Mark Mather
Associate Vice President, U.S. Programs
AUTHORS
Paola Scommegna
Contributing Senior Writer
As the large Baby Boom generation enters advanced ages, more family members and other unpaid helpers are stepping in as caregivers. In just over a decade, the number of family caregivers regularly assisting older adults with daily activities at home grew by 32%, increasing from 18.2 million to 24.1 million between 2011 and 2022.1
While the caregiving cadre has grown, who’s getting care has also changed. Older Americans receiving family care are younger, better educated, and less likely to have dementia than they were in 2011, report Jennifer L. Wolff of Johns Hopkins University, independent consultant Jennifer C. Cornman, and Vicki A. Freedman of the University of Michigan.
The increase in family caregiving partly reflects the rising share of older adults with multiple chronic conditions, such as heart disease, hypertension, stroke, and cancer. And while the share of older adults with dementia has declined, unpaid caregivers average twice as many hours each week caring for people with dementia than without dementia (about 31 hours versus 14), Wolff and team found (see Figure 1).
In addition, a new study estimates that the number of new dementia cases will double over the next 40 years as the population ages—setting the stage for more demands on dementia caregivers and more changes to the caregiving landscape.
“Understanding the changing composition and experiences of family caregiving has never been more important, but it is challenging to assess,” the researchers write. “[It] requires consistent measurement for well-characterized, generalizable samples of people who receive and provide help.”
The nationally representative National Study of Caregiving and the National Health and Aging Trends Study offer important insights. The two studies provide a snapshot of the family caregivers that help Americans ages 65+ who live in the community (i.e., at home or with a relative) or in a residential care setting other than a skilled nursing facility, such as an assisted or independent living facility, a personal care home, or a continuing care retirement community.
Family caregivers include relatives and unpaid helpers, like neighbors and friends, who assist with personal care tasks like bathing and dressing; mobility tasks like getting out of bed and getting around the house; and household activities such as laundry, food preparation, shopping, and managing money.
Dementia Caregivers See Increasing Demands on Their Time, Employment Woes
On average, the time that family caregivers spent helping older adults with dementia increased by almost 50% over the decade, rising from 21.4 hours per week in 2011 to 31.0 hours in 2022. By contrast, time spent assisting older adults without dementia fell from 15.3 hours a week in 2011 to 13.9 hours in 2022 (Figure 1).
Figure 1: Family Caregivers Are Spending 50% More Time Caring for Older Adults With Dementia
Average Weekly Family Caregiving Hours, by the Dementia Status of Older Adults (65+), 2011 and 2022
Source: Jennifer L. Wolff, Jennifer C. Cornman, and Vicki A. Freedman, “The Number of Family Caregivers Helping Older US Adults Increased From 18 Million to 24 Million, 2011–22,” Health Affairs 44, no. 2 (2025): 189-95.
People caring for older adults with dementia have high—and increasing—demands on their time. More than half (51.7%) of dementia caregivers lived with the person they were caring for in 2022, up from 39.4% in 2011, Wolff and team report. And the share able to hold jobs—outside their caregiving work—dropped from 42.5% to 34.6% during the same period.
Among caregivers with formal jobs, the share who reported challenges with their employment—including working fewer hours or being less productive—increased over the decade, regardless of whether they cared for someone for dementia.
“Challenges are exacerbated when caregivers are in poor health themselves; have a lack of choice in assuming the caregiving role; and, for the substantial proportion of family caregivers who are employed, work in low-wage jobs with limited flexibility,” the researchers note.
Care Recipients Are Mainly Older Women, but the Share of Men Receiving Care Is Growing
Which older Americans get family care? As in the past, they tend to be female, non-Hispanic white women who are married or widowed. But growing numbers of family care recipients are male and have some college education. More are also separated and divorced compared to 2011, reflecting national trends.
Adult Children Continue to Care for Their Parents
Who’s providing care? Family caregivers continue to be largely female and married, and most report being in good health. In 2022, adult children still made up the largest share of family caregivers for older adults, at 40.7%, but this represents a significant decline since 2011 (Figure 2).
Figure 2: The Share of Adult Children Caring for Older Relatives Has Declined
Relationship of Family Caregivers to Adults Ages 65 and Older Receiving Care, 2011 and 2022
Source: Jennifer L. Wolff, Jennifer C. Cornman, and Vicki A. Freedman, “The Number of Family Caregivers Helping Older US Adults Increased From 18 Million to 24 Million, 2011–22,” Health Affairs 44, no. 2 (2025): 189-95.
In 2022, adult children accounted for about half (49.1%) of family caregivers for older adults with dementia, compared with 38.4% of caregivers for those without dementia. Just 17.7% of family caregivers for older adults with dementia were spouses, compared with 24.5% of family caregivers for people without dementia.
A sizeable share of family caregivers (17.0%) had children under age 18 at home in 2022, and 6% to 13% viewed their care responsibilities for older adults as a source of financial, physical, or emotional difficulty.
Despite these challenges, the researchers report a decline in the use of support groups (4.1% to 2.5%) and respite services (12.9% to 9.3%) between 2011 and 2022.
Trends and Policy Implications
Many caregivers face extraordinary demands and should be the focus of support services, Wolff and colleagues say. They single out those caring for older adults with dementia or nearing the end of life, as well as caregivers “from racial and ethnic minority groups who are more likely to assist people who have extensive care needs in circumstances that involve scare economic resources.”
Family care needs are likely to rise as the number of U.S. adults ages 85 and older is projected to triple by 2050. The researchers note that the number of family caregivers rose even as the long-term use of skilled nursing facilities among older Americans dropped and community living increased. The challenges these caregivers continue to face is “sobering,” they write, including competing time demands from work and child care while spending an average of 17 hours per week on care. In addition, about 1 in 8 family caregivers report financial, physical, or emotional difficulties related to their caregiving roles, percentages that were largely unchanged over the 11 years examined.
Policies and programs to help reduce the financial, physical, and emotional burden of caregiving exist, but do not represent a coherent strategy, the researchers say. “Local, state, and federal policies are a patchwork that is uneven in availability and largely symbolic in magnitude,” they argue. Addressing the needs of family caregivers will require a “cohesive framework in support of the care economy.”
References
1. Jennifer L. Wolff, Jennifer C. Cornman, and Vicki A. Freedman, “The Number of Family Caregivers Helping Older US Adults Increased From 18 Million to 24 Million, 2011–22,” Health Affairs 44, no. 2 (2025): 189-95.
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AUTHORS
Paola Scommegna
Contributing Senior Writer
Mark Mather
Associate Vice President, U.S. Programs
Diana Elliott
Senior Vice President, Programs
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Older Americans with strong social connections are healthier and live longer than their socially isolated peers. Increasingly, researchers are finding that the components of good health are not only the absence of mental disorders and physical disease but also the presence of robust social relationships.
This brief explores recent research probing the dynamics of social connection and health supported by the National Institute on Aging. The findings point to myriad ways in which social ties bolster health—from slowing aging and boosting cancer-fighting hormones to preventing depression and protecting memory. Health policymakers and program planners can use this evidence to inform a variety of interventions—particularly those aimed at reducing social isolation in vulnerable groups—to support longer and healthier lives for older Americans.
Strong Social Ties Slow Aging, Lower Risk of Death
Studies have shown that both the quality and quantity of our social ties shape our mental and physical health, health behaviors, and mortality risk. Researchers are studying multiple aspects of our social lives—from the levels of social support we receive to our activities and the strength of our social networks—to understand how they link to health outcomes.
Healthy, supportive relationships with family and friends may slow aging, concluded a research team from the University of California, Los Angeles (UCLA) and the University of Southern California (USC).1 According to their DNA, older adults with the most supportive relationships with spouses, adult children, other family members, and friends were aging one to two years slower than those who lacked such ties, they found. The pace of such aging is important—rapid epigenetic aging at younger chronological ages can contribute to the early onset of chronic disease and disability and premature death.
The healthiest older adults had strong social connections and just a 4% risk of dying within five years.
Older adults with the most supportive relationships were aging one to two years slower than those who lacked such ties.
Social Connections May Improve the Well-Being of Patients With Cancer and Other Health Conditions
More new research poses that social connections may also improve the well-being of cancer patients by boosting protective hormones.
A research team from six major universities found that in ovarian cancer patients, social support was associated with higher levels of oxytocin—a hormone linked to some protection against cancer.3 Specifically, having a more positive outlook, a sense of purpose, and a role in caring for others at the time of cancer surgery were associated with higher oxytocin levels. (However, a person’s perceived closeness with others was not related to higher oxytocin levels.)
“Nurturance is consistent with the oxytocin-focused ‘tend and befriend’ hypothesis of female coping and stress response,” wrote the team, led by Michaela G. Cuneo at the University of Iowa. Thus, even though this research is in the early stages, feeling needed by others could have protective health effects for women with ovarian cancer.
Similarly, another team of researchers at the National Cancer Institute and the University of Wisconsin–Madison found that strong, supportive relationships between cancer patients and their caregivers were associated with better self-reported health for both parties.4 This was especially the case for those dealing with lung cancer, where social support was linked to better self-reported health 12 months after diagnosis. Dannielle E. Kelley and coauthors speculate that there may be a beneficial “partner effect” for lung cancer that can counter internalized and social stigmas associated with the disease for former smokers.
Strong, supportive relationships between cancer patients and their caregivers were associated with better self-reported health for both parties.
A Lifetime of Experiences Shape Social Connections at Older Ages
Some groups of people are more likely to be socially isolated than others, report Debra Umberson of the University of Texas at Austin and Rachel Donnelly of Vanderbilt University.9 Older married women may become socially isolated when a spouse needs round-the-clock care, while men who either never marry or divorce may begin experiencing social isolation in young adulthood.
In addition, non-Hispanic Black and Hispanic older adults are more likely to experience social isolation than non-Hispanic older white adults, they found (see Figure 1). Black and Hispanic Americans’ social isolation could be related to the impact of lifetime discrimination and financial stress, the researchers suggest. Using HRS data, the study measured isolation among adults ages 50 and older based on whether they are married or cohabiting, participate in volunteer activities, and have contact with parents, children, and neighbors.
Figure 1. Older Non-Hispanic Black Americans Experience Higher Levels of Social Isolation Than Other Groups
Mean Levels of Social Isolation Among U.S. Adults Ages 50 and Older in the Health and Retirement Study, by Race/Ethnicity
Source: Debra Umberson and Rachel Donnelly, “Social Isolation: An Unequally Distributed Health Hazard,” Annual Review of Sociology 49, no. 1 (2023): 379-99.
Another study out of Johns Hopkins University estimated that nearly one in four (24%) of older Americans living in the community are socially isolated, and one in 25 (4%) are severely isolated.10 To measure isolation, they examined participants’ living arrangements, religious attendance, social activities, and the number of people they spoke with about important matters. The study used data from the National Health and Aging Trends Study, which includes a nationally representative sample of Medicare beneficiaries ages 65 and older.
Being unmarried, male, and having low education and income levels increased the odds of being socially isolated, according to the analysis, led by Thomas Cudjoe. Specifically, men were four times as likely as women to be severely isolated, while people with annual incomes below $30,000 were twice as likely as people with incomes over $60,000 to be severely isolated. These findings offer “easily identifiable” factors to help program planners target those most at risk, the researchers wrote, noting that living arrangements, discussion networks, and social activities can all be modified to improve social connections.
Calling social isolation “an unequally distributed health hazard,” Umberson and Donnelly urge future researchers to undertake “a systematic assessment of social conditions that foster isolation over the life course” to better understand the root causes and identify ways to reduce isolation among those most at risk.
Being unmarried, male, and having low education and income levels increased the odds of being socially isolated.
Social Networks May Protect Mental Health and Prevent Cognitive Decline
Older adults’ social networks may protect both their mental health and cognitive abilities. Getting help with daily activities may be an important reason why—since many older adults need assistance bathing, getting in and out of bed, and doing other tasks, some built-in social interaction can accompany aging. But feelings of closeness and companionship may also help stave off memory loss, loneliness, and depression—and may matter as much or more than geographic proximity or number of family or friends, new research shows.
To try to understand the importance of relationship quality, Sarah Patterson of the University of Michigan and Rachel Margolis of the University of Western Ontario looked at four groups of older adults with different types of family connections: those who were geographically and emotionally close with family; those who were kinless and without a partner or children; those who were distanced and lived far from family; and those who were disconnected and had no family members in their social network or did not know where they lived.14
The closeness of the relationships—especially with family—buffered loneliness.
“We were interested in understanding how much the presence of family ties matters for older adults’ well-being but also in measuring the quality of those relationship ties,” said Patterson.15
They found that older adults who lived near family members and discussed important concerns with them were less likely to report unmet need for help with daily activities than the other groups (see Figure 2). Meanwhile, those who reported no partner or family or disconnection had the poorest mental health and socialized less often—even less than those who lived far away from their family.
Figure 2. Older Adults Who Are Close to Family Get More Help With Activities
Share of adults ages 70 and older reporting unmet need for help with activities by type of family connections, 2015–2019
Source: Sarah E Patterson and Rachel Margolis, “Family Ties and Older Adult Well-Being: Incorporating Social Networks and Proximity,” The Journals of Gerontology: Series B, Volume 78, no. 12 (December 2023): 2080–89.
The findings suggest that the presence and strength of family ties matter for older adults’ mental health. “As families continue to evolve, researchers should strive to capture the size and shape of family networks, as well as the level of connection that older adults have with those kin,” the authors note.
In fact, social connections can also produce distress. Stephanie T. Child and Leora E. Lawton of UC Berkeley found that social companionship and emergency help mattered most to older adults, whereas having more people from whom they sought advice was related to more psychological distress.16 The findings suggest that mental well-being may be enhanced by enjoyable and helpful relationships, while those that are more demanding may detract from it. Data are from the UC Berkeley Social Networks Study (UCNets), which includes a locally representative sample from across the San Francisco Bay Area in California.
Companionship and emergency help mattered most to older adults.
Marriage Has Diverse Effects on Older Adults’ Health
Several recent studies build on the well-established link between marriage and better physical and psychological health in old age, particularly for men. They examine some of the ways marriage may benefit health as well as the connections between marriage and other forms of social interaction.
New research finds that marriage can help men be less socially isolated throughout their lives. Umberson of University of Texas at Austin, Zhiyong Lin of University of Texas at San Antonio, and Hyungmin Cha of USC show that men tend to be more isolated in adolescence and young adulthood, while women tend to experience isolation in later life.19 Their analysis of HRS data shows that levels of social isolation increase with age for both men and women.
But gender patterns differ by marital history (see Figure 3). Among older adults in stable marriages, women are less isolated than men until age 60, but by age 68, men are slightly less isolated than women. This gender gap shrinks at older ages for those who have experienced marital disruptions, possibly because chronic health issues contribute more to social isolation among women, the authors note.
Figure 3. Social Isolation Increases With Age, but Gender Patterns Differ by Marital History
Age Trajectories of Social Isolation Among Adults Ages 50 and Older, by Gender and Relationship History, 1998-2012
Source: Debra Umberson, Zhiyong Lin, and Hyungmin Cha, “Gender and Social Isolation Across the Life Course,” Journal of Health and Social Behavior 63, no. 3 (2022): 319-35.
There may be an unexpected physiological explanation for some of marriage’s health benefits. Drawing on lessons from primate research and using stool samples from a subset of participants in the long-running Wisconsin Longitudinal Study, researchers affiliated with the University of Wisconsin-Madison find that spouses in self-described close marriages tend to have more diverse and healthful gut microbiota compared with siblings, people without a partner, or married couples in less close relationships.20 Less diverse gut microbiota is related to obesity, cardiac disease, type 2 diabetes, and other inflammatory disorders, Kimberly Dill-McFarland and coauthors note.
Not all marriages are equal when it comes to social support and its potential health effects. Both men and women in same-sex marriages are more likely than those in different-sex marriages to offer concrete support to a spouse in distress, such as taking over chores or giving extra personal time, found Mieke Thomeer of the University of Alabama at Birmingham, Amanda Pollitt of Northern Arizona University, and Umberson.21 The team used a survey of 378 midlife couples ages 35 to 65.
Among older adults in stable marriages, men are less isolated than women.
Good Neighbors (and Neighborhoods) Are Good for Well-Being
Multiple studies have shown that a neighborhood’s physical features—from broken sidewalks and high crime to plentiful parks and low air pollution—are related to older residents’ health and quality of life.25 Not surprisingly, the places older people call home also shape their social connections, thereby influencing both their physical and mental health.
Neighborhood social ties may promote sensory health, a study using NSHAP data shows. Older adults who have more social connections in their neighborhoods report better self-rated vision than those who have fewer connections, find Alyssa Goldman of Boston College and Jayant Pinto of the University of Chicago.26 More social ties may lead to more time spent engaging with people and places outside of the home, protecting visual abilities, the researchers suggest. Good vision is key to older adults’ ability to safely navigate their environment, they add.
