Forthcoming: Journal of Urban Economics
Homeownership, Housing Capital Gains and Self-Employment
John P. Harding
University of Connecticut
Center for Real Estate and Urban Economic Studies
School of Business
Storrs, CT 06269-1041
E-mail: [email protected]
Stuart S. Rosenthal
Maxwell Advisory Board Professor of Economics
Department of Economics and Center for Policy Research
Syracuse University
Syracuse, NY 13244-1020
Phone: (315) 443-3809
Email: [email protected]
http://faculty.maxwell.syr.edu/rosenthal/
December 30, 2016
Funding for this project from the Ewing Marion Kauffman Foundation and the Center for Aging
and Policy Studies at Syracuse University is gratefully acknowledged. We thank Jing Li,
Shimeng Liu, and Nuno Mota for excellent research assistance. Comments from three
anonymous referees along with conference participants at the 2013 Urban Economic Association
meetings and 2014 AREUEA meetings at the ASSA are greatly appreciated. Any errors are our
own.
Abstract
This paper measures the impact of individual-level housing capital gains on transitions into and out of
self-employment. Drawing on special features of the 1985-2013 American Housing Survey (AHS) panel,
our most robust models control for recent expenditures on home maintenance, MSA-by-year fixed effects,
lagged proxies for wealth and other household attributes. Net of home maintenance, a 20 percent real
increase in home value over a two-year period raises the likelihood of entry into self-employment by
roughly 1.5 percentage points; housing capital losses have little effect on exits. Controlling for house
fixed effects, self-employed homeowners are also more likely to hold a HELOC, facilitating easy, lowcost access to home equity that could be used to cover business expenses. These and other estimates
suggest that links between homeownership and self-employment are strong enough to be important when
home prices are rising rapidly, but modest when housing capital gains are limited or negative.
JEL Codes: J2, R2, M2
Key Words: Self-employment, homeownership, housing capital gains, mortgage
I.
Introduction
This paper demonstrates that housing capital gains encourage entry into self-employment while
housing capital losses have little effect on exits. We also show that self-employed homeowners are more
likely to hold a home equity line of credit (HELOC), facilitating easy, low-cost access to home equity that
could be used to cover business expenses. In documenting these and other patterns, the paper highlights a
link between self-employment and owning a home that until recently has received little attention. This is
true for the vast literature on homeownership and also an extensive literature on self-employment and
entrepreneurship. In the aftermath of the Great Recession, however, links between housing market
volatility and labor market outcomes have taken on new importance, contributing to a burst of new papers
that seek to assess the effect of homeownership and housing capital gains on self-employment.
Relative to other work in this area, our paper is the first to directly measure the magnitude of
homeowner response to household-level housing capital gains net of home maintenance expenditures.
This is possible because our data, the 1985-2013 American Housing Survey (AHS) panel, reports
expenditures on home maintenance. Previous work has instead proxied for homeowner capital gains
using aggregate measures of house price inflation (e.g. Hurst and Lusardi (2004), Disney and Gathergood
(2009), Adelino et al. (2015), Corradin and Popov (2015), and Kerr, Kerr, and Nanda (2015)), and/or
relied on policy events to confirm that housing capital gains increase self-employment (e.g. Jensen et al.
(2015) and Schmalz et al. (forthcoming)).1 While informative, these approaches provide only indirect
estimates of the extent to which a change in an individual’s house value affects that individual’s
1
The closest cousins to our work are Corradin and Popov (2015) and Kerr, Kerr and Nanda (2015). Corradin and
Popov (2015) use individual-level panel data from the U.S. Survey of Income and Program Participation (SIPP). In
some models, they evaluate the impact of individual-level home equity on transitions into business ownership. They
do not, however, control for home maintenance which could affect measures of home equity. In other models, they
instrument for home equity using the change in aggregate U.S. home prices between the year of home purchase and
current year scaled by an MSA-level measure of housing supply elasticity from Saiz (2010). Kerr, Kerr and Nanda
(2015) use individual level panel data from the U.S. Longitudinal Employer-Household Dynamics (LEHD).
In some of their models, they proxy for individual housing capital gains by interacting lagged measures of house
value with zipcode-level measures of house price inflation. Very different from these studies, Adelino et al. (2015)
use aggregate data and measure the impact of MSA-level house price inflation in the U.S. on MSA-level business
activity. They show that MSAs with larger increases in home prices experienced larger increases in employment
among small businesses in industries in which firms incur only modest startup costs. We comment further on these
and other studies below.
propensity for self-employment. Previous studies have also tended to focus on short time periods when
home prices were mostly rising including 1985-1988 in Hurst and Lusardi (2004), 1997-2006 in Corradin
and Popov (2015), 2002-2007 in Adelino et al. (2015), and 2000-2004 in Kerr, Kerr, and Nanda (2015).
Our models are estimated using the full 1985-2013 AHS panel which visits homes in the U.S. every two
years. The long sweep of time includes multiple home price boom-bust episodes and increases sample
variation. That, along with other special features of the AHS, enhances opportunities to reveal different
facets of the relationship between homeownership and self-employment.
Drawing on the AHS data, and conditioning on MSA-by-year fixed effects, proxies for lagged
wealth, and other controls, we find that a 20 percent real increase in home value over a two-year period
(net of maintenance) increases the probability that a homeowner transitions into self-employment by
roughly 1.5 percentage points. Conditioning on year and house fixed effects we also obtain robust
evidence that self-employed homeowners are roughly 2.5 percentage points more likely to hold a
HELOC. These and other estimates suggest that links between homeownership and self-employment are
strong enough to be important when home prices are rising rapidly, but modest when capital gains are
limited or negative.2
There are several reasons to take note of the connection between homeownership and selfemployment. Self-employment contributes to new small firm creation, it is sometimes associated with
innovation and new product creation, and it can also provide opportunities for employment when wagework is not accessible.3 For these reasons, self-employment plays a central role in the labor market and
2
It is desirable but difficult to compare the magnitude of our estimates to those of recent related studies. In part that
is because prior studies in this area have focused primarily on discriminating between alternative channels by which
home equity and housing capital gains may affect self-employment (e.g. by increasing wealth which is thought to
increase tastes for self-employment versus alleviating credit constraints), while our focus is primarily on identifying
the magnitude of homeowner response to individual-level housing capital gains. That difference contributes to
differences in model specifications. Studies differ as well in the type of data used (e.g. individual level versus
aggregate), differences in time periods, location (United States, Denmark, France), and other idiosyncratic design
features. Bearing that in mind, Jensen et al. (2015) and Kerr, Kerr and Nanda (2015) report modest effects from
home equity as a source of collateral while Adelino et al. (2015), Corradin and Popov (2015) and Schmalz et al.
(forthcoming) conclude that housing equity and capital gains have large effects.
3
Faggio and Silva (2014), for example, analyze UK data and find that self-employment is more of a source of
employment of last resort in rural areas while this is less true in urban areas. That echoes patterns in Lindh and
Ohlsson (1996) who report higher self-employment rates in rural areas of Sweden. Other studies have suggested
2
has been the subject of considerable study. It is also well known that U.S. home prices rose sharply 19962006, crashed 2007-2009, and have moved back up to their pre-crash peak as of fall 2016.4 Over this
same period, U.S. homeownership rates jumped from 64 percent in the mid-1990s to 69 percent in 2006,
and then fell to roughly 63.5 percent by fall 2016.5 Such extreme volatility amplifies possible spillovers
that may affect self-employment decisions, adding to other previously established effects of
homeownership on families and their local economies.6 Partly in response to this extensive volatility in
housing markets, a burst of new papers has sought to highlight and measure different aspects of the
impact of housing capital gains on self-employment. We discuss those and related studies below,
beginning with different ways in which homeownership may affect self-employment outcomes.7
Several channels could account for a positive effect of homeownership and housing capital gains
on self-employment. The first is that self-employment is a risky source of income and risk aversion
declines with wealth. For these reasons, positive wealth shocks from housing capital gains could increase
preferences for self-employment. This point has been stressed by Hurst and Pugsley (2011, 2015) and
Kerr, Kerr, and Nanda (2015).
A different mechanism, emphasized by Evans and Leighton (1989) and Evans and Jovanovic
(1989), is that limited personal wealth and restricted access to credit may make it difficult for aspiring
that self-employment may offer a route out of poverty for minority groups (e.g. Borjas and Bronars (1989), Fairlie
and Meyer (1996)). Others have emphasized that customer discrimination might deter minority self-employment
(Black, Holtz-Eakin, and Rosenthal (2001)). See also Holtz-Eakin et al. (1994), Fairlie and Meyer (1996), Oswald
(1997a, 1997b), Blanchflower and Oswald (1998), and Dunn and Holtz-Eakin (2000).
4
On a national basis, nominal home prices roughly doubled 1996-2006 and then fell 40 percent by 2009. Prices
began to rebound in 2012 and regained their peak in fall 2016 (see US. Federal Housing Finance Agency, AllTransactions House Price Index for the United States at: https://fred.stlouisfed.org/series/USSTHPI).
5
See http://www.census.gov/housing/hvs/data/histtabs.html, Housing Vacancies and Homeownership, (CPS/HVS),
U.S. Census, Table 14.
6
Previous literature, for example, has emphasized that homeowners are financially invested in their communities
and may make better neighbors for that reason (e.g. DiPasquale and Glaeser (1999), Rosenthal (2008), Jiang
(2016)), while others have argued that homeownership provides a healthier home environment allowing children to
do better (e.g. Greene and White (1997), Haurin et al. (2002)). For related discussion, see Haurin et al. (2007) and
Olsen and Zabel (2015).
7
Other links between housing and labor markets arise because local amenities are jointly capitalized into wages and
house prices (e.g. Blomquist et al (1988), Gyourko and Tracy (1991), Chen and Rosenthal (2008), Albouy and Lue
(2015)), residential location affects access to child care and female labor supply (e.g. Graves (2013), Compton and
Pollak (2014) and Black et al (2014) as well as word-of-mouth job networks (e.g. Bayer, Ross and Topa (2008),
Hellerstein, Kutzbach, and Neumark (2014)). High moving costs from owner-occupied housing may also impede
labor market mobility and adversely affect employment (e.g. Coulson and Fisher (2009), Oswald (1997a, 1997b).
3
entrepreneurs to overcome startup costs, impeding access to self-employment for that reason.8 Evidence
on this channel has been varied and controversial. Prior to 2000, much of the evidence in favor of this
view drew on inheritances and other sorts of windfall financial gains (e.g. lottery winnings), treating these
events as exogenous shocks to personal wealth.9 Hurst and Lusardi (2004), however, argued that in the
U.S. personal wealth only affects self-employment for the top 5th percentile of the wealth distribution and
also noted that low-wealth self-employed individuals are not disproportionately likely to concentrate in
industries with low startup costs. For these and other reasons, they argue that personal wealth and access
to credit have not been a significant impediment to self-employment for the typical entrepreneur.10
To further explore this issue both Hurst and Lusardi (2004) and Disney and Gathergood (2009)
turned to house price inflation as an alternative exogenous driver of personal wealth; these are among the
earliest studies we are aware of that considered a possible link between housing markets and selfemployment.11 Hurst and Lusardi (2004) drew on region-level measures of house price inflation (for the
nine U.S. Census regions) based on the Federal Housing Finance Agency (FHFA) home price index,
while Disney and Gathergood (2009) used county-level house price inflation in the UK. Hurst and
Lusardi (2004) fail to find any evidence that house price inflation affects self-employment. Disney and
Gathergood (2009) do find that rising house prices increase self-employment but with caveats.12
Several recent studies have revisited the role of access to credit as a driver of self-employment
and business startups. Among these, Jensen et al. (2015) and Schmalz et al. (forthcoming) consider
8
Evans and Leighton (1989) and Evans and Jovanovic (1989) noted that the tendency to shift from wage- to selfemployment was independent of age even though occupational choice studies report that younger workers are more
willing to take on risk. As a possible explanation, Evans and Leighton (1989) and Evans and Jovanovic (1989)
suggested that younger workers lack the wealth and access to credit necessary to overcome business start-up costs.
