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Working Paper Series

Intergenerational Linkages in Household
Credit

WP 15-14

Andra C. Ghent
University of Wisconsin, Madison
Marianna Kudlyak
Federal Reserve Bank of Richmond

This paper can be downloaded without charge from:
http://www.richmondfed.org/publications/

Intergenerational Linkages in Household Credit
Andra C. Ghent
University of Wisconsin,
Madison
ghent@wisc.edu

Marianna Kudlyak
Federal Reserve Bank of
Richmond
Marianna.Kudlyak@rich.frb.org

First Draft: November 5, 2015
Working Paper No. 15-14
We document economically important correlations between children’s future
credit outcomes and their parents’ credit risk scores, default, and the extent of
credit constraints – intergenerational linkages in household credit. Using
observations on siblings, we find that the linkages are due to unobserved
household heterogeneity rather than parental credit conditions directly affecting
children’s credit outcomes. In particular, in the sample of siblings, there is no
correlation between parental and child credit attributes after controlling for
household fixed effects. The linkages are stronger in cities with lower
intergenerational income mobility, implying that common factors drive both.
Finally, existing measures of state-level educational policy interventions appear to
have limited effects on the strength of intergenerational linkages.

JEL: D14, E21, G10.
Keywords: Household Finance; Intergenerational Mobility; Credit Constraints; Income
Inequality.

We thank David Min and Peter Debbaut for excellent research assistance. The views expressed here are
those of the authors and do not necessarily reflect those of the Federal Reserve Bank of Richmond, and
the Federal Reserve System, or any other institution with which the authors are affiliated.

1. Introduction
Existing evidence on economic mobility shows that there are persistent intergenerational
linkages in income (e.g., Solon, 1992; Chadwick and Solon, 2002), wealth (Charles and Hurst,
2003), and consumption (Waldkirch, Ng, and Cox,2004).1 Economists have theorized that credit
markets are an important mechanism driving intergenerational income and consumption mobility
(Grawe and Mulligan, 2002). However, intergenerational linkages in household credit markets
have not yet been explored. Understanding the nature of such linkages and the factors that
influence them can advance our understanding of intergenerational mobility.
We study intergenerational linkages in household credit using administrative data on individual
credit records. First, we document economically important correlations between children’s future
credit outcomes and their parents’ credit risk scores, default, and the extent of credit constraints –
intergenerational linkages in household credit. Second, we study whether such linkages arise
because parental credit conditions directly affect children’s credit outcomes or whether the
estimated correlations are due to other factors that differ across households - household
heterogeneity. Using observations on siblings, we find that the linkages are due to unobserved
household heterogeneity. In particular, in the sample of siblings, there is no correlation between
parental and child credit attributes after controlling for household fixed effects. Third, we
examine what local factors are correlated with the strength of the linkages. We find that
intergenerational linkages in household credit are stronger in cities with lower intergenerational
income mobility, implying that common factors drive both. However, the linkages are not
stronger in cities with more unequal distributions of income. Finally, we study the effect of local
educational policy interventions on the strength of the intergenerational linkages in credit using
time-series and state-level variation in school requirements regarding economic education,
financial literacy education, mathematics education, and school spending per pupil. We find that
economic education weakens the intergenerational linkages while other local policy interventions
(financial education requirements and the overall quality of schools) do not appear to have an
effect.
We use data from the New York Federal Reserve Bank Consumer Credit Panel/Equifax (CCP),
1999 - 2013. We focus on the following children’s credit outcomes: default behavior,
1

See also reviews in Solon (1999) and Black and Devereux (2011) and the references therein.
2

homeownership and having a credit card, and the child’s credit risk score as measured by
Equifax Risk Score (hereafter, risk score). The data allow us to study child credit outcomes over
two different horizons – between ages 19 and 24, and between ages 25 and 29. Regarding
parental credit attributes, we focus on three main attributes measured at the time when the child
is 19 years old and shares the household with parents – the incidence of serious default, the
degree of credit constraints, and the Equifax Risk Score (hereafter, risk score).
The study consists of four parts. The first part documents the existence of intergenerational
linkages in household credit. Children of parents with high credit scores, low levels of credit
utilization, and no serious defaults are less likely to default, have higher credit scores, are more
likely to become homeowners in their 20s, and are more likely to have credit cards. Controlling
for the household’s location (zip code) weakens the relationships only slightly, suggesting the
linkages are not merely caused by access to local amenities such as schools or the affluence of
the child’s peers. Rather, there exists a correlation between parents and children’s credit market
outcomes in excess of the effect of neighborhood. Furthermore, the relationships persist into ages
25 to 29.
The magnitudes of the effects are economically large. For example, after controlling for zip code
and cohort fixed effects, a one standard deviation increase in parental credit risk scores is
associated with a 35 percent reduction in the child’s likelihood of bankruptcy, a 49 percent
decrease in the child’s likelihood of other serious default, and a 6 percent increase in the child’s
credit score. It is also associated with a 12 percent increase in the likelihood of becoming a
homeowner by age 25 and a 23 percent increase by age 29. Conversely, a one standard deviation
increase in the extent to which parents are credit constrained is associated with an 8 percent
reduction in the likelihood of a child becoming a homeowner by age 25 and a 14 percent
reduction by age 29.
In the second part of the paper, we study what factors drive the intergenerational linkages in
household credit. The linkages might arise because credit conditions experienced by the parent
directly affect their children’s outcomes. For example, more credit-constrained parents might
have less resources available to directly or indirectly subsidize or invest in children.
Alternatively, the estimated correlations may be due to time-invariant household-level
heterogeneity, i.e., due to underlying factors that drive both the credit attributes of parents and
3

the future credit outcomes of children in the household. The identification of which of the two
scenarios better describes the estimated intergenerational linkages requires observations on more
than one child-parent pair from the same household and variation in the parents’ credit
characteristics within the same household. We obtain such observations by constructing a sample
of siblings. In the sample of siblings, we reject the existence of linkages beyond household-level
fixed effects. That is, the results indicate that the intergenerational linkages in household credit
are not due to parental credit attributes causing children’s future credit outcomes.
In the third part of the study, we use household location data to examine the effect of local area
characteristics on the strength of intergenerational linkages in credit markets. Overall, we find
that the linkages are stronger in cities with lower levels of income mobility indicating that some
of the same factors that drive intergenerational linkages in household credit also drive income
mobility. The linkages, however, are not consistently stronger in cities with more income
inequality as measured by the Gini coefficient.
Finally, we turn to the question of what policies can affect the strength of the intergenerational
linkages in household credit. The identification strategy exploits state-level differences over time
in economic, financial, and quantitative literacy programs. We also examine whether the quality
of public schooling affects the linkages as high-quality public schooling is often perceived as the
main equalizer of opportunity for children. Among the four policy interventions, financial
literacy, economic education, mathematics education, and school spending per pupil, higher
economic education requirements is the only policy that weakens the intergenerational linkages
in household credit.
In summary, we find economically important intergenerational linkages in household credit
behavior. The data, however, do not support the conjecture that these linkages are causal.
Instead, the linkages are due to household-level heterogeneity. More research is needed to
understand the factors that cause some households to fare better in credit markets than others. In
particular, there is a need for models that illustrate how household characteristics that persist
across generations manifest themselves in credit market outcomes, such as default and home
ownership.
1.1. Related Literature
4

