Data Overview
The primary data source is the American Community Survey (ACS). The
ACS samples roughly 1 percent of the U.S. population each year. I
downloaded the ACS samples from IPUMS USA database.
I restrict my analysis to only include ACS respondents from the
2017-2020 surveys. The sample is further restricted to include those who
are 1) not living in institutional group quarters, 2) have attained at
least a Bachelor’s degree, and 3) are ages 18 to 65. From this
restriction, the base sample includes roughly 3.7 million
observations.
# filtering all data so that age is between 18 and 65, excludes institutionalized pop, and includes only people with a Bachelor's degree and above
data <- data %>%
filter(AGE >= 18 & AGE <= 65,
!GQ %in% c(3, 4),
EDUCD %in% c(101,114,115,116)) # bachelor's, master's, professional degree beyond bach, doctoral degree
# create dataframe for 2017-2019
d17_19 <- data %>%
filter(YEAR %in% 2017:2019)
# create dataframe for 2020-2022
d20_22 <- data %>%
filter(YEAR %in% 2020:2022)
# define healthcare occupations using OCC2010 codes
healthcare_occs <- c(3000:3650)
DEGFIELD Overview
Starting in 2009, the ACS asked all respondents with a Bachelor’s
degree to report their undergraduate major. For those respondents with a
post-Bachelor’s degree, no additional information is provided for the
field of study of their advanced degree(s). If individuals have more
than one Bachelor’s degree or more than one major, they are prompted to
list multiple majors. In this analysis, I only look at respondents’
primary major.
The questionnaire has multiple lines that can be filled in.
If the first lines are skipped and later line(s) are filled in, the
later lines are used as the first and/or second degree fields.
If a person has less than a bachelor’s degree (EDUC), DEGFIELD and
DEGFIELD2 will be replaced with “Not in universe.”
If someone has a Bachelor’s degree (EDUC) and the field of degree
(DEGFIELD) is missing, it will be allocated from someone else with a
Bachelor’s degree, with a similar occupation (OCC), AGE, and SEX.
If someone has a Master’s degree (EDUC) and the field of degree
(DEGFIELD) is missing, it will be allocated from someone else with a
Master’s degree, with a similar occupation (OCC), AGE, and SEX.
#replacing blanks with NA for consistency
summary <- data %>%
mutate(DEGFIELD = as.character(DEGFIELD)) %>%
mutate(DEGFIELD = ifelse(DEGFIELD == "", NA, DEGFIELD))
# summarize data by year
degfield_summary <- summary %>%
group_by(YEAR) %>%
summarise(
Non_Empty_Count = sum(!is.na(DEGFIELD)),
NA_Count = sum(is.na(DEGFIELD))
) %>%
mutate(
Percent_Available = round((Non_Empty_Count / (Non_Empty_Count + NA_Count)) * 100, 2),
Non_Empty_Count = format(Non_Empty_Count, big.mark = ",")) %>%
kable(caption = "DEGFIELD Availability by Year for 2017-2020", format = "html", booktabs = TRUE) %>%
kable_styling(latex_options = c("striped", "hold_position"))
degfield_summary
DEGFIELD Availability by Year for 2017-2020
|
YEAR
|
Non_Empty_Count
|
NA_Count
|
Percent_Available
|
|
2017
|
608,803
|
0
|
100
|
|
2018
|
621,177
|
0
|
100
|
|
2019
|
640,132
|
0
|
100
|
|
2020
|
521,915
|
0
|
100
|
|
2021
|
657,903
|
0
|
100
|
|
2022
|
691,319
|
0
|
100
|
DEGFIELD Analysis of Healthcare Occupations
General Codes
After restricting the data above, I now focus my analysis on only
healthcare occupations. Here, I restrict the data to healthcare
occupations only and look at the top 10 occupations reported for
2017-2019 and 2020-2022.
