Consumer Expenditure Survey - CX

Data - BLS

Author

François Geerolf


First choose demographics:

Then choose item:

Last observation: 2024 (N = 96030)

First observation: 1984 (N = 10358)

Last data update: 05 sept. 2026, 23:08

Last compile: 05 sept. 2026, 23:15

Demographics

cx.demographics

Code 
cx.demographics %>%
  {if (is_html_output()) print_table(.) else .}
demographics_code demographics_text display_level selectable sort_sequence
LB01 Quintiles of income before taxes 0 T 100
LB02 Income before taxes 0 T 200
LB04 Age of reference person 0 T 300
LB05 Size of consumer unit 0 T 400
LB06 Composition of consumer unit 0 T 400
LB07 Number of earners 0 T 500
LB09 Race of reference person 0 T 700
LB10 Hispanic or Latino origin of reference person 0 T 800
LB11 Region of residence 0 T 900
LB12 Occupation of reference person 0 T 1000
LB13 Education of reference person 0 T 1100
LB14 Highest education level of any member 0 T 1200
LB15 Deciles of income before taxes 0 T 1300
LB16 Generation of reference person 0 T 1600
LB17 Housing tenure 0 T 1700
LB18 Type of area 0 T 1800
LB19 Type of area 0 T 1900
LB20 Population size of area of residence 0 T 2000
LB21 Selected age of reference person 0 T 2100

cx.characteristics

Code 
cx.characteristics %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Item

cx.category

Code 
cx.category %>%
  {if (is_html_output()) print_table(.) else .}
category_code category_text display_level selectable sort_sequence
ADDENDA Assets and liabilities, and other financial info 0 T 300
CUCHARS Consumer Characteristics 0 T 400
EXPEND Expenditures 0 T 100
INCOME Income and Taxes 0 T 200

cx.subcategory

Code 
cx.subcategory %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

cx.item

Code 
cx.item %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Income Example

By Quintile

Code 
cx.series |>
  filter(demographics_code == "LB01",
         item_code %in% c("INCAFTTX", "HOUSING", "INSPENSN", "TOTALEXP", "OWNMORTG")) |>
  inner_join(cx.data.1.AllData |>
              filter(year == 2016), by = "series_id") |>
  inner_join(cx.characteristics |>
              select(1, 2, 3), by = c("characteristics_code", "demographics_code")) |>
  select(characteristics_code, characteristics_text, item_code, value) |>
  spread(item_code, value) %>%
  {if (is_html_output()) print_table(.) else .}
characteristics_code characteristics_text HOUSING INCAFTTX INSPENSN OWNMORTG TOTALEXP
01 All Consumer Units 18886 64175 6831 2889 57311
02 Lowest 20 percent income quintile 10267 11832 645 545 25138
03 Second 20 percent income quintile 13552 29423 1766 977 36770
04 Third 20 percent income quintile 16315 47681 4227 1910 47664
05 Fourth 20 percent income quintile 20687 75065 8262 3707 64910
06 Highest 20 percent income quintile 33653 157215 19302 7325 112221

By Decile

Code 
cx.series |>
  filter(demographics_code == "LB15",
         item_code %in% c("INCAFTTX", "HOUSING", "INSPENSN", "TOTALEXP", "OWNMORTG")) |>
  inner_join(cx.data.1.AllData |>
              filter(year == 2016), by = "series_id") |>
  inner_join(cx.characteristics |>
              select(1, 2, 3), by = c("characteristics_code", "demographics_code")) |>
  select(characteristics_code, characteristics_text, item_code, value) |>
  spread(item_code, value) %>%
  {if (is_html_output()) print_table(.) else .}
characteristics_code characteristics_text HOUSING INCAFTTX INSPENSN OWNMORTG TOTALEXP
01 All consumer units 18886 64175 6831 2889 57311
02 Lowest 10 percent 9567 6774 644 512 23588
03 Second 10 percent 10961 16841 645 579 26675
04 Third 10 percent 12829 25423 1411 760 34221
05 Fourth 10 percent 14271 33404 2120 1193 39308
06 Fifth 10 percent 15511 42410 3290 1547 43975
07 Sixth 10 percent 17119 52949 5164 2272 51351
08 Seventh 10 percent 19285 66676 7058 3198 59395
09 Eighth 10 percent 22085 83424 9461 4215 70411
10 Ninth 10 percent 26719 108743 13278 5564 87432
11 Highest 10 percent 40547 205391 25290 9075 136873

