diff --git a/R/prep_pages.R b/R/prep_pages.R index ab7bdee..c2bae3f 100644 --- a/R/prep_pages.R +++ b/R/prep_pages.R @@ -26,7 +26,7 @@ library(glue) #' columns of the datasets pointed to by the url argument #' dirname - location of where knitted hmtl files should be saved #' -prep_pages <- function(url, output_directory, state_title = FALSE, bespoke = TRUE) { +prep_pages <- function(url, output_directory, state_title = FALSE, fake_labels = FALSE, bespoke = TRUE) { # read in the applicant list app_list <- read_csv(url) @@ -57,16 +57,16 @@ prep_pages <- function(url, output_directory, state_title = FALSE, bespoke = TRU # create a data frame with parameters and output file names runs <- tibble( filename = "index.html", # creates a string with output file names in the form .pdf - params = map(app_indexes, ~list(state_county = ., state_title = state_title)), - dir_name = paste0(output_directory, '/', full_name_lst, "_", app_list$random_id) + params = map(app_indexes, ~list(state_county = ., state_title = state_title, fake_labels = fake_labels)), + dir_name = paste0(output_directory, '/', full_name_lst, "_", app_list$random_id, "/") ) # creates a nest list of parameters for each object in the index } else { # create a data frame with parameters and output file names runs <- tibble( filename = "index.html", # creates a string with output file names in the form .pdf - params = map(app_indexes, ~list(state_county = ., state_title = state_title)), - dir_name = map(full_name_lst, function(x) paste0(output_directory, glue('/{x}'))) + params = map(app_indexes, ~list(state_county = ., state_title = state_title, fake_labels = fake_labels)), + dir_name = map(full_name_lst, function(x) paste0(output_directory, glue('/{x}/'))) ) # creates a nest list of parameters for each object in the index } diff --git a/create_bespoke_pages.R b/create_bespoke_pages.R index db19f24..6c02685 100644 --- a/create_bespoke_pages.R +++ b/create_bespoke_pages.R @@ -144,4 +144,9 @@ prepped24 <- prep_pages(url = "data/24_website_requests.csv", render_pages(prepped_object = prepped24) +# Upward County, MB +prepped25 <- prep_pages(url = "data/25_upward-county.csv", + output_directory = "factsheets/25_upward-county", + fake_labels = "yes") +render_pages(prepped_object = prepped25, input = "index-county.qmd") diff --git a/data/25_upward-county.csv b/data/25_upward-county.csv new file mode 100644 index 0000000..a9feafc --- /dev/null +++ b/data/25_upward-county.csv @@ -0,0 +1,2 @@ +state,county,fips,comparisons,random_id +MB, Upward County,51760,, diff --git a/factsheets/25_upward-county/51760-Upward-MB_NA/.gitignore b/factsheets/25_upward-county/51760-Upward-MB_NA/.gitignore new file mode 100644 index 0000000..075b254 --- /dev/null +++ b/factsheets/25_upward-county/51760-Upward-MB_NA/.gitignore @@ -0,0 +1 @@ +/.quarto/ diff --git a/factsheets/25_upward-county/51760-Upward-MB_NA/description.html b/factsheets/25_upward-county/51760-Upward-MB_NA/description.html new file mode 100644 index 0000000..45d3bbe --- /dev/null +++ b/factsheets/25_upward-county/51760-Upward-MB_NA/description.html @@ -0,0 +1,4688 @@ + + + + + + + + + +Upward Mobility from Poverty Metric Descriptions + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Upward Mobility from Poverty Metric Descriptions

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Pillar: Opportunity-Rich & Inclusive Neighborhoods

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PREDICTOR: HOUSING AFFORDABILITY
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Metric: Ratio of affordable and available housing units to households with low, very low, and extremely low income levels

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This metric reports the number of available housing units affordable for households with low (below 80 percent of area median income, or AMI), very low (below 50 percent of AMI), and extremely low (below 30 percent of AMI) incomes relative to every 100 households with these income levels. Housing units are defined as affordable if the monthly costs do not exceed 30 percent of a household’s income. For this metric, the stock of available housing units includes both vacant and occupied units and both rental and homeowner units. A unit is considered available for households at a given level of income if its monthly cost is affordable at that income level—regardless of the income of the current occupant.

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Validity: Affordable housing ratios of this type are widely applied in studies of local housing market conditions and trends. Both the income categories and the affordability standard are well established and accepted in both research and policy.

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Availability: This metric can be constructed using data from the US Census Bureau’s American Community Survey and income categories defined by the US Department of Housing and Urban Development, both of which are publicly available nationwide.

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Frequency: This metric can be updated annually. For less populous communities, it may be necessary to pool multiple years of data and report moving averages.

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Geography: This metric is available at the county and city level.

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Consistency: Affordable housing ratios can be computed consistently for all counties and cities over time. Because the income categories are calculated relative to AMI, the affordability metric appropriately reflects local economic conditions.

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Subgroups: Because these ratios focus on the characteristics of the housing stock, stratifying by demographic subgroups is not relevant. However, housing units in each affordability category can be stratified by size (number of bedrooms) and tenure (owned or rented).

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Limitations: These shares do not reflect the quality of the available and affordable housing units. Units counted as available and affordable for households with low or very low incomes may be of poor quality or too small to meet household needs. This metric is somewhat sensitive to patterns of residential mobility. For example, if the number of households with very low incomes were to decline (because of out-migration), this metric would show improvement even if no additional affordable units were produced. We calculate both the number of low-income households and the number of affordable houses by looking at the income threshold for a family of four, regardless of the size of the actual household in question.

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PREDICTOR: HOUSING STABILITY
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Metric: Number and share of public school children who are ever homeless during the school year

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This metric identifies the number of children (age 3 through 12th grade) who are enrolled in public schools and whose primary nighttime residence at any time during a school year was a shelter, transitional housing, or awaiting foster care placement; unsheltered (e.g., a car, park, campground, temporary trailer, or abandoned building); a hotel or motel because of the lack of alternative adequate accommodations; or in housing of other people because of loss of housing, economic hardship, or a similar reason.

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Validity: These data are reported by school administrators and generally verified by local liaisons and state coordinators. This is a direct and well-established measure of homelessness for children that results from and reflects housing instability among families and unaccompanied children. The definition of homelessness used for this measure extends beyond literal homelessness to effectively include the full range of circumstances in which a family does not have a stable home of their own.

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Availability: The US Department of Education requires every local education agency to collect and report these data and data are made public nationwide.

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Frequency: This metric can be updated annually.

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Geography: This metric is available at the county and city level. The boundaries of local education agencies can be aggregated to other geographies.

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Consistency: This measure is consistently defined, collected, and reported for all local education agencies nationwide.

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Subgroups: This metric can be disaggregated based on students’ disability status and whether they are enrolled in English-as–a-second-language courses.

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Limitations: Because this metric is limited to public-school children, it does not capture homeless adults who are childless, and it does not capture homelessness among children who do not enroll in public school. Further, it could show improvement if the families of homeless children move to a neighboring jurisdiction or if policies “push” them out. This metric is sensitive to patterns of residential mobility if large numbers of families with very low incomes flow into or out of a local education agency’s boundary.

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PREDICTOR: ECONOMIC INCLUSION
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Metric: Share of residents experiencing poverty who live in high-poverty neighborhoods

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This metric measures the share of residents in a city or county who are experiencing poverty and who live in high-poverty neighborhoods (measured by census tract). People and families are classified as being in poverty if their income (before taxes and excluding capital gains or noncash benefits) is less than their poverty threshold, as defined by the US Census Bureau. Poverty thresholds vary by the size of the family and age of its members and are updated for inflation, but do not vary geographically. A high-poverty neighborhood is one in which over 40 percent of the residents are experiencing poverty.

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Validity: Measures of poverty concentration have been widely used to measure the extent and severity of economic exclusion and isolation. The more concentrated and separate people in poverty are from better-resourced neighbors, the more isolated they are from the larger community and the social and economic resources and opportunities it can provide. Because this metric reflects the structural conditions facing a city or county’s residents, changes in the metric possibly caused by people moving into or out of a jurisdiction do represent changes to those structural conditions.

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Availability: The metric can be constructed using data from the US Census Bureau’s American Community Survey, which is publicly available nationwide.

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Frequency: This metric can be updated annually.

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Geography: This metric is available at the county and city level; however, aggregation to the city level is imprecise because census tracts and census places do not always perfectly align.

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Consistency: Poverty concentration can be consistently defined and calculated for all cities and counties over time.

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Subgroups: This metric can be disaggregated by the race and ethnicity of the head of household.

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Limitations: This measure can be sensitive to the overall poverty rate of a city or county. Therefore, changes in poverty concentrations need to be assessed with reference to the city or county’s overall poverty rate.

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PREDICTOR: RACIAL DIVERSITY
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Metric: Index of people’s exposure to neighbors of different races and ethnicities

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This metric is constructed separately for each racial or ethnic group that is readily available and reliable, and reports the share of each group’s neighbors who are members of other racial or ethnic groups. The exposure index reflects the racial and ethnic diversity of neighborhoods. For example, the exposure index would report the share of people who are Hispanic; white, non-Hispanic; and other races and ethnicities in the census tract of the average Black, non-Hispanic person. Higher values of the index indicate more neighborhood diversity and more day-to-day exposure of people to neighbors of different races and ethnicities.

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Validity: The exposure index is one of several widely used measures of residential segregation or inclusion. It captures the multiracial or multiethnic diversity of American communities today and reflects the experience of individuals of different races and ethnicities, and it provides a robust picture of neighborhood racial and ethnic composition. Because this metric reflects the structural conditions facing a city or county’s residents, changes in the metric that may be caused by people moving into or out of a jurisdiction do represent changes to those structural conditions.

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Availability: The metric can be constructed using data from the US Census Bureau’s American Community Survey, which is publicly available nationwide.

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Frequency: This metric can be updated annually.

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Geography: This metric is available at the county and city level; however, aggregation to the city level is imprecise because census tracts and census places do not always perfectly align.

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Consistency: Exposure indexes can be consistently defined and calculated for all cities and counties over time.

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Subgroups: This metric is by definition disaggregated by race or ethnicity.

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Limitations: This measure can be sensitive to the overall racial or ethnic composition of a city or county. Therefore, changes in exposure indexes need to be assessed with reference to the city or county’s overall racial or ethnic composition. Further, although this index can be constructed annually, it may take many years to observe appreciable changes.

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PREDICTOR: SOCIAL CAPITAL
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Metric: Number of membership associations per 10,000 people

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Based on the US Census Bureau’s County Business Patterns (CBP) data, which measure the total number and type of establishments for all counties in the US, this metric is a ratio of the number of membership associations (e.g., civic organizations, bowling centers, golf clubs, fitness centers, sports organizations, religious organizations, political organizations, labor organizations, business organizations, and professional organizations) per 10,000 people in a given community.

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Validity: The CBP data from the US Census Bureau are well established and widely used by academics and other researchers across the country. Research supports its use as a measure for social trust because social trust is enhanced when people belong to voluntary groups and organizations. People who belong to such groups tend to trust others who belong to the same group. The more such groups per person, the more likely that individuals in those communities belong to one or more groups.

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Availability: The metric can be constructed using the CBP dataset, which is publicly available nationwide.

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Frequency: This metric can be updated annually.

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Geography: This metric is available at the county and city level. This metric is also available at the ZIP code level.

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Consistency: This metric is clearly defined and has been consistently measured across time since 2012 and for the entire population of the US. CBP data are derived from the Business Register, maintained and updated by the Census Bureau to track all known single- and multi-establishment employer companies in the US.

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Structural equity* and subgroups: Data used to construct this metric can reflect racial or socioeconomic subgroups by matching it with demographic characteristics at the ZIP code level, such as racial or ethnic composition or concentrated poverty.

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Structural relevance*: This metric reflects the availability of opportunities for engagement and relationship-building and important structural support for social capital.

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Limitations: This metric captures only a certain aspect of social capital. It is trying to measure social associations within a community and is likely the best measure within the context of business and professional organizations. Nevertheless, it cannot capture (a) social associations at the granular, individual level, or (b) smaller, more informal organizations that would not be in a position to self-report to the Business Register.

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* These selection criteria on structural equity and structural relevance were explicitly added in a review of new metrics added to the Upward Mobility Framework in fall 2022.

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PREDICTOR: SOCIAL CAPITAL
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Metric: Ratio of Facebook friends with higher socioeconomic status to Facebook friends with lower socioeconomic status

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“Economic connectedness” measures the extent to which low- and high-socioeconomic status individuals are friends with one another. Specifically, the economic connectedness metric is twice the average share of high-socioeconomic-status friends (individuals from households ranked in the top half of all income-earning households) among low-socioeconomic-status individuals (individuals from households ranked in the lower half of all US households based on income) in a given community. An economic connectedness measure of 1 represents a community that is perfectly integrated across socioeconomic status, with half of all low-socioeconomic status individuals’ friends being of high socioeconomic status. The metric is meant to be a measure of the interconnectivity, by location, between people from different economic backgrounds (i.e., with different levels of social capital and exposure).

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Validity: This metric measures an important aspect of social capital: the extent to which members of a community associate with people with varying social statuses. The connections made through this type of social capital help facilitate and develop an individual’s power, autonomy, and sense of belonging in their community. These data come from research that has been peer reviewed and published.

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Availability: The metric is publicly available through Opportunity Insights’ Social Capital Atlas.

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Frequency: These data were released in summer 2022. Because this was an inaugural data release contingent on the publication of new research, it is unclear if it will be updated or with what frequency.

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Geography: This metric is available at the county and city level. These data are available nationally in a standardized format for all counties. The smallest geography for which these data are available is the Zip Code Tabulation Area (ZCTA) level, which can be approximately aggregated to other geographies.

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Consistency: This metric is clearly defined and is currently consistently measured across populations and geographies. We cannot know if it will be consistently measured across time, because it is new and has yet to be updated.

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Structural equity* and subgroups: Data used to construct this metric can reflect racial or socioeconomic subgroups by matching it with demographic characteristics at the ZIP code level, such as racial or ethnic composition or concentrated poverty. This metric will help identify structural equity in the community by identifying the relative abundance or lack of social cohesion between community members with different levels of socioeconomic status.

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Structural relevance*: This metric is a systemic condition of economic mobility because it speaks to what level of socioeconomic intermingling is supported in the social environment of a given area. This metric, however, is also an outcome of greater economic mobility, since economic connectedness is positively correlated with increased mobility from poverty.

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Limitations: The biggest limitation of this metric is its novelty. Although it was developed by reputable scholars and peer reviewed, it lacks the established track record of other metrics. Moreover, it focuses on the financial aspects of social capital and does not capture other important elements like popularity and community ties. This metric may be sensitive to residential mobility into and out of a city or county, but not to an extent likely to affect its aggregate values. This metric also relies on the continued popularity and use of Facebook as a social media platform, the user base of which has been skewing older in recent years. Without continued engagement from the same user groups and the introduction of younger populations as they age, comparability and consistency over time may be compromised. This metric can only be calculated for ZIP codes containing at least 100 people.

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* These selection criteria on structural equity and structural relevance were explicitly added in a review of new metrics added to the Upward Mobility Framework in fall 2022.

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PREDICTOR: TRANSPORTATION ACCESS
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Metric: Transit trips index

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This metric reflects the number of public transit trips taken annually at the census tract level by a three-person single-parent family with income at 50 percent of the Area Median Income (AMI) for renters. This number is percentile ranked nationally into an index with values ranging from 0 to 100 for each census tract. Higher scores reflect better access to public transportation.

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Validity: This metric was designed in partnership with the US Department of Transportation and has been used by the US Department of Housing and Urban Development in community efforts to affirmatively further fair housing. Several scholars have also used this metric and data in published in peer-reviewed journals. Although other arrangements of family composition, income, and housing status are possible in constructing this index and are available in the data, these characteristics were intended to more closely characterize a lower-income household in the community and are the most validated of other household combinations.

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Availability: The estimates come from the Location Affordability Index, which is publicly available.

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Frequency: This metric used to be released every three years. The Urban Institute is working to develop an updated version of the metric.

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Geography: This can be measured at the census tract or neighborhood level. Values can be averaged at higher levels of geography. For example, one can calculate a population-weighted average value among all census tracts in a county to determine a county-level value.

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Consistency: This metric can be calculated the same way over time.

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Subgroups: This metric is based on a lower-income population, notably single-parent families with two children earning half the local AMI among renters.

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Limitations: This metric cannot alone capture the concept of transportation access. This must be used in partnership with the transportation cost index to cover geographies that may not have an extensive public transportation system, such as rural areas.

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PREDICTOR: TRANSPORTATION ACCESS
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Metric: Transportation cost index

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This index reflects local transportation costs as a share of renters’ incomes. It accounts for both transit and cars. This index is based on estimates of transportation costs for a three-person, single-parent family with income at 50 percent of the median income for renters for the region (i.e., a core-based statistical area). Although other arrangements of family composition, income, and housing status are possible in constructing this index, these characteristics were intended to more closely characterize a lower-income household in the community. Values are inverted and percentile ranked nationally, with values ranging from 0 to 100. The higher the value, the lower the cost of transportation in that neighborhood.

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Validity: This metric was designed in partnership with the US Department of Transportation and has been used by the US Department of Housing and Urban Development in community efforts to affirmatively further fair housing. Several scholars have also used this metric and data in articles in peer-reviewed journals.

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Availability: The estimates come from the location affordability index, which are publicly available.

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Frequency: The location affordability index data are updated every three years.

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Geography: This can be measured at the census tract or neighborhood level. Values can be averaged at higher levels of geography. For example, one can calculate a population-weighted average value among all census tracts in a county to determine a county-level value.

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Consistency: This metric can be calculated the same way over time.

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Subgroups: This metric is based on a lower-income population, notably single-parent families earning half the local area median income among renters.

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Limitations: Transportation costs may be low for a variety of reasons, including greater access to public transportation and the density of homes, services, and jobs in the neighborhood and surrounding community. It is important not to consider this metric alone but rather in combination with the transit trips index to more fully measure the concept of transportation access.

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Pillar: High-Quality Education

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PREDICTOR: ACCESS TO PRESCHOOL
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Metric: Share of children enrolled in nursery school or preschool

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This metric measures the share of 3- and 4-year-old children in a community who are enrolled in nursery school or preschool.

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Validity: Federal agencies such as the National Center for Education Statistics use household survey data to ascertain nursery and preschool enrollment.

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Availability: The metric can be constructed using data from the US Census Bureau’s American Community Survey (ACS), which is publicly available nationwide.

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Frequency: This metric can be updated annually. Survey data are collected annually and used to create one- and five-year estimates. For less populous communities, it may be necessary to pool several years of data to obtain reliable estimates.

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Geography: This metric is available at the county and city level.

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Consistency: Information pertaining to nursery and preschool enrollment in the ACS is measured the same way across all geographies in the same year. Changes to the ACS in the future could influence comparisons over time.

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Subgroups: The data can be disaggregated by race or ethnicity, gender, and other demographic factors. For less populous communities and for certain demographic groups, several years of data may need to be pooled to provide reliable estimates.

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Limitations: This metric can change over time if fertility patterns change or if families with young children who move out of or into the community have very different propensities for enrolling their children in preschools than parents with young children who remain in the community. Because ACS data do not capture the quality of preschool, enrollment figures may overstate exposure to the kinds of programs most likely to improve short-term academic outcomes and long-term outcomes such as mobility from poverty.

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PREDICTOR: EFFECTIVE PUBLIC EDUCATION
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Metric: Average per-grade change in English language arts achievement between third and eighth grades

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This metric reports the average annual improvement in English language arts (reading comprehension and written expression) observed between the third and eighth grades.

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Validity: The state assessments are well defined and validated but vary by state. Because the Stanford Education Data Archive (SEDA) has standardized these to be nationally comparable, this metric has been widely used and a reliable measure of student achievement. Higher rates of improvement in English indicate more effective public education, which improves the upward mobility of children from less advantaged backgrounds.

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Availability: The metric can be constructed using data from the SEDA, which is publicly available nationwide.

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Frequency: This metric can be updated annually.

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Geography: This metric is available at the county, metropolitan area, and school district level.

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Consistency: Tests of student progress vary from state to state and can change over time if states modify their tests. The SEDA has standardized these to be nationally comparable.

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Subgroups: The SEDA has adjusted scores by race or ethnicity, gender, and socioeconomic status. SEDA data comes from EDFacts, which reports proficiency levels based on raw data by race or ethnicity, gender, disability status, limited English proficiency status, homeless status, migrant status, and economically disadvantaged status.

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Limitations: Literacy performance reported in “levels” is sensitive to movement in and out of a community over time.

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PREDICTOR: SCHOOL ECONOMIC DIVERSITY
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Metric: Share of students attending high-poverty schools, by student race or ethnicity

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This set of metrics is constructed separately for each racial or ethnic group and reports the share of students attending schools in which over 20 percent of students come from households earning at or below 100 percent of the federal poverty level.

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Validity: This set of metrics captures the interaction of economic and racial segregation of schools and therefore reveals whether (and to what degree) students of color are more likely than white students to attend schools with large concentrations of classmates experiencing poverty. Higher concentrations of students experiencing poverty are associated with worse achievement for all the students in a school.

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Availability: The metric can be constructed using data from the National Center for Education Statistics Common Core of Data and the Urban Institute’s Modeled Estimates of Students in Poverty, which are publicly available nationwide.

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Frequency: This metric can be updated annually.

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Geography: This metric is available at the county and city level. These data exist at the school district, city, and county levels. Because this metric reflects the structural conditions facing a city or county’s students, changes in the metric that may result from people moving into or out of a community may represent changes to those structural conditions.

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Consistency: This metric can be consistently defined and calculated for cities and counties.

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Subgroups: This metric is by definition disaggregated by race or ethnicity.

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Limitations: Because traditional proxies for school poverty (i.e., the share of free- and reduced-price-meal students or the share of students directly certified for free meals) have grown inconsistent across time and states, this metric uses the Urban Institute’s Model Estimates of Poverty in Schools to identify school poverty levels.

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PREDICTOR: PREPARATION FOR COLLEGE
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Metric: Share of 19- and 20-year-olds with a high school degree

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This metric is the share of 19- and 20-year-olds in a community who have a high school degree.

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Validity: Earning a high school degree is an important prerequisite for pursuing additional schooling, and although not all high school graduates are ready to enroll in college, high school completion is a well-understood and widely used measure of educational attainment. Data on educational attainment are collected in a variety of federal surveys.

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Availability: The metric can be constructed using data from the US Census Bureau’s American Community Survey (ACS), which is publicly available nationwide.

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Frequency: This metric can be updated annually. Survey data are collected annually and used to create one- and five-year estimates. For less populous communities, it may be necessary to pool several years of data to obtain reliable estimates.

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Geography: This metric is available at the county and city level.

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Consistency: Information pertaining to educational attainment in general and on high school graduation in particular is measured the same way across all geographies in the same year in the ACS. Changes to the ACS in the future could influence comparisons over time.

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Subgroups: This metric is disaggregated by race and ethnicity. Data used to construct this metric can be disaggregated by race or ethnicity, gender, and other demographic factors. For less populous communities and for certain demographic groups, several years of data may need to be pooled to provide reliable estimates.

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Limitations: Young adults moving in and out of an area can influence this measure. This measure also does not capture the quality of schooling received.

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PREDICTOR: DIGITAL ACCESS
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Metric: Share of people in households with broadband access in the home

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This is a measure of the share of people in households in a population that have a broadband internet subscription of any type (e.g., DSL, cable modem, fiber-optic cell phone, or satellite) at their place of residence.

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Validity: The US Census Bureau uses a series of questions to measure aspects of digital access across the nation. Existing literature makes extensive use of these measures of digital access.

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Availability: Data on broadband access are available annually from the US Census Bureau’s American Community Survey (ACS), which is publicly available nationwide.

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Frequency: This metric can be updated annually.

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Geography: This metric is available at the county and city level.

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Consistency: The metric can be measured in the same way across geographies and over time, but some changes were made in 2016 to the survey questions required to construct this metric, so values before and after 2016 should not be compared.

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Structural equity* and subgroups: The digital divide is about the degree of inequity in digital access by demographics (including race and ethnicity) and by geography (e.g., urban versus rural). Because this metric can be disaggregated along those dimensions, it can provide insights into structural equity at the community level. The data can be disaggregated by household income and by demographics of the head of household, such as race, ethnicity, and gender.

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Structural relevance*: This metric measures whether individual households have broadband connections to the internet and thus reflect individual choices as well as more structural factors such as affordability and the availability of broadband services

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Limitations: Having broadband internet at home is not useful without a device from which to access it. Access to computing devices is an important component of digital access. Moreover, measuring broadband access alone does not account for other types of digital access (such as access to cellular data), but existing scholarship supports a measure of broadband access as it relates to closing the digital divide. There is no universally available measure of digital access that includes both broadband access and access to computing devices. Those who would like to further investigate digital access in their communities could also look at the data available through the ACS on access to computing devices. Finally, not all broadband is fast enough to meet the needs of all households. Adequate minimum speeds to effectively access all content types (e.g., streaming videos or virtual classrooms) may vary by household.

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* These selection criteria on structural equity and structural relevance were explicitly added in a review of new metrics added to the Upward Mobility Framework updated in fall 2022.

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Pillar: Rewarding Work

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PREDICTOR: EMPLOYMENT OPPORTUNITIES
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Metric: Employment-to-population ratio for adults ages 25 to 54

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This metric is the share of adults ages 25 to 54 in a given community who are employed.

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Validity: An employment ratio captures what share of adults in a community are engaging in work for pay. The prime-age employment-to-population ratio focuses on age groups that are traditionally older than college age and younger than retirement age. The prime-age employment-to-population ratio is a standard labor market indicator, which can be calculated using the Current Population Survey for states and the American Community Survey (ACS) for sub-state areas.

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Availability: The metric can be constructed using data from the ACS, which is publicly available nationwide.

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Frequency: This metric can be updated annually. Survey data are collected annually and used to create one- and five-year estimates. For less populous communities, it may be necessary to pool several years of data to obtain reliable estimates.

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Geography: This metric is available at the county and city level.

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Consistency: Information on employment and age is measured the same way across all geographies in the same year in the ACS. It is highly unlikely that the ACS will change how it captures employment in the future.

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Subgroups: This metric is disaggregated by race and ethnicity. Data used to construct this metric can be disaggregated by race or ethnicity, gender, and other demographic factors. For less populous communities and for certain demographic groups, several years of data may need to be pooled to provide reliable estimates.

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Limitations: The Bureau of Labor Statistics (BLS) reports the official employment-to-population ratio monthly for those ages 16 to 19 and those age 20 and up. As such, the BLS-reported measure could be lower for communities that have many young adults attending college (and not working). Consequently, for our purposes, we recommend computing the employment-to-population ratio for adults ages 25 to 54 using data from the ACS rather than relying on BLS reports. Even when using ACS data, the employment-to-population ratio can drop if people who aren’t working leave an area or if working people move in.

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PREDICTOR: JOBS PAYING A LIVING WAGE
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Metric: Ratio of pay on an average job to the cost of living

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This metric shows average pay relative to the cost of living in a particular area. The metric is computed by dividing the average earnings for a job in an area by the cost of meeting a family of three’s basic expenses in that area.

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Validity: Communities in which typical jobs pay a greater share of the local cost of living provide more opportunities for their residents to move out of poverty. Employer-reported data on wages paid are a reliable indicator of what jobs pay, and our metric is based on data collected and disseminated by the Bureau of Labor Statistics (BLS). Data on what it costs to meet basic expenses requires detailed studies of the cost of food, clothing, shelter, health care, and work-related expenses for each community. We rely on the work of well-regarded scholars at the Massachusetts Institute of Technology (MIT) to obtain estimates of the local cost of living.

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Availability: The metric can be constructed using data from the BLS Quarterly Census of Employment and Wages, and data on living wage from the MIT, which are publicly available nationwide.

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Frequency: This metric can be updated annually.

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Geography: This metric is available at the county and metro levels. Data on wages are available for the 365 largest counties in the US. About 75 percent of the US population live in the 365 largest counties.

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Consistency: Information on weekly wages is collected in a consistent fashion by the BLS. MIT uses a consistent methodology to compute living wages by county.

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Subgroups: This metric cannot be disaggregated into subgroups because these data describe wages rather than the people earning those wages.

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Limitations: The measure cannot be disaggregated into subgroups. The measure relies on MIT’s computations of “living wages.” The living wage data is not available for every year.

