Race/Ethnicity Categories in ACS Tables

Preface

The American Community Survey API returns data in a very terse format. Each response contains four parts: geographic information, a table code, a row number, and a value. The geographic information varies by the level of detail one asks for. The value is a number that may be a population size, number of households, an income in dollars, a percentage, or several other quantities. The table code and row number are what determine the exact meaning of the value.

Generally speaking, all of the values in a table will be reporting the same kind of information. Again, the information might be counts of people or households, incomes or costs in dollars, hours commuting or working, or a percentage of just about anything. The row numbers then let you know what group of people the information is for. The first row always has a value for the entire population of a geographic area. Subsequent rows may have information about a very specific subgroup, or may contain a summary value for a combination of subgroups. For example, in a sex-and-age table, you might find the value for all men and boys (males) in row 2, then the value for boys under 5 in row 3. In short, a lot of information is packed into the two fields of table name and row number.

While packing information is efficient for storing and transmitting data, it means that users of Census information must unpack things in order to be clear. The hercacstables package has many tools and glossary tables that should make it easier and more convenient to unpack Census data.

Racial and ethnic categories in the American Community Survey

This vignette describes some features of hercacstables that help with the common and repetitive chores of unpacking racial and ethnic data from Census API responses. The Census has several ways that it reports by racial and ethnic identity. They do not always all agree or line up. That makes sense because the concepts of race and ethnicity are slippery, changing across place and time.

Broad strokes by the Census

The first racial/ethnic system that we will discuss involves ten categories. There are seven categories of race, one of ethnicity, one that combines both, and an “All” category: “Total”, “White alone”, “Black or African American alone”, “American Indian and Alaska Native alone”, “Asian alone”, “Native Hawaiian and Other Pacific Islander alone”, “Some Other Race alone”, “Two or More Races”, “White alone, not Hispanic or Latino”, and “Hispanic or Latino”. This is a very coarse and unsophisticated way of characterizing identities. Nevertheless, it occurs throughout the American Community Survey.

Subtable categories of race and ethnicity

There are 57 separate sets of tables that subdivide their information according to this ten-category scheme. Each identity is designated by a suffix at the end of a table’s name. In other words, all of the values from a table with one of these suffixes in its name will pertain to people of just one racial/ethnic category.

Since the case is so common, hercacstables describes it with the glossary table hercacstables::RACE_ETHNICITY_SUBTABLES. It maps from the suffix in a table’s name to the racial/ethnic category the table describes.

Census Race	Suffix
TOTAL
WHITE ALONE	A
BLACK OR AFRICAN AMERICAN ALONE	B
AMERICAN INDIAN AND ALASKA NATIVE ALONE	C
ASIAN ALONE	D
NATIVE HAWAIIAN AND OTHER PACIFIC ISLANDER ALONE	E
SOME OTHER RACE ALONE	F
TWO OR MORE RACES	G
WHITE ALONE, NOT HISPANIC OR LATINO	H
HISPANIC OR LATINO	I

Tables with “race” in their description

There are also 14 tables that have the word “RACE” in their description.

Group	Description	rows
B02001	Race	20
B02003	Detailed Race	71
B02008	White Alone or in Combination With One or More Other Races	2
B02009	Black or African American Alone or in Combination With One or More Other Races	2
B02010	American Indian and Alaska Native Alone or in Combination With One or More Other Races	2
B02011	Asian Alone or in Combination With One or More Other Races	2
B02012	Native Hawaiian and Other Pacific Islander Alone or in Combination With One or More Other Races	2
B02013	Some Other Race Alone or in Combination With One or More Other Races	2
B03002	Hispanic or Latino Origin by Race	42
B25006	Race of Householder	20
B98013	Total Population Coverage Rate by Weighting Race and Hispanic or Latino Groups	14
B99021	Allocation of Race	6
C02003	Detailed Race	42
C03002	Hispanic or Latino Origin by Race	12

They seem to fall into four categories. There are two tables with 10 rows each, B02001 and B25006. These are probably convenience tables that pull data from other race-specific subtables. Six tables, B02008 through B020013, have only one row each. These seem to report inclusive counts, combining people who claim a racial identity either as their sole identity or in combination with another. One table, B03002, has 21 rows. This table appears to report detailed information about Hispanic ethnicity and specific racial identities. Tables B98013 and B99021 have descriptions that suggest that they report methodological details. Each of these categories of table deserves a little more discussion.