9
Cross-country differences in self-employment rates have also been used to provide evidence that restricted access
to credit deters entrepreneurship. Examples include Loutfi (1991) on Europe; Cowling and Mitchell (1997), Robson
(1997) and Taylor (1996) on the United Kingdom; Carrasco (1999) on Spain; Johansson (2000) on Finland;
Blanchflower (2000) on the OECD as a whole; Pfeiffer and Pohlmeier (1992) on Germany.
10
Hurst and Lusardi (2004) also show that both past and future inheritances are positively associated with selfemployment status, a pattern also confirmed by Disney and Gathergood (2009) using UK data. This suggests that
self-employment could be sensitive to anticipated bequests and implies that bequests may be endogenous.
11
Black, de Meza and Jeffreys (1996) also considered the impact of house price inflation on business startups.
Using aggregate UK data, they find a positive association between rising home prices and business startups. Their
data, however, do not allow for many of the controls used in recent studies to address possible confounding factors.
12
Disney and Gathergood (2009) find a strong positive correlation between county-level house price appreciation
and self-employment but that pattern is notably weaker when controlling for unanticipated housing capital gains.
4
government mortgage regulations that affect opportunities for homeowners to draw on home equity and
mortgage debt to finance business activity. Jensen et al. (2015) consider the impact of a 1992 policy that
allowed individuals in Denmark, for the first time, to use home equity and mortgage debt to finance nonhousing expenditures including business expenses. They estimate that the policy change increased the
number of small businesses in Denmark.13 Analogously, Schmalz et al. (forthcoming) draw on the fact
that mortgage policy in France does not allow homeowners with a mortgage to use their home as
collateral against a small business loan. They then compare the impact of region-level house price
inflation on owners without a mortgage, owners with a mortgage, and also renters where the latter is used
primarily as a placebo and check on model specification. Results support expectations that house price
inflation increases self-employment most for homeowners without a mortgage. Jensen et al (2015) and
Schmalz et al. (forthcoming) both argue that their results are consistent with the view that collateral
constraints and restricted access to credit impede business activity.
Corradin and Popov (2015) obtain analogous results using the U.S. SIPP panel. In their most
robust model, they instrument for individual home equity using national-level home price appreciation
since the time of home purchase, scaled by the local MSA housing supply elasticity (taken from Saiz
(2010)). Based on that and other specifications, they estimate that rising individual home equity increases
transitions into business ownership.14
Kerr, Kerr and Nanda (2015) also consider the collateral channel using 2000-2004 US individuallevel panel data from the Longitudinal Employer-Household Dynamics database (LEHD). They conduct
a series of empirical exercises, including comparing owner-occupiers to renters (treating renters primarily
as a placebo), examining differences across states with different levels of homeowner home equity
13
In related work, Krishnan, Nandy and Puri (2014) show that improved access to bank loans following adoption of
new interstate banking provisions increased total factor productivity (TFP) for firms most affected by the policy
shifts. They interpret this as supporting the view that access to credit expands investment opportunities.
14
Adelino et al. (2015) and Kerr, Kerr and Nanda (2015) also instrument using the Saiz (2010) supply elasticities
for portions of their work. The instrumenting procedure helps to address concerns that changes in home equity may
be correlated with local labor demand shocks and endogenous for that reason. We note, however, that recent work
by Davidoff (2016) suggests that local demand-side amenities (e.g. scenic topography or proximity to a coastline)
are often associated with low supply elasticities. That weakens the case for using housing supply elasticity as an
exogenous instrument.
5
protection in the event of a bankruptcy (e.g. Cao (2014)), splitting samples by industry based on those
with and without high startup costs, and other modeling devices. They find robust evidence that housing
capital gains encourage entrepreneurship, while attributing much of the estimated effect to factors other
than collateral constraints including possibly wealth-induced shifts in preferences as noted above.
While debate about the extent to which credit access impedes self-employment persists, it is
important to recognize that savvy small business owners are aware of the possibility that home equity and
housing capital gains can provide a low cost, accessible source of funds and is sometimes an attractive
substitute to a small business loan.15 A Google search on “using home equity to finance a business”
yielded 6,860,000 hits as of August, 2016.16 Based on survey data from Barlow Research Inc., Shane
(2012, 2015) also reports that at the peak of the housing boom in 2006, 28 percent of small businesses had
used home equity as a source of business finance, up from 18 percent in 2001. These anecdotes echo
findings in Jones (1994) and Canner et al. (2002) who provide early evidence that homeowners often use
mortgage debt to finance non-housing activities. Using the SIPP panel for U.S. households, Corradin and
Popov (2015) also report that new small business owners take on additional home mortgage debt,
consistent with using home equity to help cover business expenses. Estimates in our paper that selfemployed homeowners are more likely to hold a HELOC is also consistent with this view.17
Different from above, Bracke et al. (2015) suggest a negative relationship between
homeownership and self-employment. They argue that because homeownership and self-employment are
15
Home mortgage debt in the U.S. is collateralized by the home and home mortgages are widely sold on the
secondary market (e.g. Gabriel and Rosenthal (2010)), both of which help to lower investor risk and mortgage loan
rates. Small businesses in contrast, exhibit high failure rates and face high costs of credit for that reason (e.g. Berger
and Udell (1993)). Thirty percent of new firms that employ at least one worker fail in the first two years, 50 percent
fail in the first five years, and just 25 percent survive beyond 15 years (see the US Small Business Administration
for 2001 and 2008 at http://web.sba.gov/faqs/faqIndexAll.cfm?areaid=24 and related source data from the US
Commerce Department). These high failure rates increase default risk for small business loans and are difficult to
assess unless the lender has close knowledge of the entrepreneur. For these reasons, many small businesses depend
on “relationship lending” in which the entrepreneur develops a close relationship with a local bank that holds the
originated loans in portfolio (see, for example, Berger and Udell (1993), Berger, Klapper, Udell (2001), Brevoort
and Hannan (2004), Carlstrom and Samolyk (1995), Demsetz (2000), and Drucker and Puri (2008)).
16
Many web hits also warned of risks to the family home should higher mortgage payments become burdensome in
the event that the business fails. See, for example, Neiman (2007), Mount (2011), Banker (2013), and Shane (2015).
17
We note that while anecdotal and formal evidence of home equity borrowing is compelling, it is possible that
home equity borrowing could reduce business operating costs (the intensive margin) without actually drawing
individuals into self-employment (the extensive margin).
6
both risky, homeowners may shy away from self-employment to limit their exposure to risk, similar in
spirit to arguments in papers by Sinai and Soulelles (2005) and Davidoff (2006). While the qualitative
nature of the portfolio-based argument is intuitive, the magnitude of the effect on tendencies for selfemployment is less clear and could be dominated by other arguments described above.
A common feature of all of the studies above is the need to address various empirical challenges
that complicate efforts to assess links between homeownership and self-employment. One such challenge
is that higher wealth families typically occupy more expensive homes. This increases their potential for
housing capital gains since capital gains increase with house value for a given rate of house price
inflation. Because wealth may also increase taste and opportunities for self-employment, insufficient
controls for personal wealth could cause estimates of the influence of housing capital gains on selfemployment to be upward biased. A second challenge is that unobserved local labor demand shocks
affect opportunities for wage work and related tendencies for self-employment. Those same shocks may
also increase housing demand causing home prices to increase. This could affect estimates of the effect
of capital gains although in this instance the direction of bias is less clear because positive labor demand
shocks could push tendencies for self-employment in either direction.
We address these challenges using unique features of the 1985-2013 AHS panel in conjunction
with a series of modeling strategies and identifying assumptions. Unique among panels, the AHS follows
roughly 55,000 homes – not households – every two years. Each survey provides detailed information on
the house and its current occupants, including self-employment status, income, whether the household
owns or rents, mortgage attributes, and a battery of usual socio-demographic descriptors. The MSA in
which a home is located is also reported for homes located in the 145 largest MSAs in the U.S.
In all of the estimation to follow we restrict the estimating samples to homes for which the MSA
is identified in the data. In our simplest models, we then merge in annual measures of MSA-level house
price inflation from the Federal Housing Finance Agency (FHFA) which are used to proxy for individuallevel housing capital gains. In our more robust models, we replace MSA-level measures of house price
inflation with MSA-by-year fixed effects that do much to difference away time varying unobserved labor
7
demand shocks and other time varying MSA-level factors. Identification is then based on explicit and
implicit interactions between MSA-level house price inflation and key indicators of the potential for
housing capital gains, controls for which differ across specifications.
Our initial models explore the effect of MSA-level house price inflation on owner-occupiers
relative to that of renters. These models treat renters as a control group since renters do not receive
housing capital gains, mimicking some of the models in Corradin and Popov (2015), Kerr, Kerr and
Nanda (2015), and Schmalz et al. (forthcoming). It is well known, however, that renters have less wealth
than owner-occupiers and are more mobile. In addition, arguments in Bracke et al. (2015) suggest that
renters may have different preferences for self-employment because of portfolio considerations. For these
and other reasons highlighted later, our preferred models restrict the sample to just owner-occupiers.
Identification in the homeowner-only models relies on direct measures of individual housing
capital gains and losses. This approach presents both opportunities and new challenges. One advantage
is that individual-level housing capital gains vary extensively within a given metropolitan area, most
importantly because capital gains are proportional to initial home value for a given level of house price
inflation.18 That variation greatly increases power to identify the effect of housing capital gains on selfemployment. Drawing directly on individual housing capital gains, however, requires that one subtract
off recent expenditures on home maintenance and improvements. This is necessary to avoid overstating
housing capital gains and also because home maintenance and improvements could be correlated with
labor demand shocks that affect anticipated income. Unique among major surveys, the AHS panel
includes detailed information on recent home maintenance and improvement expenditures (e.g. Harding
et al (2007)). Our preferred models, therefore, are based on individual housing capital gains over the
previous two years net of maintenance and improvement expenditures over that same period.19
18
House price inflation also sometimes differ at the sub-MSA level across market segments (e.g. Leventis (2012),
McManus (2013), Guerrieri et al (2013), Landvoigt et al (2015), Liu et al (2016) and McMillen (2016)), while the
hedonics literature has confirmed that individual property values respond to localized changes in amenities (e.g.
within one or two city blocks) as with changes in crime, school quality and access to public transport.
19
Kerr, Kerr and Nanda (2015) and Disney and Gathergood (2009) both note that controls for home maintenance
would be necessary if using individual-level housing capital gains.
8
Our primary challenges in using individual-level housing capital gains are encapsulated in two
key assumptions that are necessary for identification. The first assumption is that the two-year lagged
value of an individual’s house is exogenous to subsequent transitions into or out of self-employment after
conditioning on MSA-by-year fixed effects and the other controls. This allows us to use lagged house
value as a proxy for individual wealth and also as a starting point from which subsequent housing capital
gains are measured. A second key assumption is that within-MSA variation in house price appreciation is
exogenous to self-employment transitions conditional on the model fixed effects and other controls. This
is consistent with a view that labor demand shocks operate at the MSA level while within-MSA variation
in home price growth is driven by primarily by shocks to neighborhood-level amenities.20
We proceed as follows. Section II outlines our modeling strategy in more detail. Section III
describes our data and summary measures. This includes comparisons of self-employment rates in the
AHS to analogous measures in decennial Census and ACS data, datasets that are more commonly used
for labor market studies. Section IV presents estimates of the self-employment models while Section V
considers the effect of self-employment on the tendency to hold a primary mortgage and HELOC.
Section VI compares estimates for younger versus older individuals since older individuals are wealthier
and may exhibit different tendencies for self-employment for that reason. We conclude with Section VII.
II.
Modeling strategy
In this section we outline our modeling strategy to address issues that have challenged previous
literature and highlight identifying assumptions that affect interpretation of our results. We estimate two
primary sets of self-employment regressions, each with different strengths and weaknesses. These are
described below ending with our preferred specification.
Our initial set of models pool owner-occupiers and renters and are of the following general form.
20
Kerr, Kerr and Nanda (2015) similarly assume that lagged house value is exogenous when they measure housing
capital gains by interacting lagged house value with zipcode level home price inflation.