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PREDICTOR: OPPORTUNITIES FOR INCOME
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Metric: Household income at the 20th, 50th, and 80th percentiles

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Household income is a standard measure of financial well-being. The three levels help a community to track how and for whom incomes are changing in a given place as well as whether incomes are rising across the board or more so for those with higher incomes. To identify income percentiles, all households are ranked by income from lowest to highest. The income level threshold for the poorest 20 percent of households is the value at the 20th percentile. The 50th percentile income threshold indicates the median, with half of households earning less and half earning more. The income level threshold for the richest 20 percent of households is the value at the 80th percentile.

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Validity: These are well-established and frequently-used measures to assess the financial well-being of families by several federal agencies and many scholars.

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Availability: The metric can be constructed using data from the US Census Bureau’s American Community Survey, which is publicly available nationwide.

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Frequency: This metric can be updated annually. Survey data are collected annually and used to create one- and five-year estimates.

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Geography: This metric is available at the county and city level.

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Consistency: Income data in the ACS are measured the same way across all geographies in the same year. The measure is fairly consistent over time but there could be changes in the phrasing and sequence of income source questions that may affect comparisons over time. When such changes have occurred in other federal surveys, such as the Current Population Survey, the Census Bureau provides bridge year data so users can assess the effects of survey changes.

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Subgroups: This metric is disaggregated by race and ethnicity. Data used to construct this metric can be disaggregated by race or ethnicity, gender, and other demographic factors. For less populous communities and for certain demographic groups, several years of data may need to be pooled to provide reliable estimates.

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Limitations: The purchasing power of any particular level of income will vary based on the local cost of living. Also, because household sizes differ, the same income may be stretched across larger average households in some places relative to others. Like all metrics based on the characteristics of people living in an area, it can change because of residential mobility.

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PREDICTOR: FINANCIAL SECURITY
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Metric: Share with debt in collections

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This measure indicates the share of people in an area with a credit bureau record with debt that has progressed from being past due to being in collections. Debt in collections includes past-due credit lines that have been closed and charged off on the creditor’s books as well as unpaid bills reported to the credit bureaus that the creditor is attempting to collect. For example, credit card accounts enter collections once they are 180 days past due. The city-level measure captures the share of people in an area with a credit bureau record with any derogatory debt, which is primarily debt in collections.

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Validity: Consumers without other accessible financial resources may need to take out debt not only to pay for housing or education but also to pay for daily necessities such as food or utilities. The inability to pay back debts can signal current or near-term financial insecurity, particularly for families with lower incomes. Those households likely have few assets and as such may have negative wealth.

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Availability: Drawn directly from credit reports, the credit bureau data are nationally representative and uniform across the country. The data are restricted and are not accessible directly from credit bureaus but are made available in aggregate form on the Urban Institute’s Debt in America feature and on the Urban Institute’s Financial Health & Wealth Dashboard.

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Frequency: This metric can be updated annually.

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Geography: This metric is available at the ZIP code level which can be aggregated to the county and city level.

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Consistency: The share of consumers with debt in collections can be measured consistently for all geographies. The measure is likely to remain consistent over time, unless the credit bureaus change the way overdue debt is captured in credit reporting. However, Urban Institute features like Debt in America and the Financial Health and Wealth Dashboard have different methodologies for measuring debt in collections.

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Subgroups: This metric can reflect racial or socioeconomic subgroups by matching it with demographic characteristics at the ZIP code level, such as racial or ethnic composition or concentrated poverty.

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Limitations: Aside from the limitations related to geography, subgroup analysis, and the definition of debt in collections, these data do not capture “credit invisible” households without a credit record. And as a measure of financial well-being, even if few consumers have no debt in collections, many may still have too little wealth or savings to be primed for upward mobility. This measure is somewhat sensitive to resident turnover. If many residents without overdue debt move into a county or ZIP code, or if many residents with overdue debt move out, this measure could shift without any in-household change in debt management. The measure from the Urban Institute’s Financial Health & Wealth Dashboard captures all derogatory debt, which is primarily debt in collections, while the measure from the Urban Institute’s Debt in America feature includes debt in collections only.

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PREDICTOR: WEALTH-BUILDING OPPORTUNITIES
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Metric: Ratio of the share of a community’s housing wealth held by a racial or ethnic group to the share of households of the same group

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This metric highlights racial and ethnic disparities in an important source of wealth—disparities that likely reflect structural inequities in wealth-building opportunities. The metric compares a racial or ethnic group’s (measured by the head of household) share of primary-residence housing wealth (measured using self-reported house values) in a community to its share of the total number of households. For example, if Black homeowners have 15 percent of the community’s primary-residence housing wealth but make up 45 percent of the community’s heads of households, then the metric is a ratio of 15%:45%. The greater the gap between these percentages, the more inequity in housing wealth in the community.

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Validity: Although this ratio is not commonly used, each piece of the ratio is. The calculation of primary-residence housing wealth is consistent with the literature. The share of racial and ethnic groups among the household population is commonly used. The juxtaposition of these two shares has been used to highlight housing wealth equity and homeownership wealth gaps.

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Availability: The metric can be constructed using data from the US Census Bureau’s American Community Survey, which is publicly available nationwide.

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Frequency: This metric can be updated annually. For less populous communities, it may be necessary to pool several years of data to obtain reliable estimates.

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Geography: This metric is available at the county and city level.

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Consistency: This metric is defined consistently across race and ethnic groups, is consistently measured over time, and is comparable across geography.

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Structural equity* and subgroups: This metric captures the racial disparities apparent in the ways wealth is shared across a given population. Rather than focusing on an aggregate measure of total homeownership, this helps elucidate any racial or ethnic disparities in comparative housing wealth and how it has been accessed or distributed in that community. These disparities can signal instances of racism as a hindrance to achieving or benefiting from homeownership.

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Structural relevance*: This ratio characterizes the distribution of aggregate housing wealth to describe a community-level condition rather than an individual outcome such as the average value of household wealth.

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Limitations: This metric only captures relative wealth-building opportunities and does not capture absolute wealth-building opportunities. It is possible that a community has an equitable distribution of housing wealth in a community with very little housing wealth. Such a community would rate well under this metric. This metric will also be imprecise or suppressed for communities that lack racial or ethnic diversity. Although we refer to this metric as housing wealth, the data reflect homeowners’ self-assessments of the value of their homes and does not account for mortgage debt. Black and Latino households, on average, buy their homes with more debt, so the racial housing wealth disparities are likely to widen if mortgage debt is incorporated. Also, this metric does not account for other financial costs and benefits of homeownership that could affect wealth building, nor does it account for other important differences such as the average age of people in different racial and ethnic groups. One would expect older people to have higher-value homes than younger people, so some racial and ethnic disparities could be exaggerated by age differences. Therefore, this metric may not fully reflect the size of the actual housing wealth gap and could be misleading without a deeper understanding of homeownership and demographic circumstances in a community. Further, this metric focuses on only one form of wealth based on homeownership, and does not consider other forms of wealth like owning a business, stocks, or bonds. The metric only considers the race of the head of household and does not account for household size.

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* These selection criteria on structural equity and structural relevance were explicitly added in a review of new metrics added to the Upward Mobility Framework in fall 2022.

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Pillar: Healthy Environment and Access to Good Healthcare

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PREDICTOR: ACCESS TO HEALTH SERVICES
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Metric: Ratio of population per primary care physician

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This ratio represents the number of residents per primary care physician in a community. Primary care physicians include practicing non-federal physicians (MDs and DOs) under age 75 specializing in general practice medicine, family medicine, internal medicine, and pediatrics. If a community has a population of 50,000 and has 20 primary care physicians, for example, the ratio would be: 2,500:1. If a community has no primary care physicians, the ratio would be the population size to zero.

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Validity: This metric is defined and established by the US Department of Health and Human Services, Health Resources and Services Administration, Bureau of Health Workforce.

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Availability: The metric can be constructed using data from the US Department of Health and Human Services’ Area Health Resource File, which is publicly available nationwide.

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Frequency: This metric can be updated annually.

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Geography: This metric is available at the county level.

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Consistency: This metric can be measured in the same way across geographies and over time, but the definition of a primary care physician changed in 2013, so values before and after 2013 should not be compared.

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Structural equity* and subgroups: These data cannot be disaggregated by demographic subgroups because we are unable to connect physicians to their patients.

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Structural relevance*: This metric measures the extent to which community residents have access to primary care physicians based on the presence of physicians in the community. As such, it is a structural feature of the community.

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Limitations: Because of financial and insurance constraints, the presence of physicians in an area does not mean that all local residents can access their services or that the care they access is good quality. Conversely, physicians in one county may provide services to residents of another county, and physicians are not the only type of primary care provider available to patients. Nurse practitioners, physician assistants, or other practitioners can also provide primary care services. This metric is not available by demographic subgroups and therefore cannot speak to differences in access by race or ethnicity.

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* These selection criteria on structural equity and structural relevance were explicitly added in a review of new metrics added to the Upward Mobility Framework updated in fall 2022.

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PREDICTOR: NEONATAL HEALTH
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Metric: Share with low birth weight

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A child born weighing less than 5 pounds 8 ounces (<2,500 grams) is considered to have a low birthweight. Children born below that threshold are at elevated risk for health conditions and infant mortality. This metric looks at the share of low-birthweight babies out of all births with available birthweight information.

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Validity: This metric is the standard currently used by the Centers for Disease Control and Prevention as part of their national assessment on health among infants.

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Availability: The metric can be constructed using data from the Centers for Disease Control and Prevention’s National Center for Health Statistics, which is publicly available nationwide.

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Frequency: This metric can be updated annually.

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Geography: This metric is available at the county level. County-level estimates are available through public-use microdata files provided by the National Center for Health Statistics as well as through other data collection efforts, such as the Kids Count Data Center or the CDC WONDER system.

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Consistency: Medical advances have improved the outcomes for low-birthweight babies, implying this metric may change in the future. However, this has been consistently used over decades as a metric for neonatal health.

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Subgroups: This metric is disaggregated by race and ethnicity. The share of children born with low birthweights can be disaggregated by the race or ethnicity of the mother, as well as by the mother’s age.

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Limitations: These data are not readily available at lower levels of geography, such as neighborhoods, where disparities by race and socioeconomic status within a county are most notable. Large movements of women with risky pregnancies moving in or out of a community could influence this metric.

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PREDICTOR: ENVIRONMENTAL QUALITY
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Metric: Air quality index

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The air quality index is an index that summarizes potential exposure to harmful toxins at the neighborhood level. The index is a linear combination of standardized US Environmental Protection Agency (EPA) estimates of air quality carcinogenic, respiratory, and neurological hazards at the census tract level. Values are inverted and then percentile ranked nationally. Values per census tract range from 0 to 100. The higher the index value, the less exposure to toxins harmful to human health.

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Validity: EPA scientists and researchers link air pollutants to health effects that can manifest within a few hours or days after breathing polluted air.

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Consistency: Air pollutants can be consistently measured across time and geographies. However, EPA standards may change in the coming years and should be reevaluated for consistency moving forward.

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Availability: The metric can be constructed using data from the EPA’s AirToxScreen data, which are publicly available nationwide.

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Frequency: Air quality information from the AirToxScreen are updated every three years.

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Geography: This metric is available at the census tract level which can be aggregated to the county and city level.

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Subgroups: This metric can reflect racial or socioeconomic subgroups by matching it with demographic characteristics of residents at the census tract level, such as racial or ethnic composition or concentrated poverty.

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Limitations: These data may not be updated with enough frequency for some communities. Alternative data sources can offer information annually, or even daily, but they are only available at higher levels of geography and for a limited set of places and cannot be disaggregated by subgroups.

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PREDICTOR: SAFETY FROM TRAUMA
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Metric: Deaths due to injury per 100,000 people

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This metric represents the number of deaths of community residents, both from intentional injuries such as homicide or suicide and unintentional injuries such as motor vehicle deaths, per 100,000 people in the community.

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Validity: These data are collected by the National Center for Health Statistics and the Centers for Disease Control and Prevention. Injury can be traumatic, and people living in communities with a high incidence of injury can experience both the direct trauma from injury and vicarious trauma from injuries sustained by others, which can lead to psychological distress, increased rates of aggression, and diminished physical health. High rates of injuries that lead to death in a community, such as opioid overdose, suicide, traffic fatalities, and homicide, can lead to community-level trauma. The County Health Rankings and Roadmaps uses a five-year average to increase data reliability.

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Availability: The metric can be constructed using data from the National Center for Health Statistics Mortality Files and Centers for Disease Control and Prevention’s Wide-Ranging Online Data for Epidemiologic Research, which are publicly available nationwide. The metric is available publicly for counties through the County Health Rankings & Roadmaps website.

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Frequency: This metric can be updated annually.

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Geography: This metric is available at the county level.

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Consistency: The metric can be measured in the same way across geographies and over time.

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Structural equity* and subgroups: This metric is not disaggregated by subgroups, but the underlying data can be disaggregated by race and ethnicity, age, gender, and education level. Because this metric can be disaggregated by race and ethnicity, it can be used to see how much exposure to trauma varies between racial and ethnic groups within a community.

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Structural relevance*: This metric is concerned with individual deaths, but deaths caused by injury can be reflective of both individual-level factors and structural factors such as neighborhood design, crime rates, and access to mental health services.

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Limitations: The metric captures only one aspect of exposure to trauma. Injury more generally (i.e., injuries that don’t lead to deaths) may still cause trauma, but data on that are not nationally available. Data are not available at the city level.

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* These selection criteria on structural equity and structural relevance were explicitly added in a review of new metrics added to the Upward Mobility Framework updated in fall 2022.

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Pillar: Responsible and Just Governance

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PREDICTOR: POLITICAL PARTICIPATION
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Metric: Share of the voting-age population who turn out to vote

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This metric measures the share of the voting-age population that voted in the most recent presidential election.

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Validity: This metric is well established. Scholars of political science have used this metric in articles published in peer-reviewed journals.

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Availability: The metric can be constructed using data reported by local governments, which is publicly available nationwide.

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Frequency: This metric can be updated after every presidential election (every four years).

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Geography: This metric is available at the county and city level; however, aggregation to the city level can be imprecise. These data are broadly available at the electoral district level and can be approximately aggregated to other geographies.

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Consistency: Voter turnout is measured consistently over time and geography, but the values can be volatile from year to year, with higher turnouts in years involving a presidential election, so comparison over time should account for that.

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Subgroups: This metric cannot be directly disaggregated into subgroups because these data do not include voter demographics.

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Limitations: Residential mobility can impact this metric, so it is important to interpret changes in voter turnout in the context of demographic shifts in the community. In local communities with higher rates of non-citizens, voter turnout can inaccurately reflect a community’s political participation. Communities with a population of non-citizens may consider additional local data to better assess political participation and civic engagement. Census data only identifies the citizen voting-age population and does not account for residents who are ineligible for reasons like incarceration or disenfranchisement.

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PREDICTOR: DESCRIPTIVE REPRESENTATION
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Metric: Ratio of the share of local elected officials of a racial or ethnic group to the share of residents of the same racial or ethnic group

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This metric measures the ratio of the share of the city council or county board from specific racial and ethnic groups to the share of city or county residents from those racial or ethnic groups. For example, if a community has 10 elected officials; 9 are white, non-Hispanic; and the community’s population is half white, non-Hispanic, the metric will read as “90.0%:50.0%.” If the share of local officials is higher than the share of people in the community, then this group is overrepresented. If the share of local officials is lower than the share of people in the community, then this group is underrepresented.

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Validity: Scholars of political science have used this metric in articles published in peer-reviewed journals.

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Availability: Data on the racial or ethnic characteristics of city council or county boards can be collected locally. Communities should ask their local elected officials to self-report their racial or ethnic identity (see suggestions for collecting this information in the Boosting Upward Mobility Planning Guide (Fedorowicz et al. 2022, pg. 27)). The racial and ethnic composition of residents in those communities can be calculated using data from the US Census Bureau’s American Community Survey, which is publicly available nationwide.

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Frequency: This metric can be updated as frequently as elections occur. The population figure can be updated annually.

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Geography: This metric is available at the county and city level; however, aggregation to the city level is imprecise because census tracts and census places do not always perfectly align.

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Consistency: This metric can be calculated the same way over time and across geographies.

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Subgroups: This metric accounts for race within its definition, but it may also be calculated for other subgroups.

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Limitations: Although the movement of people in and out of the community can influence this metric, it is likely to be far more sensitive to shifts in the composition of elected officials in the short term. The number and race of public officials must be collected locally, and collecting information on the demographic characteristics of a local official may be challenging if they do not reveal this information publicly and are unwilling to report it to local data collectors.

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PREDICTOR: SAFETY FROM CRIME
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Metric: Reported property crimes per 100,000 people and reported violent crimes per 100,000 people

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The Federal Bureau of Investigation’s (FBI) National Incident-Based Reporting System (NIBRS) provides a standard, well-defined measure of crime. Reported crimes are captured as crimes against persons (e.g., assault, homicide, human trafficking, kidnapping or abduction, and sex offenses) and crimes against property (e.g., arson, bribery, burglary or breaking and entering, counterfeiting or forgery, destruction/damage/vandalism of property, embezzlement, extortion or blackmail, larceny or theft offenses, motor vehicle theft, robbery, and stolen property offenses). Crimes against society and arrest-only offenses are also captured in NIBRS but are not used in our data. This metric shows crime rates as the number of reported crimes per 100,000 people.

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Validity: NIBRS is the most widely used source to measure and compare reported crime across the country. The FBI provides definitions of each variable collected and provides technical specifications and a user manual.

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Availability: The metric can be constructed using data from NIBRS, which is gaining increased participation from agencies across the country. As of 2021, agencies reporting to NIBRS covered 66 percent of the US population. If a community is not included in NIBRS, the relevant and comparable data can be requested by the public directly from their local law enforcement agencies or may be posted on their local law enforcement website.

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Frequency: This metric can be updated annually.

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Geography: This metric is available at the county and city level. The NIBRS data are reported at the agency level. Information about the cities and counties that the agency has jurisdiction over are available in the NIBRS data. Accordingly, the data can be aggregated across all agencies to other geographies.

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Consistency: The NIBRS data are consistent across the communities that provide data to the FBI, because the FBI establishes the definitions of the crimes included. Data may be accumulated and compiled differently at the local level. Prior to January 1, 2021, the FBI’s Uniform Crime Reporting (UCR) Program provided a standard, well-defined measure of crime. Reported crimes were captured for four “index” violent felonies (murder or nonnegligent manslaughter, rape, robbery, and aggravated assault) and four index property felonies (burglary, larceny-theft, motor vehicle theft, and arson). The UCR program was retired on January 1, 2021, transitioning the national standard for crime statistics to NIBRS to improve crime measures nationally. NIBRS offers greater specificity in reporting offenses, includes more detailed information, and provides context to specific crime problems.

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Subgroups: The NIBRS data include demographic information about the age, race, and gender of victims and person suspected of committing the offense. However, demographics in these data are unreliable. Additionally, those not directly impacted by a crime can still be negatively impacted by general exposure to crime. Looking at differences in crime rates by neighborhood demographics can illustrate whether there are racial disparities in exposure to crime. NIBRS data does not include neighborhood level information necessary for this analysis, but local law enforcement agencies have anonymized geocoded incident-level data and some agencies share this data publicly on government websites. The Urban Institute’s Spatial Equity Data Tool can be used for this analysis.

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Limitations: Reporting to NIBRS is not mandatory, and although most communities provide data, NIBRS does not capture the universe of reported crimes across the US. NIBRS measures crime reported to the police, so unreported crime is not captured in these data. An FBI analysis estimated that up to half of violent crime goes unreported to the local police, and research finds that some neighborhoods are less likely to report violent crime, especially where trust of police is low. As a place-based measure, reported crime is affected by mobility in and out of the community. Crime rates are based on the number of incidents per 100,000 residents. If the number of residents increases and crime remains constant, the crime rate could go down without any change in the number of reported incidents. Also, since crime tends to be concentrated in certain areas, if new residents are moving to places where crime rates were already low, the populations and areas experiencing the most crime may not see any change even if city-wide rates decrease. Relatedly, NIBRS does not provide data on crime at the neighborhood level, so it cannot track changes in crime or compare different places within a community. NIBRS reports data at the agency level and some agencies may have jurisdiction in multiple counties and cities. Similarly, multiple law enforcement agencies (e.g., state, county, city, university, tribal) can fall within a single county. Because of this, at the county level, crime counts may be underestimated in counties where some agency data is missing.

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PREDICTOR: JUST POLICING
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Metric: Juvenile arrests per 100,000 juveniles

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The Federal Bureau of Investigation’s (FBI) National Incident-Based Reporting System (NIBRS) provides information about arrests of people under the age of 18. Because individuals can be arrested multiple times, the NIBRS data reports the number of arrests and not the number of individuals arrested. The metric is for juvenile (defined as under 18 years of age and over 9 years of age) arrest for any crime or status offense, but the data can be broken down by offense type. Arrest rates can be calculated using population data from the American Community Survey.

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Validity: Although arrest behavior of the total population may be confounded by many factors, arrests among juvenile offenders can be more closely tied to overly punitive policing behavior. Research finds that juveniles are more likely to be arrested than adult suspects, after controlling for suspect race, gender, seriousness of offense, and amount of evidence. Research also finds large and disruptive impacts of juvenile justice system involvement on adult outcomes; juvenile detention is associated with lower educational attainment, lower rates of employment, and higher rates of criminal offending and incarceration as an adult.

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Availability: The metric can be constructed using data from NIBRS, which is gaining increased participation from agencies across the country. As of 2021, agencies reporting to NIBRS covered 66 percent of the US population. If a community is not included in NIBRS, the relevant and comparable data can be requested by the public directly from their local law enforcement agencies or may be posted on their local law enforcement website.

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Frequency: This metric can be updated annually. Juvenile arrest data is available annually through the FBI. Arrest data before 2014 can be found on the Bureau of Justice Statistics Arrest Data tool.

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Geography: This metric is available at the county and city level. The NIBRS data are available at the agency level, which can be aggregated to other geographies.

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Consistency: These data are consistent across the communities that provide data to the FBI, because the FBI establishes the definitions of the crimes included in the index and of juvenile as between 10 and 17 years of age regardless of state definition.

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Subgroups: This metric necessarily measures people within a particular age group but also provides arrest-level data including age as well as race or ethnicity and gender. Ethnicity data is inconsistently collected and frequently missing.

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Limitations: Reporting to NIBRS is not mandatory, and although most communities are covered by NIBRS data, NIBRS does not capture the universe of reported crimes across the US. As a place-based measure, levels of arrests are affected by mobility in and out of the community, and because the measure is a rate, large increases or declines in the number of youth under age 18 in an area could also affect the metric.

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Mobility Metrics for Upward County, MB

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These mobility metrics data tables are designed to help local leaders in every county and over 450 cities in the United States measure the status of and progress toward increasing upward mobility and equity in their communities.

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The Urban Institute’s Upward Mobility Framework identifies five essential pillars that support mobility from poverty and a set of evidence-based predictors that are strongly correlated with the likelihood that a community can create conditions to boost the economic and social mobility of its residents while narrowing racial and ethnic inequities. These predictors were identified by an interdisciplinary group of experts and refined through testing with cross-sector partners. They cover diverse aspects of community, such as affordable housing, living-wage jobs, and political participation, and can be influenced by state and local policy.

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Communities can use this suite of metrics along with the Planning Guide for Local Action as they work to develop a strategic plan for upward mobility and monitor progress over time.

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Return here to select another community

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Pillar: Opportunity-Rich & Inclusive Neighborhoods

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Predictor: Housing Affordability

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Metric: Ratio of affordable and available housing units (per 100 households) with low-, very low-, and extremely low-income levels
Upward County, MB
Ratio for low-income households138.4
Ratio for very low-income households100.3
Ratio for extremely low-income households61.5
QualityStrong
Source: US Department of Housing and Urban Development Office of Policy Development and Research Fair Market Rents and Income Limits, FY 2021; US Census Bureau’s 2021 1-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2021)
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Metric: Ratio of affordable and available housing units (per 100 households) with low-, very low-, and extremely low-income levels+
Upward County, MB
Ratio for low-income households138.4
Ratio for very low-income households100.3
Ratio for extremely low-income households61.5
QualityStrong
Source: US Department of Housing and Urban Development Office of Policy Development and Research Fair Market Rents and Income Limits, FY 2021; US Census Bureau’s 2021 1-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2021)
Notes: This metric reports the number of housing units affordable for households with low-incomes (below 80 percent of area median income, or AMI), very low-incomes (below 50 percent of AMI), and extremely low-incomes (below 30 percent of AMI) relative to every 100 households with these income levels. Income groups are defined for a local family of 4. Housing units are defined as affordable if the monthly costs do not exceed 30 percent of a household’s income. Values above 100 suggest that there are more affordable housing units than households with those income levels. Values below 100 indicate a shortage of affordable housing for households with those income levels. Housing units are counted as affordable for a given income level regardless of whether they are currently occupied by a household at that income level.

The Confidence Interval for this metric is not applicable.
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Metric: Ratio of affordable and available housing units (per 100 households) with low-, very low-, and extremely low-income levels+
YearUpward County, MB
Ratio for low-income households2021138.4
Ratio for very low-income households2021100.3
Ratio for extremely low-income households202161.5
Quality2021Strong
Ratio for low-income households2018137.8
Ratio for very low-income households2018110.3
Ratio for extremely low-income households201871.9
Quality2018Strong
Source: US Department of Housing and Urban Development Office of Policy Development and Research Fair Market Rents and Income Limits, FY 2018 & FY 2021; US Census Bureau’s 2018 & 2021 5-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time periods: 2014-18 & 2017-21)
Notes: This metric reports the number of housing units affordable for households with low-incomes (below 80 percent of area median income, or AMI), very low-incomes (below 50 percent of AMI), and extremely low-incomes (below 30 percent of AMI) relative to every 100 households with these income levels. Income groups are defined for a local family of 4. Housing units are defined as affordable if the monthly costs do not exceed 30 percent of a household’s income. Values above 100 suggest that there are more affordable housing units than households with those income levels. Values below 100 indicate a shortage of affordable housing for households with those income levels. Housing units are counted as affordable for a given income level regardless of whether they are currently occupied by a household at that income level.

The Confidence Interval for this metric is not applicable.
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Predictor: Housing stability

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Metric: Number and share of public-school children who are ever homeless during the school year
Upward County, MB
Number homeless852
Share homeless3.4%
QualityStrong
Source: US Department of Education Local Education Agency data, SY 2019-20 (via EDFacts Homeless Students Enrolled). (Time period: School Year 2019-20)
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Metric: Number and share of public-school children who are ever homeless during the school year
Upward County, MB
Number homeless852
Lower/Upper bound(852, 852)
Share homeless3.4%
QualityStrong
Source: US Department of Education Local Education Agency data, SY 2019-20 (via EDFacts Homeless Students Enrolled). (Time period: School Year 2019-20)
Notes: The number of homeless students is based on the number of children (age 3 through 12th grade) who are enrolled in public schools and whose primary nighttime residence at any time during a school year was a shelter, transitional housing, or awaiting foster care placement; unsheltered (e.g., a car, park, campground, temporary trailer, or abandoned building); a hotel or motel because of the lack of alternative adequate accommodations; or in housing of other people because of loss of housing, economic hardship, or a similar reason. The share is the percent of public-school students who are experiencing homelessness out of all public-school students.
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Metric: Number and share of public-school children who are ever homeless during the school year
YearUpward County, MB
Number homeless2019852
Lower/Upper bound2019(852, 852)
Share homeless20193.4%
Quality2019Strong
Number homeless20181,189
Lower/Upper bound2018(1,189, 1,189)
Share homeless20184.8%
Quality2018Strong
Source: US Department of Education Local Education Agency data, SY 2018-19 & SY 2019-20 (via EDFacts Homeless Students Enrolled). (Time periods: School Years 2018-19 & 2019-20)
Notes: The number of homeless students is based on the number of children (age 3 through 12th grade) who are enrolled in public schools and whose primary nighttime residence at any time during a school year was a shelter, transitional housing, or awaiting foster care placement; unsheltered (e.g., a car, park, campground, temporary trailer, or abandoned building); a hotel or motel because of the lack of alternative adequate accommodations; or in housing of other people because of loss of housing, economic hardship, or a similar reason. The share is the percent of public-school students who are experiencing homelessness out of all public-school students. Data disaggregated by race/ethnicity became available for the first time in SY 2019-20.
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Predictor: Economic inclusion

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Metric: Share of people experiencing poverty who live in high-poverty neighborhoods
Upward County, MB
% in high poverty neighborhoods23.7%
QualityStrong
Source: US Census Bureau’s 2021 5-Year American Community Survey. (Time period: 2017-21)
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Metric: Share of people experiencing poverty who live in high-poverty neighborhoods+
Upward County, MB
% in high poverty neighborhoods23.7%
QualityStrong
Source: US Census Bureau’s 2021 5-Year American Community Survey. (Time period: 2017-21)
Notes: The share of a city’s or county’s residents living in poverty who also live in high-poverty neighborhoods (defined as census tracts). A high-poverty neighborhood is one in which over 40 percent of the residents live in poverty. People and families are classified as being in poverty if their income (before taxes and excluding capital gains or noncash benefits) is less than their poverty threshold, as defined by the US Census Bureau. Poverty thresholds vary by the size of the family and age of its members and are updated for inflation, but do not vary geographically.

