FY 2021, despite the composition of female employees in NYC’s labor force increasing by three percentage points, from 49% to 52%.
Looking at racial and gender diversification separately, Figure 2 below illustrates how diversity has changed
for each agency from FY 2012 to FY 2021. Out of 28 agencies with over 100 employees in 2021, 13 agencies
saw significant increases (more than five percentage points) in their share of ABHLO employees between
FY 2012 and FY 2021.
Within those 13 agencies, eight saw increases in their ABHLO workforce that moved the agency towards
the racial/ethnic composition of NYC’s labor force. The other five agencies saw increases towards the overrepresentation of ABHLO workers, with DOC and DHS as two notable standouts that already had workforces
made up of over 80% ABHLO in FY 2012. Notably, no agency experienced a significant decrease in theirshare of ABHLO employees from FY 2012 to FY 2021.
In contrast, agencies experienced relatively little change in their gender composition over time. Out of the same
28 agencies, only three agencies saw a change in their gender composition that was greater than five percentage points, with the Department of Design and Construction (DDC) being the only one of those agencies wherefemale employees were under-represented in FY 2012.
Figure 2. Agency Demographic Composition Changes Over Time (Percentage Points)

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Similar results can be seen within career ladders. Using the top 10 most populous titles from the DCAS
Workforce Profile Report, Figure 3 below shows the career ladder within the police service. The higher the
position on the career ladder, the higher the percentage of male and white employees. Nevertheless, there
was improvement from FY 2012 to FY 2021. In 2012, police captains were 82% white, but this decreased to
66% by 2021, a decline of 16 percentage points. In contrast, the improvement in gender diversity is muchsmaller, with only a three percentage point decrease in the male share of police captains over time.
Figure 3. Career Ladder Example: Police Service

Workplace culture is a factor that affects the retention and likelihood of promotion of female and ABHLO
employees. However, changes in culture are difficult to quantify. Future analyses should seek to better
understand whether the workplace culture in certain agencies is improving based on feedback from employeesof those agencies.

Benchmark Considerations
While not explored in this report, the Data Team acknowledges that NYC’s overall workforce population may
not be the most appropriate benchmark for certain jobs, particularly those with eligibility criteria extending
beyond typical qualifications. For instance, jobs such as police officers3 and firefighters,4 have specific age
limitations and citizenship requirements, and also entail physical, medical, and psychological examinations.
A study focusing on diversifying the U.S. Air Force highlights that eligibility and fitness requirements likethese will result in the disqualification of various groups at different rates.5 Additionally, the study identifies
unquantifiable differences in preference for military service among demographic groups. When compared to
the general population, the study found that only eight million people (10.8%) meet the age, citizenship, andhealth requirements out of the 71.8 million who meet the age requirement alone. Consequently, the general
population, including the labor force, may not be a suitable benchmark for titles with heightened eligibility andfitness requirements. Instead, a benchmark based on the racial/ethnic and gender distributions of a population meeting eligibility requirements would be more appropriate.
Although a more suitable benchmark should be considered, the reevaluation of eligibility requirements mustalso be explored, considering that these criteria may be outdated and may unnecessarily disqualify a significant portion of the population. Key questions to pose to the NYPD and FDNY include how frequently, if at all,eligibility requirements are reevaluated, and whether other factors, such as technological advancements, aretaken into consideration.

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OCCUPATIONALSEGREGATIONEXPLAINS THE
CITYWIDE PAYGAPS

OCCUPATIONAL SEGREGATIONEXPLAINS THE CITYWIDE PAY GAPS
Across and within agencies
Large agencies within City government serve as a comprehensive lens for examining job segregation at
various levels—Citywide, across agencies, and within them. The analysis demonstrates how demographic
disparities and pay disparities across agencies contribute to the gender and racial municipal pay gaps. Additionally, the data indicate that occupational segregation is not only an issue across agencies, but also withinthem. Occupational segregation within agencies, combined with pay disparities across roles within agencies,
contributes significantly to the City’s pay gaps.

Citywide pay gaps
Figure 4. Unadjusted Gender Pay Gaps Alone16

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Figure 5. Unadjusted Race/Ethnicity Pay Gaps Alone

Figures 4 and 5 present the gender and race/ethnicity pay gaps. With no improvement from 2018 to 2021,
female employees continue to earn only $0.73 for every dollar earned by male employees. In contrast, there
have been improvements in the race/ethnicity pay gap over time, with Black or African American employees
seeing the largest gains despite continuing to have the widest pay gap, earning $0.73 for every dollar earned
by white employees in 2021.
While these municipal pay gap numbers are important, they represent a general picture, and their high levelof aggregation hides more specific detail. To dig deeper, the Data Team examined large agencies. Given thesize of their workforces, such agencies can significantly influence the Citywide pay gap data. These pay gaps
partly stem from occupational segregation across agencies—many agencies have over- or under-representation of specific racial/ethnic and/or gender groups. In conjunction with pay disparities across agencies, thissegregation contributes to the observed pay gaps.

Uniformed Workforce
In the municipal workforce, the largest employee group is not an individual agency but the uniformed
workforce. Excluding civilian employees, uniformed employees within the NYPD, FDNY, DOC, andDSNY total 66,500 employees, or 44% of the municipal workforce. This workforce is 82% male andclose to 40% white, with a median salary of $85,292. In contrast, the remaining 56% of the workforce hasa higher proportion of female employees and ABHLO individuals and has a median salary of $62,899.

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Figure 6. Municipal Workforce by Uniformed or Not

Figure 7. Uniformed Workforce by Gender & Race/Ethnicity																					viivii Percentages do not sum to 100% in the race/ethnicity figure above because it excludes groups with small sample sizes.

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When excluding uniformed employees, the gender pay gap shrinks by 16%. However, the race/ethnicity pay
gap between Hispanic or Latino and white employees widens slightly, while the pay gap between the otherrace/ethnicities and white workers stays the same. This demonstrates that, while the uniformed workforce
contributes to the municipal workforce pay gaps, it is not solely responsible for these gaps.
Figure 8. Effects to the Pay Gap When Removing the Uniformed Workforce

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DEMOGRAPHIC COMPOSITION CHANGESIN THE MUNICIPAL WORKFORCE
Over-representation of Female employees and Black or African American
employees in the municipal workforce
To assess occupational segregation, the Data Team compared the demographic makeup of the municipal
workforce to that of NYC’s entire working population, which includes both the public and private sectors.
When comparing the two, the Team found that the share of white workers in the municipal workforce
closely mirrors that of the city’s entire labor force. Furthermore, there is a minor under-representationof Hispanic or Latino, Asian, and individuals identifying as other, with municipal employees trailing theoverall NYC labor force by four, five, and one percentage points, respectively. The Black or African
American share of the municipal workforce deviates the most from the city’s overall labor force. Blackor African Americans are over-represented by nine percentage points, constituting 29% of the municipal
workforce compared to only 20% of the city’s labor force. Similarly, female municipal employees areover-represented by six percentage points, constituting 58% of the municipal workforce compared to
52% of the city’s labor force. The over-representation of Black female employees within the municipal
workforce leaves them at disproportionately higher risk of losing their jobs during government layoffs,
hiring freezes, or fiscal crises.6
Figure 9. Municipal Employees Compared to Entire NYC Labor Force