For caregivers, social support can counteract the negative effects of living in less-connected neighborhoods. Researchers at the University of California, Davis and the University of Michigan show that neighborhoods with low social cohesion—lacking a sense of community and trust among neighbors—can take a toll on mental health in the absence of social support.27 This is particularly true for dementia caregivers, who face a high risk of depression related to the emotional and physical burden of their work. But dementia caregivers living in neighborhoods with low social cohesion had fewer symptoms of depression if they had family and friends to talk to and help with daily tasks, Oanh Meyer and team found.
Local opportunities for social connection may strengthen social ties.
When faced with traumatic events, older adults with fewer confidants living nearby showed more severe depression symptoms than those with more close friends in their neighborhood.
Even two years of living alone is linked to about a 10% increase in the risk of dementia.
Virtual Interaction Cannot Fully Replace the Health Benefits of Face-to-Face Contact
Phone calls and Zoom or FaceTime gatherings replaced in-person get-togethers for many people during the COVID-19 pandemic shutdowns, but a growing body of research suggests virtual interaction cannot fully replace face-to-face contact. Two recent studies, one led by Namkee Choi at the University of Texas at Austin and the other by Louise Hawkley at NORC at the University of Chicago, show that older adults who had less in-person time with family and friends and more phone calls during the first year of the pandemic were more likely to experience loneliness.32
Phone calls are an important source of social connection for older adults with impaired vision or hearing.
Older adults who had less in-person time with family and friends and more phone calls during the first year of the pandemic were more likely to experience loneliness.
Policy Implications
As many as one in four older Americans are socially isolated and face an increased risk of poor health and early death. The research documented in this report underscores the links between strong social ties and longer, healthier lives. U.S. Surgeon General Vivek H. Murthy has called for making social connectedness a national priority, “the same way we have prioritized other critical public health issues such as tobacco, obesity, and substance use disorders.”36 His recent advisory, Our Epidemic of Loneliness and Isolation, identifies multiple actions based on growing research evidence, including:
- Strengthening social infrastructure: Social ties are not just built by person-to-person interactions, but by the physical elements (parks, libraries, sidewalks, and benches) and the programs and policies in place. Communities can design environments, establish and expand programs, and invest in institutions that bring people together.
- Enacting pro-connection public policies: National, state, local, and tribal governments can play a role in establishing policies for accessible public transportation, paid family leave, and other supports that can enable more connection among communities and families.
Social Isolation and Loneliness Among Older Adults: Opportunities for the Health Care System, a recent report from the National Academies of Sciences, Engineering, and Medicine, details ways health care organizations can address social isolation among older people by better educating their staff to intervene and aligning with other community agencies. Specific recommendations include:
- Partnering directly with ride-sharing programs to help older adults’ get to medical appointments and community events.
- Working with community organizations to integrate social activities and in-person interaction into hospital discharge planning, care coordination, and transitional care planning.
“Our relationships are a source of healing and well-being hiding in plain sight,” Murthy said, “one that can help us live healthier, more fulfilled, and more productive lives.”37
Accessible public transportation can help improve older adults’ health by connecting them to both medial care and social activities.
References
- Kelly E. Rentscher et al., “Social Relationships and Epigenetic Aging in Older Adulthood: Results From the Health and Retirement Study,” Brain, Behavior, and Immunity 114, (2023): 349-59.
- Linda Waite and Yiang Li, “Bringing the Social World Into Our Understanding of Health,” paper presented at the annual meeting of the Population Association of America, Columbus, April 2024.
- Michaela G. Cuneo et al., “Positive Psychosocial Factors and Oxytocin in the Ovarian Tumor Microenvironment,” Psychosomatic Medicine 83, no. 5 (2021): 417-22.
- Dannielle E. Kelley, et al., “Dyadic Associations Between Perceived Social Support and Cancer Patient and Caregiver Health: An Actor-Partner Interdependence Modeling Approach,” Psycho-oncology 28, no. 7 (2019): 1453-60.
- Won-Tak Joo, “Educational Gradient in Social Network Changes at Disease Diagnosis,” Social Science & Medicine 317 (2023): 115626.
- Karen L. Fingerman, et al., “Functional Limitations, Social Integration, and Daily Activities in Late Life,” The Journals of Gerontology: Series B, Psychological Sciences and Social Sciences 76, no. 10 (2021): 1937-47.
- Sophie Mitra, Debra L. Brucker, and Katie M Jajtner, “Wellbeing at Older Ages: Towards an Inclusive and Multidimensional Measure,” Disability and Health Journal 13, no. 4 (2020): 100926.
- Dan Zhang et al. “What Could Interfere with a Good Night’s Sleep? The Risks of Social Isolation, Poor Physical and Psychological Health Among Older Adults in China,” Research on Aging 44, nos. 7-8 (2022): 519-30.
- Debra Umberson and Rachel Donnelly, “Social Isolation: An Unequally Distributed Health Hazard,” Annual Review of Sociology 49, no. 1 (2023): 379-99.
- Thomas K. M. Cudjoe et al., “The Epidemiology of Social Isolation: National Health and Aging Trends Study,” The Journals of Gerontology: Series B, Psychological Sciences and Social Sciences 75, no. 1 (2020): 107-13.
- Kaulie Watson, “Social Isolation Can Begin as Early as Adolescence, Research Shows,” The University of Texas at Austin College of Liberal Arts, July 20, 2023.
- Jordan Weiss, Leora E. Lawton, and Claude S. Fischer, “Life Course Transitions and Changes in Network Ties Among Younger and Older Adults,” Advances in Life Course Research 52 (2022): 100478.
- James Iveniuk, “Social Networks, Role-Relationships, and Personality in Older Adulthood,” The Journals of Gerontology: Series B, Psychological Sciences and Social Sciences, 74, no. 5, July 2019, 815–26.
- Sarah E. Patterson and Rachel Margolis, “Family Ties and Older Adult Well-Being: Incorporating Social Networks and Proximity,” The Journals of Gerontology: Series B Psychological Sciences and Social Sciences, 78, no. 12 (2023): 2080-9.
- Jon Meerdink, “New Paper Explores the Impact of Family Ties on Older Adults,” University of Michigan Institute for Social Research, November 15, 2023.
- Stephanie T. Child and Leora E. Lawton, “Personal Networks and Associations With Psychological Distress Among Young and Older Adults,” Social Science & Medicine 246 (2020): 112714.
- Stephanie T. Child and Leora Lawton, “Loneliness and Social Isolation Among Young and Late Middle-Age Adults: Associations With Personal Networks and Social Participation,” Aging & Mental Health 23, no. 2 (2019): 196-204.
- Abbey M. Hamlin et al., “Social Engagement and Its Links to Cognition Differ Across Non-Hispanic Black and White Older Adults,” Neuropsychology 36, no. 7 (2022): 640-50.
- Debra Umberson, Zhiyong Lin, and Hyungmin Cha, “Gender and Social Isolation Across the Life Course,” Journal of Health and Social behavior 63, no. 3 (2022): 319-35.
- Kimberly A. Dill-McFarland et al., “Close Social Relationships Correlate With Human Gut Microbiota Composition,” Scientific Reports 9, no. 1 (2019): 703.
- Mieke Beth Thomeer, Amanda Pollitt, and Debra Umberson, “Support in Response to a Spouse’s Distress: Comparing Women and Men in Same-Sex and Different-Sex Marriages,” Journal of Social and Personal Relationships 38, no. 5 (2021): 1513-34.
- Shira Offer, “They Drive Me Crazy: Difficult Social Ties and Subjective Well-Being,” Journal of Health and Social Behavior 61, no. 4 (2020): 418-36.
- Claudia Recksiedler and Robert S. Stawski, “Marital Transitions and Depressive Symptoms Among Older Adults: Examining Educational Differences,” Gerontology 65, no. 4 (2019): 407-18.
- James Iveniuk, Peter Donnelly, and Louise Hawkley, “The Death of Confidants and Changes in Older Adults’ Social Lives,” Research on Aging 42, nos. 7-8 (2020): 236-46.
- Mark Mather and Paola Scommegna, “How Neighborhoods Affect the Health and Well-Being of Older Americans,” Today’s Research on Aging, no. 35 (2017).
- Alyssa Goldman and Jayant Pinto, “Sensory Health Among Older Adults in the United States: A Neighborhood Context Approach,” The Journals of Gerontology: Series B; Psychological Sciences and Social Sciences, 79, no. 5, (May).
- Oanh L. Meyer et al., “Neighborhood Characteristics and Caregiver Depressive Symptoms in the National Study of Caregiving,” Journal of Aging and Health 34, no. 6-8 (2022): 1005-15.
- Keunbok Lee, “Different Discussion Partners and Their Effect on Depression Among Older Adults,” Social Sciences 10, no. 6 (2021): 215.
- Xue Zhang, Danielle Rhubart, and Shannon Monnat, “Social Infrastructure Availability and Suicide Rates Among Working-Age Adults in the United States,” Socius 10 (2024).
- Benjamin A Shaw, Tse-Chuan Yang, and Seulki Kim, “Living Alone During Old Age and the Risk of Dementia: Assessing the Cumulative Risk of Living Alone,” The Journals of Gerontology: Series B, Psychological Sciences and Social Sciences 78, no. 2 (2023): 293-301.
- Yeon Jin Choi, Jennifer A. Ailshire, and Eileen M. Crimmins, “Living Alone, Social Networks in Neighbourhoods, and Daily Fruit and Vegetable Consumption Among Middle-Aged and Older Adults in the USA,” Public Health Nutrition 23,18 (2020): 3315-23.
- Namkee G. Choi et al., “COVID-19 and Loneliness Among Older Adults: Associations With Mode of Family/Friend Contacts and Social Participation,” Clinical Gerontologist 45, no. 2 (2022): 390-402 and Louise C. Hawkley et al., “Can Remote Social Contact Replace In-Person Contact to Protect Mental Health Among Older Adults?” Journal of the American Geriatrics Society 69, no. 11 (2021): 3063-5.
- Amanda Zhang et al., “Can Digital Communication Protect Against Depression for Older Adults With Hearing and Vision Impairment During COVID-19?” The Journals of Gerontology: Series B, Psychological Sciences and Social Sciences 78, no. 4 (2023): 629-38.
- Yijung K. Kim, and Karen L. Fingerman, “Daily Social Media Use, Social Ties, and Emotional Well-Being in Later Life,” Journal of Social and Personal Relationships 39, no. 6 (2022): 1794-1813.
- Jeffrey G. Snodgrass et al. “Social Connection and Gene Regulation During the COVID-19 Pandemic: Divergent Patterns for Online and In-Person Interaction,” Psychoneuroendocrinology 144 (2022): 105885.
- U.S. Department of Health and Human Services, “New Surgeon General Advisory Raises Alarm About the Devastating Impact of the Epidemic of Loneliness and Isolation in the United States,” May 3, 2023.
- U.S. Department of Health and Human Services, “New Surgeon General Advisory Raises Alarm About the Devastating Impact of the Epidemic of Loneliness and Isolation in the United States.”
Author
Beth Jarosz
Senior Program Director
U.S. society and policy disproportionately burden women with unpaid (or underpaid) caregiving responsibilities. In her new book “Holding It Together: How Women Became America’s Safety Net,” author Jessica Calarco (University of Wisconsin-Madison) draws on five years of research to show how thinly stretched American women are.
In June 2024, PRB convened an expert panel to discuss Calarco’s key findings and their implications for reproductive health care policy and explore additional research on abortion, contraception, fertility, gender, and motherhood. Calarco was joined by Tiffany Green (University of Wisconsin-Madison), Karen Benjamin Guzzo (University of North Carolina at Chapel Hill), and Jocelyn Foye (The Womxn Project), and nearly 200 attendees.
Women in the United States are often the primary caregivers for children, older parents, and others, even when they have full-time jobs, and even when men could share in the care work. This gender gap in caregiving can have significant negative economic consequences for women, especially when that work is unpaid or underpaid. Coupled with complex and inaccessible social safety net programs, the caregiving burden can limit women’s career opportunities, reduce women’s earnings potential, and increase financial hardship for women and their families.
Systemic inequality and discrimination have taken a financial, physical, and emotional toll on marginalized groups, such as Black women, Indigenous women, Hispanic and Latina women, disabled women, and transgender women. For example, Dr. Green shared that the practice of birth cost recovery (also referred to as the “birth tax”) in Wisconsin disproportionately affects Black families and creates financial burdens for fathers, hindering their ability to participate in their children’s lives.
U.S. policies have historically burdened women with caregiving responsibilities and offered limited protections compared with other nations. When asked “What can be done?”, panelists offered suggestions based on their research:
- Invest in policies that provide adequate support for caregivers, including affordable childcare, paid family leave, and health care.
- Design policies and programs to address the specific needs of women of color, who often face additional barriers to economic opportunity and social mobility.
- Invest in women’s health, including reproductive health care, which can improve women’s well-being and reduce the burden of caregiving.
- Challenge traditional gender roles and expectations that place the primary burden of caregiving on women.
- Conduct additional research to better understand the experiences of caregivers and to inform policy decisions.
By addressing the systemic challenges faced by caregivers, we can create a more economically vibrant, equitable, and healthy society.
The first PRB Book Talk webinar discusses the book, Holding It Together: How Women Became America’s Safety Net, with author and sociologist Jessica Calarco. In Holding It Together, Calarco (University of Wisconsin-Madison) draws on five years of research to show how U.S. society and policy disproportionately burden women with caregiving responsibilities. With an expert panel, we discuss Calarco’s key findings and their implications for reproductive health care policy and explore additional research on abortion, contraception, fertility, gender, and motherhood.
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The United States is facing persistent labor shortages, limiting businesses both large and small and spanning all industries. What’s driving these shortages, and what can be done to address them? On Sept. 5, 2024, PRB and the Critical Labor Coalition discussed the latest data behind the shrinking workforce and explore potential policy solutions.
Panelists include:
- R. Alexander Acosta, Former U.S. Secretary of Labor
- Misty Chally, CEO of Capitol Solutions, LLC, and head of the Critical Labor Coalition
- Diana Elliott, Vice President of Programs, PRB
Transcript
Diana Elliott: Great. Thank you all so much for joining us today. My name is Diana Elliott, and I’m the vice president of programs at Population Reference Bureau, or PRB. PRB is a not for profit and nonpartisan research organization that uses population data to improve the health and well-being of people in the US and globally. We are delighted to be hosting this webinar today with the Critical Labor Coalition and our special guest, former Labor Secretary Alexander Acosta. On the heels of Labor Day, we are reflecting on the state of the labor force. Anyone who reads the news these days is presented with an array of data in the US that suggests that any one time that we don’t have enough workers or enough workers with the right skill sets, or we have too many workers in certain professions or sectors. This webinar is an attempt to cut through the noise to present who is in the workforce, why we have long term challenges ahead of us, and some possible solutions.
I am delighted that both former Labor Secretary Alexander Acosta and CEO of the Critical Labor Coalition, Misty Chally, are here with us today to discuss these issues. First, we’ll hear remarks from Alexander Acosta, former US Secretary of Labor. He will present background about the state of the US labor force, drawing upon his experiences as the former head of the US Department of Labor. Next, I will present how demographic trends matter for the labor force now and in the future. Then Misty Chally will present information about the Critical Labor Coalition and why they are working to find solutions to labor shortages and the legislation they are seeking to advance. We will conclude with a Q&A session with all panelists, where we welcome audience questions about the workforce now in the future, and ways we can work towards solutions.
Finally, a bit of housekeeping before we begin. If you have any questions during the presentations, please type them into the question box in your webinar control panel. Attendees will be muted. We will also share a recording of the webinar after the event on PRB’s YouTube channel. And now I am delighted to introduce Alexander Acosta, former US Secretary of Labor, for his remarks.
Alexander Acosta: Diana, thank you. I appreciate the invitation from from you and from Misty to be part of this webinar and in particular, I appreciate the opportunity to be, you know, to to go over the data because I think there is so much discussion and, and, frankly, noise in this space that it’s important to take a step back and just look at that data and say, where does the data lead us? there’s a PowerPoint that I think you have, and I want to start with the first slide if if we could.
So, the first slide is a slide going back to the year 2000. that’s very simple. This tells so much of the story. It starts out with this is the labor force participation rate. All my data, I should say is from either the Bureau of Labor Statistics or the OECD, which is an international organization. These are the aggregators of data that are sort of the gold standard, in my opinion, in labor data. And I think we often focus on the unemployment rate. But what really matters in the long term is the labor force participation rate. Put simply, the unemployment rate is of those who are working or looking for jobs, what percentage cannot find jobs? The labor force participation rate is much broader, it says of the population, of our adult population that is not institutionalized. That is by, you know, excluding individuals who are in the military, who are in jail or in other institutions. Of those who could work, what percentage are either working or looking for work? And as you see, in 2000, the number was pretty close to 68%. And then there’s a pretty dramatic and steady decrease until about 2016 where it levels off, goes up a little bit, and then Covid hits and we haven’t quite recovered to where it was.
