Family Instability... It's Not Just Mom and Dad

by Kristin Perkins
Postdoctoral Fellow

Children experience many changes in their households while they are growing up. But while we often think about divorcing and remarrying parents as common changes in household composition that affect many children, in new research, I show that changes involving extended family members and nonrelatives are far more common than changes involving a parent. This finding is significant because prior research suggests that it is likely that instability involving nonparental household members affects children's outcomes. It is therefore relevant to assess the extent to which children are exposed to these transitions and how exposure varies by race and family structure.

To gauge the extent of household changes, I used the nationally representative Survey of Income and Program Participation (SIPP) to track a sample of more than 72,000 children and their households over approximately two years. The SIPP interviews households every four months and documents the set of household members at each interview. This allowed me to identify the relationship between each child and each other household member and to determine who exited or entered the households between interviews.

Overall, by the end of two years (after six interviews), about one percent of children experienced a change in household composition involving their mother and five percent experience a change involving their father. In contrast, more than 10 percent experienced a change involving a grandparent, aunt, uncle, cousin, or other extended family member. In addition, more than four percent of children experienced a change involving a nonrelative. This means that if we think only about divorcing and remarrying parents, we miss changes that affect 14 percent of children over a period of about two years.

These rates vary significantly by type of household, by race, and by ethnicity. For example, 18 percent of children living with a single parent and 29 percent of children living with no parents had an extended family member enter or exit their households compared to only seven percent of children who live with two parents. Similarly, over 10 percent of children living with a single parent or no parent experienced a change involving a nonrelative compared to two percent of children living with two parents (Figure 1).

Moreover, 17 percent of black children and 18 percent of Hispanic children experienced a change in household composition involving extended family members compared to only six percent of white children. There is not as much difference by race and ethnicity, however, for changes involving nonrelatives. Rather, the rate clustered around five percent for all three groups (Figure 2).

Taken together, these findings mean that considering only instability involving parents may not uncover differences by family structure, race, and ethnicity that a broader conceptualization of household instability would reveal. This is important because many studies find that divorce has negative effects on children's well-being. My research showing the much more widespread exposure to changes in household composition highlights the need for future research that assesses whether these other changes in household composition are detrimental—or beneficial—for children. For example: do the distraction and stress from instability involving relatives and nonfamily members mean children perform less well in school or have more behavior problems? Or do some types of changes reflect closer relationships with extended kin that are beneficial for children? Such questions, which were beyond the scope of my analysis, clearly merit further attention.

These descriptive findings and future work on the consequences of household instability for children could also have implications for housing policy. If high housing cost burdens and a lack of affordable options contribute to changes in household composition and if those changes are detrimental to children, then expanding the supply of affordable housing and targeting it to specific households and/or specific high-cost or low-income geographies could greatly help those children. Moreover, such policies could have meaningful spillover effects because providing housing assistance to families not only might benefit children in the family that receives the assistance, but could also help children in the households their families otherwise would have joined when they doubled up with extended family members or nonrelatives.

Why is Moving to a New Home Worse for African-American and Hispanic Children than for White Children?

by Kristin Perkins
Postdoctoral Fellow

Compared to children who do not move to a new home, children who move are more likely to do worse in school, have more physical and mental health problems, and are more likely to be delinquent and use alcohol and drugs. In recent research that uses detailed data from the Project on Human Development in Chicago Neighborhoods, I find that African-American and Hispanic children showed more signs of anxiety and depression after they moved. I also find that, on average, Hispanic children demonstrated more aggressive behavior after they moved (Figure 1). White children in this sample, however, did not appear to be negatively affected by a move.
Figure 1.  

Why might moving be worse for African-American and Hispanic children than it is for white children? Perhaps non-white children are more likely to be exposed to violence or have fewer social supports in their homes and neighborhoods, which would make them more susceptible to the disruptive effects of a move? Neither of those factors, however, explained the negative effect of moving for African-American and Hispanic children (as measured by the Child Behavior Checklist, well-established scales that are frequently used as indicators of child behavior). A variety of other factors, such as being renters instead of homeowners and, for Hispanic children, their immigration history, also failed to explain the differences.

Another factor could be the differences between the types of neighborhoods that people are leaving and those they are entering. In general, most of the children in my sample whose families left their neighborhoods moved to a new neighborhood with similar characteristics. This is consistent with other research showing that it is uncommon for families to move to new neighborhoods that are radically different (in terms of poverty level and other characteristics) from the neighborhoods they are leaving. Given this, it's not surprising that among those moving to similar (or worse) neighborhoods, African-American and Hispanic children showed more signs of anxiety and depression, on average, after they moved.

I do, however, have suggestive findings that indicate that African-American children who moved to much better neighborhoods, within or beyond the city of Chicago, did not experience increases in anxiety and depression, unlike African-American children who moved to similar or worse neighborhoods. This finding is consistent with research on the Moving to Opportunity program showing better outcomes in some domains for children who moved from neighborhoods characterized by concentrated poverty to lower poverty neighborhoods.

These and similar findings from other studies of residential mobility and neighborhood effects have several possible implications for policymakers. The data suggest that the children most likely to experience negative effects of moves seem to be similar to children that Matthew Desmond's work on evictions shows are more likely to experience forced moves. If this is the case, the findings underscore the importance of efforts to prevent and reduce evictions and other forced moves.

The findings also suggest that policymakers pursuing programs that aim to improve neighborhood contexts by relocating families need to acknowledge the potential disruptive effects of residential mobility that could undermine the benefits of those moves. If further research confirm the suggestive results showing that the disruptive effects of residential mobility may differ depending on the characteristics of the destination neighborhood, then mobility programs should be designed to focus on efforts to move families to more advantaged neighborhoods.

Beyond mobility programs, policymakers might consider the extent to which other programs and policies unintentionally increase the number of moves that children make and thus increase the possibility of negative outcomes. As one example, it would be useful to determine if the Housing Choice Voucher Program's time limits for finding a unit to rent with a voucher unnecessarily result in temporary moves before a household finds a permanent unit.