9
SelfEmpt = θt,msa + θ1Tent + θ2(Tent*ΔHPIt,t-1) + θ3SelfEmpt-1 + θ4SESt + et
(1)
where θt,msa are a vector of MSA-by-year fixed effects, Ten equals 1 for owner-occupiers and 0 for renters,
ΔHPIt,t-1 is the percent MSA-level house price appreciation between adjacent two-year surveys t and t-1,
Tent*ΔHPIt,t-1 interacts these latter two measures, SelfEmpt-1 is the one-survey lagged value of the
individual’s self-employment status, and SES is a vector of sociodemographic attributes of the individual
including age, education, marital status, etc.
Given our focus on self-employment transitions we restrict the estimating samples to household
heads that are present in consecutive surveys since the AHS follows homes and not people over time.
Although this could introduce sample selection bias by skewing our sample towards relatively less mobile
individuals, the other extensive controls in the model help to mitigate this concern.21 In the estimation to
follow, this model is estimated as described above and also stratified by previous self-employment status
to allow for a complete set of interactions with previous self-employment.
The tenure fixed effect (θ1) in expression (1), is intended to capture the influence of well-known
wealth differences between owners and renters.22 It also reflects the effect of homeownership on selfemployment in the absence of house price inflation with ΔHPI equal to 0. The MSA-by-year fixed effects
difference away MSA-level time-varying factors including labor demand shocks and house price inflation
in the metropolitan area. Identification of the effect of house price inflation is therefore obtained off of
the interaction term so that the primary coefficient of interest is θ2. This coefficient allows for anticipated
differences in the effect of house price inflation on owner-occupiers relative to renters. If collateral and
home equity borrowing are the only channels by which house price inflation affects self-employment,
21
These controls include individual socioeconomic attributes and roughly 2,000 MSA-by-year fixed effects that help
to control for observed and unobserved individual attributes that may affect choice of metropolitan area. In earlier
versions of the paper we attempted to control for selection effects directly using Heckman two-step selection
methods. Those models included survey year and MSA fixed effects and yielded results that were mostly similar to
comparably specified models when selection effects were ignored. Estimating selection models was not possible,
however, with the large number of MSA-by-year fixed effects.
22
If wealth encourages homeownership and self-employment, then homeownership should have a positive effect in
our models since we do not control for wealth directly. As noted above, however, there is debate as to whether
personal wealth is an important driver of self-employment (e.g. Hurst and Lusardi (2004) and Disney and
Gathergood (2009)), while risk-based arguments in Bracke et al. (2015) suggest that homeowners may sometimes
prefer wage-work over self-employment. For these reasons the effect of housing tenure is potentially ambiguous.
10
renters should be unaffected by house price inflation. Under that assumption, provided other elements of
the model adequately control for unobserved local labor demand shocks, the coefficient on the interaction
term θ2 should be positive. This is the argument for using renters as a control group.
The model above is revealing and analogous specifications have been used in portions of the
work by Schmalz et al. (forthcoming) and Kerr, Kerr and Nanda (2015), and Corriden and Popov (2015).
Nevertheless, the specification in expression (1) also suffers from two limitations that are important to
note. The first is that despite the absence of a collateral channel, renters are imperfect as a control group.
One reason for this is that rental rates typically increase with house prices. As a result, house price
inflation will tend to increase wealth for owner-occupiers while raising costs for renters.23 If that effect is
differentially associated with housing tenure it may not difference away with the MSA-by-year fixed
effects. A second reason is that prior research suggests that renters save for downpayment when planning
a future home purchase (see Engelhardt (1994) and Engelhardt and Mayer (1998), for example) and that
tendency may be correlated with house price movements. A third reason is that homeowners are exposed
to house price volatility and other forms of risk that are less relevant for renters. This has been used to
explain why individuals in occupations closely tied to the housing market may be less likely to seek
homeownership (Davidoff (2006)) and also why some homeowners may choose away from opening a
business (Bracke et al. (2015)). Because house price inflation and house price volatility tend to be
positively correlated these risk-based mechanisms could affect estimates from expression (1). Lastly,
summary measures presented in the appendix confirm well-known patterns that renters have much lower
socioeconomic attributes relative to most owner-occupiers.24 All of these factors could affect estimates of
θ2 in expression (1) and for that reason caution is warranted when using renters as a control group.
23
This follows from standard asset pricing models which indicate that price should be driven by current rent along
with expectations of future rents. In that context, it is also possible that house prices could increase because of
rising expectations of future rent growth in which case current rents could remain flat or even fall.
24
Summary measures for the socioeconomic attributes of renters and owner-occupiers in our sample are provided in
the appendix. Compared to owners, renters have lower income and education in addition to exhibiting lower
concentrations of white or Asian ethnicity, lower marriage rates, and higher frequency of female-headed households.
11
A second limitation of the model in expression (1) is that it does not control for the primary
variable of interest. It is the magnitude of housing capital gains net of maintenance in level terms that
should affect tendencies for self-employment, not house price inflation at the MSA level. Expression (1),
however, does not readily allow for the contribution of maintenance to changes in house value and
especially so given that maintenance expenditures differ widely across homes and years (e.g. Harding et
al. (2007)). Expression (1) also does not allow for the fact that a given level of house price inflation
generates larger capital gains in levels (not percentage terms) for occupants of higher valued homes.
Finally, the specification in expression (1) reveals information about relative tendencies for selfemployment in comparison to the reference group. This makes it difficult to assess the magnitude of the
impact of house price inflation on the probability that a given individual may be self-employed.
We address these issues by controlling directly for individual housing capital gains between
adjacent bi-annual surveys net of maintenance expenditures. As discussed earlier, these models are
estimated only over owner-occupiers. As a proxy for household wealth, we include lagged measures of
individual house value. The model is specified as below.
SelfEmpt = θt,msa + θ1Valuet-1 + θ2ΔNetValuet,t-1 + θ3SelfEmpt-1 + θ4SESt + et
(2)
where Valuet-1 is the value of the home lagged one survey (two years in the past) and ΔNetValuet,t-1 is the
change in house value between adjacent surveys net of maintenance expenditures between t and t-1.
The specification in (2) presents tradeoffs relative to expression (1) beyond those issues already
noted. On the plus side, the model allows for sub-MSA variation in house price movements that may
affect individual housing capital gains. This increases variation in the capital gains measure and
estimation power. The primary downside of the specification in expression (2) is that identification
requires that individual capital gains and lagged house value are exogenous to self-employment
transitions conditional on the MSA-by-year fixed effects and other model controls. These are stronger
assumptions than in the previous models and we have no way of formally testing their validity.
12
We end this section by commenting on two extensions of the model in expression (2) that will be
presented later in the paper and which are illustrative of the difficulty of identifying underlying
mechanisms. As with the model based on expression (1), in most instances we estimate expression (2)
stratifying the sample by previous self-employment status. As previously noted, among homeowners not
currently self-employed, real housing capital gains increase entry into self-employment but there is little
effect on exits. This asymmetric pattern is consistent with the startup costs that prospective business
owners must overcome if they are to become self-employed but which should not affect exits. While that
is suggestive of a collateral effect, we note that the pattern could also emerge if self-employed individuals
develop emotional and/or financial commitments to their business enterprise as that would reduce the
sensitivity of exit to wealth shocks. For that reason, although the asymmetry just referenced is highly
robust, the pattern is only suggestive of a possible impact of housing capital gains on self-employment.
A second extension is that we decompose the capital gains measure in expression (2) into two
variables for increases and decreases in net house value. When measuring decrease in net house value we
express the change in absolute value. In addition, both variables are assigned a value of zero if the change
in net house value is in the opposite direction indicated. The model becomes,
SelfEmpt = θt,msa + θ1Valuet-1
(3)
+ θ2posΔNetPosValuet,t-1+ θ2negΔAbs[NetNegValuet,t-1] + θ3SelfEmpt-1 + θ4SESt + et
As noted earlier, in the estimation to follow, housing capital losses have no discernible effect on entry
into or exit from self-employment. This is different from what would be anticipated if wealth shocks
associated with a change in net house value affect an individual’s preference for self-employment. This
also, therefore, is suggestive that the wealth-preference channel described in the Introduction may not be
so strong as to dominate collateral and home equity borrowing effects. On the other hand, the housing
losses experienced by individuals in our sample are unrealized since we only follow individuals still in
their homes. It is possible that unrealized losses simply have little effect on household attitudes which
may account for the asymmetric effect of housing capital gains and losses for that reason. Once again,
13
while the pattern associated with gains and losses could indicate that housing collateral effects dominate
wealth-induced shocks to preferences, other credible arguments could account for these patterns as well.
III.
Data and Summary Statistics
3.1 Data
The primary data are from the 1985-2013 AHS panel. The AHS is a national panel that follows
housing units (not households) every two years and includes roughly 55,000 housing units in most years.
In all of our models we limit our estimating sample to individuals age 20 to 65 and also those with real
($2014) annual earnings above $5,000. This restricts our sample to employed individuals of prime
working age for whom self-employment is most likely to be a relevant consideration. Individuals are
coded as self-employed if they responded “yes” the survey question which asked whether any selfemployment income was received (qbus prior to the 2005 AHS survey and qself from 2005 onwards).
For all of the estimating samples we also restrict homes to just those in one of the 145 MSAs
identified in the public version of the AHS. This enables us to match these observations to MSA-level
house price indexes for portions of the empirical work to follow. Those indexes measure house price
inflation and are based on repeat sales “purchase only” models estimated by the Federal Housing Finance
Agency (FHFA) and posted to the FHFA website. Because the FHFA reports their indexes at the CBSA
level, we created a CBSA-MSA correspondence by matching the names of the MSAs and CBSAs and
then merged those indexes into the AHS data files based on MSA level geography.
All dollar valued measures were converted to constant year-2014 values using the CPI-U from the
Bureau of Labor Statistics. This included measures of household income, earnings, house value,
maintenance expenditures, housing capital gains, and also house price inflation. Only real-valued
measures for these variables were used in the summary and regression tables to follow.
It is also worth emphasizing that the AHS provides no information on an individual’s business,
industry, occupation, or hours worked. In addition, household debt information is available only for the
family’s mortgage(s) with no information on other personal loans, business loans, credit card debt, etc.
14
3.2 Summary Statistics
We begin in Tables 1 and 2 by comparing summary measures of self-employment rates in the
AHS to the decennial census (for years 1980, 1990, and 2000) and the American Community Survey (for
years since 2000). These latter data sources are more commonly used to study labor markets. In all
cases, self-employment is measured based on whether the household head reports having received any
self-employment income. All of the samples used in Tables 1 and 2 include individuals throughout the
entire United States (including those outside of identified MSAs in the AHS) and are roughly
representative of the nation. This facilitates comparison between the datasets and to summary measures
reported elsewhere in the literature.
Table 1a presents summary measures of self-employment rates for employed household heads in
the AHS stratified by survey year. In columns 1 and 2, notice that self-employment rates are always
higher for homeowners as compared to renters; in 2013, for example, 18.8 percent of homeowners were
self-employed versus 10.8 percent of renters. This underscores potential links between homeownership
and self-employment. In columns 4-8, which stratify the sample by age, self-employment rates are also
sharply higher for older individuals; in 2013, 9.3 percent for age 20-30 versus 18.4 percent for age 50-60.
Similar values and qualitative patterns are also present in Census/ACS data from 1980 to 2013 as shown
in Table 1b. The primary difference between the two tables is that self-employment rates are lower in the
ACS relative to comparable values in the AHS. As an example, in 2013 the overall self-employment rate
in column 3 in the ACS is 13.2 percent compared to 15.7 percent for the AHS. This difference likely
reflects differences in sample composition and the accuracy with which self-employment is identified in
the different samples. These differences, however, are unlikely to have any bearing on the regression
results in part because all of the self-employment models control for lagged self-employment which helps
to soak up idiosyncrasies in the coding of a given individual’s self-employment status.
Tables 2a and 2b report summary measures on the share of homeowners in the AHS that hold a
primary mortgage (Table 2a) and a HELOC (Table 2b). In each table, measures are provided for each
15
survey year in which the variable of interest is reported, 1985-2013 for the primary mortgage and 20012013 for HELOCs. In each case, separate summary measures are also reported for self-employed and not
self-employed households, and for individuals age 20-50 and age 50-65.25
Several patterns are worth noting in Table 2a. First, the share of households holding a mortgage
is relatively similar regardless of self-employment status although generally slightly lower for the selfemployed. This suggests that self-employment is not a dominant driver of the decision to hold a primary
mortgage, something we provide further evidence of later in the paper.
