Employment and the Housing and Living Arrangements of Young Men: New Evidence from the Great Recession

Gary V. Engelhardt Syracuse University

Michael D. Eriksen

University of Cincinnati

Nadia Greenhalgh-Stanley Kent State University

May 25, 2015

Abstract

We investigate how extended families cope with adverse labor-market conditions and present new estimates of the loss of full-time employment for men during the Great Recession on co-residence with parents. Based on detailed data on sons of respondents in the Health and Retirement Study (HRS), our fixed-effect estimates indicate that on average a young man moving from full-time to either part-time to non-employment raises the likelihood of co-residing with a parent by approximately 10 percentage points. These effects are economically large, statistically significant, and suggest that the ability to move in with parents is an important way that young men adjust during economic downturns. We estimate the impact of employment loss during the recession on homeownership and mortgage distress: moving from full-time to non-employment lowers the likelihood of owning a home by 8 percentage points and raises the likelihood of falling behind on payments and foreclose by 8 and 5 percentage points, respectively. Overall, we find that employment loss during the recession had a pronounced effect on the housing decisions of young men. Acknowledgements: The research reported herein was supported by a grant from the MacArthur Foundation program on How Housing Matters. The opinions and conclusions are solely those of the authors and should not be construed as representing the opinions or policy of the MacArthur Foundation, Syracuse University, Kent State University, or University of Cincinnati. All errors are our own.

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1. Introduction

How do extended families cope economically with adverse labor-market conditions such

as unemployment? The extent to which families provide informal insurance arrangements

among members against labor-market risk is a long-standing topic of interest in economics. In

addition to private transfers of time and money (Altonji et al., 1997; Cox, 1987; Dynarski and

Gruber, 1997),Taste families may provide insurance in the form of in-kind transfers such as

housing (Bianchi et al., 2006). Recently, there has been substantial interest in whether parents

use shared living arrangements as a form of risk-sharing in the face of labor-market uncertainty

for their adult children (Kaplan, 2009, 2012; Wiemers, 2012).

In this paper, we make three contributions to the literature. First, we present new

estimates of the loss of full-time employment on co-residence between parents and adult

children. We follow a large number of existing studies and focus on men, who often experience

the most difficulty in adjusting to employment loss. As labor supply of prime-age men is very

inelastic, most of the movements in and out of employment are likely to be from labor-demand

rather than labor-supply shocks. Our estimates are based on the experience of these men in the

Great Recession. In particular, we used detailed data on sons of respondents in the Health and

Retirement Study (HRS) to construct a panel dataset of young men and their parents. We are

able to track the men’s employment and residential statuses before and during the recession and,

therefore, see to what extent changes in labor-market attachment result in shared living

arrangements with parents, and how this differs across socio-economic strata.

A key empirical challenge is that there may be unobserved heterogeneity in the taste for

leisure that may be correlated with the demand for shared living arrangements. If men with a

high taste for leisure also prefer to live with their parents, then standard estimates examining the

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effect of employment loss on parental co-residence would be biased upward. As a second

contribution, we use a variety of fixed-effect estimation strategies that rely on the rich HRS panel

data in an attempt to circumvent this concern. We also account for parents’ own health, income,

employment, housing, and wealth trajectories in our estimation.

Our fixed-effect estimates indicate that on average a young man moving from full-time to

non-employment raises the likelihood of co-residing with a parent by 9 percentage points. These

effects are economically large, statistically significant, and suggest that the ability to move in

with parents is an important way that men adjust during economic downturns. We find similar

estimates for moving from full- to part-time employment, which suggests that it is extensive

margin of work that matters. Moreover, we find a pronounced effect of employment loss on

marriage: moving from full-time to non-employment lowers the likelihood of being married by 7

percentage points. Although there are some differences by age, our estimates are uniform across

other socio-economic strata, likely reflecting the fact that the depth and length of the recession

left comparatively few families unscathed.

During the recession, the national unemployment rate peaked at 10 percent,

homeownership rates dropped significantly from historical highs, and mortgage distress rose

sharply. As a third contribution, we estimate the impact of employment loss during the recession

on homeownership and mortgage distress: moving from full-time to non-employment lowers the

likelihood of owning a home by 8 percentage points and raises the likelihood of falling behind on

payments and foreclose by 8 and 5 percentage points, respectively. Overall, we find that

employment loss during the recession had a pronounced effect on the housing decisions of young

men.

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The remainder of the paper is organized as follows. Section 2 briefly reviews the

previous literature in economics on living arrangements. Section 3 describes the basic

econometric framework and HRS data. Section 4 presents the results on co-residence, as well as

a large set of extensions and robustness checks. The homeownership results are discussed in

Section 5. The paper concludes with a brief summary of findings and a discussion of caveats.

2. Related Studies

Our analysis is most closely related to five interconnected strands in the existing

literature in economics and demography. The first is centered in urban economics and has

focused on the effect of household formation on homeownership rates. The homeownership rate

is measured as the number of owner-occupier households divided by the total number of

households. Economic conditions can affect both the number of owners versus renters via tenure

choice (the numerator), as well as the number of households via household formation (the

denominator). In an early, influential study, Börsch-Supan (1986) used data from the 1976-1977

waves of the American Housing Survey (AHS) and showed empirically that household formation

was quite responsive to house prices and income. Other notable work in this area includes

Haurin, Hendershott, and Kim (1993, 1994), Yelowitz (2007), Haurin and Rosenthal (2008), Lee

and Painter (2013), and Paciorek (2013).

A second strand has treated the work and household formation decisions of young adults

as jointly determined. McElroy (1985) is the best-known study in this area.1 In particular, she

specified a structural model of labor supply and household formation, which she estimated using

data on 203 never-married white men with completed schooling between the ages of 19 and 24

                                                            1 Haurin, Hendershott, and Kim (1993, 1994) also treat work decisions and income as endogenous.

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from the 1971 National Longitudinal Survey of Young Men (NLS-YM) matched to data on their

parents from the National Longitudinal Surveys of Mature Women (NLS-MW) and Men (NLS-

MM), respectively. There are two important implications of her analysis. First, there is an

ordered relationship between wage offers, employment, and residential independence. For wage

offers below the adult child’s reservation wage, the child does not work and co-reside with the

parents; for offers above the reservation wage, the child works but still co-resides; for offers

sufficiently above the reservation wage, the child works and lives independently. Second, the

option of an adult child to co-reside with parents is a form of nonemployment insurance.

