Labor Force Statistics including the National Unemployment Rate - LN

Data - BLS

Author

François Geerolf

Info

source dataset .html .RData
bls ln 2024-06-19 NA

Data on employment

source dataset .html .RData
bls jt 2024-11-12 NA
bls la 2024-06-19 NA
bls ln 2024-06-19 NA
eurostat nama_10_a10_e 2024-11-08 2024-11-09
eurostat nama_10_a64_e 2024-11-05 2024-10-08
eurostat namq_10_a10_e 2024-11-05 2024-10-08
eurostat une_rt_m 2024-11-05 2024-10-24
oecd ALFS_EMP 2024-04-16 2024-05-12
oecd EPL_T 2024-11-12 2023-12-10
oecd LFS_SEXAGE_I_R 2024-09-15 2024-04-15
oecd STLABOUR 2024-09-15 2024-06-30

LAST_DOWNLOAD

LAST_DOWNLOAD
2024-11-12

LAST_COMPILE

LAST_COMPILE
2024-11-12

Last

date Nobs
2024-10-01 19950

ln.activity

Code 
ln.data.1.AllData %>%
  left_join(ln.series, by = "series_id") %>%
  left_join(ln.activity, by = "activity_code") %>%
  group_by(activity_code, activity_text) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional
activity_code activity_text Nobs
0 N/A 8030958
3 Enrolled in School 156067
4 Enrolled in High School 31158
5 Enrolled in College 31221
6 Enrolled in College Full-time 31182
7 Enrolled in College Part-time 30846
8 Not Enrolled 294052

ln.ages

Code 
ln.data.1.AllData %>%
  left_join(ln.series, by = "series_id") %>%
  left_join(ln.ages, by = "ages_code") %>%
  group_by(ages_code, ages_text) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional

ln.born

Code 
ln.data.1.AllData %>%
  left_join(ln.series, by = "series_id") %>%
  left_join(ln.born, by = "born_code") %>%
  group_by(born_code, born_text) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional
born_code born_text Nobs
0 N/A 8532056
1 Native born 36714
2 Foreign born 36714

ln.class

Code 
ln.data.1.AllData %>%
  left_join(ln.series, by = "series_id") %>%
  left_join(ln.class, by = "class_code") %>%
  group_by(class_code, class_text) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional
class_code class_text Nobs
0 N/A 7859879
1 Wage and salary workers 217153
2 Private wage and salary workers 234598
3 Government wage and salary workers 75429
4 Federal wage and salary workers 3723
5 State wage and salary workers 3763
6 Local wage and salary workers 3763
8 Self-employed workers, unincorporated 103766
9 Unpaid family workers 81776
11 Nonagriculture government, self employed, and unpaid family worker (3, 8, and 9 above) 3443
12 Self-employed unincorporated, and unpaid family workers (8 and 9) 3936
14 Incorporated self-employed 2453
16 Wage and salary workers, excluding incorporated self employed 6188
17 Private wage and salary workers, excluding incorporated self employed 4424
20 NA 1190

ln.duration

Code 
ln.data.1.AllData %>%
  left_join(ln.series, by = "series_id") %>%
  left_join(ln.duration, by = "duration_code") %>%
  group_by(duration_code, duration_text) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional
duration_code duration_text Nobs
0 N/A 8115149
6 Less than 5 weeks 98411
18 15 weeks and over 99467
31 27 weeks and over 75038
58 52 weeks and over 28021
105 99 weeks and over 6286
106 5 to 10 weeks 2864
107 5 to 14 weeks 74816
108 11 to 14 weeks 2848
109 15 to 26 weeks 74651
110 27 to 51 weeks 27933

ln.education

Code 
ln.data.1.AllData %>%
  left_join(ln.series, by = "series_id") %>%
  left_join(ln.education, by = "education_code") %>%
  group_by(education_code, education_text) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional
education_code education_text Nobs
0 All educational levels 8240704
11 Less than a High School diploma 65947
19 High School graduates, no college 65975
20 Some college or associate degree 65355
21 Some college, no degree 23864
25 Associate degree 23862
40 Bachelor's degree and higher 77821
41 Bachelor's degree only 20018
45 Advanced degree 21938