Second, the share of homeowners holding a mortgage increased sharply over the 1985-2013
period and especially so for individuals over age 50 (columns 5 and 6). For households over age 50,
roughly 45 percent held a mortgage in 1985 compared to roughly 70 percent in 2013, an increase of
roughly 25 percentage points. For younger households (columns 3 and 4) the corresponding values are
roughly 75 percent and 85 percent, respectively, or an increase of 10 percentage points. This increase in
the share of households holding a mortgage mirrors the dramatic decline in mortgage interest rates over
the period, from a peak of roughly 18 percent in late 1981 for 30-year fixed rate mortgages down to 4
percent in 2013.26 The sharper rise in mortgage share among older households over the period is
indicative of their greater levels of wealth and potential to use discretionary mortgage debt to finance nonhousing expenditures. In contrast, the smaller increase in mortgage share among younger homeowners,
along with their generally high rates of holding a mortgage, is indicative of their greater tendency to have
limited wealth and lesser sensitivity to interest rates when deciding whether to finance a home purchase.
Overall, the patterns in Table 2a are a reminder that house price inflation and housing capital gains may
have different effects on the self-employment decisions of young and older households.
Table 2b reports analogous summary measures for the share of homeowners that hold a HELOC
over the 2001-2013 period, the set of years in which HELOC data are reported in the survey. Recall that
a HELOC enables the homeowner to extract and pay back home equity on demand with little transaction
25
Analogous summary measures were computed using the census and ACS data. The summary measures were
similar to those in the AHS and are not reported for that reason.
26
See the Freddie Mac 30-year fixed rate mortgage loan series at http://www.freddiemac.com/pmms/pmms30.htm.
16
costs. The important pattern to note in the table is that regardless of age and survey year, self-employed
individuals are notably more likely to hold a HELOC than household heads who are not self-employed.
This pattern is also robust in the empirical work to follow and will be discussed later in the paper.
Additional summary measures on the sociodemographic attributes of owner-occupiers used in the
mortgage regressions to follow are presented in Tables 3a and 3b. Table 3a pools self-employed and notself-employed owner-occupiers while Table 3b stratifies the sample by current self-employment status.
As a broad characterization, the measures in these tables indicate that self-employed individuals are of
higher socio-economic status and are more stable in the sense that they have been in their homes longer
and are more likely to be married.27 Importantly, regardless of self-employment status, in the previous
two years, roughly 38 percent of the sample experiences an increase in the real value of the house net of
maintenance expenditures while 62 percent experiences a net decline. This latter pattern is consistent
with the general tendency for the real value of homes to decline with house age (e.g. Rosenthal (2014)).
Analogous measures for renters are reported in the appendix and confirm well-known facts that renters
are younger, less likely to be married, and are of lower socioeconomic status.
IV.
Housing capital gains and transitions into and out of self-employment
4.1 Overview
Tables 4 and 5 report estimates from linear probability specifications of the self-employment
models described in Section 2, all of which consider transitions into and out of self-employment over the
previous two years. Table 4 presents the simplest models based on expression (1). As described earlier,
these models use MSA-level house price inflation as a proxy for individual-level housing capital gains
27
In Table 3b, observe that self-employed individuals have higher family income ($144,580 versus $107,370 in
$2014), live in higher valued homes ($311,700 versus $239,800), and have been in their homes longer (11.7 years
versus 10.9 years). Self-employed individuals are also more highly educated (47.9 percent college or more versus
41.2 percent), older (46.3 versus 44.4), less likely to be female (25.5 percent versus 29.6 percent), more likely to be
white or Asian (86.6 percent versus 78.5 percent), and more likely to be married (77.3 percent versus 68.8 percent).
Number of adults in the household are nearly the same (2.2 for self-employed versus 2.17 for not self-employed).
17
using renters as a comparison group. Table 5 replaces MSA-level house price inflation with individual
housing capital gains as in expressions (2) and (3) in Section 2.
In all of the models in Tables 4 and 5, the dependent variable is 1 if the household head is
currently self-employed and 0 otherwise. To conserve space, only the coefficients on the primary
variables of interest are reported in the tables. Included in the models but not shown are controls for the
age and age squared of the household head; categorical variables describing years since moving into the
home (including less than 2 years, 2-5 years, and more than 5 years), education dummy variables for less
than high school, high school, some college, college degree, more than college; whether the household
head is married, divorced or single; gender of the household head, race (white or Asian as one group with
all others as the alternative category), and number of adults in the household.
4.2 House price inflation and self-employment
Consider now the estimates in Table 4. Column 1 presents estimates from an OLS regression
including sample year fixed effects for fourteen years. Column 2 includes MSA plus year fixed effects
and columns 3-5 include MSA-by-year fixed effects. Note also that columns 4 and 5 stratify the sample
by previous self-employment status. For reasons discussed earlier, the key control measures in the table
are housing tenure and its interaction with the 2-year real change in the FHFA house price inflation index
for the MSA in which the home is located. This variable is coded as the percentage change in the FHA
index relative to its 2-year lag and adjusted for the general rate of inflation using the CPI-U: ΔHPI =
HPIt/HPIt-1 – 1. A value for ΔHPI of zero, therefore, denotes zero price change while a value of 1
indicates an approximate doubling of real house price.
In columns 1-3, notice first that regardless of specification, there is tremendous serial correlation
in a household head’s self-employment status: the coefficient on 2-year lagged self-employment status is
always about 41 percent with t-ratios over 50. Observe also that the direct effect of house price inflation
in column 1 is positive 0.035 with a t-ratio of 1.73. That coefficient is just 0.0138 with a t-ratio of 0.79
when MSA fixed effects are included in column 2 (this variable drops out when MSA-by-year fixed
18
effects are added in column 3). The coefficient on owning a home in columns 1-3 ranges between 0.02
and 0.025 and is always highly significant (with t-ratios ranging between 5.5 and 7.1). This suggests that
in the absence of any real house price inflation, homeownership is associated with an increase in the
tendency for self-employment of 2 to 2.5 percentage points. While notable, this is much lower than the
roughly ten percentage point difference in self-employment rates for owners and renters in the raw
summary measures as reported in Tables 1a and 1b. Most important, the housing tenure-inflation
interaction terms in columns 1-3 are always close to zero and not significant. On balance, therefore, these
models yield no evidence that MSA-level house price inflation encourages self-employment.
A sharper pattern begins to emerge in columns 4 and 5 when the sample is stratified by previous
self-employment status. Consider column 5 first which restricts the sample to individuals who were
previously self-employed and therefore highlights exit from self-employment. The coefficient on owning
a home is 0.155 with a t-ratio of 9.73. This indicates that in the absence of house price inflation,
homeowners are much less likely to exit self-employment as compared to renters. That pattern is eroded
somewhat when real house prices are rising as the coefficient on the interaction term is -0.14 (with a tratio of -1.44). However, a doubling of real house prices (in two years) would be necessary before exit
among renters would be comparable to that of owner-occupiers. In nearly all years and metropolitan
areas, therefore, homeowners are more likely to remain self-employed as compared to renters.
In column 4, among individuals who were not previously self-employed, in the absence of house
price inflation the coefficient on housing tenure indicates that homeowners are roughly 1 percentage point
more likely to transition into self-employment compared to renters (the housing tenure coefficient is
0.0096 with a t-ratio of 2.86). In this instance, that tendency is amplified by house price inflation as the
coefficient on the interaction term is positive 0.038 with a t-ratio of 1.72. To put this estimate in
perspective, recall from Tables 3a and 3b that the average real two-year rate of house price inflation for
our the 1985-2013 sample is roughly 2.5 percent. Evaluated at that level, house price inflation adds just
0.1 percentage point to the propensity for owner-occupiers to transition into self-employment relative to
renters. On the other hand, in volatile markets, real house prices have jumped sharply at times as with the
19
near doubling of price in Phoenix between 2004 and 2006 (see Liu, Nowak and Rosenthal (2016)). A
change in house price on that level would add 3.8 percentage points to homeowner entry into selfemployment.
Summarizing, estimates from Table 4 support the view that house price inflation appears to
increase the tendency for homeowners to enter self-employment relative to renters. That effect is small in
most market environments but can be empirically important in volatile markets and time periods.
Estimates also indicate that there is a sharp asymmetry between the influence of homeownership and
house price inflation on transitions into versus exists from self-employment: house price inflation has a
stronger effect on entry and of opposite sign relative to exit. In the absence of house price inflation,
homeownership is associated with greater persistence in the tendency to remain self-employed as
compared to possible effects on entry into self-employment. Analogous qualitative patterns will persist in
the models to follow.
4.3 Housing capital gains and self-employment
Table 5 reports estimates based on expressions (2) and (3) from Section 2 that replace house price
inflation with individual housing capital gains net of maintenance expenditures. These estimates allow us
to characterize the degree to which individual housing capital gains have an absolute as opposed to
relative effect on propensity for self-employment. Given the nature of the capital gains variable, these
models are estimated only over owner-occupiers.
Three sets of models are presented in Table 5. The first, in columns 1-3, measures housing
capital gains by differencing the homeowner’s assessment of house value between adjacent surveys (in
year-2014, $100,000). The second, in columns 4-6, subtracts off the previous two years of home
maintenance and improvement expenditures from the change in house value. The third set of models, in
columns 7-9, decomposes net-of-maintenance capital gains into increases versus decreases in house value.
For each set of models, separate estimates are reported for all household heads and also models
stratified by previous self-employment. As before, all models include MSA-by-year fixed effects and an
20
array of individual socio-economic controls. To proxy for wealth we now control for the owner’s
assessment of house value lagged two years (in year-2014, $100,000). As noted earlier, identification in
these models requires that lagged house value and housing capital gains net of maintenance are
exogenous to self-employment transitions conditional on the MSA-by-year fixed effects and other model
controls.
Notice first that lagged house value is always positive and significant regardless of other features
of the model specification. Its coefficient is also somewhat larger for individuals who were not
previously self-employed as compared to those who were: notice that the corresponding estimates in
columns 8 and 9 are 0.023 and 0.017 (with t-ratios of 12.37 and 3.26). These estimates are consistent
with the view that greater personal wealth encourages self-employment and that it has a stronger
influence on transitions into self-employment as compared to tendencies to remain self-employed once
self-employment has been attained.
In columns 1-3, housing capital gains encourage individuals who were not previously selfemployed to become so (column 2) but have no effect on exits from self-employment (column 3). The
estimate in column 2 indicates that a $10,000 real increase in house value increases the probability of
becoming self-employed by 0.13 percentage points with a t-ratio of 3.43. For individuals already selfemployed, the analogous effect in column 3 is just 0.02 percentage points with a t-ratio of 0.18.
Differencing off maintenance expenditures in columns 4-6 reduces the magnitude of the capital gains
coefficients but does not affect the qualitative pattern: for a $10,000 real change in house value net of
maintenance, the estimates in columns 5 and 6 are 0.108 percentage points and -0.049 percentage points
for those who were not and were previously self-employed, respectively.
The models in columns 7-9 reveal further nuances in the manner in which housing capital gains
(net of maintenance) affect self-employment transitions. In column 7, notice that housing capital gains
encourage self-employment while capital loses have little effect: the corresponding coefficients for a
$10,000 net change in house value are 0.226 percentage points for a capital gain (with a t-ratio of 3.15)
and -0.003 percentage point for a capital loss (with a t-ratio of -0.06). For individuals who were not
21
previously self-employed, the effect of a $10,000 capital gain is 0.297 percentage points (with a t-ratio of
3.86) while the effect of a $10,000 capital loss is much smaller and clearly insignificant as are the
coefficients on both capital gains and losses for previously self-employed individuals (in column 9).