Convenience tables

Tables B02001 and B25006 deal with counts of individuals and households, respectively. They seem to be redundant, showing the first rows of the tables in the B01001 and B11001, respectively.

We can use hercacstables to check that.

Map meaning to Census Variables

The first step is to make a table that connects the Census’s opaque variable names to the real-world meanings that we are interested in. For example, tables B01001* and B02001 deal with counts of people, while tables B11001* and B25006 deal with counts of households. Similarly, the racial identity being counted is defined either by the subtable suffix or the row number. Finally, we are interested in whether the data come from, a subtable or a convenience table. We can lay that all out in a way that “maps” from the Census variable to the real-world meaning.

convenience_check_variables <- tibble::tribble(
    ~ Table,  ~ Suffix,   ~ Index, ~ Population, ~ Race,
    "B01001", "A",              1, "People",     "White",
    "B01001", "B",              1, "People",     "Black",
    "B02001", "",               2, "People",     "White",
    "B02001", "",               3, "People",     "Black",
    "B11001", "A",              1, "Households", "White",
    "B11001", "B",              1, "Households", "Black",
    "B25006", "",               2, "Households", "White",
    "B25006", "",               3, "Households", "Black"
) |>
    dplyr::mutate(
        Group = paste0(.data$Table, .data$Suffix),
        Variable = hercacstables::build_api_variable(group_code = .data$Table,
                                                     race_code = .data$Suffix,
                                                     item_number = .data$Index),
        `Table Type` = dplyr::if_else(nchar(.data$Suffix) > 0,
                                      "Subtable",
                                      "Convenience")
    )

Table	Suffix	Index	Population	Race	Group	Variable	Table Type
B01001	A	1	People	White	B01001A	B01001A_001E	Subtable
B01001	B	1	People	Black	B01001B	B01001B_001E	Subtable
B02001		2	People	White	B02001	B02001_002E	Convenience
B02001		3	People	Black	B02001	B02001_003E	Convenience
B11001	A	1	Households	White	B11001A	B11001A_001E	Subtable
B11001	B	1	Households	Black	B11001B	B11001B_001E	Subtable
B25006		2	Households	White	B25006	B25006_002E	Convenience
B25006		3	Households	Black	B25006	B25006_003E	Convenience

Fetch the raw data

With our variables defined, we can fetch the data from the Census API. Notice that, since this is an API call, the code block is set to cache its results. API calls are much slower than local functions, so it is usually a good idea to isolate them and run them as few times as possible.

LATEST_YEAR <- hercacstables::most_recent_vintage("acs", "acs1")

convenience_check_values_raw <- hercacstables::fetch_data(
    convenience_check_variables$Variable,
    year = LATEST_YEAR,           # the most recent one available at the time of writing
    for_geo = "us",               # the entire nation
    for_items = "*",              # all nation-level geographies
    survey_type = "acs",          # as opposed to, e.g. "dec," for Decennial survey data
    table_or_survey_code = "acs5" # the specific survey is the 5-year ACS.
)

Examine values in context

Now that we have the raw data, we can check to see if the values from the convenience tables do, in fact, match up with the values from the subtables.

convenience_check_values <- convenience_check_values_raw |>
    dplyr::inner_join(
        convenience_check_variables,
        by = c("Group", "Index")
    ) |>
    dplyr::select(
        "Population",
        "Race",
        "Table Type",
        "Value"
    ) |>
    tidyr::pivot_wider(
        names_from = "Table Type",
        values_from = "Value"
    ) |>
    dplyr::mutate(
        Identical = dplyr::if_else(.data$Subtable == .data$Convenience,
                                   "Yes",
                                   "No")
    )

convenience_check_values |>
    dplyr::mutate(
        dplyr::across(tidyselect::all_of(c("Subtable", "Convenience")),
                      scales::label_comma(accuracy = 1))
    ) |>
    knitr::kable(
        align = "llrrl"
    )

Population	Race	Subtable	Convenience	Identical
People	White	210,875,446	210,875,446	Yes
People	Black	41,070,890	41,070,890	Yes
Households	White	87,717,076	87,717,076	Yes
Households	Black	15,556,942	15,556,942	Yes

We don’t have to include any of the confusing “Group,” “Index,” or “Variable” columns in our final result.