ln.lfst

Code 
ln.data.1.AllData %>%
  left_join(ln.series, by = "series_id") %>%
  left_join(ln.lfst, by = "lfst_code") %>%
  group_by(lfst_code, lfst_text) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional

ln.hour

Code 
ln.data.1.AllData %>%
  left_join(ln.series, by = "series_id") %>%
  left_join(ln.hour, by = "hour_code") %>%
  group_by(hour_code, hour_text) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional
hour_code hour_text Nobs
0 N/A 8234248
1 1 to 34 hours 207416
2 1 to 4 hours 7695
6 5 to 14 hours 7695
10 15 to 29 hours 6979
14 30 to 34 hours 8411
16 35 hours and over 93314
17 35 to 39 hours 6979
20 40 hours 6979
21 41 hours and over 4831
23 41 to 48 hours 6979
27 49 to 59 hours 6979
29 60 hours and over 6979

ln.indy

Code 
ln.data.1.AllData %>%
  left_join(ln.series, by = "series_id") %>%
  left_join(ln.indy, by = "indy_code") %>%
  group_by(indy_code, indy_text) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional

ln.occupation

Code 
ln.data.1.AllData %>%
  left_join(ln.series, by = "series_id") %>%
  left_join(ln.occupation, by = "occupation_code") %>%
  group_by(occupation_code, occupation_text) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional

ln.orig

Code 
ln.data.1.AllData %>%
  left_join(ln.series, by = "series_id") %>%
  left_join(ln.orig, by = "orig_code") %>%
  group_by(orig_code, orig_text) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional
orig_code orig_text Nobs
0 All Origins 7656155
1 Hispanic or Latino 771886
2 Mexican 40737
6 Puerto Rican 39578
7 Cuban 39088
10 Non-Hispanic 28440
15 Central or South American 4392
20 Central American 4392
21 Salvadoran 1624
25 Other Central American (excludes Salvadoran) 4392
30 South American 4392
40 Other Hispanic or Latino 4392
41 Dominican 1624
45 Other Hispanic or Latino (excludes Dominican) 4392

ln.race

Code 
ln.data.1.AllData %>%
  left_join(ln.series, by = "series_id") %>%
  left_join(ln.race, by = "race_code") %>%
  group_by(race_code, race_text) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional
race_code race_text Nobs
0 All Races 5720368
1 White 1286531
3 Black or African American 1044875
4 Asian 532298
5 American Indian or Alaska Native 3722
6 Native Hawaiian or Other Pacific Islander 3314
7 Two or more races 3176
10 Asian Indian 1600
15 Chinese 1600
25 Filipino 1600
26 Japanese 1600
27 Korean 1600
28 Vietnamese 1600
30 Other Asian 1600

ln.seasonal

Code 
ln.data.1.AllData %>%
  left_join(ln.series, by = "series_id") %>%
  rename(seasonal_code = seasonal) %>%
  left_join(ln.seasonal, by = "seasonal_code") %>%
  group_by(seasonal_code, seasonal_text) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional
seasonal_code seasonal_text Nobs
S Seasonally Adjusted 581747
U Not Seasonally Adjusted 8023737

ln.sexs

Code 
ln.data.1.AllData %>%
  left_join(ln.series, by = "series_id") %>%
  left_join(ln.sexs, by = "sexs_code") %>%
  group_by(sexs_code, sexs_text) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional
sexs_code sexs_text Nobs
0 Both Sexes 3985561
1 Men 2324247
2 Women 2295676

ln.vets

Code 
ln.data.1.AllData %>%
  left_join(ln.series, by = "series_id") %>%
  left_join(ln.vets, by = "vets_code") %>%
  group_by(vets_code, vets_text) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional
vets_code vets_text Nobs
0 N/A 8056827
1 Veteran 144288
3 World War II or Korean War or Vietnam Era 39272
9 Gulf War Era 63975
12 Veterans who served in Gulf War Era 2 (whether or not they served in Era 1) 57611
13 Veterans who served in Gulf War Era 1 but not Gulf War Era 2 50973
16 Other Service Periods (may include peacetime) 48648
25 Nonveteran 143890

