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 the IPUMS USA database.
I restrict my analysis to only include ACS respondents from the 2017-2022 surveys. The sample is further restricted to include those who are:
From these restrictions, the base sample includes roughly 3.7 million observations from 2017-2022.
# creating a dataframe that does not have any restrictions on education variable
data2 <- data %>%
filter(AGE >= 18 & AGE <= 65,
GQTYPE == 0)
# filtering all data so that age is between 18 and 65, excludes group quarters pop, and includes only people with a Bachelor's degree and above
data <- data %>%
filter(AGE >= 18 & AGE <= 65,
GQTYPE == 0,
EDUCD %in% c(101,114,115,116)) # 101 - Bachelor's, 114 - Master's, 115 - Professional degree beyond Bachelor's, 116 - 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) 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.
Below are two tables. The first displays the total number of available cases for each year from 2017-2022 and confirms that there are no missing values of DEGFIELD in the analysis. The second table display the number of cases for the DEGFIELD variable that were either imputed given the guidelines above, or unaltered.
#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(YEAR = as.character(YEAR))
# adding total row
total_count <- sum(degfield_summary$Non_Empty_Count)
degfield_summary <- bind_rows(degfield_summary,
tibble(YEAR = "Total",
Non_Empty_Count = total_count,
NA_Count = 0,
Percent_Available = 100)) %>%
mutate(
Percent_Available = round((Non_Empty_Count / (Non_Empty_Count + NA_Count)) * 100, 2),
Non_Empty_Count = format(Non_Empty_Count, big.mark = ","))| Year | Non-Empty Count | NA Count | Percent Available |
|---|---|---|---|
| 2017 | 608,724 | 0 | 100 |
| 2018 | 621,097 | 0 | 100 |
| 2019 | 640,024 | 0 | 100 |
| 2020 | 521,818 | 0 | 100 |
| 2021 | 657,788 | 0 | 100 |
| 2022 | 691,126 | 0 | 100 |
| Total | 3,740,577 | 0 | 100 |
| Note. This table summarizes the availability of the degree field variable (DEGFIELD) by year from 2017 to 2022. “Non-Empty Count” reflects the total number of cases with a non-missing DEGFIELD value. “NA Count” represents cases where DEGFIELD was missing. “Percent Available” shows the proportion of cases with non-missing DEGFIELD values. In our dataset, DEGFIELD is fully available for all years, with no missing values. |
# summarize QDEGFIELD by year
qc_summary <- data %>%
group_by(YEAR, QDEGFIELD) %>%
summarise(count = n(), .groups = 'drop') %>%
pivot_wider(names_from = QDEGFIELD, values_from = count, values_fill = 0) %>%
rename(
Unaltered = "0",
Imputed = "4") %>%
mutate(
Percent_Imputed = round((Imputed / (Unaltered + Imputed)) * 100, 2),
Total = (Imputed + Unaltered),
YEAR = as.character(YEAR)) %>%
select(YEAR, Imputed, Unaltered, Total, Percent_Imputed)
# adding totalS
total_unaltered <- sum(qc_summary$Unaltered)
total_imputed <- sum(qc_summary$Imputed)
total_overall <- sum(qc_summary$Total)
qc_summary <- bind_rows(qc_summary,
tibble(YEAR = "Total",
Imputed = total_imputed,
Unaltered = total_unaltered,
Total = total_overall,
Percent_Imputed = (total_imputed/(total_unaltered + total_imputed))* 100))| Year | Imputed Values | Reported/Unaltered Values | Total Cases | Percent Imputed |
|---|---|---|---|---|
| 2017 | 78,438 | 530,286 | 608,724 | 12.89 |
| 2018 | 82,936 | 538,161 | 621,097 | 13.35 |
| 2019 | 85,384 | 554,640 | 640,024 | 13.34 |
| 2020 | 116,375 | 405,443 | 521,818 | 22.30 |
| 2021 | 108,214 | 549,574 | 657,788 | 16.45 |
| 2022 | 114,674 | 576,452 | 691,126 | 16.59 |
| Total | 586,021 | 3,154,556 | 3,740,577 | 15.67 |
| Note. This table summarizes the imputation of bachelor’s degree field values (DEGFIELD) by year from 2017 to 2022. “Imputed Values” represent cases where DEGFIELD was missing and subsequently imputed. Specifically, if an individual has a bachelor’s degree but the field of degree is missing, the value is imputed by allocating a degree field from another individual with a bachelor’s degree who has a similar occupation, age, and sex. “Reported/Unaltered Values” are cases with an original, non-imputed response. “Percent Imputed” reflects the share of total cases in each year that were imputed. |
After applying the above restrictions, I now focus the following specifically on individuals working in healthcare occupations. Within this subset, I examine the distribution of bachelor’s degree fields to identify the top 10 most commonly reported fields of study. This analysis is limited to individuals who hold a bachelor’s degree and work in a healthcare occupation, as defined by detailed occupation codes. The top 10 degree fields are determined separately for each period, 2017–2019 and 2020–2022, based on the fields with the highest weighted sums of reported cases. These weighted totals reflect population-level estimates and determine the ranking of the top 10 bachelor’s degree fields among healthcare workers in each period.
degree_summary_fun <- function(df_year, degfield_type, occ_code, labels) {
# first i am creating the weighted counts and percentages for each degree type for each year in period
df <- df_year %>%
mutate(DGF = as.character(.data[[degfield_type]])) %>%
filter(OCC2010 %in% occ_code) %>%
group_by(YEAR, DGF) %>%
summarise(Weighted_Count = sum(PERWT, na.rm = TRUE), .groups = 'drop') %>%
arrange(desc(Weighted_Count)) %>%
group_by(YEAR) %>%
mutate(Percentage = (Weighted_Count / sum(Weighted_Count)) * 100) %>%
ungroup()
# here i am creating the total period totals for each degree, which is how i calculate the top 10 majors
df_totals <- df %>%
group_by(DGF) %>%
summarise(Total = sum(Weighted_Count), .groups = 'drop')
# finally i am merging both the yearly counts/percentages and the period totals and pivoting the table
final_df <- df %>%
mutate(cell = paste0(format(round(Weighted_Count, 0), big.mark = ","),
"<br/>(", round(Percentage, 2), "%)")) %>%
select(DGF, YEAR, cell) %>%
arrange(YEAR) %>%
pivot_wider(names_from = YEAR, values_from = cell) %>%
left_join(df_totals, by = "DGF") %>%
mutate(Total = format(round(Total, 0), big.mark = ",")) %>%
mutate(DGF = ifelse(DGF %in% names(labels),
labels[DGF],
"Unknown")) %>%
head(10)
return(final_df)
}| Field of Bachelor’s Degree | 2017 | 2018 | 2019 | Total Healthcare Workers with Listed Degree (2017–2019) |
|---|---|---|---|---|
| Medical and Health Sciences and Services |
2,873,229 (46.49%) |
2,968,511 (45.83%) |
3,139,208 (46.47%) |
8,980,948 |
| Biology and Life Sciences |
1,023,735 (16.56%) |
1,076,272 (16.62%) |
1,140,314 (16.88%) |
3,240,321 |
| Psychology |
317,935 (5.14%) |
332,913 (5.14%) |
330,830 (4.9%) |
981,678 |
| Business |
311,957 (5.05%) |
346,264 (5.35%) |
352,345 (5.22%) |
1,010,566 |
| Physical Sciences |
263,822 (4.27%) |
264,522 (4.08%) |
283,772 (4.2%) |
812,116 |
| Education Administration and Teaching |
