Revisiting the Effect of Homeownership
on Social Capital
October 3, 2013
Roland Cheoa , Eric Fesselmeyerb,∗, Kiat Ying Seahc
a
Center for Economic Research, Shandong University, China
Department of Economics, National University of Singapore
c
Department of Real Estate, National University of Singapore
b
Abstract
This paper examines the role of homeownership in a household’s decision to
invest in social capital. Because homeowners are more likely to improve the
quality of their community and less likely to move, many housing advocates
believe that homeownership brings about better citizenship. The empirical literature on the relationship between social capital and homeownership
is, however, plagued by two particular difficulties. First, homeownership is
an endogenous variable that is correlated with unobservable characteristics
that describe good citizenship thus making instruments and more sophisticated econometric specifications necessary. Second, datasets of social capital
investment choices are hard to come by. Using confidential and detailed
household-level data, we are able to model the interrelated decisions of social capital investment and homeownership with an endogenous switching
model that controls for selection effects. With appropriate instruments, we
find strong evidence that homeownership increases the rate of social capital
investment.
Keywords: Social Capital, Homeownership, Externalities
JEL Classifications: R20, R21, D62
∗
Corresponding author
Email address: ecsef@nus.edu.sg Tel:
2646) (Eric Fesselmeyer)
(65) 6516 4873 Fax:
(65) 6775
1. Introduction
Stabilized neighborhoods, increased political activism and positive effects
on family structure and children - these are some of the social benefits that
have been espoused by advocates who believe that homeownership brings
about better citizenship. A popular argument in support for increased homeownership rates concerns the externalities in housing consumption. Just as a
homeowner has an incentive to improve his property, and such improvements
may simultaneously increase neighboring property values, a homeowner similarly has an incentive to invest in social capital that could make her community cleaner or safer with the expectation that any returns from social capital
investment is recouped through higher property values.1 In contrast, renters
may be less likely to invest in social capital since any monetary payoff from
such community-specific investments accrues only to the landlords.
A natural way to test whether homeowners invest more in social capital
than do renters is to compare social capital investment rates of homeowners
and renters. This approach, however, is plagued by a particular difficulty:
homeownership is an endogenous variable. Homeowners may have characteristics that correlate strongly with individual characteristics that predict
citizenship. That is, the qualities that make an individual invest in social
capital may be the same qualities that determine homeownership. While the
endogeneity difficulty could, in principle, be alleviated using instrumental
variable estimation, such a remedy often requires detailed micro-level data
that allow a researcher to mine carefully considered instruments – an empirical luxury that is, oftentimes, rare. Consequently, skepticism is still present
with respect to the causal effect homeownership has on social capital.
1
Social capital is a multifaceted concept. In the present study, social capital refers to the
the access to resources through interactions made in a social setting such as membership
in groups and networks (see, e.g., Bourdieu (1986) and Sobel (2002)).
1
The objective of this paper is to reexamine the role of homeownership in
a household’s decision to invest in social capital using a detailed, confidential
dataset that allows us to include instruments and explanatory variables that
were not considered before. The basis of our approach follows DiPasquale
and Glaeser (1999), whereby homeowners and renters based their decisions
to invest in social capital on the perceived benefits from these investments.
Homeowners have a larger incentive to participate in activities that are perceived to improve neighborhood capital since they are able to directly reap
any returns from these investments through an increase in property values
whereas any pecuniary payoffs from community-specific investments made
by renters only accrue to the landlords.
We use data from a confidential version of the Los Angeles Family and
Neighborhood Survey (L.A.FANS) to investigate the effect of homeownership on five “participation” variables that measure participation in the local
community and thus represent investment in social capital: volunteerism in
a local organization, participation in a neighborhood meeting or block meeting, participation in a business or civic group, participation in a local or state
political organization, and membership in a house of worship (for example,
a church or a temple).
Our paper differs from DiPasquale and Glaeser (1999) in two ways. First,
we use detailed, confidential data that surveys households within a city
whereas DiPasquale and Glaeser (1999) used the General Social Survey (GSS)
which contains coarser geographic information. The confidential version of
the L.A.FANS dataset identifies each household’s census tract, which allows
us to include potentially important neighborhood information such as ownership rates, median rents, and median house values. With this detailed
dataset we are also able to construct two new instruments for homeownership, a rent-price ratio and a measure of an individual’s ability to pay a
downpayment, in addition to the homeownership rate instrument used in
2
DiPasquale and Glaeser (1999). Second, DiPasquale and Glaeser (1999) use
OLS and IV estimation while we use the endogenous switching model2 that is
more general in that it allows for the joint determination of homeownership
and investment in one’s community as both a renter and as an owner. That
homeownership affects participation in community-enhancing activities and
that the perceived benefits associated with these social capital investments
affect the level of homeownership means that homeownership and social capital/local amenities investment are simultaneously related. This simultaneity
creates a selection bias. Consequently, homeownership is not randomly assigned. The advantage of the endogenous switching model is that it allows
us to estimate structural parameters which show the degree and direction of
the nonrandom selection of individuals into homeownership.
We find strong evidence for a positive and, in most cases, large effect
of homeownership in our preferred specification that accounts for sample
selection into homeownership. A homeowner is 19.7 percentage points more
likely to volunteer, 14.5 percentage points more likely to participate in a block
meeting, 10.5 percentage points more likely to participate in a civic group,
3.2 percentage points more likely to be part of a political organization, and
15.6 percentage points more likely to be a member of a religious group than
a renter. Moreover, we find that probit severely underestimates the marginal
effect of homeownership due to sample selection into homeownership. Our
results augment and confirm the results in DiPasquale and Glaeser (1999)
that homeownership provides a strong incentive for social capital investment.
The paper is organized as follows. The next section discusses the relevant
literature. Section 3 contains the model and estimation approach, Section 4
contains a description of the data, and Section 5 discusses the results. We
conclude in Section 6.
2
See, in particular, Maddala (1983) and Amemiya (1985).
3
2. Literature Review
The concept of social capital has its origin in sociology. Portes and Landolt (1996) attribute the genesis of the concept to the early sociological works
in the nineteenth-century. The term social capital was first used by sociologist
Bourdieu (1986) to refer to access to resources that accrue to people through
membership in certain communities. Following Putnam (1993)’s finding of a
positive correlation between civic engagement and government quality there
was a surge in empirical research documenting the effects of social capital
on socio-economic outcomes. These early empirical studies have seemingly
found a positive relationship between social capital and economic or labor
outcomes (for example, see Furstenberg and Hughes (1995) and Knack and
Keefer (1997)). However, many of these earlier endeavors suffer from identification problems that arise due to endogeneity issues. Durlauf (2002), in
his persuasive and insightful critique, discusses the potential identification
pitfalls contained in many of these oft cited studies.
