diff --git a/R/varlist.R b/R/varlist.R index 5ebf561..0f92915 100644 --- a/R/varlist.R +++ b/R/varlist.R @@ -273,10 +273,10 @@ social_capital2_varlist <- list( digital_access_varlist <- list( summary_vars = c( - "Digital access" = "digital_access" + "% Digital access" = "digital_access" ), detail_vars = c( - "Digital access" = "digital_access", + "% Digital access" = "digital_access", "digital_access_quality" = "digital_access_quality" ) ) diff --git a/data/00_metrics-summary_county.csv b/data/00_metrics-summary_county.csv index 9261414..83965f4 100644 --- a/data/00_metrics-summary_county.csv +++ b/data/00_metrics-summary_county.csv @@ -1,21 +1,21 @@ metric_name,metric_vars_prefix,quality_variable,ci_var,subgroup_id,metrics_description,source_data,source_data2,notes,notes2,notes3,years Housing affordability,share_affordable,share_affordable_quality,3,none,"Metric: Ratio of affordable and available housing units (per 100 households) with low-, very low-, and extremely low-income levels","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)","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)","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.",,,"2018, 2021" -Housing instability,"count_homeless, share_homeless",homeless_quality,1,none,Metric: Number and share of public-school children who are ever homeless during the school year,"US Department of Education Local Education Agency data, SY 2019-20 (via EDFacts Homeless Students Enrolled). (Time period: School Year 2019-20)","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)","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.,,"2018, 2021" -Economic inclusion,share_poverty_exposure,share_poverty_exposure_quality,3,race_poverty,Metric: Share of people experiencing poverty who live in high-poverty neighborhoods,US Census Bureau’s 2021 5-Year American Community Survey. (Time period: 2017-21),US Census Bureau’s 2018 & 2021 5-Year American Community Survey. (Time periods: 2014-18 & 2017-21),"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.,,"2018, 2021" +Housing instability,"count_homeless, share_homeless",homeless_quality,1,none,Metric: Number and share of public-school children who are ever homeless during the school year,"US Department of Education Local Education Agency data, SY 2019-20 (via EDFacts Homeless Students Enrolled). (Time period: School Year 2019-20)","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)","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.,,"2016, 2019" +Economic inclusion,share_poverty_exposure,share_poverty_exposure_quality,3,race_poverty,Metric: Share of people experiencing poverty who live in high-poverty neighborhoods,US Census Bureau’s 2021 5-Year American Community Survey. (Time period: 2017-21),US Census Bureau’s 2018 & 2021 5-Year American Community Survey. (Time periods: 2014-18 & 2017-21),"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.,,"2014, 2018" Racial diversity,"share_black_nh_exposure, share_hispanic_exposure, share_other_nh_exposure, share_white_nh_exposure","share_black_nh_exposure_quality, share_hispanic_exposure_quality, share_other_nh_exposure_quality, share_white_nh_exposure_quality",3,none,Metric: Index of people’s exposure to neighbors of different races and ethnicities,US Census Bureau’s 2021 5-Year American Community Survey. (Time period: 2017-21),US Census Bureau’s 2018 & 2021 5-Year American Community Survey. (Time periods: 2014-18 & 2017-21),"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.",,,"2018, 2021" Social capital1,count_membership_associations_per_10k,count_membership_associations_per_10k_quality,3,none,"Metric: Number of membership associations per 10,000 people","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)",,"This metric measures the number of membership associations (as self-reported by businesses and organizations) per 10,000 people in a given community.",,,2020 -Social capital2,ratio_high_low_ses_fb_friends,ratio_high_low_ses_fb_friends_quality,3,none,Metric: Ratio of Facebook friends with higher socioeconomic status to Facebook friends with lower socioeconomic status,"Opportunity Insights’ Social Capital Atlas, 2022. (Time period: 2022)",,"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.",,,2022 +Social capital2,ratio_high_low_ses_fb_friends,ratio_high_low_ses_fb_friends_quality,3,none,Metric: Ratio of Facebook friends with higher socioeconomic status to Facebook friends with lower socioeconomic status (‘economic connectedness’),"Opportunity Insights’ Social Capital Atlas, 2022. (Time period: 2022)",,"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.",,,2022 Transportation access,count_transportation_trips,count_transportation_trips_quality,3,race_share,Metric: Transit trips index,"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)","2016 & 2018 Location Affordability Index data using 2013-15 & 2020-22 Illinois vehicle miles travelled data; Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics data, 2013 & 2014 & 2018; US Census Bureau’s 2016 & 2021 5-Year American Community Survey. (Time periods: 2012-16 & 2017-21)","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.,,"2018, 2021" Transportation cost,transportation_cost,transportation_cost_quality,3,race_share,Metric: Transportation cost index,"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)","2016 & 2018 Location Affordability Index data using 2013-15 & 2020-22 Illinois vehicle miles travelled data; Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics data, 2013 & 2014 & 2018; US Census Bureau’s 2016 & 2021 5-Year American Community Survey. (Time periods: 2012-16 & 2017-21)","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.,,"2018, 2021" Access to preschool,share_in_preschool,share_in_preschool_quality,1,race_ethnicity,Metric: Share of (3- to 4-year-old) children enrolled in nursery school or preschool,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),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),The share of a community's children aged three to four who are enrolled in nursery or preschool.,,,"2018, 2021" -Effective public education,rate_learning,rate_learning_quality,1,"race_ethnicity, income",Metric: Average per grade change in English Language Arts achievement between third and eighth grades,"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)","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)","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.","2018, 2021" +Effective public education,rate_learning,rate_learning_quality,1,"race_ethnicity, income",Metric: Average per grade change in English Language Arts achievement between third and eighth grades,"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)","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)","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.","2016, 2017" School economic diversity,"meps20_white, meps20_black, meps20_hispanic","meps20_white_quality, meps20_black_quality, meps20_hispanic_quality",3,none,"Metric: Share of students attending high-poverty schools, by student race/ethnicity ","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)","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)",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.,,,"2018, 2021" Preparation for college,share_hs_degree,share_hs_degree_quality,1,race_ethnicity,Metric: Share of 19- and 20-year-olds with a high school degree,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),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),The share of 19- and 20-year-olds in a community who have a high school degree.,,,"2018, 2021" -Digital access,digital_access,digital_access_quality,2,none,Metric: Share of households with broadband access in the home,US Census Bureau’s 2021 1-Year American Community Survey. (Time period: 2021),,This metric represents the share of households with access to broadband in their home.,,,2021 +Digital access,digital_access,digital_access_quality,2,none,Metric: Share of people in households with broadband access in the home,US Census Bureau’s 2021 5-Year American Community Survey. (Time period: 2017-2021),,This metric represents the share of people in households with access to broadband in their home.,,,2021 Employment opportunities,share_employed,share_employed_quality,1,race_ethnicity,Metric: Employment-to-population ratio for adults ages 25 to 54,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),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),The share of adults between the ages of 25 and 54 in a given community who are employed.,,,"2018, 2021" Jobs paying a living wage,ratio_average_to_living_wage,ratio_average_to_living_wage_quality,3,none,Metric: Ratio of pay on an average job to the cost of living,"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)","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)","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.",,,"2018, 2021" Opportunities for income,pctl_income,pctl_income_quality,2,race_ethnicity,"Metric: Household income at the 20th, 50th, and 80th percentiles",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),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),"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.",,,"2018, 2021" -Financial security,share_debt_col,share_debt_col_quality,1,race_share,Metric: Share with debt in collections,2022 credit bureau data from Urban Institute’s [Debt in America](https://apps.urban.org/features/debt-interactive-map/?type=overall&variable=totcoll) feature. (Time period: 2022),2018 and 2022 credit bureau data from Urban Institute’s [Debt in America](https://apps.urban.org/features/debt-interactive-map/?type=overall&variable=totcoll) feature. (Time periods: 2018 & 2022),The county-level measure captures 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. ,"

