DATA110 Project 2 - Employee Salaries 2021

Topic: Salaries and Overtime by Gender within Agencies

Dataset: Employee Salaries 2021

Dataset Description: Annual salary information including gross pay and overtime pay for all active, permanent employees of Montgomery County, MD paid in the calendar year 2021. This information will be published annually each year.

Source: data.gov

Variable Definitions: County Agency, Gender, 2021 Compensation, 2021 Overtime Pay, 2021 Longevity Pay, and Salary Grade

Why did you choose the topic and dataset?

I chose this topic and dataset to explore if employees working for publicly funded agencies in Montgomery County, Maryland had an earnings wage gap by gender. The U.S. Census Bureau created an interactive Gender Pay Gap visualization that shows the earnings wage gap by gender for each state. According to the map, in Maryland, the median earnings for men are $7,564 higher than the median earnings for women. While I always negotiate salary offers, most of my female friends expressed that they do not negotiate. As a quasi-local government employee, I am aware of how a low starting salary can impact future earnings.

In 2019, the County Council unanimously supported the creation of the Montgomery County Pay Equity Act. This is in recognition of an identified pay gap between employees of different genders in the same grade levels. The data visualization I hope to provide in this project will show how the salaries paid to men and women differ in the same pay band within the same agency. Essentially, these are similarly qualified people doing work that is valued within the same band. I am also interested in understanding the distribution of overtime pay by gender within the same agency.

Loading Libraries and Packages

#install.packages("tidyverse")
#install.packages("tinyvex")
#install.packages("RColorBrewer")
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#install.packages("leaflet")
#install.packages("scales")
#install.packages("leaflet.extras")
#install.packages("rio")
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#install.packages("sf")
#install.packages("dplyr")
#install.packages("sp")
#tinytex::install_tinytex()
#install.packages("psych")
#install.packages("janitor")
#install.packages("CGPfunctions")
#install.packages("vtree")
#install.packages("ggpubr")
#install.packages("plotly")
library(janitor)

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library(tinytex)
library(tidyverse)

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library(ggpubr)

Loading the Dataset

library(tidyverse)
setwd("C:/Users/tgoode/Desktop")
salaries <- read_csv("employeesalaries2021.csv")

## Rows: 9907 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): Department, Department_Name, Division, Gender, Grade
## dbl (3): Base_Salary, 2021_Overtime_Pay, 2021_Longevity_Pay
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

View the Data

glimpse(salaries)

## Rows: 9,907
## Columns: 8
## $ Department           <chr> "ABS", "ABS", "ABS", "ABS", "ABS", "ABS", "ABS", …
## $ Department_Name      <chr> "Alcohol Beverage Services", "Alcohol Beverage Se…
## $ Division             <chr> "Beer Loading", "Liquor and Wine Delivery Operati…
## $ Gender               <chr> "M", "M", "M", "M", "M", "M", "M", "M", "M", "M",…
## $ Base_Salary          <dbl> 87969.00, 80086.00, 80086.00, 70814.00, 76419.00,…
## $ `2021_Overtime_Pay`  <dbl> 32953.25, 32656.04, 31369.15, 29837.85, 21379.22,…
## $ `2021_Longevity_Pay` <dbl> NA, 1105.44, 1591.20, NA, NA, NA, NA, NA, 3022.88…
## $ Grade                <chr> "20", "18", "18", "18", "17", "20", "17", "20", "…

Data Structure

str(salaries)

## spec_tbl_df [9,907 × 8] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
##  $ Department        : chr [1:9907] "ABS" "ABS" "ABS" "ABS" ...
##  $ Department_Name   : chr [1:9907] "Alcohol Beverage Services" "Alcohol Beverage Services" "Alcohol Beverage Services" "Alcohol Beverage Services" ...
##  $ Division          : chr [1:9907] "Beer Loading" "Liquor and Wine Delivery Operations" "Beer Delivery Operations" "Beer Delivery Operations" ...
##  $ Gender            : chr [1:9907] "M" "M" "M" "M" ...
##  $ Base_Salary       : num [1:9907] 87969 80086 80086 70814 76419 ...
##  $ 2021_Overtime_Pay : num [1:9907] 32953 32656 31369 29838 21379 ...
##  $ 2021_Longevity_Pay: num [1:9907] NA 1105 1591 NA NA ...
##  $ Grade             : chr [1:9907] "20" "18" "18" "18" ...
##  - attr(*, "spec")=
##   .. cols(
##   ..   Department = col_character(),
##   ..   Department_Name = col_character(),
##   ..   Division = col_character(),
##   ..   Gender = col_character(),
##   ..   Base_Salary = col_double(),
##   ..   `2021_Overtime_Pay` = col_double(),
##   ..   `2021_Longevity_Pay` = col_double(),
##   ..   Grade = col_character()
##   .. )
##  - attr(*, "problems")=<externalptr>