The Confidence Interval for this metric is not applicable.
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Metric: Share of people experiencing poverty who live in high-poverty neighborhoods+
GroupYearUpward County, MB
% in high poverty neighborhoodsAll202123.7%
QualityAll2021Strong
% in high poverty neighborhoodsBlack202129.6%
QualityBlack2021Strong
% in high poverty neighborhoodsHispanic20216.7%
QualityHispanic2021Strong
% in high poverty neighborhoodsOther Races and Ethnicities202112.9%
QualityOther Races and Ethnicities2021Strong
% in high poverty neighborhoodsWhite, Non-Hispanic202115.0%
QualityWhite, Non-Hispanic2021Strong
% in high poverty neighborhoodsAll201831.3%
QualityAll2018Strong
% in high poverty neighborhoodsBlack201834.4%
QualityBlack2018Strong
% in high poverty neighborhoodsHispanic201825.0%
QualityHispanic2018Strong
% in high poverty neighborhoodsOther Races and Ethnicities201827.0%
QualityOther Races and Ethnicities2018Strong
% in high poverty neighborhoodsWhite, Non-Hispanic201824.5%
QualityWhite, Non-Hispanic2018Strong
Source: US Census Bureau’s 2018 & 2021 5-Year American Community Survey. (Time periods: 2014-18 & 2017-21)
Notes: The share of a city’s or county’s residents living in poverty who also live in high-poverty neighborhoods (defined as census tracts). A high-poverty neighborhood is one in which over 40 percent of the residents live in poverty. People and families are classified as being in poverty if their income (before taxes and excluding capital gains or noncash benefits) is less than their poverty threshold, as defined by the US Census Bureau. Poverty thresholds vary by the size of the family and age of its members and are updated for inflation, but do not vary geographically.

’Black’ includes Black Hispanics. ‘Other Races and Ethnicities’ includes those of races not explicitly listed and those of multiple races. Those who identify as other race or multiple races and Hispanic are counted in both the ‘Hispanic’ and ’Other Races and Ethnicities’ categories.

The Confidence Interval for this metric is not applicable.
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Predictor: Racial diversity

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Metric: Index of people’s exposure to neighbors of different races and ethnicities
Upward County, MB
% for Black, Non-Hispanic36.2%
QualityStrong
% for Hispanic77.3%
QualityStrong
% for Other Races and Ethnicities91.1%
QualityStrong
% for White, Non-Hispanic36.7%
QualityStrong
Source: US Census Bureau’s 2021 5-Year American Community Survey. (Time period: 2017-21)
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Metric: Index of people’s exposure to neighbors of different races and ethnicities+
Upward County, MB
% for Black, Non-Hispanic36.2%
QualityStrong
% for Hispanic77.3%
QualityStrong
% for Other Races and Ethnicities91.1%
QualityStrong
% for White, Non-Hispanic36.7%
QualityStrong
Source: US Census Bureau’s 2021 5-Year American Community Survey. (Time period: 2017-21)
Notes: A set of metrics constructed separately for each racial/ethnic group and reports the average share of that group’s neighbors who are members of other racial/ethnic groups. This is a type of exposure index. For example, an exposure index of 90.0% in the ‘% for Black, Non-Hispanic’ row means that the average Black, non-Hispanic resident has 90.0% of their neighbors within a census tract who have a different race/ethnicity than them. The higher the value, the more exposed to people of different races/ethnicities.

The Confidence Interval for this metric is not applicable.
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Metric: Index of people’s exposure to neighbors of different races and ethnicities+
YearUpward County, MB
% for Black, Non-Hispanic202136.2%
Quality2021Strong
% for Hispanic202177.3%
Quality2021Strong
% for Other Races and Ethnicities202191.1%
Quality2021Strong
% for White, Non-Hispanic202136.7%
Quality2021Strong
% for Black, Non-Hispanic201835.1%
Quality2018Strong
% for Hispanic201878.1%
Quality2018Strong
% for Other Races and Ethnicities201891.7%
Quality2018Strong
% for White, Non-Hispanic201836.8%
Quality2018Strong
Source: US Census Bureau’s 2018 & 2021 5-Year American Community Survey. (Time periods: 2014-18 & 2017-21)
Notes: A set of metrics constructed separately for each racial/ethnic group and reports the average share of that group’s neighbors who are members of other racial/ethnic groups. This is a type of exposure index. For example, an exposure index of 90.0% in the ‘% for Black, Non-Hispanic’ row means that the average Black, non-Hispanic resident has 90.0% of their neighbors within a census tract who have a different race/ethnicity than them. The higher the value, the more exposed to people of different races/ethnicities.

The Confidence Interval for this metric is not applicable.
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Predictor: Social Capital

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Metric: Number of membership associations per 10,000 people
Upward County, MB
Membership associations14.9
QualityStrong
Source: US Census Bureau’s County Business Patterns Survey, 2020 and Population Estimation Program, 2016-20; Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2016-20)
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Metric: Number of membership associations per 10,000 people+
Upward County, MB
Membership associations14.9
QualityStrong
Source: US Census Bureau’s County Business Patterns Survey, 2020 and Population Estimation Program, 2016-20; Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2016-20)
Notes: This metric measures the number of membership associations (as self-reported by businesses and organizations) per 10,000 people in a given community.

The Confidence Interval for this metric is not applicable.
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Predictor: Social Capital

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Metric: Ratio of Facebook friends with higher socioeconomic status to Facebook friends with lower socioeconomic status (‘economic connectedness’)
Upward County, MB
Economic connectedness0.7
QualityStrong
Source: Opportunity Insights’ Social Capital Atlas, 2022. (Time period: 2022)
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Metric: Ratio of Facebook friends with higher socioeconomic status to Facebook friends with lower socioeconomic status (‘economic connectedness’)+
Upward County, MB
Economic connectedness0.7
QualityStrong
Source: Opportunity Insights’ Social Capital Atlas, 2022. (Time period: 2022)
Notes: This measures the interconnectivity, by location, between people from different economic backgrounds to estimate “economic connectedness.” Specifically, the metric is twice the average share of high-socioeconomic status (SES) friends (e.g., individuals from households ranked in the top half of all income-earning households) among low-SES individuals (e.g., individuals from households ranked in the lower half of all US households based on income) in a given community. A metric value of 1 represents a community that is perfectly integrated across socioeconomic status, with half of all low-SES individuals’ friends being of high-SES.

The Confidence Interval for this metric is not applicable.
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Predictor: Transportation access

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Metric: Transit trips index
Upward County, MB
Transit trips66
QualityStrong
Source: 2016 Location Affordability Index data based on 2013-15 Illinois vehicle miles traveled data; Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics data, 2013 & 2014; US Census Bureau’s 2016 5-Year American Community Survey (via HUD AFFH data). (Time period: 2012-16)
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Metric: Transit trips index+
Upward County, MB
Transit trips66
QualityStrong
Source: 2016 Location Affordability Index data based on 2013-15 Illinois vehicle miles traveled data; Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics data, 2013 & 2014; US Census Bureau’s 2016 5-Year American Community Survey (via HUD AFFH data). (Time period: 2012-16)
Notes: The number of public transit trips taken annually by a three-person single-parent family with income at 50 percent of the Area Median Income for renters. Values are percentile ranked nationally, with values ranging from 0 to 100 for each census tract. To get a value for the community, we generate a population-weighted average of census tracts within the community. The higher the value, the more likely residents utilize public transit in the community.

The Confidence Interval for this metric is not applicable.
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Metric: Transit trips index+
GroupYearUpward County, MB
Transit tripsAll201666.1
QualityAll2016Strong
Transit tripsMajority Non-White201664.2
QualityMajority Non-White2016Strong
Transit tripsMajority White, Non-Hispanic201674.1
QualityMajority White, Non-Hispanic2016Strong
Transit tripsMixed Race and Ethnicity201666.4
QualityMixed Race and Ethnicity2016Strong
Source: 2016 Location Affordability Index data based on 2013-15 Illinois vehicle miles traveled data; Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics data, 2013 & 2014; US Census Bureau’s 2016 5-Year American Community Survey (via HUD AFFH data). (Time period: 2012-16)
Notes: The number of public transit trips taken annually by a three-person single-parent family with income at 50 percent of the Area Median Income for renters. Values are percentile ranked nationally, with values ranging from 0 to 100 for each census tract. To get a value for the community, we generate a population-weighted average of census tracts within the community. The higher the value, the more likely residents utilize public transit in the community.

‘Majority’ means that at least 60% of residents in a census tract are members of the specified group.

The Confidence Interval for this metric is not applicable.
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Metric: Transportation cost index
Upward County, MB
Transportation cost77.4
QualityStrong
Source: 2016 Location Affordability Index data based on 2013-15 Illinois vehicle miles traveled data; Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics data, 2013 & 2014; US Census Bureau’s 2016 5-Year American Community Survey (via HUD AFFH data). (Time period: 2012-16)
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Metric: Transportation cost index+
Upward County, MB
Transportation cost77.4
QualityStrong
Source: 2016 Location Affordability Index data based on 2013-15 Illinois vehicle miles traveled data; Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics data, 2013 & 2014; US Census Bureau’s 2016 5-Year American Community Survey (via HUD AFFH data). (Time period: 2012-16)
Notes: Reflects local transportation costs as a share of renters’ incomes. It accounts for both transit and cars. This index is based on estimates of transportation costs for a family that meets the following description: a three-person, single-parent family with income at 50 percent of the median income for renters for the region (i.e., core-based statistical area). Values are inverted and percentile ranked nationally, with values ranging from 0 to 100. The higher the value, the lower the cost of transportation in that neighborhood.

The Confidence Interval for this metric is not applicable.
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Metric: Transportation cost index+
GroupYearUpward County, MB
Transportation costAll201677.4
QualityAll2016Strong
Transportation costMajority Non-White Tracts201675.2
QualityMajority Non-White Tracts2016Strong
Transportation costMajority White, Non-Hispanic Tracts201684.1
QualityMajority White, Non-Hispanic Tracts2016Strong
Transportation costNo Majority Race/Ethnicity Tracts201680.5
QualityNo Majority Race/Ethnicity Tracts2016Strong
Source: 2016 Location Affordability Index data based on 2013-15 Illinois vehicle miles traveled data; Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics data, 2013 & 2014; US Census Bureau’s 2016 5-Year American Community Survey (via HUD AFFH data). (Time period: 2012-16)
Notes: Reflects local transportation costs as a share of renters’ incomes. It accounts for both transit and cars. This index is based on estimates of transportation costs for a family that meets the following description: a three-person, single-parent family with income at 50 percent of the median income for renters for the region (i.e., core-based statistical area). Values are inverted and percentile ranked nationally, with values ranging from 0 to 100. The higher the value, the lower the cost of transportation in that neighborhood.

’Majority’ means that at least 60% of residents in a census tract are members of the specified group.

The Confidence Interval for this metric is not applicable.
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Pillar: High-Quality Education

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Predictor: Access to preschool

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Metric: Share of (3- to 4-year-old) children enrolled in nursery school or preschool
Upward County, MB
% Pre-kindergarten33.0%
QualityStrong
Source: US Census Bureau’s 2021 5-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2017-21)
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Metric: Share of (3- to 4-year-old) children enrolled in nursery school or preschool+
Upward County, MB
% Pre-kindergarten33.0%
QualityStrong
Source: US Census Bureau’s 2021 5-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2017-21)
Notes: The share of a community’s children aged three to four who are enrolled in nursery or preschool.

The Confidence Interval for this metric is not applicable.
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Metric: Share of (3- to 4-year-old) children enrolled in nursery school or preschool+
GroupYearUpward County, MB
% Pre-kindergartenAll202149.2%
QualityAll2021Strong
% Pre-kindergartenBlack, Non-Hispanic202149.9%
QualityBlack, Non-Hispanic2021Weak
% Pre-kindergartenHispanic2021NA
QualityHispanic2021NA
% Pre-kindergartenOther Races and Ethnicities2021NA
QualityOther Races and Ethnicities2021NA
% Pre-kindergartenWhite, Non-Hispanic202175.7%
QualityWhite, Non-Hispanic2021Weak
% Pre-kindergartenAll201844.5%
QualityAll2018Strong
% Pre-kindergartenBlack, Non-Hispanic201830.8%
QualityBlack, Non-Hispanic2018Weak
% Pre-kindergartenHispanic2018NA
QualityHispanic2018NA
% Pre-kindergartenOther Races and Ethnicities2018NA
QualityOther Races and Ethnicities2018NA
% Pre-kindergartenWhite, Non-Hispanic201871.6%
QualityWhite, Non-Hispanic2018Weak
Source: US Census Bureau’s 2018 & 2021 5-Year American Community Survey (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2014-18 & 2017-21)
Notes: The share of a community’s children aged three to four who are enrolled in nursery or preschool.

The Confidence Interval for this metric is not applicable.
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Predictor: Effective public education

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Metric: Average per grade change in English Language Arts achievement between third and eighth grades
Upward County, MB
Annual ELA achievement0.56
QualityStrong
Source: Stanford Education Data Archive, SY 2017-18 (Version 4.1; Reardon, S. F. et al. 2021; retrieved from http://purl.stanford.edu/db586ns4974) (Time period: School Year 2017-18)
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Metric: Average per grade change in English Language Arts achievement between third and eighth grades
Upward County, MB
Annual ELA achievement0.56
Lower/Upper bound(0.48, 0.64)
QualityStrong
Source: Stanford Education Data Archive, SY 2017-18 (Version 4.1; Reardon, S. F. et al. 2021; retrieved from http://purl.stanford.edu/db586ns4974) (Time period: School Year 2017-18)
Notes: The average per year improvement in English/language arts (reading comprehension and written expression) among public school students between the third and eighth grades. Assessments are normalized such that a typical learning growth is roughly 1 grade level per year. ‘1’ indicates a community is learning at an average rate; below 1 is slower than average, and above 1 is faster than average.
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Metric: Average per grade change in English Language Arts achievement between third and eighth grades
GroupYearUpward County, MB
Annual ELA achievementAll20170.56
Lower/Upper boundAll2017(0.48, 0.64)
QualityAll2017Strong
Annual ELA achievementBlack, Non-Hispanic20170.57
Lower/Upper boundBlack, Non-Hispanic2017(0.5, 0.63)
QualityBlack, Non-Hispanic2017Strong
Annual ELA achievementHispanic20170.67
Lower/Upper boundHispanic2017(0.1, 1.24)
QualityHispanic2017Weak
Annual ELA achievementOther Races and Ethnicities2017NA
Lower/Upper boundOther Races and Ethnicities2017NA
QualityOther Races and Ethnicities2017NA
Annual ELA achievementWhite, Non-Hispanic20170.71
Lower/Upper boundWhite, Non-Hispanic2017(0.53, 0.9)
QualityWhite, Non-Hispanic2017Strong
Annual ELA achievementAll20160.53
Lower/Upper boundAll2016(0.45, 0.6)
QualityAll2016Strong
Annual ELA achievementBlack, Non-Hispanic20160.52
Lower/Upper boundBlack, Non-Hispanic2016(0.46, 0.59)
QualityBlack, Non-Hispanic2016Strong
Annual ELA achievementHispanic20160.18
Lower/Upper boundHispanic2016(-0.13, 0.49)
QualityHispanic2016Marginal
Annual ELA achievementOther Races and Ethnicities2016NA
Lower/Upper boundOther Races and Ethnicities2016NA
QualityOther Races and Ethnicities2016NA
Annual ELA achievementWhite, Non-Hispanic20160.68
Lower/Upper boundWhite, Non-Hispanic2016(0.48, 0.88)
QualityWhite, Non-Hispanic2016Strong
Source: Stanford Education Data Archive, SY 2016-17 & SY 2017-18 (Version 4.1; Reardon, S. F. et al. 2021; retrieved from http://purl.stanford.edu/db586ns4974) (Time period: School Years 2016-17 & 2017-18)
Notes: The average per year improvement in English/language arts (reading comprehension and written expression) among public school students between the third and eighth grades. Assessments are normalized such that a typical learning growth is roughly 1 grade level per year. ‘1’ indicates a community is learning at an average rate; below 1 is slower than average, and above 1 is faster than average.

Research suggests that annual improvement in English for Hispanic children will exceed those of White, Non-Hispanic children because Hispanic children, on average, start with lower levels of English language skills and can improve more quickly than children with higher baseline skills.

Research suggests that annual improvement in English for students in low-income or economically disadvantaged households will exceed those of non-economically disadvantaged households because students in less advantaged households, on average, start with lower levels of English language skills and can improve more quickly than children with higher baseline skills. ‘Low-income’ means students are determined to be eligible for their schools’ free and reduced-price meals under the National School Lunch Program.
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Metric: Average per grade change in English Language Arts achievement between third and eighth grades
GroupYearUpward County, MB
Annual ELA achievementAll20170.56
Lower/Upper boundAll2017(0.48, 0.64)
QualityAll2017Strong
Annual ELA achievementLow Income20170.6
Lower/Upper boundLow Income2017(0.51, 0.69)
QualityLow Income2017Marginal
Annual ELA achievementNot Low-Income20170.56
Lower/Upper boundNot Low-Income2017(0.43, 0.7)
QualityNot Low-Income2017Marginal
Annual ELA achievementAll20160.53
Lower/Upper boundAll2016(0.45, 0.6)
QualityAll2016Strong
Annual ELA achievementLow Income20160.51
Lower/Upper boundLow Income2016(0.43, 0.59)
QualityLow Income2016Strong
Annual ELA achievementNot Low-Income20160.43
Lower/Upper boundNot Low-Income2016(0.3, 0.55)
QualityNot Low-Income2016Strong
Source: Stanford Education Data Archive, SY 2016-17 & SY 2017-18 (Version 4.1; Reardon, S. F. et al. 2021; retrieved from http://purl.stanford.edu/db586ns4974) (Time period: School Years 2016-17 & 2017-18)
Notes: The average per year improvement in English/language arts (reading comprehension and written expression) among public school students between the third and eighth grades. Assessments are normalized such that a typical learning growth is roughly 1 grade level per year. ‘1’ indicates a community is learning at an average rate; below 1 is slower than average, and above 1 is faster than average.

Research suggests that annual improvement in English for Hispanic children will exceed those of White, Non-Hispanic children because Hispanic children, on average, start with lower levels of English language skills and can improve more quickly than children with higher baseline skills.

Research suggests that annual improvement in English for students in low-income or economically disadvantaged households will exceed those of non-economically disadvantaged households because students in less advantaged households, on average, start with lower levels of English language skills and can improve more quickly than children with higher baseline skills. ‘Low-income’ means students are determined to be eligible for their schools’ free and reduced-price meals under the National School Lunch Program.
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Predictor: School economic diversity

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Metric: Share of students attending high-poverty schools, by student race/ethnicity
Upward County, MB
% for White, non-Hispanic48.9%
QualityStrong
% for Black, non-Hispanic82.4%
QualityStrong
% for Hispanic91.4%
QualityStrong
Source: National Center for Education Statistics Common Core of Data, SY 2018-19; Urban Institute’s Modeled Estimates of Poverty in Schools (via Education Data Portal v. 0.17.0, Urban Institute, under ODC Attribution License). (Time period: School Year 2018-19)
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Metric: Share of students attending high-poverty schools, by student race/ethnicity+
Upward County, MB
% for White, non-Hispanic48.9%
QualityStrong
% for Black, non-Hispanic82.4%
QualityStrong
% for Hispanic91.4%
QualityStrong
Source: National Center for Education Statistics Common Core of Data, SY 2018-19; Urban Institute’s Modeled Estimates of Poverty in Schools (via Education Data Portal v. 0.17.0, Urban Institute, under ODC Attribution License). (Time period: School Year 2018-19)
Notes: This set of metrics is constructed separately for each racial/ethnic group and reports the share of students attending schools in which over 20 percent of students come from households earning at or below 100% of the Federal Poverty Level.

The Confidence Interval for this metric is not applicable.
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Metric: Share of students attending high-poverty schools, by student race/ethnicity+
YearUpward County, MB
% for White, non-Hispanic201848.9%
Quality2018Strong
% for Black, non-Hispanic201882.4%
Quality2018Strong
% for Hispanic201891.4%
Quality2018Strong
% for White, non-Hispanic201488.2%
Quality2014Strong
% for Black, non-Hispanic201492.4%
Quality2014Strong
% for Hispanic201491.9%
Quality2014Strong
Source: National Center for Education Statistics Common Core of Data, SY 2017-18 & 2018-19; Urban Institute’s Modeled Estimates of Poverty in Schools (via Education Data Portal v. 0.17.0, Urban Institute, under ODC Attribution License). (Time periods: School Years 2017-18 & 2018-19)
Notes: This set of metrics is constructed separately for each racial/ethnic group and reports the share of students attending schools in which over 20 percent of students come from households earning at or below 100% of the Federal Poverty Level.

The Confidence Interval for this metric is not applicable.
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Predictor: Preparation for college

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Metric: Share of 19- and 20-year-olds with a high school degree
Upward County, MB
% HS degree95.5%
QualityStrong
Source: US Census Bureau’s 2021 5-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2017-21)
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Metric: Share of 19- and 20-year-olds with a high school degree+
Upward County, MB
% HS degree95.5%
QualityStrong
Source: US Census Bureau’s 2021 5-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2017-21)
Notes: The share of 19- and 20-year-olds in a community who have a high school degree.

The Confidence Interval for this metric is not applicable.
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Metric: Share of 19- and 20-year-olds with a high school degree+
GroupYearUpward County, MB
% HS degreeAll202197.0%
QualityAll2021Strong
% HS degreeBlack, Non-Hispanic202194.8%
QualityBlack, Non-Hispanic2021Strong
% HS degreeHispanic202196.7%
QualityHispanic2021Weak
% HS degreeOther Races and Ethnicities202199.5%
QualityOther Races and Ethnicities2021Weak
% HS degreeWhite, Non-Hispanic202199.0%
QualityWhite, Non-Hispanic2021Strong
% HS degreeAll201894.7%
QualityAll2018Strong
% HS degreeBlack, Non-Hispanic201888.0%
QualityBlack, Non-Hispanic2018Weak
% HS degreeHispanic2018NA
QualityHispanic2018NA
% HS degreeOther Races and Ethnicities2018NA
QualityOther Races and Ethnicities2018NA
% HS degreeWhite, Non-Hispanic201898.2%
QualityWhite, Non-Hispanic2018Weak
Source: US Census Bureau’s 2018 & 2021 5-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2014-18 & 2017-21)
Notes: The share of 19- and 20-year-olds in a community who have a high school degree.

The Confidence Interval for this metric is not applicable.
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Predictor: Digital access

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Metric: Share of people in households with broadband access in the home
Upward County, MB
% Digital access79.7%
QualityStrong
Source: US Census Bureau’s 2021 5-Year American Community Survey. (Time period: 2017-2021)
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Metric: Share of people in households with broadband access in the home*
Upward County, MB
% Digital access79.7%
QualityStrong
Source: US Census Bureau’s 2021 5-Year American Community Survey. (Time period: 2017-2021)
Notes: This metric represents the share of people in households with access to broadband in their home.

The Confidence Interval for this metric is not available at this time.
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Metric: Share of people in households with broadband access in the home*
GroupYearUpward County, MB
% Digital accessAll202179.7%
QualityAll2021Strong
% Digital accessBlack202174.3%
QualityBlack2021Strong
% Digital accessHispanic202161.3%
QualityHispanic2021Strong
% Digital accessOther Races and Ethnicities202175.0%
QualityOther Races and Ethnicities2021Strong
% Digital accessWhite202186.2%
QualityWhite2021Strong
Source: US Census Bureau’s 2021 5-Year American Community Survey. (Time period: 2017-2021)
Notes: This metric represents the share of people in households with access to broadband in their home.

The Confidence Interval for this metric is not available at this time.
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Pillar: Rewarding Work

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Predictor: Employment Opportunities

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Metric: Employment-to-population ratio for adults ages 25 to 54
Upward County, MB
Employment to population ratio83.4%
QualityStrong
Source: US Census Bureau’s 2021 5-Year American Community Survey Public Use Microdata Sample (PUMS) (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2017-21)
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Metric: Employment-to-population ratio for adults ages 25 to 54+
Upward County, MB
Employment to population ratio83.4%
QualityStrong
Source: US Census Bureau’s 2021 5-Year American Community Survey Public Use Microdata Sample (PUMS) (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2017-21)
Notes: The share of adults between the ages of 25 and 54 in a given community who are employed.

The Confidence Interval for this metric is not applicable.
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Metric: Employment-to-population ratio for adults ages 25 to 54+
GroupYearUpward County, MB
Employment to population ratioAll202181.6%
QualityAll2021Strong
Employment to population ratioBlack, Non-Hispanic202173.1%
QualityBlack, Non-Hispanic2021Strong
Employment to population ratioHispanic202179.4%
QualityHispanic2021Strong
Employment to population ratioOther Races and Ethnicities202182.6%
QualityOther Races and Ethnicities2021Strong
Employment to population ratioWhite, Non-Hispanic202188.6%
QualityWhite, Non-Hispanic2021Strong
Employment to population ratioAll201883.4%
QualityAll2018Strong
Employment to population ratioBlack, Non-Hispanic201868.2%
QualityBlack, Non-Hispanic2018Strong
Employment to population ratioHispanic201883.0%
QualityHispanic2018Strong
Employment to population ratioOther Races and Ethnicities201880.3%
QualityOther Races and Ethnicities2018Strong
Employment to population ratioWhite, Non-Hispanic201887.0%
QualityWhite, Non-Hispanic2018Strong
Source: US Census Bureau’s 2018 & 2021 5-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2014-18 & 2017-21)
Notes: The share of adults between the ages of 25 and 54 in a given community who are employed.

The Confidence Interval for this metric is not applicable.
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Predictor: Access to jobs paying a living wage

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Metric: Ratio of pay on an average job to the cost of living
Upward County, MB
Ratio of pay to living wage0.91
QualityStrong
Source: US Bureau of Labor Statistics Quarterly Census of Employment and Wages (QCEW) data, 2021; Massachusetts Institute of Technology Living Wage Calculator, 2022. (Time period: 2021)
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Metric: Ratio of pay on an average job to the cost of living+
Upward County, MB
Ratio of pay to living wage0.91
QualityStrong
Source: US Bureau of Labor Statistics Quarterly Census of Employment and Wages (QCEW) data, 2021; Massachusetts Institute of Technology Living Wage Calculator, 2022. (Time period: 2021)
Notes: What an average job pays relative to the cost of living in a particular area. The metric is computed by dividing the average earnings for a job in an area by the cost of meeting a family of three’s (for a 1 adult and 2 child household) basic expenses in that area. Ratio values greater than 1 indicate that the average job pays more than the cost of living, while values less than 1 suggest the average job pays less than the cost of living.

For the 2021 metric, we were only able to access the 2022 Living Wage data. We deflated the 2022 data to 2021 using the consumer price index (for all urban consumers), for a correct comparison with the 2021 QCEW.

The Confidence Interval for this metric is not applicable.
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Metric: Ratio of pay on an average job to the cost of living+
YearUpward County, MB
Ratio of pay to living wage20210.91
Quality2021Strong
Ratio of pay to living wage20180.95
Quality2018Strong
Source: US Bureau of Labor Statistics Quarterly Census of Employment and Wages (QCEW) data, 2018 & 2021; Massachusetts Institute of Technology Living Wage Calculator, 2018 & 2022. (Time period: 2018 & 2021)
Notes: What an average job pays relative to the cost of living in a particular area. The metric is computed by dividing the average earnings for a job in an area by the cost of meeting a family of three’s (for a 1 adult and 2 child household) basic expenses in that area. Ratio values greater than 1 indicate that the average job pays more than the cost of living, while values less than 1 suggest the average job pays less than the cost of living.

For the 2021 metric, we were only able to access the 2022 Living Wage data. We deflated the 2022 data to 2021 using the consumer price index (for all urban consumers), for a correct comparison with the 2021 QCEW.

