The YVI assesses vulnerability to inadequate support for wellbeing. It combines four types of data, which we call “indicators,”— the rates
at which youth became teen mothers, did not complete high school, were placed in foster care, and lived in households with very low family incomes. The YVI and related indicator data and maps are available by county for all youth, and by race/ethnicity and sex. Data for three of the four indicators are also provided for all youth across census county divisions (CCDs). These are subcounty regions that include one or more census tracts. Data for the fourth indicator was not available at this level of geography, so we were unable to calculate the YVI for CCDs.
Individual placebased indicator scores
reflect comparison to a benchmark that could be considered
excellent progress. For each indicator
the benchmark is based on the rates experienced by 10% of all
CA youth in the counties with the lowest
rates. Each place receives a “score” for each indicator:
 met the benchmark
 somewhat below benchmark (within 90% of the benchmark)
 below benchmark (between 5090% of the benchmark)
 far below benchmark (less than 50% of the benchmark)
The YVI averages the four indicator
scores for each county, and highlights places
scoring relatively low (meaning lower
vulnerability rates) and high (meaning higher vulnerability rates)
across the four indicators. In counties where one or more indicators is based on an estimate that is potentially unreliable, the YVI is not calculated. Reliability concerns and thresholds are discussed in more detail below.
Many other conditions are associated
with youth marginalization from potentially key institutional
supports for wellbeing, including
involvement in the juvenile justice system, teen fatherhood,
identification as LGBTQ, homelessness and
unauthorized immigration status. Unfortunately these data are either
unavailable or unavailable in a form that can easily be mapped for the state at a subcounty level, so we are unable to include them in the index.
New SubGroup Analysis for YVI
The YVI includes analyses by race, ethnicity and sex wherever there was an adequate population size to do so. At the county level, the YVI and its indicators are now provided for females, males, and populations identified as White, Hispanic/Latino, Black/African American, Asian, Native American/Alaskan Native, and Two or More racial/ethnic backgrounds. Due to small youth populations in many CCDs, subgroups are not analyzed separately at this geographic level. Data are provided for only three of the indicators for CCDs because foster care entry rates are not adequately georeferenced at the subcounty level. Therefore the YVI is not calculated for CCDs.
To learn more about YVI indicator data,
data sources and their limitations, click the links below.
More On YVI Calculation
To develop benchmarks for each indicator, we adopted an approach employed in health care
research called “Achievable Benchmarks of Care,” or “ABC.” The basic idea is to rank order
all the counties in order of performance on the indicator, and select the 90th percentile
as the benchmark. This would set the benchmark at the level achieved by the county which
performs better than 90% of the other counties. However, this approach can give undue
influence to very small counties, which may not be very representative of the larger
population. The “ABC” approach limits the influence that small places can have on the
benchmark. The steps are as follows:
 First, calculate the performance for each county, adjusting county size to reduce
the impact of smaller units
 rank order the counties by their adjusted performance scores
 beginning with the bestperforming county, sequentially add county population until
at least 10% of the total population is included in the sum
 calculate the performance for the subset of counties included in the sum by
aggregating the numerator and denominator counts.
Here is a worked example for teen birth rate, which assumes that the total population of
females age 1319 in all California counties is 1,932,377, 10% of which is 193,378.
The table lists only a subset of
California counties, ordered here by their adjusted teen birth rate,
lowest first.

Number of births to teens 
Number of teens 
Teen birth rate (unadjusted) 
Teen birth rate (adjusted) 
Cumulative number of teens 
Cumulative number of births 
1.  78  8,902  8.76  8.87  8,902  78 
2.  153  17,046  9.00  9.05  25,948  231 
3.  240  24,117  9.94  9.98  50,065  471 
4.  43  4,107  10.47  10.71  54,172  514 
5.  94  8,575  11.00  11.11  62,747  608 
6.  372  30,926  12.03  12.06  93,673  980 
7.  174  13,284  13.07  13.15  106,957  1,154 
8.  173  12,738  13.56  13.63  119,695  1,327 
9.  717  52,161  13.75  13.76  171,856  2,044 
10.  29  2,087  13.74  14.20  173,943  2,072 
11.  19  1,326  14.08  14.81  175,269  2,091 
12.  1200  80,484  14.91  14.92  255,753  3,291 
The unadjusted teen birth rate is
simply the number of births to teens, divided by the number of teens,
multiplied by 1,000. The adjusted
teen birth rate is the number of teen births plus one, divided by the
number of teens plus two, multiplied
by 1,000. This adjustment has little effect in large counties, but
does impact the birth rate in
smaller counties. Compare the adjusted and unadjusted teen birth rates
in
rows 9 and 10 to see this.