Inclusive identity counts

Six others, tables “B02008” through “B02013”, show the numbers of people who identified with specific races and ethnicities. The totals from these tables will be larger than the US population because someone who identified with more than one category will be counted in each corresponding table.

These tables correspond to six of the ten broad categories of race/ethnicity, so they are actually already in RACE_ETHNICITY_SUBTABLES. I just hid them before because it would have been confusing.

Census Race	Suffix	Inclusive Group
TOTAL
WHITE ALONE	A	B02008
BLACK OR AFRICAN AMERICAN ALONE	B	B02009
AMERICAN INDIAN AND ALASKA NATIVE ALONE	C	B02010
ASIAN ALONE	D	B02011
NATIVE HAWAIIAN AND OTHER PACIFIC ISLANDER ALONE	E	B02012
SOME OTHER RACE ALONE	F	B02013
TWO OR MORE RACES	G
WHITE ALONE, NOT HISPANIC OR LATINO	H
HISPANIC OR LATINO	I

The population reported in the inclusive tables should be as large, or larger, than the population reported in the exclusive tables. We can use hercacstables to check this, too.

Census variables for inclusive and exclusive counts

incl_excl_pop_variables <- hercacstables::RACE_ETHNICITY_SUBTABLES |>
    dplyr::filter(
        nchar(.data$`Inclusive Group`) > 0
    ) |>
    dplyr::mutate(
        `Exclusive Group` = paste0("B01001", .data$Suffix)
    ) |>
    dplyr::select(
        "Census Race",
        Inclusive = "Inclusive Group",
        Exclusive = "Exclusive Group"
    ) |>
    tidyr::pivot_longer(
        cols = tidyselect::ends_with("clusive"),
        names_to = "Type of count",
        values_to = "Group"
    ) |>
    dplyr::mutate(
        Variable = hercacstables::build_api_variable(.data$Group, 1)
    )

knitr::kable(incl_excl_pop_variables)

Census Race	Type of count	Group	Variable
WHITE ALONE	Inclusive	B02008	B02008_001E
WHITE ALONE	Exclusive	B01001A	B01001A_001E
BLACK OR AFRICAN AMERICAN ALONE	Inclusive	B02009	B02009_001E
BLACK OR AFRICAN AMERICAN ALONE	Exclusive	B01001B	B01001B_001E
AMERICAN INDIAN AND ALASKA NATIVE ALONE	Inclusive	B02010	B02010_001E
AMERICAN INDIAN AND ALASKA NATIVE ALONE	Exclusive	B01001C	B01001C_001E
ASIAN ALONE	Inclusive	B02011	B02011_001E
ASIAN ALONE	Exclusive	B01001D	B01001D_001E
NATIVE HAWAIIAN AND OTHER PACIFIC ISLANDER ALONE	Inclusive	B02012	B02012_001E
NATIVE HAWAIIAN AND OTHER PACIFIC ISLANDER ALONE	Exclusive	B01001E	B01001E_001E
SOME OTHER RACE ALONE	Inclusive	B02013	B02013_001E
SOME OTHER RACE ALONE	Exclusive	B01001F	B01001F_001E

Raw inclusive and exclusive counts

incl_excl_pop_values_raw <- hercacstables::fetch_data(
    variables = incl_excl_pop_variables$Variable,
    year = 2022,
    for_geo = "us",
    for_items = "*",
    survey_type = "acs",
    table_or_survey_code = "acs5"
)

Comparing inclusive and exclusive counts

incl_excl_pop_values <- incl_excl_pop_values_raw |>
    dplyr::inner_join(
        incl_excl_pop_variables,
        by = c("Group")
    ) |>
    dplyr::select(
        "Census Race",
        "Type of count",
        "Value"
    ) |>
    tidyr::pivot_wider(
        names_from = "Type of count",
        values_from = "Value"
    ) |>
    dplyr::mutate(
        Difference = .data$Inclusive - .data$Exclusive,
        `Percent Multiracial` = .data$Difference / .data$Inclusive
    ) |>
    dplyr::arrange(
        dplyr::desc(.data$Inclusive)
    )

incl_excl_pop_values |>
    dplyr::mutate(
        dplyr::across(tidyselect::all_of(c("Inclusive",
                                           "Exclusive",
                                           "Difference")),
                      scales::label_comma(accuracy = 1)),
        `Percent Multiracial` = scales::label_percent(accuracy = 1)(
            .data$`Percent Multiracial`
        )
    ) |>
    knitr::kable(
        align = "lrrrr"
    )