The Confidence Interval for this metric is not applicable.
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Predictor: Opportunities for Income

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Metric: Household income at the 20th, 50th, and 80th percentiles
Upward County, MB
20th Percentile$20,059
50th Percentile$48,629
80th Percentile$101,310
QualityStrong
Source: US Census Bureau’s 2021 1-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time Period: 2021)
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Metric: Household income at the 20th, 50th, and 80th percentiles*
Upward County, MB
20th Percentile$20,059
50th Percentile$48,629
80th Percentile$101,310
QualityStrong
Source: US Census Bureau’s 2021 1-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time Period: 2021)
Notes: To identify income percentiles, all households are ranked by income from lowest to highest. The income level threshold for the poorest 20 percent of households is the value at the 20th percentile. The 50th percentile income threshold indicates the median, with half of households earning less and half of households earning more. The income level threshold for the richest 20 percent of households is the value at the 80th percentile. The difference in income between households at the 20th percentile and the 80th percentile illustrates the level of local economic inequality.

The Confidence Interval for this metric is not available at this time.
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Metric: Household income at the 20th, 50th, and 80th percentiles*
GroupYearUpward County, MB
20th PercentileAll2021$21,458
50th PercentileAll2021$53,847
80th PercentileAll2021$116,233
QualityAll2021Strong
20th PercentileBlack, Non-Hispanic2021$14,529
50th PercentileBlack, Non-Hispanic2021$37,071
80th PercentileBlack, Non-Hispanic2021$70,411
QualityBlack, Non-Hispanic2021Strong
20th PercentileHispanic2021$25,692
50th PercentileHispanic2021$54,763
80th PercentileHispanic2021$93,240
QualityHispanic2021Strong
20th PercentileOther Races and Ethnicities2021$15,303
50th PercentileOther Races and Ethnicities2021$48,173
80th PercentileOther Races and Ethnicities2021$102,822
QualityOther Races and Ethnicities2021Strong
20th PercentileWhite, Non-Hispanic2021$35,807
50th PercentileWhite, Non-Hispanic2021$79,218
80th PercentileWhite, Non-Hispanic2021$157,969
QualityWhite, Non-Hispanic2021Strong
20th PercentileAll2018$20,059
50th PercentileAll2018$48,629
80th PercentileAll2018$101,310
QualityAll2018Strong
20th PercentileBlack, Non-Hispanic2018$11,186
50th PercentileBlack, Non-Hispanic2018$30,939
80th PercentileBlack, Non-Hispanic2018$62,819
QualityBlack, Non-Hispanic2018Strong
20th PercentileHispanic2018$20,256
50th PercentileHispanic2018$44,552
80th PercentileHispanic2018$81,127
QualityHispanic2018Strong
20th PercentileOther Races and Ethnicities2018$9,920
50th PercentileOther Races and Ethnicities2018$42,996
80th PercentileOther Races and Ethnicities2018$84,552
QualityOther Races and Ethnicities2018Strong
20th PercentileWhite, Non-Hispanic2018$28,873
50th PercentileWhite, Non-Hispanic2018$68,573
80th PercentileWhite, Non-Hispanic2018$139,770
QualityWhite, Non-Hispanic2018Strong
Source: US Census Bureau’s 2018 & 2021 5-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time Periods: 2014-18 & 2017-21)
Notes: To identify income percentiles, all households are ranked by income from lowest to highest. The income level threshold for the poorest 20 percent of households is the value at the 20th percentile. The 50th percentile income threshold indicates the median, with half of households earning less and half of households earning more. The income level threshold for the richest 20 percent of households is the value at the 80th percentile. The difference in income between households at the 20th percentile and the 80th percentile illustrates the level of local economic inequality.

The Confidence Interval for this metric is not available at this time.
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Predictor: Financial security

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Metric: Share with debt in collections
Upward County, MB
% with debt34.2%
QualityStrong
Source: February 2022 credit bureau data from Urban Institute’s Debt in America feature. (Time period: February 2022)
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Metric: Share with debt in collections
Upward County, MB
% with debt34.2%
Confidence IntervalNA
QualityStrong
Source: February 2022 credit bureau data from Urban Institute’s Debt in America feature. (Time period: February 2022)
Notes: The county-level measure captures the share of adults in an area with a credit bureau record with debt sent to collections.
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Metric: Share with debt in collections
GroupYearUpward County, MB
% with debtAll201842.6%
Confidence IntervalAll2018(40.9%, 44.2%)
QualityAll2018Strong
% with debtMajority Non-White ZIPs201857.1%
Confidence IntervalMajority Non-White ZIPs2018(54.7%, 59.5%)
QualityMajority Non-White ZIPs2018Strong
% with debtMajority White, Non-Hispanic ZIPs201813.4%
Confidence IntervalMajority White, Non-Hispanic ZIPs2018(10.6%, 16.3%)
QualityMajority White, Non-Hispanic ZIPs2018Strong
Source: August 2018 and February 2022 credit bureau data from Urban Institute’s Debt in America feature. (Time periods: August 2018 & February 2022)
Notes: The county-level measure captures the share of adults in an area with a credit bureau record with debt sent to collections.

For county-level August 2018 and February 2022 data, “majority” means that at least 60% of residents in a zip code are members of the specified population group.
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Predictor: Wealth-Building Opportunities

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Metric: Ratio of the share of a community’s housing wealth held by a racial or ethnic group to the share of households of the same group
Upward County, MB
Black, non-Hispanic Opportunity26.6%:39.6%
QualityStrong
Hispanic Opportunity3.1%:4.9%
QualityWeak
Other Races and Ethnicities Opportunity5.4%:7.5%
QualityStrong
White, non-Hispanic Opportunity64.9%:48.0%
QualityStrong
Source: US Census Bureau’s 2021 1-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2021)
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Metric: Ratio of the share of a community’s housing wealth held by a racial or ethnic group to the share of households of the same group+
Upward County, MB
Black, non-Hispanic Opportunity26.6%:39.6%
QualityStrong
Hispanic Opportunity3.1%:4.9%
QualityWeak
Other Races and Ethnicities Opportunity5.4%:7.5%
QualityStrong
White, non-Hispanic Opportunity64.9%:48.0%
QualityStrong
Source: US Census Bureau’s 2021 1-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2021)
Notes: The percentage to the left of the colon for a given racial group reflects their share of primary-residence housing wealth in a community, and the percentage to the right of the colon reflects the number of households who are headed by a member of that racial group as a share of the community’s total number of households. If the percentage on the left side of the colon is smaller than the percentage on the right side, then that group has less proportionate housing wealth compared to their presence in the community. The greater the gap between these percentages, the more inequality in housing wealth in the community. This metric is based on self-reported housing value, does not account for the extent of mortgage debt, and does not account for other important demographic variations such as differences in age composition across race and ethnic groups, and as such this metric may not fully reflect the size of the actual housing wealth gap.

The Confidence Interval for this metric is not applicable.
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Metric: Ratio of the share of a community’s housing wealth held by a racial or ethnic group to the share of households of the same group+
YearUpward County, MB
Black, non-Hispanic Opportunity202126.6%:39.6%
Quality2021Strong
Hispanic Opportunity20213.1%:4.9%
Quality2021Weak
Other Races and Ethnicities Opportunity20215.4%:7.5%
Quality2021Strong
White, non-Hispanic Opportunity202164.9%:48.0%
Quality2021Strong
Black, non-Hispanic Opportunity201815.2%:41.1%
Quality2018Strong
Hispanic Opportunity20182.2%:4.1%
Quality2018Weak
Other Races and Ethnicities Opportunity20183.4%:5.4%
Quality2018Weak
White, non-Hispanic Opportunity201879.2%:49.4%
Quality2018Strong
Source: US Census Bureau’s 2018 & 2021 1-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time periods: 2018 & 2021)
Notes: The percentage to the left of the colon for a given racial group reflects their share of primary-residence housing wealth in a community, and the percentage to the right of the colon reflects the number of households who are headed by a member of that racial group as a share of the community’s total number of households. If the percentage on the left side of the colon is smaller than the percentage on the right side, then that group has less proportionate housing wealth compared to their presence in the community. The greater the gap between these percentages, the more inequality in housing wealth in the community. This metric is based on self-reported housing value, does not account for the extent of mortgage debt, and does not account for other important demographic variations such as differences in age composition across race and ethnic groups, and as such this metric may not fully reflect the size of the actual housing wealth gap.

The Confidence Interval for this metric is not applicable.
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Pillar: Healthy Environment and Access to Good Healthcare

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Predictor: Access to health services

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Metric: Ratio of population per primary care physician
Upward County, MB
Ratio of people to physicians941:1
QualityStrong
Source: US Department of Health and Human Services, Health Resources and Services Administration, Area Health Resources File, 2020-21 (via County Health Rankings, 2022). (Time period: 2019)
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Metric: Ratio of population per primary care physician+
Upward County, MB
Ratio of people to physicians941:1
QualityStrong
Source: US Department of Health and Human Services, Health Resources and Services Administration, Area Health Resources File, 2020-21 (via County Health Rankings, 2022). (Time period: 2019)
Notes: The ratio represents the number of people served by one primary care physician in a county. It assumes the population is equally distributed across physicians and does not account for actual physician patient load. Missing values are reported for counties with population greater than 2,000 and 0 primary care physicians. The metric does not include nurse practitioners, physician assistants, or other primary care providers who are not physicians.

The Confidence Interval for this metric is not applicable.
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Predictor: Neonatal health

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Metric: Share with low birth weight
Upward County, MB
% Low birth weight11.5%
QualityStrong
Source: Centers for Disease Control and Prevention National Center for Health Statistics, Division of Vital Statistics, Natality data, 2020 (via CDC WONDER). (Time period: 2020)
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Metric: Share with low birth weight
Upward County, MB
% Low birth weight11.5%
Confidence Interval(10.4%, 12.7%)
QualityStrong
Source: Centers for Disease Control and Prevention National Center for Health Statistics, Division of Vital Statistics, Natality data, 2020 (via CDC WONDER). (Time period: 2020)
Notes: The share of babies born weighing less than 5 pounds 8 ounces (<2,500 grams) out of all births with available birthweight information.
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Metric: Share with low birth weight
GroupYearUpward County, MB
% Low birth weightAll202011.5%
Confidence IntervalAll2020(10.4%, 12.7%)
QualityAll2020Strong
% Low birth weightBlack, Non-Hispanic202016.6%
Confidence IntervalBlack, Non-Hispanic2020(14.6%, 18.5%)
QualityBlack, Non-Hispanic2020Strong
% Low birth weightHispanic20208.9%
Confidence IntervalHispanic2020(6.4%, 11.5%)
QualityHispanic2020Strong
% Low birth weightOther Races and Ethnicities202010.0%
Confidence IntervalOther Races and Ethnicities2020(4.1%, 15.9%)
QualityOther Races and Ethnicities2020Marginal
% Low birth weightWhite, Non-Hispanic20205.6%
Confidence IntervalWhite, Non-Hispanic2020(4.1%, 7.0%)
QualityWhite, Non-Hispanic2020Strong
% Low birth weightAll201810.4%
Confidence IntervalAll2018(9.3%, 11.4%)
QualityAll2018Strong
% Low birth weightBlack, Non-Hispanic201815.9%
Confidence IntervalBlack, Non-Hispanic2018(14.1%, 17.7%)
QualityBlack, Non-Hispanic2018Strong
% Low birth weightHispanic20184.3%
Confidence IntervalHispanic2018(2.5%, 6.1%)
QualityHispanic2018Marginal
% Low birth weightOther Races and Ethnicities2018NA
Confidence IntervalOther Races and Ethnicities2018NA
QualityOther Races and Ethnicities2018Weak
% Low birth weightWhite, Non-Hispanic20184.4%
Confidence IntervalWhite, Non-Hispanic2018(3.1%, 5.7%)
QualityWhite, Non-Hispanic2018Strong
Source: Centers for Disease Control and Prevention National Center for Health Statistics, Division of Vital Statistics, Natality data, 2018 & 2020 (via CDC WONDER). (Time period: 2018 & 2020)
Notes: The share of babies born weighing less than 5 pounds 8 ounces (<2,500 grams) out of all births with available birthweight information. Race and ethnicity is based on the mother’s characteristics.
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Predictor: Environmental quality

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Metric: Air quality index
Upward County, MB
Air quality index14
QualityStrong
Source: US Environmental Protection Agency’s AirToxScreen data, 2018 (based on 2017 National Emissions Inventory data). (Time period: 2017-18)
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Metric: Air quality index+
Upward County, MB
Air quality index14
QualityStrong
Source: US Environmental Protection Agency’s AirToxScreen data, 2018 (based on 2017 National Emissions Inventory data). (Time period: 2017-18)
Notes: The index is a linear combination of standardized EPA estimates of air quality carcinogenic, respiratory, and neurological hazards measured at the census tract level. Values are inverted and percentile ranked nationally and range from 0 to 100. The higher the index value, the less exposure to toxins harmful to human health.

The Confidence Interval for this metric is not applicable.
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Metric: Air quality index+
GroupYearUpward County, MB
Air quality indexAll201814
QualityAll2018Strong
Air quality indexMajority Non-White Tracts201813
QualityMajority Non-White Tracts2018Strong
Air quality indexMajority White, Non-Hispanic Tracts201815
QualityMajority White, Non-Hispanic Tracts2018Strong
Air quality indexNo Majority Race/Ethnicity Tracts201816
QualityNo Majority Race/Ethnicity Tracts2018Strong
Air quality indexAll201434
QualityAll2014Strong
Air quality indexMajority Non-White Tracts201436
QualityMajority Non-White Tracts2014Strong
Air quality indexMajority White, Non-Hispanic Tracts201431
QualityMajority White, Non-Hispanic Tracts2014Strong
Air quality indexNo Majority Race/Ethnicity Tracts201432
QualityNo Majority Race/Ethnicity Tracts2014Strong
Source: Environmental Protection Agency’s National Air Toxics Assessment data, 2014 and AirToxScreen data, 2018 (based on 2014 & 2017 National Emissions Inventory data); US Census Bureau’s 2014 & 2018 5-Year American Community Survey. (Time periods: 2010-14 & 2014-18)
Notes: The index is a linear combination of standardized EPA estimates of air quality carcinogenic, respiratory, and neurological hazards measured at the census tract level. Values are inverted and percentile ranked nationally and range from 0 to 100. The higher the index value, the less exposure to toxins harmful to human health.

‘Majority’ means that at least 60% of residents in a census tract are members of the specified group. ‘High poverty’ means that 40% or more of people in a census tract live in families with incomes below the federal poverty line.

The Confidence Interval for this metric is not applicable.
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Metric: Air quality index+
GroupYearUpward County, MB
Air quality indexAll201814
QualityAll2018Strong
Air quality indexHigh Poverty Tracts201810
QualityHigh Poverty Tracts2018Strong
Air quality indexNot High Poverty Tracts201815
QualityNot High Poverty Tracts2018Strong
Air quality indexAll201434
QualityAll2014Strong
Air quality indexHigh Poverty Tracts201434
QualityHigh Poverty Tracts2014Strong
Air quality indexNot High Poverty Tracts201434
QualityNot High Poverty Tracts2014Strong
Source: Environmental Protection Agency’s National Air Toxics Assessment data, 2014 and AirToxScreen data, 2018 (based on 2014 & 2017 National Emissions Inventory data); US Census Bureau’s 2014 & 2018 5-Year American Community Survey. (Time periods: 2010-14 & 2014-18)
Notes: The index is a linear combination of standardized EPA estimates of air quality carcinogenic, respiratory, and neurological hazards measured at the census tract level. Values are inverted and percentile ranked nationally and range from 0 to 100. The higher the index value, the less exposure to toxins harmful to human health.

‘Majority’ means that at least 60% of residents in a census tract are members of the specified group. ‘High poverty’ means that 40% or more of people in a census tract live in families with incomes below the federal poverty line.

The Confidence Interval for this metric is not applicable.
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Predictor: Safety from Trauma

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Metric: Deaths due to injury per 100,000 people
Upward County, MB
Trauma99.5
QualityStrong
Source: National Center for Health Statistics, 2016-20, drawn from the National Vital Statistics System (via County Health Rankings, 2022). (Time period: 2016-20)
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Metric: Deaths due to injury per 100,000 people
Upward County, MB
Trauma99.5
Confidence Interval(93.7, 105.3)
QualityStrong
Source: National Center for Health Statistics, 2016-20, drawn from the National Vital Statistics System (via County Health Rankings, 2022). (Time period: 2016-20)
Notes: Injury deaths is the number of deaths from planned (e.g., homicide or suicide) and unplanned (e.g., motor vehicle deaths) injuries per 100,000 people. Deaths are counted in the county of residence for the person who died, rather than the county where the death occurred. A missing value is reported for counties with fewer than 10 injury deaths in the time frame.
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Pillar: Responsible and Just Governance

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Predictor: Political participation

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Metric: Share of the voting-age population who turn out to vote
Upward County, MB
% voting62.0%
QualityStrong
Source: Massachusetts Institute of Technology Election Data and Science Lab, 2020; US Census Bureau’s 2020 5-Year American Community Survey Citizen Voting Age Population Special Tabulation. (Time period: 2016-20)
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Metric: Share of the voting-age population who turn out to vote+
Upward County, MB
% voting62.0%
QualityStrong
Source: Massachusetts Institute of Technology Election Data and Science Lab, 2020; US Census Bureau’s 2020 5-Year American Community Survey Citizen Voting Age Population Special Tabulation. (Time period: 2016-20)
Notes: This metric measures the share of the citizen voting-age population that voted in the most recent presidential election.

The Confidence Interval for this metric is not applicable.
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Metric: Share of the voting-age population who turn out to vote+
YearUpward County, MB
% voting202062.0%
Quality2020Strong
% voting201661.5%
Quality2016Strong
Source: Massachusetts Institute of Technology Election Data and Science Lab, 2016 & 2020; US Census Bureau’s 2016 & 2020 5-Year American Community Survey Citizen Voting Age Population Special Tabulation. (Time periods: 2012-16 & 2016-20)
Notes: This metric measures the share of the citizen voting-age population that voted in the most recent presidential election.

The Confidence Interval for this metric is not applicable.
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Predictor: Descriptive Representation

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Metric: Ratio of the share of local elected officials of a racial or ethnic group to the share of residents of the same racial or ethnic group. Part of this metric is shown. See the notes for information on finalizing this metric.+
Upward County, MB
Other Races/Ethnicities__:6%
Black, non-Hispanic__:45%
Hispanic__:7%
White, non-Hispanic__:41%
Source: US Census Bureau’s 2021 5-Year American Community Survey. (Time period: 2017-21)
Notes: Shown are the share of that racial or ethnic group in your community. The community will need to calculate the missing percentages in order to complete the descriptive representation metric. See the Planning Guide (pg. 27) on how to calculate the missing percentage. +Say that of your 10 elected officials, nine are White, non-Hispanic and your community’s population is half White, non-Hispanic, the metric will read as “90.0%:50.0%.” If the share of local officials is higher than the share of people in the community, then this group is over-represented. If the share of local officials is lower than the share of people in the community, then this group is under-represented. We are presenting this as a ratio of percentages because it provides important context.

The quality index reflects the data quality only of the given value.

The Confidence Interval for this metric is not applicable.
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Predictor: Safety from Crime

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Metric: Reported property crimes per 100,000 people and reported violent crimes per 100,000 people
Upward County, MB
Violent crime1,422
Property crime4,741
QualityStrong
Source: Federal Bureau of Investigations (FBI) National Incident Based Reporting System (via Kaplan J (2021). National Incident-Based Reporting System (NIBRS) Data. https://nibrsbook.com/); US Census Bureau’s 2021 1-Year American Community Survey. (Time period: 2021)
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Metric: Reported property crimes per 100,000 people and reported violent crimes per 100,000 people+
Upward County, MB
Violent crime1,422
Property crime4,741
QualityStrong
Source: Federal Bureau of Investigations (FBI) National Incident Based Reporting System (via Kaplan J (2021). National Incident-Based Reporting System (NIBRS) Data. https://nibrsbook.com/); US Census Bureau’s 2021 1-Year American Community Survey. (Time period: 2021)
Notes: Rates are calculated as the number of reported crimes against property or people per 100,000 people. Although these are the best national data source, communities should use their local data if they are available. The FBI cautions against using NIBRS data to rank or compare locales because there are many factors that cause the nature and type of crime to vary from place to place.

The Confidence Interval for this metric is not applicable.
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Predictor: Just policing

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Metric: Juvenile arrests per 100,000 juveniles
Upward County, MB
Juvenile arrest rate285.8
QualityStrong
Source: Federal Bureau of Investigations (FBI) National Incident Based Reporting System (via Kaplan J (2021). National Incident-Based Reporting System (NIBRS) Data. https://nibrsbook.com/); US Census Bureau’s 2021 1-Year American Community Survey. (Time period: 2021)
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Metric: Juvenile arrests per 100,000 juveniles+
Upward County, MB
Juvenile arrest rate285.8
QualityStrong
Source: Federal Bureau of Investigations (FBI) National Incident Based Reporting System (via Kaplan J (2021). National Incident-Based Reporting System (NIBRS) Data. https://nibrsbook.com/); US Census Bureau’s 2021 1-Year American Community Survey. (Time period: 2021)
Notes: The number of arrests of people aged 10 to 17, for any crime or status offense, per 100,000 people of that age. Because people can be arrested multiple times, the data reports the number of arrests and not people. Although these are the best national data source, communities should use their local data if it is available. The FBI cautions against using NIBRS data to rank or compare locales because there are many factors that cause the nature and type of crime to vary from place to place.

The Confidence Interval for this metric is not applicable.
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Metric: Juvenile arrests per 100,000 juveniles+
GroupYearUpward County, MB
Juvenile arrest rateAll2021285.8
QualityAll2021Strong
Juvenile arrest rateBlack2021391.2
QualityBlack2021Strong
Juvenile arrest rateHispanic202156.0
QualityHispanic2021Strong
Juvenile arrest rateOther Races and Ethnicities20210.0
QualityOther Races and Ethnicities2021Strong
Juvenile arrest rateWhite2021153.9
QualityWhite2021Strong
Source: Federal Bureau of Investigations (FBI) National Incident Based Reporting System (via Kaplan J (2021). National Incident-Based Reporting System (NIBRS) Data. https://nibrsbook.com/); US Census Bureau’s 2021 1-Year American Community Survey. (Time period: 2021)
Notes: The number of arrests of people aged 10 to 17, for any crime or status offense, per 100,000 people of that age. Because people can be arrested multiple times, the data reports the number of arrests and not people. Although these are the best national data source, communities should use their local data if it is available. The FBI cautions against using NIBRS data to rank or compare locales because there are many factors that cause the nature and type of crime to vary from place to place.

Ethnicity is inconsistently collected and often missing in the data. Those of multiple races are only included in ‘Other Races.’

The Confidence Interval for this metric is not applicable.
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Additional Notes on Data

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Data Quality

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“Strong” indicates that the metric is measured with adequate accuracy and sample size.
+“Marginal” indicates that there are known shortcomings of the data for this metric for this community, and the metric should be used with caution.
+“Weak” indicates that although the metric could be computed for this community, we have serious concerns about how accurately it is measured for this community and do not recommend its use. Instead, we recommend seeking more local data sources for this metric.
+“NA” indicates that the metric value may be suppressed or unavailable and the quality is not applicable.

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Confidence Intervals

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Confidence intervals shown are 95 percent.
+* This confidence interval is not available at this time.
++ A confidence interval is not applicable.
+Lower/Upper bound: The data used to construct this metric do not lend themselves to conventional confidence intervals. The value of the metric shown represents are best estimate; the lower and upper bounds represent alternative estimates of the metric under different assumptions about missing data.

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+“NC” in fields for confidence intervals or lower/upper bounds means that we are not able to calculate this because the underlying data lack variation. +
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Missing and Suppressed Values

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+“NA” in fields for metric values and data quality values indicates that the data are suppressed due to sample sizes or because that element is not applicable to that community (e.g., no zip code in the county is majority non-white). +
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Version: 2023-05-23 07:26:32