One strand of research that stands apart from the empirical studies on
the effects of social capital is a body of studies that seek to identify the
mechanisms behind the creation of social capital.3 These studies evaluate
the determinants of individuals’ social capital investment behavior since the
returns to social capital are inherently heterogeneous for individuals with
different endowments and characteristics. Different incentives are the antecedents to different social capital investment behavior. Modeling the effect
of homeownership on social capital investment is one such example. The
idea is that homeownership fundamentally changes an individual’s behavior
in two important ways. For one, a homeowner has a long-term financial obligation and is more vested in the well-being of his neighborhood or community
3
See, for example, DiPasquale and Glaeser (1999), Alesina and La Ferrara (2000), and
Glaeser and Sacerdote (2000).
4
than a renter. Second, owning a home involves large transaction costs and
this in turn increases the cost of moving, thus reducing households’ mobility. Residential immobility potentially induces neighborhood stability, and
this, in turn, could enhance homeowners entrenchment in their residences
and thereby increase social capital accumulation.
A number of studies have attempted to show empirical evidence of homeownership’s effect on political and social activity. Many have found that
homeowners are more likely than renters to volunteer or to participate in
local political groups and nonprofessional organizations.4 Rossi and Weber
(1996), using the data from the National Opinion Research Center’s General Social Survey (GSS), finds that homeowners are more “consistently engaged” in local politics and are more likely to vote in national elections. Rohe
and Stegman (1994) finds that homeowners are more likely to participate in
neighborhood and block associations but are not that different from renters
in terms of church, school and political organizations involvement.
Similar to the above-mentioned identification problem in the social capital literature, the main criticism of these earlier empirical studies is that
unobservables could potentially determine homeownership and good citizenship simultaneously.5 For instance, individuals who have a taste for owning
a home may also have a predilection for being politically active or forming social ties within their communities. That is, homeownership is likely
an endogenous variable; the incidence of whether or not an individual is a
homeowner is not a result of a randomized trial but a consequence of some
conscious utility-optimizing behavior which depends on factors that may not
be observable to a researcher. This creates both an omitted variable problem
4
For example, see Cox (1982). For a review on voting behavior and homeownership,
see Herbert and Belsky (2006).
5
See Dietz and Haurin (2003) for a comprehensive review of this problem.
5
and selection bias, rendering inconsistent estimates and unreliable inferences.
More recent studies on social outcomes and homeownership are more
conscious of the problem of omitted variables bias and selection effects. For
example, Aaronson (2000) finds that the study of Green and White (1997)
that examines the effect of homeownership on child outcomes may be flawed
because of omitted variable bias and that the homeownership effects found
are overstated. Nonetheless, controlling for endogeneity, homeownership still
remains an important variable in explaining positive child outcomes.6 In
many cases, though, the ability to alleviate selection bias is constrained by
the availability of data.
This paper augments the work of DiPasquale and Glaeser (1999). To
that effect, we use the endogenous switching model7 along with two instruments not previously used to evaluate the causal effect homeownership has
on social outcomes. The endogenous switching model allows for the joint determination of the homeownership decision and the decision to invest in one’s
community as a renter and as an owner. Moreover, the model allows us to
estimate the degree and direction of any non-random selection of individuals
into homeownership. The endogenous switching model has been applied to
the problem of housing demand in Lee and Trost (1978) and to the problem
of education and self-selection in Willis and Rosen (1979). It was also used
in Green and White (1997).
3. Model and Estimation
The endogenous switching model we estimate comprises three components: a homeownership decision, a social-capital participation decision as
6
Haurin et al. (2002), using a different data set and instrumental variables estimation,
corroborates this finding.
7
See, in particular, Maddala (1983) and Amemiya (1985).
6
an owner, and a social-capital participation decision as a renter. Formally,
we specify the latent propensity to own, I ∗ , as
I ∗ = γZ + where Z is a vector of exogenous observable variables, γ is a vector of parameters, and is an error term. A person is a homeowner if I ∗ > 0, and a
renter otherwise.
Further, assume that a person’s latent propensity to participate in social
∗
= βo,j X + uo,j , where X is a vector
capital activity8 j as an owner is yo,j
of observable variables, βo,j is a vector of parameters, and uo,j is an error
∗
term. A homeowner participates in activity j if yo,j
> 0. Similarly, the
∗
= βr,j X + ur,j , where X
latent propensity to participate as a renter is yr,j
is a vector of observable variables, βr,j is a vector of parameters, and ur,j is
∗
> 0. We can write the model
an error term. A renter participates if yr,j
succinctly as:
I ∗ = γZ + ,
I = 1 iff I ∗ > 0,
I = 0 otherwise
∗
if I = 1 : yo,j
= βo,j X + uo,j ,
∗
yo,j = 1 iff yo,j
> 0,
yo,j = 0 otherwise
∗
if I = 0 : yr,j
= βr,j X + ur,j ,
∗
yr,j = 1 iff yr,j
> 0,
yr,j = 0 otherwise
(1)
The error terms, , uo,j and ur,j are normally distributed with means zero
and variances normalized to 1. Finally, let the correlation between and
u0,j be denoted ρo,j , and the correlation between and ur,j be denoted ρr,j .
The signs of the correlation coefficients measure the direction and degree of
selection into homeownership.
8
We use the term ‘activity’ in this paper to refer to memberships in groups and networks
within an individual’s community. This is consistent with Bourdieu (1986), DiPasquale
and Glaeser (1999) and Sobel (2002).
7
For each social capital activity j, the log-likelihood function is
N n
X
ln Lj =
Ii · yo,i,j · ln Φ (γZi , βo,j Xi , ρo,j )
i=1
+ Ii · (1 − yo,i,j ) · ln Φ (γZi , βo,j Xi , −ρo,j )
(2)
+ (1 − Ii ) · yr,i,j · ln Φ (γZi , βr,j Xi , −ρr,j )
+ (1 − Ii ) · (1 − yr,i,j ) · ln Φ (γZi , βr,j Xi , ρr,j )
o
We estimate the model using maximum likelihood estimation. The maximum likelihood estimators are consistent and asymptotically normally distributed.