For county-level 2018 and 2022 data, “majority” means that at least 60% of residents in a zip code are members of the specified population group.",,"2018, 2021" +Financial security,share_debt_col,share_debt_col_quality,1,race_share,Metric: Share with debt in collections,February 2022 credit bureau data from Urban Institute’s [Debt in America](https://apps.urban.org/features/debt-interactive-map/?type=overall&variable=totcoll) feature. (Time period: February 2022),August 2018 and February 2022 credit bureau data from Urban Institute’s [Debt in America](https://apps.urban.org/features/debt-interactive-map/?type=overall&variable=totcoll) feature. (Time periods: August 2018 & February 2022),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.",,"2018, 2021" Wealth-building opportunities,"ratio_black_nh_house_value_households, ratio_hispanic_house_value_households, ratio_other_nh_house_value_households, ratio_white_nh_house_value_households","ratio_black_nh_house_value_households_quality, ratio_hispanic_house_value_households_quality, ratio_other_nh_house_value_households_quality, ratio_white_nh_house_value_households_quality",3,none,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,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),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),"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.",,,"2018, 2021" Access to health services,ratio_population_pc_physician,ratio_population_pc_physician_quality,3,none,Metric: Ratio of population per primary care physician,"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)",,"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.",,,"2018, 2021" Neonatal health,rate_low_birth_weight,rate_low_birth_weight_quality,1,race_ethnicity,Metric: Share with low birth weight,"Centers for Disease Control and Prevention National Center for Health Statistics, Division of Vital Statistics, Natality data, 2020 (via CDC WONDER). (Time period: 2020)","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)","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.,,"2018, 2021" diff --git a/data/00_metrics-summary_place.csv b/data/00_metrics-summary_place.csv index a41e04b..19a440d 100644 --- a/data/00_metrics-summary_place.csv +++ b/data/00_metrics-summary_place.csv @@ -4,15 +4,15 @@ Housing instability,"homeless_count, homeless_share",homeless_quality,1,none,Met Economic inclusion,poverty_exposure,poverty_exposure_quality,3,race_poverty,Metric: Share of people experiencing poverty who live in high-poverty neighborhoods,US Census Bureau’s 2021 5-Year American Community Survey; Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2017-2021),,"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.,,2021 Racial diversity,"white_nh_exposure, black_nh_exposure, hispanic_exposure, other_nh_exposure","white_nh_exposure_quality, black_nh_exposure_quality, hispanic_exposure_quality, other_nh_exposure_quality",3,none,Metric: Index of people’s exposure to neighbors of different races and ethnicities,US Census Bureau’s 2021 5-Year American Community Survey; Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2017-21),,"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.",,,2021 Social capital1,socassn,socassn_quality,3,none,"Metric: Number of membership associations per 10,000 people","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)",,"This metric measures the number of membership associations (as self-reported by businesses and organizations) per 10,000 people in a given community.",,,2020 -Social capital2,ec_zip,ec_zip_quality,3,none,Metric: Ratio of Facebook friends with higher socioeconomic status to Facebook friends with lower socioeconomic status,"Opportunity Insights’ Social Capital Atlas, 2022. (Time period: 2022)",,"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.",,,2022 +Social capital2,ec_zip,ec_zip_quality,3,none,Metric: Ratio of Facebook friends with higher socioeconomic status to Facebook friends with lower socioeconomic status (‘economic connectedness’),"Opportunity Insights’ Social Capital Atlas, 2022. (Time period: 2022)",,"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.",,,2022 Access to preschool,share_in_preschool,preschool_quality,1,race_ethnicity,Metric: Share of (3- to 4-year-old) children enrolled in nursery school or preschool,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),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),The share of a community's children aged three to four who are enrolled in nursery or preschool.,,,2021 Effective public education,learning_rate,learning_rate_quality,1,"race_ethnicity, income",Metric: Average per grade change in English Language Arts achievement between third and eighth grades,"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)","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)","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.","2016, 2017" School economic diversity,"frpl40_white, frpl40_black, frpl40_hispanic","frpl40_white_quality, frpl40_black_quality, frpl40_hispanic_quality",3,none,"Metric: Share of students attending high-poverty schools, by student race/ethnicity ","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)","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)",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.,,,"2016, 2018" Preparation for college,share_hs_degree,hs_degree_quality,1,race_ethnicity,Metric: Share of 19- and 20-year-olds with a high school degree,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),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),The share of 19- and 20-year-olds in a community who have a high school degree.,,,2021 -Digital access,digital_access,digital_access_quality,2,race,Metric: Share of households with broadband access in the home,US Census Bureau’s 2021 1-Year American Community Survey. (Time period: 2021),,This metric represents the share of households with access to broadband in their home.,,,2021 +Digital access,digital_access,digital_access_quality,2,race,Metric: Share of people in households with broadband access in the home,US Census Bureau’s 2021 5-Year American Community Survey. (Time period: 2017-2021),,This metric represents the share of people in households with access to broadband in their home.,,,2021 Employment opportunities,share_employed,employed_quality,1,race_ethnicity,Metric: Employment-to-population ratio for adults ages 25 to 54,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),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),The share of adults between the ages of 25 and 54 in a given community who are employed.,,,2021 Opportunities for income,pctl,pctl_quality,2,race_ethnicity,"Metric: Household income at the 20th, 50th, and 80th percentiles",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),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),"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.",,,2021 -Financial security,share_debt_coll,share_debt_coll_quality,1,race_share,Metric: Share with debt in collections,"2021 credit bureau data, from Urban Institute’s Financial Health and Wealth Dashboard. (Time period: 2021)",,"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.","For city-level 2021 data, ‘majority’ means that at least 50% of residents in a zip code are members of the specified population group.",,2022 +Financial security,share_debt_coll,share_debt_coll_quality,1,race_share,Metric: Share with debt in collections,"August 2021 credit bureau data, from Urban Institute’s Financial Health and Wealth Dashboard. (Time period: August 2021)",,"The city-level measure captures the share of adults in an area with a credit bureau record with any derogatory debt, which is primarily debt in collections.","For city-level August 2021 data, ‘majority’ means that at least 50% of residents in a zip code are members of the specified population group.",,2022 Wealth-building opportunities,"r_black_nh_hv_hh, r_hispanic_hv_hh, r_other_nh_hv_hh, r_white_nh_hv_hh","black_nh_wealth_quality, hispanic_wealth_quality, other_nh_wealth_quality, white_nh_wealth_quality",3,none,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,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),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),"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.",,,"2018, 2021" Environmental quality,environmental,environmental_quality,3,"race_share, poverty",Metric: Air quality index,"US Environmental Protection Agency’s AirToxScreen data, 2018 (based on 2017 National Emissions Inventory data); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2017-18)","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; Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time periods: 2010-14 & 2014-18)","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.,,"2014, 2018" Political participation,election_turnout,election_turnout_quality,3,none,Metric: Share of the voting-age population who turn out to vote,"Voting and Election Science Team, Precinct-Level Election Results 2020 (via Harvard Dataverse); US Census Bureau’s 2020 5-Year American Community Survey Citizen Voting Age Population Special Tabulation; Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2016-20)",,This metric measures the share of the citizen voting-age population that voted in the most recent presidential election.,,,2020 diff --git a/description.html b/description.html index 653c983..fc10c33 100644 --- a/description.html +++ b/description.html @@ -4337,8 +4337,8 @@
PREDICTOR: DIGITAL ACCESS
-