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+ + + + \ No newline at end of file diff --git a/factsheets/25_upward-county/51760-Upward-MB_NA/search.json b/factsheets/25_upward-county/51760-Upward-MB_NA/search.json new file mode 100644 index 0000000..92c2bd1 --- /dev/null +++ b/factsheets/25_upward-county/51760-Upward-MB_NA/search.json @@ -0,0 +1,44 @@ +[ + { + "objectID": "index.html#pillar-opportunity-rich-inclusive-neighborhoods", + "href": "index.html#pillar-opportunity-rich-inclusive-neighborhoods", + "title": "", + "section": "Pillar: Opportunity-Rich & Inclusive Neighborhoods", + "text": "Pillar: Opportunity-Rich & Inclusive Neighborhoods\n\n\n\n\n\n\nPredictor: Housing Affordability\n\nSummaryDetailMore Data\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Ratio of affordable and available housing units (per 100 households) with low-, very low-, and extremely low-income levels\n \n \n \n Upward County, MB\n \n \n \n Ratio for low-income households\n138.4\n Ratio for very low-income households\n100.3\n Ratio for extremely low-income households\n61.5\n Quality\nStrong\n \n \n \n Source: US Department of Housing and Urban Development Office of Policy Development and Research Fair Market Rents and Income Limits, FY 2021; US Census Bureau’s 2021 1-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2021)\n \n \n \n\n\n\n\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Ratio of affordable and available housing units (per 100 households) with low-, very low-, and extremely low-income levels+\n \n \n \n Upward County, MB\n \n \n \n Ratio for low-income households\n138.4\n Ratio for very low-income households\n100.3\n Ratio for extremely low-income households\n61.5\n Quality\nStrong\n \n \n \n Source: US Department of Housing and Urban Development Office of Policy Development and Research Fair Market Rents and Income Limits, FY 2021; US Census Bureau’s 2021 1-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2021)\n \n \n Notes: This metric reports the number of housing units affordable for households with low-incomes (below 80 percent of area median income, or AMI), very low-incomes (below 50 percent of AMI), and extremely low-incomes (below 30 percent of AMI) relative to every 100 households with these income levels. Income groups are defined for a local family of 4. Housing units are defined as affordable if the monthly costs do not exceed 30 percent of a household’s income. Values above 100 suggest that there are more affordable housing units than households with those income levels. Values below 100 indicate a shortage of affordable housing for households with those income levels. Housing units are counted as affordable for a given income level regardless of whether they are currently occupied by a household at that income level.The Confidence Interval for this metric is not applicable.\n \n \n \n\n\n\n\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Ratio of affordable and available housing units (per 100 households) with low-, very low-, and extremely low-income levels+\n \n \n \n Year\n Upward County, MB\n \n \n \n Ratio for low-income households\n2021\n138.4\n Ratio for very low-income households\n2021\n100.3\n Ratio for extremely low-income households\n2021\n61.5\n Quality\n2021\nStrong\n Ratio for low-income households\n2018\n137.8\n Ratio for very low-income households\n2018\n110.3\n Ratio for extremely low-income households\n2018\n71.9\n Quality\n2018\nStrong\n \n \n \n Source: US Department of Housing and Urban Development Office of Policy Development and Research Fair Market Rents and Income Limits, FY 2018 & FY 2021; US Census Bureau’s 2018 & 2021 5-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time periods: 2014-18 & 2017-21)\n \n \n Notes: This metric reports the number of housing units affordable for households with low-incomes (below 80 percent of area median income, or AMI), very low-incomes (below 50 percent of AMI), and extremely low-incomes (below 30 percent of AMI) relative to every 100 households with these income levels. Income groups are defined for a local family of 4. Housing units are defined as affordable if the monthly costs do not exceed 30 percent of a household’s income. Values above 100 suggest that there are more affordable housing units than households with those income levels. Values below 100 indicate a shortage of affordable housing for households with those income levels. Housing units are counted as affordable for a given income level regardless of whether they are currently occupied by a household at that income level.The Confidence Interval for this metric is not applicable.\n \n \n \n\n\n\n\n\n\n\n\nVisit here for more details about this predictor.\n\n\n\n\nPredictor: Housing stability\n\nSummaryDetailMore Data\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Number and share of public-school children who are ever homeless during the school year\n \n \n \n Upward County, MB\n \n \n \n Number homeless\n852\n Share homeless\n3.4%\n Quality\nStrong\n \n \n \n Source: US Department of Education Local Education Agency data, SY 2019-20 (via EDFacts Homeless Students Enrolled). (Time period: School Year 2019-20)\n \n \n \n\n\n\n\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Number and share of public-school children who are ever homeless during the school year\n \n \n \n Upward County, MB\n \n \n \n Number homeless\n852\n Lower/Upper bound\n(852, 852)\n Share homeless\n3.4%\n Quality\nStrong\n \n \n \n Source: US Department of Education Local Education Agency data, SY 2019-20 (via EDFacts Homeless Students Enrolled). (Time period: School Year 2019-20)\n \n \n Notes: The number of homeless students is based on the number of children (age 3 through 12th grade) who are enrolled in public schools and whose primary nighttime residence at any time during a school year was a shelter, transitional housing, or awaiting foster care placement; unsheltered (e.g., a car, park, campground, temporary trailer, or abandoned building); a hotel or motel because of the lack of alternative adequate accommodations; or in housing of other people because of loss of housing, economic hardship, or a similar reason. The share is the percent of public-school students who are experiencing homelessness out of all public-school students.\n \n \n \n\n\n\n\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Number and share of public-school children who are ever homeless during the school year\n \n \n \n Year\n Upward County, MB\n \n \n \n Number homeless\n2019\n852\n Lower/Upper bound\n2019\n(852, 852)\n Share homeless\n2019\n3.4%\n Quality\n2019\nStrong\n Number homeless\n2018\n1,189\n Lower/Upper bound\n2018\n(1,189, 1,189)\n Share homeless\n2018\n4.8%\n Quality\n2018\nStrong\n \n \n \n Source: US Department of Education Local Education Agency data, SY 2018-19 & SY 2019-20 (via EDFacts Homeless Students Enrolled). (Time periods: School Years 2018-19 & 2019-20)\n \n \n Notes: The number of homeless students is based on the number of children (age 3 through 12th grade) who are enrolled in public schools and whose primary nighttime residence at any time during a school year was a shelter, transitional housing, or awaiting foster care placement; unsheltered (e.g., a car, park, campground, temporary trailer, or abandoned building); a hotel or motel because of the lack of alternative adequate accommodations; or in housing of other people because of loss of housing, economic hardship, or a similar reason. The share is the percent of public-school students who are experiencing homelessness out of all public-school students. Data disaggregated by race/ethnicity became available for the first time in SY 2019-20.\n \n \n \n\n\n\n\n\n\n\nVisit here for more details about this predictor.\n\n\n\n\nPredictor: Economic inclusion\n\nSummaryDetailMore Data\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Share of people experiencing poverty who live in high-poverty neighborhoods\n \n \n \n Upward County, MB\n \n \n \n % in high poverty neighborhoods\n23.7%\n Quality\nStrong\n \n \n \n Source: US Census Bureau’s 2021 5-Year American Community Survey. (Time period: 2017-21)\n \n \n \n\n\n\n\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Share of people experiencing poverty who live in high-poverty neighborhoods+\n \n \n \n Upward County, MB\n \n \n \n % in high poverty neighborhoods\n23.7%\n Quality\nStrong\n \n \n \n Source: US Census Bureau’s 2021 5-Year American Community Survey. (Time period: 2017-21)\n \n \n Notes: The share of a city’s or county’s residents living in poverty who also live in high-poverty neighborhoods (defined as census tracts). A high-poverty neighborhood is one in which over 40 percent of the residents live in poverty. People and families are classified as being in poverty if their income (before taxes and excluding capital gains or noncash benefits) is less than their poverty threshold, as defined by the US Census Bureau. Poverty thresholds vary by the size of the family and age of its members and are updated for inflation, but do not vary geographically.The Confidence Interval for this metric is not applicable.\n \n \n \n\n\n\n\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Share of people experiencing poverty who live in high-poverty neighborhoods+\n \n \n \n Group\n Year\n Upward County, MB\n \n \n \n % in high poverty neighborhoods\nAll\n2021\n23.7%\n Quality\nAll\n2021\nStrong\n % in high poverty neighborhoods\nBlack\n2021\n29.6%\n Quality\nBlack\n2021\nStrong\n % in high poverty neighborhoods\nHispanic\n2021\n6.7%\n Quality\nHispanic\n2021\nStrong\n % in high poverty neighborhoods\nOther Races and Ethnicities\n2021\n12.9%\n Quality\nOther Races and Ethnicities\n2021\nStrong\n % in high poverty neighborhoods\nWhite, Non-Hispanic\n2021\n15.0%\n Quality\nWhite, Non-Hispanic\n2021\nStrong\n % in high poverty neighborhoods\nAll\n2018\n31.3%\n Quality\nAll\n2018\nStrong\n % in high poverty neighborhoods\nBlack\n2018\n34.4%\n Quality\nBlack\n2018\nStrong\n % in high poverty neighborhoods\nHispanic\n2018\n25.0%\n Quality\nHispanic\n2018\nStrong\n % in high poverty neighborhoods\nOther Races and Ethnicities\n2018\n27.0%\n Quality\nOther Races and Ethnicities\n2018\nStrong\n % in high poverty neighborhoods\nWhite, Non-Hispanic\n2018\n24.5%\n Quality\nWhite, Non-Hispanic\n2018\nStrong\n \n \n \n Source: US Census Bureau’s 2018 & 2021 5-Year American Community Survey. (Time periods: 2014-18 & 2017-21)\n \n \n Notes: The share of a city’s or county’s residents living in poverty who also live in high-poverty neighborhoods (defined as census tracts). A high-poverty neighborhood is one in which over 40 percent of the residents live in poverty. People and families are classified as being in poverty if their income (before taxes and excluding capital gains or noncash benefits) is less than their poverty threshold, as defined by the US Census Bureau. Poverty thresholds vary by the size of the family and age of its members and are updated for inflation, but do not vary geographically. ’Black’ includes Black Hispanics. ‘Other Races and Ethnicities’ includes those of races not explicitly listed and those of multiple races. Those who identify as other race or multiple races and Hispanic are counted in both the ‘Hispanic’ and ’Other Races and Ethnicities’ categories.The Confidence Interval for this metric is not applicable.\n \n \n \n\n\n\n\n\n\n\nVisit here for more details about this predictor.\n\n\n\n\nPredictor: Racial diversity\n\nSummaryDetailMore Data\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Index of people’s exposure to neighbors of different races and ethnicities\n \n \n \n Upward County, MB\n \n \n \n % for Black, Non-Hispanic\n36.2%\n Quality\nStrong\n % for Hispanic\n77.3%\n Quality\nStrong\n % for Other Races and Ethnicities\n91.1%\n Quality\nStrong\n % for White, Non-Hispanic\n36.7%\n Quality\nStrong\n \n \n \n Source: US Census Bureau’s 2021 5-Year American Community Survey. (Time period: 2017-21)\n \n \n \n\n\n\n\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Index of people’s exposure to neighbors of different races and ethnicities+\n \n \n \n Upward County, MB\n \n \n \n % for Black, Non-Hispanic\n36.2%\n Quality\nStrong\n % for Hispanic\n77.3%\n Quality\nStrong\n % for Other Races and Ethnicities\n91.1%\n Quality\nStrong\n % for White, Non-Hispanic\n36.7%\n Quality\nStrong\n \n \n \n Source: US Census Bureau’s 2021 5-Year American Community Survey. (Time period: 2017-21)\n \n \n Notes: A set of metrics constructed separately for each racial/ethnic group and reports the average share of that group’s neighbors who are members of other racial/ethnic groups. This is a type of exposure index. For example, an exposure index of 90.0% in the ‘% for Black, Non-Hispanic’ row means that the average Black, non-Hispanic resident has 90.0% of their neighbors within a census tract who have a different race/ethnicity than them. The higher the value, the more exposed to people of different races/ethnicities.The Confidence Interval for this metric is not applicable.\n \n \n \n\n\n\n\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Index of people’s exposure to neighbors of different races and ethnicities+\n \n \n \n Year\n Upward County, MB\n \n \n \n % for Black, Non-Hispanic\n2021\n36.2%\n Quality\n2021\nStrong\n % for Hispanic\n2021\n77.3%\n Quality\n2021\nStrong\n % for Other Races and Ethnicities\n2021\n91.1%\n Quality\n2021\nStrong\n % for White, Non-Hispanic\n2021\n36.7%\n Quality\n2021\nStrong\n % for Black, Non-Hispanic\n2018\n35.1%\n Quality\n2018\nStrong\n % for Hispanic\n2018\n78.1%\n Quality\n2018\nStrong\n % for Other Races and Ethnicities\n2018\n91.7%\n Quality\n2018\nStrong\n % for White, Non-Hispanic\n2018\n36.8%\n Quality\n2018\nStrong\n \n \n \n Source: US Census Bureau’s 2018 & 2021 5-Year American Community Survey. (Time periods: 2014-18 & 2017-21)\n \n \n Notes: A set of metrics constructed separately for each racial/ethnic group and reports the average share of that group’s neighbors who are members of other racial/ethnic groups. This is a type of exposure index. For example, an exposure index of 90.0% in the ‘% for Black, Non-Hispanic’ row means that the average Black, non-Hispanic resident has 90.0% of their neighbors within a census tract who have a different race/ethnicity than them. The higher the value, the more exposed to people of different races/ethnicities.The Confidence Interval for this metric is not applicable.\n \n \n \n\n\n\n\n\n\n\nVisit here for more details about this predictor.\n\n\n\n\nPredictor: Social Capital\n\nSummaryDetail\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Number of membership associations per 10,000 people\n \n \n \n Upward County, MB\n \n \n \n Membership associations\n14.9\n Quality\nStrong\n \n \n \n Source: US Census Bureau’s County Business Patterns Survey, 2020 and Population Estimation Program, 2016-20; Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2016-20)\n \n \n \n\n\n\n\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Number of membership associations per 10,000 people+\n \n \n \n Upward County, MB\n \n \n \n Membership associations\n14.9\n Quality\nStrong\n \n \n \n Source: US Census Bureau’s County Business Patterns Survey, 2020 and Population Estimation Program, 2016-20; Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2016-20)\n \n \n Notes: This metric measures the number of membership associations (as self-reported by businesses and organizations) per 10,000 people in a given community.The Confidence Interval for this metric is not applicable.\n \n \n \n\n\n\n\n\n\n\nVisit here for more details about this predictor.\n\n\n\n\nPredictor: Social Capital\n\nSummaryDetail\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Ratio of Facebook friends with higher socioeconomic status to Facebook friends with lower socioeconomic status (‘economic connectedness’)\n \n \n \n Upward County, MB\n \n \n \n Economic connectedness\n0.7\n Quality\nStrong\n \n \n \n Source: Opportunity Insights’ Social Capital Atlas, 2022. (Time period: 2022)\n \n \n \n\n\n\n\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Ratio of Facebook friends with higher socioeconomic status to Facebook friends with lower socioeconomic status (‘economic connectedness’)+\n \n \n \n Upward County, MB\n \n \n \n Economic connectedness\n0.7\n Quality\nStrong\n \n \n \n Source: Opportunity Insights’ Social Capital Atlas, 2022. (Time period: 2022)\n \n \n Notes: This measures the interconnectivity, by location, between people from different economic backgrounds to estimate “economic connectedness.” Specifically, the metric is twice the average share of high-socioeconomic status (SES) friends (e.g., individuals from households ranked in the top half of all income-earning households) among low-SES individuals (e.g., individuals from households ranked in the lower half of all US households based on income) in a given community. A metric value of 1 represents a community that is perfectly integrated across socioeconomic status, with half of all low-SES individuals’ friends being of high-SES.The Confidence Interval for this metric is not applicable.\n \n \n \n\n\n\n\n\n\n\nVisit here for more details about this predictor.\n\n\n\n\nPredictor: Transportation access\n\nSummaryDetailMore Data\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Transit trips index\n \n \n \n Upward County, MB\n \n \n \n Transit trips\n66\n Quality\nStrong\n \n \n \n Source: 2016 Location Affordability Index data based on 2013-15 Illinois vehicle miles traveled data; Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics data, 2013 & 2014; US Census Bureau’s 2016 5-Year American Community Survey (via HUD AFFH data). (Time period: 2012-16)\n \n \n \n\n\n\n\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Transit trips index+\n \n \n \n Upward County, MB\n \n \n \n Transit trips\n66\n Quality\nStrong\n \n \n \n Source: 2016 Location Affordability Index data based on 2013-15 Illinois vehicle miles traveled data; Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics data, 2013 & 2014; US Census Bureau’s 2016 5-Year American Community Survey (via HUD AFFH data). (Time period: 2012-16)\n \n \n Notes: The number of public transit trips taken annually by a three-person single-parent family with income at 50 percent of the Area Median Income for renters. Values are percentile ranked nationally, with values ranging from 0 to 100 for each census tract. To get a value for the community, we generate a population-weighted average of census tracts within the community. The higher the value, the more likely residents utilize public transit in the community.The Confidence Interval for this metric is not applicable.\n \n \n \n\n\n\n\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Transit trips index+\n \n \n \n Group\n Year\n Upward County, MB\n \n \n \n Transit trips\nAll\n2016\n66.1\n Quality\nAll\n2016\nStrong\n Transit trips\nMajority Non-White\n2016\n64.2\n Quality\nMajority Non-White\n2016\nStrong\n Transit trips\nMajority White, Non-Hispanic\n2016\n74.1\n Quality\nMajority White, Non-Hispanic\n2016\nStrong\n Transit trips\nMixed Race and Ethnicity\n2016\n66.4\n Quality\nMixed Race and Ethnicity\n2016\nStrong\n \n \n \n Source: 2016 Location Affordability Index data based on 2013-15 Illinois vehicle miles traveled data; Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics data, 2013 & 2014; US Census Bureau’s 2016 5-Year American Community Survey (via HUD AFFH data). (Time period: 2012-16)\n \n \n Notes: The number of public transit trips taken annually by a three-person single-parent family with income at 50 percent of the Area Median Income for renters. Values are percentile ranked nationally, with values ranging from 0 to 100 for each census tract. To get a value for the community, we generate a population-weighted average of census tracts within the community. The higher the value, the more likely residents utilize public transit in the community. ‘Majority’ means that at least 60% of residents in a census tract are members of the specified group.The Confidence Interval for this metric is not applicable.\n \n \n \n\n\n\n\n\n\n\n\nSummaryDetailMore Data\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Transportation cost index\n \n \n \n Upward County, MB\n \n \n \n Transportation cost\n77.4\n Quality\nStrong\n \n \n \n Source: 2016 Location Affordability Index data based on 2013-15 Illinois vehicle miles traveled data; Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics data, 2013 & 2014; US Census Bureau’s 2016 5-Year American Community Survey (via HUD AFFH data). (Time period: 2012-16)\n \n \n \n\n\n\n\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Transportation cost index+\n \n \n \n Upward County, MB\n \n \n \n Transportation cost\n77.4\n Quality\nStrong\n \n \n \n Source: 2016 Location Affordability Index data based on 2013-15 Illinois vehicle miles traveled data; Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics data, 2013 & 2014; US Census Bureau’s 2016 5-Year American Community Survey (via HUD AFFH data). (Time period: 2012-16)\n \n \n Notes: Reflects local transportation costs as a share of renters’ incomes. It accounts for both transit and cars. This index is based on estimates of transportation costs for a family that meets the following description: a three-person, single-parent family with income at 50 percent of the median income for renters for the region (i.e., core-based statistical area). Values are inverted and percentile ranked nationally, with values ranging from 0 to 100. The higher the value, the lower the cost of transportation in that neighborhood.The Confidence Interval for this metric is not applicable.\n \n \n \n\n\n\n\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Transportation cost index+\n \n \n \n Group\n Year\n Upward County, MB\n \n \n \n Transportation cost\nAll\n2016\n77.4\n Quality\nAll\n2016\nStrong\n Transportation cost\nMajority Non-White Tracts\n2016\n75.2\n Quality\nMajority Non-White Tracts\n2016\nStrong\n Transportation cost\nMajority White, Non-Hispanic Tracts\n2016\n84.1\n Quality\nMajority White, Non-Hispanic Tracts\n2016\nStrong\n Transportation cost\nNo Majority Race/Ethnicity Tracts\n2016\n80.5\n Quality\nNo Majority Race/Ethnicity Tracts\n2016\nStrong\n \n \n \n Source: 2016 Location Affordability Index data based on 2013-15 Illinois vehicle miles traveled data; Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics data, 2013 & 2014; US Census Bureau’s 2016 5-Year American Community Survey (via HUD AFFH data). (Time period: 2012-16)\n \n \n Notes: Reflects local transportation costs as a share of renters’ incomes. It accounts for both transit and cars. This index is based on estimates of transportation costs for a family that meets the following description: a three-person, single-parent family with income at 50 percent of the median income for renters for the region (i.e., core-based statistical area). Values are inverted and percentile ranked nationally, with values ranging from 0 to 100. The higher the value, the lower the cost of transportation in that neighborhood. ’Majority’ means that at least 60% of residents in a census tract are members of the specified group.The Confidence Interval for this metric is not applicable.\n \n \n \n\n\n\n\n\n\n\nVisit here for more details about this predictor." + }, + { + "objectID": "index.html#pillar-high-quality-education", + "href": "index.html#pillar-high-quality-education", + "title": "", + "section": "Pillar: High-Quality Education", + "text": "Pillar: High-Quality Education\n\n\n\n\n\n\nPredictor: Access to preschool\n\nSummaryDetailMore Data\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Share of (3- to 4-year-old) children enrolled in nursery school or preschool\n \n \n \n Upward County, MB\n \n \n \n % Pre-kindergarten\n33.0%\n Quality\nStrong\n \n \n \n Source: US Census Bureau’s 2021 5-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2017-21)\n \n \n \n\n\n\n\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Share of (3- to 4-year-old) children enrolled in nursery school or preschool+\n \n \n \n Upward County, MB\n \n \n \n % Pre-kindergarten\n33.0%\n Quality\nStrong\n \n \n \n Source: US Census Bureau’s 2021 5-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2017-21)\n \n \n Notes: The share of a community’s children aged three to four who are enrolled in nursery or preschool.The Confidence Interval for this metric is not applicable.\n \n \n \n\n\n\n\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Share of (3- to 4-year-old) children enrolled in nursery school or preschool+\n \n \n \n Group\n Year\n Upward County, MB\n \n \n \n % Pre-kindergarten\nAll\n2021\n49.2%\n Quality\nAll\n2021\nStrong\n % Pre-kindergarten\nBlack, Non-Hispanic\n2021\n49.9%\n Quality\nBlack, Non-Hispanic\n2021\nWeak\n % Pre-kindergarten\nHispanic\n2021\nNA\n Quality\nHispanic\n2021\nNA\n % Pre-kindergarten\nOther Races and Ethnicities\n2021\nNA\n Quality\nOther Races and Ethnicities\n2021\nNA\n % Pre-kindergarten\nWhite, Non-Hispanic\n2021\n75.7%\n Quality\nWhite, Non-Hispanic\n2021\nWeak\n % Pre-kindergarten\nAll\n2018\n44.5%\n Quality\nAll\n2018\nStrong\n % Pre-kindergarten\nBlack, Non-Hispanic\n2018\n30.8%\n Quality\nBlack, Non-Hispanic\n2018\nWeak\n % Pre-kindergarten\nHispanic\n2018\nNA\n Quality\nHispanic\n2018\nNA\n % Pre-kindergarten\nOther Races and Ethnicities\n2018\nNA\n Quality\nOther Races and Ethnicities\n2018\nNA\n % Pre-kindergarten\nWhite, Non-Hispanic\n2018\n71.6%\n Quality\nWhite, Non-Hispanic\n2018\nWeak\n \n \n \n Source: US Census Bureau’s 2018 & 2021 5-Year American Community Survey (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2014-18 & 2017-21)\n \n \n Notes: The share of a community’s children aged three to four who are enrolled in nursery or preschool.The Confidence Interval for this metric is not applicable.\n \n \n \n\n\n\n\n\n\n\nVisit here for more details about this predictor.\n\n\n\n\nPredictor: Effective public education\n\nSummaryDetailMore Data\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Average per grade change in English Language Arts achievement between third and eighth grades\n \n \n \n Upward County, MB\n \n \n \n Annual ELA achievement\n0.56\n Quality\nStrong\n \n \n \n Source: Stanford Education Data Archive, SY 2017-18 (Version 4.1; Reardon, S. F. et al. 2021; retrieved from http://purl.stanford.edu/db586ns4974) (Time period: School Year 2017-18)\n \n \n \n\n\n\n\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Average per grade change in English Language Arts achievement between third and eighth grades\n \n \n \n Upward County, MB\n \n \n \n Annual ELA achievement\n0.56\n Lower/Upper bound\n(0.48, 0.64)\n Quality\nStrong\n \n \n \n Source: Stanford Education Data Archive, SY 2017-18 (Version 4.1; Reardon, S. F. et al. 2021; retrieved from http://purl.stanford.edu/db586ns4974) (Time period: School Year 2017-18)\n \n \n Notes: The average per year improvement in English/language arts (reading comprehension and written expression) among public school students between the third and eighth grades. Assessments are normalized such that a typical learning growth is roughly 1 grade level per year. ‘1’ indicates a community is learning at an average rate; below 1 is slower than average, and above 1 is faster than average.\n \n \n \n\n\n\n\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Average per grade change in English Language Arts achievement between third and eighth grades\n \n \n \n Group\n Year\n Upward County, MB\n \n \n \n Annual ELA achievement\nAll\n2017\n0.56\n Lower/Upper bound\nAll\n2017\n(0.48, 0.64)\n Quality\nAll\n2017\nStrong\n Annual ELA achievement\nBlack, Non-Hispanic\n2017\n0.57\n Lower/Upper bound\nBlack, Non-Hispanic\n2017\n(0.5, 0.63)\n Quality\nBlack, Non-Hispanic\n2017\nStrong\n Annual ELA achievement\nHispanic\n2017\n0.67\n Lower/Upper bound\nHispanic\n2017\n(0.1, 1.24)\n Quality\nHispanic\n2017\nWeak\n Annual ELA achievement\nOther Races and Ethnicities\n2017\nNA\n Lower/Upper bound\nOther Races and Ethnicities\n2017\nNA\n Quality\nOther Races and Ethnicities\n2017\nNA\n Annual ELA achievement\nWhite, Non-Hispanic\n2017\n0.71\n Lower/Upper bound\nWhite, Non-Hispanic\n2017\n(0.53, 0.9)\n Quality\nWhite, Non-Hispanic\n2017\nStrong\n Annual ELA achievement\nAll\n2016\n0.53\n Lower/Upper bound\nAll\n2016\n(0.45, 0.6)\n Quality\nAll\n2016\nStrong\n Annual ELA achievement\nBlack, Non-Hispanic\n2016\n0.52\n Lower/Upper bound\nBlack, Non-Hispanic\n2016\n(0.46, 0.59)\n Quality\nBlack, Non-Hispanic\n2016\nStrong\n Annual ELA achievement\nHispanic\n2016\n0.18\n Lower/Upper bound\nHispanic\n2016\n(-0.13, 0.49)\n Quality\nHispanic\n2016\nMarginal\n Annual ELA achievement\nOther Races and Ethnicities\n2016\nNA\n Lower/Upper bound\nOther Races and Ethnicities\n2016\nNA\n Quality\nOther Races and Ethnicities\n2016\nNA\n Annual ELA achievement\nWhite, Non-Hispanic\n2016\n0.68\n Lower/Upper bound\nWhite, Non-Hispanic\n2016\n(0.48, 0.88)\n Quality\nWhite, Non-Hispanic\n2016\nStrong\n \n \n \n Source: Stanford Education Data Archive, SY 2016-17 & SY 2017-18 (Version 4.1; Reardon, S. F. et al. 2021; retrieved from http://purl.stanford.edu/db586ns4974) (Time period: School Years 2016-17 & 2017-18)\n \n \n Notes: The average per year improvement in English/language arts (reading comprehension and written expression) among public school students between the third and eighth grades. Assessments are normalized such that a typical learning growth is roughly 1 grade level per year. ‘1’ indicates a community is learning at an average rate; below 1 is slower than average, and above 1 is faster than average. Research suggests that annual improvement in English for Hispanic children will exceed those of White, Non-Hispanic children because Hispanic children, on average, start with lower levels of English language skills and can improve more quickly than children with higher baseline skills. Research suggests that annual improvement in English for students in low-income or economically disadvantaged households will exceed those of non-economically disadvantaged households because students in less advantaged households, on average, start with lower levels of English language skills and can improve more quickly than children with higher baseline skills. ‘Low-income’ means students are determined to be eligible for their schools’ free and reduced-price meals under the National School Lunch Program.\n \n \n \n\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Average per grade change in English Language Arts achievement between third and eighth grades\n \n \n \n Group\n Year\n Upward County, MB\n \n \n \n Annual ELA achievement\nAll\n2017\n0.56\n Lower/Upper bound\nAll\n2017\n(0.48, 0.64)\n Quality\nAll\n2017\nStrong\n Annual ELA achievement\nLow Income\n2017\n0.6\n Lower/Upper bound\nLow Income\n2017\n(0.51, 0.69)\n Quality\nLow Income\n2017\nMarginal\n Annual ELA achievement\nNot Low-Income\n2017\n0.56\n Lower/Upper bound\nNot Low-Income\n2017\n(0.43, 0.7)\n Quality\nNot Low-Income\n2017\nMarginal\n Annual ELA achievement\nAll\n2016\n0.53\n Lower/Upper bound\nAll\n2016\n(0.45, 0.6)\n Quality\nAll\n2016\nStrong\n Annual ELA achievement\nLow Income\n2016\n0.51\n Lower/Upper bound\nLow Income\n2016\n(0.43, 0.59)\n Quality\nLow Income\n2016\nStrong\n Annual ELA achievement\nNot Low-Income\n2016\n0.43\n Lower/Upper bound\nNot Low-Income\n2016\n(0.3, 0.55)\n Quality\nNot Low-Income\n2016\nStrong\n \n \n \n Source: Stanford Education Data Archive, SY 2016-17 & SY 2017-18 (Version 4.1; Reardon, S. F. et al. 2021; retrieved from http://purl.stanford.edu/db586ns4974) (Time period: School Years 2016-17 & 2017-18)\n \n \n Notes: The average per year improvement in English/language arts (reading comprehension and written expression) among public school students between the third and eighth grades. Assessments are normalized such that a typical learning growth is roughly 1 grade level per year. ‘1’ indicates a community is learning at an average rate; below 1 is slower than average, and above 1 is faster than average. Research suggests that annual improvement in English for Hispanic children will exceed those of White, Non-Hispanic children because Hispanic children, on average, start with lower levels of English language skills and can improve more quickly than children with higher baseline skills. Research suggests that annual improvement in English for students in low-income or economically disadvantaged households will exceed those of non-economically disadvantaged households because students in less advantaged households, on average, start with lower levels of English language skills and can improve more quickly than children with higher baseline skills. ‘Low-income’ means students are determined to be eligible for their schools’ free and reduced-price meals under the National School Lunch Program.\n \n \n \n\n\n\n\n\n\n\nVisit here for more details about this predictor.\n\n\n\n\nPredictor: School economic diversity\n\nSummaryDetailMore data\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Share of students attending high-poverty schools, by student race/ethnicity\n \n \n \n Upward County, MB\n \n \n \n % for White, non-Hispanic\n48.9%\n Quality\nStrong\n % for Black, non-Hispanic\n82.4%\n Quality\nStrong\n % for Hispanic\n91.4%\n Quality\nStrong\n \n \n \n Source: National Center for Education Statistics Common Core of Data, SY 2018-19; Urban Institute’s Modeled Estimates of Poverty in Schools (via Education Data Portal v. 0.17.0, Urban Institute, under ODC Attribution License). (Time period: School Year 2018-19)\n \n \n \n\n\n\n\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Share of students attending high-poverty schools, by student race/ethnicity+\n \n \n \n Upward County, MB\n \n \n \n % for White, non-Hispanic\n48.9%\n Quality\nStrong\n % for Black, non-Hispanic\n82.4%\n Quality\nStrong\n % for Hispanic\n91.4%\n Quality\nStrong\n \n \n \n Source: National Center for Education Statistics Common Core of Data, SY 2018-19; Urban Institute’s Modeled Estimates of Poverty in Schools (via Education Data Portal v. 0.17.0, Urban Institute, under ODC Attribution License). (Time period: School Year 2018-19)\n \n \n Notes: This set of metrics is constructed separately for each racial/ethnic group and reports the share of students attending schools in which over 20 percent of students come from households earning at or below 100% of the Federal Poverty Level.The Confidence Interval for this metric is not applicable.\n \n \n \n\n\n\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Share of students attending high-poverty schools, by student race/ethnicity+\n \n \n \n Year\n Upward County, MB\n \n \n \n % for White, non-Hispanic\n2018\n48.9%\n Quality\n2018\nStrong\n % for Black, non-Hispanic\n2018\n82.4%\n Quality\n2018\nStrong\n % for Hispanic\n2018\n91.4%\n Quality\n2018\nStrong\n % for White, non-Hispanic\n2014\n88.2%\n Quality\n2014\nStrong\n % for Black, non-Hispanic\n2014\n92.4%\n Quality\n2014\nStrong\n % for Hispanic\n2014\n91.9%\n Quality\n2014\nStrong\n \n \n \n Source: National Center for Education Statistics Common Core of Data, SY 2017-18 & 2018-19; Urban Institute’s Modeled Estimates of Poverty in Schools (via Education Data Portal v. 0.17.0, Urban Institute, under ODC Attribution License). (Time periods: School Years 2017-18 & 2018-19)\n \n \n Notes: This set of metrics is constructed separately for each racial/ethnic group and reports the share of students attending schools in which over 20 percent of students come from households earning at or below 100% of the Federal Poverty Level.The Confidence Interval for this metric is not applicable.\n \n \n \n\n\n\n\n\n\n\nVisit here for more details about this predictor.\n\n\n\n\nPredictor: Preparation for college\n\nSummaryDetailMore Data\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Share of 19- and 20-year-olds with a high school degree\n \n \n \n Upward County, MB\n \n \n \n % HS degree\n95.5%\n Quality\nStrong\n \n \n \n Source: US Census Bureau’s 2021 5-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2017-21)\n \n \n \n\n\n\n\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Share of 19- and 20-year-olds with a high school degree+\n \n \n \n Upward County, MB\n \n \n \n % HS degree\n95.5%\n Quality\nStrong\n \n \n \n Source: US Census Bureau’s 2021 5-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2017-21)\n \n \n Notes: The share of 19- and 20-year-olds in a community who have a high school degree.The Confidence Interval for this metric is not applicable.\n \n \n \n\n\n\n\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Share of 19- and 20-year-olds with a high school degree+\n \n \n \n Group\n Year\n Upward County, MB\n \n \n \n % HS degree\nAll\n2021\n97.0%\n Quality\nAll\n2021\nStrong\n % HS degree\nBlack, Non-Hispanic\n2021\n94.8%\n Quality\nBlack, Non-Hispanic\n2021\nStrong\n % HS degree\nHispanic\n2021\n96.7%\n Quality\nHispanic\n2021\nWeak\n % HS degree\nOther Races and Ethnicities\n2021\n99.5%\n Quality\nOther Races and Ethnicities\n2021\nWeak\n % HS degree\nWhite, Non-Hispanic\n2021\n99.0%\n Quality\nWhite, Non-Hispanic\n2021\nStrong\n % HS degree\nAll\n2018\n94.7%\n Quality\nAll\n2018\nStrong\n % HS degree\nBlack, Non-Hispanic\n2018\n88.0%\n Quality\nBlack, Non-Hispanic\n2018\nWeak\n % HS degree\nHispanic\n2018\nNA\n Quality\nHispanic\n2018\nNA\n % HS degree\nOther Races and Ethnicities\n2018\nNA\n Quality\nOther Races and Ethnicities\n2018\nNA\n % HS degree\nWhite, Non-Hispanic\n2018\n98.2%\n Quality\nWhite, Non-Hispanic\n2018\nWeak\n \n \n \n Source: US Census Bureau’s 2018 & 2021 5-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2014-18 & 2017-21)\n \n \n Notes: The share of 19- and 20-year-olds in a community who have a high school degree.The Confidence Interval for this metric is not applicable.\n \n \n \n\n\n\n\n\n\n\nVisit here for more details about this predictor.\n\n\n\n\nPredictor: Digital access\n\nSummaryDetailMore Data\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Share of people in households with broadband access in the home\n \n \n \n Upward County, MB\n \n \n \n % Digital access\n79.7%\n Quality\nStrong\n \n \n \n Source: US Census Bureau’s 2021 5-Year American Community Survey. (Time period: 2017-2021)\n \n \n \n\n\n\n\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Share of people in households with broadband access in the home*\n \n \n \n Upward County, MB\n \n \n \n % Digital access\n79.7%\n Quality\nStrong\n \n \n \n Source: US Census Bureau’s 2021 5-Year American Community Survey. (Time period: 2017-2021)\n \n \n Notes: This metric represents the share of people in households with access to broadband in their home.The Confidence Interval for this metric is not available at this time.\n \n \n \n\n\n\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Share of people in households with broadband access in the home*\n \n \n \n Group\n Year\n Upward County, MB\n \n \n \n % Digital access\nAll\n2021\n79.7%\n Quality\nAll\n2021\nStrong\n % Digital access\nBlack\n2021\n74.3%\n Quality\nBlack\n2021\nStrong\n % Digital access\nHispanic\n2021\n61.3%\n Quality\nHispanic\n2021\nStrong\n % Digital access\nOther Races and Ethnicities\n2021\n75.0%\n Quality\nOther Races and Ethnicities\n2021\nStrong\n % Digital access\nWhite\n2021\n86.2%\n Quality\nWhite\n2021\nStrong\n \n \n \n Source: US Census Bureau’s 2021 5-Year American Community Survey. (Time period: 2017-2021)\n \n \n Notes: This metric represents the share of people in households with access to broadband in their home.The Confidence Interval for this metric is not available at this time.