Income Brackets for the Community Reinvestment Act

library(hercacstables)

Context

The Community Reinvestment Act, or CRA, is Federal legislation with mandates for how lenders must engage with folks living in areas with relatively low incomes. The Minneapolis Federal Reserve has really nice explanation of it. They even have a helpful article about the definitions of “low and moderate income” with respect to a lender’s “assessment area.” These are key terms for place-based partnerships that want to demonstrate their pertinence to a CRA lender.

The CRA defines four brackets of income level relative to an assessment area’s median income. You can use hercacstables to pull an area’s median income, define the bracket ranges, and then to count the number of families in your area that fall into each bracket.

Areas

We need to identify two different geographical footprints for this analysis. The first is the “assessment area,” which is a broad geographical scope that defines a baseline economic status for the CR. The second is the specific area that your organization will impact. This will be a subset of the assessment area, probably where poverty has been concentrated by the systems that you are trying to change.

Assesssment Area

Identifying the assessment area that is relevant to your organization is not complicated: it is probably the Census-defined Metropolitan Statistical Area where your organization is located. If your organization serves part of an enormous metro area, you may need to identify a specific Metropolitan Division. If your organization serves a region with low population density, you may need to use your entire state. If you don’t already know your area, you can start looking for it on this FAQ.

For this article, we are going to use two Metropolitan Statistical Areas in southeastern Wisconsin: Kenosha County and Racine County.

ASSESSMENT_AREAS <- tibble::tribble(
    ~ Area,    ~ county,
    "Kenosha", "059",
    "Racine",  "101"
)

Impact Area

Your organization’s impact area will be more customized to your case. According to the article above, you should define it in terms of one or more entire Census Tracts.

For this article, we are going to use two cities in southeastern Wisconsin: Kenosha and Racine.

IMPACT_AREAS <- tibble::tribble(
    ~ Area,    ~ city,  ~ district,
    "Kenosha", "39225", "07320",
    "Racine",  "66000", "12360"
)

Incomes

The CRA wants us to look at income in three ways: the median in our assessment area, the brackets defined by that median income, and the number of folks in our assessment area in each bracket.

Median income of assessment areas

We’ll use table B19326, which reports median income by place of birth. A full explanation of how I chose this table appears in an appendix at the end of this article. We don’t actually need any of the details of place of birth, just the area’s overall median income. That will be reported by the table’s first row, B19326_001E.

MEDIAN_INCOMES <- "B19326_001E" |>
    fetch_data(
        year = 2024L,
        for_geo = "county",
        for_items = ASSESSMENT_AREAS$county,
        survey_type = "acs",
        table_or_survey_code = "acs1",
        state = "55"
    ) |>
    dplyr::right_join(
        ASSESSMENT_AREAS,
        by = "county"
    ) |>
    dplyr::select(
        "Area",
        `Median Income` = "Value"
    )

Personal median incomes in 2024
Area	Median Income
Kenosha	$45,091
Racine	$43,133

Defining income brackets

We can define a glossary of income brackets for each assessment area by joining and multiplying.

INCOME_BRACKETS <- tibble::tribble(
    ~ Bracket, ~ `Lower Bound`, ~ `Upper Bound`,
    "Low",                 0.0,             0.5,
    "Moderate",            0.5,             0.8,
    "Middle",              0.8,             1.2,
    "Upper",               1.2,             Inf
) |>
    dplyr::mutate(
        Bracket = forcats::fct_inorder(.data$Bracket)
    ) |>
    dplyr::cross_join(
        MEDIAN_INCOMES
    ) |>
    dplyr::mutate(
        dplyr::across(
            tidyselect::ends_with("Bound"),
            \(.) . * .data$`Median Income`
        )
    ) |>
    dplyr::select(
        "Area",
        "Bracket",
        "Lower Bound",
        "Upper Bound"
    )

Personal income brackets in 2024
Bracket	Kenosha	Racine
Low	$0 - $22,546	$0 - $21,566
Moderate	$22,546 - $36,073	$21,566 - $34,506
Middle	$36,073 - $54,109	$34,506 - $51,760
Upper	$54,109 - Inf	$51,760 - Inf

Counting bracket populations

The third step is to count how many people fall into each income bracket. To do this, we will use census table B20005 hercacstables provides GLOSSARY_OF_SEX_BY_EARNINGS, which describes how each row of this table corresponds to a specific combination of sex, employment status, and income bracket. We must assign each bracket in B20005 to one or more of our local income brackets, with the amount weighted by how much they overlap.

To do that, we’ll create a helper function that computes how much, if at all, two ranges overlap. We’ll use purrr::map2_dbl to make it work on vector inputs.

overlap_helper <- function(.lower_1, .upper_1, .lower_2, .upper_2) {
    .tops <- purrr::map2_dbl(.upper_1, .upper_2, min)
    .bottoms <- purrr::map2_dbl(.lower_1, .lower_2, max)
    purrr::map_dbl(.tops - .bottoms, \(.) max(., 0.0))
}

Now we join everything, compute the overlaps, filter out the rows without any overlap, and compute the weights as the proportion of overlap.