Summarizing, as in Table 4, we see that housing capital gains encourage transitions into selfemployment but have little effect on exits back to wage work. As noted earlier, this pattern is consistent
with the presence of hurdle costs (emotional or financial) that must be overcome to attain selfemployment and/or ongoing commitments that may tie self-employed individuals to their business. We
also see that whereas housing capital gains encourage self-employment, capital losses have little effect on
self-employment decisions.
We conclude this section by considering the magnitude of the housing capital gains effect on selfemployment. As a starting point, recall that our estimates reflect homeowner response to a two-year real
change in home value net of maintenance. For that length of time, real house price inflation averages just
2.5 percent for our estimating sample (see Table 3a), consistent with estimates elsewhere (e.g. Rosenthal
(2014)). Subtracting out expenditures for home maintenance lowers these values further so that the
average real housing capital gain net of maintenance is slightly negative for our estimating sample (see
Table 3a). This is consistent with estimates from Harding et al. (2007) which suggest that much of the
real capital gain enjoyed by homeowners is offset by home maintenance expenditures. Thus, even before
considering the magnitude of our coefficients, it is important to recognize that for the typical metropolitan
area and time period, housing capital gains have little effect on self-employment. That characterization,
however, does not preclude important effects of housing capital gains given the considerable variation in
home price appreciation across markets and time.
Consider now the coefficients on positive capital gains (per $100,000) in columns 7 and 8 of
Table 5. These pertain to all household heads and those not previously self-employed, respectively, and
equal 0.023 and 0.03. Suppose also that the 2-year real change in house price net of maintenance is
$10,000, an amount that is well within the norm. That level of capital gain would increase the
homeowner rate of entry into self-employment by roughly 0.3 percentage points (0.1 by 0.03). The
22
overall self-employment rate would increase by 0.23 percentage points, about 1.5 percent of the national
self-employment rate which we benchmark at 15 percent (see Table 1b). If instead, real home prices rose
20 percent, well below rates observed in select cities prior to the 2007 crash, that would generate a capital
gain of $50,000 for the average valued home in our sample (see Table 3a). In that case, the likelihood of
homeowner entry into self-employment would increase 1.5 percentage points while the overall selfemployment rate would increase by roughly 1.1 percentage points.28
V.
Mortgage loans and self-employment
The previous section confirmed that homeownership and housing capital gains encourage
transitions into self-employment. As discussed in the Introduction, one channel by which that can occur
is through home equity borrowing. Because mortgage debt is less expensive than a small business loan
this creates incentives for self-employed homeowners to draw on home equity and housing capital gains
to cover a portion of their business expenses. This section provides suggestive evidence of this channel.
It is well appreciated that few households have sufficient wealth to purchase their residence
without a mortgage. It is not surprising, therefore, that summary measures in Table 2a indicate that in
nearly every survey year, two-thirds or more of both self-employed and not self-employed homeowners
hold a primary mortgage. Less straight forward are two other patterns in the table. Notice that in every
survey year, self-employed individuals are less likely to hold a primary mortgage than those who are not
self-employed. This is opposite from what might be expected if home equity borrowing is an important
driver of self-employment. In 2013, for example, 75 percent for self-employed individuals held a primary
mortgage versus 80 percent for those not self-employed. Also evident is that many self-employed
homeowners do not hold a primary mortgage.
A different pattern is present in Table 2b which presents analogous summary measures of the
tendency for homeowners to hold a HELOC. Recall that a HELOC allows the homeowner to extract
28
As an upper bound event, home prices in Phoenix doubled 2004-2006 (Liu et al. (2016)), which based on our
coefficients, would yield corresponding effects on entry and the overall self-employment rate of 7.5 and 5.5
percentage points, respectively.
23
home equity on demand with close to zero transactions costs. As such, it is a perfect vehicle to have in
place if a business owner wants to periodically draw on home equity and housing capital gains to cover
business expenses.29 Summary measures in Table 2b indicate that in every survey year, self-employed
homeowners are more likely to hold a HELOC than homeowners who are not self-employed. In 2013, for
example, 9.4 percent of self-employed homeowners held a HELOC compared to 5.9 percent among
homeowners who are not self-employed.
Table 6 takes a more careful look at these patterns. Columns 1 and 2 report results from linear
regressions of whether the household holds a primary mortgage. Columns 3 and 4 present analogous
regressions based on whether a homeowner holds a HELOC and columns 5 and 6 repeat the HELOC
regressions restricting the sample to just homeowners with a primary mortgage. In all of these models,
only the coefficient on self-employment status is presented while an array of additional household
controls are suppressed to conserve space.30 For each dependent variable results are first presented
including MSA-by-year fixed effects and then again for a specification that includes year plus house fixed
effects. In all cases, house value is omitted as a control since the need for financing may be sensitive to
the house value causing it to be endogenous.31 Finally, data for the primary mortgage regressions are
from the 1985-2013 survey while the HELOC regressions are limited to the 2001-2013 survey years
because HELOC information was not reported prior to 2001.
The first column in Table 6 suggests that self-employed individuals are less likely to hold a
primary mortgage conditional on the underlying socioeconomic attributes of the household and MSA-byyear fixed effects. Controlling instead for house fixed effects, which likely help to capture unobserved
29
Although a self-employed homeowner could cash out home equity by refinancing the primary mortgage,
substantial transactions costs in both expense and time would likely make this a less appealing option than taking a
draw on a HELOC.
30
Additional controls for the primary mortgage regressions include real family income (in $2014), the age of the
household head, age squared, education dummy variables for less than high school, high school, some college,
college degree, more than college, marital status, gender of the household head, race (white or Asian with all others
as the omitted category), number of adults in the household and categorical variables describing the length of time in
the home. Additional controls for the HELOC regressions include those for the primary mortgage regressions and
also the original loan to value ratio (OLTV).
31
We also ran all of the mortgage regressions including house value which had little effect on the coefficients.
24
wealth (column 2), the coefficient on self-employment becomes positive 0.0056 with a t-ratio of 1.46.
This implies that self-employed homeowners are roughly one-half percentage point more likely to hold a
primary mortgage, a small effect given the overall tendency to hold a primary mortgage. This estimate
also has a sizable confidence band that includes zero. Overall, therefore, the evidence indicates that selfemployment has relatively little effect on the tendency to hold a primary mortgage.
The patterns in the HELOC regressions in columns 3 and 4 are quite different. Regardless of the
model specification, self-employed individuals are roughly 2.5 to 3 percentage points more likely to hold
a HELOC with t-ratios ranging from 3.18 to 6.31. This is a smaller spread than in the raw summary
measures in Table 2b but still large enough to be notable and especially relative to the mean tendency to
hold a HELOC of roughly 15 percent (see Table 2b). Restricting the sample to just households with a
primary mortgage in columns 5 and 6 has little effect on the pattern.
Summarizing, the patterns in Table 6 suggest that self-employment has little effect on the
decision to finance a home but self-employed homeowners do position themselves to make home equity
an accessible source of business financing. This later result is consistent with arguments and anecdotal
evidence discussed in the Introduction. We cannot, however, confirm whether this tendency draws
homeowners into self-employment.
VI.
Differences by age
Older individuals have more wealth and also tend to be more risk averse as they near retirement.
For both reasons the determinants of self-employment among owner-occupiers may differ with age and
this may also affect tendencies to hold a HELOC. This section considers these possibilities.32
We focus first on Table 7a which revisits the self-employment models from Table 5. In Table 7a,
the first two columns restrict the estimating samples to individuals under age 50 and then repeat the
32
For summary measures of net wealth by age of householder and other demographic attributes see tables published
by the US Census Bureau at http://www.census.gov/people/wealth/data/dtables.html. Note also that homeownership
rates increase sharply with age and are roughly 40 percent for under age 35 but 80 percent for age 55-64. For
details, see Table 19, http://www.census.gov/housing/hvs/data/histtabs.html, US Department of Housing and Urban
Development (HUD), and also Haurin et al. (2007) and Gabriel and Rosenthal (2005, 2015).
25
specifications from columns 8 and 9 of Table 5 for individuals not self-employed and those who are selfemployed. The second set of estimates in Table 7a repeat these models with the sample restricted to
individuals over age 50.
The most important pattern in the table is that for individuals not previously self-employed
(columns 1 and 3), the coefficients on increase in house value are positive and significant for both age
groups but larger for the older individuals. For the under age 50 sample, a $10,000 increase in home
value (net of maintenance) increases the propensity for entry into self-employment by 0.262 percentage
points (with a t-ratio of 2.97). For the over age 50 sample the corresponding estimate is 0.320 percentage
points (with a t-ratio of 2.31). This suggests that housing capital gains has a somewhat stronger impact
on entry into self-employment for older individuals. Other estimates in the table based on the effect of
capital loses and exit from self-employment are largely as before and do not differ between age groups.
Table 7b presents similarly age-stratified estimates of the mortgage models from Table 6. In
Panel A, the effect of self-employment on the propensity to hold a primary mortgage is similar for both
age groups and also to estimates in Table 6. The same is true for the HELOC estimates in Panel B.
Summarizing, the results in Tables 7a and 7b suggest that households over age 50 may be more
sensitive to housing capital gains when considering transitions into self-employment. Self-employed
individuals of both age groups, however (under and over 50), display a similar tendency to configure their
mortgages to facilitate access to home equity.33
VII.
Conclusions
Drawing on the 1985-2013 American Housing Survey (AHS) panel, this paper shows that a 20
percent real increase in home value over a two-year period (net of maintenance) increases the probability
that a homeowner transitions into self-employment by roughly 1.5 percentage points. Housing capital
losses, in contrast, have little effect on exits. Relative to other work in this area, these estimates are
33
The models in Tables 7a and 7b were also estimated breaking the samples into three groups for individuals of age
20-35, age 35-50, and 50-65. Coefficients for the younger two groups were always similar.
26
distinct in that they are based on individual homeowner response to household-level measures of housing
capital gains net of maintenance expenditures. This is possible because of special features of the AHS
including that it reports detailed information on home maintenance and improvements which we subtract
off of observed changes in home values. We also obtain robust evidence that self-employed homeowners
are roughly 2.5 percentage points more likely to hold a HELOC, giving them easy, inexpensive access to
home equity. That is consistent with compelling anecdotal and other evidence that small business owners
periodically draw on home equity to cover business expenses.
Overall, our estimates suggest that spillovers from the housing market to self-employment are
strong enough to be important when home prices are rising rapidly, but modest when housing capital
gains are limited or negative. These effects could be driven by several possible mechanisms including
wealth shocks induced by housing capital gains that affect preferences for operating a business or
collateral constraints that may limit access to credit. Previous studies in this area have primarily focused
on discriminating between these and other underlying mechanisms while our emphasis has been on
estimating the magnitude of the effects noted above. That difference contributes to differences in model
specifications which, along with differences in the type of data, time periods and location, make it
difficult to compare the precise magnitude of our estimates to those from related work. Bearing that in
mind, Jensen et al. (2015) and Kerr, Kerr and Nanda (2015) report modest collateral effects from home
equity and housing capital gains on business creation while Adelino et al. (2015), Corradin and Popov
(2015) and Schmalz et al. (forthcoming) claim evidence of a much larger role for home equity as a source
of collateral. Our estimates indicate that in many market environments, real housing capital gains net of
maintenance are often zero or even negative while in other instances housing capital gains can be robust.
For these and other reasons, the impact of home equity and housing capital gains on business activity
differs with market conditions which may account for some of the differences across other studies.
Links between housing and labor market outcomes have taken on new importance in the
aftermath of the Great Recession and its associated volatility in home prices and homeownership. This
27
study confirms that such links can be important and adds to previously established effects of
homeownership on families and their communities.
28
References
Adelino, M., A. Schoar, and F. Severino (2015), “ House Prices, Collateral, and Self-employment”,
Journal of Financial Economics, 117 (2), 288–306.
Albouy, David and Bert Lue (2015), “Driving to Opportunity: Local Rents, Wages, Commuting, and SubMetropolitan Quality of Life,” Journal of Urban Economics, 89, 74-92.
Banker, Joe (March 8, 2013), “Should You Use a Home Equity Loan to Fund Your Business?” Connect,
http://www.usbankconnect.com/blog-post/a-home-equity-loan-fund-business .