195,675 (3.17%) |
185,997 (2.87%) |
180,643 (2.67%) |
562,315 |
| Social Sciences |
166,411 (2.69%) |
178,089 (2.75%) |
173,977 (2.58%) |
518,477 |
| Physical Fitness, Parks, Recreation, and Leisure |
135,452 (2.19%) |
153,671 (2.37%) |
167,668 (2.48%) |
456,791 |
| Engineering |
106,606 (1.72%) |
117,548 (1.81%) |
110,642 (1.64%) |
334,796 |
| Fine Arts |
85,976 (1.39%) |
97,113 (1.5%) |
94,590 (1.4%) |
277,679 |
| Note. Each cell contains the total weighted count of healthcare workers reporting the listed bachelor’s degree field in that year. The percentage below each count represents that field’s share of all reported degree fields among healthcare workers for that specific year. The final column displays the total weighted count across all years (2017–2019), which determines the ranking of the top 10 degree fields. |
| Field of Bachelor’s Degree | 2020 | 2021 | 2022 | Total Healthcare Workers with Listed Degree (2020–2022) |
|---|---|---|---|---|
| Medical and Health Sciences and Services |
3,159,024 (44.86%) |
3,272,364 (45.04%) |
3,394,014 (44.83%) |
9,825,402 |
| Biology and Life Sciences |
1,219,900 (17.32%) |
1,172,174 (16.13%) |
1,258,494 (16.62%) |
3,650,568 |
| Psychology |
376,019 (5.34%) |
395,284 (5.44%) |
411,100 (5.43%) |
1,182,403 |
| Business |
350,038 (4.97%) |
409,343 (5.63%) |
422,628 (5.58%) |
1,182,009 |
| Physical Sciences |
263,322 (3.74%) |
279,679 (3.85%) |
275,453 (3.64%) |
818,454 |
| Social Sciences |
206,154 (2.93%) |
204,732 (2.82%) |
198,325 (2.62%) |
609,211 |
| Education Administration and Teaching |
196,925 (2.8%) |
197,724 (2.72%) |
217,045 (2.87%) |
611,694 |
| Physical Fitness, Parks, Recreation, and Leisure |
180,833 (2.57%) |
189,550 (2.61%) |
193,566 (2.56%) |
563,949 |
| Engineering |
123,982 (1.76%) |
136,011 (1.87%) |
155,600 (2.06%) |
415,593 |
| Fine Arts |
116,456 (1.65%) |
123,486 (1.7%) |
124,895 (1.65%) |
364,837 |
| Note. Each cell contains the total weighted count of healthcare workers reporting the listed bachelor’s degree field in that year. The percentage below each count represents that field’s share of all reported degree fields among healthcare workers for that specific year. The final column displays the total weighted count across all years (2020–2022), which determines the ranking of the top 10 degree fields. |
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.
| Field of Bachelor’s Degree | 2017 | 2018 | 2019 | Total Healthcare Workers with Listed Degree (2017–2019) |
|---|---|---|---|---|
| 6107 - Nursing |
1,880,374 (30.43%) |
1,965,289 (30.34%) |
2,084,697 (30.86%) |
5,930,360 |
| 3600 - Biology (NOS) |
722,724 (11.69%) |
765,793 (11.82%) |
794,657 (11.76%) |
2,283,174 |
| 5200 - Psychology (NOS) |
297,335 (4.81%) |
311,024 (4.8%) |
307,247 (4.55%) |
915,606 |
| 6109 - Treatment Therapy Professions |
266,898 (4.32%) |
258,333 (3.99%) |
271,312 (4.02%) |
796,543 |
| 6108 - Pharmacy, Pharmaceutical Sciences, and Administration |
156,737 (2.54%) |
181,884 (2.81%) |
187,224 (2.77%) |
525,845 |
| 6102 - Communication Disorders Sciences and Services |
153,756 (2.49%) |
147,360 (2.27%) |
156,227 (2.31%) |
457,343 |
| 4101 - Physical Fitness, Parks, Recreation, and Leisure |
135,452 (2.19%) |
153,671 (2.37%) |
167,668 (2.48%) |
456,791 |
| 5098 - Multi-disciplinary or General Science |
123,236 (1.99%) |
121,025 (1.87%) |
120,490 (1.78%) |
364,751 |
| 5003 - Chemistry |
118,306 (1.91%) |
119,949 (1.85%) |
137,197 (2.03%) |
375,452 |
| 6105 - Medical Technologies Technicians |
103,296 (1.67%) |
98,256 (1.52%) |
99,092 (1.47%) |
300,644 |
| Note. This table uses the ACS detailed degree field codes, where the ACS combines major responses into 176 distinct “detailed” majors, which is more specific than the 29 broader major categories used in the previous section. Degree fields labeled as “NOS” (Not Otherwise Specified) at the end refer to responses that could not be categorized into a more specific major within that field. |
| Field of Bachelor’s Degree | 2020 | 2021 | 2022 | Total Healthcare Workers with Listed Degree (2020–2022) |
|---|---|---|---|---|
| 6107 - Nursing |
2,044,541 (29.03%) |
2,169,633 (29.86%) |
2,260,667 (29.86%) |
6,474,841 |
| 3600 - Biology (NOS) |
848,894 (12.05%) |
822,611 (11.32%) |
884,644 (11.68%) |
2,556,149 |
| 5200 - Psychology (NOS) |
346,173 (4.92%) |
371,290 (5.11%) |
384,400 (5.08%) |
1,101,863 |
| 6109 - Treatment Therapy Professions |
275,619 (3.91%) |
267,063 (3.68%) |
279,216 (3.69%) |
821,898 |
| 6108 - Pharmacy, Pharmaceutical Sciences, and Administration |
190,741 (2.71%) |
175,458 (2.41%) |
168,453 (2.22%) |
534,652 |
| 4101 - Physical Fitness, Parks, Recreation, and Leisure |
180,833 (2.57%) |
189,550 (2.61%) |
193,566 (2.56%) |
563,949 |
| 6102 - Communication Disorders Sciences and Services |
164,150 (2.33%) |
175,263 (2.41%) |
179,795 (2.37%) |
519,208 |
| 5003 - Chemistry |
130,251 (1.85%) |
124,732 (1.72%) |
134,071 (1.77%) |
389,054 |
| 6100 - General Medical and Health Services (NOS) |
111,158 (1.58%) |
116,108 (1.6%) |
131,881 (1.74%) |
359,147 |
| 6203 - Business Management and Administration |
110,188 (1.56%) |
129,707 (1.79%) |
134,793 (1.78%) |
374,688 |
| Note. This table uses the ACS detailed degree field codes, where the ACS combines major responses into 176 distinct “detailed” majors, which is more specific than the 29 broader major categories used in the previous section. Degree fields labeled as “NOS” (Not Otherwise Specified) at the end refer to responses that could not be categorized into a more specific major within that field. |
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"))
# here i am finding the top 10 occupations for each degfield
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)
# combining the various unemployed/not in labor force categories into one
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")
no_work_count <- df_code_occ %>%
filter(OCC2010 == "Unemployed, with No Work Experience in the Last 5 Years or Earlier or Never Worked") %>%
pull(Weighted_Count)
df_code_temp$Weighted_Count <- df_code_temp$Weighted_Count + no_work_count
df_code_occ <- df_code_occ %>%
filter(OCC2010 != "Unemployed, with No Work Experience in the Last 5 Years or Earlier or Never Worked")
#combining everything into one
df_code_occ <- bind_rows(df_code_occ, df_code_temp) %>%
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)
}| Occupation (2010 Census Codes / Titles) | Estimated Number of People (Weighted Count) | Percent of Degree Holders in This Occupation (%) |
|---|---|---|
| Unemployed & Not in labor force | 15,001,463 | 21.95 |
| Elementary and Middle School Teachers | 10,858,750 | 15.89 |
| Managers, nec (including Postmasters) | 8,619,686 | 12.61 |
| Registered Nurses | 7,366,753 | 10.78 |
| Accountants and Auditors | 4,917,353 | 7.19 |