Census Race	Inclusive	Exclusive	Difference	Percent Multiracial
WHITE ALONE	244,954,342	218,123,424	26,830,918	11%
BLACK OR AFRICAN AMERICAN ALONE	47,498,346	41,288,572	6,209,774	13%
SOME OTHER RACE ALONE	38,354,036	20,018,544	18,335,492	48%
ASIAN ALONE	23,330,887	19,112,979	4,217,908	18%
AMERICAN INDIAN AND ALASKA NATIVE ALONE	6,749,000	2,786,431	3,962,569	59%
NATIVE HAWAIIAN AND OTHER PACIFIC ISLANDER ALONE	1,513,124	624,863	888,261	59%

Hispanic ethnicity and broad racial identity

Table B03002 contains counts of people by Hispanic ethnicity for each of the ten broad racial identities. As always, the first row is the total population size. There are then two groups of rows. Rows 2-11 count people who are not Hispanic. Rows 12-21 count people who identify as Hispanic. Rows 2 and 12 are the total populations of non-Hispanic and Hispanic people. Rows 10, 11, 20, and 21 contain subgroupings that distinguish between people who identify as biracial and those who identify as multiracial.

That means that we can take rows one through eight of hercacstables::RACE_ETHNICITY_SUBTABLES and map them onto rows in B03002. In fact, this is so useful that it is also included in hercacstables::RACE_ETHNICITY_SUBTABLES.

Census Race	non-Hispanic	Hispanic
TOTAL	B03002_002E	B03002_012E
WHITE ALONE	B03002_003E	B03002_013E
BLACK OR AFRICAN AMERICAN ALONE	B03002_004E	B03002_014E
AMERICAN INDIAN AND ALASKA NATIVE ALONE	B03002_005E	B03002_015E
ASIAN ALONE	B03002_006E	B03002_016E
NATIVE HAWAIIAN AND OTHER PACIFIC ISLANDER ALONE	B03002_007E	B03002_017E
SOME OTHER RACE ALONE	B03002_008E	B03002_018E
TWO OR MORE RACES	B03002_009E	B03002_019E

Let’s use these to look at nationwide trends across racial identities in their percentages of Hispanic ethnicity.

First, we will define our glossary table.

hispanic_and_broad_race_variables <- hercacstables::RACE_ETHNICITY_SUBTABLES |>
    dplyr::filter(
        nchar(.data$`non-Hispanic`) > 0
    ) |>
    dplyr::select(
        "Census Race",
        "non-Hispanic",
        "Hispanic"
    ) |>
    tidyr::pivot_longer(
        cols = tidyselect::ends_with("Hispanic"),
        names_to = "Ethnicity",
        values_to = "Variable"
    ) |>
    tidyr::separate_wider_position(
        cols = "Variable",
        widths = c(Group = 6, 1,
                   Index = 3, 1),
        cols_remove = FALSE
    ) |>
    dplyr::mutate(
        Index = as.integer(.data$Index)
    )

knitr::kable(hispanic_and_broad_race_variables)

Census Race	Ethnicity	Group	Index	Variable
TOTAL	non-Hispanic	B03002	2	B03002_002E
TOTAL	Hispanic	B03002	12	B03002_012E
WHITE ALONE	non-Hispanic	B03002	3	B03002_003E
WHITE ALONE	Hispanic	B03002	13	B03002_013E
BLACK OR AFRICAN AMERICAN ALONE	non-Hispanic	B03002	4	B03002_004E
BLACK OR AFRICAN AMERICAN ALONE	Hispanic	B03002	14	B03002_014E
AMERICAN INDIAN AND ALASKA NATIVE ALONE	non-Hispanic	B03002	5	B03002_005E
AMERICAN INDIAN AND ALASKA NATIVE ALONE	Hispanic	B03002	15	B03002_015E
ASIAN ALONE	non-Hispanic	B03002	6	B03002_006E
ASIAN ALONE	Hispanic	B03002	16	B03002_016E
NATIVE HAWAIIAN AND OTHER PACIFIC ISLANDER ALONE	non-Hispanic	B03002	7	B03002_007E
NATIVE HAWAIIAN AND OTHER PACIFIC ISLANDER ALONE	Hispanic	B03002	17	B03002_017E
SOME OTHER RACE ALONE	non-Hispanic	B03002	8	B03002_008E
SOME OTHER RACE ALONE	Hispanic	B03002	18	B03002_018E
TWO OR MORE RACES	non-Hispanic	B03002	9	B03002_009E
TWO OR MORE RACES	Hispanic	B03002	19	B03002_019E