Taken as a whole, such measures could potentially reduce negative outcomes among African-American and Hispanic children whose families have to move, particularly those who have to move frequently.

The Continued Growth of Multigenerational Living

by Shannon Rieger
Research Assistant

A substantial number and share of older Americans are living in “multigenerational” households, according to our analysis of recently released 2015 American Community Survey (ACS) one-year population estimates. In total, 20.3 percent of all non-institutionalized adults aged 65 and over – about 9.4 million people – live in multigenerational households that include at least two generations of adults (individuals over the age of 25). The ACS data also show large differences in the prevalence and composition of multigenerational homes by age, race, and ethnicity.

The new data not only reflect the fact that there are a growing number of older Americans, but also that the share of older Americans living in multigenerational homes has been growing steadily since the 1980s. These trends are likely to continue as baby boomers age. Importantly, multigenerational living might allow some older Americans to enjoy a higher quality of life while aging in place, as an overwhelming majority of people want to do. At the same time, for some families of limited means, multigenerational living may be a financial necessity rather than a desirable living situation. Regardless of why they are choosing multigenerational living arrangements, providing families with education and support to suitably modify their homes could help these arrangements be as safe, effective, and beneficial as possible.

Who Lives in Multigenerational Homes?

About two-thirds of the 9.4 million older adults living in multigenerational homes live in households that have exactly two adult generations (usually parents and adult children aged 25 or older). The rest are in three-or-more-generation households that typically include grandparents, adult children, and grandchildren.

Trends in multigenerational living also change with age (Figure 1). The share of people living in multigenerational settings is highest for individuals in their late 20s (mostly due to adult children still living at home), then drops for those in their 30s as young adults move out and form their own households. The share rises again for people in their early 40s until peaking at about 23 percent for people in their late 50s. This “sandwich” age group includes people who are living with their adult children, those who are living with their aging parents, who often need daily support and care, and those living with both their children and aging parents.

Notes: Multigenerational households are those with least two adult generations aged 25 or older or that include grandchildren, adult children, and grandparents. Householders and parents are considered “adults” regardless of age. Other household members include extended family members (e.g. aunts, uncles, nieces, nephews) and unrelated individuals. Source: JCHS tabulations of US Census Bureau, 2015 American Community Survey 1-year Estimates. 

Because adult children move out and elderly parents pass away, the share of people living in multigenerational households declines for people who are in their 60s and early 70s. However, the share rises steadily for older adults in their mid-70s, who often are starting to face more daunting health and financial challenges. Among the oldest age groups (aged 85 and over), 27 percent – about 1.5 million people – lived in multigenerational households in 2015.

In addition to differences in age, people of color and foreign-born individuals are far more likely to live in multigenerational settings than non-Hispanic whites and people born in the United States (Figure 2). More than 25 percent of native-born blacks, Hispanics, and Asians/others aged 65 and over live in multigenerational homes, as do more than 45 percent of foreign-born in all three of these groups. In contrast, 15 percent of native-born non-Hispanic whites of the same age, and just over 20 percent of foreign-born non-Hispanic whites, live in multigenerational households. 

Notes: Whites, blacks, and Asians/others are non-Hispanic. Hispanics may be of any race. Multigenerational households are those with least two adult generations aged 25 or older or that include grandchildren, adult children, and grandparents. Householders and parents are considered “adults” regardless of age.

Source: JCHS tabulations of US Census Bureau, 2015 American Community Survey 1-year Estimates. 

A sizeable subset of these multigenerational homes include at least three generations: usually grandparents, adult children, and grandchildren living together under the same roof. Roughly ten percent of native-born blacks, Hispanics, and Asians/others aged 65 or over live in such households, along with around 25 percent of foreign-born older adults in each group. Among non-Hispanic whites, just under 4 percent of older native-born adults and 7 percent of the foreign-born live with three or more generations.

Looking forward, projected growth and demographic shifts in the older population seem likely to increase the number of multigenerational households and the share of people living in those households. The U.S. Census Bureau’s most recent population projections estimate that by 2035, about 79 million Americans will be age 65 or older, an increase of more than 30 million people in just two decades. This growth is due to the fact that the baby boom generation is getting older and because with increases in longevity more people will live well into their 80s, 90s, and beyond.  In fact, the Census Bureau projects the number of “oldest old” adults aged 85 and over to double over the next two decades.

The racial and ethnic composition of the older population will also shift markedly over the next several decades. The non-Hispanic white share of the 65-and-over population is projected to drop nearly ten percentage points to 69 percent by 2035, while the black, Hispanic, and Asian shares will rise, respectively, by 20 percent, 67 percent, and 39 percent (Figure 3). Census Bureau projections estimate that the foreign-born share of the 65 and over population will also continue to increase, growing from 13 percent in 2015 to 19 percent in 2035. Though the direction of future residential preferences among the older population is uncertain, the sheer magnitude of growth in the older population and the fact that much of the growth will be among the very old, people of color, and the foreign born suggests there will be substantial growth in multigenerational households in the coming years. 

Notes: Whites, blacks, and Asians/others are non-Hispanic. Hispanics may be of any race.   

Source: JCHS tabulations of US Census Bureau, 2014 Population Projections. 

Impacts on Housing and Services

As this growth occurs, it will be important to consider how new and existing housing stock might be designed or modified to best meet the needs of multigenerational households. Universal design features including single-floor living, zero-step entrances, and hallways and doorways wide enough to accommodate wheelchairs, walkers, or strollers can make homes more accessible for older adults with mobility limitations as well as for their young grandchildren. Flexible layouts that can change as family needs evolve, as well as the addition of semi-private spaces for each generation (such as in-law suites with separate entrances, multiple master bedrooms or kitchens, and accessory dwelling units), can also help make the housing stock better suited for multigenerational households.