| First-Line Supervisors of Sales Workers | 4,084,225 | 5.97 |
| Software Developers, Applications and Systems Software | 4,023,940 | 5.89 |
| Postsecondary Teachers | 3,987,466 | 5.83 |
| Computer Scientists and Systems Analysts/Network systems Analysts/Web Developers | 3,478,385 | 5.09 |
| Lawyers, and judges, magistrates, and other judicial workers | 3,228,557 | 4.72 |
| Secondary School Teachers | 2,789,783 | 4.08 |
| Note. This table displays the top occupations held by individuals with a bachelors degree in Medical and Health Sciences between 2017 and 2019. Each cell shows the weighted count of individuals in the specified occupation, which can be interpreted as population-level estimates. The percentage represents the share of Medical and Health Science degree holders working in each occupation. The category “Unemployed, with No Work Experience in the Last 5 Years or Earlier or Never Worked” includes individuals who reported a Biology degree but were not employed and had limited or no recent work history. |
| Occupation (2010 Census Codes / Titles) | Estimated Number of People (Weighted Count) | Percent of Degree Holders in This Occupation (%) |
|---|---|---|
| Unemployed & Not in labor force | 15,170,795 | 21.01 |
| Managers, nec (including Postmasters) | 10,070,569 | 13.95 |
| Elementary and Middle School Teachers | 9,504,930 | 13.17 |
| Registered Nurses | 8,158,460 | 11.30 |
| Software Developers, Applications and Systems Software | 5,266,951 | 7.30 |
| Accountants and Auditors | 4,742,221 | 6.57 |
| Postsecondary Teachers | 4,067,206 | 5.63 |
| Secondary School Teachers | 4,058,430 | 5.62 |
| First-Line Supervisors of Sales Workers | 3,810,092 | 5.28 |
| Computer Scientists and Systems Analysts/Network systems Analysts/Web Developers | 3,745,635 | 5.19 |
| Lawyers, and judges, magistrates, and other judicial workers | 3,598,962 | 4.99 |
| Note. This table displays the top occupations held by individuals with a bachelors degree in Medical and Health Sciences between 2020 and 2022. Each cell shows the weighted count of individuals in the specified occupation, which can be interpreted as population-level estimates. The percentage represents the share of Medical and Health Science degree holders working in each occupation. The category “Unemployed, with No Work Experience in the Last 5 Years or Earlier or Never Worked” includes individuals who reported a Biology degree but were not employed and had limited or no recent work history. |
| Occupation (2010 Census Codes / Titles) | Estimated Number of People (Weighted Count) | Percent of Degree Holders in This Occupation (%) |
|---|---|---|
| Unemployed & Not in labor force | 14,331,737 | 21.17 |
| Elementary and Middle School Teachers | 10,858,750 | 16.04 |
| Managers, nec (including Postmasters) | 8,619,686 | 12.73 |
| Registered Nurses | 7,366,753 | 10.88 |
| Accountants and Auditors | 4,917,353 | 7.26 |
| First-Line Supervisors of Sales Workers | 4,084,225 | 6.03 |
| Software Developers, Applications and Systems Software | 4,023,940 | 5.94 |
| Postsecondary Teachers | 3,987,466 | 5.89 |
| Computer Scientists and Systems Analysts/Network systems Analysts/Web Developers | 3,478,385 | 5.14 |
| Lawyers, and judges, magistrates, and other judicial workers | 3,228,557 | 4.77 |
| Secondary School Teachers | 2,789,783 | 4.12 |
| Note. This table displays the top occupations held by individuals with a bachelors degree in Biology between 2017 and 2019. Each cell shows the weighted count of individuals in the specified occupation, which can be interpreted as population-level estimates. The percentage represents the share of Medical and Health Science degree holders working in each occupation. The category “Unemployed, with No Work Experience in the Last 5 Years or Earlier or Never Worked” includes individuals who reported a Biology degree but were not employed and had limited or no recent work history. |
| Occupation (2010 Census Codes / Titles) | Estimated Number of People (Weighted Count) | Percent of Degree Holders in This Occupation (%) |
|---|---|---|
| Unemployed & Not in labor force | 14,387,099 | 20.15 |
| Managers, nec (including Postmasters) | 10,070,569 | 14.10 |
| Elementary and Middle School Teachers | 9,504,930 | 13.31 |
| Registered Nurses | 8,158,460 | 11.42 |
| Software Developers, Applications and Systems Software | 5,266,951 | 7.38 |
| Accountants and Auditors | 4,742,221 | 6.64 |
| Postsecondary Teachers | 4,067,206 | 5.70 |
| Secondary School Teachers | 4,058,430 | 5.68 |
| First-Line Supervisors of Sales Workers | 3,810,092 | 5.34 |
| Computer Scientists and Systems Analysts/Network systems Analysts/Web Developers | 3,745,635 | 5.25 |
| Lawyers, and judges, magistrates, and other judicial workers | 3,598,962 | 5.04 |
| Note. This table displays the top occupations held by individuals with a bachelors degree in Biology between 2020 and 2022. Each cell shows the weighted count of individuals in the specified occupation, which can be interpreted as population-level estimates. The percentage represents the share of Medical and Health Science degree holders working in each occupation. The category “Unemployed, with No Work Experience in the Last 5 Years or Earlier or Never Worked” includes individuals who reported a Biology degree but were not employed and had limited or no recent work history. |
| Occupation (2010 Census Codes / Titles) | Estimated Number of People (Weighted Count) | Percent of Degree Holders in This Occupation (%) |
|---|---|---|
| Unemployed & Not in labor force | 18,608,000 | 25.86 |
| Elementary and Middle School Teachers | 10,858,750 | 15.09 |
| Managers, nec (including Postmasters) | 8,619,686 | 11.98 |
| Registered Nurses | 7,366,753 | 10.24 |
| Accountants and Auditors | 4,917,353 | 6.83 |
| First-Line Supervisors of Sales Workers | 4,084,225 | 5.68 |
| Software Developers, Applications and Systems Software | 4,023,940 | 5.59 |
| Postsecondary Teachers | 3,987,466 | 5.54 |
| Computer Scientists and Systems Analysts/Network systems Analysts/Web Developers | 3,478,385 | 4.83 |
| Lawyers, and judges, magistrates, and other judicial workers | 3,228,557 | 4.49 |
| Secondary School Teachers | 2,789,783 | 3.88 |
| Note. This table displays the top occupations held by individuals with a bachelors degree in Business between 2017 and 2019. Each cell shows the weighted count of individuals in the specified occupation, which can be interpreted as population-level estimates. The percentage represents the share of Medical and Health Science degree holders working in each occupation. The category “Unemployed, with No Work Experience in the Last 5 Years or Earlier or Never Worked” includes individuals who reported a Biology degree but were not employed and had limited or no recent work history. |