# Top 10 DEGFIELD categories --- 2017-2019 GENERAL
dg1 <- d17_19 %>%
filter(OCC2010 %in% healthcare_occs) %>%
mutate(DEGFIELD = as.character(DEGFIELD)) %>%
filter(DEGFIELD != 0) %>%
group_by(DEGFIELD) %>%
summarise(Weighted_Count = sum(PERWT, na.rm = TRUE), .groups = "drop") %>%
arrange(desc(Weighted_Count)) %>%
head(10) %>%
mutate(Percentage = (Weighted_Count / sum(Weighted_Count)) * 100) %>%
mutate(DEGFIELD = ifelse(DEGFIELD %in% names(degfield_labels),
degfield_labels[DEGFIELD],
"Unknown"))
# adding total row
total_count <- sum(dg1$Weighted_Count)
dg1 <- bind_rows(dg1,
tibble(DEGFIELD = "Total",
Weighted_Count = total_count,
Percentage = 100))
# Top 10 DEGFIELD categories --- 2020-2022 GENERAL
dg2 <- d20_22 %>%
filter(OCC2010 %in% healthcare_occs) %>%
mutate(DEGFIELD = as.character(DEGFIELD)) %>%
filter(DEGFIELD != 0) %>%
group_by(DEGFIELD) %>%
summarise(Weighted_Count = sum(PERWT, na.rm = TRUE), .groups = "drop") %>%
arrange(desc(Weighted_Count)) %>%
head(10) %>%
mutate(Percentage = (Weighted_Count / sum(Weighted_Count)) * 100) %>%
mutate(DEGFIELD = ifelse(DEGFIELD %in% names(degfield_labels),
degfield_labels[DEGFIELD],
"Unknown"))
# adding total row
total_count <- sum(dg2$Weighted_Count)
dg2 <- bind_rows(dg2,
tibble(DEGFIELD = "Total",
Weighted_Count = total_count,
Percentage = 100))
Top 10 Degree Fields (2017-2019)
|
DEGFIELD
|
Weighted_Count
|
Percentage
|
|
Medical and Health Sciences and Services
|
8981301
|
52.288730
|
|
Biology and Life Sciences
|
3240321
|
18.865003
|
|
Business
|
1010730
|
5.884424
|
|
Psychology
|
981678
|
5.715285
|
|
Physical Sciences
|
812116
|
4.728103
|
|
Education Administration and Teaching
|
562425
|
3.274413
|
|
Social Sciences
|
518477
|
3.018550
|
|
Physical Fitness, Parks, Recreation, and Leisure
|
456791
|
2.659417
|
|
Engineering
|
334796
|
1.949167
|
|
Fine Arts
|
277726
|
1.616908
|
|
Total
|
17176361
|
100.000000
|
Top 10 Degree Fields (2020-2022)
|
DEGFIELD
|
Weighted_Count
|
Percentage
|
|
Medical and Health Sciences and Services
|
9825933
|
51.106814
|
|
Biology and Life Sciences
|
3650712
|
18.988147
|
|
Psychology
|
1182683
|
6.151391
|
|
Business
|
1182395
|
6.149894
|
|
Physical Sciences
|
818480
|
4.257092
|
|
Education Administration and Teaching
|
612115
|
3.183743
|
|
Social Sciences
|
609211
|
3.168639
|
|
Physical Fitness, Parks, Recreation, and Leisure
|
563988
|
2.933424
|
|
Engineering
|
415864
|
2.162999
|
|
Fine Arts
|
364887
|
1.897857
|
|
Total
|
19226268
|
100.000000
|
Detailed Codes
For the sample years 2017-2022, the ACS combines major responses into
176 distinct “detailed” majors, which are more detailed than the 29
“broad” major categories from the previous section.
Top 10 Degree Fields Detailed (2017-2019)
|
DEGFIELDD
|
Weighted_Count
|
Percentage
|
|
6107 - Nursing
|
5930482
|
47.751875
|
|
3600 - Biology (NOS)
|
2283174
|
18.383976
|
|
5200 - Psychology (NOS)
|
915606
|
7.372403
|
|
6109 - Treatment Therapy Professions
|
796774
|
6.415575
|
|
6108 - Pharmacy, Pharmaceutical Sciences, and Administration
|
525845
|
4.234071
|
|
6102 - Communication Disorders Sciences and Services
|
457343
|
3.682498
|
|
4101 - Physical Fitness, Parks, Recreation, and Leisure
|
456791
|
3.678053
|
|
5003 - Chemistry
|
375452
|
3.023116
|
|
5098 - Multi-disciplinary or General Science
|
364751
|
2.936952
|
|
6203 - Business Management and Administration
|
313152
|
2.521481
|
|
Total
|
12419370
|
100.000000
|
Top 10 Degree Fields Detailed (2020-2022)
|
DEGFIELDD
|
Weighted_Count
|
Percentage
|
|
6107 - Nursing
|
6475334
|
47.276955
|
|
3600 - Biology (NOS)
|
2556293
|
18.663709
|
|
5200 - Psychology (NOS)
|
1102143
|
8.046838
|
|
6109 - Treatment Therapy Professions
|
821898
|
6.000746
|
|
4101 - Physical Fitness, Parks, Recreation, and Leisure
|
563988
|
4.117724
|
|
6108 - Pharmacy, Pharmaceutical Sciences, and Administration
|
534690
|
3.903816
|
|
6102 - Communication Disorders Sciences and Services
|
519208
|
3.790781
|
|
5003 - Chemistry
|
389054
|
2.840516
|
|
6203 - Business Management and Administration
|
374842
|
2.736753
|
|
6100 - General Medical and Health Services (NOS)
|
359147
|
2.622162
|
|
Total
|
13696597
|
100.000000
|
Top 5 DEGFIELDS in Healthcare to Top 10 Occupations
I now look at the top 5 degree fields from the sample years 2017-2022
and look at the top 10 occupations in that period.