To our knowledge, ours is the first empirical study of intergenerational linkages in household
credit. Our study builds on the extensive literature on intergenerational linkages in income
(Solon, 1992; Chadwick and Solon, 2002; Lee and Solon, 2009; Corak, 2013; Chetty, Hendren,
Kline, and Saez, 2014; and Chetty, Hendren, Kline, Saez, and Turner, 2014; Olivetti and
Paserman, 2015; and Olivetti, Paserman, and Salisbury, 2015). In addition, Charles and Hurst
(2003) use the Panel Survey of Income Dynamics (PSID) to document intergenerational linkages
in wealth and find that more than a third of the relationship between parental and child wealth
remains unexplained after controlling for income, propensity to own assets, education, gifts, and
expected bequests. Moreover, Charles and Hurst (2003) report that, even after controlling for
income, there are similarities in the types of assets that children and parents hold, a finding that
they attribute to similarities in risk preferences. Waldkirch, Ng, and Cox (2004) use data from
the PSID to report that there are similarities in familial consumption patterns even after
controlling for parental income. Charles, Danziger, Li, and Schoeni (2014) similarly find that
intergenerational linkages in consumption patterns persist after controlling for intergenerational
linkages in income.
The findings in Charles and Hurst (2003) and Waldkirch, Ng, and Cox (2004) suggest that
children may have similar preferences to their parents. Dohmen, Falk, Huffman, and Sunde
(2012) and Zumbuehl, Dohmen, and Pfann (2013) use German survey data to test more directly
for intergenerational linkages in preferences. They find that attitudes toward risk are linked
across generations. Cesarini, Dawes, Johannesson, Lichtenstein, and Wallace (2009), Barnea,
Cronqvist, and Siegel (2010), and Cesarini, Johannesson, Lichtenstein, Sandewall, and Wallace
(2010) use data on twins to conclude that genetics constitute a substantial component of risk
preferences. Kuhnen and Chiao (2009) and Zhong, Israel, Xue, Ebstein, and Chew (2009) link
risk-taking behavior with particular genes. Such similarity in preferences will manifest itself in
household fixed effects and suggests a reason why we observe intergenerational linkages in
credit even if the relationships are not causal.
Parents may also transmit human capital to their children in excess of what may manifest itself in
the labor market. In particular, parents may transmit financial literacy. Lusardi, Mitchell, and
Curto (2010) use the National Longitudinal Survey of Youth (NLSY) to conclude that financial

literacy among youth is generally low. Moreover, Lusardi, Mitchell, and Curto (2010) report that
youth financial literacy is strongly associated with family financial sophistication.
The remainder of the paper proceeds as follows. Section 2 describes the framework for
measuring the intergenerational linkages in household credit and the data. Section 3 documents
our benchmark measurement of linkages in household credit markets. Section 4 examines the
mechanism behind the intergenerational linkages using a sample of siblings. Section 5 explores
environmental factors that affect the strength of the intergenerational linkages. Section 6 studies
the effect of educational policy interventions on the linkages. Finally, Section 7 concludes.

2. Framework for Measuring Intergenerational Linkages in Household Credit
2.1. New York Federal Reserve Bank Consumer Credit Panel/Equifax Data
The data are from the New York Federal Reserve Bank Consumer Credit Panel/Equifax (CCP).
The CCP is an individual-level panel dataset that contains detailed records of individual debt and
borrowing on a quarterly basis from the first quarter of 1999 to the most recent quarter. The CCP
is a 5 percent random sample of all U.S. consumers with a credit record. These individuals
constitute the primary sample. In addition, the CCP has information about individuals who reside
at the same address as individuals in the primary sample. Using this information, we link
individual records to a household and then use individuals’ ages to identify children’s and
parents’ records as described below.2
The advantage of the CCP relative to survey data (e.g., PSID, Health and Retirement Study,
NLSY) is its sample size and accuracy. The data in the study cover the period from the first
quarter of 1999 to the fourth quarter of 2013. The resulting dataset on children contains over
300,000 individual records that we follow for a full decade starting in 1999-2003 and even more
individual records that we follow for five years.
2.2. Construction of Intergenerational Records

Lee and van der Klaauw (2010) provides an excellent description of the CCP data and contains
additional details on the CCP.
6

To link the data records of children and parents, we combine individual records that correspond
to the same mailing address into household records. The earliest age of the individuals included
in the CCP is generally 18. In the paper, we refer to the individuals for whom we have records at
age 18 or 19 as children. An individual who resides in a household with a 19-year-old child and
is 34 years or older is considered a parent (i.e., an adult in the household). The adult might not be
a genetic parent of the child. However, the analysis in the paper is concerned with the
transmission of credit market behavior within the household in which the child grows up rather
than solely genetic transmission (for a similar focus, see, for example, Solon, 1992).3 Having
identified children and their parents from the household identifiers at the time when children are
18 or 19 years old, we follow the individual records over time even when children and their
parents no longer reside in the same household. We present results from the sample of the
individuals whom we identify as children at 19 years old. 4
2.3. Measurement Framework for Intergenerational Linkages
We define the intergenerational linkages in household credit as the linear relationship between an
individual’s (i.e., child’s) credit outcomes and the individual’s parents’ credit characteristics at
the time when the individual was 19 years old and lived in the same household with the parents
We focus on measuring the children’s credit outcomes at two different time horizons: (1) a shortterm horizon, which covers first five years after we identify an individual as a child in our
sample, i.e., the period during which children are between 19 and 24 years old; and (2) a longterm horizon, which covers the second five years after we identify an individual as a child in our
sample, i.e., the period during which children are between 25 and 29 years old. Since the data in
the study covers the period from the first quarter of 1999 to the fourth quarter of 2013, we can
identify five cohorts of children – cohorts who are 19 in 1999, 2000, 2001, 2002, and 2003 –
whom we can follow for the full 10 years and thus construct both the short- and long-term
horizon outcomes. We use data from the fourth quarter of each year.
3

To decrease the probability of capturing some nontraditional living arrangements (for example, military
bases), we restrict our analysis to the individuals who at the age of 19 live in households with at most two
adults. We also drop households with more than 10 members.
4
In unreported analyses, instead of 19-year-olds, we use 18-year-olds (with parents being 33 or over).
The results using 18-year-olds are very similar to those with 19-year-olds. The results from this sample
are available from the authors upon request.
7

We focus on the following children’s credit outcomes: default patterns (delinquencies and
serious default), credit risk score (Equifax Risk Score), homeownership status, and having a
credit card. We define indicators of default, homeownership, or having a credit card using the
information from the years between t0 and t0  5 for short horizon outcomes and from the years
between t0  6 and t0  10 for long horizon outcomes. For the credit risk score, we consider the
average risk score over the respective horizon as well as the end-of-horizon risk score.
We define parents’ credit attributes using information from the year when the child is 19 years
old (t₀). For parents, we create variables that summarize whether a parent has a default and its
severity (e.g., bankruptcy, foreclosure, or delinquency), the extent to which the parent is creditconstrained, and the parents' average credit score.
In the benchmark analysis, we estimate the following relationship
CiaT = + Piat0   Aiat0  Dt0  Da  Dat0   iaT

(1)

where CiaT is the outcome of child 𝑖 over horizon T (T is between t0 and t0  5 for the short
horizon and between t0  6 and t0  10 for the long horizon), Piat is the outcome of parents of
0

child 𝑖 in year t₀, Aiat is the age of the parents of child 𝑖 in year t₀, Dt is the cohort effect of
0

children who are 19 in year t₀, Da is the location-specific effect of location 𝑎 where the child
resides when she is 19, and Dat is the cohort- and location-specific effect. In the estimation
0

below, we also allow the linear relationship described in (1) to differ by quartile of the
distribution of the child’s outcome for continuous outcomes.
The child’s credit outcomes analyzed are indicators of bankruptcy, serious default, and
delinquency in any of the years during the short- or long-term horizon, an indicator of
homeownership, an indicator of having a credit card, and, finally, the average risk score over the
horizon and the end-of-the-horizon risk score.5 We use three alternative variables to summarize
the parents’ credit attributes at the time their child is 19 years old. First, we consider a binary
D
variable, Piat0 , that takes value 1 if any parent has a serious default at t0, where serious default is

defined when a parent has a bankruptcy, a foreclosure, or a 90-day or greater delinquency. The
5