GLOSSARY_OF_INCOME_BRACKETS <- GLOSSARY_OF_SEX_BY_INCOME |>
    dplyr::slice(
         -1L, # grand total
         -2L, # subtotal of all males
         -3L, # subtotal of full-time-employed males
         -5L, # subtotal of full-time-employed males with earnings
        -26L, # subtotal of not-full-time-employed males
        -28L, # subtotal of not-full-time-employed males with earnings
        -49L, # subtotal of all females
        -50L, # subtotal of full-time-employed females
        -52L, # subtotal of full-time-employed females with earnings
        -73L, # subtotal of not-full-time-employed females
        -75L  # subtotal of not-full-time-employed females
    ) |>
    dplyr::cross_join(
        INCOME_BRACKETS,
        suffix = c(" Census", " CRA")
    ) |>
    dplyr::mutate(
        Overlap = overlap_helper(.data$`Lower Bound Census`,
                                 .data$`Upper Bound Census`,
                                 .data$`Lower Bound CRA`,
                                 .data$`Upper Bound CRA`)
    ) |>
    dplyr::filter(
        .data$Overlap > 0.0
    ) |>
    dplyr::mutate(
        Proportion = dplyr::coalesce(
            .data$Overlap / 
                (.data$`Upper Bound Census` - .data$`Lower Bound Census`),
            1.0
        )
    ) |>
    dplyr::arrange(
        .data$Index,
        .data$Bracket,
        .data$Area
    )

Area	Low	Moderate	Middle	Upper
Kenosha	6 - 85	15 - 88	18 - 91	21 - 95
Racine	6 - 84	14 - 87	17 - 91	21 - 95

Fetch the counts

The next step is to pull the counts of people in income brackets from the Census.

RAW_COUNTS_BY_INCOME_BRACKET <- fetch_data(
    variables = "group(B19325)",
    year = 2024L,
    for_geo = "place",
    for_items = IMPACT_AREAS$city,
#    for_geo = "school district (unified)",
#    for_items = IMPACT_AREAS$district,
    survey_type = "acs",
    table_or_survey_code = "acs1",
    state = "55"
)

COUNTS_BY_INCOME_BRACKET <- RAW_COUNTS_BY_INCOME_BRACKET |>
    dplyr::filter(
        .data$Measure == "E"
    ) |>
    dplyr::semi_join(
        GLOSSARY_OF_INCOME_BRACKETS,
        by = "Index"
    ) |>
    dplyr::inner_join(
        IMPACT_AREAS,
#        by = c(`school district (unified)` = "district")
        by = c(place = "city")
    ) |>
    dplyr::left_join(
        GLOSSARY_OF_INCOME_BRACKETS,
        by = c("Area", "Index"),
        relationship = "many-to-many"
    )

POPULATIONS_PER_BRACKET <- COUNTS_BY_INCOME_BRACKET |>
    dplyr::summarize(
        People = sum(.data$Proportion * .data$Value, na.rm = TRUE),
        .by = c("Area", "Bracket")
    ) |>
    dplyr::arrange(
        .data$Area,
        .data$Bracket
    )

Area	Bracket	People	Percent
Kenosha	Low	19,824	27%
Kenosha	Moderate	10,228	14%
Kenosha	Middle	14,326	20%
Kenosha	Upper	28,574	39%
Racine	Low	16,110	29%
Racine	Moderate	8,582	16%
Racine	Middle	14,260	26%
Racine	Upper	15,885	29%

The distributions of personal incomes differ between Kenosha and Racine. The most populous income bracket in Kenosha, Upper, makes up 39% of people. Kenosha`s next largest bracket, Lower income, also has a lot of people: 27%. Its Moderate (14%) and Middle (20%) brackets are both less populous. In contrast, Racine’s Lower and Upper brackets are tied for most populous, at 29% each. Racine’s Middle (26%) bracket is also pretty populous, leaving the Moderate income bracket as the smallest, with only 16% of the population. Both income distributions are bimodal and asymmetric, with more folks at the extreme ends of the income range.

Appendix

If, like me, have NOT memorized every subtable of the ACS, you will need to look up which tables report the data that you need. The search_in_columns function and METADATA_FOR_ACS_GROUPS gloassary of hercacstables exist for exactly this situation.

Finding a Median Income Table

We definitely want to find tables with the word “median” in them. The Census uses both “income” and “earnings” to talk about how much people make. We’ll search with both of those terms.

METADATA_FOR_ACS_GROUPS |>
    search_in_columns(
        Description = c("median", "income|earn"), # case-insensitive search for "median" AND ("income" OR "earn")
        Universe = "population",                  # this will give us individual incomes
        Group = "\\d$"                            # this will exclude tables that report within a single race
    ) |>
    knitr::kable()