The next slide sort of puts it all in numbers with a little bit more specificity. And so, you see that we started out at 67.3%. By ‘04 we dropped about 1.2. We sort of hovered through ‘08. By 2012, we dropped again, 2.5 by 2016. We dropped another point. Between 2016 and 2020, we actually managed to go up. We went counter trend, for a short period of time and and then not only have we dropped again, but if you look at the projections that come from the Labor Department, the, the drop in the labor force, participation is expected to continue, through 2028 and again to 2032. you know, what’s truly frightening about this is that by 2034, the labor force participation rate, in other words, the percentage of Americans that are working or looking for work is going to be about the same level as the worst month of Covid. So if you think back to the worst month of Covid, all those folks that said, we’re not going to be part of the labor market, by 2034, that is going to be the everyday state of play in America, not according to me, but according to the Bureau of Labor Statistics. and the question is why it’s complicated, but it’s easier to say to some extent. Why not?
Moving to the next slide if we could. You know, I put this up here and this is from 20, from the year 2000 to to the year 2015. You know, it breaks different age cohorts down by race. And I put it up there because I think it’s important to recognize that the effect is, is quite comparable, regardless of race. You know, if anything, you see a larger percentage drop among Caucasians than among other racial or ethnic groups. But you see in this slide and the next slide, you see that, you know, race is not really, something that easily explains, you know, this massive, really, really substantial, massive drop in labor participation rate.
Moving to the next slide if we could. And again, the same issue. You know race is not the the explainer if anything. You know overall you see a larger drop in Caucasians than others. But race doesn’t explain, moving to the next slide. So, this is one of my, my favorite slides. Maybe favorite isn’t the right word. I think the most interesting slides here are the Bureau of Labor Statistics. They always do projections going out about ten years, and they break those projections down by age cohorts. And, and you see that they’re overall predicting a pretty substantial decrease in labor participation rate. in four years out, eight years out. And if you look on the website data, the trend continues, and you see that the trend is much stronger with men than women. the drop is much less for for women than it is for men. but you also see that the drop is greatest among the youngest age cohorts. And this matters because we often hear that part of the problem is that the baby boomers are retiring, that we’re- that our workforce is aging. And that is an issue because the baby boomers are a very hard-working generation. And as they age out, we lose a lot of, you know, both in terms of number but also in terms of workforce participation. We lose a generation that worked very hard, that was very large and that heavily participated in the workforce. But we also see that it doesn’t fully explain it because as new age cohorts enter the labor market, the younger you go, the larger the decrease in labor force participation rate. So, there is something about this decrease that is unrelated to the retirement of the baby boomers, or something about how we’re addressing, newer generations that either discourages or does not connect them to the labor force the way previous generations were connected.
Moving on to the next slide, if we could. So, this is, I think, something we don’t talk about enough and something that’s very important. This data comes from the OECD, which, as I said, is the gold standard for international data. They aggregate data from several countries. And this shows the United States when compared against, the G7 nations, and the eurozone nations. And as you see on the far right, this is in 2000 in red, you see the United States, we had the highest labor force participation rate compared to all the eurozone nations in the G7 nations. And I should say that this particular slide, pulls individuals from 25 to 64. And that’s important because we want to adjust different countries have different university systems, different points at which individuals stop being students and, and fully enter the labor force and different retirement ages. And so, the 25 to 64, is an important comparator when you’re looking internationally because you’re sort of cutting off the two age extremes to really prevent different retirement systems and different university systems from affecting the data. So, in 2000, we had the highest labor force participation rate compared to all our competitor countries.
Let’s look at the next slide though. So here we see by 2008 the United States the red bar was was pretty much right in the center compared to the eurozone countries. And let’s look at the next bar. The next slide I’m sorry. and so, this is 2016. And in 2016 you see that the United States now is one of the lowest rates of labor force participation rate compared to the eurozone countries, you know, and we’re far to the left. We’re, basically fourth from the bottom after as I look closer, after Italy, Greece and Belgium and all the other countries had a higher labor force participation rate. And I think this is important because much of what is happening here is not attributable to a broad global change but is really attributable to something happening within the United States compared to our competitor nations. Either we have done something to change our percentage and lower our percentage, or they have done more to correct the downward trend that I think we’re seeing globally. Because we have fallen behind quite substantially. You know, from being number one to being fourth from the bottom compared to the G7 and the Eurozone nations. And so, I put this out there because I think long term, you know, we can talk about solutions and there’s some solutions in the short term. Part of the issue here is that, you know, businesses want solutions now. They don’t want to talk about societal trends that might take 5 to 10 years to fix. And I understand that because businesses need workers now. And when you talk to Congress and when you talk to policymakers, sometimes it’s easier to present solutions that you can pass a law tomorrow and say, we’re going to have this influx of workers, but I don’t think we talk enough about this, this 20 year trend that, you know, that stopped for a little while, but has now restarted and and is going in the wrong direction because, you know, the United States should not be fourth or fifth from the bottom. We should be at the very top of this.
And so how do we change it? You know, and one final point. I’m not adverse to short term solutions. We need short term solutions, but we can’t implement short term solutions without paying attention to this long-term trend. If today we were to apply a labor force participation, the labor force participation rate that we had in the year 2000, right, we would have 16 million more workers. 16 million. And so, while we can talk about the unemployment rate being lower than it has been historically, for, you know, over the course of 20 years, it’s up a little bit now and it’s, you know, on the upper trend. But it’s you know, in the grand scheme, the real employment rate as a percentage of population is lower than it’s been in a long, long time, lower than it’s been in decades. And those 16 million individuals that are not engaged with the labor force really matter.
We are a different nation of seven out of ten people working to support the three out of ten that are not. Then if only six out of ten are working to support the four out of ten who aren’t. Not simply in terms of our population dynamics, but in terms of tax structures, distribution of tax burden, ability to repay debt, not to mention, you know, the impact that has on GDP. And so, I think this is an issue that we need to address in the long term, you know, with much more specificity. And with that, I’ll, I’ll yield the time. And I look forward to hearing from my fellow panelists.
Diana Elliott: All right. Thank you so much, Secretary Acosta. Really appreciate your remarks. Working at PRB, where we think a lot about demographics and population trends, it can be somewhat frustrating to see news stories that have such short-term focus. Right? Much as Secretary Acosta was suggesting so understanding point in time, employment is incredibly important for getting a pulse check on the economy. It tends to overshadow the bigger picture story.
Next slide. For example, the unemployment rate is followed very closely in the news cycle because it’s an important piece of evidence about whether or not, for example, we’re in a recession. But the larger context is not always presented fully in the news. As of July 2024, the unemployment rate was 4.3%. This is not as low as it was a year ago when it was 3.5%. But it’s still better than what economists consider to be full employment. And that detail isn’t always shared in the news stories. But the unemployment rate, again, doesn’t tell the full story. We’ve had a longer-term trend where there are more job openings than unemployed people, and even if some of these job openings are ghost jobs, something you’ll also hear about in the news or postings that employers don’t intend to fill. We can safely assume this 1.4 million gap between jobs and workers is not all ghost jobs. So actually, back to the previous slide, please. So, the unemployment rate is also a lagging indicator and is not terribly future looking. It all misses the nuance and context around who isn’t working and why, but demographics can fill in some of this information as I’ll walk through. The U.S. has had a rapidly changing age structure and changes in mortality and illness that may be contributing to long term challenges in the labor force. Further, policy decisions in the U.S. may contribute to reasons why not everyone who wants to work is actually working.
Next slide. So, as was hinted previously in the in the prior prior remarks, one of the biggest shifts in the U.S. and one that is changing the labor force right now is how we are aging. Between 2000 and 2023 and the 21st century alone, the U.S. median age has increased by 3.8 years. If you lined up every person in the U.S. from youngest to oldest in 2023, the person in the middle would be 39.1 years old. In part, this is because baby boomers, the large cohort born after World War II between 1946 and 1964 are aging. This is affected and is currently affecting the labor force because baby boomers are retiring. To put this into perspective, as of 2022, 57% of baby boomers were over the age of 65. And we know that by age 65, 90% of Americans are. Previous slide, please. 90% of Americans are already collecting Social Security. We also know that most retirees report having retired at age 62. This means that most baby boomers are retired. The youngest of baby boomers turns 60 this year, meaning all are eligible to access their retirement savings without penalty. And by 2030, most will be over the age of 65 and will have exited the labor force. One of the themes you may hear about in the news is the prediction that older people will live longer and work longer, but the evidence so far suggests that hasn’t come to pass for the majority of older Americans. While Warren Buffett may be hard at work at 94, the data suggests this isn’t the future for most people.
Next slide. So, another way to look at this is through the age dependency ratio, which looks at the share of those aged 65 and older relative to those of working age, ages 15 to 64. Since mid-2000, the age dependency ratio has reliably gone up every year. In short, we have a growing number of retirees, which has implications for Social Security and public pensions, for example.
Next slide. An important reason why this ratio is shifting is because our birthrate has declined in the U.S. since 2000, the total fertility rate has been well below the replacement rate. Why is 2007 a significant year for thinking about the labor force? Because those born in 2007 are now 17 years old and entering the labor force, the provisional fertility rate and 2023 total fertility rate was 1.6 births per women, down 2% from the previous year. As context, the replacement rate is generally considered to be 2.1. Put simply, there are too few young Americans and future workers being born to replace retirees. In the news, you may hear complaints about how young people aren’t working. I would argue that’s an incomplete statement. In fact, there just aren’t as many young people to fill roles, which makes workforce training programs all the more important in the larger U.S. context.
Next slide. And the U.S. is not alone in confronting these challenges. Our peer countries are also facing a shrinking share of the working age population. The share of the working age population in Japan has dipped below 60% through a combination of factors increased longevity, fewer births, and tight immigration policies. And there are adaptations to that new reality underway. The U.S. and Canada have both seen a decline since the mid 2000, and Canada remains slightly above the U.S., in part because they’ve implemented various policies to try to change that trajectory.
Next slide. Unfortunately, we also need to confront that deaths among working age adults have risen in the U.S. in recent years. Since 2010, as a result of various conditions, there have been a decrease, a decline in the number of working age adults in the U.S. Covid-19 certainly exacerbated this. In 2020, it’s estimated there were 58 excess deaths per 100,000 people ages 15 to 64. So excess deaths are not just from Covid-19, but from other illnesses that went untreated because of pandemic lockdowns. Further, long Covid symptoms may be depressing labor force participation, similar to general trends with Americans with disabilities. Workplaces may not be accommodating people with long Covid optimally, and others with long Covid may be leaving the workforce to handle their symptoms. One study estimated that 1.6 million workers were missing from the workforce in 2021 because of long Covid symptoms. So, because so little is known about the trajectory and treatment of long Covid symptoms, it’s unclear how permanently this will affect the labor force in the long run.
Next slide. This graphic from Paris 2022 publication “Dying Young,” authored by Richard Rogers of the University of Colorado at Boulder and coauthors, shows that the U.S. really stands apart from other peer countries for the probability of death among teenagers and young adults in the U.S. This suggests that there are health and safety policy challenges in the U.S. that are contributing to these sad and tragic outcomes. And other countries have done a better job creating safer environments for their young adults and accordingly, their future workforce.
Next slide. Demographers also think about immigration and how it matters for our labor force. Immigrants and U.S.-born children are a significant share of the overall labor force. but in recent years, immigration policies have contributed to backlogs, which made things worse during the pandemic. While things have mostly returned to pre-pandemic levels, there remain persistent problems with issuing work permits and visas to immigrants who have entered the country legally. For context, in 2023, the overall population added 1.6 million people. This is despite a falling birth rate. The census attributes this increase to the resumption of post-pandemic norms around immigration, as well as fewer deaths following the pandemic uptick.
Next slide. Beth Jarosz at PBB analyzed the most recent Census Bureau projections in a blog published in 20th November of 2023, and describes how the most likely scenario for the future population of the U.S. is for us to reach a high point in our population around 2080, and then to begin slowly losing population with us reaching the year 2100 with about 366 million people. For perspective, a baby born today would be 76 years old in 2100. And these numbers in part reflect our slowing fertility in the U.S. However, if immigration were to slow or to be halted in the U.S., we reached these inflection points sooner, potentially within the next 1 to 2 decades.
Next slide. In fact, she shows that within 20 years the main series or the most likely scenario for the future shows that within 20 years, deaths will outnumber births. This is all accelerated with the curtailment of immigration from current levels. Next slide. But let’s move away from a doom and gloom scenario, because policy matters tremendously in terms of how we confront these demographic challenges, we’ll think about that with respect to the U.S. labor force.
Next slide. So one way to confront the demographic challenges is to think about who in the working age population would likely be more engaged with work and how we can address those barriers. One group is women, particularly those with young children. The U.S. is notable among our peer countries for a lack of family friendly policies. This limits women’s full labor force participation. That said, women’s labor force participation is now above pre-pandemic levels at 77.6%, but it is still lowest among those with young children. For perspective, Canada’s labor force participation rate for women is 84.9% and is notable for women with young children. The Canadian government has invested billions in recent years to cut childcare costs for working parents. One of the stories you may hear about in the news is that men’s labor force participation rate is also down. That is true from a historical perspective, but it remains higher than women’s rates. men, for example, have a labor force participation rate at 89.1%. Missing from these news stories is why there’s even a gap at all between men’s and women’s labor force participation rates, when women are now outpacing men in their educational training, and the U.S. states are often policy laboratories for what could be. One example to elevate here is New Mexico and its shift to prioritize early childhood education and care. They created a state level department, increased investments in early childhood education, and expanded subsidies for families. This could be a state to watch in coming years for policy examples that help working families and improve parents’ engagement in work.
Next slide. Another group that could be better engaged with the labor force are people with disabilities. In fact, this group of workers saw improved employment during the pandemic, in part because remote work enhance their abilities to engage with work. Between 2021 and 2022. There was a notable increase in their employment and a notable decline in their unemployment. That said, for people with disabilities, their unemployment rate is still two times higher than those without them. Studies have shown that accommodations matter for the employment of people with disabilities. Nearly half of needed accommodations are free, and for those with a one-time cost, the expense is typically $300. So again, states offer examples for policy ideas to engage people with disabilities who are eager to join and be a part of the labor force. For example, Ohio established the state’s vocational apprentice program in 2019, which are paid apprenticeship opportunities within state government. This not only helps people with disabilities enhance their resume and potentially find a permanent state government job, but it also helps to fill gaps within state government offices.
Next slide. So, let’s reflect on what we know about the state of the U.S. labor force. Births have slowed in the U.S., and there is neither a growing cohort of young people nor older Americans who will be tomorrow’s workers. Challenges such as mortality and morbidity present uniquely American challenges in our labor force. As we look to the future, we’re confronting a demographic future where there are fewer workers amidst an increasingly aging population. But policy can intervene to better engage workers who have been sidelined and want to be more engaged in work. There are examples happening in other countries, and even within the U.S., that suggest ways to attract, retain and train the workforce amidst overarching demographic shifts in the labor force.
Next slide. So, thank you. And now at this point, I’m going to turn it over to Misty Chally of the Critical Labor Coalition to additionally share how her group is working on legislative solutions to these issues.
Misty Chally: Thank you, Diana, and thank you for everyone who took the time to join our webinar today. Just waiting for the slides in a moment. And while we wait, I would just like to mention an add-on to the Secretary and Diana’s comments about the workforce. I think also what we’re seeing is the workforce, the nature of the workforce has changed, with a lot of people working from home and, you know, having flexibility as an Uber driver and setting their own schedules. We’re seeing a lot of people leaving places of employment where you need in-person attendance, if you will, a set schedule, somebody to be in a restaurant or a hotel or a retail location, where you actually need people physically there. And so that’s why when you go to a restaurant and you know, there’s a long wait, but you see a lot of empty tables, it’s because of the labor shortage, because there simply are not enough workers in those in person needed areas, to fill those spots. So, want to just to add that to the conversation and yes, AI does help in some ways, and definitely contributes, but you cannot replace all workers with I although it does help the employers and the workers in many ways. So, it looks like we’re we’re back up. So, thank you for letting me give my little personal opinion on that.
My name is Misty Chally, executive director of the Critical Labor Coalition. My background is in representing franchisees and franchisee associations. And so, this issue came up when I was talking to a number of different franchisee associations. called the Critical Labor Coalition, and they represent everybody from Planet Fitness and Domino’s to Burger King and Meineke. So, all different industries. And we were talking about, you know, what are our key issues, regardless of industry. And the issue was the labor shortage. Everybody was and continues to have an issue with the labor shortage. and that’s really where the Critical Labor Coalition was formed.