Bayer, Patrick, Stephen Ross, and Giorgio Topa (2008), “Place of Work and Place of Residence: Informal
Hiring Networks and Labor Market Outcomes.” Journal of Political Economy 116(6), 1150-1196.
Berger, Allen and G. Udell (1993), “Securitization, Risk and the Liquidity Problem in Banking”, in
Michael Klausner and Lawrence White (eds) Structural Change in Banking (Irwin, Homewood:IL)
Berger, Allen, Leora Klapper, and Gregory Udell (2001), “The Ability of Banks to Lend to
Informationally Opaque Small Businesses,” Journal of Banking and Finance, 25(12), 2127-2167.
Black, Dan, Douglas Holtz-Eakin, and Stuart S. Rosenthal (2001), “Racial Minorities, Economic Scale,
and the Geography of Self-Employment.” Brookings-Wharton papers on Urban Affairs, 245-282.
Black, Dan, Natalia Kolesnikova and Lowell J. Taylor (2014), “Why Do So Few Women Work in New
York (and so many in Minneapolis)? Labor Supply of Married Women Across US Cities,” Journal of
Urban Economics, 79, 59-71.
Black, Jane, David de Meza and David Jeffreys (1996), “House Prices, the Supply of Collateral and the
Enterprise Economy,” Economic Journal, 106(434), 60-75.
Blanchflower, David (2000), “Self-Employment in OECD Countries”, Labour Economics, 7, 471-505.
Blanchflower, David and A. Oswald (1998), “What Makes an Entrepreneur?” Journal of Labor
Economics, Vol.16, No.1, pp.26-60.
Borjas, George J. and Stephen G. Bronars (1989), “Consumer Discrimination and Self-Employment,”
Journal of Political Economy, 97 (June): 581-605.
Bracke, P., C. Hilber, and O. Silva (2015), “Mortgage Debt and Entrepreneurship,” Bank of England
Working Paper No. 560.
Brevoort, Kenneth and Timothy Hannan (2004), “Commercial Lending and Distance: Evidence from
Community Reinvestment Act Data,” Finance and Economics Discussion Series, 2004-24, Board of
Governors of the Federal Reserve System.
http://www.federalreserve.gov/pubs/feds/2004/200424/200424abs.htm
Canner, Glenn, Karen Dynan, and Wayne Passmore (December 2002), “Mortgage Refinancing in 2001
and Early 2002,” Federal Reserve Bulletin, 469-481.
Cao, Qianqian (2014), “Bankruptcy Exemption, Home Equity and Mortgage Credit,” Real Estate
Economics, 42(4), 938-976.
29
Carlstrom, Charles, and Katherine Samolyk (1995), “Loan Sales as a Response to Market-Based Capital
Constraints,” Journal of Banking and Finance 19, 627-646.
Carrasco, Raquel (1999), “Transitions to and from Self-Employment in Spain: An Empirical Analysis,”
Oxford Bulletin of Economics and Statistics, 61(3), 315-341.
Chen, Yong and Stuart S. Rosenthal (2008), “Local Amenities and Life-Cycle Migration: Do People
Move for Jobs or Fun?” Journal of Urban Economics, 64(3): 519-537.
Compton, Janice and Robert A. Pollak (2014), “Family Proximity, Childcare, and Women’s Labor Force
Attachment,” Journal of Urban Economics, 79: 72-90.
Corradin, Stefano and Alexander Popov (2015), “House Prices, Home Equity Borrowing, and
Entrepreneurship,” The Review of Financial Studies, 28:8, 2399-2428.
Coulson, Edward and Lynn Fisher (2009), “Housing tenure and labor market impacts: The search goes
on,” Journal of Urban Economics, 65 (3): 252–264.
Cowling, Marc and Peter Mitchell (1997), “The Evolution of U.K. Self-Employment: A Study of
Government Policy and the Role of the Macroeconomy,” The Manchester School LXV (September):.
427-442.
Davidoff, Thomas (2006), “Labor income, housing prices, and homeownership,” Journal of Urban
Economics, 59, 209–235.
Davidoff, Thomas (2016), “Supply Constraints Are Not Valid Instrumental Variables for Home Prices
Because They Are Corrleated With Many Demand Factors,” Critical Finance Review, 59, 177-206.
Demsetz, Rebecca (2000), “Bank Loan Sales: A New Look at the Motivation for Secondary Market
Activity,” Journal of Financial Research, 23, 192-222.
DiPasquale, Denise and Edward Glaeser (1999), “Incentives and Social Capital: Are Homeowners Better
Citizens?” Journal of Urban Economics, 45(2), 354–384.
Disney, R. and J. Gathergood (2009), “Housing Wealth, Liquidity Constraints and Self-Employment,”
Labour Economics, Vol.16, pp.79-88.
Drucker, Steven and M. Puri (2008), “On Loan Sales, Loan Contracting, and Lending Relationships”,
Review of Financial Studies, Advanced Access July 2, 2008.
Dunn, T. and D. Holtz-Eakin (2000), “Financial Capital, Human Capital, and the Transition to SelfEmployment: Evidence from Intergenerational Links”, Journal of Labour Economics, 18 (2), 282305.
Engelhardt, Gary (1994), “House Prices and the Decision to Save for Down Payments,” Journal of Urban
Economics, 36, 209-237.
Engelhardt, Gary and Christopher Mayer (1998), “Intergenerational Transfers, Borrowing Constraints,
and Saving Behavior: Evidence from the Housing Market,” Journal of Urban Economics, 44, 135157.
30
Evans, D. and B. Jovanovic (1989), “An Estimated Model of Entrepreneurial Choice under Liquidity
Constraints”, Journal of Political Economy, 97(4), 808-827.
Evans, D. and L. Leighton (1989), “Some Empirical Aspects of Entrepreneurship”, American Economic
Review, Vol.79, No.3, pp.519-535.
Faggio, Giulia and Olmo Silva (2014), “Does Self-Employment Measure Entrepreneurship: Evidence
from Great Britain,” Journal of Urban Economics, 84, 67–85.
Fairlie, Robert W. and Bruce D. Meyer (1996), “Ethnic and Racial Self-Employment Differences and
Possible Explanations,” Journal of Human Resources, 31: 757-93.
Gabriel, Stuart and Stuart S. Rosenthal (2005), “Homeownership in the 1980s and 1990s: Aggregate
Trends and Racial Gaps,” Journal of Urban Economics, 57, 101-127.
Gabriel, Stuart, and Stuart Rosenthal (2010), “Do the GSEs Expand the Supply of Mortgage Credit? New
Evidence of Crowd Out in the Secondary Mortgage Market,” Journal of Public Economics, 94, 953966.
Gabriel, Stuart, and Stuart Rosenthal (2015), “The Boom, the Bust and the Future of Homeownership,”
Real Estate Economics, 43(2): 334-375.
Graves, Jennifer (2013), “School Calendars, Child Care Availability and Maternal Employment,” Journal
of Urban Economics, 78, 57-70.
Green, Richard and Michelle White (1997), “Measuring the Benefits of Homeowning: Effects on
Children,” Journal of Urban Economics, 41, 441–461.
Guerrieri, Veronica, Daniel Hartley and Erik Hurst (2013), “Endogenous Gentrification and Housing
Price Dynamics,” Journal of Public Economics, 100, 45-60.
Harding, John, C. F. Sirmans, and Stuart S. Rosenthal (2007), “Depreciation of Housing Capital,
Maintenance, and House Price Inflation: Estimates from a Repeat Sales Model,” Journal of Urban
Economics, 61, 193-217.
Haurin, Donald, Toby L. Parcel, and Jean Haurin (2002), “Does Homeownership Affect Child
Outcomes?” Real Estate Economics, 30(4), 635–666.
Haurin, Donald, Christopher Herbert, and Stuart S. Rosenthal (2007), “Homeownership Gaps Among
Low-Income and Minority Borrowers and Neighborhoods,” CityScape, 9 (2), 5-52.
Hellerstein, Judith, Mark Kutzbach, and David Neumark (2014), “Do Labor Market Networks have an
Important Spatial Dimension?” Journal of Urban Economics, 79:39-58.
Holtz-Eakin, Doug, D. Joulfaian and Harvey Rosen (1994), “Entrepreneurial Decisions and Liquidity
Constraints”, the RAND Journal of Economics, 25(2), 334-347.
Hurst, Erik and Anamaria Lusardi (2004), “Liquidity Constraints, Household Wealth, and
Entrepreneurship,” Journal of Political Economy, 112 (2), 319-347.
31
Hurst, Erik and Benjamin Wild Pugsley (2011), “What Do Small Businesses Do?” Brookings Papers on
Economic Activity, 2, 73-118.
Hurst, Erik and Benjamin Pugsley (2015), “Wealth, Tastes, and Entrepreneurial Choice,” NBER working
paper no. 21644.
Jensen, Thais Laerkholm, Soren Leth-Petersen, Ramana Nanda (2015), “Home Equity Finance and
Entrepreneurial Performance – Evidence from a Mortgage Reform,” Harvard Business School
working paper 15-020.
Jiang, Boqian (2016), “Why Do People Vote Nationally But Not Locally? Homeownership, Mobility and
Investment in the Community,” working paper, Syracuse University.
Johansson, Edward (2000), “Self-Employment and Liquidity Constraints: Evidence from Finland,”
Scandinavian Journal of Economics, 102 (1), 123-134.
Jones, Larry (1994), “Home Mortgage Debt Financing of Nonhousing Investments”, Journal of Real
Estate Finance and Economics, 9, 91-112.
Kerr, Sari Pekkala, William R. Kerr, and Ramana Nanda (2015), “House Money and Entrepreneurship,”
Harvard Business School working paper 15-069.
Krishnan, Karthik, Debarshi K. Nandy, and Manju Puri (2014), “Does Financing Spur Small Business
Productivity? Evidence form a Natural Experiment,” The Review of Financial Studies, 28:6, 17681809.
Landvoigt, Tim, Monika Piazzesi, and Martin Schneider (2015). “The Housing Market(s) of San Diego,”
American Economic Review, 105(4): 1371-1407.
Leventis, Andrew (2012), “Home Price Indexes for Homes in Different Price Tiers: Biases and
Corrections,” Federal Housing Finance Agency Working Paper 12-1.
Lindh, Thomas and Henry Ohlsson (1996), “Self-Employment and Windfall Gains: Evidence from the
Swedish Lottery,” The Economic Journal, 106(439), 1515-1526.
Liu, Crocker, Adam Nowak, and Stuart S. Rosenthal (2016), “Housing Price Bubbles, New Supply, and
Within-City Dynamics,” Journal of Urban Economics, 96, 55-7.
Loutfi, Martha F. (1991), “Self-Employment Patterns and Policy Issues in Europe,” International Labour
Review, 30, 1-19.
McManus, Douglas (2013), “House Price Tiers in Repeat Sales Estimation,” Freddie Mac, mimeo.
McMillen, Daniel (2016), “Local Quantile House Price Indices,” University of Illinois, mimeo.
Mount, Ian (November 30, 2011), “Why It’s Getting Harder, and Riskier, to Bet the House,” New York
Times, http://www.nytimes.com/2011/12/01/business/smallbusiness/why-business-owners-routinelybet-the-house-and-why-its-getting-harder-to-do.html?_r=0
Neiman, Debra (Sept. 24, 2007), “Financing Your Business with Home Equity,”
http://www.entrepreneur.com/article/184692 .
32
Olsen, Edward and Jeffrey Zabel (2015), “U.S. Housing Policy,” Handbook of Regional and Urban
Economics, Eds. Gilles Duranton, J. Vernon Henderson and William C. Strange, Vol 5, Chapter 14,
pp. 887-986.
Oswald, Andrew (1997a), “Thoughts on NAIRU (Correspondence),” Journal of Economic Perspectives
11, 227–228.
Oswald, Andrew (1997b), “Theory of homes and jobs,” Working paper. University of Warwick.
Pfeiffer, E. and W. Pohlmeier (1992), “Uncertainty, and the Probability of Self-Employment,” Recherche
Economiques De Louvain, 58 (3-4), 265-281.
Robson, Martin T. (1997), “The Relative Earnings from Self and Paid Employment: A Time-Series
Analysis for the UK,” Scottish Journal of Political Economy, 44 (November), 502-518.