Calculating Summary Statistics

mean(salaries$Base_Salary)

## [1] 81339.53

median(salaries$Base_Salary)

## [1] 78789.38

sd(salaries$Base_Salary)

## [1] 30639.34

var(salaries$Base_Salary)

## [1] 938768931

summary(salaries)

##   Department        Department_Name      Division            Gender         
##  Length:9907        Length:9907        Length:9907        Length:9907       
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##                                                                             
##                                                                             
##   Base_Salary     2021_Overtime_Pay 2021_Longevity_Pay    Grade          
##  Min.   : 11147   Min.   :     1    Min.   :   19.93   Length:9907       
##  1st Qu.: 59939   1st Qu.:  1091    1st Qu.: 1981.28   Class :character  
##  Median : 78789   Median :  4818    Median : 3164.72   Mode  :character  
##  Mean   : 81340   Mean   : 10561    Mean   : 3674.41                     
##  3rd Qu.: 96665   3rd Qu.: 12669    3rd Qu.: 5419.68                     
##  Max.   :262000   Max.   :219326    Max.   :12100.40                     
##                   NA's   :4312      NA's   :7494

Employee Count By Gender

salaries %>% count(Gender, sort = TRUE)

## # A tibble: 2 × 2
##   Gender     n
##   <chr>  <int>
## 1 M       5806
## 2 F       4101

Employee Count By Gender within Department

tabyl(salaries, Gender, Department)

##  Gender ABS BOA BOE CAT CCL CEC CEX COR CUS DEP DGS DHS  DOT DPS ECM FIN  FRS
##       F  96   3  20  53  66  37  30 197  14  72  66   6  256  61   1  81  109
##       M 357   0   6  23  43  57  17 313  10  94 337   8 1036 167   1  38 1265
##  HCA  HHS HRC IGR LIB MPB NDA OAG OAS OCP OHR OIG OLO OLR OMB ORE PIO  POL PRO
##   52 1489   4   4 278   1  11   3  53   9  44   8  10   2  22   4  36  690  13
##   44  288   5   0  79   1   9   6  19   9  16   5   6   6  16   0  31 1186  14
##  REC SHF TBS ZAH
##   89  57  51   3
##   66 141  86   1

Employee Percentage By Gender within Department

Used to select department for gender wage gap comparison. The closest to 50% each gender will be considered for further review

tabyl(salaries, Gender, Department) %>%
  adorn_percentages("col") %>%
  adorn_pct_formatting(digits = 1)

##  Gender   ABS    BOA   BOE   CAT   CCL   CEC   CEX   COR   CUS   DEP   DGS
##       F 21.2% 100.0% 76.9% 69.7% 60.6% 39.4% 63.8% 38.6% 58.3% 43.4% 16.4%
##       M 78.8%   0.0% 23.1% 30.3% 39.4% 60.6% 36.2% 61.4% 41.7% 56.6% 83.6%
##    DHS   DOT   DPS   ECM   FIN   FRS   HCA   HHS   HRC    IGR   LIB   MPB   NDA
##  42.9% 19.8% 26.8% 50.0% 68.1%  7.9% 54.2% 83.8% 44.4% 100.0% 77.9% 50.0% 55.0%
##  57.1% 80.2% 73.2% 50.0% 31.9% 92.1% 45.8% 16.2% 55.6%   0.0% 22.1% 50.0% 45.0%
##    OAG   OAS   OCP   OHR   OIG   OLO   OLR   OMB    ORE   PIO   POL   PRO   REC
##  33.3% 73.6% 50.0% 73.3% 61.5% 62.5% 25.0% 57.9% 100.0% 53.7% 36.8% 48.1% 57.4%
##  66.7% 26.4% 50.0% 26.7% 38.5% 37.5% 75.0% 42.1%   0.0% 46.3% 63.2% 51.9% 42.6%
##    SHF   TBS   ZAH
##  28.8% 37.2% 75.0%
##  71.2% 62.8% 25.0%

Employee Percentage By Gender within Department (using full name)

Used to select department for gender wage gap comparison. The closest to 50% each gender will be selected

tabyl(salaries, Gender, Department_Name) %>%
  adorn_percentages("col") %>%
  adorn_pct_formatting(digits = 1)