Appendix A contains a detailed description of the variables.
8

C
second measure, Piat0 , is the credit balance as a percentage of credit limit available for use at

time t0. It captures the extent to which a parent may be able to borrow to improve their children’s
R
outcomes. Finally, Piat0 , is the parents’ risk score (the average of the scores if there are two

parents).
2.4. Summary Statistics
Tables 1 shows the summary statistics of the parental variables when the children are 19 years
old. Panel A of Table 1 shows the summary statistics for the parents for the sample of 19-yearolds that we can follow until age 24. Panel B shows the statistics for the parents of the 19-yearolds that we can follow until age 29. If a child has two parents in the CCP, the summary statistics
in Table 1 are calculated including both observations. In general, the samples in Panel A and B
are very similar.
For comparison, the last column of Table 1 presents analogous statistics for adults 34 years or
older who have at least one child 18 years or older in the 1998 through 2010 waves of the Survey
of Consumer Finances (SCF). The parents in the CCP sample look broadly similar to those in the
SCF. The CCP parents’ homeownership rate is slightly lower than the homeownership rate in the
SCF (55 percent vs. 65 percent), largely because we define homeownership as having a
mortgage. In both samples, 8.0 percent of parents have experienced a bankruptcy in the last 10
years (the longest time a bankruptcy can be retained on a credit record). The average age of
parents in the CCP is 48 while it is 45 in the SCF. The most substantial difference is that only 22
percent of parents in the CCP sample are single parents, while 30 percent of parents in the SCF
are single parents.
Table 2 presents key statistics for the children in our sample at ages 24 and 29. Panel A presents
these statistics for the sample of children that we follow to age 24, i.e., those children who turn
19 in 1999 through 2003. In this sample, 34 percent experience a serious default sometime
between ages 19 and 24, 10 percent become homeowners by age 24 and 84 percent have a credit
card at some point between ages 19 and 24.6 The average Equifax Risk Score at age 24 is 636.
6

The rate of serious default in our sample is higher than the delinquency rate because we use only data
from quarter four of the year. Serious default is akin to a stock variable while delinquency is a flow
variable such that an individual could enter serious default in Q2 and remain there in Q4 without our ever
9

Panel B presents the same statistics for individuals who are 25 to 29 years old, that is, those
individuals who turn 19 sometime between 1999 and 2003 and whom we can follow to age 29 –
the long horizon sample.7 The long horizon sample has fewer observations than the short horizon
sample because fewer individuals can be followed for 10 years than can be followed for five
years.8

3. Intergenerational Linkages in Household Credit
This section documents key empirical facts about intergenerational linkages in household credit
markets. We first measure the strength between parental and child credit outcomes controlling
only for cohort effects without conditioning on geographic location. We then analyze how much
of the linkage across generations is explained by the household’s geographic location at t₀ versus
differences in parental characteristics within the location and variation over time in the labor
market opportunities available to individuals in different locations. Finally, to absorb differential
changes in macroeconomic conditions over time, we estimate an equation with state-cohort fixed
effects.
3.1. Baseline Estimates of the Intergenerational Linkages
Tables 3 through 5 show the relationship between children’s outcomes and parental credit
attributes – parental default, extent of parents’ credit constraints, and parental credit score,
respectively. For each child outcome, we estimate three different specifications. The first row for
each outcome shows the coefficients from the specification with cohort effects. The second row
shows the coefficients from the specification with cohort and zip code fixed effects. Finally, the
having observed a delinquency. See Debbaut, Ghent, and Kudlyak (2015) for additional discussion of this
feature of the CCP data.
7
Unlike the rest of the variables, the following variables - an indicator of whether the child left the
parents’ home, the age of such an event, the age when she got her first card, and the age at which the child
bought her first home – are defined over the entire 10 years for the long horizon sample rather than
through ages 25-29.
8
Also, the long horizon sample is not strictly a subsample of the short horizon or vice versa. This is
because, to be included in the short or long horizon sample, we require the individual to have a credit
record at the end of the horizon (at 24 years old for the short horizon and at 29 years old for the long
horizon) as well as at age 19. In the appendix Table A1 presents summary statistics for an alternative
definition of the short horizon sample in which the short horizon sample is a subsample of the long
horizon sample.
10

third row shows the coefficients from the specification with cohort, zip code, and state-cohort
fixed effects. Depending on the child’s outcome, including zip code fixed effects decreases the
magnitude of the coefficients on parental variables by 10-30 percent. The results using cohort,
zip code, and state-cohort fixed effects are virtually identical to those with only cohort and zip
code fixed effects. All the results in the tables are based on specifications in which the standard
errors are clustered by state. Finally, Table 6 aids in the interpretation of the effects by providing
a summary of the magnitude of the estimated effect in response to a one standard deviation
C
R
increase in Piat0 and Piat0 for each of the child outcome variables from the regressions with zip

code fixed effects presented in Tables 3 through 5.
3.1.1. Children’s Default and Bankruptcy
As can be seen from Table 3 through 5, there exist strong intergenerational linkages between
children’s default or bankruptcy and the parental credit attributes. The linkages are found
between parental credit attributes and children outcomes measured when children are 19-24
years old as well as between parental credit attributes and children outcomes measured at 25-29.
In particular, focusing on the results with zip code fixed effects, we observe that children of
parents with stronger credit characteristics (i.e., not having a serious default, being less credit
constrained, and having a higher credit score) are less likely to experience a bankruptcy or other
serious default and less likely to be delinquent. For delinquency and other serious default, the
magnitude of the effect is slightly larger in the 19- to 24-year-olds sample than for the 25- to 29year-olds sample. For bankruptcy, the effect of parental credit is twice as large for 25- to 29year-olds than for 19- to 24-year-olds in part because there are relatively few bankruptcies
among those under the age of 25 (see Table 2).
The magnitudes of the intergenerational credit linkages are economically substantial. For
example, children of parents with a serious default are 68 percent (1.3 percentage points) more
likely to experience a bankruptcy by age 24 and 46 percent (2.4 percentage points) more likely to
R
experience a bankruptcy between ages 25 and 29. A one standard deviation increase in Piat0 is

associated with a 24-35 percent decrease in the risk of bankruptcy, a 36-49 percent decrease in

the risk of other serious default, and a 23-36 percent decrease in the risk of delinquency for the
child (see Table 6).
3.1.2. Children’s Homeownership and Credit Cards
Children’s participation in credit markets is also correlated with parental credit attributes.
Children of parents with good credit characteristics are more likely to become homeowners or
C
have a credit card early in life. In particular, a one standard deviation increase in Piat0 is

associated with an 8 percent decrease in the probability of the child becoming a homeowner by
the age of 24 and a 14 percent decrease in the probability that the child becomes a homeowner
R
by age 29. Similarly, a one standard deviation increase in Piat0 is linked to a 12 percent increase

in the probability that a child is a homeowner by age 24 and a 23 percent increase by age 29. In
the regressions for homeownership presented in Tables 3 through 5, the coefficients on parental
age are all negative and statistically significant indicating that children of older parents are less
likely to become homeowners.
3.1.3. Children’s Credit Risk Scores
Children of parents with a serious default have risk scores 51 points lower at age 24 and 46
points lower at age 29. A one standard deviation increase in the extent to which parents are credit
constrained is associated with child risk scores 4 percent lower at age 24 and a 3 percent lower
child risk score at age 29. Similarly, a one standard deviation increase in the parental risk score is
associated with child risk scores that are 6 percent higher at age 24 and 5 percent higher at age
29.9
One concern with the documented linkages in household credit could mechanically arise due to
children and parents sharing cosigned credit cards. We thus re-estimate the benchmark
specification in equation (1) by adding an indicator if a child has a cosigned account in retail
trade or credit card, and the interaction of this indicator with the parental credit variable. We find
that the documented intergenerational linkages are very similar to the ones estimated in our

C
R
We also estimate the relationship between Piat0 and children’s risk scores as well as between Piat0 and

children’s risk scores using quantile regression. The relationships are similar at the 25 th, 50th, and 75th
percentiles. These results are available from the authors upon request.
12

benchmark specification. Consequently, the benchmark results on the linkages are not driven by
the joint parent-child accounts. 10