Analyses conducted by think tanks and academic institutions have yielded similar findings.
Nationally, Black or African American employees and female employees are overrepresented in the public sector relative to their share of the private sector. According to an
Economic Policy Institute report, policy & legislative actions taken during the Civil Rights
era increased female and Black employment in government.7

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Growing Hispanic or Latino and Asian Representation
It is important to note broader demographic trends, as they influence and alter diversity
benchmarks. As indicated by Census data and the DCP in their report Stability & Change
in NYC Neighborhoods, 2010 to 2020,8 NYC’s overall population composition is experiencing a shift, witha decreasing proportion of white and Black or African American individuals, and an increasing proportionof Hispanic or Latino, Asian, and individuals of other racial backgrounds. These demographic trends areseen within both the overall NYC working population and the municipal workforce. The share of Black or
African American employees and white employees within the municipal workforce have decreased by two
and four percentage points, respectively, from 2012 to 2021, while shares of Hispanic or Latino employees,
Asian employees, and employees identifying as some other race have increased by three, two, and one
percentage points, respectively, as indicated in the DCAS Workforce Profile Report.  
Figure 10. Race/Ethnicity Composition Over Time (Stability & Change in NYC Neighborhoods, 2010 to 2020 Report)

From the latest census in 2020, NYC’s population is increasingly diverse. With no racial
demographic group surpassing one-third of the entire population, there is an approximately
76% chance that two individuals chosen at random in NYC would be of different racial/ethnic groups.

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Department of Education
DOE is the City’s largest agency, comprising nearly 150,000 employees. It is predominantly female (78%),
with an almost even split between white (44%) and ABHLO (56%) employees, and a median salary of$89,890. Notably, DOE pedagogical staff are currently absent from the Pay Equity Law data but are required to be included in the data in upcoming years. The incorporation of these employees would nearly
double the amount of data to be analyzed, due to the agency’s size.
As the largest agency, DOE has a large influence on the municipal gender and race/ethnicity pay gaps. To
estimate its impact, the Data Team incorporated DOE employee data from the Workforce Profile Report.9
The inclusion of this data flips the gender composition of the City workforce to roughly 60% female and40% male from where it currently stands at 40% female and 60% male. Due to the fact that many female
employees at DOE are paid more than the average female salary in the remaining municipal workforce,
including DOE data would reduce the gender pay gap by around 90%. While difficult to estimate, including
DOE would also reduce the racial pay gap by, at most, 40-50%. Therefore, including DOE numbers would
make the municipal pay gaps appear significantly smaller. However, as noted, these Citywide numbers
do not fully reveal disparities within DOE itself as well as the large gender and race disparities within andacross other agencies. It is important to use the Citywide numbers as only one measure of how the City
is doing, and to recognize the outsized impact of large agencies and the large disparities that continue toexist within and across other agencies.

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Figure 11. Gender and Race/Ethnicity Compositions for DOE, without and with DOE

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As mentioned above, the inclusion of DOE data reduces the municipal gender pay gap significantly.
Using the mean salary, female employees earn $0.80 on average for every dollar earned by their malecounterparts when DOE is not included; however, this figure changes to $0.97 when DOE is included.
Thus, the gender pay gap is reduced from 20% to around 3%. The 97-cents-on-the-dollar aligns moreclosely with the 90-cents-on-the-dollar that female employees earn in the entire NYC workforce.10 As noted,
this DOE-inclusive Citywide pay gap figure may create misconceptions regarding the state of occupationalsegregation and pay disparities, particularly along racial lines, within agencies.
Figure 12. Effects to the Gender Pay Gap When Including DOE

Pay Gaps Between Agencies
Focusing on the Citywide numbers masks how segregation and pay disparities across agencies contributeto the overall pay gaps. By examining the data at the agency level, the relationship between the differencesin median salaries and the over/under-representation of specific genders and/or racial/ethnic groups is observed.
When examining the relationship between agency gender composition and median salary, the five agencies
with the highest percentage of female employees all exhibit median salaries that fall below the City municipal
workforce’s median of $75,933. Conversely, the five agencies with the lowest percentage of female employees all have median salaries above the Citywide median. A similar pattern emerges when considering agencies with the highest and lowest percentage of ABHLO employees. The agencies with the highest shares of
ABHLO employees all have median salaries below the City median, while those with the lowest shares of
ABHLO employees tend to have median salaries above the City median.

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DOE stands out as an exception to this, diverging from the trends observed in other agencies with higher
concentrations of female employees. DOE has the highest percentage of female employees, at 78% female.
Despite this over-representation of female employees, DOE’s median salary of $89,890 is well above theCity median, and similar to agencies that have a higher concentration of male and white employees.
When the Data Team examined agencies with the highest and lowest percentage of female or ABHLO employees, HRA/DSS and FDNY stood out as the worst paying and best paying, respectively. HRA/DSS has
the lowest median salary at $54,100 and is 71% female and 79% ABHLO. Conversely, FDNY has the highest
median salary at $92,073 and is only 12% female and 33% ABHLO.
Historically, titles in ACS, DOHMH, DOE, DHS, and HRA/DSS have been primarily occupied by female and
ABHLO employees, and receive lower salaries. Alternatively, titles in DSNY, FDNY, NYPD, DOT, DEP, and DCP
have traditionally been occupied by a higher concentration of male and white employees and receive higher
salaries. These demographic trends continue to feed into career valuation perceptions informed by genderand race-based roles, where jobs in healthcare, early education, and domestic roles are undervalued whencompared to careers involving science, technology, engineering, math (STEM), safety, or infrastructure11.
Figure 13. Tables Showing the Agencies with the Highest and Lowest Percentages of Female Employees,
Respectively

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Figure 14. Tables Showing the Agencies with the Highest and Lowest Percentagesof ABHLO Employees, Respectively

Also of note, during and since the COVID-19 pandemic, overall headcount reductions disproportionately
affected agencies with higher concentrations of female and ABHLO employees. When looking at headcountchanges before and after COVID-19, agencies that are predominantly female and/or ABHLO, such as DHS,
HRA/DSS, DOP, and DOE, experienced substantial reductions in headcount. DHS in particular experienced
an almost 19% decrease from 2019 to 2021. In contrast, agencies with predominantly male and/or white
employees, such as the NYPD, DSNY, DOT, FDNY, and OTI maintained relatively stable headcounts, withdecreases below 5% or increases no greater than 4%.

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THE EFFECTS OF COVID-19
ON THE MUNICIPAL WORKFORCE
The data analyzed in this report reflects the City’s recovery from the COVID-19 pandemic, the effectsof which are still being felt today. COVID-19 led to a fiscal crisis which disproportionately affects female
ABHLO employees. The municipal workforce has seen a nearly 7% decline in headcount from 2019
(162,372) to 2021 (151,593). However, this decrease is not uniform across agencies and job titles.
Agencies with predominantly female and/or ABHLO employees, such as DHS, HRA/DSS, DOP, andDOE, experienced substantial reductions in headcount. In particular, DHS experienced an almost 19%
decrease. ACS, an exception, witnessed a modest 2% increase. In contrast, agencies with predominantly male and/or white employees, like NYPD, DSNY, DOT, FDNY, and OTI, maintained relatively
stable headcounts, with decreases below 5% or increases no greater than 4%, except for DOC, which
had a notable 24% decrease, losing around 2,900 employees.
Figure 15. Workforce Headcount Change Since COVID by Agency (2019 vs 2021)

When analyzing headcount reductions within specific job titles, six positions related to schools experienced significant declines. Education paraprofessionals had the largest decrease at -25% (6,700
employees). The NYPD had four job titles among the top decreases, and HRA/DSS and ACS each had
two titles experience significant decreases. Parks & Recreation, DEP, FDNY, and DSNY each had one.