The marginal effect of ownership is the effect ownership has on the probability of participation. For the endogenous switching model in (1), the
marginal effect of homeownership for a person with characteristics X is the
difference between the probability of participation of the person as an owner
and as a renter:9
marginal effect j = P (yo,j = 1 | X) − P (yr,j = 1 | X)
= Φ (βo,j X) − Φ (βr,j X) .
(3)
3.1. Selection
The correlation coefficients ρo,j and ρr,j measure the direction and degree
of selection into homeownership, and the combinations of signs of these correlation coefficients illuminate the form of ‘comparative advantage’ in utility
attainment from the respective social capital investment activities (Maddala
(1983)). If ρo,j is negative, for instance, then there is negative selection:
9
See the discussion in Manski et al. (1992).
8
controlling for the effects of observable characteristics, homeowners derive
lower latent utility from participating in the particular social capital activity
than does the average person in the population. As such, probit underestimates the marginal effect because it underestimates the first probability in
equation (3). If ρr,j is negative, then positive selection occurs for renters: controlling for the effects of observable characteristics, positive selection means
renters have a higher probability of participation than the average person
in the population.10 In this case, probit underestimates the marginal effect
of homeownership because it overestimates the second probability in equation (3). The endogenous switching model allows for non-random selection
of individuals into homeownership resulting in consistent estimates of the
marginal effect of homeownership on participation.
4. Data
We use data from a confidential version of the Los Angeles Family and
Neighborhood Survey (L.A.FANS). L.A.FANS collected longitudinal data on
neighborhoods, families, children, and on residential choice and neighborhood change. Wave 1 of the data was collected from April 2000 to January
2002. Wave 2 was collected from the fall of 2006 to November 2008.11 The
confidential version of the dataset that we use identifies each household’s
census tract, which allows us to include potentially important neighborhood
information such as ownership rates, median rents, and median house values. Our sample includes adults between the age of 25 and 65 who are not
in school. In total, the sample consists of 1,603 observations.
Table 1 contains summary statistics for the full sample, for owners, and
10
From equation (2), negative correlation indicates positive selection since ρr,j negatively
enters the likelihood contribution of renters who participate.
11
See Peterson et al. (2004) for a full description of the survey.
9
for renters. Our participation variables are in the first five rows: 17% of the
sample volunteered in a local organization (volunteer ), 13% participated in a
neighborhood or block meeting (block meeting), 6% participated in a business
or civic group (civic group), 5% participated in a local or state political
organization (political organization), and 39% were a member of a church,
temple, etc. (religious group). About 39% of the sample are homeowners.
Owners are more likely to be married, older, and have higher (real) income
and (real) non-housing wealth and education levels. Owners also have longer
durations of residence than renters, having, on average, lived in their current
residences for about 4 and 1/2 years longer than renters have.12 Finally,
in our sample, homeowners live in census tracts with a median house value
about $31,000 higher than the median house value in the renters’ census
tracts.
The (unconditional) marginal effect of homeownership on the participation variables is reported in Table 2. These values are the differences between
the participation rates of owners and of renters in the sample. One can see
that for each of the participation variables, homeowners are more likely to
participate than renters, in some cases by a large margin. For instance, the
rate of volunteerism of homeowners is 13.5 percentage points higher than
renters. Homeowners are more likely than renters to attend block meetings
by 12.1 percentage points and more likely to be members of religious groups
by 10.6 percentage points. In contrast, we do not see as great a difference
in the relative likelihood of participation in a civic group or in a political
organization, though this is not too surprising since the aggregate participation rate of each of these activities is low. Relative to renters, owners are
12
Actual duration of residence is a noisy measure of anticipated duration of residence,
a critical determinant of a household’s annualized user cost of homeownership, which in
turn determines homeownership.
10
6.4 percentage points more likely to participate in a civic group and 4.2 percentage points more likely to participate in a political organization. The full
model estimated below will show whether these effects persist after including
control variables and accounting for selection into ownership.
4.1. Instruments
To address the endogeneity of homeownership, we include three instruments in this study.13 The instruments are, namely, homeownership rate by
census tract, rent-price ratio, the ratio of capitalized median rent to median
house value by census tract, and an individual’s non-housing wealth to homevalue ratio.14 It is instructive at this juncture to discuss the motivations for
the inclusion of each of these instruments.
Like DiPasquale and Glaeser (1999), we include average homeownership
rate within a locale to indirectly capture differences in housing costs, income,
and local property tax rates. Unlike DiPasquale and Glaeser (1999), which
relies on the average homeownership rates by income quartiles and race to
achieve state-level variation, we do not segregate homeownership rate by income or race. Instead, because we are able to identify the census tract each
household resides in, we are able to merge the homeownership rate for each
census tract from the 2000 Census to provide cross-sectional variation for
identification. We also use median rent-to-price ratios for each census tract
as an instrument. Empirical evidence suggests that the rent-to-price ratio,
which acts very much like the dividend yield measure in stock valuation, encapsulates expectations about the rental growth rate, which in turn should
13
Although the model is parametrically identified without any exclusion restrictions, for
the results to be convincing there should be at least one variable in the homeownership
equation that is not in the participation equation.
14
Rents were capitalized into house values using the formula: house value = (monthly
rent)×12/0.5.
11
partially determine homeownership since homeowners could deem owning a
home as a hedge against rental risk (Sinai and Souleles (2005) and Campbell
et al. (2009)). The third instrument, the ratio of the household’s non-housing
wealth to home value, captures the role of housing as a collateral as well as
a major consumption component in an individual’s budget function, which
should conceivably affect a household’s homeownership decision.15 The validity of these instruments depends on both their relevance (whether they
determine homeownership) and exogeneity (whether they determine social
capital investment). We discuss the empirical evidence for each condition in
detail in Section 5.4 below.
The last three rows in Table 1 give the summary statistics for these
instruments. In our sample, owners live in census tracts with an average
homeownership rate of 62.2 while renters live in census tracts with an average homeownership rate of only 35.1. Owners tend to live in areas where the
rent-to-price ratio is higher: the average rent-to-price ratio for homeowners is
1.00 and 0.88 for renters. Homeowners tend to face less collateral constraints;
homeowners have a much higher average non-housing wealth to house-value
ratio than do renters. The average non-housing wealth to house-value ratio
is 0.71 for homeowners compared to only 0.19 for renters.