\n \n \n \n\n\n\n\n\n\n\nVisit here for more details about this predictor." + }, + { + "objectID": "index.html#pillar-rewarding-work", + "href": "index.html#pillar-rewarding-work", + "title": "", + "section": "Pillar: Rewarding Work", + "text": "Pillar: Rewarding Work\n\n\n\n\n\n\nPredictor: Employment Opportunities\n\nSummaryDetailMore Data\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Employment-to-population ratio for adults ages 25 to 54\n \n \n \n Upward County, MB\n \n \n \n Employment to population ratio\n83.4%\n Quality\nStrong\n \n \n \n Source: US Census Bureau’s 2021 5-Year American Community Survey Public Use Microdata Sample (PUMS) (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2017-21)\n \n \n \n\n\n\n\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Employment-to-population ratio for adults ages 25 to 54+\n \n \n \n Upward County, MB\n \n \n \n Employment to population ratio\n83.4%\n Quality\nStrong\n \n \n \n Source: US Census Bureau’s 2021 5-Year American Community Survey Public Use Microdata Sample (PUMS) (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2017-21)\n \n \n Notes: The share of adults between the ages of 25 and 54 in a given community who are employed.The Confidence Interval for this metric is not applicable.\n \n \n \n\n\n\n\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Employment-to-population ratio for adults ages 25 to 54+\n \n \n \n Group\n Year\n Upward County, MB\n \n \n \n Employment to population ratio\nAll\n2021\n81.6%\n Quality\nAll\n2021\nStrong\n Employment to population ratio\nBlack, Non-Hispanic\n2021\n73.1%\n Quality\nBlack, Non-Hispanic\n2021\nStrong\n Employment to population ratio\nHispanic\n2021\n79.4%\n Quality\nHispanic\n2021\nStrong\n Employment to population ratio\nOther Races and Ethnicities\n2021\n82.6%\n Quality\nOther Races and Ethnicities\n2021\nStrong\n Employment to population ratio\nWhite, Non-Hispanic\n2021\n88.6%\n Quality\nWhite, Non-Hispanic\n2021\nStrong\n Employment to population ratio\nAll\n2018\n83.4%\n Quality\nAll\n2018\nStrong\n Employment to population ratio\nBlack, Non-Hispanic\n2018\n68.2%\n Quality\nBlack, Non-Hispanic\n2018\nStrong\n Employment to population ratio\nHispanic\n2018\n83.0%\n Quality\nHispanic\n2018\nStrong\n Employment to population ratio\nOther Races and Ethnicities\n2018\n80.3%\n Quality\nOther Races and Ethnicities\n2018\nStrong\n Employment to population ratio\nWhite, Non-Hispanic\n2018\n87.0%\n Quality\nWhite, Non-Hispanic\n2018\nStrong\n \n \n \n Source: US Census Bureau’s 2018 & 2021 5-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2014-18 & 2017-21)\n \n \n Notes: The share of adults between the ages of 25 and 54 in a given community who are employed.The Confidence Interval for this metric is not applicable.\n \n \n \n\n\n\n\n\n\n\nVisit here for more details about this predictor.\n\n\n\n\nPredictor: Access to jobs paying a living wage\n\nSummaryDetailMore Data\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Ratio of pay on an average job to the cost of living\n \n \n \n Upward County, MB\n \n \n \n Ratio of pay to living wage\n0.91\n Quality\nStrong\n \n \n \n Source: US Bureau of Labor Statistics Quarterly Census of Employment and Wages (QCEW) data, 2021; Massachusetts Institute of Technology Living Wage Calculator, 2022. (Time period: 2021)\n \n \n \n\n\n\n\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Ratio of pay on an average job to the cost of living+\n \n \n \n Upward County, MB\n \n \n \n Ratio of pay to living wage\n0.91\n Quality\nStrong\n \n \n \n Source: US Bureau of Labor Statistics Quarterly Census of Employment and Wages (QCEW) data, 2021; Massachusetts Institute of Technology Living Wage Calculator, 2022. (Time period: 2021)\n \n \n Notes: What an average job pays relative to the cost of living in a particular area. The metric is computed by dividing the average earnings for a job in an area by the cost of meeting a family of three’s (for a 1 adult and 2 child household) basic expenses in that area. Ratio values greater than 1 indicate that the average job pays more than the cost of living, while values less than 1 suggest the average job pays less than the cost of living.For the 2021 metric, we were only able to access the 2022 Living Wage data. We deflated the 2022 data to 2021 using the consumer price index (for all urban consumers), for a correct comparison with the 2021 QCEW.The Confidence Interval for this metric is not applicable.\n \n \n \n\n\n\n\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Ratio of pay on an average job to the cost of living+\n \n \n \n Year\n Upward County, MB\n \n \n \n Ratio of pay to living wage\n2021\n0.91\n Quality\n2021\nStrong\n Ratio of pay to living wage\n2018\n0.95\n Quality\n2018\nStrong\n \n \n \n Source: US Bureau of Labor Statistics Quarterly Census of Employment and Wages (QCEW) data, 2018 & 2021; Massachusetts Institute of Technology Living Wage Calculator, 2018 & 2022. (Time period: 2018 & 2021)\n \n \n Notes: What an average job pays relative to the cost of living in a particular area. The metric is computed by dividing the average earnings for a job in an area by the cost of meeting a family of three’s (for a 1 adult and 2 child household) basic expenses in that area. Ratio values greater than 1 indicate that the average job pays more than the cost of living, while values less than 1 suggest the average job pays less than the cost of living.For the 2021 metric, we were only able to access the 2022 Living Wage data. We deflated the 2022 data to 2021 using the consumer price index (for all urban consumers), for a correct comparison with the 2021 QCEW.The Confidence Interval for this metric is not applicable.\n \n \n \n\n\n\n\n\n\n\nVisit here for more details about this predictor.\n\n\n\n\nPredictor: Opportunities for Income\n\nSummaryDetailMore Data\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Household income at the 20th, 50th, and 80th percentiles\n \n \n \n Upward County, MB\n \n \n \n 20th Percentile\n$20,059\n 50th Percentile\n$48,629\n 80th Percentile\n$101,310\n Quality\nStrong\n \n \n \n Source: US Census Bureau’s 2021 1-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time Period: 2021)\n \n \n \n\n\n\n\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Household income at the 20th, 50th, and 80th percentiles*\n \n \n \n Upward County, MB\n \n \n \n 20th Percentile\n$20,059\n 50th Percentile\n$48,629\n 80th Percentile\n$101,310\n Quality\nStrong\n \n \n \n Source: US Census Bureau’s 2021 1-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time Period: 2021)\n \n \n Notes: To identify income percentiles, all households are ranked by income from lowest to highest. The income level threshold for the poorest 20 percent of households is the value at the 20th percentile. The 50th percentile income threshold indicates the median, with half of households earning less and half of households earning more. The income level threshold for the richest 20 percent of households is the value at the 80th percentile. The difference in income between households at the 20th percentile and the 80th percentile illustrates the level of local economic inequality.The Confidence Interval for this metric is not available at this time.\n \n \n \n\n\n\n\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Household income at the 20th, 50th, and 80th percentiles*\n \n \n \n Group\n Year\n Upward County, MB\n \n \n \n 20th Percentile\nAll\n2021\n$21,458\n 50th Percentile\nAll\n2021\n$53,847\n 80th Percentile\nAll\n2021\n$116,233\n Quality\nAll\n2021\nStrong\n 20th Percentile\nBlack, Non-Hispanic\n2021\n$14,529\n 50th Percentile\nBlack, Non-Hispanic\n2021\n$37,071\n 80th Percentile\nBlack, Non-Hispanic\n2021\n$70,411\n Quality\nBlack, Non-Hispanic\n2021\nStrong\n 20th Percentile\nHispanic\n2021\n$25,692\n 50th Percentile\nHispanic\n2021\n$54,763\n 80th Percentile\nHispanic\n2021\n$93,240\n Quality\nHispanic\n2021\nStrong\n 20th Percentile\nOther Races and Ethnicities\n2021\n$15,303\n 50th Percentile\nOther Races and Ethnicities\n2021\n$48,173\n 80th Percentile\nOther Races and Ethnicities\n2021\n$102,822\n Quality\nOther Races and Ethnicities\n2021\nStrong\n 20th Percentile\nWhite, Non-Hispanic\n2021\n$35,807\n 50th Percentile\nWhite, Non-Hispanic\n2021\n$79,218\n 80th Percentile\nWhite, Non-Hispanic\n2021\n$157,969\n Quality\nWhite, Non-Hispanic\n2021\nStrong\n 20th Percentile\nAll\n2018\n$20,059\n 50th Percentile\nAll\n2018\n$48,629\n 80th Percentile\nAll\n2018\n$101,310\n Quality\nAll\n2018\nStrong\n 20th Percentile\nBlack, Non-Hispanic\n2018\n$11,186\n 50th Percentile\nBlack, Non-Hispanic\n2018\n$30,939\n 80th Percentile\nBlack, Non-Hispanic\n2018\n$62,819\n Quality\nBlack, Non-Hispanic\n2018\nStrong\n 20th Percentile\nHispanic\n2018\n$20,256\n 50th Percentile\nHispanic\n2018\n$44,552\n 80th Percentile\nHispanic\n2018\n$81,127\n Quality\nHispanic\n2018\nStrong\n 20th Percentile\nOther Races and Ethnicities\n2018\n$9,920\n 50th Percentile\nOther Races and Ethnicities\n2018\n$42,996\n 80th Percentile\nOther Races and Ethnicities\n2018\n$84,552\n Quality\nOther Races and Ethnicities\n2018\nStrong\n 20th Percentile\nWhite, Non-Hispanic\n2018\n$28,873\n 50th Percentile\nWhite, Non-Hispanic\n2018\n$68,573\n 80th Percentile\nWhite, Non-Hispanic\n2018\n$139,770\n Quality\nWhite, Non-Hispanic\n2018\nStrong\n \n \n \n Source: US Census Bureau’s 2018 & 2021 5-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time Periods: 2014-18 & 2017-21)\n \n \n Notes: To identify income percentiles, all households are ranked by income from lowest to highest. The income level threshold for the poorest 20 percent of households is the value at the 20th percentile. The 50th percentile income threshold indicates the median, with half of households earning less and half of households earning more. The income level threshold for the richest 20 percent of households is the value at the 80th percentile. The difference in income between households at the 20th percentile and the 80th percentile illustrates the level of local economic inequality.The Confidence Interval for this metric is not available at this time.\n \n \n \n\n\n\n\n\n\n\nVisit here for more details about this predictor.\n\n\n\n\nPredictor: Financial security\n\nSummaryDetailMore Data\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Share with debt in collections\n \n \n \n Upward County, MB\n \n \n \n % with debt\n34.2%\n Quality\nStrong\n \n \n \n Source: February 2022 credit bureau data from Urban Institute’s Debt in America feature. (Time period: February 2022)\n \n \n \n\n\n\n\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Share with debt in collections\n \n \n \n Upward County, MB\n \n \n \n % with debt\n34.2%\n Confidence Interval\nNA\n Quality\nStrong\n \n \n \n Source: February 2022 credit bureau data from Urban Institute’s Debt in America feature. (Time period: February 2022)\n \n \n Notes: The county-level measure captures the share of adults in an area with a credit bureau record with debt sent to collections.\n \n \n \n\n\n\n\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Share with debt in collections\n \n \n \n Group\n Year\n Upward County, MB\n \n \n \n % with debt\nAll\n2018\n42.6%\n Confidence Interval\nAll\n2018\n(40.9%, 44.2%)\n Quality\nAll\n2018\nStrong\n % with debt\nMajority Non-White ZIPs\n2018\n57.1%\n Confidence Interval\nMajority Non-White ZIPs\n2018\n(54.7%, 59.5%)\n Quality\nMajority Non-White ZIPs\n2018\nStrong\n % with debt\nMajority White, Non-Hispanic ZIPs\n2018\n13.4%\n Confidence Interval\nMajority White, Non-Hispanic ZIPs\n2018\n(10.6%, 16.3%)\n Quality\nMajority White, Non-Hispanic ZIPs\n2018\nStrong\n \n \n \n Source: August 2018 and February 2022 credit bureau data from Urban Institute’s Debt in America feature. (Time periods: August 2018 & February 2022)\n \n \n Notes: The county-level measure captures the share of adults in an area with a credit bureau record with debt sent to collections. For county-level August 2018 and February 2022 data, “majority” means that at least 60% of residents in a zip code are members of the specified population group.\n \n \n \n\n\n\n\n\n\n\nVisit here for more details about this predictor.\n\n\n\n\nPredictor: Wealth-Building Opportunities\n\nSummaryDetailMore Data\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Ratio of the share of a community’s housing wealth held by a racial or ethnic group to the share of households of the same group\n \n \n \n Upward County, MB\n \n \n \n Black, non-Hispanic Opportunity\n26.6%:39.6%\n Quality\nStrong\n Hispanic Opportunity\n3.1%:4.9%\n Quality\nWeak\n Other Races and Ethnicities Opportunity\n5.4%:7.5%\n Quality\nStrong\n White, non-Hispanic Opportunity\n64.9%:48.0%\n Quality\nStrong\n \n \n \n Source: US Census Bureau’s 2021 1-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2021)\n \n \n \n\n\n\n\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Ratio of the share of a community’s housing wealth held by a racial or ethnic group to the share of households of the same group+\n \n \n \n Upward County, MB\n \n \n \n Black, non-Hispanic Opportunity\n26.6%:39.6%\n Quality\nStrong\n Hispanic Opportunity\n3.1%:4.9%\n Quality\nWeak\n Other Races and Ethnicities Opportunity\n5.4%:7.5%\n Quality\nStrong\n White, non-Hispanic Opportunity\n64.9%:48.0%\n Quality\nStrong\n \n \n \n Source: US Census Bureau’s 2021 1-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2021)\n \n \n Notes: The percentage to the left of the colon for a given racial group reflects their share of primary-residence housing wealth in a community, and the percentage to the right of the colon reflects the number of households who are headed by a member of that racial group as a share of the community’s total number of households. If the percentage on the left side of the colon is smaller than the percentage on the right side, then that group has less proportionate housing wealth compared to their presence in the community. The greater the gap between these percentages, the more inequality in housing wealth in the community. This metric is based on self-reported housing value, does not account for the extent of mortgage debt, and does not account for other important demographic variations such as differences in age composition across race and ethnic groups, and as such this metric may not fully reflect the size of the actual housing wealth gap.The Confidence Interval for this metric is not applicable.\n \n \n \n\n\n\n\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Ratio of the share of a community’s housing wealth held by a racial or ethnic group to the share of households of the same group+\n \n \n \n Year\n Upward County, MB\n \n \n \n Black, non-Hispanic Opportunity\n2021\n26.6%:39.6%\n Quality\n2021\nStrong\n Hispanic Opportunity\n2021\n3.1%:4.9%\n Quality\n2021\nWeak\n Other Races and Ethnicities Opportunity\n2021\n5.4%:7.5%\n Quality\n2021\nStrong\n White, non-Hispanic Opportunity\n2021\n64.9%:48.0%\n Quality\n2021\nStrong\n Black, non-Hispanic Opportunity\n2018\n15.2%:41.1%\n Quality\n2018\nStrong\n Hispanic Opportunity\n2018\n2.2%:4.1%\n Quality\n2018\nWeak\n Other Races and Ethnicities Opportunity\n2018\n3.4%:5.4%\n Quality\n2018\nWeak\n White, non-Hispanic Opportunity\n2018\n79.2%:49.4%\n Quality\n2018\nStrong\n \n \n \n Source: US Census Bureau’s 2018 & 2021 1-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time periods: 2018 & 2021)\n \n \n Notes: The percentage to the left of the colon for a given racial group reflects their share of primary-residence housing wealth in a community, and the percentage to the right of the colon reflects the number of households who are headed by a member of that racial group as a share of the community’s total number of households. If the percentage on the left side of the colon is smaller than the percentage on the right side, then that group has less proportionate housing wealth compared to their presence in the community. The greater the gap between these percentages, the more inequality in housing wealth in the community. This metric is based on self-reported housing value, does not account for the extent of mortgage debt, and does not account for other important demographic variations such as differences in age composition across race and ethnic groups, and as such this metric may not fully reflect the size of the actual housing wealth gap.The Confidence Interval for this metric is not applicable.\n \n \n \n\n\n\n\n\n\n\nVisit here for more details about this predictor." + }, + { + "objectID": "index.html#pillar-healthy-environment-and-access-to-good-healthcare", + "href": "index.html#pillar-healthy-environment-and-access-to-good-healthcare", + "title": "", + "section": "Pillar: Healthy Environment and Access to Good Healthcare", + "text": "Pillar: Healthy Environment and Access to Good Healthcare\n\n\n\n\n\n\nPredictor: Access to health services\n\nSummaryDetail\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Ratio of population per primary care physician\n \n \n \n Upward County, MB\n \n \n \n Ratio of people to physicians\n941:1\n Quality\nStrong\n \n \n \n Source: US Department of Health and Human Services, Health Resources and Services Administration, Area Health Resources File, 2020-21 (via County Health Rankings, 2022). (Time period: 2019)\n \n \n \n\n\n\n\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Ratio of population per primary care physician+\n \n \n \n Upward County, MB\n \n \n \n Ratio of people to physicians\n941:1\n Quality\nStrong\n \n \n \n Source: US Department of Health and Human Services, Health Resources and Services Administration, Area Health Resources File, 2020-21 (via County Health Rankings, 2022). (Time period: 2019)\n \n \n Notes: The ratio represents the number of people served by one primary care physician in a county. It assumes the population is equally distributed across physicians and does not account for actual physician patient load. Missing values are reported for counties with population greater than 2,000 and 0 primary care physicians. The metric does not include nurse practitioners, physician assistants, or other primary care providers who are not physicians.The Confidence Interval for this metric is not applicable.\n \n \n \n\n\n\n\n\n\n\nVisit here for more details about this predictor.\n\n\n\n\nPredictor: Neonatal health\n\nSummaryDetailMore Data\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Share with low birth weight\n \n \n \n Upward County, MB\n \n \n \n % Low birth weight\n11.5%\n Quality\nStrong\n \n \n \n Source: Centers for Disease Control and Prevention National Center for Health Statistics, Division of Vital Statistics, Natality data, 2020 (via CDC WONDER). (Time period: 2020)\n \n \n \n\n\n\n\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Share with low birth weight\n \n \n \n Upward County, MB\n \n \n \n % Low birth weight\n11.5%\n Confidence Interval\n(10.4%, 12.7%)\n Quality\nStrong\n \n \n \n Source: Centers for Disease Control and Prevention National Center for Health Statistics, Division of Vital Statistics, Natality data, 2020 (via CDC WONDER). (Time period: 2020)\n \n \n Notes: The share of babies born weighing less than 5 pounds 8 ounces (<2,500 grams) out of all births with available birthweight information.\n \n \n \n\n\n\n\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Share with low birth weight\n \n \n \n Group\n Year\n Upward County, MB\n \n \n \n % Low birth weight\nAll\n2020\n11.5%\n Confidence Interval\nAll\n2020\n(10.4%, 12.7%)\n Quality\nAll\n2020\nStrong\n % Low birth weight\nBlack, Non-Hispanic\n2020\n16.6%\n Confidence Interval\nBlack, Non-Hispanic\n2020\n(14.6%, 18.5%)\n Quality\nBlack, Non-Hispanic\n2020\nStrong\n % Low birth weight\nHispanic\n2020\n8.9%\n Confidence Interval\nHispanic\n2020\n(6.4%, 11.5%)\n Quality\nHispanic\n2020\nStrong\n % Low birth weight\nOther Races and Ethnicities\n2020\n10.0%\n Confidence Interval\nOther Races and Ethnicities\n2020\n(4.1%, 15.9%)\n Quality\nOther Races and Ethnicities\n2020\nMarginal\n % Low birth weight\nWhite, Non-Hispanic\n2020\n5.6%\n Confidence Interval\nWhite, Non-Hispanic\n2020\n(4.1%, 7.0%)\n Quality\nWhite, Non-Hispanic\n2020\nStrong\n % Low birth weight\nAll\n2018\n10.4%\n Confidence Interval\nAll\n2018\n(9.3%, 11.4%)\n Quality\nAll\n2018\nStrong\n % Low birth weight\nBlack, Non-Hispanic\n2018\n15.9%\n Confidence Interval\nBlack, Non-Hispanic\n2018\n(14.1%, 17.7%)\n Quality\nBlack, Non-Hispanic\n2018\nStrong\n % Low birth weight\nHispanic\n2018\n4.3%\n Confidence Interval\nHispanic\n2018\n(2.5%, 6.1%)\n Quality\nHispanic\n2018\nMarginal\n % Low birth weight\nOther Races and Ethnicities\n2018\nNA\n Confidence Interval\nOther Races and Ethnicities\n2018\nNA\n Quality\nOther Races and Ethnicities\n2018\nWeak\n % Low birth weight\nWhite, Non-Hispanic\n2018\n4.4%\n Confidence Interval\nWhite, Non-Hispanic\n2018\n(3.1%, 5.7%)\n Quality\nWhite, Non-Hispanic\n2018\nStrong\n \n \n \n Source: Centers for Disease Control and Prevention National Center for Health Statistics, Division of Vital Statistics, Natality data, 2018 & 2020 (via CDC WONDER). (Time period: 2018 & 2020)\n \n \n Notes: The share of babies born weighing less than 5 pounds 8 ounces (<2,500 grams) out of all births with available birthweight information. Race and ethnicity is based on the mother’s characteristics.\n \n \n \n\n\n\n\n\n\n\nVisit here for more details about this predictor.\n\n\n\n\nPredictor: Environmental quality\n\nSummaryDetailMore Data\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Air quality index\n \n \n \n Upward County, MB\n \n \n \n Air quality index\n14\n Quality\nStrong\n \n \n \n Source: US Environmental Protection Agency’s AirToxScreen data, 2018 (based on 2017 National Emissions Inventory data). (Time period: 2017-18)\n \n \n \n\n\n\n\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Air quality index+\n \n \n \n Upward County, MB\n \n \n \n Air quality index\n14\n Quality\nStrong\n \n \n \n Source: US Environmental Protection Agency’s AirToxScreen data, 2018 (based on 2017 National Emissions Inventory data). (Time period: 2017-18)\n \n \n Notes: The index is a linear combination of standardized EPA estimates of air quality carcinogenic, respiratory, and neurological hazards measured at the census tract level. Values are inverted and percentile ranked nationally and range from 0 to 100. The higher the index value, the less exposure to toxins harmful to human health.The Confidence Interval for this metric is not applicable.\n \n \n \n\n\n\n\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Air quality index+\n \n \n \n Group\n Year\n Upward County, MB\n \n \n \n Air quality index\nAll\n2018\n14\n Quality\nAll\n2018\nStrong\n Air quality index\nMajority Non-White Tracts\n2018\n13\n Quality\nMajority Non-White Tracts\n2018\nStrong\n Air quality index\nMajority White, Non-Hispanic Tracts\n2018\n15\n Quality\nMajority White, Non-Hispanic Tracts\n2018\nStrong\n Air quality index\nNo Majority Race/Ethnicity Tracts\n2018\n16\n Quality\nNo Majority Race/Ethnicity Tracts\n2018\nStrong\n Air quality index\nAll\n2014\n34\n Quality\nAll\n2014\nStrong\n Air quality index\nMajority Non-White Tracts\n2014\n36\n Quality\nMajority Non-White Tracts\n2014\nStrong\n Air quality index\nMajority White, Non-Hispanic Tracts\n2014\n31\n Quality\nMajority White, Non-Hispanic Tracts\n2014\nStrong\n Air quality index\nNo Majority Race/Ethnicity Tracts\n2014\n32\n Quality\nNo Majority Race/Ethnicity Tracts\n2014\nStrong\n \n \n \n Source: Environmental Protection Agency’s National Air Toxics Assessment data, 2014 and AirToxScreen data, 2018 (based on 2014 & 2017 National Emissions Inventory data); US Census Bureau’s 2014 & 2018 5-Year American Community Survey. (Time periods: 2010-14 & 2014-18)\n \n \n Notes: The index is a linear combination of standardized EPA estimates of air quality carcinogenic, respiratory, and neurological hazards measured at the census tract level. Values are inverted and percentile ranked nationally and range from 0 to 100. The higher the index value, the less exposure to toxins harmful to human health. ‘Majority’ means that at least 60% of residents in a census tract are members of the specified group. ‘High poverty’ means that 40% or more of people in a census tract live in families with incomes below the federal poverty line.The Confidence Interval for this metric is not applicable.\n \n \n \n\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Air quality index+\n \n \n \n Group\n Year\n Upward County, MB\n \n \n \n Air quality index\nAll\n2018\n14\n Quality\nAll\n2018\nStrong\n Air quality index\nHigh Poverty Tracts\n2018\n10\n Quality\nHigh Poverty Tracts\n2018\nStrong\n Air quality index\nNot High Poverty Tracts\n2018\n15\n Quality\nNot High Poverty Tracts\n2018\nStrong\n Air quality index\nAll\n2014\n34\n Quality\nAll\n2014\nStrong\n Air quality index\nHigh Poverty Tracts\n2014\n34\n Quality\nHigh Poverty Tracts\n2014\nStrong\n Air quality index\nNot High Poverty Tracts\n2014\n34\n Quality\nNot High Poverty Tracts\n2014\nStrong\n \n \n \n Source: Environmental Protection Agency’s National Air Toxics Assessment data, 2014 and AirToxScreen data, 2018 (based on 2014 & 2017 National Emissions Inventory data); US Census Bureau’s 2014 & 2018 5-Year American Community Survey. (Time periods: 2010-14 & 2014-18)\n \n \n Notes: The index is a linear combination of standardized EPA estimates of air quality carcinogenic, respiratory, and neurological hazards measured at the census tract level. Values are inverted and percentile ranked nationally and range from 0 to 100. The higher the index value, the less exposure to toxins harmful to human health. ‘Majority’ means that at least 60% of residents in a census tract are members of the specified group. ‘High poverty’ means that 40% or more of people in a census tract live in families with incomes below the federal poverty line.The Confidence Interval for this metric is not applicable.\n \n \n \n\n\n\n\n\n\n\nVisit here for more details about this predictor.\n\n\n\n\nPredictor: Safety from Trauma\n\nSummaryDetail\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Deaths due to injury per 100,000 people\n \n \n \n Upward County, MB\n \n \n \n Trauma\n99.5\n Quality\nStrong\n \n \n \n Source: National Center for Health Statistics, 2016-20, drawn from the National Vital Statistics System (via County Health Rankings, 2022). (Time period: 2016-20)\n \n \n \n\n\n\n\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Deaths due to injury per 100,000 people\n \n \n \n Upward County, MB\n \n \n \n Trauma\n99.5\n Confidence Interval\n(93.7, 105.3)\n Quality\nStrong\n \n \n \n Source: National Center for Health Statistics, 2016-20, drawn from the National Vital Statistics System (via County Health Rankings, 2022). (Time period: 2016-20)\n \n \n Notes: Injury deaths is the number of deaths from planned (e.g., homicide or suicide) and unplanned (e.g., motor vehicle deaths) injuries per 100,000 people. Deaths are counted in the county of residence for the person who died, rather than the county where the death occurred. A missing value is reported for counties with fewer than 10 injury deaths in the time frame.\n \n \n \n\n\n\n\n\n\n\nVisit here for more details about this predictor." + }, + { + "objectID": "index.html#pillar-responsible-and-just-governance", + "href": "index.html#pillar-responsible-and-just-governance", + "title": "", + "section": "Pillar: Responsible and Just Governance", + "text": "Pillar: Responsible and Just Governance\n\n\n\n\n\n\nPredictor: Political participation\n\nSummaryDetailMore data\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Share of the voting-age population who turn out to vote\n \n \n \n Upward County, MB\n \n \n \n % voting\n62.0%\n Quality\nStrong\n \n \n \n Source: Massachusetts Institute of Technology Election Data and Science Lab, 2020; US Census Bureau’s 2020 5-Year American Community Survey Citizen Voting Age Population Special Tabulation. (Time period: 2016-20)\n \n \n \n\n\n\n\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Share of the voting-age population who turn out to vote+\n \n \n \n Upward County, MB\n \n \n \n % voting\n62.0%\n Quality\nStrong\n \n \n \n Source: Massachusetts Institute of Technology Election Data and Science Lab, 2020; US Census Bureau’s 2020 5-Year American Community Survey Citizen Voting Age Population Special Tabulation. (Time period: 2016-20)\n \n \n Notes: This metric measures the share of the citizen voting-age population that voted in the most recent presidential election.The Confidence Interval for this metric is not applicable.\n \n \n \n\n\n\n\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Share of the voting-age population who turn out to vote+\n \n \n \n Year\n Upward County, MB\n \n \n \n % voting\n2020\n62.0%\n Quality\n2020\nStrong\n % voting\n2016\n61.5%\n Quality\n2016\nStrong\n \n \n \n Source: Massachusetts Institute of Technology Election Data and Science Lab, 2016 & 2020; US Census Bureau’s 2016 & 2020 5-Year American Community Survey Citizen Voting Age Population Special Tabulation. (Time periods: 2012-16 & 2016-20)\n \n \n Notes: This metric measures the share of the citizen voting-age population that voted in the most recent presidential election.The Confidence Interval for this metric is not applicable.\n \n \n \n\n\n\n\n\n\n\nVisit here for more details about this predictor.\n\n\n\n\nPredictor: Descriptive Representation\n\nDetail\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Ratio of the share of local elected officials of a racial or ethnic group to the share of residents of the same racial or ethnic group. Part of this metric is shown. See the notes for information on finalizing this metric.+\n \n \n \n Upward County, MB\n \n \n \n Other Races/Ethnicities\n__:6%\n Black, non-Hispanic\n__:45%\n Hispanic\n__:7%\n White, non-Hispanic\n__:41%\n \n \n \n Source: US Census Bureau’s 2021 5-Year American Community Survey. (Time period: 2017-21)\n \n \n Notes: Shown are the share of that racial or ethnic group in your community. The community will need to calculate the missing percentages in order to complete the descriptive representation metric. See the Planning Guide (pg. 27) on how to calculate the missing percentage.\nSay that of your 10 elected officials, nine are White, non-Hispanic and your community’s population is half White, non-Hispanic, the metric will read as “90.0%:50.0%.” If the share of local officials is higher than the share of people in the community, then this group is over-represented. If the share of local officials is lower than the share of people in the community, then this group is under-represented. We are presenting this as a ratio of percentages because it provides important context.The quality index reflects the data quality only of the given value.The Confidence Interval for this metric is not applicable.\n \n \n \n\n\n\n\n\n\n\nVisit here for more details about this predictor.\n\n\n\n\nPredictor: Safety from Crime\n\nSummaryDetail\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Reported property crimes per 100,000 people and reported violent crimes per 100,000 people\n \n \n \n Upward County, MB\n \n \n \n Violent crime\n1,422\n Property crime\n4,741\n Quality\nStrong\n \n \n \n Source: Federal Bureau of Investigations (FBI) National Incident Based Reporting System (via Kaplan J (2021). National Incident-Based Reporting System (NIBRS) Data. https://nibrsbook.com/); US Census Bureau’s 2021 1-Year American Community Survey. (Time period: 2021)\n \n \n \n\n\n\n\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Reported property crimes per 100,000 people and reported violent crimes per 100,000 people+\n \n \n \n Upward County, MB\n \n \n \n Violent crime\n1,422\n Property crime\n4,741\n Quality\nStrong\n \n \n \n Source: Federal Bureau of Investigations (FBI) National Incident Based Reporting System (via Kaplan J (2021). National Incident-Based Reporting System (NIBRS) Data. https://nibrsbook.com/); US Census Bureau’s 2021 1-Year American Community Survey. (Time period: 2021)\n \n \n Notes: Rates are calculated as the number of reported crimes against property or people per 100,000 people. Although these are the best national data source, communities should use their local data if they are available. The FBI cautions against using NIBRS data to rank or compare locales because there are many factors that cause the nature and type of crime to vary from place to place.The Confidence Interval for this metric is not applicable.\n \n \n \n\n\n\n\n\n\n\n\nVisit here for more details about this predictor.\n\n\n\n\nPredictor: Just policing\n\nSummaryDetailMore Data\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Juvenile arrests per 100,000 juveniles\n \n \n \n Upward County, MB\n \n \n \n Juvenile arrest rate\n285.8\n Quality\nStrong\n \n \n \n Source: Federal Bureau of Investigations (FBI) National Incident Based Reporting System (via Kaplan J (2021). National Incident-Based Reporting System (NIBRS) Data. https://nibrsbook.com/); US Census Bureau’s 2021 1-Year American Community Survey. (Time period: 2021)\n \n \n \n\n\n\n\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Juvenile arrests per 100,000 juveniles+\n \n \n \n Upward County, MB\n \n \n \n Juvenile arrest rate\n285.8\n Quality\nStrong\n \n \n \n Source: Federal Bureau of Investigations (FBI) National Incident Based Reporting System (via Kaplan J (2021). National Incident-Based Reporting System (NIBRS) Data. https://nibrsbook.com/); US Census Bureau’s 2021 1-Year American Community Survey. (Time period: 2021)\n \n \n Notes: The number of arrests of people aged 10 to 17, for any crime or status offense, per 100,000 people of that age. Because people can be arrested multiple times, the data reports the number of arrests and not people. Although these are the best national data source, communities should use their local data if it is available. The FBI cautions against using NIBRS data to rank or compare locales because there are many factors that cause the nature and type of crime to vary from place to place.The Confidence Interval for this metric is not applicable.\n \n \n \n\n\n\n\n\n\n\n\n\n \n \n \n \n \n \n \n Metric: Juvenile arrests per 100,000 juveniles+\n \n \n \n Group\n Year\n Upward County, MB\n \n \n \n Juvenile arrest rate\nAll\n2021\n285.8\n Quality\nAll\n2021\nStrong\n Juvenile arrest rate\nBlack\n2021\n391.2\n Quality\nBlack\n2021\nStrong\n Juvenile arrest rate\nHispanic\n2021\n56.0\n Quality\nHispanic\n2021\nStrong\n Juvenile arrest rate\nOther Races and Ethnicities\n2021\n0.0\n Quality\nOther Races and Ethnicities\n2021\nStrong\n Juvenile arrest rate\nWhite\n2021\n153.9\n Quality\nWhite\n2021\nStrong\n \n \n \n Source: Federal Bureau of Investigations (FBI) National Incident Based Reporting System (via Kaplan J (2021). National Incident-Based Reporting System (NIBRS) Data. https://nibrsbook.com/); US Census Bureau’s 2021 1-Year American Community Survey. (Time period: 2021)\n \n \n Notes: The number of arrests of people aged 10 to 17, for any crime or status offense, per 100,000 people of that age. Because people can be arrested multiple times, the data reports the number of arrests and not people. Although these are the best national data source, communities should use their local data if it is available. The FBI cautions against using NIBRS data to rank or compare locales because there are many factors that cause the nature and type of crime to vary from place to place. Ethnicity is inconsistently collected and often missing in the data. Those of multiple races are only included in ‘Other Races.’The Confidence Interval for this metric is not applicable.\n \n \n \n\n\n\n\n\n\n\n\nVisit here for more details about this predictor." + }, + { + "objectID": "index.html#additional-notes-on-data", + "href": "index.html#additional-notes-on-data", + "title": "", + "section": "Additional Notes on Data", + "text": "Additional Notes on Data\n\nData Quality\n“Strong” indicates that the metric is measured with adequate accuracy and sample size.\n“Marginal” indicates that there are known shortcomings of the data for this metric for this community, and the metric should be used with caution.\n“Weak” indicates that although the metric could be computed for this community, we have serious concerns about how accurately it is measured for this community and do not recommend its use. Instead, we recommend seeking more local data sources for this metric.\n“NA” indicates that the metric value may be suppressed or unavailable and the quality is not applicable.\n\n\n\nConfidence Intervals\nConfidence intervals shown are 95 percent.\n* This confidence interval is not available at this time.\n+ A confidence interval is not applicable.\nLower/Upper bound: The data used to construct this metric do not lend themselves to conventional confidence intervals. The value of the metric shown represents are best estimate; the lower and upper bounds represent alternative estimates of the metric under different assumptions about missing data.\n\n“NC” in fields for confidence intervals or lower/upper bounds means that we are not able to calculate this because the underlying data lack variation.\n\n\n\nMissing and Suppressed Values\n\n“NA” in fields for metric values and data quality values indicates that the data are suppressed due to sample sizes or because that element is not applicable to that community (e.g., no zip code in the county is majority non-white).\n\n\nVersion: 2023-05-23 07:26:32" + } +] \ No newline at end of file diff --git a/factsheets/25_upward-county/51760-Upward-MB_NA/www/images/mobility-icons/mobility-infographic-book-icon.png b/factsheets/25_upward-county/51760-Upward-MB_NA/www/images/mobility-icons/mobility-infographic-book-icon.png new file mode 100644 index 0000000..1180895 Binary files /dev/null and b/factsheets/25_upward-county/51760-Upward-MB_NA/www/images/mobility-icons/mobility-infographic-book-icon.png differ diff --git a/factsheets/25_upward-county/51760-Upward-MB_NA/www/images/mobility-icons/mobility-infographic-govt-icon.png b/factsheets/25_upward-county/51760-Upward-MB_NA/www/images/mobility-icons/mobility-infographic-govt-icon.png new file mode 100644 index 0000000..03ebae7 Binary files /dev/null and b/factsheets/25_upward-county/51760-Upward-MB_NA/www/images/mobility-icons/mobility-infographic-govt-icon.png differ diff --git a/factsheets/25_upward-county/51760-Upward-MB_NA/www/images/mobility-icons/mobility-infographic-health-icon.png b/factsheets/25_upward-county/51760-Upward-MB_NA/www/images/mobility-icons/mobility-infographic-health-icon.png new file mode 100644 index 0000000..fc13c13 Binary files /dev/null and b/factsheets/25_upward-county/51760-Upward-MB_NA/www/images/mobility-icons/mobility-infographic-health-icon.png differ diff --git a/factsheets/25_upward-county/51760-Upward-MB_NA/www/images/mobility-icons/mobility-infographic-house-icon.png b/factsheets/25_upward-county/51760-Upward-MB_NA/www/images/mobility-icons/mobility-infographic-house-icon.png new file mode 100644 index 0000000..8c5721a Binary files /dev/null and b/factsheets/25_upward-county/51760-Upward-MB_NA/www/images/mobility-icons/mobility-infographic-house-icon.png differ diff --git a/factsheets/25_upward-county/51760-Upward-MB_NA/www/images/mobility-icons/mobility-infographic-icons-panel-text.png b/factsheets/25_upward-county/51760-Upward-MB_NA/www/images/mobility-icons/mobility-infographic-icons-panel-text.png new file mode 100644 index 0000000..49867b5 Binary files /dev/null and b/factsheets/25_upward-county/51760-Upward-MB_NA/www/images/mobility-icons/mobility-infographic-icons-panel-text.png differ diff --git a/factsheets/25_upward-county/51760-Upward-MB_NA/www/images/mobility-icons/mobility-infographic-money-icon.png b/factsheets/25_upward-county/51760-Upward-MB_NA/www/images/mobility-icons/mobility-infographic-money-icon.png new file mode 100644 index 0000000..739c4c9 Binary files /dev/null and b/factsheets/25_upward-county/51760-Upward-MB_NA/www/images/mobility-icons/mobility-infographic-money-icon.png differ diff --git a/factsheets/25_upward-county/51760-Upward-MB_NA/www/images/urban-institute-logo.png b/factsheets/25_upward-county/51760-Upward-MB_NA/www/images/urban-institute-logo.png new file mode 100644 index 0000000..25314a3 Binary files /dev/null and b/factsheets/25_upward-county/51760-Upward-MB_NA/www/images/urban-institute-logo.png differ diff --git a/factsheets/25_upward-county/51760-Upward-MB_NA/www/web_report.css b/factsheets/25_upward-county/51760-Upward-MB_NA/www/web_report.css new file mode 100644 index 0000000..74ac737 --- /dev/null +++ b/factsheets/25_upward-county/51760-Upward-MB_NA/www/web_report.css @@ -0,0 +1,185 @@ +/* Global Formats */ +.container-fluid { + font-family: Lato, Arial, sans-serif; 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Table of contents