Group	Universe	Description	ACS1	ACS5
B06011	Population 15 years and over in the United States with income	Median Income in the Past 12 Months by Place of Birth in the United States	TRUE	TRUE
B07011	Population 15 years and over in the United States with income	Median Income in the Past 12 Months by Geographical Mobility in the Past Year for Current Residence in the United States	TRUE	TRUE
B07411	Population 15 years and over in the United States with income	Median Income in the Past 12 Months by Geographical Mobility in the Past Year for Residence 1 Year Ago in the United States	TRUE	TRUE
B15013	Population 25 to 64 years with earnings and a Bachelor’s degree or higher attainment	Median Earnings in the Past 12 Months by Sex by Field of Bachelor’s Degree for First Major	TRUE	TRUE
B15014	Population 25 to 64 years with earnings and a Bachelor’s degree or higher attainment	Median Earnings in the Past 12 Months by Age by Field of Bachelor’s Degree for First Major	TRUE	TRUE
B18140	Civilian noninstitutionalized population 16 years and over with earnings in the past 12 months	Median Earnings in the Past 12 Months by Disability Status by Sex for the Civilian Noninstitutionalized Population 16 Years and Over With Earnings	TRUE	TRUE
B19326	Population 15 years and over with income in the past 12 months	Median Income in the Past 12 Months by Sex by Work Experience in the Past 12 Months for the Population 15 Years and Over With Income	TRUE	TRUE
B20002	Population 16 years and over with earnings	Median Earnings in the Past 12 Months by Sex for the Population 16 Years and Over With Earnings in the Past 12 Months	TRUE	TRUE
B20004	Population 25 years and over with earnings	Median Earnings in the Past 12 Months by Sex by Educational Attainment for the Population 25 Years and Over	TRUE	TRUE
B20017	Population 16 years and over with earnings	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 in the Past 12 Months	TRUE	TRUE
B20018	Population 16 years and over who worked full-time, year-round with earnings	Median Earnings in the Past 12 Months for the Population 16 Years and Over Who Worked Full-Time, Year-Round With Earnings in the Past 12 Months	TRUE	TRUE
B21004	Civilian population 18 years and over with income in the past 12 months	Median Income in the Past 12 Months by Veteran Status by Sex for the Civilian Population 18 Years and Over With Income	TRUE	TRUE
B24011	Civilian employed population 16 years and over with earnings	Occupation by Median Earnings in the Past 12 Months for the Civilian Employed Population 16 Years and Over	TRUE	TRUE
B24012	Civilian employed population 16 years and over with earnings	Sex by Occupation and Median Earnings in the Past 12 Months for the Civilian Employed Population 16 Years and Over	TRUE	TRUE
B24021	Full-time, year-round civilian employed population 16 years and over with earnings	Occupation by Median Earnings in the Past 12 Months for the Full-Time, Year-Round Civilian Employed Population 16 Years and Over	TRUE	TRUE
B24022	Full-time, year-round civilian employed population 16 years and over with earnings	Sex by Occupation and Median Earnings in the Past 12 Months for the Full-Time, Year-Round Civilian Employed Population 16 Years and Over	TRUE	TRUE
B24031	Civilian employed population 16 years and over with earnings	Industry by Median Earnings in the Past 12 Months for the Civilian Employed Population 16 Years and Over	TRUE	TRUE
B24032	Civilian employed population 16 years and over with earnings	Sex by Industry and Median Earnings in the Past 12 Months for the Civilian Employed Population 16 Years and Over	TRUE	TRUE
B24041	Full-time, year-round civilian employed population 16 years and over with earnings	Industry by Median Earnings in the Past 12 Months for the Full-Time, Year-Round Civilian Employed Population 16 Years and Over	TRUE	TRUE
B24042	Full-time, year-round civilian employed population 16 years and over with earnings	Sex by Industry and Median Earnings in the Past 12 Months for the Full-Time, Year-Round Civilian Employed Population 16 Years and Over	TRUE	TRUE
B24081	Civilian employed population 16 years and over with earnings	Class of Worker by Median Earnings in the Past 12 Months for the Civilian Employed Population 16 Years and Over	TRUE	TRUE
B24082	Civilian employed population 16 years and over with earnings	Sex by Class of Worker and Median Earnings in the Past 12 Months for the Civilian Employed Population 16 Years and Over	TRUE	TRUE
B24091	Full-time, year-round civilian employed population 16 years and over with earnings	Class of Worker by Median Earnings in the Past 12 Months for the Full-Time, Year-Round Civilian Employed Population 16 Years and Over	TRUE	TRUE
B24092	Full-time, year-round civilian employed population 16 years and over with earnings	Sex by Class of Worker and Median Earnings in the Past 12 Months for the Full-Time, Year-Round Civilian Employed Population 16 Years and Over	TRUE	TRUE
B24121	Full-time, year-round civilian employed population 16 years and over with earnings	Detailed Occupation by Median Earnings in the Past 12 Months for the Full-Time, Year-Round Civilian Employed Population 16 Years and Over	TRUE	TRUE
B24122	Full-time, year-round civilian employed male population 16 years and over with earnings	Detailed Occupation by Median Earnings in the Past 12 Months for the Full-Time, Year-Round Civilian Employed Male Population 16 Years and Over	TRUE	TRUE
B24123	Full-time, year-round civilian employed female population 16 years and over with earnings	Detailed Occupation by Median Earnings in the Past 12 Months for the Full-Time, Year-Round Civilian Employed Female Population 16 Years and Over	TRUE	TRUE
B26119	Population 16 years and over with earnings	Median Earnings in the Past 12 Months by Group Quarters Type (3 Types) by Sex	TRUE	TRUE
B26219	Population 16 years and over with earnings	Median Earnings in the Past 12 Months by Group Quarters Type (5 Types) by Sex	TRUE	TRUE

Wow … that’s a lot of tables. Poring through them, table B19326 seems promisingly broad. In fact, if we cheat by looking ahead at tables that count people by income, we find that there is a corresponding table B19325 that should make it easy to count people once we’ve determined brackets.

Let’s use B19326 to calculate the median income of an assessment area.