| Occupation (2010 Census Codes / Titles) | Estimated Number of People (Weighted Count) | Percent of Degree Holders in This Occupation (%) |
|---|---|---|
| Unemployed & Not in labor force | 19,342,127 | 25.33 |
| Managers, nec (including Postmasters) | 10,070,569 | 13.19 |
| Elementary and Middle School Teachers | 9,504,930 | 12.45 |
| Registered Nurses | 8,158,460 | 10.68 |
| Software Developers, Applications and Systems Software | 5,266,951 | 6.90 |
| Accountants and Auditors | 4,742,221 | 6.21 |
| Postsecondary Teachers | 4,067,206 | 5.33 |
| Secondary School Teachers | 4,058,430 | 5.31 |
| First-Line Supervisors of Sales Workers | 3,810,092 | 4.99 |
| Computer Scientists and Systems Analysts/Network systems Analysts/Web Developers | 3,745,635 | 4.90 |
| Lawyers, and judges, magistrates, and other judicial workers | 3,598,962 | 4.71 |
| Note. This table displays the top occupations held by individuals with a bachelors degree in Business between 2020 and 2022. Each cell shows the weighted count of individuals in the specified occupation, which can be interpreted as population-level estimates. The percentage represents the share of Medical and Health Science degree holders working in each occupation. The category “Unemployed, with No Work Experience in the Last 5 Years or Earlier or Never Worked” includes individuals who reported a Biology degree but were not employed and had limited or no recent work history. |
| Occupation (2010 Census Codes / Titles) | Estimated Number of People (Weighted Count) | Percent of Degree Holders in This Occupation (%) |
|---|---|---|
| Unemployed & Not in labor force | 14,448,531 | 21.31 |
| Elementary and Middle School Teachers | 10,858,750 | 16.02 |
| Managers, nec (including Postmasters) | 8,619,686 | 12.71 |
| Registered Nurses | 7,366,753 | 10.86 |
| Accountants and Auditors | 4,917,353 | 7.25 |
| First-Line Supervisors of Sales Workers | 4,084,225 | 6.02 |
| Software Developers, Applications and Systems Software | 4,023,940 | 5.93 |
| Postsecondary Teachers | 3,987,466 | 5.88 |
| Computer Scientists and Systems Analysts/Network systems Analysts/Web Developers | 3,478,385 | 5.13 |
| Lawyers, and judges, magistrates, and other judicial workers | 3,228,557 | 4.76 |
| Secondary School Teachers | 2,789,783 | 4.11 |
| Note. This table displays the top occupations held by individuals with a bachelors degree in Psychology between 2017 and 2019. Each cell shows the weighted count of individuals in the specified occupation, which can be interpreted as population-level estimates. The percentage represents the share of Medical and Health Science degree holders working in each occupation. The category “Unemployed, with No Work Experience in the Last 5 Years or Earlier or Never Worked” includes individuals who reported a Biology degree but were not employed and had limited or no recent work history. |
| Occupation (2010 Census Codes / Titles) | Estimated Number of People (Weighted Count) | Percent of Degree Holders in This Occupation (%) |
|---|---|---|
| Unemployed & Not in labor force | 14,535,555 | 20.31 |
| Managers, nec (including Postmasters) | 10,070,569 | 14.07 |
| Elementary and Middle School Teachers | 9,504,930 | 13.28 |
| Registered Nurses | 8,158,460 | 11.40 |
| Software Developers, Applications and Systems Software | 5,266,951 | 7.36 |
| Accountants and Auditors | 4,742,221 | 6.63 |
| Postsecondary Teachers | 4,067,206 | 5.68 |
| Secondary School Teachers | 4,058,430 | 5.67 |
| First-Line Supervisors of Sales Workers | 3,810,092 | 5.32 |
| Computer Scientists and Systems Analysts/Network systems Analysts/Web Developers | 3,745,635 | 5.23 |
| Lawyers, and judges, magistrates, and other judicial workers | 3,598,962 | 5.03 |
| Note. This table displays the top occupations held by individuals with a bachelors degree in Psychology between 2020 and 2022. Each cell shows the weighted count of individuals in the specified occupation, which can be interpreted as population-level estimates. The percentage represents the share of Medical and Health Science degree holders working in each occupation. The category “Unemployed, with No Work Experience in the Last 5 Years or Earlier or Never Worked” includes individuals who reported a Biology degree but were not employed and had limited or no recent work history. |
| Occupation (2010 Census Codes / Titles) | Estimated Number of People (Weighted Count) | Percent of Degree Holders in This Occupation (%) |
|---|---|---|
| Unemployed & Not in labor force | 13,622,707 | 20.34 |
| Elementary and Middle School Teachers | 10,858,750 | 16.21 |
| Managers, nec (including Postmasters) | 8,619,686 | 12.87 |
| Registered Nurses | 7,366,753 | 11.00 |
| Accountants and Auditors | 4,917,353 | 7.34 |
| First-Line Supervisors of Sales Workers | 4,084,225 | 6.10 |
| Software Developers, Applications and Systems Software | 4,023,940 | 6.01 |
| Postsecondary Teachers | 3,987,466 | 5.95 |
| Computer Scientists and Systems Analysts/Network systems Analysts/Web Developers | 3,478,385 | 5.19 |
| Lawyers, and judges, magistrates, and other judicial workers | 3,228,557 | 4.82 |
| Secondary School Teachers | 2,789,783 | 4.17 |
| Note. This table displays the top occupations held by individuals with a bachelors degree in Physical Sciences between 2017 and 2019. Each cell shows the weighted count of individuals in the specified occupation, which can be interpreted as population-level estimates. The percentage represents the share of Medical and Health Science degree holders working in each occupation. The category “Unemployed, with No Work Experience in the Last 5 Years or Earlier or Never Worked” includes individuals who reported a Biology degree but were not employed and had limited or no recent work history. |
| Occupation (2010 Census Codes / Titles) | Estimated Number of People (Weighted Count) | Percent of Degree Holders in This Occupation (%) |
|---|---|---|
| Unemployed & Not in labor force | 13,532,854 | 19.18 |
| Managers, nec (including Postmasters) | 10,070,569 | 14.27 |
| Elementary and Middle School Teachers | 9,504,930 | 13.47 |
| Registered Nurses | 8,158,460 | 11.56 |
| Software Developers, Applications and Systems Software | 5,266,951 | 7.46 |
| Accountants and Auditors | 4,742,221 | 6.72 |
| Postsecondary Teachers | 4,067,206 | 5.76 |
| Secondary School Teachers | 4,058,430 | 5.75 |
| First-Line Supervisors of Sales Workers | 3,810,092 | 5.40 |
| Computer Scientists and Systems Analysts/Network systems Analysts/Web Developers | 3,745,635 | 5.31 |
| Lawyers, and judges, magistrates, and other judicial workers | 3,598,962 | 5.10 |