##  Gender Alcohol Beverage Services Board of Appeals Department
##       F                     21.2%                      100.0%
##       M                     78.8%                        0.0%
##  Board of Elections Community Engagement Cluster
##               76.9%                        39.4%
##               23.1%                        60.6%
##  Community Use of Public Facilities Correction and Rehabilitation
##                               58.3%                         38.6%
##                               41.7%                         61.4%
##  County Attorney's Office County Council Department of Environmental Protection
##                     69.7%          60.6%                                  43.4%
##                     30.3%          39.4%                                  56.6%
##  Department of Finance Department of General Services
##                  68.1%                          16.4%
##                  31.9%                          83.6%
##  Department of Health and Human Services
##                                    83.8%
##                                    16.2%
##  Department of Housing and Community Affairs Department of Permitting Services
##                                        54.2%                             26.8%
##                                        45.8%                             73.2%
##  Department of Police Department of Public Libraries Department of Recreation
##                 36.8%                          77.9%                    57.4%
##                 63.2%                          22.1%                    42.6%
##  Department of Technology and Enterprise Business Solutions
##                                                       37.2%
##                                                       62.8%
##  Department of Transportation Ethics Commission Fire and Rescue Services
##                         19.8%             50.0%                     7.9%
##                         80.2%             50.0%                    92.1%
##  Merit System Protection Board Department Non-Departmental Account
##                                     50.0%                    55.0%
##                                     50.0%                    45.0%
##  Office of Agriculture Office of Animal Services Office of Consumer Protection
##                  33.3%                     73.6%                         50.0%
##                  66.7%                     26.4%                         50.0%
##  Office of Emergency Management and Homeland Security Office of Human Resources
##                                                 42.9%                     73.3%
##                                                 57.1%                     26.7%
##  Office of Human Rights Office of Intergovernmental Relations Department
##                   44.4%                                           100.0%
##                   55.6%                                             0.0%
##  Office of Labor Relations Office of Legislative Oversight
##                      25.0%                           62.5%
##                      75.0%                           37.5%
##  Office of Management and Budget Office of Procurement
##                            57.9%                 48.1%
##                            42.1%                 51.9%
##  Office of Public Information Office of Racial Equity and Social Justice
##                         53.7%                                     100.0%
##                         46.3%                                       0.0%
##  Office of the Inspector General Office of Zoning and Administrative Hearings
##                            61.5%                                        75.0%
##                            38.5%                                        25.0%
##  Offices of the County Executive Sheriff's Office
##                            63.8%            28.8%
##                            36.2%            71.2%

Plot the Crosstab of Salaries by Gender within Department

library(CGPfunctions)
PlotXTabs(salaries, Gender, Department) 

## Plotted dataset salaries variables Gender by Department

###### The departments with an even distribution by gender are too small to use for a comparison of wages by gender with a department. For example, the Merit System Protection Board (MPB) has two employees working within different pay bands.

library(vtree)
vtree(salaries, c("Gender", "Department"), 
   fillcolor = c( Department = "#e7d4e8", Gender = "#99d8c9"),
   horiz = FALSE) 

Employee Count By Gender within Pay Grade

tabyl(salaries, Gender, Grade)

##  Gender  0  10 11  12  13  14  15  16 17  18  19  20  21 22  23  24  25 26 27
##       F 33 164  9  72 179  50 380 324 82 250  64 200 306 58 295 477 251 98 14
##       M 52  84 18 138  79 163 723 106 89 175 206 117 233 78 205 119 159 96 21
##  28 29 30 31 32 33 34 35 36 38 39 40  5 7  8  9  A1 A2 A3  B1  B2 B3 B4 B6 C1
##  76  2 10  0 31  0  0  0  0  0  0  2  4 0 31  1  15  8  3   6   8  3  3  0  7
##  94  2  7  2 23  1  3  1  1  1  3  0 11 2 12 29 135 29 17 120 144 27 10  3 16
##  C2 C3 C4  C5 C6 D1 D2 D3 D4 F1  F2  F3 F3P  F4 G1 G2 G3 G4 M1 M2  M3 MD2 MD3
##   1  6 10  42 11  6  4  1  0  5  22  18   1  10  0  3  2 12 12 71 133   1   3
##   3 14 62 107 36 25  9  4  1 58 205 439   0 206  3  9 20 61 14 69 132   0   2
##  MD4 P1 P2 P3  P4 P4P P5
##    2  6 16 18 153   8  8
##    2 19 59 59 571   1 62