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Figure 16. Top Workforce Headcount Decreases Since COVID by Job Title

Note: The data points reflected in this table are limited to the top 10 most populous civil service titles.12
The State Comptroller’s report echoed these findings, highlighting sharp declines forteaching assistants and school safety agents. The report also illustrated that the decrease in the NYC municipal workforce since COVID primarily resulted from significant
declines in new hires, rather than an increase in separations.13

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Figure 17. Hiring & Separation Counts Overtime (State Comptroller Report)

Policies such as hiring freezes or layoffs, which jeopardize government employment, have a significant
impact on the municipal workforce. Given that female and Black employees are over-represented ingovernment jobs, any reduction in employment could disproportionately affect these groups. Additionally, such policies can impede efforts to diversify the workforce, by slowing down the hiring of new personnel and, thereby, prolonging the time required to reach diversity goals.

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Pay Gaps Within Agencies
As observed across agencies, occupational segregation and significant pay disparities across titles lead to pay
gaps within agencies. Higher-paying roles within an agency tend to be predominantly held by male and white
employees, while lower-paying roles within that agency tend to be predominantly held by female and ABHLO
employees. DOE is an exception with regard to gender, but less so with race.

Agencies with the largest racial/ethnic pay gap
DCAS, NYPD, and FDNY are the agencies with the largest racial/ethnic pay gaps. DCAS has the largest pay
gap between ABHLO and white employees, with a difference of $28,065.16. The NYPD and FDNY follow
close behind with pay differences of $27,766 and $24,539, respectively. There are also large racial pay gaps
in DEP ($16,089.52) and DOT ($15,716.20). FDNY and DEP also appear in the top agencies with the lowest
share of ABHLO employees.
Figure 18. Top Five Agencies with the Largest ABHLO/White Pay Differentials

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Agencies with the largest gender pay gap
FDNY, NYPD, and OTI continue to be the agencies with the largest gender pay gaps since 2018. OTI has thelargest pay gap between male and female employees, with a difference of $36,713. NYPD and FDNY follow
close behind with pay differences of $35,085 and $32,680, respectively. The data also show large gender
pay gaps in DEP ($23,965.52) and DSNY ($22,543.50). All these agencies, except for OTI, appear in the top
agencies with the lowest share of female employees.
Figure 19. Top Five Agencies with the Largest Gender Pay Differentials

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Case Studies: Examples of Uneven Gender Distribution and Pay
In agencies with large gender pay gaps, some job titles exhibit gender parity. However, few workers
occupy these roles. Instead, at agencies with demographic compositions that are least consistent withthat of the NYC workforce, most employees are clustered within high-paying roles predominantly held bymale employees or lower-paying roles predominantly held by female employees. This uneven distributionof employees across gender and salary creates large pay disparities. As seen in Figure 20 below, certain
job titles with high male representation have median salaries above the City’s median. Examples of highpaying titles predominantly held by male employees within FDNY, NYPD, OTI, and DSNY include Fireand Police Lieutenant (70360 and 70260), Computer Systems Manager (10050), and Sanitation General
Superintendent (70196).
Conversely, low-paying, titles predominantly held by female employees within these agencies encompass
roles such as Clerical Associate (10251) within both FDNY and DSNY, Police Administrative Aide (10144),
and Call Center Representative (10260).
Titles like City Research Scientist (21744), Assistant Traffic Enforcement Agent (71652), Administrative Staff
Analyst - OTI (10026), and Associate Staff Analyst (12627) exhibit gender parity, but these roles are held by
few workers.
While there are some titles that diverge from the general trend and are high-paying and predominantly
held by female employees, or low-paying and predominantly held by male employees, these titles employ
relatively few workers at these agencies. Only one exception is observed; at FDNY, the EMS-EMT title ispopulous, predominantly held by male employees, and low-paying, with a median salary of $49,000 and 75%
male representation.

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Figure 20. Example Job Titles & Median Salary for Gender Categories

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Case Studies: Examples of Uneven Racial/Ethnic Distribution and Pay
When focused on agencies with higher racial pay gaps, most of these agencies’ workers are ABHLO and are clustered together in low-paying roles; DSNY is an outlier where the most populous title (Sanitation Worker) is near racial parity. The most populous titles at ACS, DHS, and DOP all have 80% or higher shares of ABHLO employees,
and all have median salaries below the City median. Titles such as Youth Development Specialist (52287), Special
Officer (70810), Community Associate (56057), and Sanitation Enforcement Agent (71681) are characterized byhigh ABHLO shares and low salaries.
Furthermore, high-paying titles predominantly held by white employees comprise a larger share in these agencies than the titles with racial parity (again, DSNY is an exception). These titles include Executive Agency Counsel (95005), Oiler (91628), City Research Scientist (21744), and General Superintendent (70196). In contrast,
low-paying titles that are predominantly held by white employees have the smallest share of workers at theseagencies with large racial pay gaps. For example, DOP only has one employee that would fit this category.
Figure 21. Example Job Titles & Median Salary for Race/Ethnicity Categories

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Case Study: The ABHLO Female Pay Gap in DOE
As noted earlier, incorporating DOE data into the Pay Equity Lawviii dataset significantly narrows the gender
pay gap, reducing it from 20% to about 3%. However, this aggregate improvement conceals existing pay
disparities across titles in DOE. For instance, when examining the gender distribution among teachers andeducation paraprofessionals, the two most populous titles in DOE, the breakdown aligns with DOE’s overall
gender composition (about 80% female and 20% male). Yet, while the gender composition for those titles is
consistent with that of DOE as a whole, an examination of the race/ethnicity breakdown in those titles reveals
that compared with DOE as a whole, lower-paid education paraprofessional jobs are more predominantly
held by ABHLO employees while higher-paid teacher positions are more predominantly held by white employees. In job titles that are held by more than 68% ABHLO (i.e., the fraction of ABHLO workers across theentire city), the median salary ranges from about $30,000 to $46,000, while in job titles that are less than 68%
ABHLO the median salary ranges from about $65,000 to $136,000.
Figure 22. Salaries for 10 Most Populous DOE Titles (DCAS Workforce Profile Report)

Examining municipal worker gender and race pay gaps alone makes it difficult to discern the impact ofoccupational segregation. However, as the analyses zoom into the agency level, clearer pictures emerge.
Agencies primarily defined by specific job functions (such as providing social services as part of ACS or
HRA/DSS, or providing safety and protection as part of the uniformed workforce) are often predominately
occupied by one gender and/or race, and large pay gaps exist between these agencies. This combinationof occupational segregation and pay disparities becomes particularly evident when examining individual
job titles within agencies. These analyses also reveal overlap in the over-representation of both female and
ABHLO employees in lower-paying roles, which is explored further in the next section of this report.

viii See footnote 4. More information on the methodology is available in the Appendix.