After the adjusted teen birth rate
is calculated, the counties are ordered by the adjusted birth rate,
lowest first, as was done in the
table above. The cumulative number of teen females, shown in the
second to last column, crosses the
10% threshold of 193,378 in the 12th row, where the cumulative
population is 255,753. These 12
counties are used to calculate the teen birth rate using the
cumulative totals:
3291 / 255753 * 1000 = 12.87.
The “ABC” benchmark for teen birth rate is thus 12.87.
Please see
https://www.ncbi.nlm.nih.gov/pubmed/9828034
and
https://www.ncbi.nlm.nih.gov/pubmed/10461579
for more information.
Teen Birth Rate
What do the maps show?
Teen mothers are more likely to not
graduate from high school and/or pursue postsecondary education,
experience more rapid repeat
pregnancy, face the challenges of single parenting and grapple with
unemployment and poverty, all of
which can result in inadequate support for wellbeing. Teen Birth
Rate maps shows the number of births
per 1,000 females ages 1319, averaged over 20092011. It also
compares these rates to a benchmark.
This benchmark is based on the teen birth rate experienced by
10% of all CA females ages 1319 in
the counties with the lowest teen birth rates. Maps by sex and
race/ethnicity are available by
county only because the very small numbers of births in many census
tracts make analyses unreliable.
How did we calculate the teen birth rate?
This teen birth rate map represents the number of 15 to 19 year olds in each area who gave birth during the years 20092011, divided by the total number of females in the same age group over the same time period, multiplied by 1,000. Population totals are U.S. Census population estimates for intercensal years.
If a mother’s age or race/ethnic group was not specified, the record was dropped. For the countylevel analysis, if the mother’s race was listed as “other,” the record was dropped. We used mother’s county of residence as specified in the birth records to calculate the number of births per county. For CCDs, we used mother’s residential address, geocoded to the census tract, to count the number of births to teen mothers in all census tracts within each CCD. Births that could not be geocoded were excluded (6,133 out of 128,756 births to 15 to 19 year olds, or 4.8%).
The potential for error in the estimated teen birth rate for each location depends primarily on the number of teen births. The number of teen births is expected to follow a Poisson distribution, which expresses the probability that a given number of events will occur within a fixed interval of time and space. Because there is some random temporal and spatial variability in births, the estimated birth rate may vary across geographic units or years, even when the underlying average rate is the same. A statistical measure called margin of error accounts for this naturally occurring variability, and expresses the degree of uncertainty that an estimate is close to the true value. When the margin of error is large relative to the estimate, the estimate is less reliable and should be interpreted with caution. We denote places where the margin of error is 35% the size of the estimate or larger with an asterisk. For a Poisson variable, the margin of error is equal to one divided by the square root of the number of events, multiplied by 1.645. When the number of teen births is 23 or higher, the relative margin of error falls below 35%. Therefore, the estimated birth rate in places with fewer than 23 teen births is considered unreliable and should be used with caution as the true value may differ.
For the purposes of estimating reliability, we assume that the population denominators have no error. “While this assumption is technically correct only for denominators based on the census that occurs every 10 years, the error in intercensal population estimates is usually small, difficult to measure, and therefore not considered.” (National Vital Statistics Reports, Vol. 51, No. 12, August 4, 2003, page 90 https://www.cdc.gov/nchs/data/nvsr/nvsr51/nvsr51_12.pdf).
It should be noted that population estimates for areas or groups with small populations are likely to have more variability, and teen birth estimates in these places should be used with considerable caution.
Where did we get the data?
Teen birth data were obtained (after receiving approval from the Committee for the Protection of Human Subjects) from the California Department of Public Health's Health Information and Research Section, using the Birth Statistical Master Files for 200911. Countylevel female population data came from the U.S. Census postcensal (2009) and intercensal (2010 and 2011) population estimates. Postcensal population estimates are produced by updating the resident population enumerated in a decennial census with estimates of population change (births, deaths, migration) in the years before the next decennial census. Intercensal estimates update the postcensal estimates with information from the next decennial census. We added the population estimates for 2009, 2010, and 2011 to get an estimate of the total number of females ages 1519 for the race/ethnic groups of interest in each county. Intercensal population estimates are not available for subcounty regions, so we used data from the U.S. Census Bureau’s American Community Survey, table B11001, 5year estimates for 2011 to get numerator data for the CCDlevel teen birth rate indicator. We multiplied the ACS estimate by three to obtain an estimate of the total number of females ages 15 to 19 in in each CCD over the years 2009 to 2011.