Employee Percentage By Gender within Pay Grade

Used to select department for gender wage gap comparison. The closest to 50% each gender will be considered for further review

tabyl(salaries, Gender, Grade) %>%
  adorn_percentages("col") %>%
  adorn_pct_formatting(digits = 1)

##  Gender     0    10    11    12    13    14    15    16    17    18    19    20
##       F 38.8% 66.1% 33.3% 34.3% 69.4% 23.5% 34.5% 75.3% 48.0% 58.8% 23.7% 63.1%
##       M 61.2% 33.9% 66.7% 65.7% 30.6% 76.5% 65.5% 24.7% 52.0% 41.2% 76.3% 36.9%
##     21    22    23    24    25    26    27    28    29    30     31    32
##  56.8% 42.6% 59.0% 80.0% 61.2% 50.5% 40.0% 44.7% 50.0% 58.8%   0.0% 57.4%
##  43.2% 57.4% 41.0% 20.0% 38.8% 49.5% 60.0% 55.3% 50.0% 41.2% 100.0% 42.6%
##      33     34     35     36     38     39     40     5      7     8     9
##    0.0%   0.0%   0.0%   0.0%   0.0%   0.0% 100.0% 26.7%   0.0% 72.1%  3.3%
##  100.0% 100.0% 100.0% 100.0% 100.0% 100.0%   0.0% 73.3% 100.0% 27.9% 96.7%
##     A1    A2    A3    B1    B2    B3    B4     B6    C1    C2    C3    C4    C5
##  10.0% 21.6% 15.0%  4.8%  5.3% 10.0% 23.1%   0.0% 30.4% 25.0% 30.0% 13.9% 28.2%
##  90.0% 78.4% 85.0% 95.2% 94.7% 90.0% 76.9% 100.0% 69.6% 75.0% 70.0% 86.1% 71.8%
##     C6    D1    D2    D3     D4    F1    F2    F3    F3P    F4     G1    G2
##  23.4% 19.4% 30.8% 20.0%   0.0%  7.9%  9.7%  3.9% 100.0%  4.6%   0.0% 25.0%
##  76.6% 80.6% 69.2% 80.0% 100.0% 92.1% 90.3% 96.1%   0.0% 95.4% 100.0% 75.0%
##     G3    G4    M1    M2    M3    MD2   MD3   MD4    P1    P2    P3    P4   P4P
##   9.1% 16.4% 46.2% 50.7% 50.2% 100.0% 60.0% 50.0% 24.0% 21.3% 23.4% 21.1% 88.9%
##  90.9% 83.6% 53.8% 49.3% 49.8%   0.0% 40.0% 50.0% 76.0% 78.7% 76.6% 78.9% 11.1%
##     P5
##  11.4%
##  88.6%

Plot the Crosstab of Salaries by Gender within Pay Grade

library(CGPfunctions)
PlotXTabs(salaries, Gender, Grade)

## Plotted dataset salaries variables Gender by Grade

Six pay grades were selected for salary review, based on an almost equal gender distribution. The six pay grades are 17, 26, 29, M2, M3 and MD4. The pay grades with M are executive level positions, likely appointed by the County Executive and approved by the County Council.

Subsetting Selected Pay Grade 17

salaries %>%
  filter(Grade == 17)

## # A tibble: 171 × 8
##    Department Department_Name       Division Gender Base_Salary `2021_Overtime…`
##    <chr>      <chr>                 <chr>    <chr>        <dbl>            <dbl>
##  1 ABS        Alcohol Beverage Ser… Stock L… M           76419           21379. 
##  2 ABS        Alcohol Beverage Ser… Liquor … M           76419           17666. 
##  3 ABS        Alcohol Beverage Ser… Special… M           58301.          15625. 
##  4 ABS        Alcohol Beverage Ser… Beer Wa… M           55152.          11309. 
##  5 CCL        County Council        Council… F           69643.           7357. 
##  6 CCL        County Council        Council… F           76419              82.7
##  7 CCL        County Council        Council… M           61428              NA  
##  8 CCL        County Council        Council… F           64513.             NA  
##  9 CCL        County Council        CCL 01 … F           76419              NA  
## 10 CCL        County Council        Council… F           76419              NA  
## # … with 161 more rows, and 2 more variables: `2021_Longevity_Pay` <dbl>,
## #   Grade <chr>