| Note. This table displays the top occupations held by individuals with a bachelors degree in Physical Sciences between 2020 and 2022. Each cell shows the weighted count of individuals in the specified occupation, which can be interpreted as population-level estimates. The percentage represents the share of Medical and Health Science degree holders working in each occupation. The category “Unemployed, with No Work Experience in the Last 5 Years or Earlier or Never Worked” includes individuals who reported a Biology degree but were not employed and had limited or no recent work history. |
| Occupation (2010 Census Codes / Titles) | Estimated Number of People (Weighted Count) | Percent of Degree Holders in This Occupation (%) |
|---|---|---|
| Unemployed & Not in labor force | 12,757,784 | 19.30 |
| Elementary and Middle School Teachers | 10,858,750 | 16.42 |
| Managers, nec (including Postmasters) | 8,619,686 | 13.04 |
| Registered Nurses | 7,366,753 | 11.14 |
| Accountants and Auditors | 4,917,353 | 7.44 |
| First-Line Supervisors of Sales Workers | 4,084,225 | 6.18 |
| Software Developers, Applications and Systems Software | 4,023,940 | 6.09 |
| Postsecondary Teachers | 3,987,466 | 6.03 |
| Computer Scientists and Systems Analysts/Network systems Analysts/Web Developers | 3,478,385 | 5.26 |
| Lawyers, and judges, magistrates, and other judicial workers | 3,228,557 | 4.88 |
| Secondary School Teachers | 2,789,783 | 4.22 |
| Note. This table displays the top occupations held by individuals with a bachelors degree in Nursing between 2017 and 2019. Each cell shows the weighted count of individuals in the specified occupation, which can be interpreted as population-level estimates. The percentage represents the share of Medical and Health Science degree holders working in each occupation. The category “Unemployed, with No Work Experience in the Last 5 Years or Earlier or Never Worked” includes individuals who reported a Biology degree but were not employed and had limited or no recent work history. |
| Occupation (2010 Census Codes / Titles) | Estimated Number of People (Weighted Count) | Percent of Degree Holders in This Occupation (%) |
|---|---|---|
| Unemployed & Not in labor force | 12,621,990 | 18.12 |
| Managers, nec (including Postmasters) | 10,070,569 | 14.46 |
| Elementary and Middle School Teachers | 9,504,930 | 13.65 |
| Registered Nurses | 8,158,460 | 11.71 |
| Software Developers, Applications and Systems Software | 5,266,951 | 7.56 |
| Accountants and Auditors | 4,742,221 | 6.81 |
| Postsecondary Teachers | 4,067,206 | 5.84 |
| Secondary School Teachers | 4,058,430 | 5.83 |
| First-Line Supervisors of Sales Workers | 3,810,092 | 5.47 |
| Computer Scientists and Systems Analysts/Network systems Analysts/Web Developers | 3,745,635 | 5.38 |
| Lawyers, and judges, magistrates, and other judicial workers | 3,598,962 | 5.17 |
| Note. This table displays the top occupations held by individuals with a bachelors degree in Nursing between 2020 and 2022. Each cell shows the weighted count of individuals in the specified occupation, which can be interpreted as population-level estimates. The percentage represents the share of Medical and Health Science degree holders working in each occupation. The category “Unemployed, with No Work Experience in the Last 5 Years or Earlier or Never Worked” includes individuals who reported a Biology degree but were not employed and had limited or no recent work history. |
| Occupation (2010 Census Codes / Titles) | Estimated Number of People (Weighted Count) | Percent of Degree Holders in This Occupation (%) |
|---|---|---|
| Unemployed & Not in labor force | 12,757,784 | 19.30 |
| Elementary and Middle School Teachers | 10,858,750 | 16.42 |
| Managers, nec (including Postmasters) | 8,619,686 | 13.04 |
| Registered Nurses | 7,366,753 | 11.14 |
| Accountants and Auditors | 4,917,353 | 7.44 |
| First-Line Supervisors of Sales Workers | 4,084,225 | 6.18 |
| Software Developers, Applications and Systems Software | 4,023,940 | 6.09 |
| Postsecondary Teachers | 3,987,466 | 6.03 |
| Computer Scientists and Systems Analysts/Network systems Analysts/Web Developers | 3,478,385 | 5.26 |
| Lawyers, and judges, magistrates, and other judicial workers | 3,228,557 | 4.88 |
| Secondary School Teachers | 2,789,783 | 4.22 |
| Note. This table displays the top occupations held by individuals with a bachelors degree in Health and Medical Administrative Services between 2017 and 2019. Each cell shows the weighted count of individuals in the specified occupation, which can be interpreted as population-level estimates. The percentage represents the share of Medical and Health Science degree holders working in each occupation. The category “Unemployed, with No Work Experience in the Last 5 Years or Earlier or Never Worked” includes individuals who reported a Biology degree but were not employed and had limited or no recent work history. |
| Occupation (2010 Census Codes / Titles) | Estimated Number of People (Weighted Count) | Percent of Degree Holders in This Occupation (%) |
|---|---|---|
| Unemployed & Not in labor force | 12,621,990 | 18.12 |
| Managers, nec (including Postmasters) | 10,070,569 | 14.46 |
| Elementary and Middle School Teachers | 9,504,930 | 13.65 |
| Registered Nurses | 8,158,460 | 11.71 |
| Software Developers, Applications and Systems Software | 5,266,951 | 7.56 |
| Accountants and Auditors | 4,742,221 | 6.81 |
| Postsecondary Teachers | 4,067,206 | 5.84 |
| Secondary School Teachers | 4,058,430 | 5.83 |
| First-Line Supervisors of Sales Workers | 3,810,092 | 5.47 |
| Computer Scientists and Systems Analysts/Network systems Analysts/Web Developers | 3,745,635 | 5.38 |
| Lawyers, and judges, magistrates, and other judicial workers | 3,598,962 | 5.17 |
| Note. This table displays the top occupations held by individuals with a bachelors degree in Health and Medical Administrative Services between 2020 and 2022. Each cell shows the weighted count of individuals in the specified occupation, which can be interpreted as population-level estimates. The percentage represents the share of Medical and Health Science degree holders working in each occupation. The category “Unemployed, with No Work Experience in the Last 5 Years or Earlier or Never Worked” includes individuals who reported a Biology degree but were not employed and had limited or no recent work history. |
| Occupation (2010 Census Codes / Titles) | Estimated Number of People (Weighted Count) | Percent of Degree Holders in This Occupation (%) |
|---|---|---|
| Unemployed & Not in labor force | 12,757,784 | 19.30 |
| Elementary and Middle School Teachers | 10,858,750 | 16.42 |
| Managers, nec (including Postmasters) | 8,619,686 | 13.04 |
| Registered Nurses | 7,366,753 | 11.14 |
| Accountants and Auditors | 4,917,353 | 7.44 |
| First-Line Supervisors of Sales Workers | 4,084,225 | 6.18 |
| Software Developers, Applications and Systems Software | 4,023,940 | 6.09 |
| Postsecondary Teachers | 3,987,466 | 6.03 |