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Mobility Metrics for Richmond City, Virginia

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+

Mobility Metrics for Autauga, Alabama

@@ -3258,22 +3258,22 @@

Predictor: - Metric: Ratio of affordable and available housing units (per 100 households) with low-, very low-, and extremely low-income levels + Metric: Ratio of affordable housing units (per 100 households) with low-, very low-, and extremely low-income levels - Richmond City, Virginia + Autauga, Alabama Ratio for low-income households -138.4 +169.8 Ratio for very low-income households -100.3 +197.5 Ratio for extremely low-income households -61.5 +209.2 Data quality -Strong +Marginal @@ -3297,29 +3297,29 @@

Predictor: - Metric: Ratio of affordable and available housing units (per 100 households) with low-, very low-, and extremely low-income levels+ + Metric: Ratio of affordable housing units (per 100 households) with low-, very low-, and extremely low-income levels+ - Richmond City, Virginia + Autauga, Alabama Ratio for low-income households -138.4 +169.8 Ratio for very low-income households -100.3 +197.5 Ratio for extremely low-income households -61.5 +209.2 Data quality -Strong +Marginal Source: US Department of Housing and Urban Development Office of Policy Development and Research Fair Market Rents and Income Limits, FY 2021; US Census Bureau’s 2021 1-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2021) - Notes: This metric reports the number of housing units affordable for households with low-incomes (below 80 percent of area median income, or AMI), very low-incomes (below 50 percent of AMI), and extremely low-incomes (below 30 percent of AMI) relative to every 100 households with these income levels. Income groups are defined for a local family of 4. Housing units are defined as affordable if the monthly costs do not exceed 30 percent of a household’s income. Values above 100 suggest that there are more affordable housing units than households with those income levels. Values below 100 indicate a shortage of affordable housing for households with those income levels. Housing units are counted as affordable for a given income level regardless of whether they are currently occupied by a household at that income level.

The Confidence Interval for this metric is not applicable. + Notes: This metric reports the number of housing units affordable for households with low-incomes (below 80 percent of area median income, or AMI), very low-incomes (below 50 percent of AMI), and extremely low-incomes (below 30 percent of AMI) relative to every 100 households with these income levels. Income groups are defined for a local family of 4. Housing units are defined as affordable if the monthly costs do not exceed 30 percent of a household’s income. Values above 100 suggest that there are more affordable housing units than households with those income levels. Affordability addresses whether sufficient housing units would exist if allocated solely on the basis of cost, regardless of whether they are currently occupied by a household that could afford the unit. Values below 100 suggest that on this basis the affordable stock is insufficient to meet the need. The affordable housing stock includes both vacant and occupied units.

The Confidence Interval for this metric is not applicable. @@ -3339,46 +3339,46 @@

Predictor: - Metric: Ratio of affordable and available housing units (per 100 households) with low-, very low-, and extremely low-income levels+ + Metric: Ratio of affordable housing units (per 100 households) with low-, very low-, and extremely low-income levels+ Year - Richmond City, Virginia + Autauga, Alabama Ratio for low-income households 2021 -138.4 +169.8 Ratio for very low-income households 2021 -100.3 +197.5 Ratio for extremely low-income households 2021 -61.5 +209.2 Data quality 2021 -Strong +Marginal Ratio for low-income households 2018 -137.8 +167.6 Ratio for very low-income households 2018 -110.3 +158.2 Ratio for extremely low-income households 2018 -71.9 +153.3 Data quality 2018 -Strong +Marginal Source: US Department of Housing and Urban Development Office of Policy Development and Research Fair Market Rents and Income Limits, FY 2018 & FY 2021; US Census Bureau’s 2018 & 2021 1-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time periods: 2018 & 2021) - Notes: This metric reports the number of housing units affordable for households with low-incomes (below 80 percent of area median income, or AMI), very low-incomes (below 50 percent of AMI), and extremely low-incomes (below 30 percent of AMI) relative to every 100 households with these income levels. Income groups are defined for a local family of 4. Housing units are defined as affordable if the monthly costs do not exceed 30 percent of a household’s income. Values above 100 suggest that there are more affordable housing units than households with those income levels. Values below 100 indicate a shortage of affordable housing for households with those income levels. Housing units are counted as affordable for a given income level regardless of whether they are currently occupied by a household at that income level.

The Confidence Interval for this metric is not applicable. + Notes: This metric reports the number of housing units affordable for households with low-incomes (below 80 percent of area median income, or AMI), very low-incomes (below 50 percent of AMI), and extremely low-incomes (below 30 percent of AMI) relative to every 100 households with these income levels. Income groups are defined for a local family of 4. Housing units are defined as affordable if the monthly costs do not exceed 30 percent of a household’s income. Values above 100 suggest that there are more affordable housing units than households with those income levels. Affordability addresses whether sufficient housing units would exist if allocated solely on the basis of cost, regardless of whether they are currently occupied by a household that could afford the unit. Values below 100 suggest that on this basis the affordable stock is insufficient to meet the need. The affordable housing stock includes both vacant and occupied units.

The Confidence Interval for this metric is not applicable. @@ -3414,16 +3414,16 @@

Predictor: Hou - Richmond City, Virginia + Autauga, Alabama Number homeless -852 +57 Share homeless -3.4% +0.6% Data quality -Strong +Strong @@ -3451,18 +3451,18 @@

Predictor: Hou - Richmond City, Virginia + Autauga, Alabama Number homeless -852 +57 Lower/Upper bound -(852, 852) +(57, 57) Share homeless -3.4% +0.6% Data quality -Strong +Strong @@ -3494,34 +3494,34 @@

Predictor: Hou Year - Richmond City, Virginia + Autauga, Alabama Number homeless 2019 -852 +57 Lower/Upper bound 2019 -(852, 852) +(57, 57) Share homeless 2019 -3.4% +0.6% Data quality 2019 -Strong +Strong Number homeless 2018 -1,189 +58 Lower/Upper bound 2018 -(1,189, 1,189) +(58, 58) Share homeless 2018 -4.8% +0.6% Data quality 2018 -Strong +Strong @@ -3563,14 +3563,14 @@

Predictor: Ec - Richmond City, Virginia + Autauga, Alabama % in high poverty neighborhoods -23.7% +0.0% Data quality -Strong +Strong @@ -3598,14 +3598,14 @@

Predictor: Ec - Richmond City, Virginia + Autauga, Alabama % in high poverty neighborhoods -23.7% +0.0% Data quality -Strong +Strong @@ -3638,90 +3638,90 @@

Predictor: Ec Group Year - Richmond City, Virginia + Autauga, Alabama % in high poverty neighborhoods All 2021 -23.7% +0.0% Data quality All 2021 -Strong +Strong % in high poverty neighborhoods Black 2021 -29.6% +0.0% Data quality Black 2021 -Strong +Strong % in high poverty neighborhoods Hispanic 2021 -6.7% +0.0% Data quality Hispanic 2021 -Strong +Weak % in high poverty neighborhoods Other Races and Ethnicities 2021 -12.9% +0.0% Data quality Other Races and Ethnicities 2021 -Strong +Marginal % in high poverty neighborhoods White, Non-Hispanic 2021 -15.0% +0.0% Data quality White, Non-Hispanic 2021 -Strong +Strong % in high poverty neighborhoods All 2018 -31.3% +0.0% Data quality All 2018 -Strong +Strong % in high poverty neighborhoods Black 2018 -34.4% +0.0% Data quality Black 2018 -Strong +Strong % in high poverty neighborhoods Hispanic 2018 -25.0% +0.0% Data quality Hispanic 2018 -Strong +Weak % in high poverty neighborhoods Other Races and Ethnicities 2018 -27.0% +0.0% Data quality Other Races and Ethnicities 2018 -Strong +Marginal % in high poverty neighborhoods White, Non-Hispanic 2018 -24.5% +0.0% Data quality White, Non-Hispanic 2018 -Strong +Strong @@ -3763,26 +3763,26 @@

Predictor: Raci - Richmond City, Virginia + Autauga, Alabama % for Black, Non-Hispanic -36.2% +70.3% Data quality -Strong +Strong % for Hispanic -77.3% +93.0% Data quality -Strong +Marginal % for Other Races and Ethnicities -91.1% +92.1% Data quality -Strong +Strong % for White, Non-Hispanic -36.7% +24.1% Data quality -Strong +Strong @@ -3810,26 +3810,26 @@

Predictor: Raci - Richmond City, Virginia + Autauga, Alabama % for Black, Non-Hispanic -36.2% +70.3% Data quality -Strong +Strong % for Hispanic -77.3% +93.0% Data quality -Strong +Marginal % for Other Races and Ethnicities -91.1% +92.1% Data quality -Strong +Strong % for White, Non-Hispanic -36.7% +24.1% Data quality -Strong +Strong @@ -3861,58 +3861,58 @@

Predictor: Raci Year - Richmond City, Virginia + Autauga, Alabama % for Black, Non-Hispanic 2021 -36.2% +70.3% Data quality 2021 -Strong +Strong % for Hispanic 2021 -77.3% +93.0% Data quality 2021 -Strong +Marginal % for Other Races and Ethnicities 2021 -91.1% +92.1% Data quality 2021 -Strong +Strong % for White, Non-Hispanic 2021 -36.7% +24.1% Data quality 2021 -Strong +Strong % for Black, Non-Hispanic 2018 -35.1% +74.0% Data quality 2018 -Strong +Strong % for Hispanic 2018 -78.1% +92.9% Data quality 2018 -Strong +Marginal % for Other Races and Ethnicities 2018 -91.7% +95.8% Data quality 2018 -Strong +Strong % for White, Non-Hispanic 2018 -36.8% +23.3% Data quality 2018 -Strong +Strong @@ -3954,14 +3954,14 @@

- Richmond City, Virginia + Autauga, Alabama Membership associations -14.9 +12.1 Data quality -Strong +Strong @@ -3989,14 +3989,14 @@

- Richmond City, Virginia + Autauga, Alabama Membership associations -14.9 +12.1 Data quality -Strong +Strong @@ -4038,14 +4038,14 @@

- Richmond City, Virginia + Autauga, Alabama Economic connectedness -0.7 +0.7 Data quality -Strong +Strong @@ -4073,14 +4073,14 @@

- Richmond City, Virginia + Autauga, Alabama Economic connectedness -0.7 +0.7 Data quality -Strong +Strong @@ -4122,14 +4122,14 @@

Predictor: - Richmond City, Virginia + Autauga, Alabama Transit trips -66 +18.5 Data quality -Strong +Strong @@ -4157,14 +4157,14 @@

Predictor: - Richmond City, Virginia + Autauga, Alabama Transit trips -66 +18.5 Data quality -Strong +Strong @@ -4197,42 +4197,42 @@

Predictor: Group Year - Richmond City, Virginia + Autauga, Alabama Transit trips All 2016 -66.1 +18.5 Data quality All 2016 -Strong +Strong Transit trips Majority Non-White 2016 -64.2 +27.7 Data quality Majority Non-White 2016 -Strong +Strong Transit trips Majority White, Non-Hispanic 2016 -74.1 +16.4 Data quality Majority White, Non-Hispanic 2016 -Strong +Strong Transit trips Mixed Race and Ethnicity 2016 -66.4 +9.0 Data quality Mixed Race and Ethnicity 2016 -Strong +Strong @@ -4268,14 +4268,14 @@

Predictor: - Richmond City, Virginia + Autauga, Alabama Transportation cost -77.4 +16.4 Data quality -Strong +Strong @@ -4303,14 +4303,14 @@

Predictor: - Richmond City, Virginia + Autauga, Alabama Transportation cost -77.4 +16.4 Data quality -Strong +Strong @@ -4343,42 +4343,42 @@

Predictor: Group Year - Richmond City, Virginia + Autauga, Alabama Transportation cost All 2016 -77.4 +16.4 Data quality All 2016 -Strong +Strong Transportation cost Majority Non-White Tracts 2016 -75.2 +15.3 Data quality Majority Non-White Tracts 2016 -Strong +Strong Transportation cost Majority White, Non-Hispanic Tracts 2016 -84.1 +16.9 Data quality Majority White, Non-Hispanic Tracts 2016 -Strong +Strong Transportation cost No Majority Race/Ethnicity Tracts 2016 -80.5 +13.0 Data quality No Majority Race/Ethnicity Tracts 2016 -Strong +Strong @@ -4428,14 +4428,14 @@