Foster Care Entry Rate
What do the maps show?
Youth who enter the foster care system tend to be vulnerable to inadequate support, which can lead to poor educational, mental and physical health, social developmental and economic outcomes. This map shows foster care entry rates per 1,000 youth age 0 to 17, for the years 200911. The map also compares this rate to a benchmark. This benchmark is based on the foster care entry rates experienced 10% of all CA 017 year olds in the counties with the lowest rates in 200911. Maps and data are available by county only.
The maps are based on estimates that are subject to uncertainty based on small population sizes or rare events. In places where estimates do not meet a certain threshold of reliability (explained below), we display an asterisk and urge you to interpret these estimates with caution as they are less likely to be accurate.
How did we calculate the foster care entry rate?
Foster care entry rates are the number of 0 to 17 year olds in each county who entered foster care between 2009 and 2011, divided by the number of 017 year olds in 200911, multiplied by 1,000. Foster care data could not be reliably geolocated to subcounty regions, so foster care data are not presented for CCDs.
The potential for unreliability in the foster care entry rate was assessed based on the number of foster care entries. The number of entries is expected to follow a Poisson distribution, which expresses the probability that a given number of events will occur within a fixed interval of time and space. Because there is some random temporal and spatial variability in events, the estimated foster care entry rate may vary across geographic units or years, even when the underlying average rate is the same. A statistical measure called margin of error accounts for this naturally occurring variability, and expresses the degree of uncertainty that an estimate is close to the true value. When the margin of error is large relative to the estimate, the estimate is less reliable and should be interpreted with caution. We denote places where the margin of error is 35% the size of the estimate or larger with an asterisk. For a Poisson variable, the margin of error is equal to one divided by the square root of the number of events, multiplied by 1.645. When the number of foster care entries is 23 or higher, the relative margin of error falls below 35%. Therefore, the estimated foster care entry rate in counties with fewer than 23 foster care entries is considered unreliable and should be used with caution as the true value may differ.
Where did we get the data?
Raw foster care entry data were collected from the Child Welfare Dynamic Reporting System
(https://cssr.berkeley.edu/ucb_childwelfare/GeoData.aspx),
a California Department of Social Services / UC Berkeley collaboration. The use agreement
for these data included nonredistribution of the data. For that reason, we only allow
download of the foster care entry rate rank.
Population estimates were obtained from the CA Department of Finance Demographic Unit, Report P3: Population Projections by Race/Ethnicity, Detailed Age, and Gender, 20102060. (https://www.dof.ca.gov/research/demographic/reports/projections/P3/, downloaded January 30, 2013).
Very Low Income
What do the maps show?
Very low family income is associated with decreased access to support for wellbeing. This map shows the rates at which 0 to 17 year olds are living in families with incomes that are below the federal poverty level. The map also compares this rate to a benchmark. This benchmark is based on the poverty rates experienced by 10% of all CA 017 year olds in the counties with the lowest poverty rates in 2010. Maps are available by sex and race/ethnicity for counties and by CCD.
The maps are based on estimates that are subject to uncertainty based on small population sizes or rare events. In places where estimates do not meet a certain threshold of reliability (explained below), we display an asterisk and urge you to interpret these estimates with caution as they are less likely to be accurate.
How did we calculate very low family income rate?
In this map we show the percentage of 0 to 17 year olds living in families with incomes that are below the federal poverty level. To generate analyses based
on the benchmark, data were collected from the U.S. Census Bureau's American
Community Survey 5year estimates for 2011, using Table B17001 "Poverty
Status in the Past 12 Months by Sex and Age.” Since the American Community
Survey (ACS) is not a complete census, a margin of error (MOE) for each data
point is given with the raw data. The margin of error is a statistical measure that
represents the degree of uncertainty that a given estimate is close to the true
value being estimated. When the margin of error is large relative to the estimate,
the estimate is less likely to be close to the true value being estimated. We denote
places where the margin of error is 35% the size of the estimate or larger with
an asterisk. Please beware of large MOEs because estimates in these places are less
likely to be accurate and should be interpreted with caution.
Click here for more information about the MOE calculation.