McElroy’s analysis abstracts from other forms of intergenerational support to young

adults. A third strand of existing studies has treated co-residence as a type of intergenerational

transfer that may be a substitute for (or complement to) financial transfers. This is a voluminous

literature, whose focus is often on pinning down the motives for transfers. It spans both

economics and demography, and is reviewed in detail in Bianchi et al. (2006). Ermisch (1999) is

the best known paper in urban economics in this vein. He treats income as exogenous, but

considers financial transfers in addition to co-residence. Rosenzweig and Wolpin (1993) treat

human capital investments (and hence lifetime income), co-residence, and financial transfers as

jointly determined. They estimate the parameters of a structural model based on a panel of 821

matched parent-son pairs for young men from the 1967 NLS-YM matched to parents in the NLS-

MW and NLS-MM, and followed up to eleven waves between 1967 and 1981. They found that

an additional week of unemployment of the son increased the likelihood of parental co-residence

by 3 percentage points, based on fixed effects logit estimation.

In economics, much of the existing work has examined the role of economic conditions

on initial transitions out of the parental home and new household formation. In contrast, much

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effort has been spent in the demography literature in making distinctions between different types

of transitions. This includes those who initially transition out of the parental home, as well as

their complement, i.e. those who “fail to launch” (Goldsheider and DaVanzo, 1985; Billari and

Liefbroer, 2007; Bianchi et al., 2006; among many others). More recently, researchers have

begun to examine the link between the economic status and residential patterns of young and

middle-aged adults after they have had a spell living independently, with those who return to the

parental home termed “boomerang kids.”

Kaplan (2012) embedded the option of the adult child to move in and out of the parental

home into an intergenerational dynamic, stochastic lifecycle framework. Building on McElroy

(1985), co-residence in his model is a form of nonemployment insurance that allows adult

children potentially to smooth non-housing consumption in the face of labor-market uncertainty.

He estimated the structural model for 1,491 young men who did not attend college using monthly

data from 1998 through 2002 on employment and co-residence drawn from the National

Longitudinal Survey of Youth, 1997 Cohort (NLSY97) and found that labor market shocks affect

the timing of moves into and out of the parental home. This type of insurance is particularly

valuable for individuals with parents in the lower part of the income distribution, who have

comparatively lower ability to supply financial transfers to the adult child in the face of an

adverse labor-market shock. The option of moving back home also is associated with higher

future earnings by allowing for more extensive search for jobs with higher earnings potential.

Although Kaplan’s period of study was 1998-2002, his work was particularly well-timed

in that it appeared just when the United States was in the midst of the largest macroeconomic

contraction since the Great Depression. This period beginning with the financial crisis in 2007,

often termed the Great Recession, saw a historically high level of co-residence among American

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individuals (Mykyta and Macartney, 2012; Mykyta, 2012). This has led to a number of very

recent studies of the impact of the recession on living arrangements, all of which are closely

related to our analysis below.

Lee and Painter (2013) examined the impact of recessions on household formation.

Specifically, they used data on young men and women from the Panel Study of Income

Dynamics (PSID) from 1975-2009, which span four recessions as dated by the National Bureau

of Economic Research (NBER), including the Great Recession. They found that being

unemployed has a substantial negative impact on the transition out of the parental home for

young adults. In particular, treating employment status as exogenous, their multinomial logit

estimates indicate that unemployment lowers the likelihood of a transition by 50%. In addition,

they found that, independent of the individual’s unemployment status, more general

macroeconomic and housing-market conditions matter for household formation, including the

state unemployment rate, average wage, GDP growth, median gross rent, median house value,

respectively, as well as whether the national economy is in a recession year. They also found

much stronger effects for the Great Recession than earlier economic downturns.2

Rogers and Winkler (2014) used American Community Survey (ACS) data from 2005

and 2011 and examined how the differential timing of the downturn in both labor and housing

markets across metropolitan areas in the Great Recession affected household formation by young

adults. Although their focus was on the impact of broader market conditions, treating

employment status as exogenous, their logit estimates indicated that not being employed reduced

the likelihood of living independently by 11%.

                                                            2 Wiemers and Bianchi (2014) found substantial differences in co-residence among women by birth cohort, primarily driven by changes in life expectancy across cohorts.

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In a closely related study, Wiemers (2012) examined the impact of unemployment on

household composition and doubling up. She used data from the 1996, 2001, 2004, and 2008

Survey of Income and Program Participation (SIPP) panels, which span the period from

December, 1998, through December, 2010.3 With repeated observations on individuals, she

estimated individual-specific fixed effects linear probability models and found that becoming

unemployed raises the likelihood of living in a shared arrangement by 0.3%. While statistically

significant, this an economically small effect. 4

3. Econometric Framework and Data

Let i index the individual, j index the family, and t index the calendar year. Here, the

“individual” refers to the adult child. We follow previous studies and specify a linear-in-

parameters econometric specification:

ijt it it jt t ijtY U P δX . (1.1)

In the basic empirical models below, the dependent variable, Y , is an indicator that takes on a

value of one if the individual co-resides at period t with a parent and zero otherwise. In

extensions, we allow for dependent variables that measure finer gradations of household

formation and housing choices.5

Our primary interest is in the impact of the loss of full-time employment on co-residence.

There are two focal explanatory variables. The first is U , an indicator variable that takes on a

value of one if the individual is not employed and zero otherwise. The second is P , an indicator

                                                            3 There are two breaks, one in 2000 and one in 2007. 4 Dettling and Hsu (2014) examined a related topic, which is the extent to which increased debt accumulation by young adults results in increased co-residence with parents. 5 We interpret this specification as a reduced-form. We note, however, that most of the structural analyses in the literature specify indirect utility from residential choice as linear in parameters, yielding in effect a similar specification as the reduced-form we adopt above.

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variable that takes on a value of one if the individual is part-time employed and zero otherwise.

Those who are full-time employed are the excluded group in (1.1), and, therefore, represents

the (conditional) mean fraction of men who are full-time employed and co-residing with a

parent. The central objective is to obtain consistent estimates of and , which, for those who

begin the previous period in full-time work, measure the impact of moving from full-time to non-

employment and part-time to non-employment on parental co-residence, respectively.