5. Results
In this section, we first provide descriptive results from some simple regressions that ignore any selection into homeownership. We estimate a probit
model with homeownership as the dependent variable and include the instruments, homeownership rate, the rent-price ratio, and the non-housing wealth
15
On a related note, variants of the housing collateral ratio have been shown to be
an important conditioning macroeconomic variable in determining asset prices. See, for
example, Piazzesi et al. (2007) and Lustig and Van Nieuwerburgh (2005).
12
to home-value ratio, as controls in addition to other covariates. We then estimate probit models with participation in various social capital activities as
the dependent variable, treating homeownership as an exogenous covariate.
These models allows us to contrast the results with our preferred model, the
endogenous switching model, which accounts for homeownership selection.
5.1. Homeownership and Participation Probit Regressions
Table 3 contains the results of three univariate probit models of homeownership using the combinations of instruments we later use to estimate the
switching models.16 The signs of the estimates are as expected, and most
of the estimates are significant. Being married, being older, having more
children, having higher (real) income, (real) wealth, and education all have
positive and significant effects on homeownership. Duration of residence has a
positive and statistically significant effect: the longer a person is in their current residence the more likely they are to be an owner. Higher median house
values lowers homeownership. The three instruments have the expected signs
and are statistically significant at the 1% level.17 Higher aggregate homeownership rates, higher rent-price ratios, and greater non-housing wealth to
house-value ratios are positively related to homeownership at the household
level.
Probit estimates of the participation variables are in Table 4. Since homeownership is the variable of interest here, we compute the marginal effect of
16
These probits are analogous to the the first stage of a two-stage least squares regression.
In fact, these univariate homeownership probits are consistent quasi-maximum likelihood
estimators of the homeownership equations of the switching model we estimate later (see
Avery et al. (1983)).
17
Since rent-price ratio is computed from median house value, median house value is
dropped from the probit with rent-price ratio as an instrument. Similarly, the probit with
the non-housing wealth to house-value ratio as an instrument cannot include non-housing
wealth.
13
ownership for all participation variables. This is presented in the second to
last row of Table 4. The marginal effect of homeownership on volunteer and
block meeting is 0.05 and 0.074 and statistically significant.18 The marginal
effects of homeownership on the other three participation variables is positive but not statistically significant. As we have included control variables in
this probit analysis, the estimated marginal effects are naturally smaller than
the unconditional estimates in Table 2. Bearing in mind that we have not
yet controlled for homeownership selection effects in this particular analysis,
the estimates in Table 4 nonetheless indicate some general patterns. Having
more education has a positive and significant effect on social capital. Being
between 40 and 65 years old has a positive and, for most of the participation
variables, significant effect. Men are less likely than women to volunteer or
to be part of a religious group. Blacks are more likely to be a part of a religious group or participate in a block meeting or political organization than
other groups. Interestingly, duration of residence does not seem to have a big
impact, being significant only in the cases of the longest residents for block
meeting and religious group.
5.2. Endogenous switching model
Individuals may choose to invest in social capital due to the same latent characteristics that make them homeowners. This creates a selection
bias in that homeownership is not random even after controlling for observed
socio-economic variables. The probit models described above do not take
into account the selection problem, or equivalently, the missing data problem (Mare and Winship (1988)). We now discuss the results of our preferred
model, the endogenous switching model, which addresses the selection effects
18
Throughout, the standard errors of the marginal effect of homeownership are estimated
using 400 bootstrap replications as recommended in Cameron and Trivedi (2010).
14
by jointly modeling the social capital investment decision and the homeownership decision.
The estimates of the endogenous switching models using homeownership
rate as an instrument are contained in Tables 5 to 9. For each social capital
participation variable, three equations are estimated: a homeowner participation equation, a renter participation equation and a homeownership equation.
In what follows, we first discuss the robust findings across the five participation variables and then proceed to discuss the results for each participation
variable in detail.
For each of the five participation variables, the estimates of the homeownership equation are similar, in terms of magnitude and statistical significance, to the probit regression of homeownership in Table 3. This is not
surprising since univariate probit is a consistent quasi-maximum likelihood
estimator (Avery et al. (1983)). The most robust finding across the participation variables is that education has a strong, positive effect on participation
for renters and owners. In contrast to some previous findings, we find little
evidence that, in general, duration of residence helps determine participation. Further, we find that for each participation variable except political
organization, at least one of the two estimates of the correlation coefficients
is statistically non zero. This is evidence that selection is present and that
univariate probit estimators do not consistently estimate the effect of homeownership on social capital participation. Further discussion of selection is
below in Section 5.3.
The marginal effect for each social capital participation variable estimated
using the homeownership rate as an instrument is presented in the first row
of Table 10. These effects are estimated using equation (3). All estimates
are positive indicating that homeownership leads to more social capital investment. The estimates of the marginal effect for the variables volunteer,
block meeting, civic group, and religious group are significant at conventional
15
levels. The estimate for political organization is not significant. We note
that the estimates are all larger than the probit estimates in Table 4. That
is, for all variables, we find that probit underestimates the marginal effect of
homeownership, suggesting that negative sample selection is present. This is
consistent with the findings in DiPasquale and Glaeser (1999). Overall, the
marginal effects are large. An owner is 19.7 percentage points more likely
to volunteer and 14.5 percentage points more likely to participate in a block
meeting than a renter. An owner is 10.5 percentage points more likely to
participate in a civic group than a renter and 15.6 percentage points more
likely than a renter to belong to a religious group.
Volunteerism
The results in Table 5 indicate that male renters and married renters
are less likely to participate. Age has a differential impact on an individual’s propensity to volunteer, with the impact varying with a person’s tenure
choice. However, none of the age coefficients is statistically different from
zero. Education level is important for both the owner and renter equations:
more educated people are more likely to volunteer regardless of one’s housing
tenure choice.
Block Meeting
Being married decreases a homeowner’s likelihood to attend block or
neighborhood meetings. On the other hand, marital status does not affect a
renter’s propensity to attend block meetings. Age has a positive effect on attendance but the effect is statistically significant only for renters. Again, an
individual who is either a college graduate or a professional school graduate
is more likely to attend block meetings, regardless of whether this individual
is a homeowner or a renter. The value of median home values within the
census tract one resides in affects attendance in a block meeting positively.
This result is consistent with the notion that investment in social capital
16
should correlate positively with home values.
