Gender Pay Gap: An Analysis
Sidney Coates
2024-04-16
Questions
In 2015, for occupations where men made more than women, what
were the top ten occupations with the highest pay gap between men and
women?
In 2015, for occupations where men made more than women, what
were the top ten occupations with the smallest pay gap between men and
women?
In 2015, what were the top ten occupations where women made more
than men?
In 2015, what was the average pay gap for occupations where men
made more than women and the proportion of female workers was 50% or
more?
In 2015, what was the average pay gap for occupations where men
made more than women and the proportion of female workers was less than
50%?
Setup
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jobs_gender <- read.csv("C:\\Users\\coate\\Downloads\\jobs_gender.csv")
save(jobs_gender, file = "jobs_gender.RData")
load("~/jobs_gender.RData")
Description of the Data
## 'data.frame': 2088 obs. of 12 variables:
## $ year : int 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 ...
## $ occupation : chr "Chief executives" "General and operations managers" "Legislators" "Advertising and promotions managers" ...
## $ major_category : chr "Management, Business, and Financial" "Management, Business, and Financial" "Management, Business, and Financial" "Management, Business, and Financial" ...
## $ minor_category : chr "Management" "Management" "Management" "Management" ...
## $ total_workers : int 1024259 977284 14815 43015 754514 44198 109703 489048 990611 14656 ...
## $ workers_male : int 782400 681627 8375 17775 440078 16141 72873 354369 460842 3387 ...
## $ workers_female : int 241859 295657 6440 25240 314436 28057 36830 134679 529769 11269 ...
## $ percent_female : num 23.6 30.3 43.5 58.7 41.7 63.5 33.6 27.5 53.5 76.9 ...
## $ total_earnings : int 120254 73557 67155 61371 78455 74114 62187 99167 70456 71927 ...
## $ total_earnings_male : int 126142 81041 71530 75190 91998 90071 66579 101318 90278 97552 ...
## $ total_earnings_female: int 95921 60759 65325 55860 65040 66052 55079 90940 57406 68207 ...
## $ wage_percent_of_male : num 76 75 91.3 74.3 70.7 ...
## year occupation major_category minor_category
## Min. :2013 Length:2088 Length:2088 Length:2088
## 1st Qu.:2014 Class :character Class :character Class :character
## Median :2014 Mode :character Mode :character Mode :character
## Mean :2014
## 3rd Qu.:2015
## Max. :2016
##
## total_workers workers_male workers_female percent_female
## Min. : 658 Min. : 0 Min. : 0 Min. : 0.00
## 1st Qu.: 18687 1st Qu.: 10765 1st Qu.: 2364 1st Qu.: 10.73
## Median : 58997 Median : 32302 Median : 15238 Median : 32.40
## Mean : 196055 Mean : 111515 Mean : 84540 Mean : 36.00
## 3rd Qu.: 187415 3rd Qu.: 102644 3rd Qu.: 63327 3rd Qu.: 57.31
## Max. :3758629 Max. :2570385 Max. :2290818 Max. :100.00
##
## total_earnings total_earnings_male total_earnings_female
## Min. : 17266 Min. : 12147 Min. : 7447
## 1st Qu.: 32410 1st Qu.: 35702 1st Qu.: 28872
## Median : 44437 Median : 46825 Median : 40191
## Mean : 49762 Mean : 53138 Mean : 44681
## 3rd Qu.: 61012 3rd Qu.: 65015 3rd Qu.: 54813
## Max. :201542 Max. :231420 Max. :166388
## NA's :4 NA's :65
## wage_percent_of_male
## Min. : 50.88
## 1st Qu.: 77.56
## Median : 85.16
## Mean : 84.03
## 3rd Qu.: 90.62
## Max. :117.40
## NA's :846
Cleaning and Preparation
jobs_gender_2015 <- subset(jobs_gender, year == 2015)
Question 1
In 2015, for occupations where men made more than women, what were
the top ten occupations with the highest pay gap between men and
women?
Solution 1
jobs_gender_2015$pay_gap <- jobs_gender_2015$total_earnings_male - jobs_gender_2015$total_earnings_female
desc_pay_gap <- jobs_gender_2015[order(-jobs_gender_2015$pay_gap), ]
top10_high_pay_gap <- desc_pay_gap %>% select("occupation", "pay_gap") %>% head(10)
top10_high_pay_gap
## occupation pay_gap
## 1209 Physicians and surgeons 70553
## 1172 Judges, magistrates, and other judicial workers 49731
## 1312 Securities, commodities, and financial services sales agents 43060
## 1426 Electrical and electronics repairers, industrial and utility 41974
## 1102 Financial specialists, all other 41915
## 1547 Ship and boat captains and operators 39744
## 1115 Mathematicians 38571
## 1045 Chief executives 38561
## 1096 Personal financial advisors 38346
## 1054 Compensation and benefits managers 36341
Graph 1
ggplot(top10_high_pay_gap, aes(x = reorder(occupation, -pay_gap), y = pay_gap)) +
geom_bar(stat = "identity", fill = "red", color = "black") +
labs(title = "Top Ten Occupations With Highest Pay Gap Between Men and Women",
x = "Occupation",
y = "Pay Gap (USD)") +
theme_minimal() +
coord_flip() +
scale_y_continuous(breaks = seq(0, max(top10_high_pay_gap$pay_gap), by = 10000)) +
theme(axis.text.x = element_text(size = 10, angle = 45, hjust = 1),
axis.text.y = element_text(size = 10),
axis.title = element_text(size = 12),
plot.title = element_text(size = 14, hjust = 1))
Question 2
In 2015, for occupations where men made more than women, what were
the top ten occupations with the smallest pay gap between men and
women?