We construct our analysis dataset from the HRS, a stratified random sample of over

25,000 individuals 50 and older, and their spouses (regardless of age). Individuals in the study

are interviewed every even-numbered calendar year until they die, at which point an “exit”

interview is conducted with their next of kin to gather information on the health and economic

circumstances prior to and at the time of death. The study began in 1992, and every six years

(e.g., 1998, 2004, 2010, 2016, etc.), a new birth cohort of individuals in their mid-50s enters the

study, refreshing the panel. We use data from 2004-2012 waves that span both before and

immediately after the financial crisis and recession.

A key feature of the HRS is that it asks respondents in each wave about the economic,

locational, and demographic status of their children. Given that the HRS sampling frame

consists of individuals in their 50’s and older, the great majority of children of HRS respondents

are adults. The implication then is that the HRS can be used to construct paired data on parents

and adult children to analyze the impact of labor-market dynamics on co-residence. We follow

the bulk of the previous literature in economics and focus on adult male children due to their

inelastic labor supply (McElroy, 1985; Rosenzweig and Wolpin, 1993; Kaplan, 2009, 2012,

among others).

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In particular, we construct a panel dataset of adult men who are the children of HRS

respondents. We restrict ourselves to men born between 1960 and 1982, who would have been

between the ages of 23 and 52 during our sample period. To this, we merge economic, health,

and demographic information about their parents, who are HRS respondents. This results in a

panel dataset of paired data on adult men and their parents.

Column 1 of Panel A of Table 1 presents basic summary statistics on the adult men in the

sample. There are 45,958 person-year observations on 11,989 men. The average age was 40.

Almost 64% were married. About 82% worked in full-time employment at the time of the

interview; 5% worked part-time; and almost 12% did not work. The top row of Panel B shows

summary statistics for the primary outcome in the empirical analysis: the fraction of men who

co-reside with a biological parent or parent-in-law. About 9% of men in the sample resided with

a parent.

Panel C shows summary statistics for the parents of those men. The average age of

fathers was 67, and 42% were employed and the time of their son’s interview. The average age

of mothers was 66, and 35% were employed.

4. Empirical Results for Parental Co-Residence

Columns 2-4 of Table 1 show summary statistics for subsamples defined by the three

employment categories: full-time, part-time, and not working, respectively. Reading across

columns in the first row of Panel B, parental co-residence is lower for the full-time employed

relative to the other two categories. In total, 6.5% of men who work full-time co-resided with a

parent (column 2), whereas 22.9% of those not employed did (column 4). The raw difference in

co-residence between the groups is 16.4 percentage points (i.e., 0.164 0.229 0.065 ). That is,

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on average moving from full-time to non-employment raises the likelihood of co-residing with a

parent by 16.4 percentage points. The raw difference in co-residence between the full- and part-

time employed is 14.7 percentage points (i.e., 0.147 0.212 0.065 ). These are large effects.

However, as shown in the other rows of Panels A and C, the own and parental

characteristics of men vary across employment groups, and these characteristics themselves

might have independent effects on parental co-residence. To control for these other observed

differences between men, we move in Table 2 to a regression framework. Specifically, the first

column in this table shows the ordinary least squares (OLS) linear probability model estimates of

and in (1.1). This specification includes a full set of calendar-year indicators, , and a

vector of control variables, X , that consist of the man’s age, plus the parental-level

characteristics shown in Panel B of Table 1, all of which are time-varying.6 The controls include

parental age, income, wealth; indicator variables for employment, homeownership, and being

married; and three measures of parental health status. The first measure is the number of limits

to the parent’s Activities of Daily Living (ADLs). The HRS collects information on five

activities—bathing, eating, dressing, walking across a room, and getting in and out of bed—each

designed to measure various dimensions of an individual’s ability to function in his or her

residential space. For each of the five tasks, the HRS records a 1 if the respondent had difficulty

with that task and a zero otherwise. The scores are summed for the five tasks, so that the ADL

variable ranges from 0 (no difficulties with any of the tasks) to 5 (difficulties with all of the

tasks). The second measure is a count of the number of medical conditions a doctor had ever

told the parent that he or she had. The eight conditions were high blood pressure, diabetes,

                                                            6 We control for family attributes of the biological parents separately to allow for parent’s no longer residing together. Unfortunately, the HRS did not gather information on the biological parents of the spouses of married adult children of HRS respondents. So, we only have parental information for the parents of young men, which we control for the attributes of the biological mother and father separately.

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cancer, lung disease, heart disease, stroke, psychiatric problems, and arthritis. The index ranges

from 0 (the absence of all eight conditions) to 8 (the presence of all eight conditions) where, as

before, a larger index value indicates poorer health. The third measure is a count of the number

of limits to Instrumental Activities of Daily Living (IADLs), which are the ability to use a

telephone, meal preparation, shopping, handle the household finances, and take medication.

The OLS parameter estimates in column 1 of Table 2 show the regression-adjusted

differences in co-residence between the three employment categories.7 For example, controlling

or adjusting for differences in age and parental demographic, economic, and health

characteristics across men, ˆ 0.144OLS . That is, on average moving from full-time to non-

employment raises the likelihood of co-residing with a parent by 14.4 percentage points.

Similarly, ˆ 0.153OLS . That is, on average moving from full- to part-time employment raises

the likelihood of co-residing with a parent by 10.3 percentage points. These are large effects and

similar to the unadjusted differences, suggesting that much of the variation in parental co-

residence of men is not due to observable differences in family background.

One empirical challenge with these findings is that there may be unobserved

heterogeneity in a child’s taste for co-residency with parents. Re-writing (1.1), this can be

represented by ,

ijt it it jt t i ijtY U P δX . (1.2)

If labor-market transitions are correlated with this unobserved heterogeneity, then OLS estimates

of and will be biased and inconsistent. This could occur, for example, if individuals with

high taste for leisure also have a high taste for co-residency. Then it might appear that those who

become unemployed are more likely subsequently to co-reside with a parent, but this would not                                                             7 The estimates of the parameters associated with the control variables are not shown, but available upon request.

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necessarily be a causal effect.  