Plotting Salaries by Gender in Grade 17

salaries %>%
  filter(Grade == 17) %>%
  ggplot() +
  geom_point(aes(x=Base_Salary, y=Grade, color=Gender)) +
  ggtitle("2021 Salaries in Pay Grade 17") +
  ylab("Pay Grade 17") + 
  xlab("2021 Salaries") + 
  labs(fill = "Gender")

Subsetting Selected Pay Grade 26

salaries %>%
  filter(Grade == 26)

## # A tibble: 194 × 8
##    Department Department_Name       Division Gender Base_Salary `2021_Overtime…`
##    <chr>      <chr>                 <chr>    <chr>        <dbl>            <dbl>
##  1 ABS        Alcohol Beverage Ser… IT Admi… F          116825                NA
##  2 ABS        Alcohol Beverage Ser… Adminis… M          116825                NA
##  3 CCL        County Council        CCL 01 … F           17963                NA
##  4 CCL        County Council        CCL 01 … F           48000                NA
##  5 CCL        County Council        CCL 01 … M           52816.               NA
##  6 CCL        County Council        CCL 01 … F           55825                NA
##  7 CCL        County Council        CCL 01 … F           63497.               NA
##  8 CCL        County Council        CCL 01 … M           68000                NA
##  9 CCL        County Council        CCL 01 … M           68284.               NA
## 10 CCL        County Council        CCL 01 … M           78189.               NA
## # … with 184 more rows, and 2 more variables: `2021_Longevity_Pay` <dbl>,
## #   Grade <chr>

Plotting Salaries by Gender in Grade 26

salaries %>%
  filter(Grade == 26) %>%
  ggplot() +
  geom_point(aes(x=Base_Salary, y=Grade, color=Gender)) +
  ggtitle("2021 Salaries in Pay Grade 26") +
  ylab("Pay Grade 26") + 
  xlab("2021 Salaries") + 
  labs(fill = "Gender")

Subsetting Selected Pay Grade 29

salaries %>%
  filter(Grade == 29)

## # A tibble: 4 × 8
##   Department Department_Name        Division Gender Base_Salary `2021_Overtime…`
##   <chr>      <chr>                  <chr>    <chr>        <dbl>            <dbl>
## 1 FIN        Department of Finance  Divisio… M          134750               NA 
## 2 FRS        Fire and Rescue Servi… Behavio… F          105560               NA 
## 3 HHS        Department of Health … Childre… F          134750               NA 
## 4 OAS        Office of Animal Serv… Animal … M          131316.             931.
## # … with 2 more variables: `2021_Longevity_Pay` <dbl>, Grade <chr>

Plotting Salaries by Gender in Grade 29

salaries %>%
  filter(Grade == 29) %>%
  ggplot() +
  geom_point(aes(x=Base_Salary, y=Grade, color=Gender)) +
  ggtitle("2021 Salaries in Pay Grade 29") +
  ylab("Pay Grade 29") + 
  xlab("2021 Salaries") + 
  labs(fill = "Gender")

Subsetting Selected Pay Grade M2

salaries %>%
  filter(Grade == "M2")

## # A tibble: 140 × 8
##    Department Department_Name       Division Gender Base_Salary `2021_Overtime…`
##    <chr>      <chr>                 <chr>    <chr>        <dbl>            <dbl>
##  1 ABS        Alcohol Beverage Ser… Licensu… F          147755.               NA
##  2 ABS        Alcohol Beverage Ser… ABS 85 … F          166656.               NA
##  3 ABS        Alcohol Beverage Ser… Retail … M          166729.               NA
##  4 ABS        Alcohol Beverage Ser… Adminis… M          169856                NA
##  5 ABS        Alcohol Beverage Ser… Wholesa… M          169856                NA
##  6 CAT        County Attorney's Of… Health … M          169853.               NA
##  7 CAT        County Attorney's Of… Insuran… F          169854.               NA
##  8 CAT        County Attorney's Of… Human R… F          169856                NA
##  9 CAT        County Attorney's Of… Governm… M          169856                NA
## 10 CAT        County Attorney's Of… Zoning,… M          169856                NA
## # … with 130 more rows, and 2 more variables: `2021_Longevity_Pay` <dbl>,
## #   Grade <chr>

Plotting Salaries by Gender in Grade M2

salaries %>%
  filter(Grade == "M2") %>%
  ggplot() +
  geom_point(aes(x=Base_Salary, y=Grade, color=Gender)) +
  ggtitle("2021 Salaries in Pay Grade M2") +
  ylab("Pay Grade M2") + 
  xlab("2021 Salaries") + 
  labs(fill = "Gender")