By Income

Code 
cx.series |>
  filter(demographics_code == "LB02",
         item_code %in% c("INCAFTTX", "HOUSING", "INSPENSN", "TOTALEXP", "OWNMORTG")) |>
  inner_join(cx.data.1.AllData |>
              filter(year == 2016), by = "series_id") |>
  inner_join(cx.characteristics |>
              select(1, 2, 3), by = c("characteristics_code", "demographics_code")) |>
  select(characteristics_code, characteristics_text, item_code, value) |>
  spread(item_code, value) |>
  filter(!is.na(INCAFTTX)) %>%
  {if (is_html_output()) print_table(.) else .}
characteristics_code characteristics_text HOUSING INCAFTTX INSPENSN OWNMORTG TOTALEXP
01 All Consumer Units 18886 64175 6831 2889 57311
07 $30,000 to $39,999 before tax income 14533 34381 2222 1274 40144
08 $40,000 to $49,999 before tax income 15575 43047 3400 1554 44150
09 $50,000 to $69,999 before tax income 17331 54782 5307 2451 52088
18 Less than $15,000 9698 8732 614 551 23657
19 $15,000 to $29,999 12268 23012 1161 678 31913
20 $70,000 to $99,999 20564 74744 8270 3632 65086
21 $100,000 to $149,999 26003 103504 12543 5299 84154
22 $150,000 to $199,999 33319 140130 17609 7391 109516
23 $200,000 and more 46076 254890 31079 10333 158896

Example: All Items

Code 
cx.series |>
  filter(demographics_code == "LB15",
         characteristics_code == "01") |>
  inner_join(cx.data.1.AllData |>
              filter(year == 2016), by = "series_id") |>
  inner_join(cx.item |>
              select(2, 3), by = c("item_code")) |>
  select(3, 4, 5, item_text, value) |>
  arrange(-value) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Consumption and Saving (Econ 102)

$ Saving

Code 
cx.series |>
  filter(demographics_code == "LB15",
         item_code %in% c("INCAFTTX", "HOUSING", "PENSIONS", "TOTALEXP", "OWNMORTG")) |>
  inner_join(cx.data.1.AllData |>
              filter(year == 2016), by = "series_id") |>
  inner_join(cx.characteristics |>
              select(1, 2, 3), by = c("characteristics_code", "demographics_code")) |>
  select(characteristics_text, item_code, value) |>
  spread(item_code, value) |>
  transmute(characteristics_text,
            INCAFTTX,
            `Saving` = INCAFTTX - TOTALEXP,
            `Total Saving (incl. Pensions)` = INCAFTTX - TOTALEXP + PENSIONS) |>
  gather(variable, value, -1, -2) |>
  ggplot() + geom_line(aes(x = INCAFTTX/1000, y = value/1000, color = variable)) + 
  theme_minimal() + xlab("Income after taxes") + ylab("Saving") +
  theme(legend.position = c(0.45, 0.9),
        legend.title = element_blank()) +
  scale_x_continuous(breaks = seq(0, 200, 20),
                     labels = dollar_format(suffix = "K", accuracy = 1)) +
  scale_color_manual(values = viridis(3)[1:2]) +
  scale_y_continuous(breaks = seq(-100, 200, 10),
                     labels = dollar_format(suffix = "K", accuracy = 1))

Saving Rate

Code 
cx.series |>
  filter(demographics_code == "LB15",
         item_code %in% c("INCAFTTX", "HOUSING", "PENSIONS", "TOTALEXP", "OWNMORTG")) |>
  inner_join(cx.data.1.AllData |>
              filter(year == 2016), by = "series_id") |>
  inner_join(cx.characteristics |>
              select(1, 2, 3), by = c("characteristics_code", "demographics_code")) |>
  select(characteristics_text, item_code, value) |>
  spread(item_code, value) |>
  transmute(characteristics_text,
            INCAFTTX,
            `Saving Rate` = (INCAFTTX - TOTALEXP)/INCAFTTX,
            `Total Saving Rate (incl. Pensions)` = (INCAFTTX - TOTALEXP + PENSIONS)/INCAFTTX) |>
  gather(variable, value, -1, -2) |>
  ggplot() + geom_line(aes(x = INCAFTTX/1000, y = value, color = variable)) + 
  theme_minimal() + xlab("Income after taxes") + ylab("Saving Rate") +
  theme(legend.position = c(0.65, 0.3),
        legend.title = element_blank()) +
  scale_x_continuous(breaks = seq(0, 200, 20),
                     labels = dollar_format(suffix = "K", accuracy = 1)) +
  scale_color_manual(values = viridis(3)[1:2]) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 20),
                     labels = percent_format(accuracy = 1))