| Computer Scientists and Systems Analysts/Network systems Analysts/Web Developers | 3,478,385 | 5.26 |
| Lawyers, and judges, magistrates, and other judicial workers | 3,228,557 | 4.88 |
| Secondary School Teachers | 2,789,783 | 4.22 |
| Note. This table displays the top occupations held by individuals with a bachelors degree in Medical Assisting Services between 2017 and 2019. Each cell shows the weighted count of individuals in the specified occupation, which can be interpreted as population-level estimates. The percentage represents the share of Medical and Health Science degree holders working in each occupation. The category “Unemployed, with No Work Experience in the Last 5 Years or Earlier or Never Worked” includes individuals who reported a Biology degree but were not employed and had limited or no recent work history. |
| Occupation (2010 Census Codes / Titles) | Estimated Number of People (Weighted Count) | Percent of Degree Holders in This Occupation (%) |
|---|---|---|
| Unemployed & Not in labor force | 12,621,990 | 18.12 |
| Managers, nec (including Postmasters) | 10,070,569 | 14.46 |
| Elementary and Middle School Teachers | 9,504,930 | 13.65 |
| Registered Nurses | 8,158,460 | 11.71 |
| Software Developers, Applications and Systems Software | 5,266,951 | 7.56 |
| Accountants and Auditors | 4,742,221 | 6.81 |
| Postsecondary Teachers | 4,067,206 | 5.84 |
| Secondary School Teachers | 4,058,430 | 5.83 |
| First-Line Supervisors of Sales Workers | 3,810,092 | 5.47 |
| Computer Scientists and Systems Analysts/Network systems Analysts/Web Developers | 3,745,635 | 5.38 |
| Lawyers, and judges, magistrates, and other judicial workers | 3,598,962 | 5.17 |
| Note. This table displays the top occupations held by individuals with a bachelors degree in Medical Assisting Services between 2020 and 2022. Each cell shows the weighted count of individuals in the specified occupation, which can be interpreted as population-level estimates. The percentage represents the share of Medical and Health Science degree holders working in each occupation. The category “Unemployed, with No Work Experience in the Last 5 Years or Earlier or Never Worked” includes individuals who reported a Biology degree but were not employed and had limited or no recent work history. |
| Occupation (2010 Census Codes / Titles) | Estimated Number of People (Weighted Count) | Percent of Degree Holders in This Occupation (%) |
|---|---|---|
| Unemployed & Not in labor force | 12,757,784 | 19.30 |
| Elementary and Middle School Teachers | 10,858,750 | 16.42 |
| Managers, nec (including Postmasters) | 8,619,686 | 13.04 |
| Registered Nurses | 7,366,753 | 11.14 |
| Accountants and Auditors | 4,917,353 | 7.44 |
| First-Line Supervisors of Sales Workers | 4,084,225 | 6.18 |
| Software Developers, Applications and Systems Software | 4,023,940 | 6.09 |
| Postsecondary Teachers | 3,987,466 | 6.03 |
| Computer Scientists and Systems Analysts/Network systems Analysts/Web Developers | 3,478,385 | 5.26 |
| Lawyers, and judges, magistrates, and other judicial workers | 3,228,557 | 4.88 |
| Secondary School Teachers | 2,789,783 | 4.22 |
| Note. This table displays the top occupations held by individuals with a bachelors degree in Health and Medical Preparatory Programs between 2017 and 2019. Each cell shows the weighted count of individuals in the specified occupation, which can be interpreted as population-level estimates. The percentage represents the share of Medical and Health Science degree holders working in each occupation. The category “Unemployed, with No Work Experience in the Last 5 Years or Earlier or Never Worked” includes individuals who reported a Biology degree but were not employed and had limited or no recent work history. |
| Occupation (2010 Census Codes / Titles) | Estimated Number of People (Weighted Count) | Percent of Degree Holders in This Occupation (%) |
|---|---|---|
| Unemployed & Not in labor force | 12,621,990 | 18.12 |
| Managers, nec (including Postmasters) | 10,070,569 | 14.46 |
| Elementary and Middle School Teachers | 9,504,930 | 13.65 |
| Registered Nurses | 8,158,460 | 11.71 |
| Software Developers, Applications and Systems Software | 5,266,951 | 7.56 |
| Accountants and Auditors | 4,742,221 | 6.81 |
| Postsecondary Teachers | 4,067,206 | 5.84 |
| Secondary School Teachers | 4,058,430 | 5.83 |
| First-Line Supervisors of Sales Workers | 3,810,092 | 5.47 |
| Computer Scientists and Systems Analysts/Network systems Analysts/Web Developers | 3,745,635 | 5.38 |
| Lawyers, and judges, magistrates, and other judicial workers | 3,598,962 | 5.17 |
| Note. This table displays the top occupations held by individuals with a bachelors degree in Health and Medical Preparatory Programs between 2020 and 2022. Each cell shows the weighted count of individuals in the specified occupation, which can be interpreted as population-level estimates. The percentage represents the share of Medical and Health Science degree holders working in each occupation. The category “Unemployed, with No Work Experience in the Last 5 Years or Earlier or Never Worked” includes individuals who reported a Biology degree but were not employed and had limited or no recent work history. |
I now do the same analysis as above, but I restrict my sample to only look at nurses. In this case, that will mean that I will only look at ACS respondents from 2017-2022 with the OCC2010 code of “3130 - Registered Nurses” and “3500 - Licensed Practical and Licensed Vocational Nurses,” which is the similar combination of codes used in the HCW Shortage Analysis using CPS data. First, I look at education levels, and then at degree fields.
# define nurse from occ2010 codes
nursing_occs <- c(3130,3500)
# create dataframe for 2017-2019 with no education restrictions (using data2 df, defined at beginning of code)
n17_19 <- data2 %>%
filter(YEAR %in% 2017:2019)
# create dataframe for 2020-2022 with no education restrictions(using data2 df, defined at beginning of code)
n20_22 <- data2 %>%
filter(YEAR %in% 2020:2022)
# merge together education variable categoriesnurses_educ_summary_fun <- function(df_year) {
educ_labels <- c(
"0" = "N/A or No Schooling",
"1" = "Less than high school",
"2" = "Less than high school",
"3" = "Less than high school",
"4" = "Less than high school",
"5" = "Less than high school",
"6" = "High school",
"7" = "1 year of college",
"8" = "2 years of college",
"9" = "3 years of college",
"10" = "4 years of college",
"11" = "5+ years of college")
# creating recoded education variables
df_base <- df_year %>%
filter(OCC2010 %in% nursing_occs) %>%
mutate(EDUC = as.character(EDUC),
EDUC_Collapsed = recode(EDUC, !!!educ_labels),
EDUC_Bach = case_when(EDUC %in% c("10", "11") ~ "Bachelor's degree and above",
EDUC %in% as.character(0:9) ~ "No Bachelor's degree",
TRUE ~ NA_character_))