Subsetting Selected Pay Grade M3

salaries %>%
  filter(Grade == "M3")

## # A tibble: 265 × 8
##    Department Department_Name       Division Gender Base_Salary `2021_Overtime…`
##    <chr>      <chr>                 <chr>    <chr>        <dbl>            <dbl>
##  1 ABS        Alcohol Beverage Ser… Wholesa… M          126976.               NA
##  2 ABS        Alcohol Beverage Ser… Adminis… M          129102.               NA
##  3 ABS        Alcohol Beverage Ser… Wholesa… M          133219.               NA
##  4 ABS        Alcohol Beverage Ser… Adminis… F          143447.               NA
##  5 ABS        Alcohol Beverage Ser… IT Admi… M          146923                NA
##  6 ABS        Alcohol Beverage Ser… Adminis… M          146923                NA
##  7 BOA        Board of Appeals Dep… Executi… F          146923                NA
##  8 BOE        Board of Elections    Directo… F          146917.               NA
##  9 CAT        County Attorney's Of… County … F          146921.               NA
## 10 CCL        County Council        Council… F           99274.               NA
## # … with 255 more rows, and 2 more variables: `2021_Longevity_Pay` <dbl>,
## #   Grade <chr>

Plotting Salaries by Gender in Grade M3

salaries %>%
  filter(Grade == "M3") %>%
  ggplot() +
  geom_point(aes(x=Base_Salary, y=Grade, color=Gender)) +
  ggtitle("2021 Salaries in Pay Grade M3") +
  ylab("Pay Grade M3") + 
  xlab("2021 Salaries") + 
  labs(fill = "Gender")

Subsetting Selected Pay Grade MD4

salaries %>%
  filter(Grade == "MD4")

## # A tibble: 4 × 8
##   Department Department_Name        Division Gender Base_Salary `2021_Overtime…`
##   <chr>      <chr>                  <chr>    <chr>        <dbl>            <dbl>
## 1 HHS        Department of Health … Behavio… F          110683.            6661.
## 2 HHS        Department of Health … Behavio… M          113656               NA 
## 3 HHS        Department of Health … Public … F          204581.              NA 
## 4 HHS        Department of Health … Behavio… M          227312               NA 
## # … with 2 more variables: `2021_Longevity_Pay` <dbl>, Grade <chr>

Plotting Salaries by Gender in Grade MD4

salaries %>%
  filter(Grade == "MD4") %>%
  ggplot() +
  geom_point(aes(x=Base_Salary, y=Grade, color=Gender)) +
  ggtitle("2021 Salaries in Pay Grade MD4") +
  ylab("Pay Grade MD4") + 
  xlab("2021 Salaries") + 
  labs(fill = "Gender")

Subsetting Selected Six Pay Grade

salaries2 <- salaries %>%
  filter(Grade %in% c("17", "26", "29", "M2", "M3", "MD4"))
salaries2

## # A tibble: 778 × 8
##    Department Department_Name       Division Gender Base_Salary `2021_Overtime…`
##    <chr>      <chr>                 <chr>    <chr>        <dbl>            <dbl>
##  1 ABS        Alcohol Beverage Ser… Stock L… M           76419            21379.
##  2 ABS        Alcohol Beverage Ser… Liquor … M           76419            17666.
##  3 ABS        Alcohol Beverage Ser… Special… M           58301.           15625.
##  4 ABS        Alcohol Beverage Ser… Beer Wa… M           55152.           11309.
##  5 ABS        Alcohol Beverage Ser… IT Admi… F          116825               NA 
##  6 ABS        Alcohol Beverage Ser… Adminis… M          116825               NA 
##  7 ABS        Alcohol Beverage Ser… Wholesa… M          126976.              NA 
##  8 ABS        Alcohol Beverage Ser… Adminis… M          129102.              NA 
##  9 ABS        Alcohol Beverage Ser… Wholesa… M          133219.              NA 
## 10 ABS        Alcohol Beverage Ser… Adminis… F          143447.              NA 
## # … with 768 more rows, and 2 more variables: `2021_Longevity_Pay` <dbl>,
## #   Grade <chr>

Structure of Salaries2

str(salaries2)