While multigenerational living works well for many households, it is important to note that it is not necessarily a desirable option for every family. Rather, multigenerational living may be a financial necessity rather than an attractive housing option not only for families with lower incomes but also for moderate-income families living in higher-cost areas. Further, sharing a home with multiple generations can be challenging, particularly if the house is small, has inadequate amenities, or there are unclear or unrealistic expectations about responsibilities for both finances and personal care. Finally, informal help from family members may not be an adequate replacement for professional care, particularly for aging adults with serious health conditions. Providing families with guidance about how to live successfully in multigenerational settings, and, perhaps, with financial assistance to make home upgrades and modifications, will therefore be critical if multigenerational living is going to be an appealing, comfortable option for families of all means. While designing and carrying out such policies and programs will be challenging, such efforts have the potential to provide a more appealing and cost-effective housing option for older Americans and their families.

How are Community Development Organizations Helping Build Healthy Places?

by Alina Schnake-Mahl
Gramlich Fellow

The great majority of America’s high-performing community development organizations (CDOs) are actively tackling health challenges in their communities. In a new working paper* published by NeighborWorks® America and the Joint Center for Housing Studies, Sarah Norman (NeighborWorks’ Director of Healthy Homes & Communities) and I examine how CDOs engaged in activities at the nexus of health, housing and community development. 

Drawing on a survey of the 242 high-performing CDOs in the NeighborWorks network, we found that 89 percent of the surveyed organizations reported activities and strategies that explicitly promoted health in 2015 – from green and healthy building standards to on-site, coordinated health services. We also found that 83.3 percent of organizations worked with partners to support their efforts.  Increases in these activities, we noted, have been spurred by recent changes in the American health-care system and philanthropic grantmaking that together have provided new opportunities for CDOs to partner with other community entities to address health challenges.

CDOs used a variety of approaches, many of them focused on healthy homes and access to healthy food. For example, Foundation Communities, a nonprofit affordable housing provider based in north Texas since 1990, addresses the health and social needs of residents through health and wellness classes; smoke-free, green and healthy rental homes; community gardens and walking paths; as well as childcare and after school programming that addresses literacy and physical fitness. An evaluation of these programs showed improvements on measures of quality of life and well-being for program participants.

Photo courtesy of Foundation Communities

Similarly, REACH CDC – an affordable housing developer and property management company serving Portland, Oregon – is a member of a Limited Liability Corporation (LLC) that provides enhanced health and social service coordination for 1,400 residents at 11 federally subsidized, independent-living, affordable-housing properties in Portland. Project elements include an on-site Federally Qualified Health Center; culturally specific services for non-English-speaking residents; food distribution for homebound residents and other residents experiencing food insecurity; health navigators; and free mental health consultations. Multiple evaluations documented improvements in quality of life and well-being for residents as well as cost savings for Medicaid. 

Taken as a whole, the study shows that CDOs have undertaken significant efforts to explicitly improve the health of the communities they serve. Additionally, as the health care system increasingly targets the social determinants of health, there are new opportunities for engagement.  For example, housing-based services could help address gaps between formal medical care and community health to help older residents to age in their communities. More broadly, CDOs’ long-standing relationships with local communities provide a strong base to support cross-sector partnerships to tackle health inequities.

Alina Schnake-Mahl is a doctoral student in the Department of Social and Behavioral Sciences, at the Harvard T.H. Chan School of Public Health.  She was a 2016 recipient of the The Edward M. Gramlich Fellowship in Communityand Economic Development, which is co-sponsored by the Joint Center and NeighborWorks®America.

*The full article is under consideration in Cities & Health; the journal is available online. 

Addressing the Housing Insecurity of Low-Income Renters

Irene Lew
Research Analyst

As our recently released 2016 State of the Nation’s Housing report highlights, rental housing affordability remains a pervasive—and growing—problem for millions of renter households in the US. The number of renter households devoting more than half of their income to housing costs (those considered severely burdened) climbed to a record high of 11.4 million in 2014. Among renter households earning under $15,000 a year, severe cost burdens are widespread, with 72 percent falling into this category. Severe cost burdens can adversely impact the housing security of very low-income households, leaving them little money left over to pay for necessities or to cover unexpected expenses. Indeed, compared to those with similar incomes who live in housing they can afford, very low-income renters paying more than half of their income on housing in 2013 were nearly two times more likely to fall behind on their rent, were at higher risk of having their utilities being shut off due to nonpayment, and were more likely to believe that they would be evicted within the next two months—all elements of housing insecurity (Figure 1).

Notes: Very low-income refers to households with incomes no higher than 50% of area medians. Severely cost burdened refers to households that pay more than 50% of income for housing. Households with zero or negative income are assumed to be severely burdened. Rent payment(s) were missed within the previous three months. Felt under threat of eviction refers to households who reported that they were likely to be evicted within the next two months. 
Source: JCHS tabulations of HUD, 2013 American Housing Survey. 

Furthermore, very low-income renter households with children are also more likely than those without children to be housing insecure and believe that they are at risk for eviction (Figure 2). Eviction is a leading cause of homelessness for families with children living in major cities like Washington, DC, Philadelphia and Baltimore, according to the most recent US Conference of Mayors Hunger and Homelessness Survey. As I point out in a previous blog post, homelessness among people in families with children persists in the highest-cost cities even as homelessness continues to decline steadily among veterans and those with chronic patterns of homelessness.

Notes: Very low-income refers to households with incomes no higher than 50% of area medians. Severely cost burdened refers to households that pay more than 50% of income for housing. Households with zero or negative income are assumed to be severely burdened. Rent payment(s) were missed within the previous three months. Felt under threat of eviction refers to households who reported that they were likely to be evicted within the next two months. Households with children refer to any households headed by an adult aged 18 and over with at least one child (related or unrelated). 
Source: JCHS tabulations of HUD, 2013 American Housing Survey.

Permanent federal housing subsidies that account for changes in tenant incomes, such as housing choice vouchers,  have proven to be the best option for improving housing stability, especially among homeless families exiting shelter. However, spending on federal housing assistance remains scarce, with direct housing subsidies representing just 4 percent of total discretionary funding approved by Congress in FY2015, a share that has barely budged over the past two decades.