# step one is creating a table with the "full" education variable (with some collapsed categories)
df_full <- df_base %>%
group_by(YEAR, EDUC_Collapsed) %>%
summarise(Weighted_Count = sum(PERWT, na.rm = TRUE), .groups = "drop") %>%
group_by(YEAR) %>%
mutate(Percentage = (Weighted_Count / sum(Weighted_Count))*100) %>%
ungroup() %>%
mutate(cell = paste0(format(round(Weighted_Count, 0), big.mark = ","),
"<br/>(", round(Percentage, 2), "%)"))
# pivoting to wide format
df_full_wide <- df_full %>%
select(EDUC_Collapsed, YEAR, cell) %>%
arrange(YEAR) %>%
pivot_wider(names_from = YEAR, values_from = cell)
# adding total column (sum across years)
df_full_totals <- df_full %>%
group_by(EDUC_Collapsed) %>%
summarise(
Total = sum(Weighted_Count),
.groups = "drop"
) %>%
mutate(Total = format(round(Total, 0), big.mark = ",")) %>%
select(EDUC_Collapsed, Total)
# joining total column to the main table
df_educ <- df_full_wide %>%
left_join(df_full_totals, by = "EDUC_Collapsed")
# step two is creating a table with only bach/no bach education variable
df_grouped <- df_base %>%
group_by(YEAR, EDUC_Bach) %>%
summarise(Weighted_Count = sum(PERWT), .groups = "drop") %>%
group_by(YEAR) %>%
mutate(Percentage = (Weighted_Count / sum(Weighted_Count)) * 100) %>%
ungroup() %>%
mutate(cell = paste0(format(round(Weighted_Count, 0), big.mark = ","),
"<br/>(", round(Percentage, 2), "%)"))
# pivoting to wide format
df_grouped_wide <- df_grouped %>%
select(EDUC_Bach, YEAR, cell) %>%
arrange(YEAR) %>%
pivot_wider(names_from = YEAR, values_from = cell)
# adding total column (sum across years)
df_grouped_totals <- df_grouped %>%
group_by(EDUC_Bach) %>%
summarise(Total = sum(Weighted_Count), .groups = "drop") %>%
mutate(Total = format(round(Total, 0), big.mark = ","))
df_bach <- df_grouped_wide %>%
left_join(df_grouped_totals, by = "EDUC_Bach")
return(list(full_table = df_educ, grouped_table = df_bach))
}
nurses_educ_17 = nurses_educ_summary_fun(df_year = n17_19)
nurses_educ_20 = nurses_educ_summary_fun(df_year = n20_22)| Education Level | 2017 | 2018 | 2019 | Total Nurses with Listed Education (2017–2019) |
|---|---|---|---|---|
| 1 year of college |
518,634 (11.36%) |
512,792 (10.89%) |
452,808 (9.97%) |
1,484,234 |
| 2 years of college |
1,231,744 (26.98%) |
1,218,818 (25.89%) |
1,168,955 (25.73%) |
3,619,517 |
| 4 years of college |
1,793,225 (39.28%) |
1,881,411 (39.97%) |
1,926,317 (42.4%) |
5,600,953 |
| 5+ years of college |
597,563 (13.09%) |
636,191 (13.52%) |
659,545 (14.52%) |
1,893,299 |
| High school |
403,124 (8.83%) |
427,461 (9.08%) |
314,185 (6.92%) |
1,144,770 |
| Less than high school |
15,638 (0.34%) |
22,697 (0.48%) |
14,652 (0.32%) |
52,987 |
| N/A or No Schooling |
5,252 (0.12%) |
7,433 (0.16%) |
6,461 (0.14%) |
19,146 |
| Note: | ||||
| Each cell contains the weighted count of nurses by education level and year, along with their percentage of the annual total. Categories corresponding to schooling lower than a high school degree were collapsed into a single ‘Less than high school’ category. |
| Education Level | 2020 | 2021 | 2022 | Total Nurses with Listed Education (2020–2022) |
|---|---|---|---|---|
| 1 year of college |
374,845 (8.64%) |
387,339 (8.43%) |
398,012 (8.41%) |
1,160,196 |
| 2 years of college |
1,064,540 (24.55%) |
1,091,955 (23.77%) |
1,082,805 (22.87%) |
3,239,300 |
| 4 years of college |
1,871,188 (43.15%) |
2,016,339 (43.88%) |
2,092,686 (44.21%) |
5,980,213 |
| 5+ years of college |
703,913 (16.23%) |
760,354 (16.55%) |
809,098 (17.09%) |
2,273,365 |
| High school |
297,883 (6.87%) |
309,945 (6.75%) |
325,298 (6.87%) |
933,126 |
| Less than high school |
17,166 (0.4%) |
17,493 (0.38%) |
16,687 (0.35%) |
51,346 |
| N/A or No Schooling |
7,410 (0.17%) |
11,298 (0.25%) |
9,092 (0.19%) |
27,800 |
| Note: | ||||
| Each cell contains the weighted count of nurses by education level and year, along with their percentage of the annual total. Categories corresponding to schooling lower than a high school degree were collapsed into a single ‘Less than high school’ category. |
# Bachelor's/No Bachelor's table (2017–2019)
kable(nurses_educ_17$grouped_table, escape = FALSE, format = "html",
caption = "Nurses by Bachelor’s Degree Attainment (2017–2019)",
col.names = c("Education Level","2017","2018","2019",
"Total Nurses with Listed Education (2017–2019)")) %>%
kable_styling(full_width = FALSE, bootstrap_options = c("striped", "hover")) %>%
footnote(general = "Education levels are grouped into 'No Bachelor's degree' (codes 01–09) and 'Bachelor's degree and above' (codes 10–11). Weighted counts and percentages are shown per year.") %>%
column_spec(ncol(nurses_educ_17$grouped_table), width = "250px",
extra_css = "word-wrap: break-word; white-space: normal;text-align: center;")| Education Level | 2017 | 2018 | 2019 | Total Nurses with Listed Education (2017–2019) |
|---|---|---|---|---|
| Bachelor’s degree and above |
2,390,788 (52.37%) |
2,517,602 (53.49%) |
2,585,862 (56.92%) |
7,494,252 |
| No Bachelor’s degree |
2,174,392 (47.63%) |
2,189,201 (46.51%) |
1,957,061 (43.08%) |
6,320,654 |
| Note: | ||||
| Education levels are grouped into ‘No Bachelor’s degree’ (codes 01–09) and ‘Bachelor’s degree and above’ (codes 10–11). Weighted counts and percentages are shown per year. |
# Bachelor's/No Bachelor's table (2020–2022)
kable(nurses_educ_20$grouped_table, escape = FALSE, format = "html",
caption = "Nurses by Bachelor’s Degree Attainment (2020–2022)",
col.names = c("Education Level","2020","2021","2022",
"Total Nurses with Listed Education (2020–2022)")) %>%
kable_styling(full_width = FALSE, bootstrap_options = c("striped", "hover")) %>%
footnote(general = "Education levels are grouped into 'No Bachelor's degree' (codes 01–09) and 'Bachelor's degree and above' (codes 10–11). Weighted counts and percentages are shown per year.") %>%
column_spec(ncol(nurses_educ_20$grouped_table), width = "250px",
extra_css = "word-wrap: break-word; white-space: normal;text-align: center;")| Education Level | 2020 | 2021 | 2022 | Total Nurses with Listed Education (2020–2022) |
|---|---|---|---|---|
| Bachelor’s degree and above |
2,575,101 (59.38%) |
2,776,693 (60.43%) |
2,901,784 (61.3%) |
8,253,578 |
| No Bachelor’s degree |
1,761,844 (40.62%) |
1,818,030 (39.57%) |
1,831,894 (38.7%) |
5,411,768 |
| Note: | ||||
| Education levels are grouped into ‘No Bachelor’s degree’ (codes 01–09) and ‘Bachelor’s degree and above’ (codes 10–11). Weighted counts and percentages are shown per year. |
nurses_degree_summary_fun <- function(df_year, degfield_type, occ_code, labels) {
# first i am creating the weighted counts and percentages for each degree type for each year in period
df <- df_year %>%
mutate(DGF = as.character(.data[[degfield_type]])) %>%
filter(OCC2010 %in% occ_code) %>%
group_by(YEAR, DGF) %>%
summarise(Weighted_Count = sum(PERWT, na.rm = TRUE), .groups = 'drop') %>%