occ_clean_fun = function(df_yr, deg){
df_code_emp <- df_yr %>%
filter(DEGFIELD == deg) %>%
group_by(EMPSTAT) %>%
summarise(Weighted_Count = sum(PERWT, na.rm = TRUE), .groups = "drop") %>%
mutate(EMPSTAT = case_when(
EMPSTAT == 1 ~ "Employed",
EMPSTAT == 2 ~ "Unemployed",
EMPSTAT == 3 ~ "Not in labor force"))
df_code_occ <- df_yr %>%
group_by(OCC2010) %>%
summarise(Weighted_Count = sum(PERWT, na.rm = TRUE), .groups = "drop") %>%
arrange(desc(Weighted_Count)) %>%
mutate(OCC2010 = as.character(OCC2010)) %>%
head(11)
df_code_temp <- df_code_emp %>%
filter(EMPSTAT %in% c("Unemployed", "Not in labor force")) %>%
summarise(
Weighted_Count = sum(Weighted_Count, na.rm = TRUE),.groups = "drop") %>%
mutate(OCC2010 = "Unemployed & Not in labor force")
df_code_occ <- bind_rows(df_code_occ, df_code_temp)
df_code_occ <- df_code_occ %>%
arrange(desc(Weighted_Count)) %>%
mutate(
Percentage = round((Weighted_Count / sum(Weighted_Count)) * 100, 2),
Weighted_Count = format(Weighted_Count, big.mark = ",")
)
return(df_code_occ)
}
Medical and Health Sciences
Top 10 Medical and Health Sciences Occupations (2017-2019)
|
OCC2010
|
Weighted_Count
|
Percentage
|
|
Unemployed, with No Work Experience in the Last 5 Years or Earlier or
Never Worked
|
12,761,567
|
18.67
|
|
Elementary and Middle School Teachers
|
10,859,271
|
15.88
|
|
Managers, nec (including Postmasters)
|
8,620,313
|
12.61
|
|
Registered Nurses
|
7,367,038
|
10.78
|
|
Accountants and Auditors
|
4,917,453
|
7.19
|
|
First-Line Supervisors of Sales Workers
|
4,084,531
|
5.97
|
|
Software Developers, Applications and Systems Software
|
4,024,629
|
5.89
|
|
Postsecondary Teachers
|
3,987,817
|
5.83
|
|
Computer Scientists and Systems Analysts/Network systems Analysts/Web
Developers
|
3,478,461
|
5.09
|
|
Lawyers, and judges, magistrates, and other judicial workers
|
3,229,132
|
4.72
|
|
Secondary School Teachers
|
2,790,154
|
4.08
|
|
Unemployed & Not in labor force
|
2,243,722
|
3.28
|
Top 10 Medical and Health Sciences Occupations (2020-2022)
|
OCC2010
|
Weighted_Count
|
Percentage
|
|
Unemployed, with No Work Experience in the Last 5 Years or Earlier or
Never Worked
|
12,626,579
|
17.49
|
|
Managers, nec (including Postmasters)
|
10,071,918
|
13.95
|
|
Elementary and Middle School Teachers
|
9,505,997
|
13.16
|
|
Registered Nurses
|
8,159,151
|
11.30
|
|
Software Developers, Applications and Systems Software
|
5,268,454
|
7.30
|
|
Accountants and Auditors
|
4,742,623
|
6.57
|
|
Postsecondary Teachers
|
4,068,977
|
5.63
|
|
Secondary School Teachers
|
4,059,015
|
5.62
|
|
First-Line Supervisors of Sales Workers
|
3,811,133
|
5.28
|
|
Computer Scientists and Systems Analysts/Network systems Analysts/Web
Developers
|
3,746,325
|
5.19
|
|
Lawyers, and judges, magistrates, and other judicial workers
|
3,599,406
|
4.98
|
|
Unemployed & Not in labor force
|
2,549,839
|
3.53
|
Biology
Top 10 Biology Occupations (2017-2019)
|
OCC2010
|
Weighted_Count
|
Percentage
|
|
Unemployed, with No Work Experience in the Last 5 Years or Earlier or
Never Worked
|
12,761,567
|
18.85
|
|
Elementary and Middle School Teachers
|
10,859,271
|
16.04
|
|
Managers, nec (including Postmasters)
|
8,620,313
|
12.73
|
|
Registered Nurses
|
7,367,038
|
10.88
|
|
Accountants and Auditors
|
4,917,453
|
7.26
|
|
First-Line Supervisors of Sales Workers
|
4,084,531
|