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COMPOUNDEFFECT: THE
ABHLO FEMALE
EMPLOYEE PAYGAPCOMPOUND EFFECT: THE ABHLO FEMALE
EMPLOYEE PAY GAPBoth the gender and race/ethnicity pay gaps are the result of female ABHLO employees being paid less.
For example, by excluding ABHLO female employees from the municipal workforce data, the gender pay
gap decreases by nearly 50% and the racial/ethnic pay gap decreases by nearly 40%. This is due to ABHLO
female employees being highly concentrated in particularly low-paying roles, even relative to white female
employees and ABHLO male employees.

Citywide
ABHLO female employees saw the smallest improvements in the pay gap over time
The municipal worker gender and race/ethnicity pay gaps persisted or improved slightly between 2018 and2021. ABHLO female employees saw the smallest improvements (less than 3.8%), compared to almost allother demographic groups. In contrast, white female employees, Black or African American male employees,
and male employees who identify as Other race/ethnicity saw the largest reduction of their pay gap over thisperiod. Hispanic or Latino male employees came the closest to eliminating the pay gap with white male employees in 2021 ($0.95 on the dollar), followed by white female employees ($0.91 on the dollar) and Asian male
employees ($0.90 on the dollar).
Figure 23. Gender and Race/Ethnicity Combined Pay Gap Cents on the dollar (Percent Change Between 2018 and 2021)

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Pay Gaps Are Largely Driven by ABHLO Female Employees
To better understand the pay gaps for female and ABHLO employees, a decomposition analysis was performed to assess how compensation for white female employees, ABHLO female employees, and ABHLO
male employees individually contribute to the gender and racial pay gaps.
Using the mean salary difference, the data show that female employees earn approximately $0.84 for every
dollar earned by male employees, resulting in a gender pay gap of around $0.16 per dollar. Similarly, ABHLO
employees earn roughly $0.84 for every dollar earned by a white employee, indicating a racial pay gap of
about $0.16 per dollar. Finally, when using white male employees as the reference group, all other workers
earn approximately $0.82 for every dollar a white male makes, indicating a pay gap of about $0.18 per dollar,
which is called the “combined” pay gap in this report.
Figure 24. Pay Gaps Based on Mean Salary Differences

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Taking these observed pay gaps and the total number of employees found in each demographic subgroup,
the Data Team determined how much the wages for each subgroup contributed to the overall pay gaps.
The Team found that ABHLO female employees account for the largest share of each pay gap. Specifically,
ABHLO female employees account for 85% of the gender pay gap, 68% of the racial pay gap, and 55% of
the combined pay gap.
Figure 25. Pay Gap Contributions by Subgroup (Decomposition)

This analysis reveals that the relatively lower wages of ABHLO female employees are a significant driver ofpay disparity in the municipal workforce. On average, if ABHLO female workers received the same pay as
male employees, white employees, and white male employees, the gender, racial, and combined pay gaps
would shrink by approximately 85%, 68%, and 55%, respectively. The City could achieve substantial reductions in both the gender and racial pay gaps by prioritizing wage equity resources for ABHLO female employees, such as training and career counseling.

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Agency-Level
Agencies with many ABHLO female employees have salaries below the City’s
median; those with few ABHLO female employees surpass the City’s median
The five agencies with the highest percentage of ABHLO female employees have median salaries below
the municipal workforce’s median of $75,933. Among these, the Law Department (LAW) stands out with thelowest median salary of $49,701. Conversely, the reverse pattern emerges when considering the top fiveagencies with the lowest percentages of ABHLO female employees. These agencies, all of which containfewer than 25% female ABHLO employees, have median salaries above the City’s median.
Figure 26. Tables Showing the Agencies with the Highest and Lowest Percentages of ABHLO Female Employees,
Respectively

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Title-Level
As the Share of ABHLO Female Employees Increases, Median Salary Decreases
The relationship between the share of ABHLO female employees and median salary is not only observed
across agencies, but also within agencies at the title level, where ABHLO female employees occupy particularly
low-paying roles compared to others.
In general, titles with lower shares of ABHLO female employees tend to be higher paid, with some exceptions
among select high-ranking positions, which employ few people. This inverse relationship between theshare of ABHLO female employees and salaries is consistent with the trends that have been reported
across the municipal workforce. When evaluating the distribution of employees, the Data Team observedthat most employees work in titles with low ABHLO female representation (0-30% share of ABHLO female
employees) and have a median salary of $85,292 or higher. In contrast, the second-largest group of
employees represent civil service titles with a 60-70% share of ABHLO female employees and have amuch lower median salary of $51,427.
Figure 27. Median Salary for Civil Service Title Codes by Share of ABHLO Female Employees per Title

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Female and ABHLO employees often fall below their salary band’s midpoint, raisingquestions about salary assignments
Each job title has a defined salary band that fixes the minimum and maximum salary for that title. A broadsalary band offers less clarity to applicants about eventual earnings and provides employers with greater
flexibility to determine compensation, leaving greater room for potential discrimination. Analysis reveals anotable trend: across the municipal workforce, female employees are significantly more likely to receive salaries below the midpoint of their salary band (56% of female employees compared to 41% of male employees). Similarly, salaries for ABHLO employees are more likely to fall below the midpoint of their salary band(ranging from 49-54% for ABHLO employees compared to 36% for white employees). As shown in Figure 28
below, this trend extends to ABHLO female employees—a title’s share of ABHLO female workers is positively
associated with the likelihood that the employees’ salaries are below their pay band’s midpoint. Even after
accounting for years of experience, there is no discernible reduction in the likelihood that a worker’s salarywill fall below their salary band’s midpoint.
Figure 28. Average Percentage of Employees with Salaries Below Pay Band’s Midpoint by Share of ABHLO
Female Employees per Title

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TARGETING
TITLES FOR
INTERVENTIONTARGETING TITLES FOR INTERVENTION
The City’s gender and racial pay gaps are not due to a few problematic agencies or titles, rather they are theresult of occupational segregation across the City’s workforce. ABHLO female employees are concentrated
in the roles that receive the lowest pay. Targeting these titles for interventions such as career counseling andpay increases to match their role’s worth could considerably reduce the City’s pay disparities. In 2023, theCouncil passed Local Law 27, which requires the City to conduct a comparable worth analysis to evaluate
pay inequities between titles as well as the cost to provide pay adjustments. The results of this analysis willoffer insight into how the administration values and compensates roles within and across various agencies.
The Data Team also found that one way to significantly narrow pay gaps would be to promote greater
diversity in well-paying roles currently predominantly held by either male or white employees. By simulating
the impact of aligning the demographic composition of such job titles with that of NYC’s overall workforce, theData Team found a significant reduction in the gender and racial pay gaps.
However, budget cuts and policies that limit the hiring of new employees hinder the reduction in pay gaps
by slowing diversification efforts and preventing salary increases, delaying trainings, and restricting career
improvement efforts in low-paying titles with high concentrations of female and ABHLO employees.

Increasing Diversity
Gender Diversification Push for Medium Income Titles
Below, the Data Team located examples of titles that have “high return” to intervention with respect to reducing
pay gaps. The analysis demonstrates that focusing interventions on a few significant titles can significantly
narrow the City’s gender and racial pay gaps. Of course, intervention in similar titles, beyond those listed
below, would lead to even larger reductions in pay gaps.
Each of the nine titles in Figure 29 are held by more than 450 workers, are over 50% male, and hold mediansalaries largely between $60,000 and $100,000. In total, the nine titles employ 48,933 workers, 19% of whom
are female. However, the gender distribution of NYC’s overall workforce is 52% female. The Data Team
created a simulation aligning the gender composition in these titles with the gender composition in NYC’s
overall workforce. This scenario leads to a modest reduction in the gender pay gap of 4.3%.