Given the scarcity of federal funding, how can we address financial instability among low-income renters and reduce housing insecurity among this group? Enterprise recently proposed a promising master lease model program with built-in tenant savings accounts that could, without federal subsidies, improve the stability of low-income renters. Under this program, rents would remain affordable because a nonprofit or mission-driven organization would obtain long-term access to units in existing buildings through a multi-year master lease arrangement with fixed prices similar to the ones used for commercial leases. Unique to this model is a savings component in which a small amount of money from a tenant’s monthly lease payment would be allocated toward a custodial account in the tenant’s name. Tenants would not only have stable housing costs but would also be able to accumulate a savings cushion to pay for unanticipated expenses such as emergency room visits, and bounce back from income disruptions such as involuntary job loss or a significant reduction in income. In fact, a recent Urban Institute report analyzing data from the Census Bureau’s Survey of Income and Program Participation panel found that low-income families with savings of at least $2,000 to $4,999 are more financially resilient than middle-income families without any savings. Among low-income families with savings of $2,000–$4,999, just 20 percent experienced hardship after an income disruption, compared to about 30 percent among middle-income families without any savings.

However, financial issues are not the only contributor to housing insecurity among low-income households—some households may also struggle with additional challenges such as domestic violence, former incarceration, and mental health and substance abuse issues. As a result, improving housing insecurity may also require expanding access to supportive services that help address these underlying issues.

The MacArthur Foundation’s annual How Housing Matters Survey released last month confirms that a majority of Americans have a grim outlook on housing affordability—81 percent of respondents stated that they believe housing affordability is a problem in America today. Nearly seven in ten adults responded that it is more challenging to secure stable, affordable housing today than it was for previous generations. Furthermore, a recent Gallup poll found that 63 percent of renters with annual household income of less than $30,000 were worried about being able to pay their rent or other housing costs. Existing proposals to increase the number of affordable rentals built or preserved through the Low Income Housing Tax Credit program, and to reform federal rental assistance programs in order to serve more low-income households, can help alleviate the rental affordability crisis. However, it is equally important to offer programs that can help low-income renters better weather income disruptions or unexpected financial emergencies and avoid missed rent payments that can lead to eviction.

Article review- “Patriarchy, Power, and Pay: The Transformation of American Families, 1800-2015”

George Masnick
Senior Research Fellow

At my age, there is little that makes my jaw drop, especially while reading an article in one of my professional journals. However, this is exactly what happened when “Patriarchy, Power, and Pay: The Transformation of American Families, 1800-2015” by Steven Ruggles appeared in the latest issue of Demography. (Another almost identical version of this paper is available free of charge here.) What struck me as amazing is the way Ruggles provides a long-term perspective on many of the demographic and economic trends taking place today that I have studied using a much shorter time frame. And by long-term, we are talking 150-200 years!

The Joint Center for Housing Studies' early effort to describe changes in household structure and the labor force participation of American womenThe Nation's Families, 1960-1990– adopted a temporal perspective from 1960-1990. Published in 1980, we thought at the time that a three-decade perspective was all that was needed to understand the dramatic changes of that era. Wrong! The longer historical perspective sheds much more light on the origins of today’s demographic shifts, particularly in household structure, and what they might mean for housing.

Ruggles begins with the trend in the share of persons age 65+ who live in multi-generational families. We have noted the increase in this household type during the past two decades, primarily due to the increasing share of Hispanic and Asian immigrants for whom multi-generational residence is more common, and have speculated about its implications for housing consumption. But since we housing researchers rarely look at trends spanning more than 30 or 40 years, we have no sense of whether the upward trend in multi-generational living is indeed all that significant.

Ruggles’ Figure 1, reproduced below, shows how slight the recent turnaround has been relative to longer-term historical levels. The high share of the labor force based in an agricultural economy drove the very high historical incidence of older Americans living in multi-generational households. Three quarters of the labor force in 1800 worked in agriculture, and farm labor still was in the majority in 1850 when the share of 65+ living in multi-generational families was also 75 percent. Ruggles explains convincingly why an agricultural based economy tied the generations together, and why the rise of wage labor off the farm split them apart.



Ruggles’ main theme is that the decline of what he calls the “corporate family” – those working in agriculture and other (often related) family businesses – and the gradual transformation of the workforce to include first only male breadwinners, and later dual earner and female breadwinner households – had the effect of making household structures both simpler and more fluid. Once again, his long-term perspective is enlightening in looking at the recent trend in such things as delayed marriage and divorce. Age at first marriage for both men and women has been rising steadily since 1960, and he predicts that the share of never-married 40-44 year old women will almost double in the near future, rising from 15 percent in 2010 to about 28 percent in 2030. Similarly, the rate at which married women are divorcing has increased steadily since 1960, showing no sign of this trend slowing. Consequently, the share of all households without a married couple present – which held near 20 percent between 1850 and 1950 – has risen to over 50 percent in 2010, and continues its upward trajectory.

Nor is it simply the case that young adults are just trading marriage for cohabitation. To be sure, this is happening to some degree, but Ruggles notes that the share of 25-29 year olds without a co-residing partner has grown from 23 percent in 1970 to 48 percent in 2007 to 54 percent today. The fastest growing household type is single-person rather than cohabiting couples, as more and more adults of all ages who never married, are separated/divorced, and are widowed live alone.

If the household is the unit of both production and consumption, greater fragmentation and instability in household structures is troublesome. The primary household production good today is the next generation, and the U.S. appears to be following the lead of many European countries in developing fertility levels below replacement. Nothing that Ruggles presents in his paper provides comfort that the recent declining fertility rates are simply due to the lingering effects of the Great Recession and will likely reverse themselves.

One contributing factor to declining fertility may be trends in income. Households have always provided the mechanism for combining incomes. To Ruggles’ dismay, the evolving global economy is leaving more American households without secure incomes. The long slide in the relative earning power of young men over the past 40 years has been mitigated by the steady rise in employment of wives. But now that fewer and fewer households contain a married couple, and given that women’s real wages have also begun to decline, aggregate household incomes for married couples has begun to decline as well. Ruggles suggests that the largest source of decline in economic opportunity for young people, especially over the past two decades and in future decades, may be the automation of both manufacturing and services made possible by new technologies.