Civic Group
A married homeowner is less likely to be a civic or a business group
member. The number of children has a positive and significant effect only
for renters. Renters who are 40 and above are more likely to belong to a
civic group. Age has a positive effect for homeowners but the result is not
statistically significant. The more educated an individual is, the more likely
he or she will participate in a civic group.
Political Organization
Blacks are more likely to participate in local and state organizations regardless of tenure choice but the effect is stronger for renters. Education has
a positive effect on participation in local political organizations for homeowners and renters alike and the effect increases with one’s age.
Religious Group
Male owners and renters are less likely to belong to a religious group than
do females. Being married has a positive effect but only for owners. Black
homeowners and renters are more likely to be members of a religious group
than others. Older individuals are more likely to be members of a religious
group but the effect is stronger for renters. Also, having more children has
a positive effect on membership for both owners and renters. Having more
education makes one more likely to be a religious group member whether as a
homeowner or a renter. Increases in a renter’s duration of residence increases
the likelihood of religious membership but duration of residence has no effect
for homeowners.
5.3. Selection Effects
Estimates of ρo,j are negative and statistically significant for volunteer,
block meeting, and civic group. By the arguments made in Section 3.1, this
17
means that, for these variables, owners exhibit negative selection but that
there is no simultaneity bias for owners with regards to political organization
or religious group. Further, estimates of ρr,j are negative and statistically
significant for volunteer and religious group, indicating positive selection for
renters for these participation variables. Due to both types of selection,
the estimates of the marginal effect of homeownership computed using the
endogenous switching model are larger than those computed from probits.
That is, not accounting for selection leads to misleadingly low estimates of
the role homeownership plays in the decision to investment in social capital.
5.4. Validity of the Instruments
In this section we discuss the results of estimating the switching models
using the homeownership rate as an instrument along with a second instrument, either the rent-price ratio or the non-housing wealth to house-value
ratio. Often, deciding whether or not a potential instrument is exogenous is
based on introspection. In the present study, we instead rely on the results
of DiPasquale and Glaeser (1999) and suppose that a locale’s homeownership rate is a valid instrument. By including a second instrument, our model
is over-identified and this allows us to test the exogeneity of the additional
instrument by including it in the participation equations of the switching
model. Failure to reject the null hypothesis that the coefficient of a potential
instrument is zero is evidence that the variable is indeed exogenous.19
One concern in implementing the above test is that components of our
two potential instruments are also explanatory variables in the participation
equations. Specifically, the denominator of the rent-price ratio is equal to
the median house value and the numerator of the non-housing wealth to
19
This is analogous to including additional potential instruments in the second-stage
regression of two-stage least squares when the model is over-identified. See Murray (2006)
for more discussion on this approach.
18
house-value ratio is equal to non-housing wealth. If the median house value
determines participation in the switching models, then the rent-price ratio is
ruled out as an instrument prior to performing the overidentification tests. A
similar argument holds for non-housing wealth and the non-housing wealth
to house-value ratio. According to the results in Tables 5 to 9, median house
value has a statistically significant effect on block meeting, and, thus, we do
not instrument for homeownership with the median rent-price ratio for block
meeting. Non-housing wealth has a statistically significant effect on civic
group, political organization, and religious group, which rules out using the
non-housing wealth to house-value ratio as an instrument for these participation variables. For the remaining cases in which we cannot rule out the
instruments based on the significance of their components, we cannot reject the null hypothesis that either the rent-to-price ratio or the non-housing
wealth to housing wealth ratio have zero coefficients in the participation
equations. Given the results in Table 3 that show that the rent-price ratio
and the non-housing wealth to house-value ratio determine homeownership,
we can therefore conclude that these instruments are valid for the remaining
cases.
The results of using the additional instruments to estimate the marginal
effect of homeownership are in Table 10.20 The results show that adding
an instrument makes little difference in the estimates. The estimates of the
marginal effects are similar in magnitude to the case of using the homeownership rate alone, suggesting some robustness in our findings. The most
consistent change is that, as one might expect, the standard error is smaller
with the addition of a second instrument. Attempts to estimate the model
20
To save space, we only report the estimates of the marginal effects of homeownership.
There was little change in the estimates of the other parameters of the models. These
results are available upon request.
19
using only the rent-price ratio or the non-housing wealth to house-value ratio as an instrument were less successful. In most cases, the estimates of the
marginal effects were not statistically significant, indicating that there may
not be enough variation or explanatory power in these instruments alone to
result in accurate estimates.
6. Conclusion
Public policies promoting homeownership are often justified by claims
that homeownership improves an individual’s well-being as well as society at
large by promoting good citizenship, neighborhood stability, and vibrant and
strong communities. Because homeowners can reap any pecuniary benefits
that accrue to the value of their property, they are more likely to invest in
social capital that are expected to improve the quality of their living environment. Homeowners are also less likely to move and therefore are more
likely to interact and network within their local communities, the result of
which is a higher propensity to invest in local social capital. While the above
claims have sound economic motivation and are intuitively appealing, empirical studies attempting to establish a causal link between homeownership
and social capital investment have to account for the endogeneity of homeownership.
In this paper, we revisit the relationship between homeownership and social capital, relying on the key insight that homeownership provides a strong
incentive for social capital investment since any perceived benefits through
social capital accumulation will accrue to homeowners and landlords. We
test this hypothesis by estimating an endogenous switching model using
individual-level data on community participation and two instruments for
homeownership not previously used in this literature. Our paper augments
the study of DiPasquale and Glaeser (1999), who find a positive relationship
between homeownership and social capital. We estimate that selection into
20
homeownership is non-trivial and that as a result, univariate regressions underestimate the effect of homeownership on social capital investment. After
accounting for selection, we find that homeownership has a positive and in
some cases quite large effect on the rate of community participation, indicating that homeownership contains positive externality effects on the local
community.
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25
Full Sample
Owners
Renters
Mean
St. Dev.
Mean
St. Dev.
Mean
St. Dev.