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Figure 29. “Entry-level” (medium-income), populous titles predominantly held by male employees

Gender Diversification Push for High-Income Titles
The Data Team also conducted an analysis on the impact of gender diversification on high-income titles.
Figure 30 lists nine populous titles predominantly held by male employees and that largely have mediansalaries higher than $100,000. In total, these titles employ 13,659 people, of whom 86.5% are male, and are
higher ranking on the career ladder than those titles listed in Figure 29.
If employees of all genders remained in their roles at equal rates and fair promotional practices were used,greater gender diversification of the titles in Figure 29 would be expected to percolate over time to the higherlevel, better-paying positions in Figure 30. The Data Team simulated the impact on the gender pay gap if all
18 of these titles aligned with NYC’s gender composition. The Team found that this would reduce the gender
pay gap by 37%. The outcome presented in this scenario would take time and rely on societal change toremedy workplace culture issues and unfair work practices.

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Figure 30. “High-level” (high-income), populous titles predominantly held by male employees

Race/Ethnicity Diversification Push for Medium-Income Titles
Applying the same approach to race and ethnicity, the nine titles listed in Figure 31 are populous titles predominantly held by white employees and have median salaries between $60,000 and $100,000. In total, they
employ approximately 43,800 employees, of whom 55% are white. In contrast, the racial/ethnic distribution in
NYC’s workforce is approximately 68% ABHLO and 32% white. The Data Team found that if the racial composition of these titles aligned with NYC’s demographics, it would lead to about a 1% increase in the racial
pay gap. However, as with gender, this diversification push could reduce the pay gap in the long run. Overtime, with the use of fair promotional practices, increased racial diversity in lower-level positions could leadto greater diversification in higher-level positions.

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Figure 31. “Entry-level” (medium-income), populous titles predominantly held by white employees

Race/Ethnicity Diversification Push for High-Income Titles
Figure 32 displays the same nine high-level titles seen in Figure 30. In addition to being predominantly
male, they are also over 60% white. The Data Team found that if all 18 of these titles that are predominantly
held by white employees aligned with NYC’s racial composition, it would reduce the racial pay gap by 24%.
Although biases in workplaces present barriers to diversifying well-paying career tracks, the Council hopes todraw upon the findings above to motivate a collection of policies that will help overcome these biases. Suchpolicies could include offering career and promotional guidance to employees, identifying issues in workplaceculture, and requiring City agencies to effectuate meaningful reform in hiring and promotion practices.

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Figure 32. “High-level” (high-income), populous titles predominantly held by white employees

Diversification Progress Will Take Decades with No Intervention
The Data Team’s analysis shows that diversifying well-paid career tracks that historically have highconcentrations of male and white employees could significantly reduce the City’s gender and racial/ethnic pay
gaps. However, without intentional intervention, diversification of these careers could take decades. Consider
the racial composition of police officers and firefighters, outlined in the DCAS Workforce Profile Report, whichshows increases in ABHLO workers from 2012 to 2021. If these trends continue in a linear fashion, policeand firefighters would not match NYC’s racial composition until around 2040. The diversification of these
professions at senior levels would take an additional 15 to 20 years, given that high-ranking firefighters andpolice officers typically have served the City for 15 to 20 years. Interventions to provide additional pathways
into high-paying, promotional titles are necessary to achieve gender and racial pay parity.

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Figure 33. Racial Diversification Forecast with No Interventionix

Assuming no interventions, the timeline for gender diversification of the municipal workforce is longer than thatfor racial diversification. In 2012, only 18% of police officers and less than 1% of firefighters were female. Overthe next decade, there was minimal progress. By 2021, female employees still only accounted for 20% of police
officers and 1% of firefighters. Given these trends, achieving gender parity in these professions seems highly
unlikely without deliberate action to recruit and retain female employees.
Diversifying titles predominantly held by white and male employees is challenging given the cultural andworkplace norms that prevent female and ABHLO workers from entering and advancing within these titles.
Career counseling and workplace culture reform could help to close pay gaps that negatively affect female and
ABHLO workers who are paid less.

ix The horizontal line at 68% is the fraction of ABHLO workers across the city as a whole.

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RECOMMENDATIONSRECOMMENDATIONS
Implement and Conduct Oversight on Existing Local Laws
Since the Pay Equity Law was passed in 2019, the Council has adopted more legislation and the City has
created new programs, both aimed at improving pay equity. The Council will continue to conduct oversighton the implementation of these initiatives.
In particular, as highlighted in this report, implementation of Local Law 27 of 2023, requiring the City to conduct
a comparable worth analysis, will evaluate whether municipal titles may be undervalued and could be targeted
for interventions, such as increased pay.
The Council is committed to improving pay equity for all City employees. Acquiring more insightful data points,
increasing agency accountability measures, and reducing obstacles that have historically prevented female
and ABHLO employees from attaining high-paying, promotional titles are all measures that are necessary toreach gender- and race- based pay parity.

Strategies to Diversify High-Income Titles
To address the under-representation of female and ABHLO employees in agencies with higher concentrationsof male and white employees, as well as in promotional, higher-paid titles, the Council reaffirms its commitment
to exploring opportunities to:
1. Assess and expand the civil service pipeline, and
2. Improve diverse recruitment and hiring practices.
Further, to erode occupational segregation and eliminate pay disparities, the Council will pursue multiple
legislative approaches designed to channel eligible applicants into promotional titles and uncover the root
cause of workplace barriers that contribute to segregated agencies and titles.
First, the Council will introduce legislation to establish a municipal career counselor within DCAS to advise
municipal employees of professional growth and promotional opportunities, and to provide guidance
regarding the process of taking promotional exams and agency-provided trainings. Current employees are avaluable resource and well-positioned to advance to senior-level and managerial roles within their agencies.
Career counseling should be offered to tap more broadly into the pool of these eligible potential applicants
who otherwise may not receive consideration for promotional titles due to their lack of engagement with thecomplex and daunting promotional examination process.
Second, the Council will introduce legislation to require distribution of workplace culture surveys tomunicipal employees. Surveys would offer employees the opportunity to provide feedback on perceived
barriers to promotion; working conditions that may motivate employees to leave City civil service before
they obtain promotion eligibility; and management or hiring practices that may contribute to occupationalsegregation. In response to survey distribution, the agencies will be required to report the steps that they
are going to take to remedy occupational segregation and pay disparities. This legislation will build upon
the Council’s efforts, through Local Law 130 of 2023, to gather qualitative feedback from resigning andretiring municipal employees in exit surveys and interviews. This legislation would also build upon Local
Law 27 of 2023 by enhancing existing requirements that mandate agency heads to submit annual equal
employment opportunity plans.