Housing consumption broadly should follow the downward trends in employment and income. Boosting household formation and homeownership rates, especially among the young, will require a reversal of many of the long-term demographic and economic trends that Ruggles discusses.

Ruggles’ article has sixteen figures, some only going back in time to 1940, but many spanning 150 or more years. I highly recommend you take a look. Some will surely make your jaw drop too.

For Housing Demographers It’s All About the Data – But Sometimes the Data Come Up Woefully Short

by George Masnick
Senior Research Fellow

Housing demographers are often frustrated by data that range from inconsistent to totally unavailable when attempting to research demographic and housing trends. The inconsistencies between various data sources on estimates of household numbers and household growth, vacancy rates, and homeownership rates are well documented and continue to be dissected and discussed, but there are other metrics that have been even more elusive to pin down that would help enormously to better understand today’s demographic/economic trends and their housing implications.

Two broad areas of housing consumption are particularly difficult to measure.  The first concerns the doubling up of generations living in a single residence.  The second is the opposite – when a single household lives in more than one housing unit on a regular basis. 

We would like to be able to answer many questions about the increasing trend of young adults who live with their parents. We have also identified a growing trend of grandparents who live with their grandchildren (and in many cases the grandchildren’s parent or parents as well) but we cannot identify grandparents who might not live with their grandchildren but live close by and provide support in childrearing.  We would like to know more about how delayed marriage/partnering, and divorce/remarriage, affect housing consumption of multiple units.

For the most part, existing data cannot tell us what actually takes place in the housing history of specific households over time as individuals age and change their household configurations, marital/partnership status, and employment/income profiles.  Most difficult to measure are the linkages between generations in structuring patterns of geographic mobility (affecting both those who move and those who do not move in order to be close to family), young adult household formation, and housing consumption across the age spectrum.

Data sets that do have information about life-course household transitions and some information about relationships between generations rarely have any data on housing.  Data sets that have housing information lack information about life-course changes preceding and during current occupancy.  Information about extended family members that do not reside in the household being interviewed is generally totally lacking.

We would like to know not only how many adult children presently live with their parents, but how many have boomeranged and the type of housing boomerang children moved out of when they moved back home.  How often does moving back home occur for specific households, and how long does it last?  Short spells of returning home presumably have much different consequences than long ones.  Chronic returns might have very different causes and consequences than one-off situations.  Returns to large houses with higher-income parents have different consequences than returns to small homes having low household income. 

We would like to know more about the background details of children when they leave a parental household – reasons for leaving, characteristics of housing (on both ends of the move), and, household size and composition (again on both ends of the move).  Are boomerang kids and their household/housing characteristics different from those who leave and do not return?  This information would be immensely helpful in better understanding the present and future housing consumption of those Millennials who have been slow to form independent households and become homeowners.  

Ideally, answers to the kinds of questions just raised require panel data that track individuals and their housing over time.  The few nationally representative panel surveys with public use micro data, such as the Panel Study of Income Dynamics (PSID) or the National Longitudinal Survey of Youth (NLSY), have limited housing data.  And even these surveys have historically been deficient on the collection of data on individuals and their extended families: the PSID has collected data at regular intervals since 1968, but only in 2013 added a Family Roster and Transfer Module in which respondents and their spouses are asked to enumerate all living parents and children over 18 and to report about recent and long-term transfers of time and money to these individuals.  The new PSID module is the first to fully enumerate all biological, adopted, and step-relationships of parents, parents-in-law, and adult children, and it is the first major data collection effort on transfers of time and money in the PSID since 1988.

Other efforts to assemble panel data to directly study co-residence patterns between adult children and parents, such as in a recent Federal Reserve Bank of New York Report (utilizing its own Consumer Credit Panel (CCP)) are neither a nationally representative sample of all households nor available to other researchers, raising concerns about the reliability of the data. For example, the CCP data set reports a much higher rate of co-residence than other data sources such as the Current Population Survey.  Still, because of the scarcity of panel data to answer some of our questions, data sets the FRBNY CCP cannot be entirely dismissed.

Another reason for growing inter-generational co-residence is the need to support and take care of grandchildren.  Increasing grandparent-grandchild co-residence certainly has important consequences for housing choices, further postponing independent household formation among some Millennials, and perhaps delaying downsizing among the Baby Boomer grandparents.  We would like to know if older Americans who live close to their grandchildren are different from grandparents who live with their grandchildren.  Do they also play financial and childcare roles with respect to their grandchildren?  Are retirees more likely to move to be close to their children if their grandchildren are young?  Does the existence of young grandchildren make retirement moves that put greater distance between them and their grandchildren, or that are to age restricted communities, less probable?  Do older empty nesters with young grandchildren actually downsize less than those with older grandchildren? 

Available data on the rise of co-resident grandparents and their grandchildren are mostly from cross-sectional surveys, like the Current Population Survey, and are biased toward intergenerational families where those in the first wave of Millennials had their children relatively quickly, often while teenagers or still in school, or when not yet absorbed into the labor force.  Such early births are more likely to be to parents without a college education and to be non-marital.  The large and growing share of Millennials who pursue higher education are more likely to postpone childbearing (thus postponing grandparenthood for many Baby Boomers) and their births are more likely to be marital.  Will first grandchildren who come along later in life, when their parents are older and more economically secure and their grandparents are more likely to be retired, be more or less likely live with grandparents?  Live close to their grandparents?   

Unfortunately, nationally representative data that allow us to identify who is even a non-coresident grandparent are practically non-existent.  The sole exception is from the Survey of Income and Program Participation (SIPP), a longitudinal survey following panels of households for 2½-to-4 years, which for three of its panels has asked if a person has any biological children and if those children, in turn, have any biological or adopted children.  The SIPP data overlook persons who are not biological grandparents but are grandparents through marriage, either as stepparents themselves or who became a grandparent when their children partnered with someone who already has children, but has not adopted them.