$ Saving (Log)

Code 
cx.series |>
  filter(demographics_code == "LB15",
         item_code %in% c("INCAFTTX", "HOUSING", "PENSIONS", "TOTALEXP", "OWNMORTG")) |>
  inner_join(cx.data.1.AllData |>
              filter(year == 2016), by = "series_id") |>
  inner_join(cx.characteristics |>
              select(1, 2, 3), by = c("characteristics_code", "demographics_code")) |>
  select(characteristics_text, item_code, value) |>
  spread(item_code, value) |>
  transmute(characteristics_text,
            INCAFTTX,
            `Saving` = INCAFTTX - TOTALEXP,
            `Total Saving (incl. Pensions)` = INCAFTTX - TOTALEXP + PENSIONS) |>
  gather(variable, value, -1, -2) |>
  ggplot() + geom_line(aes(x = INCAFTTX/1000, y = value/1000, color = variable)) + 
  theme_minimal() + xlab("Income after taxes") + ylab("Saving") +
  theme(legend.position = c(0.45, 0.9),
        legend.title = element_blank()) +
  scale_x_log10(breaks = c(10, 20, 40, 80, 100, 160, 200),
                     labels = dollar_format(suffix = "K", accuracy = 1)) +
  scale_color_manual(values = viridis(3)[1:2]) +
  scale_y_continuous(breaks = seq(-100, 200, 10),
                     labels = dollar_format(suffix = "K", accuracy = 1))

Saving Rate (Log)

Code 
cx.series |>
  filter(demographics_code == "LB15",
         item_code %in% c("INCAFTTX", "HOUSING", "PENSIONS", "TOTALEXP", "OWNMORTG")) |>
  inner_join(cx.data.1.AllData |>
              filter(year == 2016), by = "series_id") |>
  inner_join(cx.characteristics |>
              select(1, 2, 3), by = c("characteristics_code", "demographics_code")) |>
  select(characteristics_text, item_code, value) |>
  spread(item_code, value) |>
  transmute(characteristics_text,
            INCAFTTX,
            `Saving Rate` = (INCAFTTX - TOTALEXP)/INCAFTTX,
            `Total Saving Rate (incl. Pensions)` = (INCAFTTX - TOTALEXP + PENSIONS)/INCAFTTX) |>
  gather(variable, value, -1, -2) |>
  ggplot() + geom_line(aes(x = INCAFTTX/1000, y = value, color = variable)) + 
  theme_minimal() + xlab("Income after taxes") + ylab("Saving Rate") +
  theme(legend.position = c(0.65, 0.3),
        legend.title = element_blank()) +
  scale_x_log10(breaks = c(10, 20, 40, 80, 100, 160, 200),
                     labels = dollar_format(suffix = "K", accuracy = 1)) +
  scale_color_manual(values = viridis(3)[1:2]) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 20),
                     labels = percent_format(accuracy = 1))

Saving Rate (Log) > 30K

Code 
cx.series |>
  filter(demographics_code == "LB15",
         item_code %in% c("INCAFTTX", "HOUSING", "PENSIONS", "TOTALEXP", "OWNMORTG")) |>
  inner_join(cx.data.1.AllData |>
              filter(year == 2016), by = "series_id") |>
  inner_join(cx.characteristics |>
              select(1, 2, 3), by = c("characteristics_code", "demographics_code")) |>
  select(characteristics_text, item_code, value) |>
  spread(item_code, value) |>
  transmute(characteristics_text,
            INCAFTTX,
            `Saving Rate` = (INCAFTTX - TOTALEXP)/INCAFTTX,
            `Total Saving Rate (incl. Pensions)` = (INCAFTTX - TOTALEXP + PENSIONS)/INCAFTTX) |>
  gather(variable, value, -1, -2) |>
  filter(INCAFTTX > 30000) |>
  ggplot() + geom_line(aes(x = INCAFTTX/1000, y = value, color = variable)) + 
  theme_minimal() + xlab("Income after taxes") + ylab("Saving Rate") +
  theme(legend.position = c(0.65, 0.3),
        legend.title = element_blank()) +
  scale_x_log10(breaks = c(10, 20, 30, 40, 50, 60, 70, 80, 100, 120, 140, 160, 200),
                     labels = dollar_format(suffix = "K", accuracy = 1)) +
  scale_color_manual(values = viridis(3)[1:2]) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 10),
                     labels = percent_format(accuracy = 1))