4. Household-Level Heterogeneity or Causal Effects of Parental Credit Conditions:
Evidence from the Sample of Siblings
The previous section documents correlations between an individual’s credit market outcomes
and the credit market attributes of the individual’s parents at the time when the individual is 19
years old. For example, individuals whose parents are more credit-constrained are more likely to
be in default and have lower risk scores in the future. There are two potential mechanisms that
could generate such correlations. One possibility is that the credit conditions experienced by the
parents directly affect their children’s outcomes, i.e., the documented intergenerational linkages
are causal. The other possibility is that the correlations are entirely due to time-invariant
household-level heterogeneity, i.e., underlying factors that drive both the credit attributes of
parents and the future credit outcomes of children in the household.
To better understand these alternatives, consider, for example, the case under which all
individuals are homogeneous in terms of their preferences and endowments and are subject to
similar shocks. Under this scenario, less credit-constrained parents have the means to invest
more in their children’s human capital or, more generally, are able to provide better insurance
against shocks. As a result, children of less credit constrained parents are also less credit
constrained, fare better when faced with negative shocks, and their measurable credit market
outcomes are better – they have higher risk scores and lower default rates. Under such a scenario,
the documented intergenerational linkages are caused by parental credit characteristics.
Alternatively, consider the scenario wherein there is no causal effect of parental credit on the
children’s credit market outcomes. Instead, households differ in their preferences and/or
endowments such that measurable credit market characteristics – risk scores, default rates,
delinquency rates, homeownership rates – differ systematically across households. That is,
children of parents with high risk scores tend to also have higher risk score, children of less

These results are in Table A2-A4.
13

credit-constrained parents tend to be less-credit constrained, and children from households where
parents default less tend to also default less. However, higher parent risk score does not
contribute to higher children’s risk score and parents being less credit-constrained does not
directly cause less credit-constrained children. Instead, the linkage arises due to, for example,
some common saving and consumption habits passed from generation to generation or some
other common factor. Under such a scenario, the documented intergenerational linkages are a
result of household-level heterogeneity rather than being caused by parental credit market
behavior or opportunities.
The identification of which of the two scenarios better describes the estimated intergenerational
linkages requires observations on more than one child-parent pair from the same household with
some variability in the measured parents’ credit characteristics in the pairs from the same
household. We obtain such observations by constructing a sample of siblings. Using the sample
of siblings, we estimate the following specification









CiaT  CiaT     Piat0  PiaT   Aiat0  AiaT  Dt0  Da  Dat0   iaT ,

(2)

where CiaT is the average outcome of all children in the household that child 𝑖 is associated with
at age 19, and PiaT is the average outcome of the parents at the time the children in the
household are 19 years old. All other variable definitions are as described after equation (1).
The specification in equation (2) removes the household-level fixed effect. Thus, any correlation
between the difference in child i ′𝑠 outcome and the average outcome of all children in the
household,  CiaT  CiaT  , and

P

iaT

 PiaT  is the causal effect of the parental attribute on the

child’s outcome.
Table 7 contains the results from estimating the intergenerational linkages in the sample of
siblings with controls for household-level fixed effects. To construct the household-level fixed
effect, we estimate the specifications with continuous parental variables only – parental risk
score and the extent of parental credit constraints. Column 1 shows the estimates of equation (1)
for the sample of siblings. We confirm that the benchmark results on intergenerational linkages
described in Section 3 are present in the sample of siblings. Column 2 shows the results from
estimating equation (2). When we control for household-level heterogeneity, the estimated
14

coefficient on the parental credit attribute, either parental risk score or parental credit constraint,
is close to zero and not statistically significant across all child credit outcomes that we consider.
Consequently, the estimates reject the existence of linkages beyond household-level fixed
effects. That is, the results suggest that the documented intergenerational linkages in household
credit cannot be attributed to a causal link between parental credit attributes and future children
credit outcomes. The same conclusion holds in the regressions without controls for zip code
fixed effects (these results available upon request).

5. Intergenerational Linkages in Household Credit and Local Characteristics
The results in Section 4 suggest that intergenerational linkages in household credit markets are
due to household-level heterogeneity rather than being manifestations of causal effects of
parental credit conditions on children’s future credit outcomes. In this section, we examine what
observables can help explain some of the heterogeneity. The credit bureau data do not contain
information on socio-demographic characteristics of households beyond individuals’ ages. We
thus use the household’s location information at the time when the child is 19 years old to
examine what local area characteristics are correlated with stronger intergenerational linkages.
5.1. Local Income Levels, Income Inequality, and Racial Composition
To estimate the role of the local area characteristics in the estimated intergenerational linkages,
we estimate the following specification:

CiaT = + Piat0 + Piat0 La   Aiat0  Dt0  Da  Dat0   iaT ,

(3)

where L a is the characteristic of area a in year t0 and Piat La is the interaction term between the
0

area characteristic and the parental credit attribute.
Tables 8 and 9 contain estimates of the benchmark regressions with the local characteristic
interacted with the extent of parental credit constraints and parental risk score, respectively.
Following Chetty, Hendren, Kline, and Saez (2014), we consider the following area
characteristics: household income per capita for working-age adults per capita in the county,
15

percent of middle class in the county (i.e., the fraction of parents in the county who have family
incomes between the 25th and 75th percentiles of the national parent income distribution), county
Gini coefficient, county-level income segregation, and the fraction of black residents in the
county.11
For all local characteristics, we find that the coefficient on the parental credit attribute remains
economically and statistically significant. In addition, the coefficients on the interaction terms
between local characteristics and parental credit attributes are statistically significant. In
particular, the coefficient on the interaction between local area income level and parental credit
attribute indicates that intergenerational linkages vary with local income. In locations with higher
income levels, we find stronger intergenerational linkages for children’s delinquency and risk
score but weaker intergenerational linkages for homeownership and the presence of a credit card.
Intergenerational credit linkages are generally weaker in cities with higher inequality as
measured by the Gini coefficient. They are also often weaker in cities with high income
segregation. The coefficient on the interaction on the percent of middle class is often statistically
insignificant but sometimes also indicates that the linkages are stronger in cities with a larger
middle class, consistent with our results for the Gini coefficient. Overall, the linkages are not
much stronger in cities in which a higher share of the population is black. The only case in which
the interaction term between the percent of the population that is black and the parental credit
attribute is consistently statistically significant is with parent risk score in the regression for child
delinquency.
5.2. Intergenerational Linkages in Household Credit and Income Mobility
Finally, we examine the extent to which intergenerational linkages in household credit are driven
by factors that are also associated with intergenerational income mobility. We use the MSA-level
estimates of intergenerational income mobility from Chetty, Hendren, Kline, and Saez (2014),
which are based on data from IRS tax records. We estimate the following specification
CiaT = + Piat0 + Piat0 M a  Dt0  Da   iaT ,

(4)

See Chetty, Hendren, Kline, and Saez (2014) for the calculation of county income segregation
indicators.
16

where M a is the income mobility measure in area a and Piat M a is the interaction between the
0

mobility measure and the parental attribute variable.
We estimate equation (4) for 381 MSAs and use the two measures of mobility provided by
Chetty, Hendren, Kline, and Saez (2014). The first, relative mobility, is the difference in
outcomes for children with the wealthiest parents from those for children with the poorest
parents. A higher number indicates a greater correlation between parent and child outcomes and
may thus perhaps be thought of as a measure of immobility. The second measure, absolute
mobility, is the predicted rank for a child born to parents at the 25th percentile and indicates a
measure of upward mobility. The results of the estimation are contained in Tables 8 and 9 with
the extent of parental credit constraints and parental risk score, respectively.
We find that intergenerational linkages in household credit are stronger in cities with lower
intergenerational income mobility. While our analysis does not speak to the direction of the
causality between intergenerational linkages in credit and income, the results suggest that
common factors drive both. This conclusion is consistent with our finding in Section 4 that
household-level heterogeneity drives the intergenerational linkages in household credit.