6.03
|
|
Software Developers, Applications and Systems Software
|
4,024,629
|
5.95
|
|
Postsecondary Teachers
|
3,987,817
|
5.89
|
|
Computer Scientists and Systems Analysts/Network systems Analysts/Web
Developers
|
3,478,461
|
5.14
|
|
Lawyers, and judges, magistrates, and other judicial workers
|
3,229,132
|
4.77
|
|
Secondary School Teachers
|
2,790,154
|
4.12
|
|
Unemployed & Not in labor force
|
1,575,250
|
2.33
|
Top 10 Biology Occupations (2020-2022)
|
OCC2010
|
Weighted_Count
|
Percentage
|
|
Unemployed, with No Work Experience in the Last 5 Years or Earlier or
Never Worked
|
12,626,579
|
17.68
|
|
Managers, nec (including Postmasters)
|
10,071,918
|
14.10
|
|
Elementary and Middle School Teachers
|
9,505,997
|
13.31
|
|
Registered Nurses
|
8,159,151
|
11.42
|
|
Software Developers, Applications and Systems Software
|
5,268,454
|
7.38
|
|
Accountants and Auditors
|
4,742,623
|
6.64
|
|
Postsecondary Teachers
|
4,068,977
|
5.70
|
|
Secondary School Teachers
|
4,059,015
|
5.68
|
|
First-Line Supervisors of Sales Workers
|
3,811,133
|
5.34
|
|
Computer Scientists and Systems Analysts/Network systems Analysts/Web
Developers
|
3,746,325
|
5.25
|
|
Lawyers, and judges, magistrates, and other judicial workers
|
3,599,406
|
5.04
|
|
Unemployed & Not in labor force
|
1,766,206
|
2.47
|
Business
Top 10 Business Occupations (2017-2019)
|
OCC2010
|
Weighted_Count
|
Percentage
|
|
Unemployed, with No Work Experience in the Last 5 Years or Earlier or
Never Worked
|
12,761,567
|
17.73
|
|
Elementary and Middle School Teachers
|
10,859,271
|
15.09
|
|
Managers, nec (including Postmasters)
|
8,620,313
|
11.98
|
|
Registered Nurses
|
7,367,038
|
10.24
|
|
Unemployed & Not in labor force
|
5,850,992
|
8.13
|
|
Accountants and Auditors
|
4,917,453
|
6.83
|
|
First-Line Supervisors of Sales Workers
|
4,084,531
|
5.68
|
|
Software Developers, Applications and Systems Software
|
4,024,629
|
5.59
|
|
Postsecondary Teachers
|
3,987,817
|
5.54
|
|
Computer Scientists and Systems Analysts/Network systems Analysts/Web
Developers
|
3,478,461
|
4.83
|
|
Lawyers, and judges, magistrates, and other judicial workers
|
3,229,132
|
4.49
|
|
Secondary School Teachers
|
2,790,154
|
3.88
|
Top 10 Business Occupations (2020-2022)
|
OCC2010
|
Weighted_Count
|
Percentage
|
|
Unemployed, with No Work Experience in the Last 5 Years or Earlier or
Never Worked
|
12,626,579
|
16.53
|
|
Managers, nec (including Postmasters)
|
10,071,918
|
13.19
|
|
Elementary and Middle School Teachers
|
9,505,997
|
12.45
|
|
Registered Nurses
|
8,159,151
|
10.68
|
|
Unemployed & Not in labor force
|
6,722,017
|
8.80
|
|
Software Developers, Applications and Systems Software
|
5,268,454
|
6.90
|
|
Accountants and Auditors
|
4,742,623
|
6.21
|
|
Postsecondary Teachers
|
4,068,977
|
5.33
|
|
Secondary School Teachers
|
4,059,015
|
5.31
|
|
First-Line Supervisors of Sales Workers
|
3,811,133
|
4.99
|
|
Computer Scientists and Systems Analysts/Network systems Analysts/Web
Developers
|
3,746,325
|
4.90
|
|
Lawyers, and judges, magistrates, and other judicial workers
|
3,599,406
|
4.71
|
Psychology
Top 10 Psychology Occupations (2017-2019)
|
OCC2010
|
Weighted_Count
|
Percentage
|
|
Unemployed, with No Work Experience in the Last 5 Years or Earlier or
Never Worked
|
12,761,567
|
18.82
|
|
Elementary and Middle School Teachers
|