$ Consumption

Code 
cx.series |>
  filter(demographics_code == "LB15",
         item_code %in% c("INCAFTTX", "HOUSING", "PENSIONS", "TOTALEXP", "OWNMORTG")) |>
  inner_join(cx.data.1.AllData |>
              filter(year == 2016), by = "series_id") |>
  inner_join(cx.characteristics |>
              select(1, 2, 3), by = c("characteristics_code", "demographics_code")) |>
  select(characteristics_text, item_code, value) |>
  spread(item_code, value) |>
  transmute(characteristics_text,
            INCAFTTX,
            `Total Expenditures (incl. Pensions)` = TOTALEXP,
            `Total Expenditures` = TOTALEXP - PENSIONS) |>
  gather(variable, value, -1, -2) |>
  ggplot() + geom_line(aes(x = INCAFTTX/1000, y = value/1000, color = variable)) + 
  theme_minimal() + xlab("Income after taxes") + ylab("Consumption") +
  theme(legend.position = c(0.45, 0.9),
        legend.title = element_blank()) +
  scale_x_continuous(breaks = seq(0, 200, 20),
                     labels = dollar_format(suffix = "K", accuracy = 1)) +
  scale_color_manual(values = viridis(3)[1:2]) +
  scale_y_continuous(breaks = seq(-100, 200, 10),
                     labels = dollar_format(suffix = "K", accuracy = 1))

Consumption Rate

Code 
cx.series |>
  filter(demographics_code == "LB15",
         item_code %in% c("INCAFTTX", "HOUSING", "PENSIONS", "TOTALEXP", "OWNMORTG")) |>
  inner_join(cx.data.1.AllData |>
              filter(year == 2016), by = "series_id") |>
  inner_join(cx.characteristics |>
              select(1, 2, 3), by = c("characteristics_code", "demographics_code")) |>
  select(characteristics_text, item_code, value) |>
  spread(item_code, value) |>
  transmute(characteristics_text,
            INCAFTTX,
            `Consumption Rate` = (TOTALEXP)/INCAFTTX,
            `Consumption Rate (incl. Pensions)` = (TOTALEXP - PENSIONS)/INCAFTTX) |>
  gather(variable, value, -1, -2) |>
  ggplot() + geom_line(aes(x = INCAFTTX/1000, y = value, color = variable)) + 
  theme_minimal() + xlab("Income after taxes") + ylab("Consumption Rate") +
  theme(legend.position = c(0.65, 0.3),
        legend.title = element_blank()) +
  scale_x_continuous(breaks = seq(0, 200, 20),
                     labels = dollar_format(suffix = "K", accuracy = 1)) +
  scale_color_manual(values = viridis(3)[1:2]) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 20),
                     labels = percent_format(accuracy = 1))

$ Consumption (Log)

Code 
cx.series |>
  filter(demographics_code == "LB15",
         item_code %in% c("INCAFTTX", "HOUSING", "PENSIONS", "TOTALEXP", "OWNMORTG")) |>
  inner_join(cx.data.1.AllData |>
              filter(year == 2016), by = "series_id") |>
  inner_join(cx.characteristics |>
              select(1, 2, 3), by = c("characteristics_code", "demographics_code")) |>
  select(characteristics_text, item_code, value) |>
  spread(item_code, value) |>
  transmute(characteristics_text,
            INCAFTTX,
            `Total Expenditures (incl. Pensions)` = TOTALEXP,
            `Total Expenditures` = TOTALEXP - PENSIONS) |>
  gather(variable, value, -1, -2) |>
  ggplot() + geom_line(aes(x = INCAFTTX/1000, y = value/1000, color = variable)) + 
  theme_minimal() + xlab("Income after taxes") + ylab("Consumption") +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank()) +
  scale_x_continuous(breaks = seq(0, 200, 20),
                     labels = dollar_format(suffix = "K", accuracy = 1)) +
  scale_color_manual(values = viridis(3)[1:2]) +
  scale_y_log10(breaks = seq(-100, 200, 10),
                     labels = dollar_format(suffix = "K", accuracy = 1))