To account for this, column 2 of Table 2 presents fixed-effects linear probability model

estimates of and . Similar to Wiemers (2012), this allows us, in principle, to obtain

consistent estimates of the parameters. With this estimator, controlling for calendar-year effects,

, the estimates of and are identified by differences across time in the employment

trajectories of sons. Our central identifying assumption is that these are (conditionally)

uncorrelated with the error term, , in (1.2). Now, ˆ 0.093FE . That is, on average moving

from full-time employment to being not employed raises the likelihood of co-residing with a

parent by 9.3 percentage points. With a standard error of 0.017, this effect is statistically

different from zero at conventional significance levels. Similarly, ˆ 0.103FE . That is, on

average moving from full-time employment to being part-time employed raises the likelihood of

co-residing with a parent by 10.3 percentage points. With a standard error of 0.018, this effect is

statistically different from zero at conventional significance levels.

a. Extensions

Columns 3-4 show OLS and fixed-effect estimates, respectively, for the subsample of

men who were co-residing in 2004, the baseline calendar year of the sample. Similarly, Columns

5-6 show OLS and fixed-effect estimates, respectively, for the subsample of men who were

living independently at baseline in 2004. Those who return after a spell of independent living to

co-reside with parents have been termed “boomerang kids.” The estimates in column 6 indicate

that on average moving from full-time employment to being not employed for this group raises

the likelihood of co-residing with a parent by 3 percentage points, whereas moving from full-

time employment to being part-time employed raises the likelihood of co-residing with a parent

by 3.3 percentage points.

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Table 3 shows estimates for further sample splits. Many existing studies have

documented a higher frequency of co-residence after employment loss for men from families

with low lifetime socio-economic status (SES). Columns 1-8 show splits for a variety of proxies

for parental SES: educational attainment, wealth, the number of functional limitations, and

homeownership, respectively.8 Interestingly, there are few differences by parental characteristics

in the co-residence response to employment. Men whose parents were homeowners were more

likely to co-reside after employment loss. Columns 9-10 show separate estimates by the son’s

age. The co-residence of younger men under 40 is much more responsive to employment.

b. Robustness Checks

The empirical results thus far show evidence that loss of full-time employment generates

significant increases in co-residence for young men. A potential empirical challenge to these

findings, however, is that there might be unobserved factors that vary across families and are

correlated with child labor-market dynamics. To represent this, in (1.2) can be decomposed as

,

ijt it it jt i j t ijtY U P δX , (1.3)

where represents time-invariant unobserved heterogeneity at the family level. For example,

across extended families, parents may differ in permanent income (Lee and Painter, 2012), which

is typically difficult to measure, but might be correlated with child labor-market attachment.9

Alternatively, across extended families, parents might differ in their provision of informal

insurance via co-residence in a way that is correlated with child labor-market attachment. In the

                                                            8 For educational attainment and functional status, which are measured for both parents in the HRS, we use the higher of the two values to define the sample split.

9 Or in a lambda-constant dynamic model of private income transfers, the unobserved heterogeneity could represent the parents’ marginal utility of (full) income.

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presence of such an unobserved family fixed effect, estimates of and , would be biased and

inconsistent.

To address this, we construct a sibling panel dataset on sons for HRS respondents with

more than one son, as the HRS gathers child information for all children. With a matched

sibling-pair-to-parent dataset, we track residential and employment status of brothers and their

parents over time. Hence, we can control both for time-variant unobserved heterogeneity at the

family level and at the individual level with a two-way fixed-effect estimator: within a son over

time, and between sons. With this estimator, controlling for calendar-year effects, , the

estimates of and are identified by differences across time in the employment trajectories

between sons in the same family. Our central identifying assumption is that these are

(conditionally) uncorrelated with the error term, , in (1.3). In principle, to ensure consistent

estimates, the sibling differences in employment trajectories should due to differential shocks to

labor demand between sons across time, and not to differential shocks to labor supply. Since

most estimates in the literature indicate that prime-age male labor supply is quite inelastic, we

restrict our sample to brothers, a group whose changes in employment is most likely due to

shocks to labor demand across the recession.

These results are presented in Table 4. In total, there are 3,382 HRS respondents who

have at least 2 biological sons represented in the survey. Those families form the sample for this

estimation. In all specifications in the table, we include the same vector of control variables for

the son’s age and the time-varying parental characteristics, X , that were used in Tables 2-3.

Column 1 shows the estimates of and for this sample using the same individual fixed

effects estimator as for the full sample in Table 2. The estimates indicate that on average moving

from full-time employment to being not employed for this sample of brothers raises the

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likelihood of co-residing with a parent by 4.2 percentage points, whereas moving from full-time

employment to being part-time employed raises the likelihood of co-residing with a parent by 4.3

percentage points. Column 2 shows estimates with both individual and family fixed effects. To

the third decimal place, the results are identical to those with just individual fixed effects.

One potential concern about the identification in column 2 is that it depends on the

exogeneity of differential shocks to labor demand between sons across time. These shocks could

be correlated if brothers were exposed to the same local economic conditions (in the extreme, for

example, laid off by the same employer). Column 3 provides a further robustness check by

controlling for a sibling-pair locational fixed effect. Specifically, for each child, the HRS asks

the respondent (i.e., the parent) whether the child lives within a 10-mile radius of the respondent.

The sibling-pair locational effect takes on a value of one if both sons in the sibling pair live

within 10 miles of the parent and zero otherwise. Since even within metropolitan areas, much of

the recession’s effect was localized (e.g., unemployment, foreclosures, etc.), this fixed effect

accounts for whether the sons were subject to the same local economic conditions. As the results

in column 3 indicate, the estimated impacts of employment loss are virtually unchanged.

Finally, one time-varying factor that does not appear as a control variable in

specifications (1.1)-(1.3) is the marital status of the son. As Lee and Painter (2013) and others

have pointed out, marriage is an endogenous household formation outcome for young men that

itself most likely is heavily influenced by employment status. To explore this, Table 5 presents

individual fixed-effect estimates for the full sample of specifications isomorphic to those in (1.2),

but where the dependent variable is an indicator for whether the son is married or not. In column

1, the estimates indicate that on average moving from full-time to non-employment decreases the

likelihood of being married by 4.5 percentage points, whereas moving from full- to part-time

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employed decreases the likelihood of being married by 3.4 percentage points. Columns 2-3

show splits by age. Men who were older had greater responsiveness of their marriage decisions

to employment.