An explanation of the margin of error calculation for aggregated count data and derived proportions is shown here.
Variable list in the ACS report.
HD01_VD07  Estimate; Income in the past 12 months below poverty level:  Male:  12 to 14 years 
HD02_VD07  Margin of Error; Income in the past 12 months below poverty level:  Male:  12 to 14 years 
HD01_VD08  Estimate; Income in the past 12 months below poverty level:  Male:  15 years 
HD02_VD08  Margin of Error; Income in the past 12 months below poverty level:  Male:  15 years 
HD01_VD09  Estimate; Income in the past 12 months below poverty level:  Male:  16 and 17 years 
HD02_VD09  Margin of Error; Income in the past 12 months below poverty level:  Male:  16 and 17 years 
HD01_VD21  Estimate; Income in the past 12 months below poverty level:  Female:  12 to 14 years 
HD02_VD21  Margin of Error; Income in the past 12 months below poverty level:  Female:  12 to 14 years 
HD01_VD22  Estimate; Income in the past 12 months below poverty level:  Female:  15 years 
HD02_VD22  Margin of Error; Income in the past 12 months below poverty level:  Female:  15 years 
HD01_VD23  Estimate; Income in the past 12 months below poverty level:  Female:  16 and 17 years 
HD02_VD23  Margin of Error; Income in the past 12 months below poverty level:  Female:  16 and 17 years 
HD01_VD36  Estimate; Income in the past 12 months at or above poverty level:  Male:  12 to 14 years 
HD02_VD36  Margin of Error; Income in the past 12 months at or above poverty level:  Male:  12 to 14 years 
HD01_VD37  Estimate; Income in the past 12 months at or above poverty level:  Male:  15 years 
HD02_VD37  Margin of Error; Income in the past 12 months at or above poverty level:  Male:  15 years 
HD01_VD38  Estimate; Income in the past 12 months at or above poverty level:  Male:  16 and 17 years 
HD02_VD38  Margin of Error; Income in the past 12 months at or above poverty level:  Male:  16 and 17 years 
HD01_VD50  Estimate; Income in the past 12 months at or above poverty level:  Female:  12 to 14 years 
HD02_VD50  Margin of Error; Income in the past 12 months at or above poverty level:  Female:  12 to 14 years 
HD01_VD51  Estimate; Income in the past 12 months at or above poverty level:  Female:  15 years 
HD02_VD51  Margin of Error; Income in the past 12 months at or above poverty level:  Female:  15 years 
HD01_VD52  Estimate; Income in the past 12 months at or above poverty level:  Female:  16 and 17 years 
HD02_VD52  Margin of Error; Income in the past 12 months at or above poverty level:  Female:  16 and 17 years 
Note that the number ranges
indicate the ratio of poverty status in the past 12 months. When working
with the
margins of error, it is
generally considered best practice to use the fewest number of variables
or components
possible to reduce error.
Section 1. Calculate MOE for Aggregated Numerator.
Step 1.1. Define the MOE for each numerator component estimate.
COMPUTE N1=HD02_VD07.
COMPUTE N2=HD02_VD08.
COMPUTE N3=HD02_VD09.
COMPUTE N4=HD02_VD21.
COMPUTE N5=HD02_VD22.
COMPUTE N6=HD02_VD23.
Step 1.2. Sum the component estimates that comprise the numerator.
COMPUTE X_NUM = HD01_VD07 + HD01_VD88 + HD01_VD09 + HD01_VD21+ HD01_VD22+ HD01_VD23.
The MOE for the numerator is given by MOE_NUM.
Step 1.3. Square the MOE for each numerator component estimate.
Step 1.4. Sum the squared MOEs.
Step 1.5. Take the square root of the sum of the squared MOEs.
COMPUTE MOE_NUM = SQRT((N1^2) + (N2^2) + (N3^2) + (N4^2) + (N5^2) +(N6^2)).
Section 2. Calculate the MOE for the Aggregated Denominator.
Step 2.1. Define the MOE for each denominator component estimate.
COMPUTE D1=HD02_VD07.
COMPUTE D2=HD02_VD08.
COMPUTE D3=HD02_VD09.
COMPUTE D4=HD02_VD21.
COMPUTE D5=HD02_VD22.
COMPUTE D6=HD02_VD23.
COMPUTE D7=HD02_VD36.
COMPUTE D8=HD02_VD37.
COMPUTE D9=HD02_VD38.
COMPUTE D10=HD02_VD50.