Consumption Rate (Log)

Code 
cx.series |>
  filter(demographics_code == "LB15",
         item_code %in% c("INCAFTTX", "HOUSING", "PENSIONS", "TOTALEXP", "OWNMORTG")) |>
  inner_join(cx.data.1.AllData |>
              filter(year == 2016), by = "series_id") |>
  inner_join(cx.characteristics |>
              select(1, 2, 3), by = c("characteristics_code", "demographics_code")) |>
  select(characteristics_text, item_code, value) |>
  spread(item_code, value) |>
  transmute(characteristics_text,
            INCAFTTX,
            `Consumption Rate` = (TOTALEXP)/INCAFTTX,
            `Consumption Rate (incl. Pensions)` = (TOTALEXP - PENSIONS)/INCAFTTX) |>
  gather(variable, value, -1, -2) |>
  ggplot() + geom_line(aes(x = INCAFTTX/1000, y = value, color = variable)) + 
  theme_minimal() + xlab("Income after taxes") + ylab("Consumption Rate") +
  theme(legend.position = c(0.65, 0.8),
        legend.title = element_blank()) +
  scale_x_log10(breaks = c(10, 20, 40, 80, 100, 160, 200),
                     labels = dollar_format(suffix = "K", accuracy = 1)) +
  scale_color_manual(values = viridis(3)[1:2]) +
  scale_y_continuous(breaks = 0.01*seq(-500, 600, 20),
                     labels = percent_format(accuracy = 1))

Expenditures

Main: Housing, Food, Transportation

Code 
cx.series |>
  filter(demographics_code == "LB15",
         item_code %in% c("TOTALEXP", "HOUSING", "FOODTOTL", "TRANS", "INCAFTTX")) |>
  inner_join(cx.data.1.AllData |>
              filter(year == 2016), by = "series_id") |>
  inner_join(cx.characteristics |>
              select(1, 2, 3), by = c("characteristics_code", "demographics_code")) |>
  select(characteristics_text, item_code, value) |>
  spread(item_code, value) |>
  transmute(characteristics_text,
            INCAFTTX,
            `Housing (% of Expenditure)` = HOUSING/TOTALEXP,
            `Food (% of Expenditure)` = FOODTOTL/TOTALEXP,
            `Transportation (% of Expenditure)` = TRANS/TOTALEXP) |>
  gather(variable, value, -1, -2) |>
  filter(INCAFTTX > 20000) |>
  ggplot() + geom_line(aes(x = INCAFTTX/1000, y = value, color = variable)) + 
  theme_minimal() + xlab("Income after taxes") + ylab("% of Expenditure") +
  theme(legend.position = c(0.65, 0.9),
        legend.title = element_blank()) +
  scale_x_continuous(breaks = seq(0, 200, 20),
                     labels = dollar_format(suffix = "K", accuracy = 1)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 5),
                     labels = percent_format(accuracy = 1),
                     limits = c(0, 0.5))

Housing Rate

Code 
cx.series |>
  filter(demographics_code == "LB15",
         item_code %in% c("INCAFTTX", "HOUSING", "PENSIONS", "TOTALEXP", "OWNMORTG")) |>
  inner_join(cx.data.1.AllData |>
              filter(year == 2016), by = "series_id") |>
  inner_join(cx.characteristics |>
              select(1, 2, 3), by = c("characteristics_code", "demographics_code")) |>
  select(characteristics_text, item_code, value) |>
  spread(item_code, value) |>
  transmute(characteristics_text,
            INCAFTTX,
            `Housing (% of Expenditure)` = HOUSING/TOTALEXP,
            `Housing (% of Income)` = HOUSING/INCAFTTX) |>
  gather(variable, value, -1, -2) |>
  filter(INCAFTTX > 20000) |>
  ggplot() + geom_line(aes(x = INCAFTTX/1000, y = value, color = variable)) + 
  theme_minimal() + xlab("Income after taxes") + ylab("Housing Consumption (%)") +
  theme(legend.position = c(0.65, 0.8),
        legend.title = element_blank()) +
  scale_x_continuous(breaks = seq(0, 200, 20),
                     labels = dollar_format(suffix = "K", accuracy = 1)) +
  scale_color_manual(values = viridis(3)[1:2]) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 5),
                     labels = percent_format(accuracy = 1))