So next slide please. So, I formed a Critical Labor Coalition in July of 2022. It’s a nonprofit 501(c)(4) to address the issues of the labor shortage. We seek legislative solutions to address labor shortage, understanding that there is no silver bullet here. I mean, there are different ways to approach it. and that’s what we do. Our members are trade associations, corporations, individual business owners. And I should note that we advocate for bipartisan policies, that incentivize individuals to return to work and that grow the workforce. And how we do that is by focusing on different communities of workers. How do we get them into the workforce? Everybody from guest workers to seniors to, entry level workers to veterans, to those in the Second Chance community, the disability community and caregivers. We look at them individually and collectively to determine how do we get them to return or enter the workforce.
Next slide. These are our members right now, as I mentioned, trade associations, individual employers like Chipotle, the Restaurant Association, hoteliers, um staffing services, the um Amusement Park Association, food distributors. I mean, everybody of every industry is still having this problem. SHRM, representing the human resource community of everybody continues to have this issue. And so, I am honored to have them as members of our coalition.
Next slide. So how do we get something done? How do we address this labor shortage? Well, the Critical Labor coalition focuses on two issue areas. One: How do we grow our workforce? And two: How do we promote tax incentives to get those in the country already in the country, incentivized to enter or reenter the workforce? And I’ll go into these, individually.
So next slide please. So, when we’re talking about what are the tax incentives, CLC uh supports a number of different tax incentives. one being a bill that would expand the work opportunity tax credit or WOTC, and that is a tax credit for employers to hire from, certain communities that face roadblocks to entry. That bill was initially or WOTC was initially passed in 1997 and hasn’t been updated since. So, this bill would increase the percentage of the credit from 40 to 50% of up to $6,000. Again, these are not silver bullets. These are not significant amounts of money, but they help get people back to work. And then it has a provision that would provide additional support if those workers work at least 400 hours or stay in their employment. EITC for Older Workers Act. EITC stands for the Earned Income Tax Credit, and this is a tax credit that would expand to include seniors over 65 without an eligible dependent. Right now, to qualify for the Earned Income Tax Credit, you have to be part of a specific community similar to WOTC and earn under a specific amount. But right now, if you are older than 65, you can’t get the earned income tax credit. And so, what this bill does is it eliminates the top age restriction for EitC eligibility. And the third one, the credit for Caring Act is bipartisan bicameral bill. And it gives a tax credit to people that are family caregivers, those that go to work, come home and have caregiving expenses that they then have to go and take care of a loved one, which is very broadly defined. and so again, it helps those people that have to go to work and then come home and take care of a loved one. And I should mention, our Coalition works with what we call strange bedfellows. So, while we represent the business community, we’re working with AARP, on these issues, as well as nonprofits, antipoverty centers like Golden State opportunity, out of California and where we want to show Congress that this is not a partisan issue. Businesses support this. AARP supports this, antipoverty group supports this. This is just a workforce issue.
Next slide please. And then when we focus on workforce growth how do we get more people here? Again, we are working with groups like Refugees International, the Asylum Seekers Advocacy Project and other groups to promote these pieces of legislation that will help get more workers here, one being the Essential Workers for Economic Advancement Act. Right now, there is no real visa program for those that are in nonagricultural less skilled positions. There are seasonal workers, but our members like restaurants and hoteliers and others, they need people year-round. So, this bill would introduce an H-2C visa program, that would allow for individuals to come over. It’s a nonimmigrant visa program, and work in the country, get some experience based on need. and there are economic triggers to make sure that those workers are needed, that there is low unemployment in the areas, and that the employers have been looking for somebody to fill that position for a certain period of time as well. The Asylum Seekers Work Authorization Act, is a great solution, I believe, to get people back into the workforce that are already here. Again, a bipartisan, bicameral bill, that would shorten the waiting period for asylum seekers to receive work authorizations. as many of you know, when asylum seekers come here, they have to wait 180 days to even apply for a for work authorization. This bill would reduce that deadline to 30 days and then they could apply and start working. and so, this bill would get the people who are staying in hotels and being subsidized by the government to actually work in those hotels who actually need the workers, and they want to work, we want them to work. So, it’s common sense, a bill that we really would love to pass Congress. The Senate and the House bills are, are two different versions of the same bill, but we support both of them because we just want people to get back to work.
Next slide. And just some ways in how we do that. We do congressional briefings quite frequently. That right there is the future of the workforce caucus briefing. On your left and on your right. We did a briefing for the New Democrat Coalition. talking to them about workforce issues. And we do many of these every year.
Next slide. Just a couple other things. We had the pleasure of having Diana speak to our group and provide, data and information, really, really interesting data. To our group, we do many, many hill visits to House and Senate offices. and we do briefings with groups like the Problem Solvers, and other coalitions. whose goal is to pass bipartisan legislation. We do webinars like this. We did a webinar on fair chance hiring, second chance hiring, with, the American Probation and Parole Association, which was, great to have them on board.
Next slide. Finally, we do digital ads in Politico and Bloomberg. And this on the top is an ad that we put in a number of different electronic media. And it’s just an astounding number, as Diana mentioned, if every unemployed person had a job, we would still have over a million unfilled jobs. And that’s kind of a startling number for some people and really kind of hits home to what the problem is.
Next slide. And just as an opportunity, I was on a podcast that will be available tomorrow, but wanted to give you guys a sneak peak. The Friday Reporter is a great podcast that goes into some of the issues that are going on. in DC, a lot of reporters, and congressional staff, are subscribed. So if you scan that QR code, you can get a sneak peek of what we were talking about on this podcast, which is more in depth discussion of the labor shortage.
Next slide. And I would just conclude by saying, you know, we welcome everyone to the conversation. So please feel free. We would love for you to follow us and check out our website. talk to us more. That’s my email address. and and get involved in the conversation. We certainly need the help. And again, thank you all for joining. And I’m going to send it back to Diana, and Secretary Acosta to answer some questions.
Diana Elliott: Great. Thank you. and just a reminder to put questions in the Q&A. I also have a few questions where I’m going to use, you know, sort of moderator’s privilege and ask a question of my co-panelists. So first off, I think I’d love to hear from both of you. You know, we’ve heard a lot about challenges right now. are there bright spots that you see on the horizon for our labor force? in the wake of demographic change in the wake of structural changes that we’re seeing?
Alexander Acosta: So, let me take it. Take it first. if I could. I do think there’s a bright spot in that. I think this is, you know, the first time that I sort of addressed the issue and I focused my personal view is that a lot of this has to do with our education system. I saw a lot of heads nodding, and I’m seeing more and more heads nodding. You know, I, I had early on as secretary and meeting with, community members from a city in Texas and a businessperson said, we need more welders and I’m willing to start a welder out at 60,000 right now. And the president of a community college said, that’s great. We’ll start a bachelor’s degree in welding. And I’m thinking, excuse me. The answer to more welders is we’re going to start a bachelor’s degree in welding. Is that really the case? You know, I think starting in the ‘70s, for all the right reasons. we started subsidizing education, particularly college education, in the matter of trillions. But along the way, we forgot that there are all these folks out there that aren’t going to college.
And if you were to break down, I showed a lot of data. If you were to break down the the data from the Bureau of Labor Statistics by college, whether you’ve attained a college degree, whether it’s, more than high school or high school, only you see that the biggest drop in labor force participation rate isn’t among the college students. It’s among those that don’t have college. And it’s really among what we’ll call the service workers, the construction workers, folks that work with their hands. And along the way, we’ve done three things. One, we say that college education is education at the Department of Labor. And if you don’t go to college, you get workforce training, I’m sorry, call it education at the Department of Education. And if you don’t go to college, it’s workforce training at the Department of Labor. Why is learning to be a welder any less an educational experience than going to college and learning to be a nurse’s aide? It is not. And language matters. It’s all education.
Two, we subsidize college education to the trillions of dollars, but if you want to learn to be a welder, you have to pay for it yourself. All these college loans are not available. All the Pell Grants are not available. And that matters. And so, we are biasing folks in favor of jobs that may not be their first line of preference. I gave this talk in Boston once, and the person running the audiovisual came up to me afterward and said, you know, I went to, I forget if it was Boston College or Boston University, I’m still paying off the loans. And then afterwards I had to go and get educated to run audio-visual equipment, because I make more running audio-visual equipment than I do from my college degree. And not only do we subsidize them, but now we’re forgiving all those college loans. But what are we doing for all those folks that that are working with their hands?
And then finally, I’d say even the way we talk about this. Right. You know, how often in our communities have we heard we have a health care crisis? We need to expand our nursing schools or medical schools. and we do, and I have no issue with that. But then how often have you heard we don’t have enough folks to build those hospitals to, you know, to work inside those hospitals as service workers. Do we then say we need to go out and create job educations for them, or do we talk about immigration as a solution? Do we really treat all tracks equally and say, is the goal a family sustaining wage, or have we started biasing the conversation in favor of those that have our backgrounds? All of us on this talk on this panel went to college is our tendency. Just think about this from the perspective of the college graduate. And, and if you look at the labor force data, you see that the biggest declines are not among the college graduates, but among those that didn’t go to college. And what are we doing to address them?
Final point, and this is where I think the business community and I disagree a little bit. I have no issue with short term solutions, but if short term solutions bring in more workers from abroad, you’re driving down wages. And that’s discouraging a lot of Americans from joining the labor force. And so think back 20 years ago, how many neighborhood kids would go and, let’s say, mow lawns and and are they doing that now? And if they’re not, why are they not doing that? Is it that the wages have not kept pace and that therefore what what economists would call the reservation price of labor and a lot of these service industries, are really to some extent reduced because of the immigration flows? Because, you know, according to, to the data that we saw, if we have low immigration, we’ll have 12 million fewer workers. But if we return to the 2000 labor force participation numbers we’ll have 16 million more workers. And so, to some extent, the short-term solutions undermine the long-term policy shifts that sort of my perspective.
Diana Elliott: I think I can piggyback off of that with a bright spot I see which prior to coming to PRB, I worked on apprenticeship programs, and I feel like apprenticeships are a bright spot for helping people who, perhaps cannot afford to do not want to go to college right away, or that’s not something that they’re planning to do to have that training program sort of with the employer, have that combination of employer based learning and learning in the classroom is really a tremendous benefit. And I’ve been really encouraged by an uptick in apprenticeship programs, for example, to that welder who or that, you know, the welder who needed more welders? I would say. Have you considered, have you considered an apprenticeship program or sponsoring apprentices yourself? In, in the past, right, post-World War II, we had more employers who took an active role in training, and that kind of faded away. And there is a role for employers to be more involved in training. Misty, do you have a bright spot? Before I go to some of these great questions coming in from from, participants?
Misty Chally: Yes, and I would like to respond to some of the, the income and wage questions, but I will say that, bright spot, I think whether because there are many expiring tax credits at the end of next year or because Congress wants to get something done, I do think we are going to see legislation that will help address the workforce shortage pass next session. Because it, honestly, it has to. A lot of the provisions passed in the Tax Cuts and Jobs Act expire at the end of next year. And so, you are seeing a lot, even now, of members organizing themselves and discussing how to help, and the Critical Labor Coalition is certainly part of that discussion.
Alexander Acosta: If I could, Diana, I know that there are a lot of questions, but to respond a little bit to, to your, I think, point about apprenticeships, which is absolutely correct. And it’s a bright spot. You know, one of the weaknesses is that we’re asking employers to step up and fund these apprenticeship programs. Yet we fund college education, right? We don’t ask hospitals to fund the cost of educating nurses. So why should we ask welding companies to fund the cost of educating welders and one substantial difference between us and a lot of Europe is Europe is much better than we are at treating all types of education horizontally equally. And so, you know, community colleges, you know, aren’t what they once were. They’re focusing much more on college, you know, on full college degrees rather than vocational.
There’s a fascinating school in San Antonio. It’s called the Construction Career Academy. It’s a public school, and it’s a magnet program, just like we have high achieving Stem magnet programs. and they have more students and more families applying that they can fill. And every student graduates with the focus on either pipe-fitting, on carpentry, on construction management. And I forget the fourth and the, the fascinating part about it is they then teach math in the context of, how do you keep your books in English in the context of how do you write a business proposal? And you have students that might not otherwise be engaged in high school, fully engaged in high school. And those students have amazing success. And when I visited, the principal said, every year we have X number of students that graduate with scholarships and go on to college, and Y number of students that graduate with job certifications that have a job. And I thought that that equating of the two career paths, the two tracks, the family sustaining wages was wonderful. But why am I only aware of one public school district that has that?
Diana Elliott: Excellent. Um, fair point. Misty, there is a question that feels designed for you to address based on your experience with the coalition. So, it says, what is your opinion on the role of wages to incentivize people to enter the labor force?
Misty Chally: Right. And thank you for that. And I think Secretary Acosta and I will disagree on this, but, you know, coming from the franchisee small business world, I will tell you that the average starting wage, and don’t quote me on this, is about $20 an hour to work in a Burger King. And there’s a reason for that because there are not enough workers. So, they will, their income has increased, their salary has increased, their wages have increased. They’re getting a lot of our members to provide benefits like savings plans, college tuition plans, all of those. And I will say that, you know, the service industry, honestly, I think gets a bad rap for, for being kind of a starting job. I will tell you that many of these jobs, if you have no experience, need to put something on your resume. Want to get started? You can start. And I know many franchisees have started as a dishwasher and if you are a hard worker, I will tell you you will move up in that, in that business, in that restaurant, in that hotel, very quickly. And they have their own training programs for individuals that want to do that. So, they do provide many opportunities that people don’t often see to people that need jobs.
And I’ll, I’ll further say, that the wages issue, and I know it’s been front and center. It, there are, there are resulting effects from an increase in wages that I honestly didn’t understand until I spoke to franchisees that were suffering from this. So, let’s say you are a McDonald’s franchisee, and you raise the wages of your workers, your entry level workers, which means you then have to raise the wages of those managers and the like. And as a franchisee, you are, what you take home is the bottom line, right? What the franchisor takes is the top line. So, if you are paying individuals more, then you’re taking home less. And honestly, if you earn one franchise, this is not, you’re not buying yachts and sailing along the Riviera. And then you’re talking about raising the price of a Big Mac. So, when we’re seeing people complaining about a Happy, or not a Happy Meal, you know, an extra value meal being $15, $20, that’s, that’s a reason why. And so, what do they do? They can go get a kiosk that will be cheaper than paying for somebody to stand and take your order. So, there are a lot of different effects of raising wages. And while I understand that argument, you know, there are, that may actually decrease the number of available jobs in the U.S. So, that is my two cents and you’re seeing it in California where, you know, there’s a law that requires, I think it’s $20 an hour for quick service, and all the franchisees I know are switching to kiosks because they need to make a living as well.
Alexander Acosta: So let me let me just respond, Misty, because, because you said that we might disagree, and I don’t disagree with your, your specific point. You know, national franchises pay very well and and one of the results that you saw with the minimum wage in California, where a number of restaurants are closing down, switching to kiosks, I agree with all of that. My point was a broader one. And, and I think it applies less in the franchise area than other industries that, simply, if we believe in the free market, we need to be equal opportunity free market, you know, folks. And if we believe in the free market for goods, we also have to believe in the free market for labor. And at the end of the day, you know, while legal immigration is necessary and brings incredible, incredible people and, and knowledge to the U.S. – and this is not an anti-immigration statement because I think legal immigration is critical. The reality is that if we focus immigration on just, we need to fill the service sector. So, let’s bring in folks to fill that in.
To some extent, supply-demand means we are intervening in the free market for labor. I don’t think, you know, personally, I think one of the key drivers for franchises is not just wages, but all the regulations and the benefit costs, you know, around hiring a person that can, you know, almost increase the cost of a hire by 50, 60, 70%. And there’s so many other issues around hires, particularly, you know, in franchised as the franchise or to get liability for their franchisee that it makes hiring the franchise world very expensive. So, I don’t think we disagree with respect to, let’s say, the McDonald’s, which is the example that you used.
Misty Chally: No, I agree with that. And I think what you have to look at, what we used as a tool is look at the profit per employee, look at the industries that are not making a lot of profit per employee and see if they can handle, you know, any type of additional regulation, legislation, price increases and the like. So, thank you for that question, Diana.
Diana Elliott: Yeah. Thank you. I want to note that we are actually over time, but looking through the questions that people sent in, although we may not have asked your exact question, I think we touched upon a host of issues raised from vocational school apprenticeships, the wage challenges, to even robotics or kiosks being used. So absolutely, I encourage everyone to keep the conversation going via social media. Um, stay tuned. I think there’s a lot of material here for us to think about a post-event blog as well.
Keep asking your questions. We would love to think more about this and respond to you on social media. And if you haven’t already, go to prb.org. Consider making a donation so we can keep fantastic panels like this going. We’d love to do more of this. So, thank you so much for tuning in and joining us. And I want to thank Misty and Secretary Acosta for participating and really kicking off our Labor Day week in the best way I could imagine. So, thank you to everyone for joining and for asking great questions, and we hope to see you soon. Enjoy your afternoon.