5. Results for Homeownership

Next, we turn to impacts on homeownership. The HRS asks respondents (parents) about

the homeownership status of their children only every other wave, yielding data on the

homeownership of sons only for 2004, 2008, and 2012. Table 6 shows estimates of the impacts

of non- and part-time employment on homeownership using the following model, isomorphic to

(1.2),

ijt it it jt i t ijtH U P δX , (1.4)

where the dependent variable, H, is an indicator that is one if the son owned a home and zero

otherwise. For the purposes of comparison, column 1 presents estimates for the specification

(1.2) with parental co-residence as the outcome, but estimated just for the subsample of

observations from 2004, 2008, and 2012. Those estimates indicate that on average moving from

full-time to non-employment raises the likelihood of co-residing with a parent by 7 percentage

points, whereas moving from full- to part-time employment raises the likelihood of co-residing

with a parent by 6.1 percentage points.

The second column in the table presents individual fixed-effect estimates for a

specification isomorphic to (1.2), but where the dependent variable is an indicator for whether

the son is a homeowner or not. The estimates indicate that on average moving from full-time to

non-employment decreases the likelihood of being a homeowner by 2.9 percentage points;

moving from full- to part-time employment decreases the likelihood of being a homeowner by

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3.4 percentage points.10 When the sample is split by age, almost of this reduction in

homeownership occurs for men under 40 (columns 3-4).11

For the purposes of comparison, column 5 shows estimates for what is often the sample

in many studies of homeownership, which is men who live independently. As Lee and Painter

(2013) among others pointed out, this is an endogenous sample, because independent living is

itself responding to employment loss, as evidenced in the previous analysis in the current paper.

Comparing columns 2 (full sample) and 5 (endogenous sample), the estimated impacts of

employment loss do not differ significantly qualitatively nor statistically.

6. Impact of Employment on Mortgage Distress

The recession was characterized by historically high levels of mortgage distress, brought

on by the financial crisis and the real estate bust, as well as the consequent rise in unemployment

and decline in incomes. We end our analysis by presenting in Table 7 estimates the impact of

employment on mortgage distress using the following model, isomorphic to (1.2),

ijt it it jt i t ijtM U P δX , (1.5)

where the dependent variable, M, is an indicator of mortgage stress for the son. In 2008, 2010,

and 2012, the HRS asked respondents (i.e., parents) whether they or any of their children had

fallen behind in payments or were in foreclosure. Unfortunately, the HRS did not ask which

child was affected. Therefore, in panel A of column 1 the dependent variable is an indicator that

takes on a value of one if the son is in a family in which someone had fallen behind in payments,

and zero otherwise. The parameter in (1.5), therefore, measures the impact of the son moving

                                                            10 These findings are robust to sibling-pair fixed effects as were done in Table 4 for co-residence, and sibling-part locational fixed effects. These results are shown in Appendix Table 1. 11 Appendix Table 2 shows homeownership results for a variety of other sample splits.

19  

from full-time to non-employment on the likelihood of being in a family in which someone had

fallen behind in payments.

Estimates in column 1 are restricted to only those sons who were reported to previously

be homeowners in earlier waves, and thus at risk for mortgage distress. In panel A, moving from

full-time to non-employment increases the likelihood of falling behind in payments by 7.8

percentage points. The distress measure in panel B is an indicator for having experienced a

foreclosure. Moving from full-time to non-employment increases the likelihood of foreclosure

by 5 percentage points.

As explained above, the HRS did not ask parents to separately identify which child was

in mortgage distress, so the estimates in column 1 represent a weighted average of the impact of

loss of full-time employment for the focal son plus possibly other sons and daughters who

experienced distress. As a robustness check, we report in column 2 estimates where we

alternatively restrict the sample to families in which only one son was a homeowner in 2004 to

better identify the treated child. Those estimates are qualitatively similar and not statistically

different from those in column 1. Overall, we conclude that full-time employment loss had a

substantial effect on increasing mortgage stress for men during the recession.

7. Summary

We present new estimates of the impact of the loss of full-time employment on co-

residence between parents and adult children. We follow a large number of existing studies and

focus on young men, who often experience the most difficulty in adjusting to unemployment.

Our estimates are based on the experience of these men in the Great Recession. We use detailed

data on sons of respondents in the Health and Retirement Study (HRS) to construct a panel

20  

dataset of young men and their parents that tracks employment and residential statuses before

and during the recession. Our fixed-effect estimates indicate that on average a young man

moving from full-time employment to being not employed raises the likelihood of co-residing

with a parent by 9.3 percentage points. Similarly, on average moving from full-time

employment to being part-time employed raises the likelihood of co-residing with a parent by

10.3 percentage points. These effects are economically large, statistically significant, and

suggest that the ability to move in with parents is an important way that young men adjust during

economic downturns. Interestingly, these effects are quite uniform across socio-economic strata,

likely reflecting the fact that the depth and length of the recession left comparatively few

families unscathed.

One methodological contribution of our work is that we assess the robustness of our main

individual fixed-effect estimates with a set of two-way fixed effect estimates, based on both

individual- and family-level effects. Specifically, in each wave, the HRS asks respondents, who

are in their early 50’s and older, about the economic, locational, and demographic status of their

children. From these data, we construct a panel of adult men and their brothers, who are the

children of HRS respondents, for which we can track residential and employment status. This

allows us to track sibling pairs across time, both before and during the recession, to account for

time-invariant unobserved heterogeneity via individual-specific fixed effects, as well as family

fixed effects. These estimates, therefore, are identified by the differential time pattern of

employment losses across brothers within the same family. Using this new approach, we find

very similar effects. Overall, our results are very robust.

We temper these conclusions with a couple of caveats. First, we limited our analysis to

young men, primarily because men traditionally have had more difficulty adjusting to economic

21  

downturns, but also so that we could more readily compare our results to previous finds in the

literature. Naturally, it would important to expand the analysis to women, especially given

women’s secular increase in labor force participation over the last three decades. Second, our

sample only runs through 2012. This means that our findings should be thought of the short-

term effects of employment loss on housing and living arrangements. Once more recent data

become available, we will be able to estimate longer-run impacts. This will be one of many

avenues of future research.