Next, we pull 18 years of ACS data.

hispanic_and_broad_race_raw <- c(2005:2019, 2021:LATEST_YEAR) |>
    purrr::map(
        ~ hercacstables::fetch_data(
            hispanic_and_broad_race_variables$Variable,
            year = .,
            for_geo = "us",
            for_items = "*",
            survey_type = "acs",
            table_or_survey_code = "acs1"
        )
    )

Then, we put the data into a nice, tidy format.

hispanic_and_broad_race <- hispanic_and_broad_race_raw |>
    purrr::list_rbind() |>
    dplyr::inner_join(
        hispanic_and_broad_race_variables,
        by = "Index"
    ) |>
    dplyr::select(
        "Census Race",
        "Ethnicity",
        "Year",
        "Value"
    ) |>
    tidyr::pivot_wider(
        names_from = "Ethnicity",
        values_from = "Value",
        values_fill = 0
    ) |>
    dplyr::mutate(
        Total = .data$Hispanic + .data$`non-Hispanic`,
        `Percent Hispanic` = .data$Hispanic / .data$Total
    )
    
hispanic_and_broad_race |>
    dplyr::slice_sample(
        n = 1,
        by = "Year"
    ) |>
    dplyr::mutate(
        dplyr::across(c("Hispanic", "non-Hispanic", "Total"),
                      scales::label_comma(accuracy = 1)),
        dplyr::across("Percent Hispanic",
                      scales::label_percent(accuracy = 1))
    ) |>
    knitr::kable(
        align = "lrrrrr"
    )

Census Race	Year	non-Hispanic	Hispanic	Total	Percent Hispanic
ASIAN ALONE	2005	12,312,949	158,866	12,471,815	1%
SOME OTHER RACE ALONE	2006	768,782	18,238,347	19,007,129	96%
AMERICAN INDIAN AND ALASKA NATIVE ALONE	2007	2,019,204	346,143	2,365,347	15%
SOME OTHER RACE ALONE	2008	701,823	14,290,365	14,992,188	95%
ASIAN ALONE	2009	13,627,633	146,978	13,774,611	1%
NATIVE HAWAIIAN AND OTHER PACIFIC ISLANDER ALONE	2010	474,799	33,117	507,916	7%
SOME OTHER RACE ALONE	2011	565,213	14,202,943	14,768,156	96%
SOME OTHER RACE ALONE	2012	614,725	13,947,953	14,562,678	96%
SOME OTHER RACE ALONE	2013	641,423	14,169,602	14,811,025	96%
AMERICAN INDIAN AND ALASKA NATIVE ALONE	2014	2,103,422	498,292	2,601,714	19%
NATIVE HAWAIIAN AND OTHER PACIFIC ISLANDER ALONE	2015	502,876	52,070	554,946	9%
TOTAL	2016	265,728,796	57,398,719	323,127,515	18%
ASIAN ALONE	2017	17,999,846	215,482	18,215,328	1%
ASIAN ALONE	2018	18,193,513	221,685	18,415,198	1%
SOME OTHER RACE ALONE	2019	839,270	15,513,283	16,352,553	95%
TWO OR MORE RACES	2021	14,298,433	27,588,006	41,886,439	66%
SOME OTHER RACE ALONE	2022	1,912,680	22,531,802	24,444,482	92%
ASIAN ALONE	2023	19,769,752	282,571	20,052,323	1%