Finding counts by income

Once again, we’ll look for tables with either “income” or “earn” in their descriptions. This time, however, we want to specifically exclude tables that summarize dollars. We’ll exclude tables with words like “median,” “average,” or “aggregate” in their descriptions.

METADATA_FOR_ACS_GROUPS |>
    search_in_columns(
        Description = "income|earn",                          # seems obvious, right?
        `-Description` = c("aggregate", "median", "average"), # no summaries, we want counts of people
        Universe = "population",                              # this should give personal incomes
        Group = "\\d$"                                        # this will exclude tables that report within a single race
    ) |>
    knitr::kable()

Group	Universe	Description	ACS1	ACS5
B06010	Population 15 years and over in the United States	Place of Birth by Individual Income in the Past 12 Months in the United States	TRUE	TRUE
B07010	Population 15 years and over in the United States	Geographical Mobility in the Past Year by Individual Income in the Past 12 Months for Current Residence in the United States	TRUE	TRUE
B07410	Population 15 years and over in the United States	Geographical Mobility in the Past Year by Individual Income in the Past 12 Months for Residence 1 Year Ago in the United States	TRUE	TRUE
B09010	Population under 18 years in households	Receipt of Supplemental Security Income (SSI), Cash Public Assistance Income, or Food Stamps/SNAP in the Past 12 Months by Household Type for Children Under 18 Years in Households	TRUE	TRUE
B17002	Population for whom poverty status is determined	Ratio of Income to Poverty Level in the Past 12 Months	TRUE	FALSE
B17024	Population for whom poverty status is determined	Age by Ratio of Income to Poverty Level in the Past 12 Months	TRUE	TRUE
B18131	Civilian noninstitutionalized population for whom poverty status is determined	Age by Ratio of Income to Poverty Level in the Past 12 Months by Disability Status and Type	TRUE	FALSE
B19301	Total population	Per Capita Income in the Past 12 Months	TRUE	TRUE
B19325	Population 15 years and over	Sex by Work Experience in the Past 12 Months by Income in the Past 12 Months for the Population 15 Years and Over	TRUE	TRUE
B20001	Population 16 years and over with earnings	Sex by Earnings in the Past 12 Months for the Population 16 Years and Over With Earnings in the Past 12 Months	TRUE	TRUE
B20005	Population 16 years and over	Sex by Work Experience in the Past 12 Months by Earnings in the Past 12 Months for the Population 16 Years and Over	TRUE	TRUE
B26117	Population 16 years and over with earnings	Group Quarters Type (3 Types) by Sex With Earnings in the Past 12 Months	TRUE	TRUE
B26217	Population 16 years and over with earnings	Group Quarters Type (5 Types) by Sex With Earnings in the Past 12 Months	TRUE	TRUE
B27015	Civilian population living in households	Health Insurance Coverage Status and Type by Household Income in the Past 12 Months	TRUE	TRUE
B27016	Civilian noninstitutionalized population for whom poverty status is determined	Health Insurance Coverage Status and Type by Ratio of Income to Poverty Level in the Past 12 Months by Age	TRUE	FALSE
B27017	Civilian noninstitutionalized population for whom poverty status is determined	Private Health Insurance by Ratio of Income to Poverty Level in the Past 12 Months by Age	TRUE	FALSE
B27018	Civilian noninstitutionalized population for whom poverty status is determined	Public Health Insurance by Ratio of Income to Poverty Level in the Past 12 Months by Age	TRUE	FALSE
B99191	Population 15 years and over	Allocation of Individuals’ Income in the Past 12 Months for the Population 15 Years and Over - Percent of Income Allocated	TRUE	TRUE
B99201	Population 16 years and over	Allocation of Earnings in the Past 12 Months for the Population 16 Years and Over - Percent of Earnings Allocated	TRUE	TRUE
C17002	Population for whom poverty status is determined	Ratio of Income to Poverty Level in the Past 12 Months	TRUE	TRUE
C17024	Population for whom poverty status is determined	Age by Ratio of Income to Poverty Level in the Past 12 Months	TRUE	FALSE
C18131	Civilian noninstitutionalized population for whom poverty status is determined	Ratio of Income to Poverty Level in the Past 12 Months by Disability Status	TRUE	TRUE
C27016	Civilian noninstitutionalized population for whom poverty status is determined	Health Insurance Coverage Status by Ratio of Income to Poverty Level in the Past 12 Months by Age	TRUE	TRUE
C27017	Civilian noninstitutionalized population for whom poverty status is determined	Private Health Insurance by Ratio of Income to Poverty Level in the Past 12 Months by Age	TRUE	TRUE
C27018	Civilian noninstitutionalized population for whom poverty status is determined	Public Health Insurance by Ratio of Income to Poverty Level in the Past 12 Months by Age	TRUE	TRUE

We get a large number of tables, just like when we searched for tables of median incomes. Once again, if we “cheat” by looking for tables that seem to go with each other, three tables jump out: B19325, B20001, and B20005. The difference between the three is that B19325 counts people with any income, B20005 counts everyone over 16, and B20001 only counts people with earnings.

Let’s stick with income, rather than earnings. We’ll use B19325 to count how many people fall into each income bracket.

Detailed bracket information

Here is the full table of each bracket from the Census and the proportion of its members that go into each bracket from the CRA.