Metric: Share of households with broadband access in the home

-

This is a measure of the share of 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.

+

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

+

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.

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.

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

Frequency: This metric can be updated annually.

@@ -4399,9 +4399,9 @@
PREDICTOR: FINANCIAL SECURITY
-

Metric: Share of households with debt in collections

+

Metric: Share with debt in collections

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.

-

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. Though not a standard measure, this metric has been used by researchers to distinguish between “good” debts (e.g., mortgages paid on time every month) and “bad” debts.

+

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.

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.

Frequency: This metric can be updated annually.

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

diff --git a/description.qmd b/description.qmd index 2c57b5d..9ed643f 100644 --- a/description.qmd +++ b/description.qmd @@ -319,9 +319,9 @@ This metric is the share of 19- and 20-year-olds in a community who have a high ##### [PREDICTOR: DIGITAL ACCESS](https://upward-mobility.urban.org/digital-access) -**Metric: Share of households with broadband access in the home** +**Metric: Share of people in households with broadband access in the home** -This is a measure of the share of 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. +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. **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. @@ -415,11 +415,11 @@ Household income is a standard measure of financial well-being. The Working Grou ##### [PREDICTOR: FINANCIAL SECURITY](https://upward-mobility.urban.org/financial-security-and-wealth-building-opportunities) -**Metric: Share of households with debt in collections** +**Metric: Share with debt in collections** 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. -**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. Though not a standard measure, this metric has been used by researchers to distinguish between “good” debts (e.g., mortgages paid on time every month) and “bad” debts. +**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. **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](https://apps.urban.org/features/debt-interactive-map/?type=overall&variable=pct_debt_collections) and on the Urban Institute’s [Financial Health & Wealth Dashboard](https://apps.urban.org/features/financial-health-wealth-dashboard/). diff --git a/index-county.html b/index-county.html index 6faaa22..ee0e2dd 100644 --- a/index-county.html +++ b/index-county.html @@ -3055,29 +3055,29 @@