22  

References

Altonji, J., Hayashi, F., Kotlikoff, L. 1997. Parental altruism and inter vivos transfers: Theory and evidence. Journal of Political Economy 105 (6), 1121-1166. Bianchi, S., Hotz, J., McGarry, K., Seltzer, J. 2006. Intergenerational ties: Alternative theories, empirical findings and trends, and remaining challenges. California Center for Population Research Working Paper No. 024-06. Billari, F., Liefbroer, A. 2007. Should I stay or should I go? The impact of age norms on leaving home. Demography, 44 (1), 181-198. Börsch-Supan, A. 1986. Household formation, housing prices, and public policy impacts. Journal of Public Economics 30, 145-164. Cox, D. 1987. Motives for private income transfers. Journal of Political Economy, 95 (3), 508-546. Dettling, L. Hsu, J. 2014. Returning to the nest: Debt and parental co-residence among young adults. Federal Reserve Board Finance and Economics Discussion Series Paper No. 2014-80. Dynarski, S., Gruber, J. 1997. Can families smooth variable earnings? Brookings Papers on Economic Activity 1, 229-303. Ermisch J. 1999. Prices, parents, and young people’s household formation. Journal of Urban Economics 45, 47-71. Goldscheider, F., DaVanzo, J. 1985. Living arrangements and the transition to adulthood. Demography 22 94), 545-563. Haurin, D., Hendershott, P., Kim, D. 1993. The impact of real rents and wages on household formation. Review of Economics and Statistics 75, 284-293. Haurin, D., Rosenthal, S. 2008. The influence of household formation on homeownership rates across time and space. Real Estate Economics 35 (4), 411-450. Kahn, J., Goldscheider, F. 2013. Growing parental economic power in parent-adult child households: Co-residence and financial dependency in the United States, 1960-2010. Demography 50, 1449-1475. Kaplan, G. 2009. Boomerang kids: Labor market dynamics and moving back home. Federal Reserve Bank of Minneapolis Research Department Working Paper No. 675. Kaplan, G. 2012. Moving back home: Insurance against labor market risk. Journal of Political Economy 120 (3), 446-512.

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Lee, K., Painter, G. 2013. What happens to household formation in a recession? Journal of Urban Economics 76, 93-109. Mykyta, L. 2012. Economic downturns and the failure to launch: The living arrangements of young adults in the U.S., 1995-2011. U.S. Census Bureau SEHSD Working Paper No. 2012-24. Mykyta, L. Macartney, S. 2011. The effects of recession on household composition: Doubling up. U.S. Census Bureau SEHSD Working Paper No. 2011-4. Mykyta, L. Macartney, S. 2012. Sharing a household: Household composition and economic well-being, 2007-2010. Current Population Report P60-242. Washington, DC: U.S. Census Bureau. Rogers, W., Winkler, A. 2014. How did the housing and labor market crises affect young adults’ living arrangements? IZA Discussion Paper No. 8586. Rosenzweig, M., Wolpin, K. 1993. Intergenerational support and the life-cycle incomes of young men and their parents: Human capital investments, coresidence, and intergenerational financial transfers. Journal of Labor Economics 11 (1), 84-112. Wiemers, E. 2011. The effect of unemployment on household composition and doubling up. National Poverty Center Working Paper No. 11-12. Wiemers, E. Bianchi, S. 2014. Sandwiched between aging parents and boomerang kids in two cohorts of American women. University of Massachusetts Boston Department of Economics working Paper no. 2014-06. Yelowitz, A. 2007. Young adults leaving the nest: The role of the cost of living. In Danziger, S., Rouse, C. (Eds.) The Price of Independence: The Economics of Early Adulthood. Russell Sage Press: New York, pp. 170-206.

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Table 1. Sample Means for Variables Used in Analysis (1) (2) (3) (4)

Full Sample

Subsample:

Characteristics Working

Full-Time Working

Part-Time Not

Working A. Individual Characteristics Age 39.9 39.9 38.6 40.2 Working full-time 0.824 1 0 0 Working part-time 0.054 0 1 0 Not working 0.123 0 0 1 B. Outcomes Co-resides with a parent 0.092 0.065 0.229 0.212 Married 0.647 0.701 0.405 0.391 Homeowner 0.350 0.401 0.123 0.121 Behind on mortgage payments 0.097 0.090 0.143 0.124 Home foreclosure 0.037 0.033 0.055 0.054 C. Parental Characteristics Father is working 0.420 0.427 0.452 0.346 Mother is working 0.346 0.354 0.366 0.279

Father is homeowner 0.850 0.860 0.829 0.774 Mother is homeowner 0.780 0.800 0.727 0.661

Father’s age 67.594 67.634 66.564 67.771 Mother’s age 65.839 65.864 64.715 66.141

Father’s ADL limits 0.277 0.348 0.348 0.439 Mother’s ADL limits 0.344 0.402 0.402 0.614

Father’s IADL limits 0.129 0.117 0.160 0.218 Mother’s IADL limits 0.114 0.098 0.138 0.208

Number of father’s medical conditions 2.089 2.072 1.991 2.276 Number of mother’s medical conditions 2.156 2.090 2.160 2.596

Father's family income 78,750 82,188 69,494 53,892 Mother's family income 58,259 61,729 51,193 37,876

Father's family wealth 545,764 578,378 429,353 324,339 Mother's family wealth 415,177 454,112 306,934 199,852

Number of person-year observations

45,958

37,879

2,466

5,637

Note: Authors’ calculations based on HRS sample described in text.

25  

Table 2. OLS and Fixed-Effect Estimates of the Impact of Employment Status on Parental Co-Residence for All Sons, Robust Standard Errors in Parentheses

(1) (2) (3) (4) (5) (6)

Sample and Estimator:

All Sons

Sons Co-Residing at Baseline in 2004

Sons Living Independently at Baseline

in 2004 Explanatory Variable

OLS

Individual

Fixed Effects

OLS

Individual

Fixed Effects

OLS

Individual

Fixed Effects

Not employed

0.144

0.093

0.294

0.095

0.069

0.030

(0.008) (0.017) (0.020) (0.046) (0.006) (0.006) Part-time employed 0.153 0.103 0.261 0.093 0.075 0.033 (0.010) (0.018) (0.023) (0.017) (0.009) (0.007) Number of men 11,898 11,898 2,535 2,535 9,488 9,488

Note: The dependent variable for all regressions is an indicator that takes on a value of one if the son co-resided with the parent and zero otherwise. Each column in the table shows the parameter estimates of beta and phi in equation (1) in the text from a separate regression, with the associated sample and estimator shown in the column heading. There are three employment statuses: full-time, part-time, and not employed. Therefore, the excluded group is comprised of those who are full-time employed. All regressions included a set of controls for the calendar year and the variables for the parent: age, number of ADLs, number of IADLs, number of medical conditions, income, wealth, and indicators for homeownership, employment, and marital status. All standard errors are heteroscedasticity-robust and clustered at the individual-level.