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Third, the Council will explore opportunities to optimize the civil service promotional examination system
for applicants who acquire skills and training. The current civil service promotional system must do more
to ensure the recruitment and retention of a highly motivated and skilled workforce. Under New York State
law, the City must rank candidates for competitive class title positions, including promotions, based on their
exam results. Agencies must fill vacancies by selecting from the top three ranking candidates. This system
recognizes employees for their test-taking ability, which may or may not best represent the applicant’s ability
to perform the duties of the promotional position. To prioritize an employee’s skills and motivation to excel, theCity should reward participants who complete DCAS’s managerial and supervisory trainings with additional
points on their civil service promotional exam. DCAS offers many professional development training coursesthat teach core managerial competencies and tools to enhance creative thinking decision-making. Employees
who complete these trainings deserve recognition for taking the initiative to learn new professional skills tofoster their leadership abilities and advance their careers.

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PAY DISPARITIES IN NYCACKNOWLEDGEMENTS
The New York City Council’s Data Team produced this report in partnership with staff of the Council’s Legislative
Division. The report was designed by Omany Luna and the Event and Production Services unit of the Council’sCommunity Engagement Division. The authors extend their heartfelt appreciation to all individuals whose
contributions have been instrumental in the development of this report.
Special thanks to Jeffrey Baker, Malcom Butehorn, Alaa Moussawi, and Andrea Vazquez for listening to ourfindings, thinking through policy considerations, and providing edits to the report. We also would like to thankElizabeth Arzt, Rose Martinez, and Melissa Nuñez for their project and operational support.
We also gratefully acknowledge the assistance of Stacy Gardner and others at the New York City Office ofTechnology and Innovation.

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APPENDIXAPPENDIX
Terminology
Pay Gaps & Occupational Segregation
The Data Team’s earlier findings brought attention to significant pay gaps resulting from occupational segregation. After adjusting for job segregation, pay gaps are quite small and nearly imperceptible. This reportdelves into the unadjusted pay gaps and occupational segregation. Notably, occupational segregation andpay gaps are distinct concepts and one does not necessarily imply the other. Pay gaps may exist among different job categories, even when they have equal demographic representation.
Occupational segregation is where demographic groups are unevenly distributed across job categories ortitles. This report mainly focuses on “horizontal” occupational segregation, the uneven demographic distribution of individuals across different job categories or fields as compared to ”vertical” segregation that relates
to disparities across hierarchical levels within an organization or occupation.
This report measures occupational segregation by comparing the demographic composition of municipal
workers, an agency, or an occupation to that of NYC’s workforce as a whole. This involves a diversity index
that measures the similarity or dissimilarity of the two demographic compositions.
The unadjusted pay gap is the difference between the median pay for two demographic groups when notaccounting for other variables that may affect salary. For example, the unadjusted gender pay gap compares
the median salary for male employees to the median salary for female employees, regardless of their race,
age, civil service title, or other known characteristics of the individuals in those groups.
Throughout this report, the terms “unadjusted pay gap” or “pay gap” refer to the difference in median pay. In
some instances, which will be stated, the report uses the difference in mean/average pay.

Data Sources
This report draws from three sources of employee data to produce a more comprehensive analysis of pay
disparities in the municipal workforce. The first source comprises municipal employees covered under the
Pay Equity Law. The second encompasses a broader range of municipal employees, as detailed in the Workforce Profile Report and dashboard compiled by DCAS14. The third represents information about the entire
labor force in NYC, from the Census Bureau. Throughout this report, unless stated otherwise, the terms “municipal” or “City” workforce specifically refer to employees covered under the Pay Equity Law.

Pay Equity Law Data

Employee demographic and pay data was provided to the Council under the Pay Equity Law. It includes employees from 36 City agencies and offices. The data are a point-in-time snapshot of employees as of December
31, 2021. Data points from the 2018, 2019, and 2020 snapshots as summarized in the previous reports arereferenced. The Pay Equity Law was amended in 2023 to include additional data points. Analysis on the newdata will be available in future reports, including DOE’s pedagogical employees.

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Workforce Profile Report

DCAS produces a Fiscal Annual NYC Government Workforce Profile Report with comprehensive data onmunicipal employee demographics and pay. It includes employees from 72 City agencies, including elective
bodies, boards, commissions, and authorities. The most recent available data is a point-in-time snapshot of
employees as of June 30, 2021. Employee data spanning back ten years is also available.

NYC Labor Force

Throughout this report, the municipal workforce is sometimes compared to the entire labor force in NYC. Information on NYC’s labor force is drawn from the Census’s 2021 5-Year Estimates Public Use Microdata Sample15.

Employee, Pay & Demographic Variables
GenderMost employees identified as “Male” or “Female,” with 104 identifying as “Non-Binary” or “Other Gender,”and around 300 are categorized as “Unknown.” This report primarily focuses on the “Male” or “Female”
categories. Due to the smaller samples in the other groups, making conclusive statements about their unadjusted pay gaps may lack statistical robustness. In the report’s findings, where applicable, values are either
left or grayed out to acknowledge this limitation.

Race/Ethnicity

About 2% of employees self-identified as “some other race,” had an unknown race, or chose not to disclose
their race. About 12% of employees had an unknown ethnicity or chose not to disclose their ethnicity. This report primarily focuses on the remaining 86% of employees who identified as either Hispanic or Latino (20%),
Asian (9%), Black or African American (28%), or white (30%). As above, where applicable, values are either
left or grayed out to acknowledge this.
When the terms “Asian”, “Black or African American”, “Some Other Race”, and “White” are used, theyrefer to non-Hispanic employees, whereas “Hispanic or Latino’’ employees may or may not have selfidentified a race. The term “Asian, Black or African American, Hispanic or Latino, or Some Other Race” which
may be abbreviated as “ABHLO” is a designation representing individuals who are “Asian”, “Black or African
American”, “Some Other Race”, or “Hispanic or Latino”. Alternatively this designation can be described asincluding all races known not to be “White” in addition to individuals who are known to be “Hispanic or Latino”.

Education

Information on employees’ education history is largely unavailable. Most employees did not provide this information to the City.

Job Title

Throughout the report, when the terms “job,” “position,” “job title,” or “title code” are used, they specificallyrefer to the civil service title code. The civil service title code serves as a distinctive alphanumeric identifier foreach civil service title. Because identifying a job solely by the code could be challenging, the report includes
both the civil service job title names and their corresponding codes. Use of codes is necessary as job title
names alone are not unique identifiers. For visual clarity, some tables and charts only have the civil service title
name. Their corresponding codes are available in the Appendix.

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Uniformed Titles

About 44% of employees are uniformed. These uniformed titles are housed in the NYPD and FDNY, DOC,
and DSNY, and exist alongside civilian positions, which are non-uniformed titles such as administrative aides.
Distinctive uniforms are generally worn to identify and distinguish employees in roles that involve public
safety, law enforcement, emergency response, and similar functions. Uniformed titles were not indicated in
the dataset. At the Council’s request, DCAS provided a list of uniformed titles.

Agencies

The Pay Equity Law dataset includes 36 agencies and does not include every employee in those agencies.
In particular, the dataset does not include pedagogical employees in DOE. However, the Workforce Profile
Report from DCAS includes DOE in its entirety and is utilized in this report.