## spec_tbl_df [778 × 8] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
##  $ Department        : chr [1:778] "ABS" "ABS" "ABS" "ABS" ...
##  $ Department_Name   : chr [1:778] "Alcohol Beverage Services" "Alcohol Beverage Services" "Alcohol Beverage Services" "Alcohol Beverage Services" ...
##  $ Division          : chr [1:778] "Stock Liquor and Wine Warehouse Operations" "Liquor and WIne Loading" "Special Order Liquor and Wine Warehouse Operations" "Beer Warehouse Operations" ...
##  $ Gender            : chr [1:778] "M" "M" "M" "M" ...
##  $ Base_Salary       : num [1:778] 76419 76419 58301 55152 116825 ...
##  $ 2021_Overtime_Pay : num [1:778] 21379 17666 15625 11309 NA ...
##  $ 2021_Longevity_Pay: num [1:778] NA NA NA NA 3480 ...
##  $ Grade             : chr [1:778] "17" "17" "17" "17" ...
##  - attr(*, "spec")=
##   .. cols(
##   ..   Department = col_character(),
##   ..   Department_Name = col_character(),
##   ..   Division = col_character(),
##   ..   Gender = col_character(),
##   ..   Base_Salary = col_double(),
##   ..   `2021_Overtime_Pay` = col_double(),
##   ..   `2021_Longevity_Pay` = col_double(),
##   ..   Grade = col_character()
##   .. )
##  - attr(*, "problems")=<externalptr>

Plotting the Salaries in Select Pay Grades By Gender

salaries2 %>%
  filter(Grade %in% c("17", "26", "29", "M2", "M3", "MD4")) %>%
  ggplot() +
  geom_point(aes(x=Base_Salary, y=Grade, color=Gender)) +
  ggtitle("2021 Salaries in Select Pay Grades by Gender") +
  ylab("Select Pay Grades") + 
  xlab("2021 Salaries") + 
  labs(fill = "Gender")

Plotting All Salaries By Pay Grades and Gender

The salaries noted in grade 0 are spread across divisions. They are primarily director level or higher positions.

salaries %>%
  ggplot() +
  geom_point(aes(x=Base_Salary/100, y=Grade, color=Gender)) +
  ggtitle("2021 Salaries by Pay Grade") +
  ylab("Pay Grade") + 
  xlab("2021 Salaries (000s)") + 
  labs(fill = "Gender")

Gender Wage Gap Analysis

Some sources calculate the gender wage gap using the median income for each gender. Other sources use the average income for each gender. All the sources divide women’s wages by men’s wages to identity the gender wage gap.

Wage Gap by Gender Using the Median (Average)

76809.92/80086

## [1] 0.959093

aggregate(x = salaries$Base_Salary,               
          by = list(salaries$Gender),             
          FUN = median)

##   Group.1        x
## 1       F 76809.92
## 2       M 80086.00

Wage Gap by Gender Using the Mean (Average)

79709.61/82490.81

## [1] 0.9662847

aggregate(x = salaries$Base_Salary,               
          by = list(salaries$Gender),             
          FUN = mean)

##   Group.1        x
## 1       F 79709.61
## 2       M 82490.81

Plot Wage Distribution to Show Gender Wage Gap by Select Grades

salaries2 %>%
  ggplot() +
  geom_boxplot(aes(x=Base_Salary/100, y=Grade, color=Gender, outcol="red")) +
  ggtitle("2021 Salaries by Select Pay Grade") +
  ylab("Select Pay Grade") + 
  xlab("2021 Salaries (000s)") + 
  labs(fill = "Gender")

## Warning: Ignoring unknown aesthetics: outcol

Overtime Pay Distribution by Select Grades

library(dplyr)
library(ggplot2)
library(plotly)

(ggplot_object <- salaries2 %>%
  ggplot(aes(x = `2021_Overtime_Pay`, y = Grade)) + 
  geom_point(mapping = aes(color = Gender)) + 
  geom_smooth())+
  ggtitle("Overtime Pay Distribution by Select Grades")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## Warning: Removed 647 rows containing non-finite values (stat_smooth).

## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : span too small. fewer data values than degrees of freedom.

## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : at 56.683

## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : radius 16.137

## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : all data on boundary of neighborhood. make span bigger

## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 56.683

## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 4.017

## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 1

## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : at 868.13

## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : radius 16.137

## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : all data on boundary of neighborhood. make span bigger

## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 16.137

## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : zero-width neighborhood. make span bigger

## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : zero-width neighborhood. make span bigger

## Warning: Computation failed in `stat_smooth()`:
## NA/NaN/Inf in foreign function call (arg 5)

## Warning: Removed 647 rows containing missing values (geom_point).

#### Overtime Pay Distribution by Select Grades ##### Using Plotly

salaries2 %>%
  plot_ly(x = ~`2021_Overtime_Pay`, y = ~Grade, color = ~Gender, type = 'scatter')%>%
  layout(title = 'Overtime Pay Distribution by Select Grades', plot_bgcolor = "#e5ecf6")