6. Intergenerational Linkages in Household Credit and Educational Policies
In this section, we examine whether local policy on financial education or, more generally, better
school quality affect the strength of the intergenerational linkages.
6.1. Financial Literacy, Economic Education, and Math Education
To estimate the effect of educational policies on the strength of the intergenerational linkages in
household credit, we augment equation (1) with interactions between parents’ credit
characteristics and state-level policy variables regarding the financial education. Financial
education may be associated with lower intergenerational linkages in household credit by
reducing the influence of underlying household heterogeneity on financial decision-making.
The schooling curriculum regarding financial literacy, economic education or mathematical
education varies by state and by year. We use the state-year classification for these three
17

components from Brown, Grigsby, van der Klaauw, Wen, and Zafar (2014).12 For each state,
Brown, Grigsby, van der Klaauw, Wen, and Zafar identify years in which each state changed
requirements for each of the three components. Consequently, the educational policy varies
across locations (states) as well as across time (for those states that experienced the policy
change during the sample period). We estimate the following specification with different
measures of local educational policy
CiaT = + Piat0   Eat0 + Piat0 Eat0  Dt0  Da   iaT ,

(5)

where, for economic and financial literacy, Eat is a dummy variable for whether a financial
0

literacy or economic education mandate was enacted or strengthened before the individual turned
18; for math, Eat takes values of 0,1, or 2 for no reform, one reform, or two reforms.
0

Tables 10 and 11 contain the results of estimating educational policy equation (5) with two
alternative parental credit attributes - the extent of parental credit constraints and parental risk
score, respectively, separately for each educational policy.
If economic education improves decision-making in credit markets, the expected effect of the
(level of) economic education on child’s bankruptcy, delinquency, and serious default, ceteris
paribus, is negative, while the expected effect on child’s homeownership, having a credit card, or
on the child’s risk score is positive. In addition, economic education might weaken
intergenerational linkages in household credit by mitigating the influence of household
endowments. That is, under the null hypothesis, the sign on the interaction term between the
economic education measure and the parental credit attribute is opposite to the sign of the
coefficient on the parental credit attribute variable.
As can be seen from Tables 10 and 11, the estimated coefficient on the economic education
measure is small in magnitude and often not statistically significant for all children credit
outcomes except for the end-period children risk score, in which the coefficient is statistically
significant and has an expected positive sign. However, since we cannot include state-year fixed
effects in a regression that also includes a state-specific and time-varying measure of economic
education in levels and cohort fixed effects, the coefficient on the economic education measure
12

See Table 1 in Brown, Grigsby, van der Klaauw, Wen, and Zafar (2014).
18

might capture state effects other than educational policy. We thus focus on the interaction
between the educational policy variables and parental credit attributes.
In the regressions with child bankruptcy, delinquency, serious default, homeownership, or risk
score, the interaction term has the sign opposite to the sign on the parental credit attribute
variable. Consequently, the results suggest that a higher level of economic education is
associated with weaker intergenerational linkages in household credit. The interaction term is
close to zero and not statistically significant only in the regressions on the child having a credit
card. Importantly, the inclusion of the interaction term of the economic education measure and a
parental credit attribute in equation (1) leaves the coefficient on the parental credit attribute
almost unchanged.
We next proceed to analyzing the effect of financial literacy. We estimate equation (5) using the
financial literacy measure from Brown et al. (2014). As can be seen from Tables 10 and 11, the
estimated coefficient on the financial literacy measure is small in magnitude and often not
statistically significant. As can be seen from Tables 10 and 11, the inclusion of the interaction
terms between financial literacy and parental credit attributes in equation (1) leaves the
coefficient on the parental credit attribute almost unchanged and the interaction term itself is
rarely statistically significant. Consequently, it appears that the changes in the state-specific
measures of financial literacy requirements do not affect intergenerational linkages in household
credit.
Similarly, we find no statistically significant effect of existing measures of mathematical reform
on intergenerational linkages in household credit.13
6.2 School Quality
Finally, we examine the effects of school quality on intergenerational linkages in credit. We use
local tax rates as a proxy for the quality of inputs into public schooling. Because local taxes

We also re-estimate educational policy equation (5) using alternative measures of financial and
economic education across states. In particular, we use the state graduation requirement variables
developed by Urban and Schmeiser (2015). However, the same conclusions carry through. These results
are contained in Table A5 (for the extent of parents’ being credit constrained) and Table A6 (for the
parents’ risk score).
19

primarily fund public schools in the United States, areas with higher local taxes are likely to
spend more on schooling. In particular, we estimate
CiaT = + Piat0 + Piat0 Sa  Dt0  Da   iaT ,

(6)

where S a is the average total spending per pupil on K-12 education in student i’s state of
residence. The data on state-level spending per pupil is available from the U.S. Census Bureau
tables “Public Elementary-Secondary Education Finance Data” for 2001-2012. We use the
earliest year available, 2001-2002. As in estimating equations (3) and (4), the first order effect of
S a is subsumed in the zip code fixed effects.

The results of the estimation are contained in Tables 10 and 11. In general, higher levels of
school spending are associated with weaker transmission of parental credit characteristics on
homeownership and participation in credit card markets. However, the effect of parental credit
characteristics on delinquency and risk scores is actually stronger in states with higher spending
per pupil. As such, high-quality schooling does not seem to level the playing field.

7. Conclusions
We document the existence and quantify the extent of intergenerational linkages in household
credit. By studying a sample of siblings, we further show that these intergenerational linkages are
the result of household heterogeneity rather than parental credit market conditions directly
affecting the credit outcomes of children. Furthermore, we use state variation in educational
policies and find that educational policy has a limited influence on the strength of the linkages.
The results are encouraging as they indicate that credit market frictions for parents are not so
severe that transitory shocks affect their children’s future credit market outcomes. Our results
also suggest that any disadvantages children face from family endowments that manifest
themselves in adverse credit market outcomes might be hard to overcome with policy. Our
results also suggest that financial and economic literacy policies are improving credit market
outcomes through some channel other than mitigation of the influence of family endowments.

More empirical and theoretical research on the household heterogeneity that drives both parents’
and children’s outcomes in credit markets is needed to better understand what causes default,
homeownership, and credit market participation across generations.

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Appendix A. Variables Definitions
The child credit outcomes that we study are
1. An indicator for having a credit card that is a dummy variable that takes a value of 1 if
the child has a credit card between age 19 and 24 for the short horizon sample and
between age 25 and 29 for the long horizon sample.
2. The age at which the child first has a credit card.
3. A dummy variable that takes value 1 if the child has a mortgage between age 19 and 24
for the short horizon sample and between 19 and 29 for the long horizon sample.
4. The age at which the child first has a mortgage.
5. A dummy variable that takes value 1 if the child has a bankruptcy flag on his or her credit
record between age 19 and 24 for the short horizon sample and between age 25 and 29
for the long horizon sample.
6. A dummy variable that takes value 1 if the child has a foreclosure on his or her credit
record between age 19 and 24 for the short horizon sample and between age 25 and 29
for the long horizon sample.
7. A dummy variable that takes value 1 if the child has an account 90DPD or greater default
(excluding bankruptcy and foreclosure) on his or her credit record between age 19 and 24
for the short horizon sample and between age 25 and 29 for the long horizon sample.
8. A dummy variable that takes value 1 if the child has an account 30DPD or 60DPD on his
or her credit record between age 19 and 24 for the short horizon sample and between age
25 and 29 for the long horizon sample.
9. The average Equifax Risk Score for the child between age 19 and 24 for the short horizon
sample and between age 25 and 29 for the long horizon sample.
10. The child’s Equifax Risk Score at the age 24 for the short horizon sample and at the age
29 for the long horizon sample.
11. An indicator if individual moves to address different from the parental residence between
age 19 and 24 for the short horizon sample and between age 19 and 29 for the long
horizon sample.
12. Age (by end of year) at which the child first moves away from the parental residence.
The parental attributes that we study are
1. An indicator for parents’ homeownership that takes value 1 if a parent has a mortgage
and 0 otherwise, measured when the child is 19 years old.
2. An indicator for parents’ bankruptcy that takes value 1 if a parent has any account in
bankruptcy and 0 otherwise, measured when the child is 19 years old.
3. An indicator for parents’ foreclosure that takes value 1 if a parent has a foreclosure and 0
otherwise, measured when the child is 19 years old.