Alexander Acosta: Thank you.
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An array of resources and tools can be used with American Community Survey (ACS) data to enhance the efficiency and proficiency of data users. However, given the volume of information available from the U.S. Census Bureau and elsewhere, learning about these resources and tools may be challenging for some users.
In this 90-minute workshop, expert data users from PRB, the Census Bureau, and the Southern California Association of Governments (SCAG) walked through some of their favorite shortcuts, resources, and tools to help data users maximize their experience analyzing ACS data.
Attendees were first introduced to the ACS data users group, an online community that provides help to members seeking to better understand ACS data and methods. The second presentation focused on accessing Census data via the API and MDAT, including basics such as how to create a call for an estimate in the API and access data through the public microdata sets (MDAT) on data.census.gov.
The third panelist provided a high-level overview of how to use R and the tidycensus package to execute commands such as switching between spatial scales, outputting a map, and looping through a query to assemble a longitudinal series from the ACS.
Transcript
Mark Mather, PRB: Okay, well, I think we should go ahead and get started. Hi, everyone. Thanks for joining today’s webinar on ACS resources, shortcuts, and tools. I’m Mark Mather, and for those who don’t know me, I help manage the ACS Online Community website and other activities in partnership with the U.S. Census Bureau.
I am very excited to introduce the three speakers in today’s webinar. Lillian Kilduff is a Research Analyst at PRB and will provide a brief overview of the ACS Data Users Group and Online Community. Following Lillian, we’ll have Mary McKay, who’s a survey statistician in the American Community Survey Office. Mary is going to show you how to access the ACS through the Census Bureau’s API and microdata extraction tool, also known as the MDAT. And then we have Kevin Kane, who’s a program manager with the Southern California Association of Governments. Kevin is going to describe how he uses R and the tidycensus package to access and output ACS data.
A few housekeeping notes. We’re going to save the Q&A until the end. We do have a large number of participants. We encourage you to use the raise hand feature in Zoom, and then we’ll try to unmute you to ask your question, but you can also feel free to use the question box at the bottom of your panel there, and you can type in your questions at any time during the webinar.
Closed captioning is also available as an option at the bottom of your screen. And in addition to our three panelists, we also have several other Census Bureau staff members on standby to answer your questions today. And finally, this webinar is being recorded, and we will send you a link to the recording after the event. And with that, I’m going to turn it over to Lillian.
Lillian Kilduff, PRB: Thanks, Mark. I’m going to be talking about the ACS Online Community today and also showing the new upgrades. If you haven’t already seen to the look and feel of the website, I’m going to go ahead and share my screen real quick. Right here. Okay. Sorry about that. Okay. Um, so I’m going to provide the brief introduction to the ACS Online Community.
So here’s an overview of the presentation today. First we’re going to do a quick recap of the American Community Survey itself. Then we’re going to talk about the ACS Data Users Group and Online Community. Then we can go over the tabs of the ACS Online Community, and that includes the discussion forum, the ACS resources, webinars, and conferences tabs.
After that, we’re going to talk about the ACS Online Community itself. So behind the scenes, how many members do we have, threads and replies, page views, response rates, and then also talk about the discussion forum topics that often get viewed. We’ll go over the site upgrade if you haven’t already seen the changes and talk about how to join the ACS Online Community.
So just to review, if you’re new to the American Community Survey, people use the American Community Survey to get, uh, data on the demographic characteristics. So that would include social characteristics, economic, housing, and demographic. And you can see some of the examples in those parentheses there. The data products include one-year estimates, one-year supplemental estimates, five-year estimates, and you can access those through many tools including tables, the summary file, and PUMS.
Here is a quick hierarchy of the geographies available. So we have from the nation down to block groups.
When it comes to the ACS Online Community, this is a partnership between us at PRB and the American Community Survey Office at the Census Bureau. The ACS Online Community’s purpose is so that ACS data users can share tips and tricks, questions, materials, and then also we post announcements about things like today, the webinar. Membership is free and open to all ACS data users and new ACS data users. The group is led by our steering committee, and we try to pick a steering committee that represents all different data users, local governments’ data users, geography, geography data users. And we just had a new steering committee this this year.
So I’m going to show you the home page. Okay. This is fine. Here is the home screen of the new ACS Online Community. Here is just what I talked about, the purpose of it. Here’s some quick facts about it. And we also have the most frequently asked questions. That’s based on questions from data user surveys and also from the most viewed and interacted discussion forum post. You can view more FAQs on the FAQs page from there. We also have latest discussions, people who are posting in the ACS Online Community. We have a link to the Census Bureau website.
The discussion forum is the main part of the ACS Online Community. Here is an example of a discussion forum post. So a data user is asking a question, and then we get a reply from another data user. You can upvote replies, and if you become a member of the ACS Online Community, you can do things like uploading, replying, and adding to the discussion forum. Here you can see the views, replies. You can also add tags to new discussion forum posts. And then we have more information over here.
Next is the ACS resources page. Here you can see a lot of different links to ACS resources under these helpful headings. If you aren’t already familiar, the ACS handbooks are a great place to start, and we also have handbooks that are catered to certain data users.
Here is our webinar page. So this is the webinar we’re having today. And then we also have links to past webinars with recordings, information, and even the slide decks.
We hold a biannual conference every year. The latest conference was the 2023 ACS Data Users Conference. We have the agenda from that. That includes the recordings of the presentations and also the slides as well. We have the previous conferences, and those include that information as well.
I’m going to go back to my PowerPoint now.
Again, this is the discussion forum. Here is an example of a notification of a Federal Register Notice. That’s one of the examples of a discussion forum post that’s helpful to ACS data users.
Okay, so behind the scenes we can talk about the membership. We have over 5,600 members as of the end of May. Here you can see the fiscal year 2022 and 2023, and membership can vary over that time. And usually when we, when we have events like conferences or a new series of events called ACS on the road—we just went to the Texas Demographic Conference—we can see an increase in membership.
There’s a lot of discussion forum posts, and they get a lot of replies. Here again we have, uh, the total number of the threads and replies across the two last, last fiscal years.
And here are the number of page views that the ACS Online Community gets. If you’ve ever googled a question about the American Community Survey and its data, a lot of times the first Google result is the ACS Online Community itself. And you can see that overall, uh, with the last fiscal year, the page views and the ACS Online Community have increased.
The great thing about the ACS Online Community is that we do have a great response rate. So you can see that just within one day, if you post a question or an announcement or a comment, you get a pretty good, uh, you know, response rate.
And here are the top 10 discussion topics. We get a lot of questions about calculating margin of error, especially, uh, zip code–level geographic questions.
So onto our site upgrade. This is how the ACS Online Community used to look. You may remember it this way, but now live on the site, we have this new upgrade that’s, uh, more intuitive and more modern in the look and feel. This is how the discussion forum used to look. And now here is the upgraded website.
So finally, how do you get involved with this site? You don’t need to be a member of the ACS Online Community to view the posts, but you do need to be a member to post in the Online Community, comment, uh, and also upvote. You can tailor the email notifications that you get, so, uh, to new threads and comments. And these are all optional. So if you’re hesitant about joining the ACS Online Community because you’re worried about a lot of email notifications, you can cater those. You can also bookmark discussion forum threads so you can reference those whenever you have questions about a certain topic.
And again membership is free and signing up is very simple. First, you click on the sign up button in the top right and then just answer a few questions. We use this information so that we can better cater to different data user groups.
Finally, there is a picture of one of the ACS data users conferences, and please give us your feedback or suggestions.
Thank you so much. Here is my contact information if you ever have any questions. And I can either answer the questions or direct you to someone who will know your, the answer to it.
Mark Mather: Great. Thank you, Lillian. Next up we have Mary.
Mary Ana McKay, American Community Survey Office: Hello? Hello. Okay. All right, I can share my screen once Lillian is done sharing hers.
Lillian Kilduff: Yep. Um. Stop there.
Mary Ana McKay: Perfect. Knock on wood. Awesome.
Okay, so hello, everyone. My name is Mary Ana McKay. I’m a survey statistician with the Census Bureau’s American Community Survey Office. I’m here to highlight two data products and tools that you may be familiar with or you’ve never heard of before. And just a little bit of housekeeping, I’m going to apologize in advance if I speak quickly. I just have a ton of information that I want to share with you all, and I’m very excited to be here. I’m excited that you’re all here.
So without further ado, let me get started. I want to give a broad roadmap of what I’m going to be presenting during this workshop. So to start, I’m going to dive into the ACS Public Use Microdata Sample, or PUMS. This portion is going to cover basics. Then I’m going to run through the Census Bureau’s tool to access these data, and then I’ll wrap up that section with some resources for you as you dive in on your own.
And then immediately following the PUMS, I’m going to jump over and give a very brief introduction to the application programming interface, the API. We won’t go too much into details, but you will learn the basics, so you’re hopefully able to build off what we do today as you go off onto your own data journey. And we’ll go through an example API call, and then I’ll share just a sample of the many, many resources available to you as an API data user before I turn it over to Kevin for the last leg of this workshop.
So before I dive into the PUMS and API, I want to remind everybody about data.census.gov. It’s a really powerful tool for you as you grow your ACS data accessing skills. So many of you here today are probably familiar with data.census.gov, which is the primary way to access data from the American Community Survey, 2020 Census, and more. And I’ll be sprinkling my use of it throughout my two demonstrations, but it’s not the star of the show, so I’m kind of going to run through them a little bit more quickly than I would otherwise. But in an effort to be brief, I will let you know that there are a variety of how-to materials, video tutorials, webinars, and FAQs to help you use data.census.gov.
And I’m going to step aside again and just mention there are links at the bottoms of a lot of my slides. I have a colleague who will be sharing some of them in the chat, but also the PDF version of this presentation is going to have clickable links too.
So the ACS Public Use Microdata Sample can be overwhelming, but we’re going to briefly cover basics to start to get you familiar and hopefully comfortable with this powerful data set.
And I want you to think about these questions: What are your main goals when accessing ACS data? Are you primarily accessing pretabulated estimates? Are you finding that the data you need are not published in these estimates? And what about when you’re looking at cross tabulated estimates? How do you primarily access ACS data? Are you using data.census.gov or a third-party tool such as Social Explorer? What do the data look like on a daily basis? And finally, with the tool or tools you are using, what limitations do you face accessing ACS data?
So these questions might have different answers depending on the day or the data you need. So in some cases, the tool that we are going to explore will be the best option, but other times another method will work better. It’s all about the best way to address your needs. And I always check data.census.gov—I’m going to say this constantly throughout my portion—just to see if there’s pretabulated estimates for the data product and the geography of interest. But in cases that I need something a little bit more specific, I’ll hop over to PUMS.
So, for example, today I’m curious about poverty among veterans by age, and I know I can find tables in data.census.gov that might get close to what I need but not quite exact. And luckily, PUMS is going to be able to step in and get us the table that I want.
So I want to introduce a few PUMS basics before we work on an example. And finally I will share some resources that you can access on our website.
So again, when I say PUMS, I am referring to the Public Use Microdata Sample. ACS data products are released about one year after the data are collected, and the PUMS is a publicly available subsample of ACS records. The one-year PUMS estimates are a subsample of data collected over a calendar year, 12 months, and they constitute approximately 1% of U.S. households. Whereas the five-year PUMS combines data collected over 60 months, or five years, and they constitute approximately 5% of all U.S. households.
Additional restrictions are added to protect data confidentiality, such as including broader categories of data or grouping together extreme values in the form of top and bottom coding. And you’re going to see a couple examples of this top coding in my demonstration.
PUMS files allow data users to calculate their own estimates and margins of errors that may not be available on data.census.gov. Statistical software is recommended when working with PUMS data unless you are working with our microdata access tool on data.census.gov, and this is the tool that I’m going to be demonstrating today.
So here are some examples of why you might want to use the PUMS. These data come in handy when you are looking for cross tabulations that might not be part of the standard table packages released in the ACS. For example, you could be looking for specific poverty thresholds or income levels for veterans at a specific age ranges like I am today. Again, always check data.census.gov and the pretabulated estimates. They may have exactly what you need.
This information is going to be a little bit heavy, but I want to mention it before we continue. So PUMS data provide individual records that data users must aggregate to form estimates. Unlike in data.census.gov, there are no pretabulated data. Weights are included on the PUMS files so that data users may create weighted population estimates. If you are working with housing records, you will use the housing weights. And if you’re working with person records, you’re going to use person weights.
When working with a merged file that includes both housing and person records, person weights should be used to produce estimates for person characteristics. Housing characteristics cannot be tallied from this merged file without taking extra steps to ensure that each housing weight is only counted once per household. In today’s example, I am using all person records.
And then replicate weights, those numbered one through 80 are used for calculating replica estimates needed to calculate standard errors. These standard errors are necessary in order to calculate the associated margins of error or MOEs, and we won’t be going this in-depth for this presentation, but there are guided examples that I can direct you to for more.
The five-year PUMS is the equivalent of five one-year files, so again includes about 5% of all U.S. households. So people often ask, and you may be wondering, what is the benefit of the five-year PUMS? So there’s some nice standardization for the five-year PUMS that you can’t necessarily get by merging five- to one-year files. For example, there are new weights that are produced for these records so that the weighted population matches the latest population estimate. Dollar amounts have an adjustment factor to standardize them to the latest year, so that no one is comparing varying levels of inflation. Other coding schemes are updated, such as ancestry and occupation, so you don’t have to recode those yourself.
I’m going to focus on a limitation data users might experience someone accessing PUMS, and that’s geography. To ensure the confidentiality of ACS respondents, the Census Bureau has to balance geographic detail with detail in the data. There are more than 250 variables on a single PUMS person record. This means that we cannot identify as many small geographies in the PUMS as users might hope. We can put the region, division, and state on the file, but the only other geography is something called a Public Use Microdata Area, a PUMA. PUMS is not designed for statistical analysis of small geographic areas, but the PUMAs can still be used for focus analysis in counties and cities of about 100,000 people or more as well as many metro areas.
So I want to spend a little bit more time here on PUMAs. PUMAs are areas with a population of, again, at least 100,000, which is large enough to meet disclosure avoidance requirements. PUMAs are identified by a five-digit code that is unique within each state. These geographies are redefined after each decennial census and are defined by either the state data center or, in some cases, the Census Bureau’s regional geography staff. For example, the 2020 PUMA definitions were introduced with the 2022 PUMS files.
As with many geographic concepts, seeing PUMAs on a map may help you understand them better. So as you can see, some PUMAs are small and others are large, because, again, PUMAs are built on population and not geography. The smaller PUMAs here on this map are mainly concentrated in the Buffalo and Rochester regions of this map, and some counties in this region that have smaller populations are combined together as part of a multi-county PUMA.
So I use data.census.gov here to visualize geographies. This is a screenshot that shows, um, the PUMAs that make up Marin County, California. So as you can see, there are two that make up the county. So you can combine data from both to approximate estimates for the county. The primary difficulties occur when we get further away from urban centers to counties with smaller populations, which are then again combined with other counties to make PUMAs. And in these cases it becomes less feasible to infer data about the individual county. Furthermore, while I am showing you an example here of PUMAs that adhere to county boundaries, it is not actually a requirement that PUMAs be designed that way, although it is recommended.
And I want to acknowledge really quickly that some of you might know that data.census.gov now has an address lookup option in the search bar. I just want to let you know that right now, PUMA geographies do not pop up when you use that option. I just tried it before, but hopefully someday you’ll be able to put in an address and see what PUMA that falls into.
All right, let’s get our hands dirty with PUMS data. And to start, I’m going to heed my own advice and go directly to data.census.gov. I’m going to first see what tables I might find. And again, I’m going to zip through this because I want to focus more on the microdata access tool. I’m going to use the advanced search feature.
And again, today I’m interested in poverty among veterans by age. I’m going to apply two filters: “veterans” and then I’m going to select “poverty” to see what tables come up. I’m going to click the search bar. And I see here there’s actually a table age by veteran status by poverty status. And it’s a little bit more detailed; it also has disability status. But it does have generally what I’m looking for. So again I said poverty among veterans by age.
But as I’m looking through this table, the age ranges are not quite what I’m looking for, and I’m actually interested at below, at, and above poverty. So this just has two thresholds; I want to add a third. So in any other day but today this table might actually serve the exact purpose I’m looking for, but now I’m going to use the PUMS data to get what I really want.
I’m going to click on the logo to go back to data.census.gov home page, and on the top right, you probably can’t see it, there’s a little button that says apps. I’m going to click on that. And it’s this first option here that says microdata. So this is what you’re going to see. The default data set is the ACS one-year PUMS. And the select vintage is 2022. And perfect, that is exactly what I want. I’ll click next so I can select my variables.