Longest Series

Code 
ln.series %>%
  arrange(begin_year) %>%
  head(100) %>%
  select(series_id, series_title, begin_year, end_year) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Employment / Population Ratio

All Series - lfst_code = 23

Code 
ln.data.1.AllData %>%
  left_join(ln.series, by = "series_id") %>%
  filter(lfst_code == 23) %>%
  group_by(series_id, series_title) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional

Men, Women, All

All

Code 
ln.data.1.AllData %>%
  left_join(ln.series, by = "series_id") %>%
  #filter(series_id %in% c("LNS12300001", "LNS12300000", "LNS12300002")) %>%
  left_join(ln.sexs, by = "sexs_code") %>%
  filter(lfst_code == 23,
         orig_code == 0,
         born_code == 0,
         mari_code == 0,
         race_code == 0,
         seasonal == "S",
         ages_code == 0,
         activity_code == 0,
         duration_code == 0) %>%
  month_to_date() %>%
  mutate(value = as.numeric(value)) %>%
  ggplot(.) + theme_minimal() + xlab("") + 
  ylab("Employment / Population Ratio") +
  geom_line(aes(x = date, y = value/100, color = sexs_text)) +
  geom_rect(data = nber_recessions %>%
              filter(Trough >= as.Date("1947-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) +
  scale_x_date(breaks = seq(1910, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*c(seq(0, 100, 10), seq(100, 500, 50)),
                     labels = percent_format(accuracy = 1, prefix = "")) +
  scale_color_manual(values = c("black", "darkblue", "purple")) +
  theme(legend.position = c(0.8, 0.2),
        legend.title = element_blank())

1980-

Code 
ln.data.1.AllData %>%
  left_join(ln.series, by = "series_id") %>%
  #filter(series_id %in% c("LNS12300001", "LNS12300000", "LNS12300002")) %>%
  left_join(ln.sexs, by = "sexs_code") %>%
  filter(lfst_code == 23,
         orig_code == 0,
         born_code == 0,
         mari_code == 0,
         race_code == 0,
         seasonal == "S",
         ages_code == 0,
         activity_code == 0,
         duration_code == 0) %>%
  month_to_date() %>%
  arrange(desc(date)) %>%
  filter(date >= as.Date("1980-01-01")) %>%
  mutate(value = as.numeric(value)) %>%
  ggplot(.) + theme_minimal() + xlab("") + 
  ylab("Employment / Population Ratio") +
  geom_line(aes(x = date, y = value/100, color = sexs_text)) +
  geom_rect(data = nber_recessions %>%
              filter(Trough >= as.Date("1980-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) +
  scale_x_date(breaks = seq(1910, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*c(seq(0, 100, 5), seq(100, 500, 50)),
                     labels = percent_format(accuracy = 1, prefix = "")) +
  scale_color_manual(values = c("black", "darkblue", "purple")) +
  theme(legend.position = c(0.8, 0.2),
        legend.title = element_blank())

Veterans, Non Veterans

Code 
ln.series %>%
  filter(lfst_code == 23,
         sexs_code == 1,
         race_code == 0,
         seasonal == "U",
         ages_code == 28,
         orig_code == 0,
         born_code == 0,
         education_code == 0,
         periodicity_code == "M") %>%
  left_join(ln.data.1.AllData, by = "series_id") %>%
  #filter(series_id %in% c("LNS12300001", "LNS12300049", "LNS12300061")) %>%
  left_join(ln.vets, by = "vets_code") %>%
  month_to_date() %>%
  mutate(value = as.numeric(value)) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Men Employment / Population Ratio") +
  geom_line(aes(x = date, y = value/100, color = vets_text)) +
  geom_rect(data = nber_recessions %>%
              filter(Trough >= as.Date("1947-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) +
  
  theme(legend.position = c(0.2, 0.2),
        legend.title = element_blank()) +
  scale_x_date(breaks = seq(1910, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*c(seq(0, 100, 5), seq(100, 500, 50)),
                     labels = percent_format(accuracy = 1, prefix = ""))