Rosenthal, Stuart S. (2008), “Old Homes, Externalities, and Poor Neighborhoods: A Model of Urban
Decline and Renewal,” Journal of Urban Economics, 63(3): 816-840.
Rosenthal, Stuart S. (2014), “Are Private Markets and Filtering a Viable Source of Low-Income Housing?
Estimates from a ‘Repeat Income’ Model,” American Economic Review, 104 (2), 687-706.
Saiz, Albert (2010), “The Geographic Determinants of Housing Supply,” Quarterly Journal of
Economics, 125, 1253–96.
Schmalz, Martin C., David A. Sraer and David Thesmar (forthcoming), “Housing Collateral and
Entrepreneurship,” Journal of Finance.
Shane, Scott (Jan. 3, 2012), “The Home-Equity Credit Crunch (Opinion),” Entrepreneur,
http://www.entrepreneur.com/article/222410.
Shane, Scott (June 8, 2015), “The Downside of Encouraging Small Business Owners to Tap Home
Equity,” Small Business Trends, http://smallbiztrends.com/2015/06/using-home-equity-for-smallbusiness.html .
Sinai, Todd and Nicholas S. Soulelles (2005), “Owner-Occupied Housing as a Hedge Against Rent Risk,”
Quarterly Journal of Economics, 120 (2), 763-789.
Small Business Administration (2001), “The Small Business Economy - A Report to the President: 2001”
pg. 84, Table A.3.
Small Business Administration (2008), “U.S. Small Business Administration Office of Advocacy: The
Small Business Economy 2008, A Report to the President,”
http://www.sba.gov/advo/research/sb_econ2008.pdf.
Taylor, Mark P. (1996), “Earnings, Independence of Unemployment: Why Become Self-Employed,”
Oxford Bulletin of Economics and Statistics, 58, 253-266.
33
Table 1a
Self-Employment Rates Among Employed Household Heads
in the American Housing Surveya
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
Renters
Age >= 20
(1)
0.087
0.082
0.073
0.073
0.078
0.084
0.071
0.060
0.056
0.053
0.120
0.110
0.107
0.124
0.108
Owners
Age >= 20
(2)
0.211
0.211
0.198
0.213
0.205
0.206
0.191
0.171
0.171
0.166
0.216
0.208
0.200
0.201
0.188
Renters +
Owners
Age >= 20
(3)
0.167
0.167
0.153
0.165
0.159
0.162
0.149
0.132
0.133
0.129
0.186
0.177
0.170
0.172
0.157
Renters +
Owners
Age 20-30
(4)
0.081
0.075
0.064
0.069
0.070
0.067
0.058
0.050
0.051
0.055
0.100
0.098
0.101
0.095
0.093
Renters +
Owners
Age 30-40
(5)
0.155
0.153
0.141
0.140
0.138
0.151
0.128
0.106
0.109
0.098
0.162
0.158
0.144
0.144
0.139
Renters +
Owners
Age 40-50
(6)
0.205
0.197
0.174
0.192
0.179
0.183
0.166
0.149
0.146
0.142
0.204
0.192
0.176
0.182
0.157
Renters +
Owners
Age 50-60
(7)
0.206
0.215
0.197
0.220
0.205
0.208
0.204
0.173
0.175
0.167
0.220
0.205
0.202
0.207
0.184
Renters +
Owners
Age 60-65
(8)
0.237
0.247
0.242
0.254
0.259
0.229
0.224
0.231
0.226
0.216
0.272
0.254
0.238
0.243
0.227
Individuals are coded as self-employed if they responded “yes” the survey question which asked whether any self-employment income was received (qbus prior
to 2005 and qself from 2005 onwards). Data are from the American Housing Survey Panel 1985-2013. Sample is restricted to household heads from age 20 to 65
with earnings above $5,000 ($2014). Observations are drawn from all locations in the AHS files.
a
34
Table 1b
Self-Employment Rates Among Employed Household Heads
in the Census and American Community Surveys
Year
1980
1990
2000
2003
2005
2007
2009
2011
2013
Renters
Age >= 20
(1)
0.062
0.070
0.092
0.095
0.098
0.096
0.092
0.093
0.094
Owners
Age >= 20
(2)
0.139
0.146
0.175
0.172
0.177
0.175
0.164
0.158
0.150
Renters +
Owners
Age >= 20
(3)
0.113
0.122
0.149
0.152
0.156
0.154
0.144
0.138
0.132
Renters +
Owners
Age 20-30
(4)
0.056
0.056
0.068
0.067
0.073
0.071
0.066
0.065
0.063
a
Renters +
Owners
Age 30-40
(5)
0.107
0.110
0.125
0.124
0.127
0.124
0.115
0.107
0.105
Renters +
Owners
Age 40-50
(6)
0.136
0.138
0.165
0.162
0.165
0.162
0.151
0.142
0.136
Renters +
Owners
Age 50-60
(7)
0.147
0.156
0.189
0.188
0.189
0.185
0.173
0.166
0.156
Renters +
Owners
Age >= 60
(8)
0.165
0.191
0.227
0.226
0.231
0.220
0.200
0.193
0.185
Individuals are classified as self-employed if they report having received any self-employment income based on the IPUMS variable INCBUS00 or are identified
as self-employed based on the IPUMS variable classwkr. The sample is restricted to household heads age 20-65 with earnings above $5,000 ($2014) in the
previous year. Data are from the 5 percent 1980, 1990, and 2000 census files in addition to the 1 percent 2003-2013 ACS files. Total sample size across years is
13,216,124 and includes all locations in the ACS files.
35
Table 2a
Percent Homeowners With a Primary Mortgage
in the American Housing Survey
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
Full Sample
Not
Self-Emp
Self-Emp
(1)
(2)
0.670
0.620
0.642
0.588
0.666
0.622
0.694
0.647
0.718
0.666
0.816
0.756
0.731
0.651
0.748
0.690
0.782
0.704
0.784
0.717
0.789
0.749
0.795
0.750
0.802
0.767
0.818
0.767
0.802
0.747
Age ≤ 50
Not
Self-Emp
Self-Emp
(3)
(4)
0.764
0.725
0.722
0.688
0.739
0.715
0.762
0.732
0.781
0.744
0.874
0.827
0.785
0.729
0.804
0.779
0.834
0.794
0.835
0.801
0.840
0.830
0.849
0.826
0.863
0.843
0.884
0.856
0.865
0.833
a
Age > 50
Not
Self-Emp
Self-Emp
(5)
(6)
0.448
0.432
0.436
0.401
0.474
0.447
0.510
0.480
0.542
0.512
0.659
0.604
0.583
0.506
0.611
0.544
0.660
0.559
0.673
0.595
0.682
0.619
0.689
0.635
0.697
0.671
0.715
0.668
0.709
0.655
Data are from the American Housing Survey Panel 1985-2013. Sample is restricted to household heads from age 20 to 65
with earnings above $5,000 ($2014). Observations are drawn from all locations in the AHS files.
36
Table 2b
Percent Homeowners With a Home Equity Line of Credit (HELOC)
in the American Housing Survey by Yeara
2001
2003
2005
2007
2009
2011
2013
Full Sample
Not
Self-Emp
Self-Emp
(1)
(2)
0.122
0.156
0.133
0.178
0.180
0.221
0.167
0.228
0.153
0.217
0.073
0.113
0.059
0.094
Age ≤ 50
Not
Self-Emp
Self-Emp
(3)
(4)
0.111
0.151
0.123
0.178
0.170
0.219
0.157
0.224
0.136
0.210
0.061
0.086
0.047
0.070
a
Age > 50
Not
Self-Emp
Self-Emp
(5)
(6)
0.147
0.164
0.156
0.178
0.202
0.223
0.187
0.234
0.182
0.227
0.091
0.143
0.076
0.119
Data are from the American Housing Survey Panel 1985-2013. Sample is restricted to household heads from age 20 to 65
with earnings above $5,000 ($2014). Observations are drawn from all locations in the AHS files.
37
Table 3a
Summary Statistics Pooling Self-Employed and Not Self-Employeda
Variable
Observations
Mean
Std. Dev.
Self-Employed (household head)
120,192
0.170
0.375
Real family income ($2014, 1,000s)
120,192
113.681
94.456
Hold a primary mortgage
120,192
0.771
0.420
Hold a HELOC
49,722
0.140
0.347
Moved into home < 2 yrs ago
120,192
0.2066
0.4048
Moved into home 2 to 5 yrs ago
120,192
0.1705
0.3761
Moved into home > 5 yrs ago
120,192
0.6227
0.4847
Percent change in real FHFA HPI index in previous 2 years
111,344
0.025
0.129
Purchase price of home ($2014; 100,000s)
105,555
2.734
2.381
Current assessed home value ($2014; 100,000s)
81,723
2.515
1.814
Percent positive real change in assessed value prev. 2 yrs. net maint.
50,623
0.385
0.486
All observations
50,623
-0.105
0.649
Conditional on increase in value
19,467
0.353
0.454
Conditional on decrease in value
31,156
-0.391
0.586
High School Degree (household head)
120,192
0.240
0.427
Some College (household head)
120,192
0.257
0.437
College Degree (household head)
120,192
0.257
0.437
More than College (household head)
120,192
0.166
0.372
Age of household head
120,192
44.761
10.361
Female household head
120,192
0.289
0.453
White or Asian ((household head)
120,192
0.799
0.401
Married (household head)
120,192
0.702
0.457
Divorced (household head)
120,192
0.020
0.140
Number of adults in household
120,192
2.178
0.901
Change in assessed value prev. 2 yrs. net maint. ($2014; 100,000s)
a
Summary statistics are reported for owner-occupied homes included in the sample used for column (1) of Table 6. Observation counts for
individual variables differ because not all of the variables are defined or present in each year of the AHS.
38
Table 3b
Summary Statistics Stratified by Self-Employed Statusa
Not Self-Employed
Variable
Observations
Self-Employed
Mean
Std. Dev.
Observations
Mean
Std. Dev.
Self-Employed (household head)
99,811
0.000
0.000
20,381
1.000
0.000
Real family income ($2014, 1,000s)
99,811
107.37
83.51
20,381
144.58
131.57
Hold a primary mortgage
99,811
0.772
0.419
20,381
0.762
0.426
Hold a HELOC
41,110
0.130
0.337
8,612
0.190
0.392
Moved into home < 2 yrs ago
99,811
0.2136
0.4099
20,381
0.1720
0.3774
Moved into home 2 to 5 yrs ago
99,811
0.1719
0.3773
20,381
0.1637
0.3700
Moved into home > 5 yrs ago
99,811
0.6142
0.4878
20,381
0.6639
0.4724
Percent change in real FHFA HPI index in previous 2 144.58 in table
92,534
0.025
0.128
18,810
0.023
0.136
Purchase price of home ($2014; 100,000s)
87,690
2.589
2.217
17,865
3.449
2.961
Current assessed home value ($2014; 100,000s)
68,418
2.398
1.719
13,305
3.117
2.141
Percent positive real change in assessed value prev. 2 yrs. net maint.