Analyses of SIPP data on grandparents have been published for the 2001 panel, the 2004 panel, and the 2008 panel.  A 2014 panel is now in the process of data collection.  These data estimate that there were 64 million grandparents in 2009 (second wave questionnaire of the 2008 panel), of which one-in-ten lived with their grandchildren. According to these data, only 22 percent of co-resident grandparents were over the age of 70 compared to 34 percent of grandparents who did not live with grandchildren. We have little insight into proximity of these non-coresident grandparents to their children and the housing choices they have made if they have recently moved.

One additional panel survey that collects data to answer questions about grandparents is the University ofMichigan’s Health and Retirement Study (HRS).  It does allow identification of all grandparents, co-resident grandparents, reasons for moving, and does have some housing data, but it is somewhat limited by a sample design that selects particular birth cohorts.  Still, more analysis of these data, collected annually from 1992 to 1996 and on alternate years since, can help us better answer some of our questions.  

Shifting gears, how people utilize multiple housing units (their own and/or other’s) at different times during the week, month, or year is almost a complete mystery.  We would like to know more about middle-aged and older people who sometimes dwell in two or more housing units while maintaining control of each.  There is a catch-all category of households in some data sets identifying people who have a primary or usual residence elsewhere, and this category has been growing in recent years.  However, households interviewed at their primary residence are not asked if they sometimes live in another home, and data are not collected about the characteristics of that home and the reasons for living in it.  Are grandparents spending some time living with their grandchildren on a regular basis, or buying or renting a residence that they occupy occasionally to be close to their young grandchildren?  Are retirees who once lived close by their young grandchildren retaining a previous residence for a longer period of time to facilitate occasional visiting after retirement migration?  Are more adult children still living in retirees’ previous homes after they retire and move elsewhere?

We would also like to know how long individuals maintain an active consumption of multiple housing units when they change jobs or form new relationships.  Is “living together” increasingly less a status and more a process that could involve two or more housing units over an extended period of time?  Is the rise of long-distance telecommuting predicated on being able to spend some time in two or more locations, and is it the case that housing units in multiple places are owned or rented to facilitate this?  If people are now better able to rent out or share housing with others on a part-time basis (for example, through VRBO and Airbnb), are they more likely to maintain multiple units for their own occasional use? 

  

Until panel surveys from nationally representative samples collect data on life-course transitions, on intergenerational relationships, and on housing consumption more broadly defined, analysts will continue to try to research trends with data that are usually inadequate to the task, and to have questions that simply cannot be answered.

Survey: Many Homeowners Concerned about Invisible Health Risks

by Elizabeth La Jeunesse
Research Analyst

What makes a home healthy or unhealthy?  As Mariel Wolfson illustrated in her recent blog, this question is a multifaceted one. Old hazards persist, including lead paint, combustion pollution, formaldehyde, and radon. There is also growing awareness of other invisible pollutants, including volatile and semi-volatile organic compounds and endocrine disrupting chemicals. These elements enter homes not only through household products and goods but, as research from the Healthy Building Network shows, through building materials themselves. Achieving optimal ventilation remains essential to healthy indoor air quality. Attention should also be paid to the surrounding neighborhood, including access to health services and healthy food, walkability and accessibility, and levels of outdoor air pollution. Some communities are disproportionately affected by polluting industries and waste disposal sites in their neighborhoods, making it even more difficult for residents to enjoy a healthy home environment.

Households’ perceptions of health risks influence behaviors and in turn affect the home environment, so the Joint Center recently surveyed homeowners to learn about their ‘healthy housing’ concerns. These include but are not limited to worries about mold/moisture, indoor air quality, chemicals at home, and noise and lighting issues which might affect household health.

We found that roughly one out of four homeowners expressed some level of concern about an aspect of their home negatively impacting their household’s health. One out of ten households described their concerns as ‘moderate’ or ‘major’. High income households (earning $100K or more) were slightly more likely to express concern, as were households with one or more children.

By far the most frequently cited problem was indoor air quality, with more than two thirds of the concerned households identifying it as an issue. Water quality and harmful chemicals/materials followed, with around 30-40 percent of households citing them. As the chart below shows, these indoor health risks ranked even above basic safety issues. Least commonly cited problems were light and noise issues. 

Notes: Sample size is 529.  Households that expressed some basic level of healthy housing interest/concern were asked, “Which general category(ies) best describes your concern about the impact of your home on your household’s health?” Safety or comfort of the structure includes trip hazards, inadequate heating/cooling etc. Other basic safety issues include pests, lack of smoke detectors/locks/child safety features, etc.

Source: JCHS tabulations of Healthy Home Owner Survey, The Farnsworth Group.

When asked to be more specific about the source of their indoor air quality concerns, top issues cited by owners included managing household dust and/or pet dander, air pollution from indoor cooking/heating, and lack of sufficient ventilation. Over half of households concerned about residential indoor health risks identified these as problems. Just under half of those worried about indoor health cited chemicals from interior furnishings and from the building/structure itself as a source of concern.

Among all homeowners expressing concerns related to indoor health, more than half took at least one specific action to remediate their concern. Most frequent actions completed or planned in the near future included water filter installation, choice of paint with no or low airborne toxins, mold removal, and installation of room darkening curtains/shades. Less frequent actions included removal of asbestos and lead paint. 

Over the coming months, the Joint Center will analyze results from similar surveys of renter households, as well as of remodeling contractors, to better understand how healthy housing concerns and behaviors are playing out in the current residential remodeling market.

