Labor Force Statistics including the National Unemployment Rate - LN

Data - BLS

Last observation: 2024 (N = 258146)

First observation: 1940 (N = 10)

Last data update: 02 août 2026, 08:53

Last compile: 04 sept. 2026, 00:48

ln.activity

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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-

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

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

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-

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

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

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-

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

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-

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 = ""))

Related Data

Data on employment

source	dataset	Title	Updated
bls	jt	NA	NA
bls	la	NA	NA
bls	ln	NA	NA
eurostat	nama_10_a10_e	Employment by A*10 industry breakdowns - nama_10_a10_e	2026-08-31
eurostat	nama_10_a64_e	National accounts employment data by industry (up to NACE A*64) - nama_10_a64_e	2026-08-04
eurostat	namq_10_a10_e	Employment A*10 industry breakdowns - namq_10_a10_e	2026-08-31
eurostat	une_rt_m	Unemployment by sex and age – monthly data - une_rt_m	2026-08-28
oecd	ALFS_EMP	Employment by activities and status (ALFS) - ALFS_EMP	2026-08-02
oecd	EPL_T	Strictness of employment protection – temporary contracts - EPL_T	2026-08-02
oecd	LFS_SEXAGE_I_R	LFS by sex and age - indicators - LFS_SEXAGE_I_R	2026-08-02
oecd	STLABOUR	Short-Term Labour Market Statistics - STLABOUR	2026-08-02