42,200
0.386
0.487
8,423
0.377
0.485
All observations
42,200
-0.095
0.605
8,423
-0.152
0.836
Conditional on increase in value
16,288
0.338
0.438
3,179
0.432
0.522
Conditional on decrease in value
25,912
-0.368
0.532
5,244
-0.506
0.790
High School Degree (household head)
99,811
0.248
0.432
20,381
0.198
0.399
Some College (household head)
99,811
0.260
0.439
20,381
0.239
0.427
College Degree (household head)
99,811
0.254
0.435
20,381
0.269
0.443
More than College (household head)
99,811
0.158
0.364
20,381
0.210
0.407
Age of household head
99,811
44.443
10.388
20,381
46.319
10.087
Female household head
99,811
0.296
0.457
20,381
0.255
0.436
White or Asian ((household head)
99,811
0.785
0.411
20,381
0.866
0.341
Married (household head)
99,811
0.688
0.463
20,381
0.773
0.419
Divorced (household head)
99,811
0.020
0.139
20,381
0.021
0.144
Number of adults in household
99,811
2.167
0.908
20,381
2.232
0.866
Change in assessed value prev. 2 yrs. net maint. ($2014; 100,000s)
a
Summary statistics are reported for owner-occupied homes included in the sample used for column (1) of Table 6. Observation counts for individual variables differ because not all of
the variables are defined or present in each year of the AHS
39
Table 4
House Price Inflation and Self-Employment By Housing Tenurea,b
All
Household
Heads
(1)
All
Household
Heads
(2)
All
Household
Heads
(3)
Not SelfEmployed 2
Years Earlier
(4)
SelfEmployed 2
Years Earlier
(5)
Own home (T)
0.0210
(5.50)
0.0239
(5.71)
0.0246
(7.08)
0.0096
(2.86)
0.1554
(9.73)
Δ real HPI in previous 2 yrs (ΔHPI)c
0.0345
(1.73)
0.0138
(0.79)
-
-
-
ΔHPI x Tc
0.0000
(0.00)
0.0008
(0.05)
-0.0046
(-0.20)
0.0380
(1.72)
-0.1443
(-1.41)
Self-employed 2 years earlier
0.4141
(52.00)
0.4073
(50.75)
0.4094
(80.80)
-
-
Year Fixed Effects
MSA Fixed Effects
MSA by Year Fixed Effects
R-squared
Observations
14
0.177
92,483
14
145
0.172
92,483
2,027
0.171
92,483
2,025
0.010
78,978
1,772
0.037
13,505
a
The dependent variable always equals 1 if currently self-employed and 0 otherwise. T-ratios are based on standard errors clustered at
the MSA level in columns 1 and 2 and at the MSA by year level in columns 3-5. Samples include household heads age 20-65 with
earnings above $5,000 ($2014) and who are located in one of the 145 MSAs identified in the AHS. Data are from the American Housing
Survey Panel 1985-2013.
b Additional controls not shown include age and age squared of the household head, dummy variables for years since moving into the
home (including less than 2 years, 2-5 years, and 5 years or more), education dummy variables for less than high school (the omitted
category), high school, some college, college degree, more than college, marital status, gender of the household head; race as measured
by white or Asian versus all other race and ethnicity, and number of adults in the household.
ΔHPI is equal to the ratio of the current to the 2-year lagged values of the HPI index minus 1 deflated by the CPI-U. A
value of 0 indicates real house price inflation is zero.
c
40
Table 5: Housing Capital Gains and Self-Employmenta,b
Control for 2-Year Capital Gain
Control for 2-Year Capital Gain Net of Maintenance
Not SelfSelfNot SelfEmployed
Employed
All
Employed
2 Years
2 Years
Household
2 Years
Earlier
Earlier
Heads
Earlier
(5)
(6)
(7)
(8)
0.0252
0.0161
0.0218
0.0232
(14.49)
(3.37)
(12.35)
(12.37)
SelfEmployed
2 Years
Earlier
(9)
0.0168
(3.26)
All
Household
Heads
(1)
0.0235
(15.09)
Not SelfEmployed 2
Years Earlier
(2)
0.0253
(14.77)
SelfEmployed 2
Years Earlier
(3)
0.0173
(3.69)
All
Household
Heads
(4)
0.0233
(14.37)
0.0112
(3.16)
0.0130
(3.43)
0.0020
(0.18)
-
-
-
-
-
-
-
-
-
0.0084
(2.38)
0.0108
(2.84)
-0.0049
(-0.45)
-
-
-
-
-
-
-
-
-
0.0226
(3.15)
0.0297
(3.86)
-0.0110
(-0.44)
Decrease in house value
-
-
-
-
-
-
-0.0003
(-0.06)
-0.0003
(-0.06)
0.0013
(0.09)
Self-employed 2 years earlier
0.4377
(66.35)
-
-
0.4404
(66.66)
-
-
0.4303
(66.67)
-
-
MSA by Year Fixed Effects
R-squared
Observations
2,004
0.204
46,236
1,992
0.017
39,115
1,566
0.016
7,121
2,002
0.206
45,487
1,989
0.018
38,473
1,559
0.017
7,014
2,002
0.206
45,487
1,989
0.018
38,473
1,559
0.017
7,014
House value 2 years earlier
($2014; 100,000s)
Δ in house value
($2014; 100,000s)
Δ in house value net of maint.
prev 2 years ($2014; 100,000s)
Absolute value of change in house
value net of maint. in previous 2
years ($2014; in 100,000s)
Increase in house value
a
The dependent variable always equals 1 if currently self-employed and 0 otherwise. T-ratios are based on standard errors clustered at the MSA by year level in all models. Samples include
homeowner household heads age 20-65 with earnings above $5,000 ($2014) and who are located in one of the 145 MSAs identified in the AHS. Data are from the American Housing Survey
Panel 1985-2013.
b Additional controls not shown include age and age squared of the household head, categorical variables for years since moving into the home (including less than 2 years, 2-5 years, and 5 years
or more), education dummy variables for less than high school (the omitted category), high school, some college, college degree, more than college, marital status, gender of the household head;
race as measured by white or Asian versus all other race and ethnicity, and number of adults in the household.
41
Table 6: The Decision to Hold a Mortgagea
Self Employed
Year Fixed Effects
House Fixed Effects
MSA by Yr Fixed Effects
R-squared
Observations
Primary Mortgage
(1 if yes)b
(1)
(2)
-0.0126
0.0056
(-3.95)
(1.46)
HELOC (1 if yes)c
(3)
(4)
0.0296
0.0236
(5.23)
(3.18)
2,172
0.010
120,165
1,012
0.033
36,876
14
24,410
0.046
120,165
a
6
11,344
0.031
36,876
HELOC Conditional
on Holding a Primary
Mortgage (1 if yes)c
(5)
(6)
0.0326
0.0286
(6.31)
(4.20)
1,004
0.034
32,366
6
10,817
0.031
32,366
Dependent variables are 1 (if yes) and 0 (if no) based on whether a family holds a primary mortgage (columns 1 and 2) or a
HELOC (columns 3 to 6). T-ratios are based on standard errors clustered at the MSA by year level in columns 1, 3 and 5, and at the
house level in columns 2, 4, and 6. Sample is restricted to homeowner household heads from age 20 to 65 with earnings above
$5,000 ($2014) and who are located in one of the 145 MSAs identified in the AHS. The sample in columns 1 and 2 includes only
homeowners with and without a primary mortgage. The sample in columns 3-6 includes only homeowners with a primary
mortgage. Data are from the American Housing Survey Panel 1985-2013.
b Additional controls not shown in columns 1 and 2 include the real family income, age of the household head, age squared,
education dummy variables for less than high school (the omitted category), high school, some college, college degree, more than
college, marital status (married, divorced and single as the omitted category), gender of the household head, race (white or Asian
with all others as the omitted category), number of adults in the household, and categorical variables for years since moving into the
home (including less than 2 years, 2-5 years, and 5 years or more).
c Additional controls for the HELOC regressions include those for the primary mortgage regressions and also the original loan to
value ratio. The HELOC variable is only reported from 2001 to 2013 which accounts for the smaller sample size relative to the
primary mortgage regressions. The sample in columns 3 and 4 include homeowners with and without a primary mortgage. The
sample in columns 5 and 6 is restricted to just homeowners with a primary mortgage.
42
Table 7a: Self-Employment By Age of Household Heada,b
House value 2 yrs earlier ($2014; 100,000s)
Age ≤ 50
Not SelfSelfEmployed Employed
2 Years
2 Years
Earlier
Earlier
0.0215
0.0219
(9.08)
(3.19)
Absolute value of change in house value net of
maintenance in previous 2 years ($2014; in
100,000s)
Increase in house value
Age > 50
Not SelfSelfEmployed Employed
2 Years
2 Years
Earlier
Earlier
0.0254
0.0024
(7.55)
(0.28)
0.0262
(2.97)
0.0041
(0.13)
0.0320
(2.31)
0.0074
(0.15)
Decrease in house value
-0.0035
(-0.52)
-0.0017
(-0.08)
0.0080
(0.83)
0.0228
(0.81)
MSA by Year Fixed Effects
R-squared
Observations
1,959
0.014
27,805
1,344
0.016
4,711
1,738
0.028
10,668
1,036
0.027
2,303
a
The dependent variable always equals 1 if currently self-employed and 0 otherwise. T-ratios are based on
standard errors clustered at the MSA by year level. Samples include homeowner household heads age 20-65
with earnings above $5,000 ($2014) and who are located in one of the 145 MSAs identified in the AHS. Data
are from the American Housing Survey Panel 1985-2013.
b Additional controls not shown include age and age squared of the household head, categorical variables for
years since moving into the home (including less than 2 years, 2-5 years, and 5 years or more), education dummy
variables for less than high school (the omitted category), high school, some college, college degree, more than
college, marital status, gender of the household head; race as measured by white or Asian versus all other race
and ethnicity, and number of adults in the household.
43
Table 7b: The Decision to Hold a Mortgage By Age of Household Heada
Self Employed
Year Fixed Effects
House Fixed Effects
MSA by Year Fixed Effects
R-squared
Observations
Self Employed
Year Fixed Effects
House Fixed Effects
MSA by Year Fixed Effects
R-squared
Observations
Panel A - Primary Mortgage (1 if yes)b
Age ≤ 50
Age ≤ 50
-0.0164
0.0026
(-4.52)
(0.59)
2,167
0.029
81,911
14
19,801
0.017
81,911
Panel B - HELOC (1 if yes)c
Age ≤ 50
Age ≤ 50
0.0256
0.0281
(3.85)
(2.99)
995
0.028
22,318
6
8,594
0.032
22,318
a
Age > 50
-0.0094
(-1.53)
Age > 50
0.0101
(0.1.27)
2,118
0.078
38,254
14
13,335
0.047
38,254
Age > 50
0.0333
(3.17)
Age > 50
0.0279
(1.86)
947
0.039
10,048
6
4,527
0.027
10,048
Dependent variables in Panels A and B are 1 (if yes) and 0 (if no) based on whether a family holds a primary mortgage or a HELOC.
T-ratios are based on standard errors clustered at the level of the fixed effects specified in the model (either MSA by year or house
level fixed effects). All samples are restricted to household heads from age 20 to 65 with earnings above $5,000 ($2014) and who are
located in one of the 145 MSAs identified in the AHS. The sample in Panel A includes only homeowners with and without a primary
mortgage. The sample in Panel B includes only homeowners with a primary mortgage. Data are from the American Housing Survey
Panel 1985-2013.
b Additional controls not shown in Panel A (the primary mortgage regressions) include the real family income, age of the household
head, age squared, education dummy variables for less than high school (the omitted category), high school, some college, college
degree, more than college, marital status (married, divorced and single as the omitted category), gender of the household head, race
(white or Asian with all others as the omitted category), number of adults in the household, categorical variables for years since
moving into the home (including less than 2 years, 2-5 years, and 5 years or more).
c Additional controls not shown in Panel B (the HELOC regressions) include those for the primary mortgage regressions and also the
original loan to value ratio. The HELOC variable is only reported from 2001 to 2013 which accounts for the smaller sample size
relative to the primary mortgage regressions.
44
Appendix: Supplemental Table
Table A-1: Average Attributes of Owner-Occupiers and Rentersa
OwnerOccupied
(75,941)
RenterOccupied
(16,542)
0.18
0.10
116.86
60.61
Moved into home < 2 yrs ago
0.05
0.29
Moved into home 2 to 5 yrs ago
0.22
0.14
Moved into home > 5 yrs ago
0.73
0.56
High School Degree (household head)
0.24
0.29
Some College (household head)
0.26
0.28
College Degree (household head)
0.25
0.20
More than College (household head)
0.17
0.09
Age of household head (years)
46.22
38.09
Female household head
0.28
0.43
White or Asian ((household head)
0.80
0.64
Married (household head)
0.72
0.40
Divorced (household head)
0.03
0.11
Self-Employed (household head)
Real family income ($2014, 1,000s)
Number of adults in household
2.23
1.89
a
Samples are based on AHS panel observations used in Table 4, columns 1-3,
and include only homes present in at least two consecutive surveys. Sample
sizes are reported in the parentheses at the top of each column. All dollar values
are in $2014.
45