U.S. Employment During COVID19 by Education Attainment

Unemployment Rate

Less Than a High School Diploma, 25 Yrs. & Over (LNU04027659)
High School Graduates, No College, 25 Yrs. & Over (LNU04027660)
Some College or Associate Degree, 25 Yrs. & Over (LNU04027689)
College Graduates - Bachelor’s Degree, 25 years and over (CGBD25O)
College Graduates - Professional Degree, 25 years and over (CGPD25O)

no_hs <-fredr_series_observations(series_id = "LNU04027659",
        observation_start = as.Date("2019-01-01")) 
hs <-fredr_series_observations(series_id = "LNU04027660",
        observation_start = as.Date("2019-01-01")) 
as <-fredr_series_observations(series_id = "LNU04027689",
        observation_start = as.Date("2019-01-01")) 
ba <-fredr_series_observations(series_id = "CGBD25O",
        observation_start = as.Date("2019-01-01")) 


indicator <-as.data.frame(no_hs$date)

colnames(indicator) <- "date"
indicator$no_highschool <- no_hs$value
indicator$highschool <- hs$value
indicator$some_college <- as$value
indicator$bachelor <- ba$value



indicator %>% kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
   column_spec(2, T, color = "black" ) %>%
  column_spec(3, T, color = "blue") %>%
  column_spec(4, T, color = "green") %>%
   column_spec(5, T, color = "#CC79A7") %>%
  row_spec(16,T,color="red",background = "lightyellow")

date	no_highschool	highschool	some_college	bachelor
2019-01-01	7.4	4.3	3.7	2.8
2019-02-01	6.7	4.2	3.4	2.4
2019-03-01	6.9	4.0	3.5	2.2
2019-04-01	5.2	3.3	2.9	2.1
2019-05-01	4.4	3.3	2.5	2.1
2019-06-01	4.6	3.7	3.0	2.3
2019-07-01	4.6	3.6	3.3	2.5
2019-08-01	4.7	3.6	3.2	2.5
2019-09-01	4.0	3.3	2.8	2.2
2019-10-01	4.7	3.5	2.7	2.1
2019-11-01	5.1	3.5	2.8	1.9
2019-12-01	5.9	3.7	2.6	1.9
2020-01-01	7.4	4.4	3.0	2.2
2020-02-01	7.2	4.1	3.3	2.2
2020-03-01	8.1	4.8	3.9	2.5
2020-04-01	20.9	17.0	14.8	9.4
2020-05-01	18.5	15.0	12.9	8.4
2020-06-01	15.4	11.9	10.8	8.1
2020-07-01	14.9	10.8	10.2	8.1

#---------------------------
library(reshape2)

# Specify id.vars: the variables to keep but not split apart on
data_long <- melt(indicator, id.vars=c("date"))
colnames(data_long) <- c("date","Education","Percent")

# plotting data
data_long %>% ggplot() + 
  geom_line(mapping = aes(x=date,y=Percent,color=Education),size=1)  +
        
   labs(title = "Unemployment rates by education attainment", 
       subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
       x="Monthly", y="Percent",
       caption = "Data source: FRED Federal Reserve.   Illustration by @JoeLongSanDiego")+
    theme_economist()

Unemployment Rate - High School Graduates, No College, 18 to 19 years

_ Men (HSGS1819M) - Women (HSGS1819W)

men <-fredr_series_observations(series_id = "HSGS1819M",
        observation_start = as.Date("2019-01-01")) 
women <-fredr_series_observations(series_id = "HSGS1819W",
        observation_start = as.Date("2019-01-01")) 



indicator <-as.data.frame(men$date)

colnames(indicator) <- "date"
indicator$men <- men$value
indicator$women <- women$value




indicator %>% kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
   column_spec(2, T, color = "green" ) %>%
  column_spec(3, T, color = "blue")  %>%
  row_spec(16,T,color="red",background = "lightyellow")

date	men	women
2019-01-01	14.5	14.0
2019-02-01	18.4	13.6
2019-03-01	18.4	9.2
2019-04-01	20.4	7.1
2019-05-01	13.7	11.7
2019-06-01	17.6	11.3
2019-07-01	13.7	11.4
2019-08-01	13.7	13.3
2019-09-01	16.6	15.9
2019-10-01	16.2	11.5
2019-11-01	15.6	12.7
2019-12-01	14.5	12.9
2020-01-01	20.9	18.3
2020-02-01	16.3	20.7
2020-03-01	18.8	10.6
2020-04-01	30.3	36.4
2020-05-01	30.3	26.8
2020-06-01	26.6	15.9
2020-07-01	23.8	14.2

#---------------------------

# Specify id.vars: the variables to keep but not split apart on
data_long <- melt(indicator, id.vars=c("date"))
colnames(data_long) <- c("date","Gender","Percent")