Volunteer
0.17
0.37
0.25
0.43
0.11
0.32
Block meeting
0.13
0.34
0.21
0.41
0.09
0.28
Civic group
0.06
0.25
0.10
0.31
0.04
0.20
Political organization
0.05
0.22
0.08
0.27
0.03
0.18
Religious group
0.39
0.49
0.46
0.50
0.35
0.48
Own
0.39
0.49
1.00
0.00
0.00
0.00
Married
0.56
0.50
0.70
0.46
0.46
0.50
Male
0.43
0.50
0.46
0.50
0.42
0.49
Good health
0.82
0.39
0.88
0.32
0.78
0.42
Black
0.10
0.31
0.09
0.29
0.11
0.31
Age 25 - 30
0.18
0.38
0.10
0.30
0.23
0.42
Age 30 - 40
0.41
0.49
0.38
0.49
0.43
0.50
Age 40 - 50
0.28
0.45
0.36
0.48
0.23
0.42
Age 50 - 65
0.12
0.33
0.16
0.36
0.10
0.30
Number of children
1.55
1.26
1.60
1.22
1.52
1.28
High school or less
0.50
0.50
0.34
0.47
0.60
0.49
Some college
0.27
0.45
0.31
0.46
0.25
0.43
College graduate
0.14
0.34
0.20
0.40
0.10
0.30
Prof school graduate
0.09
0.29
0.15
0.36
0.05
0.22
29.52
33.07
43.86
43.20
20.38
19.67
101.57
292.84
210.05
418.90
32.38
127.89
< 1 year
0.17
0.38
0.10
0.31
0.21
0.41
1 to 4 years
0.42
0.49
0.37
0.48
0.45
0.50
Wage ($1000's)
Non-housing wealth ($1000's)
Duration of residence
4 to 10 years
0.30
0.46
0.34
0.47
0.28
0.45
> 10 years
0.11
0.31
0.18
0.39
0.06
0.23
Ln(median house value)
12.22
0.49
12.31
0.57
12.16
0.43
Homeownership rate
45.62
26.16
62.17
23.64
35.08
21.89
Rent-price ratio
0.93
0.36
1.00
0.44
0.88
0.29
Non-housing weath to
house value ratio
0.39
1.20
0.71
1.52
0.19
0.88
Number of observations
1,603
624
Table 1: Summary Statistics
979
owner
volunteer
block_meet~g
civic_group
local_org
church_mem~r
0.25
0.21
0.10
0.08
0.46
renter
volunteer
block_meet~g
civic_group
local_org
church_mem~r
0.11
0.09
0.04
0.03
0.35
difference
0.135
0.121
0.064
0.042
0.106
Mean Marginal Effect
Volunteer
0.135
Block meeting
0.121
Civic group
0.064
Political organization
0.042
Religious group
0.106
Table 2: Unconditional Marginal Effect of Homeownership on Participation
Homeownership Probits
Coef.
Std. Err.
Coef.
0.082
0.270
-0.011
0.077
-0.035
0.107
0.161
0.107
0.181
-0.054
0.131
0.006
0.128
-0.050
0.130
Age 30 - 40
0.045
0.113
0.040
0.113
0.044
0.114
Age 40 - 50
0.289
**
0.120
0.276
**
0.120
0.303
***
0.120
Age 50 - 65
0.461
***
0.150
0.456
***
0.150
0.502
***
0.149
Number of children
0.055
*
0.034
0.058
*
0.034
0.061
*
0.034
Some college
0.201
**
0.096
0.168
*
0.095
0.191
**
0.095
College graduate
0.382
***
0.126
0.293
**
0.122
0.391
***
0.125
0.563
***
0.418
***
0.619
***
0.153
0.012
***
0.002
0.011
***
0.002
0.012
***
0.002
Non-housing wealth ($1000's) 0.001
***
0.000
0.001
***
0.000
Married
Std. Err.
Coef.
0.082
0.260
-0.030
0.078
0.174
0.249
Male
Good health
Black
Prof school graduate
Wage ($1000's)
***
0.156
***
0.151
Std. Err.
***
0.082
0.077
*
0.107
Duration of residence
1 to 4 years
0.210
*
0.112
0.220
**
0.112
0.207
*
0.112
4 to 10 years
0.399
***
0.118
0.401
***
0.118
0.369
***
0.118
> 10 years
0.814
***
0.156
0.825
***
0.156
0.786
***
0.156
-0.552
***
0.101
-0.427
***
0.096
0.024
***
0.002
0.024
***
0.002
0.098
***
0.035
2.403
**
1.143
Ln(median house value)
Homeownership rate
Rent-price ratio
Non-housing weath to
house-value ratio
Constant
3.931
Pseudo-R2
0.326
***
***
1.209
0.020
***
0.002
0.584
***
0.125
-3.115
***
0.322
p < 0.01, ** p < 0.05, * p < 0.1, n = 1,603
Table 3: Ownership Probit Regressions
0.210
0.315
Volunteer
Coef.
Own
0.226
Married
**
-0.015
Male
Good health
Black
Age 40 - 50
Age 50 - 65
Number of children
Some college
College graduate
Prof school graduate
Wage ($1000's)
Non-housing wealth ($1000's)
Coef.
0.091
0.361
0.091
-0.180
0.084
-0.079
0.310
**
0.129
0.003
0.130
-0.112
0.122
0.060
0.129
0.104
0.162
0.066
*
0.562
***
0.751
***
1.004
***
0.001
0.036
0.103
0.128
0.149
0.001
***
-0.133
**
0.112
Age 30 - 40
Block Meeting
Std.Err.
Civic Group
Std.Err.
Coef.
Std.Err.
0.095
0.165
0.121
0.094
-0.131
0.021
0.120
-0.128
0.159
0.076
0.133
-0.079
0.283
0.141
0.193
0.352
**
0.360
**
0.329
0.147
0.174
0.037
0.108
0.154
0.132
0.162
0.001
-0.015
0.110
**
**
0.052
0.027
0.273
0.117
Coef.
0.086
**
0.003
0.122
Political Organization
0.001
0.423
**
0.649
***
0.062
0.178
0.415
0.197
0.017
0.200
0.228
0.048
0.683
***
0.842
***
0.852
***
0.004
***
0.072
0.442
-0.039
0.810
***
0.201
1.126
***
0.001
0.001
0.000
0.000
0.000
0.000
0.000
0.000
0.000
-0.122
0.118
-0.029
0.124
0.010
0.164
-0.129
0.016
*
Std.Err.
0.077
0.276
***
0.073
0.120
-0.375
***
0.068
0.192
0.103
0.166
0.577
0.198
0.053
0.090
***
0.113
0.098
0.312
***
0.105
0.300
**
0.133
0.104
***
0.029
0.263
***
0.083
0.271
**
0.112
0.208
0.447
***
0.138
0.002
-0.001
0.226
0.058
0.534
0.178
0.133
0.206
**
***
0.149
Coef.
