Healthy individuals form the foundation of strong and healthy families. Individual chronic health problems or other serious health concerns can constrain not only a person’s time, energy, and resources, but also those of the whole household. Good and stable health helps people surmount life’s challenges, excel in school and on the job, and fully participate in their communities. Conversely, poor health and debilitating health conditions rob individuals of a sense of control over their lives and potentially of the dignity that comes from being able to fully participate in society.
To see more information about these predictors, see our updated report, Boosting Upward Mobility: Metrics to Inform Local Action, Second Edition. For more insight into the decisions behind updates to the predictors and metrics, see that paper’s accompanying Technical Report.
PREDICTORS
Access to Health Services
Neonatal Health
Environmental quality
Safety from trauma
Family Stability
The Working Group determined that family structure and stability is a strong predictor of mobility from poverty. Research finds that growing up in a household with two married parents is a powerful predictor of intergenerational upward mobility. However, considerable debate exists among researchers and experts, including within the Working Group, about whether two married parents reflect demographic reality for many households or whether other structures may provide equally effective stability and support. Further, the Working Group was more interested in the concept of family stability over structure, but measures of stability were not widely available. Despite this debate, the Working Group consistently acknowledged the importance of family structure and stability on child well-being and mobility. Further, the security of a stable, long-term relationship with a partner contributes to adults’ security and mobility.
Metric: Share of Children in Various Family Living Arrangements
The share of children living in each of six mutually exclusive arrangements (which sum to 100 percent): two married biological or adoptive parents; one biological or adoptive parent and that parent’s current spouse or partner; one biological or adoptive parent and at least one other adult; one biological or adoptive parent; at least two adults, but no parent; all other arrangements.
Validity: Children’s living arrangements are recorded in household rosters used in several federal datasets.
Availability: Data on living arrangements are available from the Census Bureau’s American Community Survey and Public Use Microdata Sample.
Frequency: New data for the metric are available annually. For subgroup analyses in less populated areas, several years of data may need to be pooled to obtain reliable estimates.
Geography: Data are available at the county and metropolitan levels.
Consistency: Data on children’s living arrangements is measured the same way across all geographies and over time in the American Community Survey.
Subgroups: The metric can be disaggregated by race or ethnicity, gender, and other demographic factors. For less populated areas and for certain demographic groups, multiple years of data may need to be pooled to obtain reliable estimates.
Limitations: A large body of evidence suggests that both the presence and relationship of parents directly relates to children’s mobility. Ideally, the metric would directly reflect the continuous presence of loving adults in a child’s life, but data to construct such a measure are not consistently available at the city or county level. We recommended a metric detailing children’s living arrangements. Further, research shows that the influence of growing up in a single-parent household on later economic outcomes has been diminishing over the past few decades, and some research suggests that the strength of the relationship between married parents and child outcomes is much stronger for white children than for children of color. Like all metrics based on the characteristics of people living in an area, it can change because of residential mobility.
Overall Health
Research shows that self-rated health provides an accurate measure of a person's overall health, and it is a strong predictor of later-life outcomes. Good health helps people surmount life’s challenges and excel in school and on the job. When people’s health is compromised, their overall well-being and their personal autonomy are compromised.
Metric: Share of adults who rate their own and their children’s health as good or excellent
This metric is measured through one question that asks “How would you rate your health?” and has respondents answer along a five-point Likert scale of “very poor,” “poor,” “fair,” “good,” and “excellent.” The share of people who respond “good” or “excellent” constitutes this metric.
Validity: Asking people to rate their own health provides one of the most reliable measures of food security and other health problems (including mortality) and remains a significant predictor even after controlling for other demonstrated health-related issues and socioeconomic status. Though the research focuses on mortality rather than mobility, we argue that good health is a condition that supports autonomy and promotes mobility.
Availability: This information is not available widely enough in existing data sources to provide coverage at the local level across many geographies. Data may be available for limited geographies through the Centers for Disease Control and Prevention’s Behavioral Risk Factor Surveillance System.
Frequency: The frequency of how often data for the metric would be collected would depend upon local data collection efforts, but we recommend regular follow-up data collection at least every two years.
Geography: The level of geography that the metric would represent (e.g., county, city, or zip code) would depend on the sampling frame, stratification, and the number of people ultimately surveyed to obtain sufficient power for the survey.
Consistency: Self-rated health can be consistently defined and determined over time. The degree of consistency in this metric across different places will vary with the extensiveness of the survey design and number of people surveyed in each place.
Subgroups: Like geography, the range of subgroups represented and the ability to compare subgroups (e.g., by age, race or ethnicity, and gender) would depend on the sampling frame, stratification, and the number of people surveyed.