Predictor: Hou Number homeless -2021 +2019 NA Lower/Upper bound -2021 +2019 NA Share homeless -2021 +2019 NA Quality -2021 +2019 NA Number homeless -2018 -4,320 +2016 +NA Lower/Upper bound -2018 -(4,302, 4,338) +2016 +NA Share homeless -2018 -2.2% +2016 +NA Quality -2018 -Strong +2016 +NA @@ -3478,7 +3478,7 @@

- Metric: Ratio of Facebook friends with higher socioeconomic status to Facebook friends with lower socioeconomic status + Metric: Ratio of Facebook friends with higher socioeconomic status to Facebook friends with lower socioeconomic status (‘economic connectedness’) @@ -3513,7 +3513,7 @@

- Metric: Ratio of Facebook friends with higher socioeconomic status to Facebook friends with lower socioeconomic status+ + Metric: Ratio of Facebook friends with higher socioeconomic status to Facebook friends with lower socioeconomic status (‘economic connectedness’)+ @@ -4183,7 +4183,7 @@

Predicto

Predictor: Digital access

- + 
Error in `all_of()`:
@@ -4196,6 +4196,12 @@ 
Predictor: Digita
 ! Can't rename columns that don't exist.
 ✖ Column `digital_access` doesn't exist.
+
+
Error in `filter()`:
+ℹ In argument: `year %in% metrics_info$years`.
+Caused by error in `match()`:
+! 'match' requires vector arguments
+
@@ -4565,7 +4571,7 @@

Predictor: Fi - Source: 2022 credit bureau data from Urban Institute’s Debt in America feature. (Time period: 2022) + Source: February 2022 credit bureau data from Urban Institute’s Debt in America feature. (Time period: February 2022) @@ -4602,10 +4608,10 @@

Predictor: Fi - Source: 2022 credit bureau data from Urban Institute’s Debt in America feature. (Time period: 2022) + 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 people in an area with a credit bureau record with debt that has progressed from being past-due to being in collections. + Notes: The county-level measure captures the share of adults in an area with a credit bureau record with debt sent to collections. @@ -5514,7 +5520,7 @@

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: 2023-04-10 19:38:16

+

Version: 2023-04-10 20:11:34

diff --git a/index-county.qmd b/index-county.qmd index 5049822..8cff91f 100644 --- a/index-county.qmd +++ b/index-county.qmd @@ -759,6 +759,19 @@ create_tb_detail( ``` +##### More Data + +```{r} +#| label: digital-access-more-data + +create_tb_more_data( + data = data_race_ethnicity, + metrics_info = get_vars_info("Digital access", data_summary_file = m_info)$info_lst, + varname_maps = digital_access_varlist +) + +``` + ::: diff --git a/index-place.html b/index-place.html index a1b26f6..4fa217a 100644 --- a/index-place.html +++ b/index-place.html @@ -3399,7 +3399,7 @@

Predicto

Predictor: Digital access

- + 
Error in `all_of()`:
@@ -3412,6 +3412,10 @@ 
Predictor: Digita
 ! Can't rename columns that don't exist.
 ✖ Column `digital_access` doesn't exist.
+
+
Error in `pivot_longer()`:
+! `cols` must select at least one column.
+
@@ -3866,7 +3870,7 @@

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 city is majority non-white).