COUNTS_BY_INCOME_BRACKET |>
    dplyr::select(
        "Area",
        "Sex",
        "Full-time",
        "Index",
        "Lower Bound Census",
        "Upper Bound Census",
        "Bracket",
        "Lower Bound CRA",
        "Upper Bound CRA",
        "Overlap",
        "Proportion"
    ) |>
    knitr::kable()

Area	Sex	Full-time	Index	Lower Bound Census	Upper Bound Census	Bracket	Lower Bound CRA	Upper Bound CRA	Overlap	Proportion
Kenosha	Male	TRUE	6	1	2499	Low	0.0	22545.5	2498.0	1.0000000
Kenosha	Male	TRUE	7	2500	4999	Low	0.0	22545.5	2499.0	1.0000000
Kenosha	Male	TRUE	8	5000	7499	Low	0.0	22545.5	2499.0	1.0000000
Kenosha	Male	TRUE	9	7500	9999	Low	0.0	22545.5	2499.0	1.0000000
Kenosha	Male	TRUE	10	10000	12499	Low	0.0	22545.5	2499.0	1.0000000
Kenosha	Male	TRUE	11	12500	14999	Low	0.0	22545.5	2499.0	1.0000000
Kenosha	Male	TRUE	12	15000	17499	Low	0.0	22545.5	2499.0	1.0000000
Kenosha	Male	TRUE	13	17500	19999	Low	0.0	22545.5	2499.0	1.0000000
Kenosha	Male	TRUE	14	20000	22499	Low	0.0	22545.5	2499.0	1.0000000
Kenosha	Male	TRUE	15	22500	24999	Low	0.0	22545.5	45.5	0.0182073
Kenosha	Male	TRUE	15	22500	24999	Moderate	22545.5	36072.8	2453.5	0.9817927
Kenosha	Male	TRUE	16	25000	29999	Moderate	22545.5	36072.8	4999.0	1.0000000
Kenosha	Male	TRUE	17	30000	34999	Moderate	22545.5	36072.8	4999.0	1.0000000
Kenosha	Male	TRUE	18	35000	39999	Moderate	22545.5	36072.8	1072.8	0.2146029
Kenosha	Male	TRUE	18	35000	39999	Middle	36072.8	54109.2	3926.2	0.7853971
Kenosha	Male	TRUE	19	40000	44999	Middle	36072.8	54109.2	4999.0	1.0000000
Kenosha	Male	TRUE	20	45000	49999	Middle	36072.8	54109.2	4999.0	1.0000000
Kenosha	Male	TRUE	21	50000	54999	Middle	36072.8	54109.2	4109.2	0.8220044
Kenosha	Male	TRUE	21	50000	54999	Upper	54109.2	Inf	889.8	0.1779956
Kenosha	Male	TRUE	22	55000	64999	Upper	54109.2	Inf	9999.0	1.0000000
Kenosha	Male	TRUE	23	65000	74999	Upper	54109.2	Inf	9999.0	1.0000000
Kenosha	Male	TRUE	24	75000	99999	Upper	54109.2	Inf	24999.0	1.0000000
Kenosha	Male	TRUE	25	100000	Inf	Upper	54109.2	Inf	Inf	1.0000000
Kenosha	Male	FALSE	29	1	2499	Low	0.0	22545.5	2498.0	1.0000000
Kenosha	Male	FALSE	30	2500	4999	Low	0.0	22545.5	2499.0	1.0000000
Kenosha	Male	FALSE	31	5000	7499	Low	0.0	22545.5	2499.0	1.0000000
Kenosha	Male	FALSE	32	7500	9999	Low	0.0	22545.5	2499.0	1.0000000
Kenosha	Male	FALSE	33	10000	12499	Low	0.0	22545.5	2499.0	1.0000000
Kenosha	Male	FALSE	34	12500	14999	Low	0.0	22545.5	2499.0	1.0000000
Kenosha	Male	FALSE	35	15000	17499	Low	0.0	22545.5	2499.0	1.0000000
Kenosha	Male	FALSE	36	17500	19999	Low	0.0	22545.5	2499.0	1.0000000
Kenosha	Male	FALSE	37	20000	22499	Low	0.0	22545.5	2499.0	1.0000000
Kenosha	Male	FALSE	38	22500	24999	Low	0.0	22545.5	45.5	0.0182073
Kenosha	Male	FALSE	38	22500	24999	Moderate	22545.5	36072.8	2453.5	0.9817927
Kenosha	Male	FALSE	39	25000	29999	Moderate	22545.5	36072.8	4999.0	1.0000000
Kenosha	Male	FALSE	40	30000	34999	Moderate	22545.5	36072.8	4999.0	1.0000000
Kenosha	Male	FALSE	41	35000	39999	Moderate	22545.5	36072.8	1072.8	0.2146029
Kenosha	Male	FALSE	41	35000	39999	Middle	36072.8	54109.2	3926.2	0.7853971
Kenosha	Male	FALSE	42	40000	44999	Middle	36072.8	54109.2	4999.0	1.0000000
Kenosha	Male	FALSE	43	45000	49999	Middle	36072.8	54109.2	4999.0	1.0000000
Kenosha	Male	FALSE	44	50000	54999	Middle	36072.8	54109.2	4109.2	0.8220044
Kenosha	Male	FALSE	44	50000	54999	Upper	54109.2	Inf	889.8	0.1779956