26  

Table 3. Individual Fixed-Effect Estimates of the Impact of Employment Status on Parental Co-Residence for All Sons by Selected Subgroups, Robust Standard Errors in Parentheses (1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

Sample: All Sons, by

Highest Education Level Achieved by

Parents

Parental Wealth

Parental

Limitations to ADL or IADL

Parental Homeownership

Son’s Age Explanatory Variable

High

School or Less

More than

High School

Below Median

Above Median

At Least 1

None

Own

Rent

23-39 Years Old

40-52 Years

old Not employed

0.047

0.055

0.045

0.058

0.057

0.045

0.056 0.027 0.070 0.023

(0.008) (0.011) (0.008) (0.011) (0.013) (0.008) (0.008) (0.011) (0.011) (0.008) Part-time employed 0.048 0.060 0.048 0.051 0.046 0.054 0.056 0.047 0.079 0.025 (0.010) (0.012) (0.010) (0.012) (0.017) (0.009) (0.009) (0.013) (0.012) (0.009) Number of men 7,420 5,393 7,532 6,904 5,186 10,654 9,682 3,716 7,124 8,013

Note: The dependent variable for all regressions is an indicator that takes on a value of one if the son co-resided with the parent and zero otherwise. Each column in the table shows the parameter estimates of beta and phi in equation (1) in the text from a separate regression, with the associated sample and estimator shown in the column heading. There are three employment statuses: full-time, part-time, and not employed. Therefore, the excluded group is comprised of those who are full-time employed. All regressions included a set of controls for the calendar year and the variables for the parent: age, number of ADLs, number of IADLs, number of medical conditions, income, wealth, and indicators for homeownership, employment, and marital status. All standard errors are heteroscedasticity-robust and clustered at the individual-level.

27  

Table 4. Individual and Family Fixed-Effect Estimates of the Impact of Employment Status on Parental Co-Residence for All Sons with Brothers, Robust Standard Errors in Parentheses (1) (2) (3) (4) (5) (6)

Sample and Estimator:

All Sons

Sons Living Independently at Baseline in 2004 Explanatory Variable

Individual Fixed Effects

Individual and Family Fixed

Effects

Individual, Family, and

Sibling-Location Fixed Effects

Individual Fixed Effects

Individual and Family Fixed

Effects

Individual, Family, and

Sibling-Location Fixed Effects

Not employed

0.042

0.042 0.043

0.026

0.026 0.026

(0.008) (0.009) (0.030) (0.007) (0.009) (0.009) Part-time employed 0.043 0.043 0.043 0.027 0.027 0.027 (0.009) (0.030) (0.010) (0.009) (0.010) (0.010) Number of brothers 8,033 8,033 8,033 6,453 6,453 6,453

Note: The dependent variable for all regressions is an indicator that takes on a value of one if the son co-resided with the parent and zero otherwise. Each column in the table shows the parameter estimates of beta and phi in equation (1) in the text from a separate regression, with the associated sample and estimator shown in the column heading. There are three employment statuses: full-time, part-time, and not employed. Therefore, the excluded group is comprised of those who are full-time employed. All regressions included a set of controls for the calendar year and the variables for each biological the parent: age, number of ADLs, number of IADLs, number of medical conditions, income, wealth, and indicators for homeownership, employment, and marital status. All standard errors are heteroscedasticity-robust and clustered at the individual-level in columns 1 and 4, and at the family level in columns 2-3 and 5-6.

28  

Table 5. Individual Fixed-Effect Estimates of the Impact of Employment Status on Being Married for All Sons by Selected Subgroups, Robust Standard Errors in Parentheses (1) (2) (3)

Sample Explanatory Variable

All Sons

Under Age 40

Age 40

and Older

Not employed -0.045 -0.033 -0.095 (0.008) (0.029) (0.029) Part-time employed -0.034 -0.026 -0.042 (0.009) (0.027) (0.085) Number of men 11,898 3,428 10,554

Note: The dependent variable for all regressions is an indicator that takes on a value of one if the son was married and zero otherwise. Each column in the table shows the parameter estimates of beta and phi in equation (1) in the text from a separate regression, with the associated sample and estimator shown in the column heading. There are three employment statuses: full-time, part-time, and not employed. Therefore, the excluded group is comprised of those who are full-time employed. All regressions included a set of controls for the calendar year and the variables for each biological parent: age, number of ADLs, number of IADLs, number of medical conditions, income, wealth, and indicators for homeownership, employment, and marital status. All standard errors are heteroscedasticity-robust and clustered at the individual-level.

29  

Table 6. Individual Fixed-Effect Estimates of the Impact of Employment Status on Homeownership for All Sons, Robust Standard Errors in Parentheses (1) (2) (3) (4) (5)

Dependent Variable and Sample: Explanatory Variable

Co-Reside with Parent

Own Home

Own Home, Under Age 40

Own Home, Age 40 and

Older

Own Home, Conditional on Living

Independently Not employed 0.070

-0.029 -0.052 0.010

-0.027

(0.010) (0.012) (0.017) (0.019) (0.014) Part-time employed 0.061 -0.034 -0.030 -0.020 -0.023 (0.012) (0.013) (0.018) (0.022) (0.016) Number of men 11,706 11,695 5,576 6,119 11,509

Note: The dependent variable for all regressions is an indicator that takes on a value of one if the son owned a home and zero otherwise. Each column in the table shows the parameter estimates of beta and phi in equation (1) in the text from a separate regression, with the associated sample and estimator shown in the column heading. There are three employment statuses: full-time, part-time, and not employed. Therefore, the excluded group is comprised of those who are full-time employed. All regressions included a set of controls for the calendar year and the variables for each biological parent: age, number of ADLs, number of IADLs, number of medical conditions, income, wealth, and indicators for homeownership, employment, and marital status. All standard errors are heteroscedasticity-robust and clustered at the individual-level.