# plotting data
data_long %>% ggplot() + 
  geom_line(mapping = aes(x=date,y=Percent,color=Gender),size=1)  +
        
   labs(title = "Unemployment rates _ High School Graduates by Gender", 
       subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
       x="Monthly", y="Percent",
       caption = "Data source: FRED Federal Reserve.   Illustration by @JoeLongSanDiego")+
    theme_economist()

Unemployment Rate - Less Than a High School Diploma, 25 Yrs. & Over (LNS14027659)

Less than a High School Diploma, 25 to 64 years, Men (LHSD2564M)
Less than a High School Diploma, 25 to 64 years, Women (LHSD2564W)

women <-fredr_series_observations(series_id = "LHSD2564W",
        observation_start = as.Date("2019-01-01")) 
men <-fredr_series_observations(series_id = "LHSD2564M",
        observation_start = as.Date("2019-01-01")) 



indicator <-as.data.frame(women$date)

colnames(indicator) <- "date"
indicator$women <- women$value
indicator$men <- men$value


indicator %>% kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
   column_spec(2, T, color = "green" ) %>%
  column_spec(3, T, color = "blue") %>%
  row_spec(16,T,color="red",background = "lightyellow")

date	women	men
2019-01-01	7.6	7.5
2019-02-01	6.6	7.0
2019-03-01	7.4	6.8
2019-04-01	5.9	4.9
2019-05-01	6.8	3.2
2019-06-01	5.9	3.9
2019-07-01	6.5	3.5
2019-08-01	6.6	3.7
2019-09-01	5.2	3.5
2019-10-01	6.1	4.2
2019-11-01	6.9	4.4
2019-12-01	5.6	6.3
2020-01-01	7.2	7.7
2020-02-01	7.0	7.3
2020-03-01	8.5	7.7
2020-04-01	25.5	18.2
2020-05-01	22.8	16.0
2020-06-01	19.3	13.2
2020-07-01	17.8	13.2

#---------------------------

# Specify id.vars: the variables to keep but not split apart on
data_long <- melt(indicator, id.vars=c("date"))
colnames(data_long) <- c("date","Gender","Thousands")

# plotting data
data_long %>% ggplot() + 
  geom_line(mapping = aes(x=date,y=Thousands,color=Gender),size=1)  +
        
   labs(title = "Unemployment Rates _ Less than a High School Diploma, 25 to 64 years", 
       subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
       x="Monthly", y="Rate",
       caption = "Data source: FRED Federal Reserve.   Illustration by @JoeLongSanDiego")+
    theme_economist()

### Unemployment Rate - 18-19 Yrs. - Men (LNU04000154) - Women (LNU04000319)

women <-fredr_series_observations(series_id = "LNU04000319",
        observation_start = as.Date("2019-01-01")) 
men <-fredr_series_observations(series_id = "LNU04000154",
        observation_start = as.Date("2019-01-01")) 



indicator <-as.data.frame(women$date)

colnames(indicator) <- "date"
indicator$women <- women$value
indicator$men <- men$value


indicator %>% kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
   column_spec(2, T, color = "green" ) %>%
  column_spec(3, T, color = "blue") %>%
  row_spec(16,T,color="red",background = "lightyellow")

date	women	men
2019-01-01	12.0	14.6
2019-02-01	10.7	14.5
2019-03-01	9.1	14.6
2019-04-01	6.8	14.7
2019-05-01	11.9	12.2
2019-06-01	11.2	14.9
2019-07-01	10.1	12.1
2019-08-01	11.5	13.1
2019-09-01	11.2	14.7
2019-10-01	9.6	12.9
2019-11-01	10.0	12.6
2019-12-01	9.7	11.1
2020-01-01	13.7	14.4
2020-02-01	12.4	12.2
2020-03-01	10.5	13.7
2020-04-01	37.3	30.1
2020-05-01	31.4	28.9
2020-06-01	19.8	25.8
2020-07-01	17.6	20.0

#---------------------------

# Specify id.vars: the variables to keep but not split apart on
data_long <- melt(indicator, id.vars=c("date"))
colnames(data_long) <- c("date","Gender","Thousands")

# plotting data
data_long %>% ggplot() + 
  geom_line(mapping = aes(x=date,y=Thousands,color=Gender),size=1)  +
        
   labs(title = "Unemployment Rates _  18-19 Years.", 
       subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
       x="Monthly", y="Rate",
       caption = "Data source: FRED Federal Reserve.   Illustration by @JoeLongSanDiego")+
    theme_economist()

U.S. Employment During COVID19 by Education Attainment

Joe Long

August 2020

Unemployment Rate

Unemployment Rate - High School Graduates, No College, 18 to 19 years

Unemployment Rate - Less Than a High School Diploma, 25 Yrs. & Over (LNS14027659)