-

Version: 2023-04-10 19:37:58

+

Version: 2023-04-10 20:11:21

diff --git a/index-place.qmd b/index-place.qmd index 732b3df..f4595f3 100644 --- a/index-place.qmd +++ b/index-place.qmd @@ -772,6 +772,19 @@ create_tb_detail( ``` +##### More Data + +```{r} +#| label: digital-access-more-data + +create_tb_more_data( + data = data_race_ethnicity, + metrics_info = get_vars_info("Digital access", data_summary_file = m_info)$info_lst, + varname_maps = digital_access_varlist +) + +``` + ::: diff --git a/search.json b/search.json index b30ab39..ad07a02 100644 --- a/search.json +++ b/search.json @@ -11,14 +11,14 @@ "href": "description.html#pillar-high-quality-education", "title": "Upward Mobility from Poverty Metric Descriptions", "section": "Pillar: High-Quality Education", - "text": "Pillar: High-Quality Education\n\n\n\n\n\n\nPREDICTOR: ACCESS TO PRESCHOOL\nMetric: Share of children enrolled in nursery school or preschool\nThis metric measures the share of 3- and 4-year-old children in a community who are enrolled in nursery school or preschool.\nValidity: Federal agencies such as the National Center for Education Statistics use household survey data to ascertain nursery and preschool enrollment.\nAvailability: The metric can be constructed using data from the US Census Bureau’s American Community Survey (ACS), which is publicly available nationwide.\nFrequency: 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.\nGeography: This metric is available at the county and city level.\nConsistency: 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.\nSubgroups: 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.\nLimitations: 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.\n\n\n\nPREDICTOR: EFFECTIVE PUBLIC EDUCATION\nMetric: Average per-grade change in English language arts achievement between third and eighth grades\nThis metric reports the average annual improvement in English language arts (reading comprehension and written expression) observed between the third and eighth grades.\nValidity: 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.\nAvailability: The metric can be constructed using data from the SEDA, which is publicly available nationwide.\nFrequency: This metric can be updated annually.\nGeography: This metric is available at the county, metropolitan area, and school district level.\nConsistency: 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.\nSubgroups: 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.\nLimitations: Literacy performance reported in “levels” is sensitive to movement in and out of a community over time.\n\n\n\nPREDICTOR: SCHOOL ECONOMIC DIVERSITY\nMetric: Share of students attending high-poverty schools, by student race or ethnicity\nThis 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.\nValidity: 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.\nAvailability: 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.\nFrequency: This metric can be updated annually.\nGeography: 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.\nConsistency: This metric can be consistently defined and calculated for cities and counties.\nSubgroups: This metric is by definition disaggregated by race or ethnicity.\nLimitations: 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.\n\n\n\nPREDICTOR: PREPARATION FOR COLLEGE\nMetric: Share of 19- and 20-year-olds with a high school degree\nThis metric is the share of 19- and 20-year-olds in a community who have a high school degree.\nValidity: 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.\nAvailability: The metric can be constructed using data from the US Census Bureau’s American Community Survey (ACS), which is publicly available nationwide.\nFrequency: 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.\nGeography: This metric is available at the county and city level.\nConsistency: 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.\nSubgroups: 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.\nLimitations: Young adults moving in and out of an area can influence this measure. This measure also does not capture the quality of schooling received.\n\n\n\nPREDICTOR: DIGITAL ACCESS\nMetric: Share of households with broadband access in the home\nThis is a measure of the share of 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.\nValidity: 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.\nAvailability: Data on broadband access are available annually from the US Census Bureau’s American Community Survey (ACS), which is publicly available nationwide.\nFrequency: This metric can be updated annually.\nGeography: This metric is available at the county and city level.\nConsistency: 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.\nStructural 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.\nStructural 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\nLimitations: 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.\n* 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." + "text": "Pillar: High-Quality Education\n\n\n\n\n\n\nPREDICTOR: ACCESS TO PRESCHOOL\nMetric: Share of children enrolled in nursery school or preschool\nThis metric measures the share of 3- and 4-year-old children in a community who are enrolled in nursery school or preschool.\nValidity: Federal agencies such as the National Center for Education Statistics use household survey data to ascertain nursery and preschool enrollment.\nAvailability: The metric can be constructed using data from the US Census Bureau’s American Community Survey (ACS), which is publicly available nationwide.\nFrequency: 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.\nGeography: This metric is available at the county and city level.\nConsistency: 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.\nSubgroups: 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.\nLimitations: 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.\n\n\n\nPREDICTOR: EFFECTIVE PUBLIC EDUCATION\nMetric: Average per-grade change in English language arts achievement between third and eighth grades\nThis metric reports the average annual improvement in English language arts (reading comprehension and written expression) observed between the third and eighth grades.\nValidity: 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.