COMPUTE D11=HD02_VD51.
COMPUTE D12=HD02_VD52.
Step 2.2. Sum the component estimates that comprise the denominator.
COMPUTE X_DEN = HD01_VD07 + HD01_VD08 + HD01_VD09 + HD01_VD21 + HD01_VD22 + HD01_VD23
+ HD01_VD36 + HD01_VD37 + HD01_VD38 + HD01_VD50 + HD01_VD51 + HD01_VD52.
The MOE for the denominator is given by: MOE_DEN.
Step 2.3. Square the MOE for each numerator component estimate.
Step 2.4. Sum the squared MOEs.
Step 2.5. Take the square root of the sum of the squared MOEs.
COMPUTE MOE_DEN = SQRT((D1^2) + (D2^2) + (D3^2) + (D4^2) + (D5^2) + (D6^2) + (D7^2)
+ (D8^2) + (D9^2) + (D10^2) + (D11^2) + (D12^2)).
Section 3. Calculate the MOE for the Derived Proportion.
The derived ratio is:
COMPUTE R = X_NUM/X_DEN.
The calculation of the MOE is as follows:
Step 3.1. Square the derived proportion.
Step 3.2. Square the MOE of the numerator.
Step 3.3. Square the MOE of the denominator.
Step 3.4. Multiply the squared MOE of the denominator by the squared proportion (R).
Step 3.5. Subtract the results of (3.4) from the squared MOE of the numerator.
Step 3.6. Take the square root of the result of (3.5).
COMPUTE MOE_XNUM = SQRT((MOE_NUM**2) ((R**2)*(MOE_DEN**2))).
Step 3.7. Divide the result of (3.6) by the denominator of the proportion (X_Den).
COMPUTE MOE_X = MOE_XNUM/X_DEN.
Section 4. Define variables for analysis.
Step 4.1. Define MOE of the percentage of 1217 year olds in the tract who are below 200% of the Federal Poverty Level.
References:
Bidita J. Tithi and Chris Benner, Technical Paper for Vulnerability and Opportunity Indices Calculation, UC Davis, 2011.
Appendix 3 of the US Census
Bureau (October 2008) manual A Compass for Understanding and Using the
American Community Survey Data: What General Data Users Need to
Know.https://www.census.gov/acs/www/guidance_for_data_users/handbooks/
High School NonCompletion (Dropout) Rate
What does this map show?
Youth who do not complete high school are more likely to experience a variety of challenges to their wellbeing and less access to support. This indicator measures the rates at which young people ages 18 to 24 have less than a 9th grade education or entered 9th to 12th grades but received no diploma. The map also compares this rate to a benchmark. This benchmark is based on the lowest noncompletion (or “dropout”) rates experienced by 10% of all CA 1824 year olds. Data by sex and race/ethnicity are available for counties and by CCD.
The maps are based on estimates that are subject to uncertainty based on small population sizes or rare events. In places where estimates do not meet a certain threshold of reliability (explained below), we display an asterisk and urge you to interpret these estimates with caution as they are less likely to be accurate.
How did we calculate the high school noncompletion (dropout) rate?
This indicator was created by dividing the number individuals who did not complete high school by the total population of 1824 year olds. Data were collected from the U.S. Census Bureau's American Community Survey 5year estimates for 200711, using Table B15001, "Sex by Age by Educational Attainment for the Population 18 years and Over."
Since the American Community Survey (ACS) is not a complete census, a margin of error (MOE) for each data point is given with the raw data. The margin of error is a statistical measure that represents the degree of uncertainty that a given estimate is close to the true value being estimated. When the margin of error is large relative to the estimate, the estimate is less likely to be close to the true value being estimated. We denote places where the margin of error is 35% the size of the estimate or larger with an asterisk. Please beware of large MOEs because estimates in these places are less likely to be accurate and should be interpreted with caution.
Click here for more information
Here we provide an explanation of the margin of error calculation for aggregated count data and derived proportions.