4. An indicator for parents’ serious default that takes value 1 if a parent has any account
90DPD or greater default (excluding bankruptcy and foreclosure) and 0 otherwise,
measured when the child is 19 years old.
5. An indicator for parents’ delinquency that takes value 1 if a parent has any account
30DPD or 60DPD and 0 otherwise, measured when the child is 19 years old.
6. A degree of parents being credit constrained is the ratio of credit balance to card limit,
measured when the child is 19 years old. It is the maximum credit balance as a percentage
of the combined credit limit available for use at time t0 of the two parents if there are
two.
7. Parents’ credit Equifax Risk Score, measured when the child is 19 years old and the
average if there are two parents in the household.
8. Parents’ age when the child is 19 years old, measured as the average age if there are two
parents in the household.

Table 1: Parent Summary Statistics
Mean

Std. Dev.

Min

25th %

Median

75th %

Max

Obs.

Mean
(SCF)

1
2
3
4
Panel A: Parents of 19 year olds that can be followed to age 24
Parent Homeowner
0.56
0.50
0

862,835

0.64

Parent Bankrupt

0.08

0.27

862,835

0.08

Parent Serious Default
Parent Delinquency

0.18
0.08

0.38
0.26

0
0

1
1

862,835
862,835

Parent Credit Constrained

0.33

0.32

0.04

0.20

0.58

693,210

Parent Equifax Risk Score
Parent Age
Parent Single
Total Number in Household

686.2
48.4
0.20
2.8

104.2
8.6
0.40
0.4

293
34
0
2

617
43
0
3

712
47
0
3

772
52
0
3

842
102
1
5

831,885
862,835
862,835
862,835

Panel B: Parents of 19 year olds that can be followed to age 29
Parent Homeowner
0.55
0.50
0
0
Parent Bankrupt
0.08
0.27
0
0
Parent Serious Default
0.18
0.39
0
0

1
0
0

1
1
1

682,324
682,324
682,324

Parent Delinquency

0.08

0.27

682,324

Parent Credit Constrained
Parent Equifax Risk Score
Parent Age
Parent Single
Total Number in Household

0.33
685.1
48.4
0.22
2.8

0.32
104.1
8.7
0.41
0.4

0
293
34
0
2

0.04
616
43
0
3

0.20
710
47
0
3

0.58
771
52
0
3

1
842
102
1
5

547,274
657,547
682,324
682,324
682,324

45.2
0.30
3.0
0.64
0.08

45.2
0.30
3.0

Notes: 1) Authors’ calculations using the New York Federal Reserve Bank Consumer Credit Panel/Equifax dataset and
the Survey of Consumer Finance (SCF). 2) The table contains statistics of parents of 19 year-olds at time t0 (1999,
2000, 2001, 2002, 2003). If an individual has two parents, an observation for each parent is included. 3) Mean (SCF) is
the average for adults aged 34+ with one child aged 18+. 4) Mean (SCF) for Total Number in Household is the number
of individuals in the household aged 18+.

Table 2: Child Summary Statistics
Mean

Std.
Dev.

Min

25th %

Median

75th %

Max

Obs.

1
Panel A: Between t0 and t0+5 (Ages 19-24)
Child Household Formation

0.50

523,980

Child Household Formation First Age

21.6

1.5

259,782

Child Has Credit Card
Child Has Credit Card First Age

0.84
19.8

0.37
1.6

0
15

1
19

1
20

1
24

523,980
440,434

Child Homeowner

0.10

0.30

523,980

Child Homeowner First Age

22.5

1.6

54,123

Child Bankrupt
Child Foreclosure

0.019
0.077

0.136
0.266

0
0

1
1

523,980
523,980

Child Other Serious Default
Child Delinquency
Child Equifax Risk Score Average
Child Equifax Risk Score End
Child Credit Constrained

0.34
0.25
632.1
635.7
0.627

0.47
0.43
78.0
98.5
0.341

0
0
312
288
0

0
0
567
563
0.325

0
0
647.7
648
0.731

1
0
697.3
721
0.944

1
1
829
834
1

500,654
500,654
511,811
504,677
444,942

Panel B: Between t0+6 and t0+10 (Ages 25-29)
Child Household Formation
0.74
Child Household Formation First Age
22.86
Child Has Credit Card
0.80
Child Has Credit Card First Age
20.18

0.44
2.72
0.40
2.36

0
20
0
15

0
20
1
19

1
22
1
19

1
25
1
21

1
29
1
29

417,705
309,428
417,705
377,368

Child Homeowner
Child Homeowner First Age
Child Bankrupt
Child Foreclosure
Child Other Serious Default

0.28
25.24
0.052
0.03
0.38

0.45
2.61
0.222
0.17
0.49

0
15
0
0
0

0
23
0
0
0

0
25
0
0
0

1
27
0
0
1

1
29
1
1
1

417,705
123,488
417,705
417,705
392,930

Child Delinquency
Child Equifax Risk Score Average

0.21
644.10

0.41
94.65

0
312

0
565

0
645

0
732.4

1
833

392,930
411,339

Child Equifax Risk Score End
Child Credit Constrained

654.20
0.591

102.90
0.345

310
0

578
0.263

661
0.669

746
0.925

838
1

399,483
332,024

Notes: 1) Authors’ calculations using the New York Federal Reserve Bank Consumer Credit Panel/Equifax dataset. 2)
The individuals in Panel A are subsample of the individuals for whom we have an observation at 19 and at 24 years of
age. 3) The individuals in Panel B are subsample of the individuals for whom we have an observation at 19 and at 29
years of age. 4) The sample in Panel B is not necessarily a subsample of the sample in Panel A or vice versa. 5) See
Table A1 for the summary statistics for individuals at 24 years old who are the subsample of the sample in Panel B.

Table 3: Child Credit Outcomes and Parental Default

Child
Outcome

Coef. On Parent Serious
Default

Coef. On Parent Age

Fixed effects controls

5-Year
Horizon

10-Year
Horizon

5-Year
Horizon

10-Year
Horizon

Cohort

Zip
Code

StateCohort

5Year

10Year

0.014***
0.013***
0.013***

0.028***
0.024***
0.024***

-0.000022
0.000050
0.000050

-0.00021***
-0.0000088
-0.0000066

Yes
Yes
Yes

No
Yes
Yes

No
No
Yes

0.5%
6.8%
6.9%

0.6%
8.8%
8.9%

0.14***
0.11***

0.079***
0.057***

-0.00098***
-0.00072***

-0.00071***
-0.00049***

Yes
Yes

No
Yes

No
No

2.1%
9.2%

1.1%
9.0%

0.11***
-0.022***
-0.017***
-0.017***

0.057***
-0.11***
-0.085***
-0.085***

-0.00072***
-0.00082***
-0.00035***
-0.00035***

-0.00049***
-0.0017***
-0.00097***
-0.00097***

Yes
Yes
Yes
Yes

Yes
No
Yes
Yes

Yes
No
No
Yes

9.3%
0.2%
8.1%
8.1%

9.1%
1.4%
11.0%
11.0%

Other Serious
Default (Ex.
Foreclos. and
Bankruptcy)

0.30***

0.24***

-0.0011***

-0.0013***

Yes

7.3%

4.9%

0.23***
0.23***

0.18***
0.18***

-0.00087***
-0.00087***

-0.00098***
-0.00098***

Yes
Yes

No
Yes

18.0%
18.0%

16.0%
17.0%

Average
Equifax Risk
Score

-64.5***
-50.2***

-62.7***
-46.9***

0.31***
0.23***

0.34***
0.25***

Yes
Yes

No
Yes

No
No

13.0%
27.0%

8.4%
23.0%

Date T
Equifax Risk
Score

-50.2***
-67.3***
-51.4***
-51.4***
-0.094***

-46.9***
-62.3***
-46.2***
-46.2***
-0.14***

0.23***
0.42***
0.31***
0.31***
0.00039***

0.25***
0.33***
0.24***
0.24***
0.00042**

Yes
Yes
Yes
Yes
Yes

Yes
No
Yes
Yes
No

Yes
No
No
Yes
No

27.0%
8.7%
21.0%
21.0%
1.2%

23.0%
6.9%
21.0%
21.0%
2.3%

Has Credit
Card

-0.065***

-0.099***

0.00014**

0.00014

Yes

11.0%

13.0%

-0.065***

-0.099***

0.00014**

0.00014

Yes

11.0%

13.0%

1
Bankruptcy

Delinquency

Homeowner

Note: 1) Results using data from the New York Federal Reserve Bank Consumer Credit Panel/Equifax dataset. 2) Each
row of table presents results from two regressions of child outcome variable on parental variables indicated and fixed
effects.