10,859,271
|
16.01
|
|
Managers, nec (including Postmasters)
|
8,620,313
|
12.71
|
|
Registered Nurses
|
7,367,038
|
10.86
|
|
Accountants and Auditors
|
4,917,453
|
7.25
|
|
First-Line Supervisors of Sales Workers
|
4,084,531
|
6.02
|
|
Software Developers, Applications and Systems Software
|
4,024,629
|
5.94
|
|
Postsecondary Teachers
|
3,987,817
|
5.88
|
|
Computer Scientists and Systems Analysts/Network systems Analysts/Web
Developers
|
3,478,461
|
5.13
|
|
Lawyers, and judges, magistrates, and other judicial workers
|
3,229,132
|
4.76
|
|
Secondary School Teachers
|
2,790,154
|
4.11
|
|
Unemployed & Not in labor force
|
1,691,051
|
2.49
|
Top 10 Psychology Occupations (2020-2022)
|
OCC2010
|
Weighted_Count
|
Percentage
|
|
Unemployed, with No Work Experience in the Last 5 Years or Earlier or
Never Worked
|
12,626,579
|
17.64
|
|
Managers, nec (including Postmasters)
|
10,071,918
|
14.07
|
|
Elementary and Middle School Teachers
|
9,505,997
|
13.28
|
|
Registered Nurses
|
8,159,151
|
11.40
|
|
Software Developers, Applications and Systems Software
|
5,268,454
|
7.36
|
|
Accountants and Auditors
|
4,742,623
|
6.63
|
|
Postsecondary Teachers
|
4,068,977
|
5.68
|
|
Secondary School Teachers
|
4,059,015
|
5.67
|
|
First-Line Supervisors of Sales Workers
|
3,811,133
|
5.32
|
|
Computer Scientists and Systems Analysts/Network systems Analysts/Web
Developers
|
3,746,325
|
5.23
|
|
Lawyers, and judges, magistrates, and other judicial workers
|
3,599,406
|
5.03
|
|
Unemployed & Not in labor force
|
1,914,453
|
2.67
|
Physical Sciences
Top 10 Psychology Occupations (2017-2019)
|
OCC2010
|
Weighted_Count
|
Percentage
|
|
Unemployed, with No Work Experience in the Last 5 Years or Earlier or
Never Worked
|
12,761,567
|
19.05
|
|
Elementary and Middle School Teachers
|
10,859,271
|
16.21
|
|
Managers, nec (including Postmasters)
|
8,620,313
|
12.87
|
|
Registered Nurses
|
7,367,038
|
11.00
|
|
Accountants and Auditors
|
4,917,453
|
7.34
|
|
First-Line Supervisors of Sales Workers
|
4,084,531
|
6.10
|
|
Software Developers, Applications and Systems Software
|
4,024,629
|
6.01
|
|
Postsecondary Teachers
|
3,987,817
|
5.95
|
|
Computer Scientists and Systems Analysts/Network systems Analysts/Web
Developers
|
3,478,461
|
5.19
|
|
Lawyers, and judges, magistrates, and other judicial workers
|
3,229,132
|
4.82
|
|
Secondary School Teachers
|
2,790,154
|
4.17
|
|
Unemployed & Not in labor force
|
864,951
|
1.29
|
Top 10 Physical Sciences Occupations (2020-2022)
|
OCC2010
|
Weighted_Count
|
Percentage
|
|
Unemployed, with No Work Experience in the Last 5 Years or Earlier or
Never Worked
|
12,626,579
|
17.89
|
|
Managers, nec (including Postmasters)
|
10,071,918
|
14.27
|
|
Elementary and Middle School Teachers
|
9,505,997
|
13.47
|
|
Registered Nurses
|
8,159,151
|
11.56
|
|
Software Developers, Applications and Systems Software
|
5,268,454
|
7.47
|
|
Accountants and Auditors
|
4,742,623
|
6.72
|
|
Postsecondary Teachers
|
4,068,977
|
5.77
|
|
Secondary School Teachers
|
4,059,015
|
5.75
|
|
First-Line Supervisors of Sales Workers
|
3,811,133
|
5.40
|
|
Computer Scientists and Systems Analysts/Network systems Analysts/Web
Developers
|
3,746,325
|
5.31
|
|
Lawyers, and judges, magistrates, and other judicial workers
|
3,599,406
|
5.10
|
|
Unemployed & Not in labor force
|
911,636
|
1.29
|