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About the Data

Data Sources:

Unless otherwise noted in its “Source and Notes” drop-down, all data in the Economic Development, Human Development, and Neighborhood Development sections of the dashboard come from the Mobility Metrics. The data from the Fresno Demographics tab comes from the American Community Survey’s 2008-2012, 20013-2017, and 2018-2022 5-year surveys. The data powering the Inclusive Recovery Indices tab comes from two sources: pre-2020 calculations were downloaded from the Measuring Inclusion in America’s Cities feature on Urban’s website (Christina Stacy, Brady Meixell, Ananya Hariharan, Erika Poethig, and Solomon Greene 2020). Urban Institute researchers performed the same calculations on updated 2020 data for this project.

For more information on the Mobility Metrics from the Upward Mobility Framework used in this dashboard, please see the following resources:

Data Organization:

The Comparing Fresno Against Other Geographies plots compare Fresno to other peer cities and counties. CVCF staff members identified other relevant cities and counties both within and outside of California. The goal of these selections was to provide greater context to the Fresno data among other cities and counties in the Central Valley, across California, and among peer cities throughout the United States.

Some metrics are broken down by race and ethnicity or, more infrequently, disaggregated by other variables. The dashboard was built to show the highest degree of racial and ethnic disaggregation possible. If a metric does not show data for specific races or ethnicities, that is because the source data also lacks that racial and ethnic disaggregation.

Departures from the Mobility Metrics and the Upward Mobility Framework:

As noted above, much of the data from the dashboard came directly from the Urban Institute’s Upward Mobility project. While we followed that project’s framework of the predictors and metrics for the majority of the data, we made some adjustments to the data to meet the needs of this dashboard. This section describes those changes and explains why those decisions were made:

  1. We did not include the “Just Policing” and “Safety from Crime” predictors because the Mobility Metric data was incomplete for the counties and cities in California which are the focus on this dashboard. We did not include the “Effective Public Education” predictor because the Mobility Metrics were most recently from 2017 and because its data quality was “Marginal” (see below for more details).
  2. We did not include the “Descriptive Representation” predictor. The Planning Guide for Local Action (p27) encourages data users to identify the race and ethnicity of elected officials for this predictor. In the absence of self-reported race and ethnicity information from Fresno elected officials, we chose to report the demographic details of Fresno in greater detail in the Demographic Breakdown tab and direct users to Fresno government webpages to learn more about their elected representatives. See the Source(s) and Notes drop-down for more details.
  3. In the Upward Mobility Framework, the metric used is “the ratio of the share of a community’s home values held by a racial or ethnic group to the share of households of the same group.” Instead of showing this as a ratio of two numbers (share of home values and share of households of a particular racial or ethnic group), we found that taking the difference between these two values was more intuitive. This becomes a one-number summary and therefore no longer reflects the raw shares of home values and households, but we found that the difference is more intuitive and easier to visualize.
  4. To respond to the desire to examine within-Fresno variation in outcomes, we provide tract-level data related to some of the Mobility Metrics. In most cases, the exact metric was not available at the tract level, and we were able to identify a related metric available at the tract level. In such cases, we report the related metric and explain the difference in the variable definitions in the notes and references.
  5. Urban’s Upward Mobility Framework is oriented around five pillars. DRIVE’s portfolio includes three buckets: Economic Development, Human Development, and Neighborhood Development. Consequently, this dashboard shows the predictors and metrics organized by those buckets.
  6. The Mobility Metrics do not include state-wide estimates. All state-wide estimates were calculated specifically for this project. See below for more details.
  7. We incorporate data from four sources outside of Urban’s Upward Mobility Framework metrics: performance on standardized testing in English Language Arts and Mathematics from the California Department of Education, a water contamination index from CalEnviroScreen, the rate of juvenile arrests from the California Department of Justice, and the share of pre-term births from the California Department of Public Health.

California Estimates:

For data collected independent of Urban’s Mobility Metrics, we collected data both at the county and state scales. To determine a reasonable estimate for Urban’s metrics at the State of California scale, we took a weighted average of the county-level data for each county in California. We chose one of four weights for the data: total population by race and ethnicity, number of households by race and ethnicity, total population under 18 years old by race and ethnicity, and share of voting-age citizens. The expression below shows how we took the weighted average, where \(i\) refers to a specific county and \(j\) refers to a specific mobility metric and its corresponding ACS variable used for weighting.

\[\begin{equation} \frac{\sum_{County_i}^\text{All Counties} Metric_j \cdot ACS_j} {\sum_{County_i}^\text{All Counties} ACS_j}\end{equation}\]

As an example, consider the employment-to-population ratio for adults 25-54. For this metric, for each racial or ethnic subgroup, we used the total population of that subgroup as the weighting variable. See the GitHub README for this project to see more details about which weights we used for each metric. To be transparent, these are estimates because the weights do not perfectly correspond to the metric. In the employment-to-population ratio case, the perfect weight likely would be adults ages 25-54, but we mimic this with the total population which is likely quite proportionately similar.

Data Quality:

The Upward Mobility Framework metrics provide a data quality indicator with three values:

  • “Strong” indicates the metric is measured with adequate accuracy and sample size.
  • “Marginal” indicates that there are known shortcomings of the data for this metric, and the metric should be used with caution.
  • “Weak” indicates that although the metric could be computed, Urban researchers have serious concerns about how accurately it is measured for this community. They recommend seeking more local data sources for this metric.

The majority of the Mobility Metrics are “Strong.” For Fresno County, CA, the following metrics are not deemed “Strong”:

  • Access to Preschool: Share of 3- to 4-year-old Black, Non-Hispanic children enrolled in nursery school or preschool (weak)
  • Preparation for College: Share of Black, Non-Hispanic 19- and 20-year-olds with a high school degree (weak)

The following metrics for Fresno City, CA are not “Strong”:

  • Wealth-Building Opportunities: Percentage point difference in the share of a community’s home values held by a racial or ethnic group and the share of households of the same group (marginal)
  • Social Capital: Ratio of Facebook friends with higher socioeconomic status to Facebook friends with lower socioeconomic status (marginal)

For each of these metrics, we provide a warning in the notes section.

This dashboard provides alternative sources of data for most of these metrics. The California Department of Education data provides a supplemental source of information to assess educational quality in Fresno County, and the Mobility Metrics include two social capital metrics.

Suggested Citation:

Urban Institute’s Upward Mobility Framework Mobility Metrics: Williams et al. 2023. Boosting Upward Mobility: Metrics to Inform Local Action 1.0. Accessible from https://datacatalog.urban.org/dataset/boosting-upward-mobility-metrics-inform-local-action-10. Data originally sourced from various sources, developed at the Urban Institute, and made available under the ODC-BY 1.0 Attribution License.

Other Resources:

To learn more about data privacy and data equity and to see an example of different statistical disclosure techniques, see this dashboard developed by Urban Institute data scientists.

In conjunction with researchers and technical assistance experts at the Urban Institute, eight city or county governments put together Mobility Action Plans using the Mobility Metrics. They can serve as examples of how local governments use these data to increase upward mobility and racial equity.

This dashboard was built in Quarto using the R programming language. To learn more about Quarto, you can read its extensive documentation page. The best place to learn more about R is R for Data Science (2e), though there are many excellent resources.

This dashboard is a collaboration between the Central Valley Community Foundation and the Urban Institute.