Kenosha	Male	FALSE	45	55000	64999	Upper	54109.2	Inf	9999.0	1.0000000
Kenosha	Male	FALSE	46	65000	74999	Upper	54109.2	Inf	9999.0	1.0000000
Kenosha	Male	FALSE	47	75000	99999	Upper	54109.2	Inf	24999.0	1.0000000
Kenosha	Male	FALSE	48	100000	Inf	Upper	54109.2	Inf	Inf	1.0000000
Kenosha	Female	TRUE	53	1	2499	Low	0.0	22545.5	2498.0	1.0000000
Kenosha	Female	TRUE	54	2500	4999	Low	0.0	22545.5	2499.0	1.0000000
Kenosha	Female	TRUE	55	5000	7499	Low	0.0	22545.5	2499.0	1.0000000
Kenosha	Female	TRUE	56	7500	9999	Low	0.0	22545.5	2499.0	1.0000000
Kenosha	Female	TRUE	57	10000	12499	Low	0.0	22545.5	2499.0	1.0000000
Kenosha	Female	TRUE	58	12500	14999	Low	0.0	22545.5	2499.0	1.0000000
Kenosha	Female	TRUE	59	15000	17499	Low	0.0	22545.5	2499.0	1.0000000
Kenosha	Female	TRUE	60	17500	19999	Low	0.0	22545.5	2499.0	1.0000000
Kenosha	Female	TRUE	61	20000	22499	Low	0.0	22545.5	2499.0	1.0000000
Kenosha	Female	TRUE	62	22500	24999	Low	0.0	22545.5	45.5	0.0182073
Kenosha	Female	TRUE	62	22500	24999	Moderate	22545.5	36072.8	2453.5	0.9817927
Kenosha	Female	TRUE	63	25000	29999	Moderate	22545.5	36072.8	4999.0	1.0000000
Kenosha	Female	TRUE	64	30000	34999	Moderate	22545.5	36072.8	4999.0	1.0000000
Kenosha	Female	TRUE	65	35000	39999	Moderate	22545.5	36072.8	1072.8	0.2146029
Kenosha	Female	TRUE	65	35000	39999	Middle	36072.8	54109.2	3926.2	0.7853971
Kenosha	Female	TRUE	66	40000	44999	Middle	36072.8	54109.2	4999.0	1.0000000
Kenosha	Female	TRUE	67	45000	49999	Middle	36072.8	54109.2	4999.0	1.0000000
Kenosha	Female	TRUE	68	50000	54999	Middle	36072.8	54109.2	4109.2	0.8220044
Kenosha	Female	TRUE	68	50000	54999	Upper	54109.2	Inf	889.8	0.1779956
Kenosha	Female	TRUE	69	55000	64999	Upper	54109.2	Inf	9999.0	1.0000000
Kenosha	Female	TRUE	70	65000	74999	Upper	54109.2	Inf	9999.0	1.0000000
Kenosha	Female	TRUE	71	75000	99999	Upper	54109.2	Inf	24999.0	1.0000000
Kenosha	Female	TRUE	72	100000	Inf	Upper	54109.2	Inf	Inf	1.0000000
Kenosha	Female	FALSE	76	1	2499	Low	0.0	22545.5	2498.0	1.0000000
Kenosha	Female	FALSE	77	2500	4999	Low	0.0	22545.5	2499.0	1.0000000
Kenosha	Female	FALSE	78	5000	7499	Low	0.0	22545.5	2499.0	1.0000000
Kenosha	Female	FALSE	79	7500	9999	Low	0.0	22545.5	2499.0	1.0000000
Kenosha	Female	FALSE	80	10000	12499	Low	0.0	22545.5	2499.0	1.0000000
Kenosha	Female	FALSE	81	12500	14999	Low	0.0	22545.5	2499.0	1.0000000
Kenosha	Female	FALSE	82	15000	17499	Low	0.0	22545.5	2499.0	1.0000000
Kenosha	Female	FALSE	83	17500	19999	Low	0.0	22545.5	2499.0	1.0000000
Kenosha	Female	FALSE	84	20000	22499	Low	0.0	22545.5	2499.0	1.0000000
Kenosha	Female	FALSE	85	22500	24999	Low	0.0	22545.5	45.5	0.0182073
Kenosha	Female	FALSE	85	22500	24999	Moderate	22545.5	36072.8	2453.5	0.9817927
Kenosha	Female	FALSE	86	25000	29999	Moderate	22545.5	36072.8	4999.0	1.0000000
Kenosha	Female	FALSE	87	30000	34999	Moderate	22545.5	36072.8	4999.0	1.0000000
Kenosha	Female	FALSE	88	35000	39999	Moderate	22545.5	36072.8	1072.8	0.2146029
Kenosha	Female	FALSE	88	35000	39999	Middle	36072.8	54109.2	3926.2	0.7853971
Kenosha	Female	FALSE	89	40000	44999	Middle	36072.8	54109.2	4999.0	1.0000000
Kenosha	Female	FALSE	90	45000	49999	Middle	36072.8	54109.2	4999.0	1.0000000
Kenosha	Female	FALSE	91	50000	54999	Middle	36072.8	54109.2	4109.2	0.8220044
Kenosha	Female	FALSE	91	50000	54999	Upper	54109.2	Inf	889.8	0.1779956
Kenosha	Female	FALSE	92	55000	64999	Upper	54109.2	Inf	9999.0	1.0000000