## No scatter mode specifed:
##   Setting the mode to markers
##   Read more about this attribute -> https://plotly.com/r/reference/#scatter-mode

## Warning: Ignoring 647 observations

## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels

## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels

Overtime Pay Distribution by Pay Grades

(ggplot_object <- salaries %>%
  ggplot(aes(x = `2021_Overtime_Pay`, y = Grade)) + 
  geom_point(mapping = aes(color = Gender)) + 
  geom_smooth())+
  ggtitle("Overtime Pay Distribution by Pay Grades")

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## Warning: Removed 4312 rows containing non-finite values (stat_smooth).

## Warning: Computation failed in `stat_smooth()`:
## NA/NaN/Inf in foreign function call (arg 3)

## Warning: Removed 4312 rows containing missing values (geom_point).

#### Overtime Pay Distribution by Pay Grades ##### Using Plotly

salaries %>%
  plot_ly(x = ~`2021_Overtime_Pay`, y = ~Grade, color = ~Gender, type = 'scatter')%>%
  layout(title = 'Overtime Pay Distribution by Pay Grades', plot_bgcolor = "#e5ecf6")

## No scatter mode specifed:
##   Setting the mode to markers
##   Read more about this attribute -> https://plotly.com/r/reference/#scatter-mode

## Warning: Ignoring 4312 observations

## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels

## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels

Summary Results

The dataset includes salaries of 9,907 Montgomery County government employees, categorized by department, pay grade, and gender. Males represented 59% of the employees represented in the dataset. The county’s largest departments are the department of transportation, fire and rescue service, health and human services, and department of police. The health and human services department, which includes public school health care workers, is 84% female. The other large departments employ significantly more male employees than female employees. I sought to investigate the wage distribution within departments but found that departments with an almost even distribution of employees by gender were too small to use for a comparison of wages by gender.

The analysis shifted to review salaries within pay grades with an almost even distribution of employees by gender. The six pay grades selected for review included ranges of mid-level and executive-level pay grades. The executive-level pay grades are likely appointed by the County Executive and approved by the County Council. The pay grades selected for review were 17, 26, 29, M2, M3, and MD4. The salary plots show that within each salary range, the male salaries cluster towards the higher end of the pay range. The trend holds when all salaries are plotted by pay grade.

The analysis continued to investigate if a gender wage gap exists within the selected pay grades. Using the median wage, the calculation showed that females earned 96% of the salary earned by males employed in the Montgomery County Government. Using the mean wage, females earned 97% of the salary earned by males. The bar plot which shows the wage distribution of the six selected pay grades shows that the mean wage is higher for males than females.

Using the plotly package, the overtime pay for the six select pay grades and all pay grades were plotted to visualize the distribution of overtime pay by gender. Each scatterplot displays the amount of overtime compensation paid to each employee. Both graphs show that males are overwhelmingly the beneficiaries of overtime compensation. The gender wage gap uses the base salary. An analysis using total compensation is an area of future study and may show that the gender wage gap for Montgomery County employees is wider than calculated in this project.

The first report on gender pay equity among county employees was due to the county council by July 1, 2022. The report was not found after an online search. While the gender wage gap in my analysis is smaller than the national and state gender wage gap, any gap can have a long-term impact on lifetime earnings. The local government should actively work towards eliminating this disparity. Since research shows that there is wage disparity by race, the county council should add race as a category in the data collected each year.

References

Carranza, V. (2019, March 5). Montgomery County Maryland. https://www2.montgomerycountymd.gov/mcgportalapps/Press_Detail.aspx?Item_ID=22880&Dept=1

DATA.GOV. (2022, March 30). Employee Salaries 2021. Datasets - CKAN. Retrieved July 8, 2022, from https://catalog.data.gov/dataset?organization=montgomery-county-of-maryland&res_format=CSV&ext_location=&q=salaries&ext_prev_extent=-141.6796875%2C8.754794702435618%2C-59.4140625%2C61.77312286453146&sort=views_recent+desc&ext_bbox=

U.S. Census Bureau. (2022, March 1). In Puerto Rico, no gap in median earnings between men and women. Census.gov. Retrieved July 9, 2022, from https://www.census.gov/library/stories/2022/03/what-is-the-gender-wage-gap-in-your-state.html#:~:text=The%20gender%2Dbased%20wage%20gap,2019%20American%20Community%20Survey%20(ACS)