How Does Geographic Diversity in Age Structures Impact Housing Market Dynamics?

by George Masnick
Senior Research Fellow

As the youngest of the baby boom generation has now turned 50, there is much talk about the overall aging of the U.S. population. But recently released Census Bureau population estimates for states and counties tell a more nuanced story about the diversity in age structures in the U.S.  The census release notes that the oldest county (Sumter County-FL) has a median age of 65.5, while the youngest (Madison-ID) has a median age of 23.1.  Quite a difference!  Other counties among the oldest include Charlotte-FL (57.5), Alcona-MI (56.9), Llano-TX (56.9), and Jefferson-WA (55.9).  The five youngest counties also include Radford City-VA (23.3), Chattahoochee-GA (23.9), and Harrisonburg City-VA (24.2), and Utah County-UT (24.2).  The U.S. median age is 37.6. 

We should perhaps not be surprised that the county with the oldest population is in Florida, or that Idaho and Utah, with their Mormon influences, should have the counties with the youngest populations. But what is going on in Michigan, Texas, and Washington counties to rank among the oldest, and in Georgia and Virginia to produce places with the youngest populations?

There are three main demographic factors that influence the age structure of a population: 

1. Domestic migration patterns of both young adults and the elderly; 
2. Settlement patterns of international immigrants; 
3. Levels of fertility of both the immigrant and native born populations.  

Differences in life expectancy could also influence age structures if those differences are large.  For states and counties in the U.S., however, mortality differences are not sufficient to affect differences in median age. 

Places with net domestic out-migration of young adults, and/or in-migration of elderly will be older (younger if these migration patterns are reversed).  Florida is a destination state for retirement migration, as are North Carolina, Arizona, and other warm weather and low-tax states in the south and west.  Maine, West Virginia and many rust belt and Great Plains states lose young adults on net, so places in these states will also have an older age structure.

Immigrants tend to be young and have higher fertility compared to the native-born, so places that are immigrant destinations will be younger.  While states on the coasts and along our southern border still attract the majority of immigrants, states in the interior have increasingly become immigrant destinations as immigrant networks have spread beyond gateway states. 

Finally, fertility levels are the primary determinant of a population’s age structure.  When fertility is above replacement (more children born than reproductive-age adults in a family) the population pyramid is broader at the base, and median age is lower. The pyramid becomes more mushroom-shaped when fertility is below replacement, and median age is higher. 

When the population unit is relatively small, as with most of the counties listed above, these demographic factors can reinforce one another and create extreme values.  For larger units of population, such as large counties, metropolitan areas and states, differences should be less extreme, but they can still be significant. 

The population estimates from which median ages were calculated contain detail by race/Hispanic origin and sex, allowing us to examine the percent minority as a surrogate for the influence of immigration and the boost to overall fertility levels that immigrants and native-born minorities provide.  We can also look at a measure of recent total fertility by calculating the ratio of children age 0-4 to women in the primary reproductive ages of 20-44.  We cannot get a direct estimate of net domestic migration by age group from the published population estimates, however.  

The table at the bottom of this post, constructed from the 2013 population estimates, ranks states on median age, percent minority, and fertility.  While Florida has the county with the highest median age, the state as a whole is only the 5^th oldest, surpassed by Maine, Vermont, New Hampshire and West Virginia.  The lower the percentage minority in a state, the higher the median age (Figure 1). The oldest states are those where young immigrants and native-born minorities with higher fertility have not settled.  Maine, Vermont, West Virginia and New Hampshire rank the lowest on percent minority. In addition, the lower the total fertility rate, the higher the median age (Figure 2). This second relationship is the stronger of the two that are graphed, and the relationship holds fairly well across the entire range of fertility (discounting DC as an outlier).  The New England states collectively are also near the bottom of the ranking on total fertility.

Source: U.S. Census Bureau Population Estimates

Older states may be destination states for retirement migration, but can also have lost young adults from out-migration to states with bigger cities and more job opportunities.  For example, according to the 2012 American Community Survey, Maine gained 27,500 residents from other states during the previous year, but lost 38,500.  If most of the out-migration from Maine were young adults, the effect would be to increase the median age.

The youngest states, however, are more of a mixed bag.  Utah’s very high fertility level – the highest in the nation – is sufficient to secure its ranking as the state with the youngest median age. Utah is not completely lacking in diversity - its percent minority (20.3%) is just the 18^th lowest, but the total fertility rate in Utah is primarily driven by its non-Hispanic white population’s high rate of childbearing.  Alaska, the second youngest state, has a large minority population (mostly native Alaskans), as well as levels of fertility that are well above the U.S. average.  Its young ranking, however, is likely also determined by in-migration of young adults to work in energy and nature oriented jobs, and out-migration of the elderly to warmer climates.  The District of Columbia has achieved its ranking as the third youngest in all likelihood because of in-migration of young adults to work in Washington for a spell.  These adults are largely single, as suggested by DC’s extremely low fertility. But also contributing to DC’s young age structure is the fact that the percent minority is the highest on the mainland (64.2%).  Texas is the 4^th youngest state, both due to its high percent minority (56%) and high fertility.  Texas has received consistent growth from both immigrants and young domestic migrants in recent years.  The final state among the top five youngest is North Dakota, which has been the beneficiary of considerable in-migration of young adults to work in the booming energy sector in the western part of the state.  North Dakota’s fertility rate is also among the highest, attesting to the impact of a favorable economy on family formation.   

Geographic diversity in age structures has direct implications for housing market dynamics.  Places with younger age structures will require new construction to house young adults, both now and in the future.  If the young age structure is created by higher fertility, homes will need to be larger to accommodate larger families.  If the younger age is created by in-migration of singles, a different housing mix is required, at least in the short run. 

Places with older populations are expected to show a greater balance between supply and demand for existing housing.  An older age structure brought about by low fertility and out-migration of young adults will have less need for new construction.  This is especially true if the existing housing is located in places where young adults want to and can afford to live.  However, if future demand for existing housing by young adults or older in-migrants is not there, older adults may be less able to sell their homes, and we can expect higher rates of aging in place. In these places there would be a greater need for modification and upgrading of existing housing to help the elderly safely stay in their homes.  On the other hand, if the older age structure is primarily the result of in-migration of retirees, and if that in-migration is sustained, there will be more opportunities for new construction and for the elderly to sell their homes in order to adjust their housing needs.  