By age, Men

Seasonal

All

Code 
ln.series %>%
  filter(lfst_code == 23,
         sexs_code == 1,
         race_code == 0,
         mari_code == 0,
         seasonal == "S",
         ages_code %in% c(0, 28, 33, 22),
         periodicity_code == "M") %>%
  left_join(ln.data.1.AllData, by = "series_id") %>%
  #filter(series_id %in% c("LNS12300001", "LNS12300049", "LNS12300061")) %>%
  left_join(ln.ages, by = "ages_code") %>%
  month_to_date() %>%
  mutate(value = as.numeric(value)) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Men Employment / Population Ratio") +
  geom_line(aes(x = date, y = value/100, color = ages_text)) +
  geom_rect(data = nber_recessions %>%
              filter(Trough >= as.Date("1947-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) +
  theme(legend.position = c(0.2, 0.2),
        legend.title = element_blank()) +
  scale_x_date(breaks = seq(1910, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*c(seq(0, 100, 5), seq(100, 500, 50)),
                     labels = percent_format(accuracy = 1, prefix = ""))

1980-

Code 
ln.series %>%
  filter(lfst_code == 23,
         sexs_code == 1,
         race_code == 0,
         mari_code == 0,
         seasonal == "S",
         ages_code %in% c(0, 28, 33, 22),
         periodicity_code == "M") %>%
  left_join(ln.data.1.AllData, by = "series_id") %>%
  #filter(series_id %in% c("LNS12300001", "LNS12300049", "LNS12300061")) %>%
  left_join(ln.ages, by = "ages_code") %>%
  month_to_date() %>%
  filter(date >= as.Date("1980-01-01")) %>%
  mutate(value = as.numeric(value)) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Men Employment / Population Ratio") +
  geom_line(aes(x = date, y = value/100, color = ages_text)) +
  geom_rect(data = nber_recessions %>%
              filter(Trough >= as.Date("1980-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) +
  theme(legend.position = c(0.2, 0.2),
        legend.title = element_blank()) +
  scale_x_date(breaks = seq(1910, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*c(seq(0, 100, 5), seq(100, 500, 50)),
                     labels = percent_format(accuracy = 1, prefix = ""))

Seasonal

Code 
ln.series %>%
  filter(lfst_code == 23,
         sexs_code == 1,
         race_code == 0,
         seasonal == "S",
         ages_code %in% c(33),
         periodicity_code == "M") %>%
  left_join(ln.data.1.AllData, by = "series_id") %>%
  #filter(series_id %in% c("LNS12300001", "LNS12300049", "LNS12300061")) %>%
  left_join(ln.ages, by = "ages_code") %>%
  month_to_date() %>%
  mutate(value = as.numeric(value)) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("25-54 Men Employment / Population Ratio") +
  geom_line(aes(x = date, y = value/100)) +
  geom_rect(data = nber_recessions %>%
              filter(Trough >= as.Date("1947-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) +
  theme(legend.position = c(0.2, 0.2),
        legend.title = element_blank()) +
  scale_x_date(breaks = seq(1910, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*c(seq(0, 100, 5), seq(100, 500, 50)),
                     labels = percent_format(accuracy = 1, prefix = ""))