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LIST OF FIGURES
Figure 1. Agency Similarity or Dissimilarity Scores: 2021 Agency Demographics Compared to NYC’s Working
Population
Figure 2. Agency Demographic Composition Changes Over Time (Percentage Points)
Figure 3. Career Ladder Example: Police Service
Figure 4. Unadjusted Gender Pay Gaps Alone
Figure 5. Unadjusted Race/Ethnicity Pay Gaps Alone
Figure 6. Municipal Workforce by Uniformed or Not
Figure 7. Uniformed Workforce by Gender & Race/Ethnicity
Figure 8. Effects to the Pay Gap When Removing the Uniformed Workforce
Figure 9. Municipal Employees Compared to Entire NYC Labor Force
Figure 10. Race/Ethnicity Composition Over Time (Stability & Change in NYC Neighborhoods, 2010 to 2020
ReportFigure 11. Gender and Race/Ethnicity Compositions for DOE, without and with DOE
Figure 12. Effects to the Gender Pay Gap When Including DOE
Figure 13. Tables Showing the Agencies with the Highest and Lowest Percentages of Female Employees,
Respectively
Figure 14. Tables Showing the Agencies with the Highest and Lowest Percentages of ABHLO Employees,
Respectively
Figure 15. Workforce Headcount Change Since COVID by Agency
Figure 16. Top Workforce Headcount Decreases Since COVID by Job Title
Figure 17. Hiring & Separation Counts Overtime (State Comptroller Report)
Figure 18. Top Five Agencies with the Largest ABHLO/White Pay Differentials
Figure 19. Top Five Agencies with the Largest Gender Pay Differentials
Figure 20. Example Job Titles & Median Salary for Gender Categories
Figure 21. Example Job Titles & Median Salary for Race/Ethnicity Categories
Figure 22. Salaries for 10 Most Populous DOE Titles (DCAS Workforce Profile Report)
Figure 23. Gender and Race/Ethnicity Combined Pay Gap
Figure 24. Pay Gaps Based on Mean Salary Differences
Figure 25. Pay Gap Contributions by Subgroup (Decomposition)
Figure 26. Tables Showing the Agencies with the Highest and Lowest Percentages of ABHLO Female
Employees, Respectively
Figure 27. Median Salary for Civil Service Title Codes by Share of ABHLO Female Employees per Title
Figure 28. Average Percentage of Employees with Salaries Below Pay Band’s Midpoint by Share of ABHLO
Female Employees per Title
Figure 29. Entry-level” (medium-income), populous titles predominantly held by male employees
Figure 30. “High-level” (high-income), populous titles predominantly held by male employees
Figure 31. “Entry-level” (medium-income), populous titles predominantly held by white employees
Figure 32. “High-level” (high-income), populous titles predominantly held by white employees
Figure 33. Racial Diversification Forecast with No Intervention

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Agency Abbreviations
List of City Agencies Under the Purview of Local Law 18 of 2019
ACS - ADMINISTRATION FOR CHILDREN’S SERVICES
BIC - BUSINESS INTEGRITY COMMISSIONDFTA -DEPARTMENT FOR THE AGING
DOB - DEPARTMENT OF BUILDINGSDCP - DEPARTMENT OF CITY PLANNING
DCAS - DEPARTMENT OF CITYWIDE ADMINISTRATIVE SERVICESDCWP* – DEPARTMENT OF CONSUMER AND WORKER PROTECTIONx
DOC -DEPARTMENT OF CORRECTION
DCLA - DEPARTMENT OF CULTURAL AFFAIRSDDC -DEPARTMENT OF DESIGN AND CONSTRUCTION
DOE* - DEPARTMENT OF EDUCATION (NON-PEDAGOGICAL)
DEP - DEPARTMENT OF ENVIRONMENTAL PROTECTION
DOF - DEPARTMENT OF FINANCE
DOHMH - DEPARTMENT OF HEALTH AND MENTAL HYGIENEDHS - DEPARTMENT OF HOMELESS SERVICESDOITT - DEPARTMENT OF INFORMATION TECHNOLOGY AND TELECOMMUNICATIONSDOI - DEPARTMENT OF INVESTIGATIONPARKS - DEPARTMENT OF PARKS AND RECREATION
DOP - DEPARTMENT OF PROBATIONDORIS - DEPARTMENT OF RECORDS AND INFORMATION SERVICESDSNY - DEPARTMENT OF SANITATIONSBS - DEPARTMENT OF SMALL BUSINESS SERVICES
DOT - DEPARTMENT OF TRANSPORTATION
DVS - DEPARTMENT OF VETERANS’ SERVICESDYCD - DEPARTMENT OF YOUTH AND COMMUNITY DEVELOPMENTFDNY - FIRE DEPARTMENT
HPD - HOUSING PRESERVATION AND DEVELOPMENT
HRA/DSS - HUMAN RESOURCES ADMINISTRATION/DEPARTMENT OF SOCIAL SERVICES
LAW - LAW DEPARTMENT
MOCS - MAYORS OFFICE OF CONTRACT SERVICESOATH - OFFICE OF ADMINISTRATIVE TRIALS AND HEARINGSNYCEM - OFFICE OF EMERGENCY MANAGEMENT
OLR - OFFICE OF LABOR RELATIONSOMB - OFFICE OF MANAGEMENT AND BUDGET
MAYORALTY - OFFICE OF THE MAYOR
NYPD - POLICE DEPARTMENTx

The name of the department changed in 2019 to the Department of Consumer Affairs (DCA). The data provided to the Council
as required by the Pay Equity Law continues to use the prior name.

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Civil Service Title and Associated Title Codes
Admin Job Opportunity Spec NM - 10248

Administrative Probation Officer - 10029

Police Officer - 70210
P.O. DA Det GR3 - 7021A
P.O. Det Spec – 7021D
P.O. D/A Det 2nd GR - 7021B
Police Sergeant - 70235

Administrative Procurement Analyst - 8297A

Probation Officer - 51810

Administrative Staff Analyst - 10026

Protection Agent (ACS) - 52368

Administrative Staff Analyst (Non-Managerial) - 1002DRadio and Television Operator - 90411

Agency Attorney - 30087
Assistant Commissioner (FD) - 95039

Sanitation Enforcement Agent - 71681
Sanitation Supervisor - 70150

Assistant Principal – SUAPQ

Sanitation Worker - 70112

Associate Staff Analyst - 12627

School Aide - RAIDN

Associate Traffic Enforcement Agent - 71652

School Lunch Helper – RSLHN

Auto Mechanic - 92510

School Secretary – SYSYQ

Call Center Representative - 10260

Senior School Lunch Helper – RSSLN
Sewage Treatment Worker - 90739

Administrative Director of Social Services - 10056
Administrative Engineer – 10015

Child Protective Specialist – 52366
City Research Scientist - 21744
Clerical Associate Most Mayoral AG - 10251

Special Officer – 70810
Supervising EMS - 53055

Community Associate - 56057
Computer Specialist - 13632

Teacher - TRTRQ

Computer Systems Manager - 10050
Correction Officer – 70410
Criminalist - 21849

Youth Development Specialist - 52287

Traffic Enforcement Agent - 71651

Director of Field Operations (CPS) - 95600
Education Paraprofessional – AREPP
Emergency Medical Specialist (EMT) - 53053
Emergency Medical Specialist (Paramedic) - 53054
Executive Agency Counsel - 95005
Fire Captain - 70365
Firefighter - 70310
Fire Lieutenant - 70360
General Superintendent - 7019B
General Superintendent (LVL 1) - 70196
Guidance Counselor – GCGCQHighway Repairer - 92406
Lieutenant (FD) - 70360
Lieutenant (PD) - 70260
Occupational Therapist - 51221
Oiler - 91628
Police Administrative Aide - 10144
Police Lieutenant - 70260

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Methodology
Data Details:

The data provided to the Council by MODA is a point-in-time dataset of City of New York employees who
were either active or on temporary leave as of December 31, 2021. The raw version of this data includes
166,968 employees.
For most of the analysis, the Data Team used a subset of the raw data. Specifically, the Team analyzed asubset of full-time workers, who have base salaries greater than or equal to $15,000, are 16 years of age
or older, and have job titles that are classified as “Competitive” or “Non-Competitive.” This subset consistsof 151,593 workers.