\nAvailability: The metric can be constructed using data from the SEDA, which is publicly available nationwide.\nFrequency: This metric can be updated annually.\nGeography: This metric is available at the county, metropolitan area, and school district level.\nConsistency: 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.\nSubgroups: 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.\nLimitations: Literacy performance reported in “levels” is sensitive to movement in and out of a community over time.\n\n\n\nPREDICTOR: SCHOOL ECONOMIC DIVERSITY\nMetric: Share of students attending high-poverty schools, by student race or ethnicity\nThis 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.\nValidity: 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.\nAvailability: 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.\nFrequency: This metric can be updated annually.\nGeography: 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.\nConsistency: This metric can be consistently defined and calculated for cities and counties.\nSubgroups: This metric is by definition disaggregated by race or ethnicity.\nLimitations: 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.\n\n\n\nPREDICTOR: PREPARATION FOR COLLEGE\nMetric: Share of 19- and 20-year-olds with a high school degree\nThis metric is the share of 19- and 20-year-olds in a community who have a high school degree.\nValidity: 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.\nAvailability: The metric can be constructed using data from the US Census Bureau’s American Community Survey (ACS), which is publicly available nationwide.\nFrequency: 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.\nGeography: This metric is available at the county and city level.\nConsistency: 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.\nSubgroups: 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.\nLimitations: Young adults moving in and out of an area can influence this measure. This measure also does not capture the quality of schooling received.\n\n\n\nPREDICTOR: DIGITAL ACCESS\nMetric: Share of people in households with broadband access in the home\nThis 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.\nValidity: 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.\nAvailability: Data on broadband access are available annually from the US Census Bureau’s American Community Survey (ACS), which is publicly available nationwide.\nFrequency: This metric can be updated annually.\nGeography: This metric is available at the county and city level.\nConsistency: 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.\nStructural 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.\nStructural 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\nLimitations: 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.\n* 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." }, { "objectID": "description.html#pillar-rewarding-work", "href": "description.html#pillar-rewarding-work", "title": "Upward Mobility from Poverty Metric Descriptions", "section": "Pillar: Rewarding Work", - "text": "Pillar: Rewarding Work\n\n\n\n\n\n\nPREDICTOR: EMPLOYMENT OPPORTUNITIES\nMetric: Employment-to-population ratio for adults ages 25 to 54\nThis metric is the share of adults ages 25 to 54 in a given community who are employed.\nValidity: 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.\nAvailability: The metric can be constructed using data from the ACS, which is publicly available nationwide.\nFrequency: 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.\nGeography: This metric is available at the county and city level.\nConsistency: 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.\nSubgroups: 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.\nLimitations: 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.\n\n\n\nPREDICTOR: JOBS PAYING A LIVING WAGE\nMetric: Ratio of pay on an average job to the cost of living\nThis 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.\nValidity: 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.\nAvailability: 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.\nFrequency: This metric can be updated annually.\nGeography: 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.\nConsistency: Information on weekly wages is collected in a consistent fashion by the BLS. MIT uses a consistent methodology to compute living wages by county.\nSubgroups: This metric cannot be disaggregated into subgroups because these data describe wages rather than the people earning those wages.\nLimitations: 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. To compute the 2021 metric, the 2022 living wage data was deflated using the BLS’ consumer price index for all urban consumers. For the 2018 and 2014 metrics, the living wage data was deflated from 2019.\n\n\n\nPREDICTOR: OPPORTUNITIES FOR INCOME\nMetric: Household income at the 20th, 50th, and 80th percentiles\nHousehold income is a standard measure of financial well-being. The Working Group recommended the metrics at these three levels 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.\nValidity: These are well-established and frequently-used measures to assess the financial well-being of families by several federal agencies and many scholars.\nAvailability: The metric can be constructed using data from the US Census Bureau’s American Community Survey, which is publicly available nationwide.\nFrequency: This metric can be updated annually. Survey data are collected annually and used to create one- and five-year estimates.\nGeography: This metric is available at the county and city level.\nConsistency: 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.\nSubgroups: 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.\nLimitations: 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.\n\n\n\nPREDICTOR: FINANCIAL SECURITY\nMetric: Share of households with debt in collections\nThis 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.\nValidity: 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. Though not a standard measure, this metric has been used by researchers to distinguish between “good” debts (e.g., mortgages paid on time every month) and “bad” debts.