Variable list in the ACS report
HD01_VD01  'Total:' 
HD01_VD02  'Total Male:' 
HD01_VD03  'Total: Male: 18 to 24 years:' 
HD01_VD04  'Total: Male: 18 to 24 years: Less than 9th grade' 
HD01_VD05  'Total: Male: 18 to 24 years: 9th to 12th grade, no diploma' 
HD01_VD43  'Total: Female:' 
HD01_VD44  'Total: Female: 18 to 24 years:' 
HD01_VD45  'Total: Female: 18 to 24 years: Less than 9th grade' 
HD01_VD46  'Total: Female: 18 to 24 years: 9th to 12th grade, no diploma' 
HD02_VD01  'Total: (MOE)' 
HD02_VD02  'Total: Male: (MOE)' 
HD02_VD03  'Total: Male: 18 to 24 years: (MOE)' 
HD02_VD04  'Total: Male: 18 to 24 years: Less than 9th grade (MOE)' 
HD02_VD05  'Total: Male: 18 to 24 years: 9th to 12th grade, no diploma (MOE)' 
HD02_VD43  'Total: Female: (MOE)' 
HD02_VD44  'Total: Female: 18 to 24 years: (MOE)' 
HD02_VD45  'Total: Female: 18 to 24 years: Less than 9th grade (MOE)' 
HD02_VD46  'Total: Female: 18 to 24 years: 9th to 12th grade, no diploma (MOE)'. 
PURPOSE: Calculating the MOE
for the Percentage of 1824 year olds who completed either less than 9th
grade, or 9th through 12th grade, without receiving a diploma.
Section 1. Calculate MOE for Aggregated Numerator.
Step 1.1. Define the MOE for each numerator component estimate.
COMPUTE N1=HD02_VD04.
COMPUTE N2= HD02_VD05.
COMPUTE N3= HD02_VD45.
COMPUTE N4= HD02_VD46.
Step 1.2. Sum the component estimates that comprise the numerator.
COMPUTE X_NUM = HD01_VD04 + HD01_VD05 + HD01_VD45 + HD01_VD46.
The MOE for the numerator is given by MOE_NUM.
Step 1.3. Square the MOE for each numerator component estimate.
Step 1.4. Sum the squared MOEs.
Step 1.5. Take the square root of the sum of the squared MOEs.
COMPUTE MOE_NUM = SQRT((N1^2) + (N2^2) + (N3^2) + (N4^2)).
Section 2. Calculate the MOE for the Aggregated Denominator.
Step 2.1. Define the MOE for each denominator component estimate.
COMPUTE D1= HD02_VD03.
COMPUTE D2= HD02_VD44.
Step 2.2. Sum the component estimates that comprise the denominator.
COMPUTE X_DEN = (HD01_VD03+HD01_VD44).
The MOE for the denominator is given by: MOE_DEN.
Step 2.3. Square the MOE for each numerator component estimate.
Step 2.4. Sum the squared MOEs.
Step 2.5. Take the square root of the sum of the squared MOEs.
COMPUTE MOE_DEN = SQRT((D1^2) + (D2^2)).
Section 3. Calculate the MOE for the Derived Proportion.
The derived ratio is:
COMPUTE R = X_NUM/X_DEN.
The calculation of the MOE is as follows:
Step 3.1. Square the derived proportion.
Step 3.2. Square the MOE of the numerator.
Step 3.3. Square the MOE of the denominator.
Step 3.4. Multiply the squared MOE of the denominator by the squared proportion (R).
Step 3.5. Subtract the results of (3.4) from the squared MOE of the numerator.
Step 3.6. Take the square root of the result of (3.5).
COMPUTE MOE_XNUM = SQRT((MOE_NUM^2) ((R^2)*(MOE_DEN^2))).
Step 3.7. Divide the result of (3.6) by the denominator of the proportion (X_Den).
COMPUTE MOE_X = MOE_XNUM/X_DEN.
Section 4. Define variables for analysis.
Step 4.1. Define MOE of the
Percentage of 1824 year olds in the tract who (1) completed less than
9th grade OR (2) completed 9th12th grade without receiving a diploma.
COMPUTE M_P1824_Dropouts= MOE_X.
Step 4.2. Define percentage
of 1824 year olds in the tract who (1) completed less than 9th grade OR
(2) completed 9th12th grade without receiving a diploma.
COMPUTE P1824_Dropouts = R.
References:
Bidita J. Tithi and Chris Benner, Technical Paper for Vulnerability and Opportunity Indices Calculation, UC Davis, 2011.
Appendix 3 of the US Census
Bureau (October 2008) manual A Compass for Understanding and Using the
American Community Survey Data: What General Data Users Need to
Know.https://www.census.gov/acs/www/guidance_for_data_users/handbooks/
Where did we get the data?
To search the U.S. Census website
for data, please see the American Fact Finder query tool at:
https://factfinder2.census.gov/faces/nav/jsf/pages/index.xhtml.