30  

Table 7. Individual Fixed-Effects Estimates of the Effect of Employment on Selected Measures of Mortgage Distress and Co-Residence for Sons who were Homeowners (1) (2)

Explanatory Variable All Sons

Sons in Families with

only One Homeowning Child in 2004

A. Fall Behind on Payments Not employed 0.078 0.109

(0.034) (0.052)

Part-time employed 0.064 0.138 (0.054) (0.096)

B. Home Foreclosed Not employed 0.050 0.046

(0.021) (0.038)

Part-time employed 0.092 0.134 (0.042) (0.083)

C. Co-Residence Not employed 0.043 0.047 (0.017) (0.029) Part-time employed 0.022 0.005 (0.013) (0.106) Number of sons 3,767 1,396

Note: The dependent variable for all regressions in Panel A is an indicator that takes on a value of one if either parent reported a child fell behind in mortgage payments during last 2 years and zero otherwise. The dependent variable for all regressions in Panel B is an indicator that takes on a value of one if either parent reported a child lost a home due to foreclosure during last 2 years and zero otherwise. Each column in the table shows the parameter estimates of beta and phi in equation (1) in the text from a separate regression, with the associated sample and estimator shown in the column heading. There are three employment statuses: full-time, part-time, and not employed. Therefore, the excluded group is comprised of those who are full-time employed. All regressions included a set of controls for the calendar year and the variables for each biological parent: age, number of ADLs, number of IADLs, number of medical conditions, income, wealth, and indicators for homeownership, employment, and marital status. All standard errors are heteroscedasticity-robust and clustered at the individual-level.

31  

Appendix Table 1. Individual Fixed-Effect Estimates of the Impact of Employment Status on Being Married for All Sons by Selected Subgroups, Robust Standard Errors in Parentheses (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)

Sample: All Sons, by

Highest Education Level Achieved by

Parents

Parental Wealth

Parental

Limitations to ADL or IADL

Parental Homeownership

Son’s Age Explanatory Variable

All Sons

High

School or Less

More than

High School

Below Median

Above Median

At Least 1

None

Own

Rent

23-39

40-52 Not employed -0.045

-0.047

-0.042

-0.042

-0.052

-0.036

-0.052

-0.044 -0.113 -0.033 -0.095

(0.008) (0.010) (0.013) (0.011) (0.013) (0.013) (0.010) (0.023) (0.036) (0.029) (0.029) Part-time employed -0.034 -0.030 -0.043 -0.040 -0.028 -0.053 -0.021 -0.031 -0.104 -0.026 -0.042 (0.009) (0.012) (0.014) (0.013) (0.013) (0.016) (0.011) (0.023) (0.041) (0.027) (0.085) Number of men 11,898 7,420 5,393 7,314 7,119 3,399 8,679 9,682 3,716 3,428 10,554

Note: The dependent variable for all regressions is an indicator that takes on a value of one if the son was married and zero otherwise. Each column in the table shows the parameter estimates of beta and phi in equation (1) in the text from a separate regression, with the associated sample and estimator shown in the column heading. There are three employment statuses: full-time, part-time, and not employed. Therefore, the excluded group is comprised of those who are full-time employed. All regressions included a set of controls for the calendar year and the variables for each biological parent: age, number of ADLs, number of IADLs, number of medical conditions, income, wealth, and indicators for homeownership, employment, and marital status. All standard errors are heteroscedasticity-robust and clustered at the individual-level.

32  

Appendix Table 2. Individual and Family Fixed-Effect Estimates of the Impact of Employment Status on Homeownership for All Sons with Brothers, Robust Standard Errors in Parentheses (1) (2) (3)

Estimator: Explanatory Variable

Individual Fixed Effects

Individual and Family Fixed

Effects

Individual, Family, and

Sibling-Location Fixed Effects

Not employed

-0.030

-0.030 -0.030

(0.015) (0.020) (0.020) Part-time employed -0.014 -0.014 -0.014 (0.016) (0.022) (0.022) Number of brothers 7,851 7,851 7,851

Note: The dependent variable for all regressions is an indicator that takes on a value of one if the son owned a home and zero otherwise. Each column in the table shows the parameter estimates of beta and phi in equation (1) in the text from a separate regression, with the associated sample and estimator shown in the column heading. There are three employment statuses: full-time, part-time, and not employed. Therefore, the excluded group is comprised of those who are full-time employed. All regressions included a set of controls for the calendar year and the variables for each biological parent: age, number of ADLs, number of IADLs, number of medical conditions, income, wealth, and indicators for homeownership, employment, and marital status. All standard errors are heteroscedasticity-robust and clustered at the individual-level in column 1, and at the family level in columns 2-3.

33  

Appendix Table 3. Individual Fixed-Effect Estimates of the Impact of Employment Status on Homeownership for All Sons by Selected Subgroups, Robust Standard Errors in Parentheses (1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

Sample: All Sons, by

Highest Education Level Achieved by

Parents

Parental Wealth

Parental

Limitations to ADL or IADL

Parental Homeownership

Son’s Age Explanatory Variable

High

School or Less

More than

High School

Below Median

Above Median

At Least 1

None

Own

Rent

23-39

40-52 Not employed

-0.027

-0.041

-0.029

-0.026

-0.028

-0.039

-0.036 -0.018 -0.052 0.010

(0.015) (0.021) (0.014) (0.023) (0.029) (0.015) (0.015) (0.020) (0.017) (0.019) Part-time employed -0.013 -0.061 -0.023 -0.034 -0.062 -0.038 -0.031 -0.038 -0.030 -0.020 (0.017) (0.021) (0.016) (0.025) (0.032) (0.016) (0.016) (0.025) (0.018) (0.022) Number of men 7,132 5,154 7,061 6,412 4,068 10,134 9,420 3,396 6,921 7,595

Note: The dependent variable for all regressions is an indicator that takes on a value of one if the son owned a home and zero otherwise. Each column in the table shows the parameter estimates of beta and phi in equation (1) in the text from a separate regression, with the associated sample and estimator shown in the column heading. There are three employment statuses: full-time, part-time, and not employed. Therefore, the excluded group is comprised of those who are full-time employed. All regressions included a set of controls for the calendar year and the variables for each biological parent: age, number of ADLs, number of IADLs, number of medical conditions, income, wealth, and indicators for homeownership, employment, and marital status. All standard errors are heteroscedasticity-robust and clustered at the individual-level.