Finally, we plot it

hispanic_and_broad_race |>
    dplyr::mutate(
        `Census Race` = stringr::str_to_title(.data$`Census Race`)
    ) |>
    ggplot2::ggplot(
        ggplot2::aes(x = .data$Year,
                     y = .data$`Percent Hispanic`,
                     color = .data$`Census Race`)
    ) +
    ggplot2::geom_line(
        linewidth = 1,
        lineend = "round",
        linejoin = "mitre"
    ) +
    ggplot2::geom_point(
        size = 3
    ) +
    ggplot2::scale_x_continuous(
        name = NULL,
        labels = scales::label_number(big.mark = ""),
        limits = c(2005, 2025),
        breaks = scales::breaks_width(5),
        minor_breaks = scales::breaks_width(1)
    ) +
    ggplot2::scale_y_continuous(
        name = "Percentage Hispanic",
        labels = scales::label_percent(accuracy = 1),
        limits = c(0, 1),
        breaks = scales::breaks_width(0.2),
        minor_breaks = scales::breaks_width(0.05)
    ) +
    ggplot2::scale_color_discrete(
        guide = ggplot2::guide_legend(
            title = NULL,
            position = "top",
            nrow = 4
        )
    ) +
    ggplot2::theme_minimal()

Methodological detail tables

Tables B98013 and B99021 give information about the Census’s data collection methods. Statisticians can use to describe how much uncertainty there is in the data concerning racial identities. We can skip those.

Appendix

Subtables of race and ethnicity

Group	Description
B01001	Sex by Age (Hispanic or Latino)
B01002	Median Age by Sex (Hispanic or Latino)
B05003	Sex by Age by Nativity and Citizenship Status (Hispanic or Latino)
B06004	Place of Birth (Hispanic or Latino) in the United States
B07004	Geographical Mobility in the Past Year (Hispanic or Latino) for Current Residence in the United States
B07404	Geographical Mobility in the Past Year (Hispanic or Latino) for Residence 1 Year Ago in the United States
B08105	Means of Transportation to Work (Hispanic or Latino)
B08505	Means of Transportation to Work for Workplace Geography (Hispanic or Latino)
B10051	Grandparents Living With Own Grandchildren Under 18 Years by Responsibility for Own Grandchildren and Age of Grandparent (Hispanic or Latino)
B11001	Household Type (Including Living Alone) (Hispanic or Latino)
B11002	Household Type by Relatives and Nonrelatives for Population in Households (Hispanic or Latino)
B12002	Sex by Marital Status for the Population 15 Years and Over (Hispanic or Latino)
B12007	Median Age at First Marriage (Hispanic or Latino)
B13002	Women 15 to 50 Years Who Had a Birth in the Past 12 Months by Marital Status (Hispanic or Latino)
B14007	School Enrollment by Detailed Level of School for the Population 3 Years and Over (Hispanic or Latino)
B15002	Sex by Educational Attainment for the Population 25 Years and Over (Hispanic or Latino)
B16005	Nativity by Language Spoken at Home by Ability to Speak English for the Population 5 Years and Over (Hispanic or Latino)
B17001	Poverty Status in the Past 12 Months by Sex by Age (Hispanic or Latino)
B17010	Poverty Status in the Past 12 Months of Families by Family Type by Presence of Related Children Under 18 Years by Age of Related Children (Hispanic or Latino)
B17020	Poverty Status in the Past 12 Months by Age (Hispanic or Latino)
B18101	Age by Disability Status (Hispanic or Latino)
B19001	Household Income in the Past 12 Months (Hispanic or Latino Householder)
B19013	Median Household Income in the Past 12 Months (Hispanic or Latino Householder)
B19025	Aggregate Household Income in the Past 12 Months (Hispanic or Latino Householder)
B19037	Age of Householder by Household Income in the Past 12 Months (Hispanic or Latino Householder)
B19101	Family Income in the Past 12 Months (Hispanic or Latino Householder)
B19113	Median Family Income in the Past 12 Months (Hispanic or Latino Householder)
B19202	Median Nonfamily Household Income in the Past 12 Months (Hispanic or Latino Householder)
B19301	Per Capita Income in the Past 12 Months (Hispanic or Latino)
B19313	Aggregate Income in the Past 12 Months for the Population 15 Years and Over (Hispanic or Latino)
B20005	Sex by Work Experience in the Past 12 Months by Earnings in the Past 12 Months for the Population 16 Years and Over (Hispanic or Latino)
B20017	Median Earnings in the Past 12 Months by Sex by Work Experience in the Past 12 Months for the Population 16 Years and Over With Earnings (Hispanic or Latino)
B21001	Sex by Age by Veteran Status for the Civilian Population 18 Years and Over (Hispanic or Latino)
B22005	Receipt of Food Stamps/SNAP in the Past 12 Months by Race of Householder (Hispanic or Latino)
B23002	Sex by Age by Employment Status for the Population 16 Years and Over (Hispanic or Latino)
B24010	Sex by Occupation for the Civilian Employed Population 16 Years and Over (Hispanic or Latino)
B25003	Tenure (Hispanic or Latino Householder)
B25008	Total Population in Occupied Housing Units by Tenure (Hispanic or Latino Householder)
B25010	Average Household Size of Occupied Housing Units by Tenure (Hispanic or Latino Householder)
B25014	Occupants per Room (Hispanic or Latino Householder)
B25032	Units in Structure (Hispanic or Latino Householder)
B25077	Median Value (Dollars, Hispanic or Latino Householder)
B25140	Housing Costs as a Percentage of Household Income in the Past 12 Months (Hispanic or Latino Householder)
B26103	Group Quarters Type (3 Types) (Hispanic or Latino)
B26203	Group Quarters Type (5 Types) (Hispanic or Latino)
B27001	Health Insurance Coverage Status by Age (Hispanic or Latino)
B28009	Presence of a Computer and Type of Internet Subscription in Household (Hispanic or Latino)
C01001	Sex by Age (Hispanic or Latino)
C14007	School Enrollment by Detailed Level of School for the Population 3 Years and Over (Hispanic or Latino)
C15002	Sex by Educational Attainment for the Population 25 Years and Over (Hispanic or Latino)
C15010	Field of Bachelor’s Degree for First Major for the Population 25 Years and Over (Hispanic or Latino)
C17001	Poverty Status in the Past 12 Months by Sex by Age (Hispanic or Latino)
C17010	Poverty Status in the Past 12 Months of Families by Family Type by Presence of Related Children Under 18 Years (Hispanic or Latino)
C21001	Sex by Age by Veteran Status for the Civilian Population 18 Years and Over (Hispanic or Latino)
C23002	Sex by Age by Employment Status for the Population 16 Years and Over (Hispanic or Latino)
C24010	Sex by Occupation for the Civilian Employed Population 16 Years and Over (Hispanic or Latino)
C27001	Health Insurance Coverage Status by Age (Hispanic or Latino)