\nAvailability: 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.\nFrequency: This metric can be updated annually.\nGeography: This metric is available at the ZIP code level which can be aggregated to the county and city level.\nConsistency: 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.\nSubgroups: 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.\nLimitations: 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 a large number of residents without overdue debt move into a county or ZIP code, or if a large number of 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.\n\n\n\nPREDICTOR: WEALTH-BUILDING OPPORTUNITIES\nMetric: 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\nThis 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.\nValidity: 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.\nAvailability: The metric can be constructed using data from the US Census Bureau’s American Community Survey, which is publicly available nationwide.\nFrequency: This metric can be updated annually. For less populous communities, it may be necessary to pool several years of data to obtain reliable estimates.\nGeography: This metric is available at the county and city level.\nConsistency: This metric is defined consistently across race and ethnic groups, is consistently measured over time, and is comparable across geography.\nStructural 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.\nStructural 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.\nLimitations: 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.\n* 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." + "text": "Pillar: Rewarding Work\n\n\n\n\n\n\nPREDICTOR: EMPLOYMENT OPPORTUNITIES\nMetric: Employment-to-population ratio for adults ages 25 to 54\nThis metric is the share of adults ages 25 to 54 in a given community who are employed.\nValidity: 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.\nAvailability: The metric can be constructed using data from the ACS, which is publicly available nationwide.\nFrequency: 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.\nGeography: This metric is available at the county and city level.\nConsistency: 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.\nSubgroups: 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.\nLimitations: 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.\n\n\n\nPREDICTOR: JOBS PAYING A LIVING WAGE\nMetric: Ratio of pay on an average job to the cost of living\nThis 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.\nValidity: 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.\nAvailability: 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.\nFrequency: This metric can be updated annually.\nGeography: 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.\nConsistency: Information on weekly wages is collected in a consistent fashion by the BLS. MIT uses a consistent methodology to compute living wages by county.\nSubgroups: This metric cannot be disaggregated into subgroups because these data describe wages rather than the people earning those wages.\nLimitations: 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. To compute the 2021 metric, the 2022 living wage data was deflated using the BLS’ consumer price index for all urban consumers. For the 2018 and 2014 metrics, the living wage data was deflated from 2019.\n\n\n\nPREDICTOR: OPPORTUNITIES FOR INCOME\nMetric: Household income at the 20th, 50th, and 80th percentiles\nHousehold income is a standard measure of financial well-being. The Working Group recommended the metrics at these three levels 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.\nValidity: These are well-established and frequently-used measures to assess the financial well-being of families by several federal agencies and many scholars.\nAvailability: The metric can be constructed using data from the US Census Bureau’s American Community Survey, which is publicly available nationwide.\nFrequency: This metric can be updated annually. Survey data are collected annually and used to create one- and five-year estimates.\nGeography: This metric is available at the county and city level.\nConsistency: 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.\nSubgroups: 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.\nLimitations: 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.\n\n\n\nPREDICTOR: FINANCIAL SECURITY\nMetric: Share with debt in collections\nThis 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.\nValidity: 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.\nAvailability: 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.\nFrequency: This metric can be updated annually.\nGeography: This metric is available at the ZIP code level which can be aggregated to the county and city level.\nConsistency: 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.\nSubgroups: 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.\nLimitations: 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 a large number of residents without overdue debt move into a county or ZIP code, or if a large number of 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.\n\n\n\nPREDICTOR: WEALTH-BUILDING OPPORTUNITIES\nMetric: 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\nThis 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.\nValidity: 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.\nAvailability: The metric can be constructed using data from the US Census Bureau’s American Community Survey, which is publicly available nationwide.\nFrequency: This metric can be updated annually. For less populous communities, it may be necessary to pool several years of data to obtain reliable estimates.\nGeography: This metric is available at the county and city level.\nConsistency: This metric is defined consistently across race and ethnic groups, is consistently measured over time, and is comparable across geography.\nStructural 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.\nStructural 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.\nLimitations: 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.\n* 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." }, { "objectID": "description.html#pillar-healthy-environment-and-access-to-good-health-care",