Unseasonal

Code 
ln.series %>%
  filter(lfst_code == 23,
         sexs_code == 1,
         race_code == 0,
         seasonal == "U",
         ages_code %in% c(0, 28, 33),
         orig_code == 0,
         mari_code == 0,
         born_code == 0,
         vets_code == 0,
         education_code == 0,
         periodicity_code == "M") %>%
  left_join(ln.data.1.AllData, by = "series_id") %>%
  #filter(series_id %in% c("LNS12300001", "LNS12300049", "LNS12300061")) %>%
  left_join(ln.ages, by = "ages_code") %>%
  month_to_date() %>%
  arrange(desc(date)) %>%
  mutate(value = as.numeric(value)) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Men Employment / Population Ratio") +
  geom_line(aes(x = date, y = value/100, color = ages_text)) +
  geom_rect(data = nber_recessions %>%
              filter(Trough >= as.Date("1947-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) +
  theme(legend.position = c(0.2, 0.2),
        legend.title = element_blank()) +
  scale_x_date(breaks = seq(1910, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*c(seq(0, 100, 5), seq(100, 500, 50)),
                     labels = percent_format(accuracy = 1, prefix = ""))

Men, Women

1994-

Code 
ln.series %>%
  filter(lfst_code == 23,
         race_code == 0,
         seasonal == "S",
         ages_code %in% c(33),
         periodicity_code == "M") %>%
  left_join(ln.data.1.AllData, by = "series_id") %>%
  #filter(series_id %in% c("LNS12300001", "LNS12300049", "LNS12300061")) %>%
  left_join(ln.sexs, by = "sexs_code") %>%
  month_to_date() %>%
  filter(date >= as.Date("1994-01-01")) %>%
  mutate(value = as.numeric(value)) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("25-54 Men Employment / Population Ratio") +
  geom_line(aes(x = date, y = value/100, color = sexs_text)) +
  geom_rect(data = nber_recessions %>%
              filter(Trough >= as.Date("1994-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) +
  scale_color_manual(values = c("black", "darkblue", "purple")) +
  theme(legend.position = c(0.8, 0.2),
        legend.title = element_blank()) +
  scale_x_date(breaks = seq(1910, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*c(seq(0, 100, 5), seq(100, 500, 50)),
                     labels = percent_format(accuracy = 1, prefix = ""))

All

Code 
ln.series %>%
  filter(lfst_code == 23,
         race_code == 0,
         seasonal == "S",
         ages_code %in% c(33),
         periodicity_code == "M") %>%
  left_join(ln.data.1.AllData, by = "series_id") %>%
  #filter(series_id %in% c("LNS12300001", "LNS12300049", "LNS12300061")) %>%
  left_join(ln.sexs, by = "sexs_code") %>%
  month_to_date() %>%
  mutate(value = as.numeric(value)) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("25-54 Men Employment / Population Ratio") +
  geom_line(aes(x = date, y = value/100, color = sexs_text)) +
  geom_rect(data = nber_recessions %>%
              filter(Trough >= as.Date("1947-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) +
  scale_color_manual(values = c("black", "darkblue", "purple")) +
  theme(legend.position = c(0.8, 0.2),
        legend.title = element_blank()) +
  scale_x_date(breaks = seq(1910, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*c(seq(0, 100, 5), seq(100, 500, 50)),
                     labels = percent_format(accuracy = 1, prefix = ""))

2009-

Code 
ln.series %>%
  filter(lfst_code == 23,
         race_code == 0,
         seasonal == "S",
         ages_code %in% c(33),
         periodicity_code == "M") %>%
  left_join(ln.data.1.AllData, by = "series_id") %>%
  #filter(series_id %in% c("LNS12300001", "LNS12300049", "LNS12300061")) %>%
  left_join(ln.sexs, by = "sexs_code") %>%
  month_to_date() %>%
  filter(date >= as.Date("2000-01-01")) %>%
  mutate(value = as.numeric(value)) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("25-54 Men Employment / Population Ratio") +
  geom_line(aes(x = date, y = value/100, color = sexs_text)) +
  geom_rect(data = nber_recessions %>%
              filter(Trough >= as.Date("2000-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) +
  scale_color_manual(values = c("black", "darkblue", "purple")) +
  theme(legend.position = c(0.15, 0.2),
        legend.title = element_blank()) +
  scale_x_date(breaks = seq(1910, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*c(seq(0, 100, 5), seq(100, 500, 50)),
                     labels = percent_format(accuracy = 1, prefix = ""))