So before I select my variables, I want to search for what they might be called. I know I want poverty, I want veteran status, and I want age. So I like to use the label option here—and I’m going to zoom in, I might have to zoom in and out—I like to use the label here to use keywords to see what pops up. And we also have PUMS documentation with data dictionaries, so you can do the same thing before you get into this tool.
So for the first one I’m going to type in “poverty,” and I see this income-to-poverty ratio recode; I selected this for, uh, today’s demonstration because this is the poverty variable in PUMS, so I want to show people how to use it. It does give me a little bit of a warning here that the variable is continuous, but we’re going to make a custom group with this variable to be able to put on our table, so we don’t have to worry about that quite yet.
And so for my veteran’s variable I’m going to type in “veterans” or “veteran.” And I’m going to open the detail of the three variables that show up. And this isn’t quite what I’m looking for. This veteran period of service is a little bit more detailed. I just want to know if a person has ever served in the military or not.
So now I’m going to try typing another keyword. So I’ll do “military.” And luckily for me I have this military service. Let’s cross our fingers. And yes, okay, this is exactly what we want. We have a value 2 that says “On active duty in the past, but not now.” So that’s how I’m operationalizing veterans. I’m going to select this variable. So now I have two. And my final one is age. So it’s right here at the top. It’s going to give me that same warning that the variable is continuous, but that’s totally fine.
So from here we have our three in the data cart. We’re going to click on View Table and see what we have to start with. So for most situations simply selecting the variables is not going to be the last step for you, for your table, unless by some chance it’s laid out exactly how you want it and the categories are exactly what you want.
So at first glance, there is a lot going on, and I’m going to rename the table just to keep myself organized up here. You can go in and change that title as much as you want, but I’m just going to do “Poverty x Age for Veterans,” so that’s just going to keep it organized in my head as to what we’re doing.
So we see that the default table has military, that military variable on the columns. We have nothing on rows. And then we have two variables here in the values in table cells. Then in this drop-down this is the first thing I’m going to change. I’m going to click on this and select Count. So this is going to give us a value for how many fall in each category.
So I’m going to organize to make variables, and then I’m going to put them so we have our universe limited to just veterans. And then I’m going to create grouped categories for age. And then income-to-poverty ratios on the columns will be three thresholds. Or I’ll make a threshold of three.
So to put in simple terms, our universe is going to be just veterans. My columns are going to be the recode of that income-to-poverty ratio. And then finally the rows are going to be simplified categories of age. And what’s great about this tool is you can organize and flip-flop your rows and columns super easily, so if you don’t like what we have planned, we can change it when we’re done.
So we’re going to start first with making our universe what we want, which is just veterans. So I’m clicking on the variable. I’m going to deselect everything that says Include in Universe. And I’m only interested in Value 2: “On active duty in the past, but not now.” I’m going to select that option, and I like to click into View Table just to see kind of what we’re working with with every change that I make. So now I see my universe is only limited to my definition of veterans.
So now let’s move on and make the age category. So I’m going to click on the Age variable. I’m going to click on Create Custom Group. From here we’re going to use the Auto Group feature. I’m going to change the start age to 17 because that’s generally the cutoff date to join the military. And then for this, this is an example of a top-coded variable, we have 99. So anybody who’s 99 years or older is going to be in this category. And then I want groups of 10 years. It’s not going to be perfect with the values that I have, but for what I need, this is going to be fine. And I’m going to click Auto Group, and you see that it makes those groups for you.
The last thing I’m going to do is there’s a Not Elsewhere Classified category. I’m going to click on Edit Group. These are all the values that aren’t in the groups that I just designated. I’m going to toggle to show off the table. So I’m going to toggle that on, and you have to click Save Group. So now this isn’t going to show in my table. Let’s view the table and see what we have. It doesn’t show up, but we’re just going to click and drag, and to keep myself organized, we have the rows is what we’re going to have for age. So I just clicked it and dragged it over to On Rows. And we’ll see. Now we have account for the people who are veterans in these different age groups.
And the last thing we have is to make the poverty variable. So again I’m clicking on the POVPIT variable. And just to look at this, it is continuous. And I want to explain a little bit more about what the numbers mean before I go in and make my custom group. So for this variable, less than 1 or 100%, because this is a percentage, is below poverty; 1 or 100% is at poverty; and above 1 or 100% is above poverty.
So these are the actually the three categories I’m going to create. But this is an instance where you can really go where your research question or your need takes you. For example, I know that 200% poverty is a threshold a lot of data users need, and there are limited options on data.census.gov. So using PUMS here is, you’re going to be able to get that.
So the calculation for this specific variable is simply to divide income by poverty thresholds, which are determined by number of children, sze of family, and inflation. So for this I’m going to click on Create Custom Group. I am not going to use the Auto Group feature. I’m going to dig in right here where it says Group Label. I’m going to start with Below Poverty. And again you can go in and change these group labels. Um, as you’re going through, if you want to relabel it, you’re able to do that.
So I’m going to click on below 501%. The bottom value I want is zero. And then the top value I want for this one is 99. I’ll click Save Group. So it makes that for me I’m going to click back into Not Elsewhere Classified. Let’s do at poverty. And this is going to be a single value. You can do that. So just when we’re looking at estimates, note that this only has one single value in it. So we have 100 to 100, Save Group.
And then finally we’re going to have above poverty. We’re going to select the remaining of the between 101 and 500. And then since this is another top-coded variable, I want this 500% or more because that’s above poverty. I’ll click Save Group. The last step similar to that Auto Group you’re going to click into, Not Elsewhere Classified. I don’t want this on my table so I’m going to toggle it off, Save Group. And now we’ll view table.
So again right now POVPIT doesn’t show, that Recode doesn’t show. But I’m going to click hold and drag on to columns. I can actually take the military variable off the table because it is my universe. I don’t need to have it on there. It’s included. And here is the example of the table. So now I have the poverty thresholds for different age ranges among veterans.
So I didn’t dive into this. But I want to mention that you can click Change Geography up here at the top. And you see that we have the geographies that we talked about. And the default is going to be the United States. And since PUMAs, the Public Use Microdata Areas, have populations of 100,000 or more, all of them and all of these geographies are going to be included in both the one-year and the five-year PUMS. So from here you can click, download, and share what you’ve made. And remember that you can calculate the error with resources available on the ACS website.
So now I want to go briefly and share some few links with valuable resources for you. So I do my best learning when I am practicing. So if you’re like me, I like to follow along with webinars that have some activities to check, and I put together a list of videos to see step-by-step directions for various aspects of the MDAT tool. So the data gems are going to be shorter, more brief videos, whereas the webinars go into a little bit more detail.
And I’m going to make a plug for the PUMS documentation page. I did mention it, but we didn’t go into it. It has all the resources you’re going to need for every data release. You can explore user guides, data dictionaries, and more. And this is also where you’re going to find directions for calculating variances.
And finally, I think a really great resource that we spent a lot of time perfecting, and Lillian talked about it briefly, are the data users handbooks. We do have one for PUMS users, and I also don’t want to spoil the next part of my presentation too much, but you can find the PUMS on the API.
Um, and with that, that’s the worst segue I’ve ever had, so again, I apologize, but now we’re going to jump immediately into talking about the Census Bureau’s application programming interface. So let’s take a deep breath and move on to the next part of the workshop.
So I want you to think again about the same questions we, we had when we were exploring PUMS data. So what are your main goals when accessing ACS data? Are you primarily accessing pretabulated estimates? Are there a few variables within a single table that you find yourself going to more and more? And what about variables across different geographies or across years? How do you primarily access ACS data? Are you using data.census.gov or third-party tools such as Social Explorer? And what do the data look like on a daily basis? With the tool or tools you are using, what limitations do you face accessing ACS data? Being able to answer these questions can determine if the API is a good option for your needs.
Now on to the basics. When you use the API, imagine that you are in a strawberry field since it is summer. The strawberries are data points you seek, and in order to go get them, you are going to be running calls or going around the field and picking the ripe strawberries. Data.census.gov itself is a fellow strawberry picker. What we are doing today is just a smaller example of what data.census.gov does through its website. We are trying to directly access the data in a very simple way.
So some of you may be creating dashboards on your websites that users will access to get different data to display, given certain criteria. Others might be trying to make data visualizations, and there may be some of you who are using R to run analysis. It’s also okay if you are none of these types of users. The API can still be a very simple process to get the estimates that you want.
As I was just describing what uses the Census API might be for, here are some more specific examples. What if you simply need just one variable, let’s say percent below poverty level for individuals under 18 and nothing else within the table? What if you wanted to grab all the census tracts within a county in Delaware? How about an estimate for an individual below poverty level at the census tract, county, state, and national level? It could just be that you have a data point that you’re trying to easily access year after year. I’m going to show you some ways to simplify that process for you using the API. And I will say this, and I’ve said it several times before using the API, consider checking out data.census.gov.
So with that let’s run through an API call. These are the ACS data tables that you can find on the API. In data.census.gov, the second column here is what the table ID starts with. For our example today we’re going to be using subject tables from the five-year estimates. So we’re going to be using this here. So after you put the beginning of the call, you’re going to put in the variables the tables and the geographies you want, but we’re going to get there in a second.
We’re going to start with data.census.gov, like I’ve said a million times already. And just for the purposes of time, I have screenshots here. So I typed in “poverty” because that’s what I’m interested in for this example. I found Table S1701. And then I limited my geography to Wyoming County, New York. That’s my hometown is there. And this is a smaller county, so it’s going to be the five-year estimates. It has a population of fewer than 65,000 residents, so we’re going to be using the ACS five-year estimates.
Now on this table I see, and I’m sorry if it’s hard to read, we have below poverty levels. So we have the estimate and the margin of error. That’s what I’m interested in. Just those two pieces of the entire table. This table also has percent below poverty level, which is a measure I would prefer, especially if I’m going to be comparing with other counties of varying sizes, but for this example, I’m just going to stick with the estimate and its margin of error.
I’ll mention one cool thing about data.census.gov, there are many, but if you look along the top of your table, there’s actually an API button now that you can click and it’ll create the call for the table that you’re looking at. So this can be really helpful if you’re using the filter options to select geographies, and you might just want that entire table you’re looking at. You can also use it as a starting point to build off. If you want a little bit more detail with your call. And I highly recommend always working off an example when you’re working on calls; it makes it a lot easier than building from the ground up.
So we only want two variables: the estimate and then the margin of error. And what I’m showing here is the entire call. But we’re going to dissect it before running and seeing what happens. I use the slide a few back to figure out what table type I had. And then I did a few additional steps, using some web pages to figure out (1) the variables that I need and (2) the geography.
So to start to break it down, this is the base for all Census API queries. This second set pulls out the data product year, 2022; the program, ACS; the date, the data set, ACS five-year, so this is the 2018–2022 ACS five-year; and then, finally, the table type, which is subject. And again you can refer back a few slides to see the base of all the table types. That slide will get you the portions up until this point. So once we get to this after ?get, that’s where the customization gets started.
So this pulls out, this is where I’m picking the variables. And how did I get here? We’re going to hop over to the website, and just for transparency, I’m using Google Chrome because that’s what I prefer to use when I’m doing API. So I’m going to census.gov/api, the main website, and I’m going to scroll down to latest available available APIs and view all available APIs. From here you see what’s available. I’m going to click on American Community Survey, in theory. And we divide it by the different data products, um, which I find they’re all pretty similar for all of them. So it’s easy once you know how to use one, you can jump around and use the other ones.
So we’re selecting the five-year data. We release this for every data release. So we’re here in 2022. I’m going to scroll down, and I find Subject Tables. So this is again the same for all table types, what I’m doing; you just have to make sure that you’re following along with your table type.
So the first thing I’m going to start with is the second bullet down: the 2022 ACS Subject Table Variables. I’m going to click on the HTML. So for API, Ctrl+F is going to be your best friend, if it’s not already. So I’m going to click Ctrl+F on my keyboard. And we’re going to type in “poverty” because I want to overwhelm you briefly with what shows up.
So as it’s loading, in theory, we’re going to have thousands of options. So it’s loading, um, there’s so many of it that now it doesn’t want to do it. So there’s actually over 3,700 results on this page for poverty. And that’s a lot to go through. So I’m going to show you a little bit of an insider secret, or at least that’s what I like to call it.
Um, I’m back on S1701. I’ve magically loaded it for us here, and I’m going to talk about the different columns. So this is a column set 1. We have the total. And then for this table, there’s a column set 2. Now what does that mean? We’re going to go back to this table, the variable lists. And if I start to scroll down, you hopefully can see that there’s a table ID, then there’s an underscore, and a CO1 that corresponds with column 1. So I can use this as my base to Ctrl+F again. And since I’m looking at S1701, I’m going to type that in. It’s going to jump me to the first time that that shows up. When I do the underscore, it’s going to jump me to the section for this table.
And I know I’m looking for the second set of columns, so I’m just going to write in CO2. And luckily for me it’s this first estimate in column set 2. So we have below poverty level population for whom poverty status is determined. Then the one that ends in E is going to be my estimate, and I want that margin of error, and you should too. That’s going to be the one that just ends in N.
So let’s hop back over to the slides to see what I did here. So I have the two variables that I found and I put them in here. I also put Name here. So to make sure that I get the geography names when I run the call. But this is not a necessary component of your call. I tend to use it just to confirm that I have the right geography, so I can run it with that, confirm I have the right geography, and then you can run it again without if you don’t need it for the larger purposes of your call.
One thing I will note, you separate the variable names with just a comma. if you add a space or an additional character, you are going to get an error when you run your call. So working backwards, if you get an error, double-check your call and make sure that there’s no spaces in between the commas. You can pull up to 50 variables with this method, and if you want more than 50, it’s likely that you just have to pull the entire table and then work from there.
I also want to mention one more thing. You can pull variables from different tables of the same type. Say, for instance, you want to pull all of the same variable in a table series for different race iterations. So we have detailed tables for the different race and ethnicity iterations that end in A through I. You can pull the same variable from those different tables.
I also want to jump back to this name variable and give you a little bit of a warning. So it does cause a shift in Excel, especially if it’s a geography within a geography. And you’re going to see this when we open the file from our example here. And I’m not sure if this happens with every table type, but just keep that in mind that I know for a fact that we do not recommend using it for group calls, particularly with data profiles. So just keep that in mind that it can get a little bit messy. But again, I like to have it as a little check for me.
So before I move on, what happens if you want all variables in the table? What if you want the entire S1701? You can use a group call. So I have that down here. Um, you can also use data.census.gov if you have the geographies you selected already. That API button is going to do exactly what this is going to do for us.
So now we have the last part, which is the geography. And in many instances you will want to limit to a specific geography. And in this example I want one county. And you may be wondering how I got these numbers. And I did not, in fact, memorize every county code for every state to figure this out. I’m going to share another secret, and I think this one’s a little bit more exciting, but who knows? You’ll have to tell me.
So we’re back on the ACS five-year API page, and we’re still in the subject table section. I’m going to click on the fourth bullet down that says Examples. So this breaks it up by geographies. And since I’m looking at state and county, I’m going to look at the example API calls that I have here. And fortunately for me I’ve used this so much that it’s already, um, calling itself out.
There’s one here that has a wild called, wild card or the asterisks for county and state. So if I click on this, it’s going to actually give me, um, and hopefully let’s, that we’ve zoomed in, it’s giving me all counties in all states. It does have a random variable. Um, just to call it out again, as an example, you can leave that in there, or you can delete it with the comma and just have name. So now you have the call to get all of the counties in all of the states.
And again, your best friend, at least for now, is Ctrl+F. You’re going to start to type in your geography of interest. And luckily for me, the first Wyoming on this list is actually Wyoming County. So I can use context clues here and see 36 for all of the New York counties that shows up. So I know that’s my state code. And then the second three digit code, 121, is going to be my county code.
So now we have all the pieces we need. I’m going to jump back. And we have the &for county 121 and &in state is 36. So the nice thing here is that you don’t have to remember the little syntax components, the codes. If you follow an example, you’re going to be able to always have access to what you want, and then you can customize from there.
So much like getting the full table, that group call, you can get full geographies. So what if you wanted all counties within a state? You can use that wildcard in your calls like we just did. For some geographies, as we just did to get our geocodes, you can do the wild cards for both components. It’s really trial and error.
So let’s take this call. I’m going to copy it from my document, and I’m going to run it in the browser. So I copy paste it, and now I’m going to run, and this is what we have. So we are getting the number the estimate of those in Wyoming County in New York who live below poverty with the corresponding margin of error. And we see we have the name here, we have the estimate, the margin of error, and then the state and the county codes.
So I want to just show you back on this slide that your output might look different than what you see here. Sometimes it’s the browser you’re using or the settings. But it’s okay, because when you download it, it’s all the same.
So jumping back over to the browser, if all you needed was the estimate, you can stop here, but you can also download it. And what you’re going to do is you’re going to right click. You’re going to click Save As. You’re going to name your file. And this is important, you’re going to type in the file name .csv. The last step for the Save As type you’re going to select All Files. So you’re going to click Save. And it’s going to download that CSV. And I’ll open it up just to show you what we have.