Predictor: A - Richmond City, Virginia + Autauga, Alabama % Pre-kindergarten -33.0% +52.7% Data quality -Strong +Weak @@ -4463,14 +4463,14 @@

Predictor: A - Richmond City, Virginia + Autauga, Alabama % Pre-kindergarten -33.0% +52.7% Data quality -Strong +Weak @@ -4503,90 +4503,90 @@

Predictor: A Group Year - Richmond City, Virginia + Autauga, Alabama % Pre-kindergarten All 2021 -49.2% +28.7% Data quality All 2021 -Strong +Marginal % Pre-kindergarten Black, Non-Hispanic 2021 -49.9% +21.3% Data quality Black, Non-Hispanic 2021 -Weak +Weak % Pre-kindergarten Hispanic 2021 -NA +NA Data quality Hispanic 2021 -NA +NA % Pre-kindergarten Other Races and Ethnicities 2021 -NA +NA Data quality Other Races and Ethnicities 2021 -NA +NA % Pre-kindergarten White, Non-Hispanic 2021 -75.7% +30.4% Data quality White, Non-Hispanic 2021 -Weak +Weak % Pre-kindergarten All 2018 -44.5% +19.4% Data quality All 2018 -Strong +Marginal % Pre-kindergarten Black, Non-Hispanic 2018 -30.8% +36.3% Data quality Black, Non-Hispanic 2018 -Weak +Weak % Pre-kindergarten Hispanic 2018 -NA +NA Data quality Hispanic 2018 -NA +NA % Pre-kindergarten Other Races and Ethnicities 2018 -NA +NA Data quality Other Races and Ethnicities 2018 -NA +NA % Pre-kindergarten White, Non-Hispanic 2018 -71.6% +39.0% Data quality White, Non-Hispanic 2018 -Weak +Weak @@ -4628,14 +4628,14 @@

Predi - Richmond City, Virginia + Autauga, Alabama Annual ELA achievement -0.56 +0.96 Data quality -Strong +Strong @@ -4663,16 +4663,16 @@

Predi - Richmond City, Virginia + Autauga, Alabama Annual ELA achievement -0.56 +0.96 Lower/Upper bound -(0.48, 0.64) +(0.84, 1.08) Data quality -Strong +Strong @@ -4705,130 +4705,130 @@

Predi Group Year - Richmond City, Virginia + Autauga, Alabama Annual ELA achievement All 2017 -0.56 +0.96 Lower/Upper bound All 2017 -(0.48, 0.64) +(0.84, 1.08) Data quality All 2017 -Strong +Strong Annual ELA achievement Black, Non-Hispanic 2017 -0.57 +1.06 Lower/Upper bound Black, Non-Hispanic 2017 -(0.5, 0.63) +(0.88, 1.25) Data quality Black, Non-Hispanic 2017 -Strong +Strong Annual ELA achievement Hispanic 2017 -0.67 +0.72 Lower/Upper bound Hispanic 2017 -(0.1, 1.24) +(-0.34, 1.78) Data quality Hispanic 2017 -Weak +Weak Annual ELA achievement Other Races and Ethnicities 2017 -NA +NA Lower/Upper bound Other Races and Ethnicities 2017 -NA +NA Data quality Other Races and Ethnicities 2017 -NA +NA Annual ELA achievement White, Non-Hispanic 2017 -0.71 +1 Lower/Upper bound White, Non-Hispanic 2017 -(0.53, 0.9) +(0.88, 1.12) Data quality White, Non-Hispanic 2017 -Strong +Strong Annual ELA achievement All 2016 -0.53 +0.95 Lower/Upper bound All 2016 -(0.45, 0.6) +(0.84, 1.06) Data quality All 2016 -Strong +Strong Annual ELA achievement Black, Non-Hispanic 2016 -0.52 +0.97 Lower/Upper bound Black, Non-Hispanic 2016 -(0.46, 0.59) +(0.8, 1.15) Data quality Black, Non-Hispanic 2016 -Strong +Strong Annual ELA achievement Hispanic 2016 -0.18 +0.06 Lower/Upper bound Hispanic 2016 -(-0.13, 0.49) +(-1.33, 1.45) Data quality Hispanic 2016 -Marginal +Weak Annual ELA achievement Other Races and Ethnicities 2016 -NA +NA Lower/Upper bound Other Races and Ethnicities 2016 -NA +NA Data quality Other Races and Ethnicities 2016 -NA +NA Annual ELA achievement White, Non-Hispanic 2016 -0.68 +0.94 Lower/Upper bound White, Non-Hispanic 2016 -(0.48, 0.88) +(0.83, 1.06) Data quality White, Non-Hispanic 2016 -Strong +Strong @@ -4858,82 +4858,82 @@

Predi Group Year - Richmond City, Virginia + Autauga, Alabama Annual ELA achievement All 2017 -0.56 +0.96 Lower/Upper bound All 2017 -(0.48, 0.64) +(0.84, 1.08) Data quality All 2017 -Strong +Strong Annual ELA achievement Low Income 2017 -0.6 +0.96 Lower/Upper bound Low Income 2017 -(0.51, 0.69) +(0.8, 1.12) Data quality Low Income 2017 -Marginal +Strong Annual ELA achievement Not Low-Income 2017 -0.56 +0.98 Lower/Upper bound Not Low-Income 2017 -(0.43, 0.7) +(0.84, 1.12) Data quality Not Low-Income 2017 -Marginal +Strong Annual ELA achievement All 2016 -0.53 +0.95 Lower/Upper bound All 2016 -(0.45, 0.6) +(0.84, 1.06) Data quality All 2016 -Strong +Strong Annual ELA achievement Low Income 2016 -0.51 +0.93 Lower/Upper bound Low Income 2016 -(0.43, 0.59) +(0.79, 1.07) Data quality Low Income 2016 -Strong +Strong Annual ELA achievement Not Low-Income 2016 -0.43 +0.94 Lower/Upper bound Not Low-Income 2016 -(0.3, 0.55) +(0.8, 1.09) Data quality Not Low-Income 2016 -Strong +Strong @@ -4975,22 +4975,22 @@

Predic - Richmond City, Virginia + Autauga, Alabama % for White, non-Hispanic -48.9% +40.2% Data quality -Strong +Strong % for Black, non-Hispanic -82.4% +40.0% Data quality -Strong +Strong % for Hispanic -91.4% +33.3% Data quality -Strong +Strong @@ -5018,22 +5018,22 @@

Predic - Richmond City, Virginia + Autauga, Alabama % for White, non-Hispanic -48.9% +40.2% Data quality -Strong +Strong % for Black, non-Hispanic -82.4% +40.0% Data quality -Strong +Strong % for Hispanic -91.4% +33.3% Data quality -Strong +Strong @@ -5064,46 +5064,46 @@

Predic Year - Richmond City, Virginia + Autauga, Alabama % for White, non-Hispanic 2018 -48.9% +40.2% Data quality 2018 -Strong +Strong % for Black, non-Hispanic 2018 -82.4% +40.0% Data quality 2018 -Strong +Strong % for Hispanic 2018 -91.4% +33.3% Data quality 2018 -Strong +Strong % for White, non-Hispanic 2014 -88.2% +32.5% Data quality 2014 -Strong +Strong % for Black, non-Hispanic 2014 -92.4% +36.4% Data quality 2014 -Strong +Strong % for Hispanic 2014 -91.9% +23.9% Data quality 2014 -Strong +Strong @@ -5145,14 +5145,14 @@

Predicto - Richmond City, Virginia + Autauga, Alabama % HS degree -95.5% +65.8% Data quality -Strong +Weak @@ -5180,14 +5180,14 @@

Predicto - Richmond City, Virginia + Autauga, Alabama % HS degree -95.5% +65.8% Data quality -Strong +Weak @@ -5220,90 +5220,90 @@

Predicto Group Year - Richmond City, Virginia + Autauga, Alabama % HS degree All 2021 -97.0% +80.9% Data quality All 2021 -Strong +Marginal % HS degree Black, Non-Hispanic 2021 -94.8% +NA Data quality Black, Non-Hispanic 2021 -Strong +NA % HS degree Hispanic 2021 -96.7% +NA Data quality Hispanic 2021 -Weak +NA % HS degree Other Races and Ethnicities 2021 -99.5% +NA Data quality Other Races and Ethnicities 2021 -Weak +NA % HS degree White, Non-Hispanic 2021 -99.0% +93.9% Data quality White, Non-Hispanic 2021 -Strong +Weak % HS degree All 2018 -94.7% +85.3% Data quality All 2018 -Strong +Weak % HS degree Black, Non-Hispanic 2018 -88.0% +NA Data quality Black, Non-Hispanic 2018 -Weak +NA % HS degree Hispanic 2018 -NA +NA Data quality Hispanic 2018 -NA +NA % HS degree Other Races and Ethnicities 2018 -NA +NA Data quality Other Races and Ethnicities 2018 -NA +NA % HS degree White, Non-Hispanic 2018 -98.2% +91.7% Data quality White, Non-Hispanic 2018 -Weak +Weak @@ -5345,14 +5345,14 @@

Predictor: Digita - Richmond City, Virginia + Autauga, Alabama % Digital access -79.7% +88.0% Data quality -Strong +Strong @@ -5380,14 +5380,14 @@

Predictor: Digita - Richmond City, Virginia + Autauga, Alabama % Digital access -79.7% +88.0% Data quality -Strong +Strong @@ -5419,50 +5419,50 @@

Predictor: Digita Group Year - Richmond City, Virginia + Autauga, Alabama % Digital access All 2021 -79.7% +88.0% Data quality All 2021 -Strong +Strong % Digital access Black 2021 -74.3% +76.5% Data quality Black 2021 -Strong +Strong % Digital access Hispanic 2021 -61.3% +65.1% Data quality Hispanic 2021 -Strong +Weak % Digital access Other Races and Ethnicities 2021 -75.0% +88.6% Data quality Other Races and Ethnicities 2021 -Strong +Marginal % Digital access White 2021 -86.2% +91.0% Data quality White 2021 -Strong +Strong @@ -5512,14 +5512,14 @@

Predict - Richmond City, Virginia + Autauga, Alabama Employment to population ratio -83.4% +76.3% Data quality -Strong +Marginal @@ -5547,14 +5547,14 @@

Predict - Richmond City, Virginia + Autauga, Alabama Employment to population ratio -83.4% +76.3% Data quality -Strong +Marginal @@ -5587,90 +5587,90 @@

Predict Group Year - Richmond City, Virginia + Autauga, Alabama Employment to population ratio All 2021 -81.6% +76.3% Data quality All 2021 -Strong +Marginal Employment to population ratio Black, Non-Hispanic 2021 -73.1% +70.9% Data quality Black, Non-Hispanic 2021 -Strong +Marginal Employment to population ratio Hispanic 2021 -79.4% +81.2% Data quality Hispanic 2021 -Strong +Weak Employment to population ratio Other Races and Ethnicities 2021 -82.6% +75.0% Data quality Other Races and Ethnicities 2021 -Strong +Weak Employment to population ratio White, Non-Hispanic 2021 -88.6% +78.5% Data quality White, Non-Hispanic 2021 -Strong +Marginal Employment to population ratio All 2018 -83.4% +76.3% Data quality All 2018 -Strong +Marginal Employment to population ratio Black, Non-Hispanic 2018 -68.2% +67.2% Data quality Black, Non-Hispanic 2018 -Strong +Marginal Employment to population ratio Hispanic 2018 -83.0% +80.7% Data quality Hispanic 2018 -Strong +Weak Employment to population ratio Other Races and Ethnicities 2018 -80.3% +79.3% Data quality Other Races and Ethnicities 2018 -Strong +Weak Employment to population ratio White, Non-Hispanic 2018 -87.0% +75.3% Data quality White, Non-Hispanic 2018 -Strong +Marginal @@ -5712,14 +5712,14 @@

- Richmond City, Virginia + Autauga, Alabama Ratio of pay to living wage -0.91 +0.6 Data quality -Strong +Strong @@ -5747,14 +5747,14 @@

- Richmond City, Virginia + Autauga, Alabama Ratio of pay to living wage -0.91 +0.6 Data quality -Strong +Strong @@ -5786,22 +5786,22 @@

Year - Richmond City, Virginia + Autauga, Alabama Ratio of pay to living wage 2021 -0.91 +0.60 Data quality 2021 -Strong +Strong Ratio of pay to living wage 2018 -0.95 +0.67 Data quality 2018 -Strong +Strong @@ -5843,18 +5843,18 @@

Predict - Richmond City, Virginia + Autauga, Alabama 20th Percentile -$20,059 +$20,161 50th Percentile -$48,629 +$53,694 80th Percentile -$101,310 +$102,323 Data quality -Strong +Marginal @@ -5882,18 +5882,18 @@

Predict - Richmond City, Virginia + Autauga, Alabama 20th Percentile -$20,059 +$20,161 50th Percentile -$48,629 +$53,694 80th Percentile -$101,310 +$102,323 Data quality -Strong +Marginal @@ -5926,170 +5926,170 @@

Predict Group Year - Richmond City, Virginia + Autauga, Alabama 20th Percentile All 2021 -$21,458 +$26,120 50th Percentile All 2021 -$53,847 +$63,061 80th Percentile All 2021 -$116,233 +$116,010 Data quality All 2021 -Strong +Marginal 20th Percentile Black, Non-Hispanic 2021 -$14,529 +$17,128 50th Percentile Black, Non-Hispanic 2021 -$37,071 +$42,470 80th Percentile Black, Non-Hispanic 2021 -$70,411 +$88,574 Data quality Black, Non-Hispanic 2021 -Strong +Marginal 20th Percentile Hispanic 2021 -$25,692 +$29,059 50th Percentile Hispanic 2021 -$54,763 +$50,720 80th Percentile Hispanic 2021 -$93,240 +$84,251 Data quality Hispanic 2021 -Strong +Weak 20th Percentile Other Races and Ethnicities 2021 -$15,303 +$24,704 50th Percentile Other Races and Ethnicities 2021 -$48,173 +$49,286 80th Percentile Other Races and Ethnicities 2021 -$102,822 +$98,873 Data quality Other Races and Ethnicities 2021 -Strong +Weak 20th Percentile White, Non-Hispanic 2021 -$35,807 +$34,256 50th Percentile White, Non-Hispanic 2021 -$79,218 +$73,237 80th Percentile White, Non-Hispanic 2021 -$157,969 +$125,323 Data quality White, Non-Hispanic 2021 -Strong +Marginal 20th Percentile All 2018 -$20,059 +$20,161 50th Percentile All 2018 -$48,629 +$53,694 80th Percentile All 2018 -$101,310 +$102,323 Data quality All 2018 -Strong +Marginal 20th Percentile Black, Non-Hispanic 2018 -$11,186 +$12,434 50th Percentile Black, Non-Hispanic 2018 -$30,939 +$36,269 80th Percentile Black, Non-Hispanic 2018 -$62,819 +$81,083 Data quality Black, Non-Hispanic 2018 -Strong +Marginal 20th Percentile Hispanic 2018 -$20,256 +$20,515 50th Percentile Hispanic 2018 -$44,552 +$38,396 80th Percentile Hispanic 2018 -$81,127 +$79,149 Data quality Hispanic 2018 -Strong +Weak 20th Percentile Other Races and Ethnicities 2018 -$9,920 +$23,217 50th Percentile Other Races and Ethnicities 2018 -$42,996 +$47,551 80th Percentile Other Races and Ethnicities 2018 -$84,552 +$86,536 Data quality Other Races and Ethnicities 2018 -Strong +Weak 20th Percentile White, Non-Hispanic 2018 -$28,873 +$27,846 50th Percentile White, Non-Hispanic 2018 -$68,573 +$63,591 80th Percentile White, Non-Hispanic 2018 -$139,770 +$115,008 Data quality White, Non-Hispanic 2018 -Strong +Marginal @@ -6131,14 +6131,14 @@

Predictor: Fi - Richmond City, Virginia + Autauga, Alabama % with debt -34.2% +30.0% Data quality -Strong +Strong @@ -6166,16 +6166,16 @@

Predictor: Fi - Richmond City, Virginia + Autauga, Alabama % with debt -34.2% +30.0% Confidence Interval -NA +NA Data quality -Strong +Strong @@ -6208,46 +6208,46 @@

Predictor: Fi Group Year - Richmond City, Virginia + Autauga, Alabama % with debt All 2018 -42.6% +36.0% Confidence Interval All 2018 -(40.9%, 44.2%) +(32.9%, 39.1%) Data quality All 2018 -Strong +Strong % with debt Majority Non-White ZIPs 2018 -57.1% +NA Confidence Interval Majority Non-White ZIPs 2018 -(54.7%, 59.5%) +NA Data quality Majority Non-White ZIPs 2018 -Strong +NA % with debt Majority White, Non-Hispanic ZIPs 2018 -13.4% +35.3% Confidence Interval Majority White, Non-Hispanic ZIPs 2018 -(10.6%, 16.3%) +(32.1%, 38.5%) Data quality Majority White, Non-Hispanic ZIPs 2018 -Strong +Strong @@ -6289,26 +6289,26 @@

Pr - Richmond City, Virginia + Autauga, Alabama Black, non-Hispanic Opportunity -26.6%:39.6% +20.8%:27.5% Data quality -Strong +Weak Hispanic Opportunity -3.1%:4.9% +1.2%:2.8% Data quality -Weak +Weak Other Races and Ethnicities Opportunity -5.4%:7.5% +2.7%:2.8% Data quality -Strong +Weak White, non-Hispanic Opportunity -64.9%:48.0% +75.4%:66.9% Data quality -Strong +Weak @@ -6336,26 +6336,26 @@

Pr - Richmond City, Virginia + Autauga, Alabama Black, non-Hispanic Opportunity -26.6%:39.6% +20.8%:27.5% Data quality -Strong +Weak Hispanic Opportunity -3.1%:4.9% +1.2%:2.8% Data quality -Weak +Weak Other Races and Ethnicities Opportunity -5.4%:7.5% +2.7%:2.8% Data quality -Strong +Weak White, non-Hispanic Opportunity -64.9%:48.0% +75.4%:66.9% Data quality -Strong +Weak @@ -6387,58 +6387,58 @@

Pr Year - Richmond City, Virginia + Autauga, Alabama Black, non-Hispanic Opportunity 2021 -26.6%:39.6% +20.8%:27.5% Data quality 2021 -Strong +Weak Hispanic Opportunity 2021 -3.1%:4.9% +1.2%:2.8% Data quality 2021 -Weak +Weak Other Races and Ethnicities Opportunity 2021 -5.4%:7.5% +2.7%:2.8% Data quality 2021 -Strong +Weak White, non-Hispanic Opportunity 2021 -64.9%:48.0% +75.4%:66.9% Data quality 2021 -Strong +Weak Black, non-Hispanic Opportunity 2018 -15.2%:41.1% +17.4%:24.6% Data quality 2018 -Strong +Weak Hispanic Opportunity 2018 -2.2%:4.1% +0.1%:1.5% Data quality 2018 -Weak +Weak Other Races and Ethnicities Opportunity 2018 -3.4%:5.4% +0.2%:1.5% Data quality 2018 -Weak +Weak White, non-Hispanic Opportunity 2018 -79.2%:49.4% +82.2%:72.4% Data quality 2018 -Strong +Weak @@ -6488,14 +6488,14 @@

Predic - Richmond City, Virginia + Autauga, Alabama Ratio of people to physicians -941:1 +2235:1 Data quality -Strong +Strong @@ -6523,14 +6523,14 @@

Predic - Richmond City, Virginia + Autauga, Alabama Ratio of people to physicians -941:1 +2235:1 Data quality -Strong +Strong @@ -6572,14 +6572,14 @@

Predictor: Neona - Richmond City, Virginia + Autauga, Alabama % Low birth weight -11.5% +10.3% Data quality -Strong +Weak @@ -6607,16 +6607,16 @@

Predictor: Neona - Richmond City, Virginia + Autauga, Alabama % Low birth weight -11.5% +10.3% Confidence Interval -(10.4%, 12.7%) +(9.9%, 10.7%) Data quality -Strong +Weak @@ -6649,130 +6649,130 @@

Predictor: Neona Group Year - Richmond City, Virginia + Autauga, Alabama % Low birth weight All 2020 -11.5% +10.3% Confidence Interval All 2020 -(10.4%, 12.7%) +(9.9%, 10.7%) Data quality All 2020 -Strong +Weak % Low birth weight Black, Non-Hispanic 2020 -16.6% +16.5% Confidence Interval Black, Non-Hispanic 2020 -(14.6%, 18.5%) +(15.5%, 17.6%) Data quality Black, Non-Hispanic 2020 -Strong +Weak % Low birth weight Hispanic 2020 -8.9% +8.4% Confidence Interval Hispanic 2020 -(6.4%, 11.5%) +(7.2%, 9.5%) Data quality Hispanic 2020 -Strong +Weak % Low birth weight Other Races and Ethnicities 2020 -10.0% +11.5% Confidence Interval Other Races and Ethnicities 2020 -(4.1%, 15.9%) +(9.0%, 14.0%) Data quality Other Races and Ethnicities 2020 -Marginal +Weak % Low birth weight White, Non-Hispanic 2020 -5.6% +8.4% Confidence Interval White, Non-Hispanic 2020 -(4.1%, 7.0%) +(7.9%, 8.8%) Data quality White, Non-Hispanic 2020 -Strong +Weak % Low birth weight All 2018 -10.4% +10.1% Confidence Interval All 2018 -(9.3%, 11.4%) +(9.7%, 10.5%) Data quality All 2018 -Strong +Weak % Low birth weight Black, Non-Hispanic 2018 -15.9% +16.0% Confidence Interval Black, Non-Hispanic 2018 -(14.1%, 17.7%) +(15.0%, 17.0%) Data quality Black, Non-Hispanic 2018 -Strong +Weak % Low birth weight Hispanic 2018 -4.3% +6.5% Confidence Interval Hispanic 2018 -(2.5%, 6.1%) +(5.3%, 7.6%) Data quality Hispanic 2018 -Marginal +Weak % Low birth weight Other Races and Ethnicities 2018 -NA +10.5% Confidence Interval Other Races and Ethnicities 2018 -NA +(7.9%, 13.0%) Data quality Other Races and Ethnicities 2018 -Weak +Weak % Low birth weight White, Non-Hispanic 2018 -4.4% +8.6% Confidence Interval White, Non-Hispanic 2018 -(3.1%, 5.7%) +(8.2%, 9.1%) Data quality White, Non-Hispanic 2018 -Strong +Weak @@ -6814,14 +6814,14 @@

Predictor: - Richmond City, Virginia + Autauga, Alabama Air quality index -14 +18 Data quality -Strong +Strong @@ -6849,14 +6849,14 @@

Predictor: - Richmond City, Virginia + Autauga, Alabama Air quality index -14 +18 Data quality -Strong +Strong @@ -6889,74 +6889,74 @@

Predictor: Group Year - Richmond City, Virginia + Autauga, Alabama Air quality index All 2018 -14 +18 Data quality All 2018 -Strong +Strong Air quality index Majority Non-White Tracts 2018 -13 +13 Data quality Majority Non-White Tracts 2018 -Strong +Strong Air quality index Majority White, Non-Hispanic Tracts 2018 -15 +18 Data quality Majority White, Non-Hispanic Tracts 2018 -Strong +Strong Air quality index No Majority Race/Ethnicity Tracts 2018 -16 +26 Data quality No Majority Race/Ethnicity Tracts 2018 -Strong +Strong Air quality index All 2014 -34 +6 Data quality All 2014 -Strong +Strong Air quality index Majority Non-White Tracts 2014 -36 +8 Data quality Majority Non-White Tracts 2014 -Strong +Strong Air quality index Majority White, Non-Hispanic Tracts 2014 -31 +6 Data quality Majority White, Non-Hispanic Tracts 2014 -Strong +Strong Air quality index No Majority Race/Ethnicity Tracts 2014 -32 +NA Data quality No Majority Race/Ethnicity Tracts 2014 -Strong +NA @@ -6986,58 +6986,58 @@

Predictor: Group Year - Richmond City, Virginia + Autauga, Alabama Air quality index All 2018 -14 +18 Data quality All 2018 -Strong +Strong Air quality index High Poverty Tracts 2018 -10 +NA Data quality High Poverty Tracts 2018 -Strong +NA Air quality index Not High Poverty Tracts 2018 -15 +19 Data quality Not High Poverty Tracts 2018 -Strong +Strong Air quality index All 2014 -34 +6 Data quality All 2014 -Strong +Strong Air quality index High Poverty Tracts 2014 -34 +NA Data quality High Poverty Tracts 2014 -Strong +NA Air quality index Not High Poverty Tracts 2014 -34 +6 Data quality Not High Poverty Tracts 2014 -Strong +Strong @@ -7079,14 +7079,14 @@

Predictor: Sa - Richmond City, Virginia + Autauga, Alabama Trauma -99.5 +68.6 Data quality -Strong +Strong @@ -7114,16 +7114,16 @@

Predictor: Sa - Richmond City, Virginia + Autauga, Alabama Trauma -99.5 +68.6 Confidence Interval -(93.7, 105.3) +(58.8, 78.3) Data quality -Strong +Strong @@ -7173,14 +7173,14 @@

Predicto - Richmond City, Virginia + Autauga, Alabama % voting -62.0% +66.2% Data quality -Strong +Strong @@ -7208,14 +7208,14 @@

Predicto - Richmond City, Virginia + Autauga, Alabama % voting -62.0% +66.2% Data quality -Strong +Strong @@ -7247,22 +7247,22 @@

Predicto Year - Richmond City, Virginia + Autauga, Alabama % voting 2020 -62.0% +66.2% Data quality 2020 -Strong +Strong % voting 2016 -61.5% +61.4% Data quality 2016 -Strong +Strong @@ -7304,18 +7304,18 @@

- Richmond City, Virginia + Autauga, Alabama Other Races/Ethnicities -__:6% +__:4% Black, non-Hispanic -__:45% +__:20% Hispanic -__:7% +__:3% White, non-Hispanic -__:41% +__:73% @@ -7358,16 +7358,16 @@

Predictor: Saf - Richmond City, Virginia + Autauga, Alabama Violent crime -1,422 +1,047 Property crime -4,741 +1,369 Data quality -Strong +Strong @@ -7395,16 +7395,16 @@

Predictor: Saf - Richmond City, Virginia + Autauga, Alabama Violent crime -1,422 +1,047 Property crime -4,741 +1,369 Data quality -Strong +Strong @@ -7447,14 +7447,14 @@

- Richmond City, Virginia + Autauga, Alabama Juvenile arrest rate -285.8 +41.4 Data quality -Strong +Strong @@ -7482,14 +7482,14 @@

- Richmond City, Virginia + Autauga, Alabama Juvenile arrest rate -285.8 +41.4 Data quality -Strong +Strong @@ -7522,50 +7522,50 @@

Group Year - Richmond City, Virginia + Autauga, Alabama Juvenile arrest rate All 2021 -285.8 +41.4 Data quality All 2021 -Strong +Strong Juvenile arrest rate Black 2021 -391.2 +127.8 Data quality Black 2021 -Strong +Strong Juvenile arrest rate Hispanic 2021 -56.0 +0.0 Data quality Hispanic 2021 -Strong +Strong Juvenile arrest rate Other Races and Ethnicities 2021 -0.0 +83.5 Data quality Other Races and Ethnicities 2021 -Strong +Strong Juvenile arrest rate White 2021 -153.9 +15.9 Data quality White 2021 -Strong +Strong @@ -7615,7 +7615,7 @@

Missing and “NA” in fields for metric values and data quality values indicates that the data are suppressed due to sample sizes or because that element is not applicable to that community (e.g., no zip code in the county is majority non-white).


-

Version: 2023-06-21 18:03:09

+

Version: 2023-06-23 11:56:31

diff --git a/index-county.qmd b/index-county.qmd index 0a2bd5e..9e3d1dd 100644 --- a/index-county.qmd +++ b/index-county.qmd @@ -14,8 +14,8 @@ format: editor_options: chunk_output_type: console params: - state_county: !expr c("51760") - fake_labels: FALSE + state_county: !expr c("01001") + fake_labels: "no" state_title: FALSE data: NULL execute: @@ -97,7 +97,7 @@ relabel_data <- function(data) { } -if (params$fake_labels) { +if (params$fake_labels == "yes") { data_recent <- relabel_data(data_recent) data_years <- relabel_data(data_years)