Limitations: A primary limitation is that these data will need to be collected directly by communities. Communities will need to identify through which vehicles data can be gathered. Further, this metric can be sensitive to residential mobility if the same people cannot be followed over time. A community transitioning out residents of poorer health in favor of those who are healthier may appear to be improving the overall health of the community. Therefore, it is important to collect these data along with demographic characteristics to ensure improved health is felt by people of all races and socioeconomic backgrounds.
Access to Health Services
Access to and utilization of health services can help ensure children receive basic care through critical formative years and obtain needed prescriptions and that adults can obtain early screenings for diseases so as to enhance the likelihood of effective treatment. A lack of regular medical care can compromise one’s short- and long-term health and have negative effects on later life outcomes. Access to and utilization of health services leads to improved physical health, which promotes power and autonomy
Metric: Health professional shortage area (HPSA) ranking for primary care providers
This metric denotes whether a geography has a shortage of primary care providers based on four elements: weighted population-to-provider ratio, share of individuals with incomes below 100 percent of the federal poverty level, infant health index, and travel time or distance to the nearest source of nondesignated accessible care. The Division of Policy and Shortage Designation through the US Department of Health and Human Services calculates a score between 0 to 25 for primary care HPSAs; the higher the score, the greater the shortage.
Validity: This metric is defined and established by the US Department of Health and Human Services.
Availability: Data for this metric are nationally available, are identified at the facility level, and can be aggregated by state and county through the US Department of Health and Human Services.
Frequency: Data are updated daily. Geographies and facilities are evaluated for their shortage status at the facility’s request, but all have been evaluated at some time in the past four years.
Geography: HPSAs are designated as a geography, population, or facility. We focus on the geography for the purpose of getting a county-level HPSA flag. HPSAs are available by state, county, or service area. Although data are not available at the neighborhood level, facility addresses can be geocoded and therefore could be used to derive a value for the neighborhood. Data are available for urban and rural areas.
Consistency: HPSA status can be measured the same way over time and across geographies.
Subgroups: This metric can be disaggregated by low-income population and by low-income members of the following groups: migrant farm or seasonal workers, Medicaid-eligible individuals, people experiencing homelessness, and Native Americans.
Limitations: Even though HPSA status is a characteristic of the local area, some elements used to determine the designation are based on the characteristics of the local population. As such, changing residential mobility patterns may influence this metric. Because of data quality limitations at the geographic level, we present the result in the HPSA score (0 to 25) as either a yes (1) or no (0) value.
Neonatal Health
Strong evidence demonstrates that neonatal health has lasting effects throughout the life course. Poor childhood health has short-term effects on educational attainment and long-term negative effects on adult physical health and mental health, which in turn can affect employment opportunities and wages.
Metric: Share of low-weight births
A child born weighing less than 5 pounds 8 ounces (about 2,500 grams) is considered to have a low birth weight. Children with low birth weight are at elevated risk for health conditions and infant mortality. This metric looks at the share of low birth weight babies out of all births.
Validity: This metric is the standard currently used by the Centers for Disease Control and Prevention (CDC) as part of its national assessment on health among infants.
Availability: Data on the share of children born with low birth weights are nationally available through the CDC’s National Center for Health Statistics, Division of Vital Statistics.
Frequency: New data for the metric are available annually.
Geography: County-level estimates are available through public-use microdata files provided by the National Center for Health Statistics as well as through other data collection efforts, such as the Kids Count Data Center or the CDC WONDER system.
Consistency: Medical advances have improved the outcomes for low birth weight babies, so this metric may change in the future. However, it has been consistently used for decades as a metric for neonatal health.
Subgroups: The share of children born with low birth weights can be disaggregated by race or ethnicity and mother’s age.
Limitations: Data are not readily available at lower levels of geography, such as neighborhoods, where disparities by race and socioeconomic status within a city are most notable. Large numbers of women with risky pregnancies moving in or out of a jurisdiction could influence this metric. Counties with populations under 100,000 persons based on the decennial census are pooled into “Unidentified Counties” in the CDC WONDER data.
Environmental Quality
Living in communities with poor environmental quality—including hazardous wastes and other toxins, ambient and indoor air pollutants, poor water quality, and high levels of ambient noise—can place people at higher risk of health complications that impose costs and may undermine school or work performance. Exposure to hazardous environmental conditions can have negative implications for the health of residents, especially those who are more susceptible to health problems, such as children and elderly people.