Kenosha	Female	FALSE	93	65000	74999	Upper	54109.2	Inf	9999.0	1.0000000
Kenosha	Female	FALSE	94	75000	99999	Upper	54109.2	Inf	24999.0	1.0000000
Kenosha	Female	FALSE	95	100000	Inf	Upper	54109.2	Inf	Inf	1.0000000
Racine	Male	TRUE	6	1	2499	Low	0.0	21566.5	2498.0	1.0000000
Racine	Male	TRUE	7	2500	4999	Low	0.0	21566.5	2499.0	1.0000000
Racine	Male	TRUE	8	5000	7499	Low	0.0	21566.5	2499.0	1.0000000
Racine	Male	TRUE	9	7500	9999	Low	0.0	21566.5	2499.0	1.0000000
Racine	Male	TRUE	10	10000	12499	Low	0.0	21566.5	2499.0	1.0000000
Racine	Male	TRUE	11	12500	14999	Low	0.0	21566.5	2499.0	1.0000000
Racine	Male	TRUE	12	15000	17499	Low	0.0	21566.5	2499.0	1.0000000
Racine	Male	TRUE	13	17500	19999	Low	0.0	21566.5	2499.0	1.0000000
Racine	Male	TRUE	14	20000	22499	Low	0.0	21566.5	1566.5	0.6268507
Racine	Male	TRUE	14	20000	22499	Moderate	21566.5	34506.4	932.5	0.3731493
Racine	Male	TRUE	15	22500	24999	Moderate	21566.5	34506.4	2499.0	1.0000000
Racine	Male	TRUE	16	25000	29999	Moderate	21566.5	34506.4	4999.0	1.0000000
Racine	Male	TRUE	17	30000	34999	Moderate	21566.5	34506.4	4506.4	0.9014603
Racine	Male	TRUE	17	30000	34999	Middle	34506.4	51759.6	492.6	0.0985397
Racine	Male	TRUE	18	35000	39999	Middle	34506.4	51759.6	4999.0	1.0000000
Racine	Male	TRUE	19	40000	44999	Middle	34506.4	51759.6	4999.0	1.0000000
Racine	Male	TRUE	20	45000	49999	Middle	34506.4	51759.6	4999.0	1.0000000
Racine	Male	TRUE	21	50000	54999	Middle	34506.4	51759.6	1759.6	0.3519904
Racine	Male	TRUE	21	50000	54999	Upper	51759.6	Inf	3239.4	0.6480096
Racine	Male	TRUE	22	55000	64999	Upper	51759.6	Inf	9999.0	1.0000000
Racine	Male	TRUE	23	65000	74999	Upper	51759.6	Inf	9999.0	1.0000000
Racine	Male	TRUE	24	75000	99999	Upper	51759.6	Inf	24999.0	1.0000000
Racine	Male	TRUE	25	100000	Inf	Upper	51759.6	Inf	Inf	1.0000000
Racine	Male	FALSE	29	1	2499	Low	0.0	21566.5	2498.0	1.0000000
Racine	Male	FALSE	30	2500	4999	Low	0.0	21566.5	2499.0	1.0000000
Racine	Male	FALSE	31	5000	7499	Low	0.0	21566.5	2499.0	1.0000000
Racine	Male	FALSE	32	7500	9999	Low	0.0	21566.5	2499.0	1.0000000
Racine	Male	FALSE	33	10000	12499	Low	0.0	21566.5	2499.0	1.0000000
Racine	Male	FALSE	34	12500	14999	Low	0.0	21566.5	2499.0	1.0000000
Racine	Male	FALSE	35	15000	17499	Low	0.0	21566.5	2499.0	1.0000000
Racine	Male	FALSE	36	17500	19999	Low	0.0	21566.5	2499.0	1.0000000
Racine	Male	FALSE	37	20000	22499	Low	0.0	21566.5	1566.5	0.6268507
Racine	Male	FALSE	37	20000	22499	Moderate	21566.5	34506.4	932.5	0.3731493
Racine	Male	FALSE	38	22500	24999	Moderate	21566.5	34506.4	2499.0	1.0000000
Racine	Male	FALSE	39	25000	29999	Moderate	21566.5	34506.4	4999.0	1.0000000
Racine	Male	FALSE	40	30000	34999	Moderate	21566.5	34506.4	4506.4	0.9014603
Racine	Male	FALSE	40	30000	34999	Middle	34506.4	51759.6	492.6	0.0985397
Racine	Male	FALSE	41	35000	39999	Middle	34506.4	51759.6	4999.0	1.0000000
Racine	Male	FALSE	42	40000	44999	Middle	34506.4	51759.6	4999.0	1.0000000
Racine	Male	FALSE	43	45000	49999	Middle	34506.4	51759.6	4999.0	1.0000000
Racine	Male	FALSE	44	50000	54999	Middle	34506.4	51759.6	1759.6	0.3519904
Racine	Male	FALSE	44	50000	54999	Upper	51759.6	Inf	3239.4	0.6480096
Racine	Male	FALSE	45	55000	64999	Upper	51759.6	Inf	9999.0	1.0000000
Racine	Male	FALSE	46	65000	74999	Upper	51759.6	Inf	9999.0	1.0000000
Racine	Male	FALSE	47	75000	99999	Upper	51759.6	Inf	24999.0	1.0000000