So like I mentioned briefly, or maybe not briefly, I think briefly, name does cause a shift, especially when you have a geography within a geography. So I had a county within a state. So here it shifted my variables, and all I’m going to do is highlight these. I’m going to cut and paste to move them over. Um, so that is just what you’re going to look like, what it’s going to look like when you download your file.
So hopefully that was not too overwhelming. Um, and that was just a little bit of a breakdown of what the API is. So really quick, I now want to share some resources as you go on your own. But don’t worry, I do have contact information so you can always be connected with our team if you get stuck.
So when you’re on your own, start with checking the example calls to get yourself started. I sound like a broken record when I say that. That’s what we did today. So I want to emphasize how useful they can be and how much time you can save. You can always edit them to fit your needs, but having the base like we walked through can be really helpful.
Unfortunately, some variable names change with every data release, so variables are added and subtracted from tables, so it’s important that you check the variable names if you’re looking at data year after year, to make sure you are extracting the same data variable. It’s super easy when you use that variable list, so I always just open that HTML as soon as I get started, as you saw in the walkthrough. And then the other one is that Examples page. So these are the two that I use when I’m customizing the components of my call.
And one thing I want to mention is keys. Um, some of you may be wondering what or why, and a key is essentially just that: a way to open the door to more calls. Without a key, you are maxed at 500 calls a day, and if it’s just you and your organization running calls here, there, a key isn’t necessary. But if you are creating a dashboard that’s going to get a lot of traffic, you might consider a key. It’s completely free, and it takes mere moments.
Um, and I will mention that if you’re going to use the R package tidycensus, you need a key. And Kevin’s probably going to repeat that as well. Can’t do it without a key.
Um, this is just a start regarding the resources. Again, this PDF is going to be able to be clickable if you can’t get access, um, in the chat to the links. So there’s a lot on here. And if you’re lost I can always connect you. The last two in this webinar list, um, are going to be a good run-through of an example similar to what we did today with a little bit more detail. And I also included some resources for using open-source data and programs, which is really helpful if you’re using the API.
One really unique and valuable tool we have to offer is the Slack channel. There are Census staff that engage on their every day to help with data user questions, especially if you’re accessing data through different ways such as R or Python. And finally, as I mentioned, tidycensus, it’s a great R package to use with the Census API. It is not maintained by us, but it has great resources to guide you.
Um, and I finally want to mention a few final things before turning it over to Kevin to wow us with his expertise with tidycensus. There is a team at Census that has live workshops to go over that MDAT tool and the Census API. I highly recommend you sign up if you’re curious to learn more about either. These are great for both beginners and advanced users. Please consider joining the ACS Data Users Group that Lillian highlighted at the beginning of this workshop if you aren’t members already.
And I know these were very quick demonstrations of the PUMS and API, but you can email our team at acso.users.support@census.gov if you have any questions in the future. Thank you so much. And Kevin, the floor is yours.
Kevin Kane, Southern California Association of Governments: Well, goodness, Mary, thanks for such a thorough and comprehensive, uh, you know, overview of both PUMS and, uh, Census API calls. Hopefully I can build on it. Um, doing these is kind of your job, for the most part, I just kind of, uh, do this as somewhat of a service to a degree.
I’m Kevin Kane. I’m the Program Manager for demographics and growth visioning here at the Southern California Association of Governments. Uh, why do I do this, uh, type of, this type of a webinar? Just, you know, um, I find it extremely useful to kind of have effective workflows, certainly in my field, which is regional planning and demographics. But, uh, I also teach this material to a course at the University of Southern California.
So, um, you know, bottom line, uh, I find, Mary’s API call workflow, uh, to be really useful, but you are a little bit limited in terms of the replicability of it, um, by putting calls into a URL. And, uh, she gives me a hard time every time I follow her after a webinar, um, because of what I’ve titled this, uh, “R tidycensus: Your graceful exit from data.census.gov.” And what I’ll share with you here is basically the workflow that I kind of developed once data.census.gov, um, started a few years back in order to just kind of help, uh, you know, be a little bit more replicable.
Uh, Southern California Association of Governments has 191 cities under its purview across six counties in Southern California. So we’re working with a lot of county-, place-, uh, and tract-level data longitudinally, uh, and kind of that’s buried within either PUMS or other detailed tables. I’m sure that’s the workflow for a lot of folks here.
So, um, I’ll be very brief in terms of, uh, slides here, but, uh, really, what I want to mostly show to you is a demonstration. Um, because frankly, it’s not possible in 20 or so minutes to actually get into R or RStudio or a coding environment. But basically what I’m going to pick up where Mary left off, uh, and wrap that within an R, or a code-based workflow.
So R is an open-source, uh, programming language. RStudio is a freeware wrapper of it that just makes it a little bit easier to use. Um, I’ve included here some very easy installation instructions, uh, for you, uh, like teaching in this because, uh, it’s not a commercial product. You can take it to wherever you work, uh, and not have to worry about a license.
The second thing that I’ll say is I’ve posted a lot of training materials here on this GitHub, uh, website here. I’m not sure who, uh, you know, the level of folks are GitHub users or not. I frankly just use this for file transfer. I am going to have to confess, I’m more of an intermediate-level user of this and frankly of some of the R packages. But like all of us, you know, hey, we’re, we’re doing this to do our jobs better.
Um, so what I’ve done here is included a package which I call the kind of a half-day R introduction. There’s also a video where I did the full webinar for this, uh, if you like the workflow. Um, I would say it probably would take you about a half a day, roughly, to get through it and to actually learn R to a point where you can use the Census API usefully. Um, if you hit this code here, you can download a ZIP file containing all of this. The key file is one that has a dot R at the end of it. And that’s what we’re going to be kind of going through mostly today.
Um, switching back here to kind of all the information you’ll need. Um, Mary already gave you a lot of the Census API information, so I won’t repeat that. Um, uh, there’s a full recording, uh, of, of the webinar that takes you through how to actually get up and running in our studio so that you can get to the point where we’ll start here today. Um, and also the details on Kyle Walker is amazing, tidycensus package, uh, which, although not maintained by the Census Bureau, uh, clearly is good enough to make a make a guest appearance in a Census Bureau closing slide. So, uh, certainly has kind of revolutionized how I interact with American Community Survey material.
So, um, how to get up and running here. Basically, uh, I’m going to open up this particular dot R file for you in our studio. If you’ve gone to the GitHub page that I shared with you before, uh, and I’m sure perhaps, uh, if you do want to follow along, maybe I could task Lillian, who has this slide deck to toss it into the chat for folks. Um, but if you’re, uh, I’ll just go through a couple of ways to, uh, to kind of access and use code here.
But, um, at the, at the bottom bullet here, uh, is what’s in this, Rbootcamp file. I basically have 10 modules here. Module sections 1 through 6 are just basic data usage skills and visualization skills using R. I’m not going to go over those today. I’m going to skip them and start with section 7, which is how to use the Census API. Um, and then I’m going to provide you with section 8, which is basically a replicable code block for doing those API calls. Uh, once you’re, uh, kind of up and running in R, you can use that to basically declare whatever variables you want, geographies, etc., um, and get them in, in a nice tabular format, in Excel format, even a shapefile format, if you like to do that.
Um, and, uh, new since last time we’ve done this, I’ve added a little bit of a code block to get longitudinal ACS data if you want the full series from 2005 or 2009, uh, when when ACS one and five years started respectively until now on the same thing. Uh, and then a new little section here at the end on, um, doing a tract-level map of something in your census place or in your city, uh, as, uh, Lillian shared in one of her earlier slides. I’m going to nab it here, um, you know, a lot of kind of how you interact with this, the API is, as Mary also said, uh, it follows the Census Bureau’s geographic hierarchy. Um, you know, and there’s, there’s a difference whether you’re on kind of this main vertical or if you’re off the main vertical.
Um, you know what I tend to focus on, uh, are counties or, you know, as kind of a reflection of the overall trend or census tracts to kind of be reflective of neighborhood-type dynamics. ACS oftentimes does go down to block group as well, but you tend to get those high margin of errors, which, uh, you know, well, I’ll leave it to you to decide the level of importance of the margin of error for your for your, uh, for your workflow.
But one of the challenges is that, um, cognitively, uh, and electorally and everything like that, places are pretty important census places, uh, which are basically cities, towns, CDPs, etc., are really, uh, you know, how people interact with information. So if you’re looking to get an understanding of how a phenomenon, uh, is dispersed across the neighborhoods of a city, you really need this tract-to-place relationship. So I’ll go into that a little bit, um, as I do the demo.
Um, apologies. I’m not really able to see the chat right now, but, uh, please, please holler if any issues. And thanks, Lillian, uh, for putting those, uh, those links up there.
So I’m going to, uh, go over now to RStudio, where I’ve just opened up Rbootcamp, uh, 2024.R. So really basic two ways, two main ways to enter code. On the righthand side here I’ve got a script file, which is, um, I prepared this, this one for you here. It’s about 500 lines or so and goes through those 10 modules. You can update it, change it, change things, um, and using this nice hashtag here could kind of comment something out. So, for example, line 22 here, um, the command is Print; I’m gonna print something, and then I made myself a little note behind the hashtag here.
On the left side is actually where you’re executing code. It’s got this little triangle called a chevron and a blinking cursive. So if I want to use the Print command to say “hello world,” which is sometimes what folks do when they start a new programming language, it’s going to return to me a line that says “hello world” back, because that’s what I asked it to do. Um, certainly when we get a little bit more sophisticated with our calls and things that we want to put into the console here, uh, typing it is not going to be efficient. So that’s why we have the script file up on the righthand side here.
So long as your cursor is on a line or has highlighted a portion of code, there are a lot of easier ways to run that code. The first one is to go up here to the top right and hit run. It’s going to do the same thing. Or if your cursor is just on it and you hit Ctrl+R if on a PC, Command-R on a Mac, or in some instances it’s Ctrl+Enter. I’m not sure exactly why people’s computers all have slightly different setups. That’s going to be the other way that you can run this line of code.
The second thing that I’ll mention about kind of RStudio in general, um, in terms of this workflow, is to just be really careful what you’re working directory is. What that means is a file path on your computer somewhere where you’re saving data, where you’re saving images, where you’re saving your output, or sometimes reading in data as well.
Um, there are a few ways to do this. Um, you can, uh, if I type “getwd,” it’s going to get my working directory. Goodness. The default is, uh, what appears to be somewhat something of a My Documents on a C drive. Um, I can go up here to Session, Set Working Directory, and choose, uh, where I want to pull information from. Or I can declare it in the code here. I’ve already written it down here is “setwd.” So if I “setwd,” um, something I’d like to do kind of early in the workflow, uh, I’m going to be working with this folder. Um, and you can see it’s in Dropbox, Rbootcamp as, as the folder.
So, um, that’s just the absolute basics again. Um, if you want more information, you know, certainly I would suggest downloading, uh, the package from GitHub, including this dot R file following along yourself or following it along in the video link there. And right now I’m going to scroll down to the fun step to actually the, uh, using Census API here in R, which is, uh, what I have as section 7 here.
So, um, the way that, uh, R is, is useful is that it kind of has a lot of base functionality kind of built into it. And then it’s very customizable. Folks have built, um, tons of different packages in it. And the one that’s really helpful is called tidycensus. I’m also going to be using a few other packages here to be able to work with spatial data and to do some other data manipulation.
Um, when you install it, you have to do two things to use a package in R, first you have to install it, and you just have to do that once. But then every time you open R or RStudio, you do have to kind of invoke the package or activate the package. So you install it with this line here, Install Packages. And I’m not going to run that because it’s installed already on my machine. But I am going to highlight all of these and activate these four packages here by running this line of code. So this is basically telling our studio, hey, add this new functionality to this instance of the program that you’re working on.
Mary already mentioned getting a Census API key, which you will need. Uh, it takes, she said, mere moments to sign up. I think it takes probably like 2.5 seconds perhaps. Uh, and that, that is an alphanumeric code that’s a little bit ugly here, but, um, it allows you to actually use this because you are going to be iterating and pulling a lot of things. Um, it’s nice not to overwhelm the, uh, the, you know, our, our federal government’s, uh, servers, uh, the Census Bureau.
So they’re, uh, the first thing that you’ll have to do is to enter your Census API key here. And tidycensus has a command called, you know, what do you know, Census API key. So you put it in here like this and hit Run. Here’s my Census API key and boom, you’re done. Um, it gives you a new flashing chevron. Uh, so that means it’s taking the line of code, uh, effectively.
So, um, really we’re just working with a couple of key commands here. Um, as Mary had mentioned earlier, there are a lot of things available through the Census API, the Economic Census, the decennial, various other programs that the Bureau has, and ACS being the key one.
In tidycensus, you’ve got decennial and you’ve got ACS. So “get_decennial” is the command here for how to how to get something from the decennial census. And this gets decennial census command takes a few different arguments. And you can see what I’ve set up here in line 405 is, well let’s see, I want state-level geography. So I want state-level data. I want this variable. We’ll get to how you search for variables in a little bit. You know a little bit already.
Um, I want the census summary file, and I want from the year 2000. So I’m going to run this “get_decennial” command. And then what this equal sign does is it puts it in an object or thing or a, you know, something that you can call back called medrent00. I’ve just called it medrent00. I could call it whatever I want. So I’m going to run this line here, and what it’s doing there, uh, for that a quarter second is it’s actually getting the data. And now if I just type that run, oh, um, it’s going to show me the median rent across all the 50 states.
Uh, I can make it a little bit easier by using the view command and view medrent00. What that will do is pop it up into something that looks a little bit more like Excel or tabular data and see that, um, goodness, in Alabama in 2000, rent is probably a heck of a lot less than it is today. Um, quite a bit higher in Alaska. So, you know, this passes the smell test. Always a good check. Uh, when you’re, when you’re doing a new data extraction process.
Um, so that’s useful. Um, you know, you can certainly there’s, there’s commands, right dot CSV commands to save this in Excel. You know, if you really want to, you can just grab and copy or what have you, uh, from here. But R has a lot of really nice visualization capabilities, so it’s nice to be able to take advantage of them.
I’ve left you with a few examples in this code here that you can, you know, certainly, uh, you know, modify the name of the, the variable, the data set you’ve extracted or the variable or change some of the other parameters. But if I run this line here, it’s going to make a nice little bar plot, um, of states by rent. And you can see here. Oh, Hawaii is quite, by quite a bit the highest. And this is alphabetized, um, well, not quite alphabetized by FIPS code, but, you know, thereabouts.
I’m going to close this here, and, and I’ve made a slightly fancier bar plot here with some bells and whistles by sorting the data, adding some color, adding a label, adding some guidelines. And I can highlight all of this and hit Run or Ctrl+R or what have you. And it gives me a really nice little bar plot here of state median rents in the year 2000. Again, seeing how it varies from a high of Hawaii to a low of North Dakota. Um, did not expect that to be lower than in Puerto Rico even, but goodness.
So, uh, here. So that’s, that’s just the way to kind of get a little bit of a visualization. And I haven’t uploaded any data into my program, which you usually have to do. Um, as long as you have the internet and a Census API key and tidycensus, uh, as a package installed, uh, you’re able to just extract it in one clean flow.
Now, in order to find good variables to use, uh, Mary already gave a little bit of a tutorial to that, but, um, you can do that within tidycensus if you want to. So, um, load variables is a, is a command here. And I’ve just asked it to look at 2022, five-year ACS, um, and put it into an object that I’ll call “acsvars” and, um, oh goodness, I have 28,152 entries for, for, uh, you know, explicit ACS variables that are coming in through what I imagine, uh, Mary can correct me if I’m wrong, what I imagine are the detailed tables rather than the summary tables.
Um, in any case, uh, this is a little bit cumbersome, you know, certainly. Um, and Ctrl+F is one of your friends. You can write this to a CSV here as comma-separated values file and, and open it up if you want to. But in the GitHub site I’ve included, um, my little cheat sheet. Um, if it’s useful to you, happy to share. But these are my top one, top most commonly used 125 ACS variables with their code and a somewhat intuitive abbreviation, um, that I’ve, that I’ve, uh, renamed it, “totpop,” for example, or median age. Um, this includes just some of the, the age structure, basics, race, race, ethnicity, commuting, educational attainment, income, and housing. Just to give kind of a smattering. Um, so if you want to start there, um, that’s, that’s not a bad way, at least, at least in my view.
So, um. Right. Uh, so, so now that we’ve found some good census variables to use, and we’ll scroll down just a little bit here and try to assemble, um, some tract-level variables for a county. Um, now this is the kind of the main command here. It’s get underscore ACS and you pass it a lot of information. I want tract-level data. I want the state of California, Orange County, and this variable here, 25035, which is the median age of the housing stock in each tract. You’ll notice that I’ve also added this argument called geometry equals true. This will also extract the data as spatial data so that you can visualize it right here in R. Or you can export it as a shapefile if you’re a GIS user.