OMB Minimum Reporting Categories

Minimum Race/Ethnicity Reporting Category	Definition
American Indian or Alaska Native	Individuals with origins in any of the original peoples of North, Central, and South America, including, for example, Navajo Nation, Blackfeet Tribe of the Blackfeet Indian Reservation of Montana, Native Village of Barrow Inupiat Traditional Government, Nome Eskimo Community, Aztec, and Maya.
Asian	Individuals with origins in any of the original peoples of Central or East Asia, Southeast Asia, or South Asia, including, for example, Chinese, Asian Indian, Filipino, Vietnamese, Korean, and Japanese.
Black or African American	Individuals with origins in any of the Black racial groups of Africa, including, for example, African American, Jamaican, Haitian, Nigerian, Ethiopian, and Somali.
Hispanic or Latino	Includes individuals of Mexican, Puerto Rican, Salvadoran, Cuban, Dominican, Guatemalan, and other Central or South American or Spanish culture or origin.
Middle Eastern or North African	Individuals with origins in any of the original peoples of the Middle East or North Africa, including, for example, Lebanese, Iranian, Egyptian, Syrian, Iraqi, and Israeli.
Multiracial and/or Multiethnic	Those who identify with multiple race/ethnicity minimum reporting categories.
Native Hawaiian or Pacific Islander	Individuals with origins in any of the original peoples of Hawaii, Guam, Samoa, or other Pacific Islands, including, for example, Native Hawaiian, Samoan, Chamorro, Tongan, Fijian, and Marshallese.
White	Individuals with origins in any of the original peoples of Europe, including, for example, English, German, Irish, Italian, Polish, and Scottish.