Racine	Male	FALSE	48	100000	Inf	Upper	51759.6	Inf	Inf	1.0000000
Racine	Female	TRUE	53	1	2499	Low	0.0	21566.5	2498.0	1.0000000
Racine	Female	TRUE	54	2500	4999	Low	0.0	21566.5	2499.0	1.0000000
Racine	Female	TRUE	55	5000	7499	Low	0.0	21566.5	2499.0	1.0000000
Racine	Female	TRUE	56	7500	9999	Low	0.0	21566.5	2499.0	1.0000000
Racine	Female	TRUE	57	10000	12499	Low	0.0	21566.5	2499.0	1.0000000
Racine	Female	TRUE	58	12500	14999	Low	0.0	21566.5	2499.0	1.0000000
Racine	Female	TRUE	59	15000	17499	Low	0.0	21566.5	2499.0	1.0000000
Racine	Female	TRUE	60	17500	19999	Low	0.0	21566.5	2499.0	1.0000000
Racine	Female	TRUE	61	20000	22499	Low	0.0	21566.5	1566.5	0.6268507
Racine	Female	TRUE	61	20000	22499	Moderate	21566.5	34506.4	932.5	0.3731493
Racine	Female	TRUE	62	22500	24999	Moderate	21566.5	34506.4	2499.0	1.0000000
Racine	Female	TRUE	63	25000	29999	Moderate	21566.5	34506.4	4999.0	1.0000000
Racine	Female	TRUE	64	30000	34999	Moderate	21566.5	34506.4	4506.4	0.9014603
Racine	Female	TRUE	64	30000	34999	Middle	34506.4	51759.6	492.6	0.0985397
Racine	Female	TRUE	65	35000	39999	Middle	34506.4	51759.6	4999.0	1.0000000
Racine	Female	TRUE	66	40000	44999	Middle	34506.4	51759.6	4999.0	1.0000000
Racine	Female	TRUE	67	45000	49999	Middle	34506.4	51759.6	4999.0	1.0000000
Racine	Female	TRUE	68	50000	54999	Middle	34506.4	51759.6	1759.6	0.3519904
Racine	Female	TRUE	68	50000	54999	Upper	51759.6	Inf	3239.4	0.6480096
Racine	Female	TRUE	69	55000	64999	Upper	51759.6	Inf	9999.0	1.0000000
Racine	Female	TRUE	70	65000	74999	Upper	51759.6	Inf	9999.0	1.0000000
Racine	Female	TRUE	71	75000	99999	Upper	51759.6	Inf	24999.0	1.0000000
Racine	Female	TRUE	72	100000	Inf	Upper	51759.6	Inf	Inf	1.0000000
Racine	Female	FALSE	76	1	2499	Low	0.0	21566.5	2498.0	1.0000000
Racine	Female	FALSE	77	2500	4999	Low	0.0	21566.5	2499.0	1.0000000
Racine	Female	FALSE	78	5000	7499	Low	0.0	21566.5	2499.0	1.0000000
Racine	Female	FALSE	79	7500	9999	Low	0.0	21566.5	2499.0	1.0000000
Racine	Female	FALSE	80	10000	12499	Low	0.0	21566.5	2499.0	1.0000000
Racine	Female	FALSE	81	12500	14999	Low	0.0	21566.5	2499.0	1.0000000
Racine	Female	FALSE	82	15000	17499	Low	0.0	21566.5	2499.0	1.0000000
Racine	Female	FALSE	83	17500	19999	Low	0.0	21566.5	2499.0	1.0000000
Racine	Female	FALSE	84	20000	22499	Low	0.0	21566.5	1566.5	0.6268507
Racine	Female	FALSE	84	20000	22499	Moderate	21566.5	34506.4	932.5	0.3731493
Racine	Female	FALSE	85	22500	24999	Moderate	21566.5	34506.4	2499.0	1.0000000
Racine	Female	FALSE	86	25000	29999	Moderate	21566.5	34506.4	4999.0	1.0000000
Racine	Female	FALSE	87	30000	34999	Moderate	21566.5	34506.4	4506.4	0.9014603
Racine	Female	FALSE	87	30000	34999	Middle	34506.4	51759.6	492.6	0.0985397
Racine	Female	FALSE	88	35000	39999	Middle	34506.4	51759.6	4999.0	1.0000000
Racine	Female	FALSE	89	40000	44999	Middle	34506.4	51759.6	4999.0	1.0000000
Racine	Female	FALSE	90	45000	49999	Middle	34506.4	51759.6	4999.0	1.0000000
Racine	Female	FALSE	91	50000	54999	Middle	34506.4	51759.6	1759.6	0.3519904
Racine	Female	FALSE	91	50000	54999	Upper	51759.6	Inf	3239.4	0.6480096
Racine	Female	FALSE	92	55000	64999	Upper	51759.6	Inf	9999.0	1.0000000
Racine	Female	FALSE	93	65000	74999	Upper	51759.6	Inf	9999.0	1.0000000
Racine	Female	FALSE	94	75000	99999	Upper	51759.6	Inf	24999.0	1.0000000
Racine	Female	FALSE	95	100000	Inf	Upper	51759.6	Inf	Inf	1.0000000

Higher Expectations for Racine County

Ben Taft