Metric: Air quality index
The air quality index is an index that summarizes potential exposure to harmful toxins at a neighborhood level. The index is a linear combination of standardized Environmental Protection Agency (EPA) estimates of carcinogenic, respiratory, and neurological hazards in the air measured at the census-tract level. Values are inverted and then percentile ranked nationally and range from 0 to 100. The higher the index value, the less exposure to toxins harmful to human health. The index tells how clean or polluted the air is and what associated health effects might be a concern in the community. The air quality index includes five major air pollutants regulated by the Clean Air Act: ground-level ozone, particle pollution (also known as particulate matter), carbon monoxide, sulfur dioxide, and nitrogen dioxide.
Validity: EPA scientists and researchers link levels of air pollutants to health effects that can manifest within a few hours or days after breathing polluted air. For each of the pollutants, the EPA has established national air quality standards to protect public health.
Consistency: Levels of air pollutants can be consistently measured over time and space.
Availability: Air quality systems data are produced by the EPA and are publicly available.
Frequency: Air quality information from the National Air Toxics Assessment data were updated every three years since 1996, but the most recent update was in 2014.
Geography: This metric is available at the neighborhood (census tract) level. Values can be averaged at higher levels of geography. For example, one can calculate a population-weighted average value among all census tracts in a county to determine a county-level value.
Subgroups: This metric can be disaggregated by subarea when used in combination with the ACS to identify the racial or ethnic composition of neighborhoods (census tracts) with different levels of air quality. We distinguish census tracts that are majority nonwhite, that have no majority race or ethnicity, and that are majority non-Hispanic white. We define a majority as at least 60 percent of residents.
Limitations: Data are not updated with enough frequency. Other data sources can offer information annually or daily, but they are at higher levels of geography and could not be disaggregated by subgroup.
Safety from Trauma
Experiencing trauma can have significant negative consequences that persist long after the trauma has ended. Early exposure to trauma undermines brain development, socioemotional development, ability to develop secure attachments, emotion regulation, sense of agency, and self-efficacy. Children who have been exposed to trauma, particularly multiple traumas, are at risk for developing emotional and behavioral problems, such as depression, anxiety, dissociation, post-traumatic stress disorder, low self-esteem, hopelessness, withdrawn behaviors, and impaired peer relationships. The effects of trauma on adults can range from subtle to destructive and can manifest in diminished cognitive ability as well as worsening physical and mental health. More broadly, “community trauma” affects social groups or neighborhoods long subjected to interpersonal violence, structural violence, and historical harms. Community and systems trauma, like individual trauma, affects cognitive decisionmaking that can lead to reduced civic engagement and weakened social networks and social cohesion; it can also adversely influence how individuals view themselves, their capabilities, and their social status.
Metric: Adverse Childhood Experiences scale
The Adverse Childhood Experiences (ACE) scale is a survey-based scale comprising 17 items that measure childhood exposure to trauma such as psychological, physical, or sexual abuse; neglect; mental illness; domestic violence; divorce; and having a parent in prison. Each question relates to an experience growing up during the first 18 years of life and solicits a “yes” or “no” response. More “yes” answers mean that the respondent has gone through more types of childhood trauma. To construct this metric, community leaders would need to collect data locally.
Validity: A significant body of research finds that that higher ACE scores, indicating more childhood trauma, correlate with several outcomes related to lower mobility. Higher ACE scores are associated with poor performance at work and financial problems as adults as well as higher rates of chronic disease, depression, and lower health-related quality of life as adults. The metric may be most useful as an indicator of a need for trauma-informed care at the community level.
Availability: This information is not available widely enough in existing data sources to provide coverage at the local level across many geographies.
Frequency: The frequency of how often data for the metric would be collected would depend upon local data collection efforts, but we recommend regular follow-up data collection at least every two years.
Geography: The level of geography that the metric would represent (e.g., county, city, or zip code) would depend on the sampling frame, stratification, and the number of people ultimately surveyed to obtain sufficient power for the survey.
Consistency: The degree of consistency in this metric across different places will vary with the extensiveness of the survey design and number of people surveyed in each place. Ideally, the metric would be consistent across some base level of geography (such as the city or county), but some places would likely have more extensive coverage of residents who have completed the ACE scale.
Subgroups: Like geography, the range of subgroups represented and the ability to compare subgroups (people of color and white people; married and single people; people with children and those without) would depend on the sampling frame, stratification, and the number of people surveyed.
Limitations: The key limitation is the need for local partners to collect representative data. Original data collection may also make benchmarking against other places challenging, depending on the scale and representativeness of data collection in other places. This metric may be sensitive to residential mobility because those reporting experiences of trauma in the past may have lived in a different jurisdiction at the time.