Source: 2013 Census Bureau population estimates for states and counties.

*Fertility Rate is the number of children age 0-4 per 1000 women age 20-44. 

Census Bureau Takes a Small Step in Better Describing the Structure of the Modern Family -- but More Can Be Done

by George Masnick
Fellow

On November 25 the Census Bureau released its latest package of tables describing American families and living arrangements. These tables highlight the growing complexity of living arrangements among children—and the challenges that demographers and housing analysts face in charting changing household composition.

Since 2007 these tables have included a breakdown of family groups that identify couples who were not legally married but were joint parents of at least one minor child in the household.  This change reflects the trend for families to increasingly be started by the birth of a child rather than by marriage. Over 85 percent of births to teens are out of wedlock, as are over 60 percent of births to 20-24 year olds and over 30 percent of births to 25-29 year olds. Among those in their 20s co-residence of the parents is usually the norm, but in many cases, marriage does not take place for several years, and may never take place, certainly if the couple splits up.  Prior to 2007, these particular family groups were lumped into the category of “other families” with either a male or female reference person as head.  It was impossible under this old definition to distinguish in the tabulated data when unmarried family groups contained joint parents.

Many who referred to the older data assumed (incorrectly) that if adults in such family groups were not “currently married,” then the child or children were living in a “single”-parent household.  The implication was that unmarried two-parent households would behave more like one-parent households than like married couples across a wide range of issues of importance for public policy, including housing consumption.

The magnitude of the numbers of two-parent families under the old and new definitions can be seen in Exhibit 1.  While only about 7 percent of two-parent families are not married, that number is up from 5 percent in 2007. (Click exhibits to enlarge.)

  

Source: Current Population Survey March and annual Social and Economic Supplement, 2012 and earlier, http://www.census.gov/hhes/families/data/families.html. Table FM-2; http://www.census.gov/hhes/families/data/cps2013.html

In 2013 about 76 percent of all parents of minor children were married. Among young adults with minor children, however, the share that is currently married is much lower than this average (Exhibit 2). Only 43 percent of such parents under the age of 25 are married, as are just 65 percent of parents age 25-29.  The higher shares of older parents of minor children that are married reflect both the lower share of births to unmarried women when these parents were younger as well as the tendency for people to marry later. Whether today’s younger cohorts of parents will carry forward higher percentages of unmarried two-parent and “single”-parent living arrangements when they reach middle age remains to be seen.  I have put the word “single” in parentheses because it refers to legal marital status only, and these parents may well be partnered. 

Source: Census Bureau tabulations of 2013 CPS/ASEC  

When the parents of minor children are broken down by race/ethnicity we can see quite a large amount of variability in marriage/living arrangements (Exhibit 3).  The largest discrepancy is between Blacks and Asians.  Only 51 percent of Black parents are currently married compared to 89 percent of Asian parents of minor children. The share of non-Hispanic White parents of minor children who are married is almost 82 percent. Fully 42 percent of Black parents are in “single” parent living arrangements compared to only 9 percent of Asians. We would like to be able to identify the degree to which these differences are accounted for by differences in age of parents and by nativity status, but the data in the Census Bureau’s releases do not allow us to fully do this.  The data are especially silent when attempting to determine the presence of non-parent adults in the “single” parent category.

Source: Census Bureau tabulationsof 2013 CPS/ASEC  

While identifying joint-parent unmarried couples as a separate category is a step forward, especially among parents in their 20s, a further breakdown of the data is still needed to better describe the modern family.  Married couples consist of persons in their first marriage and those who have been remarried.  If we are now identifying unmarried parents that are both the biological parent of at least one minor child in the household, shouldn’t we also identify married couples where only one parent is the biological parent of any child?  Some “single” parents are living with a partner to whom they are not married, who for all intents and purposes are helping to support the family and acting like a parent.  Some “single” parents are living with non-partner adults who also might be playing parental roles.  Many children are in “joint custody” households.  These “blended” and “extended” living arrangements are all very much part of the modern family, but cannot be readily identified in the Census data, especially by age cohort.

The next steps that the Census Bureau can take to present a better picture of the modern family seem straightforward.  Marital status could include a category “remarried,” and married couples should be further broken down by marriages in which one or both partners are remarried. Unmarried parents of minor children could be broken down by those living with a partner and those not. Among those not living with a partner, the presence or absence of other adults could be identified.  Minor children in married couple living arrangements could be identified as the biological child of both parents or as a stepchild of one parent.  And minor children in the household could be identified as living exclusively in the household or regularly spending some of their time in another household.

Generational differences in living arrangements at the onset of family formation, and the extent to which these differences persist as cohorts age, are key descriptors of the modern family. Therefore, many of the CPS tables should provide the age of the reference parent as a variable that is cross tabulated against other variables.  It would also be helpful if these new tables are produced separately by race/Hispanic origin of the reference parent.  This detailed breakdown by age and race/Hispanic origin will stretch the CPS data quite thin, to be sure, but the user can always aggregate up to gain robustness.

Finally, a few comments about the sharp decline since 2007 in Exhibit 1 in the number of two-parent families with minor children. This decline is certainly related to the effects of the Great Recession.  One reason for the decline is that immigration fell sharply in 2006 and has just begun to recover. Immigrant women have higher fertility than native born and experienced the greatest fertility decline during the economic down turn. These are trends consistent with the poor economic conditions that have affected young adults most severely.  Immigrants also have a much higher share of births to married couples compared to native born (76.4 percent versus 61.2 percent), and the decline in immigration during the Great Recession thus contributed to the recent rise in the share of all births that are to unmarried women.

It is normal that during a recession, both marriages and births are postponed.  A recovery in marriages would be expected to lag the recovery in the economy to allow for some planning of the event.  Meanwhile, both the decline and the recovery in births should each lag the trend in the economy by a year or more.  Although year-to-year instability in the CPS series is often the result of simple random variability, perhaps the upturn in 2013 in the number of families with minor children is further evidence that the economic recovery has begun in earnest.