Inclusion of DOE Data from DCAS:

For DOE, the data only includes non-pedagogical employees, who make up only a small fraction of
DOE’s nearly 150,000 workers. To gauge how inclusion of DOE data would affect the results, the Data
Team used aggregated DOE data from DCAS. Unfortunately, DCAS does not allow multi-level subsettingof the data, so the analysis is unable to match the DCAS sample to the sample used in the main analysis.
To facilitate a more apples-to-apples comparison, all (non-pedagogical) DOE workers were dropped
from the raw data, leaving a sample of 153,775.
DCAS only provides job-level information on each agency’s 10 most populous jobs, so the Data Teamwas unable to determine average salaries for each DOE role. Additionally, DCAS does not allow viewers
to see average salaries by gender and race. Instead, DOE’s average salary was applied to all workers.
For gender, this approximation is less problematic, because DOE overall is 80/20 female-to-male, which
also reflects the demographic of the DOE’s most populous jobs. However, this approximation is lessaccurate for race, because the available data currently shows that higher paying jobs in DOE tend to havea higher concentration of white employees while lower paying jobs tend to have a higher concentrationof ABHLO employees.
To assess how DOE inclusion would affect citywide pay gaps, the analysis combined gender and race
sample sizes and gender and race average salaries for the data from MODA and the DOE data from
DCAS. For example, the average female salary and the number of female employees can be viewed
in the data from MODA. From the DCAS dataset, the Data Team applied DOE’s average salary to thefemale employee numbers. Using the available information, the Team computed a sample-size weighted
average to estimate what the citywide female average salary would be if DOE data was included. This
analysis was conducted for each gender and race, computed “with DOE” pay gaps, and compared to“without DOE” pay gaps.

Pay Gap Decompositions:

To decompose pay gaps into constituent parts, the Data Team recomputed a pay gap under the assumptionthat all but one of the subgroups made the same average salary as the base group, and the remaining
subgroup still made their actual average salary. This new, reduced pay gap was then compared to theoriginal pay gap to determine how much the remaining subgroup’s lower wages were contributing to theoriginal pay gap. This process was repeated for different subgroups and for different base groups.

Diversification Forecasts:

To assess how long it will take for police officers and firefighters to have gender and racial compositions thatmatch that of NYC’s workforce, the Data Team assessed the data on the two agencies’ gender and racial
makeups from 2012-2021 and applied a linear forecast to estimate future compositions. Specifically, the Team
used R’s “ets” forecasting function with the model parameter set to “ZAN.” Please note that the Data Team
makes no claims about the accuracy of these forecasts; rather, they are intended to make a qualitative point.

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2021 Agency Demographics Compared to NYC’s Population

Diversity indices, or similarity/dissimilarity scores, were calculated using FY 2012-2021 data from the
DCAS Workforce Profile Report and 5-Year Estimates from the American Community Survey (ACS). To
create the chart, an agency’s demographic breakdown is compared to that of NYC’s working population.
Euclidean distance is used, a smaller score (close to 0) means an agency’s demographic breakdownis ‘closer in distance’ or matches that of NYC’s, while a larger score (close to 100) means ‘farther indistance’, doesn’t match. Racial/ethnic and gender diversity indices are then plotted along the chart axes
(0 to 100), with agencies closely matching the labor force demographic compositions of NYC appearing
closer to the origin (0,0). Larger label size indicates a larger agency head count, and red indicates worse
diversity performance.

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ENDNOTES
1 Local Law 18/2019, available at https://legistar.council.nyc.gov/LegislationDetail.
aspx?ID=3371662&GUID=5FCAFC03-035E-45D9-BE1A-4EBE7D6DF43C&Options=ID|Text|&Search=pay+eq
uity.
2 2021 Pay Equity Report, available at http://council.nyc.gov/data/wp-content/uploads/sites/73/2021/08/080221.
OC03.PAY-EQUITY-INNYC_v8.pdf and 2022 Pay Equity Report, available at https://council.nyc.gov/data/wp-content/
uploads/sites/73/2022/09/Speaker-Pay-Equity-Report-092122DIGITAL-VERSION-v2.pdf.
3

NYPD’s Hiring Process, available at https://www.nyc.gov/site/nypd/careers/police-officers/po-hiring.page.

4 FDNY’s Firefighter Eligibility Requirements, available at https://www.joinfdny.com/firefighter-eligibilityrequirements.
5 Lim, Nelson, Louis T. Mariano, Amy G. Cox, David Schulker, and Lawrence M. Hanser, Improving Demographic
Diversity in the U.S. Air Force Officer Corps, RAND Corporation, May 2014, available at https://www.rand.org/pubs/
research_reports/RR495.html.
6 David Cooper and Julia Wolfe, Cuts to the State and Local Public Sector Will Disproportionately Harm Womenand Black Workers, EPI Working Economics Blog, (July 2020), available at https://www.epi.org/blog/cuts-to-thestate-and-local-public-sector-will-disproportionately-harm-women-and-black-workers/.
7

Id. at p. 1.

8 Population Division, Stability & Change in NYC Neighborhoods, 2010 to 2020, NYC Department of City Planning,(March 2023), available at https://storymaps.arcgis.com/stories/c7bf9175168f4a2aa25980cf31992342.
9

Fiscal Annual NYC Government Workforce Profile Report, available at https://dcas.shinyapps.io/WFPR/.

10 Women Creating Change, Closing the Gender Pay Gap: Why Pay Equity Has Stalled in New York City and theUrgent Need for Action, (November 2023), available at https://wccny.org/wp-content/uploads/2023/11/WCC-GenderPay-Equity-Report-FINALv2_newdata_11_20_23.pdf .
11 Block, K., Croft, A., & Schmader, T. (2018). Worth Less?: Why Men (and Women) Devalue Care-Oriented
Careers. Frontiers in psychology, 9, 1353. https://doi.org/10.3389/fpsyg.2018.01353, accessed at https://pubmed.
ncbi.nlm.nih.gov/30147665/
12 Fiscal Annual NYC Government Workforce Profile Report, available at https://dcas.shinyapps.io/WFPR/.
13 Impact of the Pandemic on New York City’s Municipal Workforce Report, Office of the State Deputy Comptroller
for the City of New York, (February 2022), available at https://www.osc.ny.gov/reports/impact-pandemic-new-yorkcitys-municipal-workforce-february-2022.
14 Fiscal Annual NYC Government Workforce Profile Report, available at https://dcas.shinyapps.io/WFPR/.
15 2021 5-Year Census Bureau Estimate: Micro Data Access Tool, (2021), available at https://data.census.gov/
mdat/#/search?ds=ACSPUMS5Y2021

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