0.136
**
Religious Group
Std.Err.
0.167
0.193
0.000
0.000
0.174
0.063
0.001
*
0.000
Duration of residence
1 to 4 years
4 to 10 years
> 10 years
Ln(median house value)
Constant
-0.059
-0.022
Own marginal effect
0.050
2
0.128
***
0.159
0.042
-2.301
Pseudo-R
0.126
0.098
*
***
1.177
0.019
-0.071
0.133
-0.090
0.176
-0.044
0.184
0.097
0.187
*
0.103
***
0.135
0.333
**
0.157
0.024
0.208
0.034
0.220
0.367
0.231
**
0.101
-0.010
0.127
-0.080
0.139
-0.001
0.086
-4.492
***
1.222
-2.355
1.534
-1.403
1.675
-0.994
1.038
0.074
***
0.021
0.018
0.013
0.005
0.012
0.006
0.029
0.088
0.133
p < 0.01, ** p < 0.05, * p < 0.1, n = 1,603
Table 4: Participation Probit Regressions
0.150
0.081
Volunteer
Owner
participation
equation
Married
Male
Good health
Black
Age 30 - 40
Age 40 - 50
Age 50 - 65
Number of children
Some college
College graduate
Prof school
Wage ($1000's)
Non-housing wealth ($1000's)
Duration of residence
1 to 4 years
4 to 10 years
> 10 years
Ln(median house value)
Homeownership rate
Constant
Correlation coefficient
***
Renter
participation
equation
Coef.
Std. Err.
Coef.
0.100
-0.065
0.352
0.287
0.052
0.240
0.229
0.033
0.329
0.566
0.642
0.000
0.000
0.143
0.114
0.218
0.194
0.227
0.232
0.265
0.050
0.165
0.193
0.214
0.002
0.000
-0.302
-0.282
0.198
-0.040
-0.211
-0.196
-0.101
0.073
0.575
0.567
1.087
-0.003
0.000
0.200
0.203
0.228
0.137
-0.226
-0.042
0.028
0.065
**
***
***
-0.127
-0.224
-0.321
0.186
-3.429
**
1.626
-2.246
-0.432
***
0.135
-0.536
**
**
***
***
***
Std. Err.
Coef.
0.122
0.121
0.157
0.175
0.144
0.166
0.220
0.050
0.139
0.190
0.236
0.003
0.000
0.243
-0.027
0.185
-0.043
0.055
0.300
0.462
0.055
0.202
0.364
0.564
0.011
0.001
***
0.146
0.158
0.256
0.144
0.210
0.400
0.812
-0.548
0.024
3.873
*
1.723
***
0.208
p < 0.01, ** p < 0.05, * p < 0.1, n = 1,603
-3.192
-2.575
Table 5: Endogenous
- Volunteer
0.0007 Switching Model0.005
0.0014
0.010
Home ownership
equation
Std. Err.
*
***
***
*
**
***
***
***
***
***
***
***
***
***
0.082
0.077
0.107
0.130
0.113
0.120
0.150
0.033
0.095
0.125
0.155
0.002
0.000
0.112
0.118
0.156
0.100
0.002
1.202
Block Meeting
Owner
participation
equation
Coef.
**
Renter
participation
equation
Std. Err.
Coef.
Std. Err.
Coef.
0.142
0.120
0.213
0.198
0.247
0.251
0.280
0.054
0.167
0.195
0.219
0.002
0.000
-0.107
-0.188
-0.048
0.145
0.352
0.264
0.432
0.051
0.099
0.324
0.150
-0.004
0.000
0.133
0.129
0.151
0.190
0.176
0.201
0.245
0.052
0.158
0.210
0.289
0.004
0.000
0.252
-0.034
0.170
-0.053
0.049
0.289
0.459
0.054
0.200
0.381
0.566
0.012
0.001
***
0.211
0.218
0.243
0.144
-0.074
-0.130
0.540
0.258
0.158
0.178
0.247
0.157
0.209
0.394
0.808
-0.559
0.024
4.014
*
Married
Male
Good health
Black
Age 30 - 40
Age 40 - 50
Age 50 - 65
Number of children
Some college
College graduate
Prof school
Wage ($1000's)
Non-housing wealth ($1000's)
Duration of residence
1 to 4 years
4 to 10 years
> 10 years
Ln(median house value)
Homeownership rate
Constant
-0.281
0.081
0.188
0.432
0.172
0.371
0.219
-0.071
0.116
0.296
0.136
0.002
0.000
-4.609
***
1.723
-4.728
Correlation coefficient
-0.265
*
0.157
-0.042
***
-0.038
-0.091
0.098
0.292
**
**
**
*
**
*
**
1.886
0.248
p < 0.01, ** p < 0.05, * p < 0.1, n = 1,603
-1.689
-0.17
Table 6: Endogenous
Switching Model -0.4326
Block Meeting
0.0456
0.0912
Home ownership
equation
0.8652
Std. Err.
**
***
**
***
***
***
***
***
***
***
***
***
0.082
0.078
0.107
0.131
0.113
0.120
0.150
0.034
0.096
0.126
0.156
0.002
0.000
0.113
0.119
0.157
0.101
0.002
1.209
Civic Group
Owner
participation
equation
Coef.
Married
Male
Good health
Black
Age 30 - 40
Age 40 - 50
Age 50 - 65
Number of children
Some college
College graduate
Prof school
Wage ($1000's)
Non-housing wealth ($1000's)
Duration of residence
1 to 4 years
4 to 10 years
> 10 years
Ln(median house value)
Homeownership rate
Constant
Correlation coefficient
***
-0.421
-0.012
0.106
0.266
0.063
0.132
0.223
-0.048
0.379
0.481
0.491
0.003
0.000
***
*
*
*
-0.267
-0.293
-0.169
0.017
-1.185
-0.549
***
Renter
participation
equation
Std. Err.
Coef.
Std. Err.
Coef.