So, um, it just takes maybe two or three seconds or so to get all the tracks, uh, in Orange County, California. Um, if I look at what this is, “head” just gives me the first five rows of any given data set. Uh, let’s see, I’ve got a GEOID. This looks like my FIPS code. I’ve got estimate, which is actually the value I’m looking for. So this tracks median housing home year, built year was 1971, 1959. All right. So this passes the smell test. Certainly these are reasonable values especially in the western United States.
Um, so I can do just a little bit of manipulation, renaming it old age. Um, you know, getting rid of the old one. And if I want to see how many rows there are, take a quick look and see that there are 614 tracts in Orange County, California. So now I have a good understanding of, of the rows and columns, which at the end of the day, that’s all data are.
What if you need more than one variable? Um, tidycensus will extract it for you, but it’s a little bit trickier because it does it long. Um, to show you what I mean, I’m going to, um, make a list of three variables: population; housing stock age, which we already did; uh, and median household income. And I can extract those in one single call by, uh, declaring this list that I made as the variables that I want. So I’m going to call this one TR underscore plus. Again, it just took a second.
And if I want to see how many rows are in underscore plus, oh goodness, it’s 1,842. Well I know there are 614 tracts. So, um, I can take a look at it and see that, hmm, this is not stacked in a terribly intuitive way. I’ve got three records for each tract, and each one’s for a different variable. Kind of a pain in the butt when you want to do math, compare it, put things as a rate, uh, put them on a map, uh, or anything like that. So, um, you know, if you really want to use it, you can use something called the match command, which is described in the earlier sections that I totally glossed over. Uh, and, and do a subset of this lengthy file and then and then bind it to your original text file. So now I have 614 entries here and eight total columns. I’ve got one for home age, total population, median income. My apologies to the Bureau for omitting the margins of error here, but you can grab those as well, especially for tracts. Mea culpa.
Um, some of the other nice features within R is that you can actually just plot this as a map using one of the, using what’s called the SF package. So if I hit line 450 here, sorry, um, uh, goodness, I can get a nice little map already of the tracks in Orange County. And again, uh, let’s see, we’ve got 1940s, 1950s here, kind of in the North Side. This is downtown Santa Ana, the older neighborhoods of city of Anaheim. Those look a little bit older, uh, then used to get to the south, to Irvine, to Laguna, Niguel, Coto de Caza. These are the newish developments up in the hills. You can see that reflected in the more curvilinear boundaries but also in the, the newer home ages there.
So, um, neat little trick there. And if you are a GIS user, you can use this “st_write” command here. Um, whoops. To write an entire shapefile. Um, now this is, uh. Let’s see. What did I call it? I called it orange underscore merge. So if I go back to here now, I have four files here I’ve seen if you’re a GIS user, you know, you’ve got somewhere between three and eight files typically together in a shapefile format. But now I can use this in GIS. I have orange underscore merge. All right.
So, uh, racing along right here. Um, hope folks are getting a little bit out of this at least. But what I’ve built here in section 8, um, is a way to group a lot of variables together. Uh, like I said, it’s a little bit clunky to extract variables one by one because they’re stacked long. So you want to make a loop and, uh, loops are, you know, a little bit more advanced coding skill. Uh, but I’ve built this to hopefully make it so that you can just enter your parameters here, um, and, uh, and then run this big block of code in section 8 and then get a good data set.
So I’m going to ask the audience here for somebody to put in the chat a state and a county, like not a tiny county, at least the medium-sized one. You know, five more seconds before I use Tampa. Okay. Sacramento. Let’s do, let’s do Sacramento okay. Thank you.
So Sacramento County, California, my state equals CA. My county equals Sacramento. Let’s see. Let’s I’ll run the first chunk of this. And the first chunk. The way I’ve set this up is it’s just grabbing total population B01001 underscore 001. And then what I’m doing is taking this whole big list of 125 variables that I’ve shared with you earlier in this spreadsheet here. Uh, and then I’m renaming them to something that’s a little bit intuitive. Um, not perfect, of course, but, uh, you know, if you follow a logic, uh, you know, commute, walk, uh, median household income, you know, female aged 5 to 9, etc. Uh, you know, should be logical. Select all of this, even do a little bit of math on the end of it. And it’s really only going to take probably a few seconds to extract this for, um, 125 different variables for, um, the tracts in Sacramento County.
All right. There we go. I can view this. I just called it D to keep it a little bit easier. Uh, and now you can see all the tracts, uh, total households, median age of 29. Goodness, that’s quite a bit under median. So that must be a young area. Um, race ethnicity, variables, etc. Um, how I put them up, I’ve got 135 total columns in this, uh, in this data frame right now. I can write it to a CSV right here. Whoops. I called it Hillsboro, Florida. Sacramento. Don’t get confused now. My Hillsborough file is messed up, but, uh, that was, uh, that was from somebody else. So Sacramento tracts and ACS can just open it up in Excel in a comma-separated values format, um, and, um, manipulate that however you like.
So there you go. You’ve got, um, uh. Uh, you can also write it to a shapefile here. Um, Again, make sure you name it the right thing so you don’t forget that that’s Cook County, Illinois. Um, and, uh, you know, you can do some plotting. Um, here is median median home value in Sacramento County. I’m not super familiar with the urban geography of Sacramento, but I’m assuming this is a little bit more kind of an inner ring neighborhoods in the downtown core and then the fringe, you see some higher income as well. This is all in the SF package. So there are a lot of parameters that you can do here.
Uh, the nice thing is, well, by doing this workflow is that you can just do math right here. So what if I want to know if the the share of commuters who work from home. Uh, a question that we get asked all the time. Uh, so I could just do the, uh, number who work from home divided by the total population of commuters. Do a little math here and then plot that variable. So, okay, the work from home share in Sacramento County is way high out here, fairly high in somewhat of the downtown core, and a little bit mixed. Again, you can do quite a bit of a different analysis here if you’d like.
Um. And then if you want to plot the variable a little bit more neatly, um, I’ve got median home value pulled up here with the Jenks optimization so that it gets some nice natural breaks. You can do a reasonable looking plot just right here in R without having to open up GIS or anything else.
Two more quick tricks here before the getting in under the gun at 12:30, uh, Pacific time. that is, um, is a task we often need is to get longitudinal ACS data. Um, I find this a little bit tricky, um, because you do have to iterate quite a bit. Um, can somebody, let’s see. I’m going to pull, um, Milwaukee County, Wisconsin, from, from the chat here for this example. But basically what I’m doing here, um, is I’m making a sequence of all the ACS years that are available. Um, sending, I use a lot of one-year data because I tend to work in big counties. Um, so it’s a little bit tricky because it didn’t exist for 2020. So you have to make sure to make a list that has that gap in there. Um, five year, that’s not an issue.
But in any case, um, what I’ve kind of given here is a not quite as sleek of a, of a loop as, as earlier, but a mechanism to, uh, go through and enter whatever I’d like to here, Milwaukee County. So, um, this is going to take a couple of seconds, in fact, to run this because it’s pulling, um, well, that’s what I’ve come across. Oh, I don’t think that. I think there we go. So now you can see as this runs here in the red text, it’s 2008, 2009. It’s just looping through, uh, all of the available ACS years to get me, um, two variables here. I put them in. I kind of snuck it in here. One is, it’s what I just showed you earlier, the number of people who work from home versus the total commuters. So, um, and what this can give you right here is total commuters in 2005, the number who work from home, and then a really nice time series of how work from home has evolved since the ACS started collecting data on it, uh, in 2005.
So again, you can write that, you can use it later. Um, I can plot it here, make a little plot and see. Goodness, that’s what happened here during COVID. Uh, and then in the most recent year, a little bit of a dip. I can make a better line graph that I’ve put a few bells and whistles into. Uh, whoops, I forgot to change this to Milwaukee County, Wisconsin. I’ll do that in just a second here. Um, and I can even make a, um, a comparative graph. Change that to Milwaukee, just so I don’t get confused. So an example of how to do a little bit of these edits here.
And what I’m also going to do is I’m going to, I’m going to make a comparative graph here. I’m going to also extract Sangamon County, Illinois, which is Springfield, which is kind of a smallish mid-sized city. Uh, and then, um, and then run through this again. And once I run through this again, I’ll be able to have a graph that compares two different places in their work from home trajectories, which is kind of interesting. And this is probably the slowest part of the Census API, at least the way I built this here.
All right. So now I can see Milwaukee County work from home. Goodness, shot up during COVID and went down, but a much smaller, um, you know, uh, metro area, uh, had not only a lower level overall but didn’t see a kind of a drop in 2022 as a return to office happened. So again, just an example of some of the analysis you might be able to do with this.
I’ll share one final tip and trick in the last couple of minutes that I have with you here. Um, and it’s something that we just, just figured out. Um, my colleague Echo Xiang, who’s also on the call, and I, um, is to do a tract-level map of something in a single city. So while I’m doing this, if somebody can, um, tell me a city and the county it’s in, uh, to make a tract-level map, something that actually has not just a few tracts, something that’s a little bit at least medium size.
And this is going to, um, uh, this is going to require a few new packages: terra, readr, and mapview. All right. Let’s do, uh, let’s do, um, Oklahoma City. Actually, it has city twice. I’m not 100% sure it’s going to work. Um, how about, um, Tempe, Arizona, Maricopa County. Tempe, Arizona.
Um, I’m just going to work with median household income right now to show you this. And, um, you know, also one, one thing that I’ve given you here, um, in the GitHub is a file, that’s a relationship file developed from geo core that I use to relate tracts to census places, because, again, it’s not on that main spine of the census geographic hierarchy. Um, so, you know, it gives you the percentage, you know, tracts don’t necessarily nest within cities or places. And this, this tells you, for example, uh, Autauga County, Alabama, which we always see when we’re doing census work nationwide. Uh, nine, 98.42% is in this tract, and 1.5% is apparently outside of Prattville, Alabama. Um, so just, that’s all to say that you can define a threshold, um, to kind of get rid of some of the superfluous stuff that’s, you know, 99% outside of the city.
So I’m going to declare a place, a variable household income. I’m going to make sure that I asked for Maricopa County, Arizona. Uh, solicit this tract data here. Make sure everything works. Okay. Looks like it all works. And then I’m going to use this neat mapview feature here, see. All right. Some, some issues here. So I’m going to go back to, uh, press the old Riverside County, California. What is this for? Apologies for the work. If you troubleshoot, I’m sure.
All right, here we go. A dynamic map of Riverside County, California, by median household income. Uh, mapview even allows you to hover and see what the household incomes are. You can do a pretty yeoman’s job of exporting the image. And, uh, there you go. There is your analysis.
So anyways, check the GitHub. Um, hope this was a helpful demonstration. A little bit sloppy, albeit, but, um, uh, enjoy. And thanks for participating. I think I’ll turn it back to, uh, Lillian and/or Mark for the kind of the closing.
Mark Mather: Great. Thanks so much, Kevin. Um, we are we, it’s 3:20, it’s 3:29 East Coast time. So I know we’re almost at the end of the time for the webinar, but, um, and this was an incredible amount of information. So just as a reminder, we will be sending out a recording and the slides that have all of the relevant links. I think that, um, flew by in many of these, in many of these presentations.
Um, I think because of the time we are going to officially close the webinar, but the panelists have agreed to, I think that you all agreed to stay for a few more minutes. If anybody wants to stay behind, uh, more informally and ask them some questions, we can, um, unmute you and, um, you know, five or 10 more minutes, I think, and we can, uh, turn off the recording so we can just speak more informally. But, uh, with that, I do want to officially close the webinar. I’ll stop recording. And thank you all for joining.
Sara Srygley
Research Associate
Print Publication
New data released today by PRB and the Appalachian Regional Commission shows that rates of labor force participation, educational attainment, income, and poverty continue to improve in Appalachia.
The 14th annual update of The Appalachian Region: A Data Overview from the 2018-2022 American Community Survey draws from the latest American Community Survey and comparable 2022 Census Population Estimates. Known as “The Chartbook,” the report contains more than 300,000 data points comparing Appalachia’s regional, subregional, state, and county economic status with the rest of the nation.
Key improvements in the region’s economic indicators are as follows.
Increased income and lower poverty rates
- Poverty rates declined in every Appalachian subregion, state, and type of county (urban and rural). The region’s overall poverty rate (14.3 percent) decreased two percentage points between 2013-2017 and 2018-2022.
- Median family income increased 9.3 percent between 2013-2017 and 2018-2022, which was on par with national median income growth.
- All income measures increased for every subregion, state, and type of county (urban and rural)—even after adjusting for inflation.
Higher educational attainment and labor force participation
- The share of individuals ages 25 and older who held Bachelor’s degrees increased by three percentage points, with more than one in four Appalachian adults reaching or surpassing this level of educational attainment in 2022.
- Between 2013-2017 and 2018-2022, labor force participation increased in every Appalachian subregion and type of county (urban and rural).
Increased population growth in south
- Southern Appalachia’s population increased 11.8 percent between 2010 and 2022, which surpassed the nation’s population growth average by more than four percentage points.
Increase in broadband access
- The share of Appalachian households with at least one computer device rose 8.6 percentage points between 2013-2017 and 2018-2022, while the share with broadband internet access increased by 12.2 percentage points. Both increases surpassed the national average, with federal and state programs designed to narrow persistent gaps in digital resources likely contributing to improvements.
“We celebrate the progress Appalachia has made, including declined poverty rates and increased broadband access. However, we know that there is still much work to be done for our entire region to reach economic parity with the rest of the country,” said ARC Federal Co-Chair Gayle Manchin. “ARC will continue to prioritize the quality of life of Appalachia’s 26 million residents, and remains committed to continued collaboration across federal, state, and local levels to ensure our people have a bright future.”
Despite positive trends, several data points revealed vulnerabilities that emphasize the inequities in Appalachia compared to the rest of the nation:
Overall population decline
- Nearly 60 percent (252) of the region’s 423 counties saw a population decline between mid-2010 and mid-2022. Rural counties were especially susceptible—77 of the 107 rural Appalachian counties lost residents.
Poverty rates for children and families and specific counties
- Though regional poverty rates have declined overall, rates have stayed the same or increased in 76 Appalachian counties. Poverty rates are highest for Appalachians under 18 (19.2 percent) and ages 18-24 (22.1 percent).
- Though the percentage of Appalachian households receiving payments from the federal Supplemental Nutrition Assistance Program (SNAP) decreased slightly more than the national average, participation was still higher (over 13 percent) compared to all U.S. households (over 11 percent). Participation of Central Appalachian households reached more than 20 percent.
- For households with children under the age of 18, Appalachia’s SNAP participation rate (21 percent) is nearly three percentage points higher than all U.S. households.
Disability and poverty in older adults
- Appalachia’s population trends older than the nation as a whole, with individuals ages 65 and older reaching at least 19.5 percent in 292 Appalachian counties.
- Additionally, the percentage of Appalachians ages 65 and older with a disability is more than three percentage points higher than the national rate. This was also the only age group for which poverty rates increased slightly.
Despite gains in access, digital divides persist
- Even with higher-than-average increases, Appalachian households still lagged nearly four percentage points behind U.S. rates for broadband subscriptions and device ownership. In 73 Appalachian counties, households were at least 13.3 percentage points below the U.S. average for broadband subscriptions. This gap in high-speed internet connectivity impacts residents’ access to remote work, online learning, telehealth, and more.
“The data in this year’s Chartbook highlight strides being made in the Appalachian Region, with noteworthy improvements across economic, educational, and health-related measures,” said Sara Srygley, a senior research analyst at PRB. “Yet, these data also emphasize considerable variation throughout the region—particularly the persistent challenges facing rural communities.”
The data show that Appalachia’s rural areas continue to be more vulnerable than its urban areas. Appalachia’s 107 rural counties are also more uniquely challenged, compared to 841 similarly designated rural counties across the rest of the U.S. Though rural Appalachians did have higher health insurance coverage than the rest of rural America, rural Appalachian counties continue to lag behind on educational attainment, labor force participation, broadband access, household income and population growth.
The Appalachian Region: A Data Overview from the 2018-2022 American Community Survey was written by PRB and the Appalachian Regional Commission.
In addition to the written report, ARC offers companion web pages on Appalachia’s population, employment, education, income and poverty, computer and broadband access, and rural Appalachian counties compared to the rest of rural America’s counties. For more information, visit www.arc.gov/chartbook.
About the Appalachian Regional Commission
The Appalachian Regional Commission is an economic development entity of the federal government and 13 state governments focusing on 423 counties across the Appalachian Region. ARC’s mission is to innovate, partner, and invest to build community capacity and strengthen economic growth in Appalachia to help the region achieve socioeconomic parity with the nation.
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