Table 4: Child Credit Outcomes and Parental Credit Constraints
Child
Outcome
1
Bankruptcy

Delinquency

Homeowner
Other Serious
Default (Ex.
Foreclos. and
Bankruptcy)
Average
Equifax Risk
Score
Date T
Equifax Risk
Score
Has Credit
Card

Coef. On Parent Credit
Constrained

Coef. On Parent Age

Fixed effects controls

5-Year
Horizon

10-Year
Horizon

5-Year
Horizon

10-Year
Horizon

Cohort

Zip
Code

StateCohort

5Year

10Year

0.016***
0.014***
0.014***
0.20***

0.034***
0.028***
0.028***
0.13***

-0.0000027
0.000063*
0.000062*
-0.00056***

-0.00022**
-0.000017
-0.000014
-0.00042**

Yes
Yes
Yes
Yes

No
Yes
Yes
No

No
No
Yes
No

0%
8%
8%
3%

1%
10%
10%
2%

0.17***

0.11***

-0.00038**

-0.00024*

Yes

10%

0.17***
-0.025***
-0.025***
-0.025***

0.11***
-0.14***
-0.12***
-0.12***

-0.00038**
-0.0014***
-0.00076***
-0.00076***

-0.00024*
-0.0030***
-0.0020***
-0.0020***

Yes
Yes
Yes
Yes

Yes
No
Yes
Yes

Yes
No
No
Yes

11%
0%
9%
9%

10%
2%
11%
12%

0.33***

0.30***

-0.0000027

-0.00033

Yes

0.26***
0.26***
-79.6***

0.24***
0.24***
-85.2***

0.000015
0.000015
0.055

-0.00020
-0.00019
0.052

Yes
Yes
Yes

Yes
Yes
No

No
Yes
No

17%
17%
13%

17%
17%
10%

-65.2***
-65.2***
-87.7****

-69.0***
-69.0***
-83.8***

0.011
0.012
0.14**

0.0085
0.0075
0.036

Yes
Yes
Yes

Yes
Yes
No

No
Yes
No

27%
27%
10%

23%
23%
8%

-71.4***
-71.4***
-0.095***

-67.4***
-67.4***
-0.14***

0.079**
0.078**
-0.00039**

-0.0092
-0.0094
-0.00040*

Yes
Yes
Yes

Yes
Yes
No

No
Yes
No

21%
21%
1%

21%
21%
2%

-0.070***
-0.069***

-0.11***
-0.11***

-0.00054***
-0.00053***

-0.00054***
-0.00054***

Yes
Yes

No
Yes

10%
11%

12%
13%

Notes: 1) Results using data from the New York Federal Reserve Bank Consumer Credit Panel/Equifax dataset. 2) Each
row in the table presents results from two regressions for child outcome variable on parental variables indicated and fixed
effects.

Table 5: Child Credit Outcomes and Parental Credit Risk Score
Child
Outcome
1
Bankruptcy

Delinquency

Homeowner
Other Serious
Default (Ex.
Foreclos. and
Bankruptcy)
Average
Equifax Risk
Score
Date T
Equifax Risk
Score
Has Credit
Card

Coef. On Parent Equifax Risk
Score

Coef. On Parent Age

Fixed effects controls

5-Year
Horizon

10-Year
Horizon

5-Year
Horizon

10-Year
Horizon

Cohort

Zip
Code

StateCohort

5Year

10Year

2
-0.000071***

3
-0.00014***

4
0.000052

5
-0.000085

6
Yes

7
No

8
No

9
1%

10
1%

-0.000064***

-0.00012***

0.00012***

0.000092

Yes

-0.000064***
-0.00097***

-0.00012***
-0.00057***

0.00012***
0.00047***

0.000095
0.00015

Yes
Yes

Yes
No

7%
5%

9%
2%

-0.00086***

-0.00046***

0.00052***

0.00018

Yes

12%

10%

-0.00086***
0.00012***
0.00012***
0.00012***

-0.00046***
0.00069***
0.00062***
0.00062***

0.00052***
-0.0011***
-0.00063***
-0.00063***

0.00018
-0.0030***
-0.0021***
-0.0021***

Yes
Yes
Yes
Yes

Yes
No
Yes
Yes

Yes
No
No
Yes

12%
0%
8%
8%

10%
3%
12%
12%

-0.0019***

-0.0016***

0.0017***

0.0011***

Yes

16%

11%

-0.0016***
-0.0016***
0.42***

-0.0013***
-0.0013***
0.42***

0.0014***
0.0014***
-0.32***

0.00088***
0.00088***
-0.31***

Yes
Yes
Yes

Yes
Yes
No

No
Yes
No

23%
23%
30%

20%
20%
21%

0.36***
0.36***
0.45***
0.38***
0.38***

0.35***
0.35***
0.42***
0.34***
0.34***

-0.29***
-0.29***
-0.26***
-0.24***
-0.24***

-0.27***
-0.27***
-0.32***
-0.27***
-0.27***

Yes
Yes
Yes
Yes
Yes

Yes
Yes
No
Yes
Yes

No
Yes
No
No
Yes

38%
38%
21%
29%
29%

30%
30%
17%
26%
26%

0.00066***

0.00093***

-0.00072***

-0.0011***

Yes

0.00050***
0.00050***

0.00073***
0.00073***

-0.00070***
-0.00069***

-0.00099***
-0.00099***

Yes
Yes

No
Yes

12%
12%

15%
15%

Notes: 1) Results using data from the New York Federal Reserve Bank Consumer Credit Panel/Equifax dataset. 2) Each row
of table presents results from two regressions for the indicated child outcome variable on parental variables indicated and
fixed effects.

Table 6: Economic Effect of One Standard Deviation Increase in Parental Credit Attributes on Child Credit Outcomes

Child Outcome

Parent Credit Constrained
5-Year Horizon
10-Year Horizon

Parent Equifax Risk Score
5-Year Horizon
10-Year Horizon

Level

Percent
of mean

Level

Percent
of mean

Level

Percent
of mean

Level

Percent
of mean

Bankruptcy
Delinquency

2
0.004
0.055

3
24%
22%

4
0.009
0.035

5
17%
17%

6
-0.007
-0.090

7
-35%
-36%

8
-0.012
-0.048

9
-24%
-23%

Homeowner

-0.008

-8%

-0.039

-14%

0.013

12%

0.065

23%

Other Serious Default (Ex.
Foreclosure and Bankruptcy)

0.083

25%

0.077

20%

-0.167

-49%

-0.135

-36%

Average Equifax Risk Score
End-of-period Equifax Risk Score
Has Credit Card

-20.9
-22.9
-0.022

-3%
-4%
-3%

-22.2
-21.7
-0.035

-3%
-3%
-4%

37.5
39.6
0.052

6%
6%
6%

36.4
35.4
0.076

6%
6%
9%

Notes: 1) Results using data from the New York Federal Reserve Bank Consumer Credit Panel/Equifax dataset. 2) The
table presents results from OLS estimation with zip code fixed effects from Tables 3-5.