0.163
0.140
0.258
0.217
0.297
0.302
0.332
0.064
0.218
0.254
0.278
0.002
0.000
0.012
0.050
-0.344
-0.551
0.205
0.487
0.869
0.197
0.875
0.999
1.023
0.006
0.001
0.189
0.179
0.216
0.347
0.269
0.284
0.345
0.074
0.222
0.279
0.321
0.004
0.000
0.246
-0.036
0.168
-0.060
0.043
0.287
0.460
0.056
0.205
0.381
0.552
0.012
0.001
***
0.235
0.241
0.265
0.169
0.106
-0.081
-0.225
0.064
0.234
0.265
0.431
0.205
0.203
0.398
0.807
-0.558
0.023
4.020
*
*
**
***
***
***
***
*
1.995
-3.659
2.462
0.141
0.024
0.271
p < 0.01, ** p < 0.05, * p < 0.1, n = 1,603
-3.902
0.0899
Table 7: Endogenous
- Civic Group
5E-05Switching Model 0.5358
1E-04
Home ownership
equation
1.0716
Std. Err.
**
***
*
**
***
***
***
***
***
***
***
***
***
0.082
0.078
0.107
0.131
0.113
0.120
0.150
0.034
0.095
0.126
0.156
0.002
0.000
0.112
0.118
0.156
0.101
0.002
1.205
Political Organization
Owner
participation
equation
Married
Male
Good health
Black
Age 30 - 40
Age 40 - 50
Age 50 - 65
Number of children
Some college
College graduate
Prof school
Wage ($1000's)
Non-housing wealth ($1000's)
Duration of residence
1 to 4 years
4 to 10 years
> 10 years
Ln(median house value)
Homeownership rate
Constant
Correlation coefficient
***
Renter
participation
equation
Home ownership
equation
Coef.
Std. Err.
Coef.
Std. Err.
Coef.
-0.055
0.112
0.597
0.239
0.224
0.027
0.356
-0.026
0.622
0.741
1.197
0.000
0.000
0.207
0.167
0.434
0.265
0.368
0.379
0.401
0.077
0.290
0.315
0.326
0.002
0.000
-0.043
-0.096
-0.177
0.631
-0.262
0.061
0.489
-0.074
0.453
0.905
0.943
0.003
0.000
0.203
0.185
0.239
0.227
0.257
0.265
0.302
0.094
0.232
0.281
0.332
0.004
0.001
0.251
-0.030
0.173
-0.054
0.043
0.288
0.460
0.054
0.200
0.383
0.565
0.011
0.001
***
0.285
0.289
0.326
0.194
-0.127
-0.104
0.074
-0.067
0.230
0.258
0.360
0.223
0.207
0.393
0.811
-0.553
0.024
3.943
*
-0.270
-0.149
-0.148
-0.069
**
**
***
*
***
*
***
***
-1.778
2.350
-1.324
2.656
-0.167
0.249
-0.225
0.329
p < 0.01, ** p < 0.05, * p < 0.1, n = 1,603
-0.671
-0.685
Table 8: Endogenous 0.251
Switching Model - Political
0.2465Organization
0.5019
0.4931
Std. Err.
**
***
**
***
***
***
***
***
***
***
***
***
0.082
0.078
0.107
0.131
0.113
0.120
0.151
0.034
0.096
0.126
0.156
0.002
0.000
0.112
0.119
0.156
0.101
0.002
1.208
Religious Group
Owner
participation
equation
Coef.
***
Renter
participation
equation
Std. Err.
Coef.
Std. Err.
Coef.
0.131
0.107
0.174
0.193
0.199
0.203
0.237
0.049
0.145
0.174
0.202
0.002
0.000
0.150
-0.361
0.031
0.586
0.105
0.302
0.282
0.080
0.181
0.195
0.389
-0.006
-0.001
0.093
0.089
0.105
0.139
0.112
0.131
0.172
0.037
0.110
0.160
0.211
0.003
0.000
0.246
-0.037
0.185
-0.046
0.048
0.294
0.469
0.055
0.201
0.378
0.557
0.012
0.001
***
0.187
0.191
0.219
0.133
0.124
0.270
0.379
0.117
0.114
0.126
0.207
0.120
0.223
0.404
0.806
-0.540
0.023
3.778
**
Married
Male
Good health
Black
Age 30 - 40
Age 40 - 50
Age 50 - 65
Number of children
Some college
College graduate
Prof school
Wage ($1000's)
Non-housing wealth ($1000's)
Duration of residence
1 to 4 years
4 to 10 years
> 10 years
Ln(median house value)
Homeownership rate
Constant
0.359
-0.366
0.236
0.635
-0.208
0.062
0.023
0.121
0.234
0.221
0.408
-0.001
0.000
-0.601
1.558
-2.345
*
1.444
Correlation coefficient
-0.171
0.161
-0.384
**
0.162
***
-0.242
-0.161
0.014
0.005
***
***
***
**
*
***
***
**
**
*
*
**
**
*
p < 0.01, ** p < 0.05, * p < 0.1, n = 1,603
-1.064
-2.373
Table 9: Endogenous
Switching Model - 0.0088
Religious Group
0.1438
0.2875
Home ownership
equation
0.0176
Std. Err.
*
**
***
*
**
***
***
***
***
***
***
***
***
***
0.082
0.078
0.107
0.130
0.113
0.120
0.150
0.033
0.095
0.126
0.155
0.002
0.000
0.112
0.118
0.156
0.101
0.002
1.210
Volunteer
Instruments
M.E.
Ownership rate
0.197
Ownership rate and rent-price ratio
a
Ownership rate and non-housing wealth
to house value ratiob
***
Block Meeting
Std.Err.
M.E.
***
0.056
0.145
0.191
***
0.052
---
0.211
***
0.054
0.151
***
***
Civic Group
Std.Err.
M.E.
0.053
0.105
---
0.104
0.049
---
Political Organization Religious Group
Std.Err.
M.E.
Std.Err.
M.E.
Std.Err.
**
0.054
0.032
0.036
0.156
**
0.064
**
0.054
0.033
0.030
0.136
**
0.062
---
---
---
---
---
0.182
2.424
0.008
p < 0.01, ** p < 0.05, * p < 0.1
a
Rent-price ratio is not a valid instrument since median house value was found to be a determinant of block meeting participation.
b
Non-housing wealth to house value ratio is not a valid instrument since non-housing wealth was found to be a determinant
of civic group, local organization, and religious group participation.
Table 10: Homeownership Marginal Effect (Switching Model)
3.549
0.000
2.742
0.000
3.693
3.919
0.000
0.000
0.000
0.000
0.003
1.944
0.006
0.001
0.002
0.909
0.052
1.948
3.058
0.026
0.026
0.051
0.363
1.085
0.139
0.278
0.015
2.206
0.014
0.027