arrange(desc(Weighted_Count)) %>%
group_by(YEAR) %>%
mutate(Percentage = (Weighted_Count / sum(Weighted_Count)) * 100) %>%
ungroup()
# here i am creating the total period totals for each degree, which is how i calculate the top 10 majors
df_totals <- df %>%
group_by(DGF) %>%
summarise(Total = sum(Weighted_Count), .groups = 'drop')
# finally i am merging both the yearly counts/percentages and the period totals and pivoting the table
final_df <- df %>%
mutate(cell = paste0(format(round(Weighted_Count, 0), big.mark = ","),
"<br/>(", round(Percentage, 2), "%)")) %>%
select(DGF, YEAR, cell) %>%
arrange(YEAR) %>%
pivot_wider(names_from = YEAR, values_from = cell) %>%
left_join(df_totals, by = "DGF") %>%
mutate(Total = format(round(Total, 0), big.mark = ",")) %>%
mutate(DGF = ifelse(DGF %in% names(labels),
labels[DGF],
"Unknown")) %>%
head(10)
return(final_df)
}| Field of Bachelor’s Degree | 2017 | 2018 | 2019 | Total Nurses with Listed Degree (2017–2019) |
|---|---|---|---|---|
| Do Not Hold Bachelor’s Degree |
2,174,392 (47.63%) |
2,189,201 (46.51%) |
1,957,061 (43.08%) |
6,320,654 |
| Medical and Health Sciences and Services |
1,765,298 (38.67%) |
1,850,031 (39.31%) |
1,958,850 (43.12%) |
5,574,179 |
| Biology and Life Sciences |
97,571 (2.14%) |
101,124 (2.15%) |
105,878 (2.33%) |
304,573 |
| Business |
89,212 (1.95%) |
115,224 (2.45%) |
102,289 (2.25%) |
306,725 |
| Psychology |
69,513 (1.52%) |
75,346 (1.6%) |
67,357 (1.48%) |
212,216 |
| Education Administration and Teaching |
60,797 (1.33%) |
57,815 (1.23%) |
46,736 (1.03%) |
165,348 |
| Social Sciences |
45,809 (1%) |
44,796 (0.95%) |
42,136 (0.93%) |
132,741 |
| Physical Sciences |
39,752 (0.87%) |
36,209 (0.77%) |
41,262 (0.91%) |
117,223 |
| Communications |
23,563 (0.52%) |
29,905 (0.64%) |
24,289 (0.53%) |
77,757 |
| Fine Arts |
22,039 (0.48%) |
25,260 (0.54%) |
23,503 (0.52%) |
70,802 |
| Note: | ||||
| Each cell contains the total weighted count of nurses reporting the listed bachelor’s degree field in that year. The percentage below each count represents that field’s share of all reported degree fields among nurses for that specific year. The final column displays the total weighted count across all years (2017–2019), which determines the ranking of the top 10 degree fields. |
| Field of Bachelor’s Degree | 2020 | 2021 | 2022 | Total Nurses with Listed Degree (2020–2022) |
|---|---|---|---|---|
| Medical and Health Sciences and Services |
1,892,071 (43.63%) |
2,030,895 (44.2%) |
2,127,548 (44.94%) |
6,050,514 |
| Do Not Hold Bachelor’s Degree |
1,761,844 (40.62%) |
1,818,030 (39.57%) |
1,831,894 (38.7%) |
5,411,768 |
| Biology and Life Sciences |
121,205 (2.79%) |
115,454 (2.51%) |
124,885 (2.64%) |
361,544 |
| Business |
101,723 (2.35%) |
122,245 (2.66%) |
122,498 (2.59%) |
346,466 |
| Psychology |
76,303 (1.76%) |
83,018 (1.81%) |
89,013 (1.88%) |
248,334 |
| Education Administration and Teaching |
57,231 (1.32%) |
57,981 (1.26%) |
65,738 (1.39%) |
180,950 |
| Social Sciences |
48,928 (1.13%) |
51,027 (1.11%) |
51,412 (1.09%) |
151,367 |
| Physical Sciences |
39,295 (0.91%) |
45,539 (0.99%) |
40,551 (0.86%) |
125,385 |
| Physical Fitness, Parks, Recreation, and Leisure |
25,452 (0.59%) |
24,389 (0.53%) |
23,668 (0.5%) |
73,509 |
| Fine Arts |
24,529 (0.57%) |
26,944 (0.59%) |
28,508 (0.6%) |
79,981 |
| Note: | ||||
| Each cell contains the total weighted count of nurses reporting the listed bachelor’s degree field in that year. The percentage below each count represents that field’s share of all reported degree fields among nurses for that specific year. The final column displays the total weighted count across all years (2020–2022), which determines the ranking of the top 10 degree fields. |
| Field of Bachelor’s Degree | 2017 | 2018 | 2019 | Total Nurses with Listed Degree (2017–2019) |
|---|---|---|---|---|
| 0000 - Do Not Hold Bachelor’s Degree |
2,174,392 (47.63%) |
2,189,201 (46.51%) |
1,957,061 (43.08%) |
6,320,654 |
| 6107 - Nursing |
1,697,100 (37.17%) |
1,770,127 (37.61%) |
1,882,468 (41.44%) |
5,349,695 |
| 3600 - Biology (NOS) |
73,524 (1.61%) |
77,847 (1.65%) |
78,450 (1.73%) |
229,821 |
| 5200 - Psychology (NOS) |
66,104 (1.45%) |
72,339 (1.54%) |
64,463 (1.42%) |
202,906 |
| 5098 - Multi-disciplinary or General Science |
28,082 (0.62%) |
24,523 (0.52%) |
27,102 (0.6%) |
79,707 |
| 6203 - Business Management and Administration |
26,959 (0.59%) |
34,617 (0.74%) |
30,086 (0.66%) |
91,662 |
| 2300 - General Education (NOS) |
24,019 (0.53%) |
20,686 (0.44%) |
16,723 (0.37%) |
61,428 |
| 6200 - General Business (NOS) |
20,788 (0.46%) |
28,619 (0.61%) |
25,077 (0.55%) |
74,484 |
| 4101 - Physical Fitness, Parks, Recreation, and Leisure |
19,408 (0.43%) |
20,368 (0.43%) |
20,885 (0.46%) |
60,661 |
| 5507 - Sociology |
17,189 (0.38%) |
15,702 (0.33%) |
15,967 (0.35%) |
48,858 |
| Note: | ||||
| This table uses the ACS detailed degree field codes, where the ACS combines major responses into 176 distinct “detailed” majors, which is more specific than the 29 broader major categories used in the previous section. Degree fields labeled as “NOS” (Not Otherwise Specified) at the end refer to responses that could not be categorized into a more specific major within that field. |
| Field of Bachelor’s Degree | 2020 | 2021 | 2022 | Total Nurses with Listed Degree (2020–2022) |
|---|---|---|---|---|
| 6107 - Nursing |
1,792,720 (41.34%) |
1,945,423 (42.34%) |
2,031,856 (42.92%) |
5,769,999 |
| 0000 - Do Not Hold Bachelor’s Degree |
1,761,844 (40.62%) |
1,818,030 (39.57%) |
1,831,894 (38.7%) |
5,411,768 |
| 3600 - Biology (NOS) |
89,058 (2.05%) |
84,635 (1.84%) |
91,585 (1.93%) |
265,278 |
| 5200 - Psychology (NOS) |
71,585 (1.65%) |
77,655 (1.69%) |
83,391 (1.76%) |
232,631 |
| 6203 - Business Management and Administration |
33,585 (0.77%) |
36,944 (0.8%) |
39,246 (0.83%) |
109,775 |
| 4101 - Physical Fitness, Parks, Recreation, and Leisure |
25,452 (0.59%) |
24,389 (0.53%) |
23,668 (0.5%) |
73,509 |
| 5098 - Multi-disciplinary or General Science |
24,933 (0.57%) |
32,231 (0.7%) |
26,610 (0.56%) |
83,774 |
| 6200 - General Business (NOS) |
21,985 (0.51%) |
34,346 (0.75%) |
29,664 (0.63%) |
85,995 |
| 6100 - General Medical and Health Services (NOS) |
19,890 (0.46%) |
21,614 (0.47%) |
25,174 (0.53%) |
66,678 |
| 3301 - English Language and Literature |
18,268 (0.42%) |
19,020 (0.41%) |
18,324 (0.39%) |
55,612 |
| Note: | ||||
| This table uses the ACS detailed degree field codes, where the ACS combines major responses into 176 distinct “detailed” majors, which is more specific than the 29 broader major categories used in the previous section. Degree fields labeled as “NOS” (Not Otherwise Specified) at the end refer to responses that could not be categorized into a more specific major within that field. |