Solution 2
asc_pay_gap <- jobs_gender_2015[order(jobs_gender_2015$pay_gap), ]
top10_low_pay_gap <- asc_pay_gap %>% filter(pay_gap >= 0) %>% select("occupation", "pay_gap") %>% head(10)
top10_low_pay_gap
## occupation
## 1 Milling and planing machine setters, operators, and tenders, metal and plastic
## 2 Baggage porters, bellhops, and concierges
## 3 Actors
## 4 Miscellaneous therapists, including exercise physiologists
## 5 Cargo and freight agents
## 6 Miscellaneous personal appearance workers
## 7 Food preparation and serving related workers, all other
## 8 Drywall installers, ceiling tile installers, and tapers
## 9 Hotel, motel, and resort desk clerks
## 10 Automotive and watercraft service attendants
## pay_gap
## 1 78
## 2 132
## 3 169
## 4 170
## 5 206
## 6 238
## 7 414
## 8 460
## 9 575
## 10 646
Graph 2
ggplot(top10_low_pay_gap, aes(x = reorder(occupation, -pay_gap), y = pay_gap)) +
geom_bar(stat = "identity", fill = "blue", color = "black") +
labs(title = "Top Ten Occupations With Smallest Pay Gap Between Men and Women",
x = "Occupation",
y = "Pay Gap (USD)") +
coord_flip() +
scale_y_continuous(breaks = seq(0, max(top10_high_pay_gap$pay_gap), by = 100)) +
theme_minimal() +
theme(axis.text.x = element_text(size = 10, angle = 45, hjust = 1),
axis.text.y = element_text(size = 10),
axis.title = element_text(size = 12),
plot.title = element_text(size = 14, hjust = 1))
Question 3
In 2015, what were the top 10 occupations where women made more than
men?
Solution 3
pay_gap_favors_women <- jobs_gender_2015[order(jobs_gender_2015$pay_gap), ]
top10_pay_gap_favors_women <- pay_gap_favors_women %>% select("occupation", "pay_gap") %>% head(10)
top10_pay_gap_favors_women
## occupation
## 1501 Fabric and apparel patternmakers
## 1527 Cooling and freezing equipment operators and tenders
## 1412 Derrick, rotary drill, and service unit operators, oil, gas, and mining
## 1436 Small engine mechanics
## 1454 Helpers--installation, maintenance, and repair workers
## 1133 Mining and geological engineers, including mining safety engineers
## 1255 Fish and game wardens
## 1130 Marine engineers and naval architects
## 1376 Animal breeders
## 1387 Cement masons, concrete finishers, and terrazzo workers
## pay_gap
## 1501 -38337
## 1527 -37427
## 1412 -32229
## 1436 -23008
## 1454 -15921
## 1133 -13961
## 1255 -12118
## 1130 -10309
## 1376 -9934
## 1387 -8498
Graph 3
ggplot(top10_pay_gap_favors_women, aes(x = reorder(occupation, -pay_gap), y = pay_gap)) +
geom_bar(stat = "identity", fill = "green", color = "black") +
labs(title = "Top Ten Occupations Where Women Made More Than Men",
x = "Occupation",
y = "Pay Gap (USD)") +
coord_flip() +
theme_minimal() +
theme(axis.text.x = element_text(size = 10, angle = 45, hjust = 1),
axis.text.y = element_text(size = 10),
axis.title = element_text(size = 12),
plot.title = element_text(size = 14, hjust = 1))
Question 4
In 2015, what was the average pay gap for occupations where men made
more than women and the proportion of female workers was 50% or
more?
Solution 4
over_half_women <- jobs_gender_2015 %>% filter(percent_female >= 50.0, pay_gap > 0)
pg_more_women <- over_half_women$pay_gap
avg_pg_more_women <- mean(pg_more_women)
avg_pg_more_women
## [1] 8486.482
Graph 4
ggplot(over_half_women, aes(x = percent_female, y = pay_gap)) +
geom_point() +
labs(title = "Proportion of Women (>= 50%) vs Pay Gap",
x = "Proportion of Women (%)",
y = "Pay Gap (USD)") +
theme_minimal() +
theme(axis.text.x = element_text(size = 10, hjust = 1),
axis.text.y = element_text(size = 10),
axis.title = element_text(size = 12),
plot.title = element_text(size = 14, hjust = .5))
Question 5
In 2015, what was the average pay gap for occupations where men made
more than women and the proportion of female workers was less than
50%?
Solution 5
under_half_women <- jobs_gender_2015 %>% filter(percent_female < 50.0, pay_gap > 0)
pg_less_women <- under_half_women$pay_gap
avg_pg_less_women <- mean(pg_less_women)
avg_pg_less_women
## [1] 11055.19
Graph 5
ggplot(under_half_women, aes(x = percent_female, y = pay_gap)) +
geom_point() +
labs(title = "Proportion of Women (< 50%) vs Pay Gap",
x = "Proportion of Women (%)",
y = "Pay Gap (USD)") +
theme_minimal() +
theme(axis.text.x = element_text(size = 10, hjust = 1),
axis.text.y = element_text(size = 10),
axis.title = element_text(size = 12),
plot.title = element_text(size = 14, hjust = .5))