Table 7: Intergenerational Linkages in Household Credit with and without Controls for Household Heterogeneity
Parental Equifax Risk Score
Child Outcome

No controls for
household
heterogeneity

With controls for
household
heterogeneity

Cohort
Fixed
Effects

Zip Code
Fixed
Effects

-0.0012***
0.35***
0.37***

-0.0000021
0.022*
-0.010

Yes
Yes
Yes

31,510
35,438
35,011

Credit Constrained
Average Equifax Risk Score
Date T Equifax Risk Score

Parental Credit Constraints

Credit Constrained
Average Equifax Risk Score
Date T Equifax Risk Score

No controls for
household
heterogeneity

With controls for
household
heterogeneity

Cohort
Fixed
Effects

Zip Code
Fixed
Effects

0.27***
-59.9***
-67.5***

-0.005
-2.450
0.470

Yes
Yes
Yes

28,220
31,760
31,453

Notes: 1) Results using data from the New York Federal Reserve Bank Consumer Credit Panel/Equifax dataset.
2) Regressions in this table are run on the sibling sample, i.e., the subsample of children who have at least one
sibling in our data. 3) Column (2) shows the coefficient estimates from the regression of the child outcome on the
parental credit attribute as described in eq. (1). Each regression in column (2) also controls for parental age in
levels. 4) Column (3) shows the coefficient estimates from the regression of the demeaned child outcome on the
demeaned parental credit attribute as described in eq. (2). Each regression in column (3) also controls for
demeaned parental age. Demeaned variables subtract off within-family mean for that variable. 5) Table presents
results for the 5-year horizon; results for the 10-year horizon are similar and available upon request.

Table 8: Geographic Variables and Strength of Intergenerational Linkages between Child Credit Outcomes and Parental Credit
Constraints
Child
Outcome
1

Delinquency

Homeowner

Date T
Riskscore

Has Credit
Card

Covariate
Specification

Coef. On Parent
Constrained

Coef. On (Parent Constrained) *
Geographical Variable

Other Controls

5-Year
Horizon

10-Year
Horizon

5-Year Horizon

10-Year Horizon

Cohort

Zip
Code

Parent
Age

HH Inc. per Capita
% Middle Class
Gini
Inc. Segregation
% Black
Rel. Immobility
Abs. Mobility
None (Benchmk)
HH Inc. per Capita
% Middle Class
Gini
Inc. Segregation
% Black
Rel. Immobility
Abs. Mobility
None (Benchmk)
HH Inc. per Capita
% Middle Class
Gini
Inc. Segregation
% Black
Rel. Immobility
Abs. Mobility
None (Benchmk)

3
0.20***
0.15***
0.16***
0.20***
0.18***
0.17***
0.11***
0.19***
-0.025***
-0.066***
0.028*
-0.041***
-0.042***
-0.021***
0.016
-0.14***
-87.7****
-65.3***
-75.9***
-75.1***
-73.0***
-69.9***
-44.9***
-85.3***
-0.095***

4
0.13***
0.066***
0.100***
0.13***
0.11***
0.11***
0.071***
0.095**
-0.14***
-0.18***
-0.03
-0.17***
-0.15***
-0.12***
-0.029
-0.30***
-83.8***
-60.2***
-69.4***
-72.0***
-69.4***
-65.8***
-38.3***
-87.8***
-0.14***

5
x
0.00000048
0.011
-0.073***
-0.15*
-0.00099
0.17***
-0.00044
x
0.0000010***
-0.11***
0.037
0.23***
-0.034*
-0.12**
0.0028***
x
-0.00015***
9.17
8.37
20.3
-13.0**
-78.0***
0.34
x

6
x
0.0000010***
0.012
-0.064***
-0.088
-0.031
0.10**
0.00026
x
0.0000013***
-0.19***
0.11***
0.43***
-0.016
-0.26***
0.0045***
x
-0.00018*
4.04
10.3
26.2
-13.9*
-85.5***
0.51
x

7
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes

8
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes

9
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes

HH Inc. per Capita
% Middle Class
Gini
Inc. Segregation
% Black
Rel. Immobility
Abs. Mobility

-0.11***
-0.040*
-0.076***
-0.084***
-0.071***
-0.041**
-0.18***

-0.18***
-0.062***
-0.091***
-0.12***
-0.10***
-0.065***
-0.23***

0.0000011***
-0.06
0.013
0.18***
0.012
-0.080*
0.0026***

0.0000018***
-0.092**
-0.039
0.17***
-0.055**
-0.12***
0.0030***

Yes
Yes
Yes
Yes
Yes
Yes
Yes

2
None (Benchmk)

Note: Results using data from the New York Federal Reserve Bank Consumer Credit Panel/Equifax dataset and other data
sources as described in the text.

Table 9: Geographic Variables and Strength of Intergenerational Linkages between Child Credit Outcomes and Parental Credit
Risk Score

Child Outcome

Covariate
Specification

Homeownership

5-Year Horizon

10-Year Horizon

Cohort

Zip
Code

Parent
Age

None (Benchmk)

-0.00086***

-0.00046***

Yes

HH Inc. per Capita

-0.00062***

-0.00021***

-6.2e-09***

-6.5e-09***

Yes

% Middle Class

-0.00087***

-0.00051***

0.000015

0.000085

Yes

Gini

-0.0011***

-0.00061***

0.00049***

0.00033***

Yes

Inc. Segregation

-0.00091***

-0.00048***

0.00055***

0.00026

Yes

% Black

-0.00090***

-0.00050***

0.00030***

0.00026***

Yes

Rel. Immobility

-0.00075***

-0.00044***

-0.00032*

-0.00007

Yes

Abs. Mobility

-0.00055***

-0.000026

-0.0000075*

-0.000011***

Yes

None (Benchmk)

0.00012***

0.00062***

Yes

HH Inc. per Capita

0.00036***

0.00093***

-6.0e-09***

-7.8e-09***

Yes

% Middle Class

-0.00017**

0.000065

0.00058***

0.0011***

Yes

Gini

0.00021***

0.00094***

-0.00019*

-0.00071***

Yes

Inc. Segregation

0.00021***

0.00081***

-0.0012***

-0.0025***

Yes

% Black

0.00011***

0.00063***

0.0001

-0.0001

Yes

-0.00011*

0.00011

0.00066***

0.0014***

Yes

0.00075***

0.0015***

-0.000016***

-0.000022**

Yes

None (Benchmk)

0.38***

0.34***

Yes

HH Inc. per Capita

0.32***

0.30***

0.0000014***

0.0000012***

Yes

% Middle Class

0.39***

0.35***

-0.022

-0.008

Yes

Gini

0.42***

0.38***

-0.097***

-0.093***

Yes

Inc. Segregation

0.39***

0.36***

-0.16***

-0.17***

Yes

% Black

0.38***

0.34***

-0.047*

-0.019

Yes

Rel. Immobility

0.30***

0.24***

0.23***

0.29***

Yes

Abs. Mobility

0.35***

0.00068

-0.00019

Yes

None (Benchmk)

0.00050***

0.00073***

Yes

HH Inc. per Capita

0.00094***

0.0012***

-0.000000011***

Yes

0.000035

0.00033***

0.00094***

0.00080***

Yes

Gini

0.00065***

0.00079***

-0.00032**

-0.00014

Yes

Inc. Segregation

0.00065***

0.00086***

-0.0019***

-0.0018***

Yes

% Black

0.00052***

0.00073***

-0.00015

-3.6e-08

Yes

Rel. Immobility

0.00026**

0.00047***

0.00064**

0.00068***

Yes

Abs. Mobility

0.0013***

0.0014***

-0.000020***

-0.000018***

Yes

% Middle Class
Has Credit Card

Other Controls

Geographical Variable

10-Year
Horizon

Rel. Immobility

Date T Equifax Risk
Score

Coef. On (Parent Equifax Risk Score) *

5-Year
Horizon
1

Delinquency

Coef. On Parent Equifax Risk
Score

Note: Results using data from the New York Federal Reserve Bank Consumer Credit Panel/Equifax dataset and other data
sources as described in the text.

Table 10: Educational Policies and Strength of Intergenerational Linkages between Child Credit Outcomes and Parental Credit Constraints
Coef. On Parent Constrained
Child Outcome

Delinquency

Homeownership

Date T Riskscore

Has Credit Card

Covariate Specification

Coef. On Educational
Policy

Coef. On (Parent Constrained) *
Educational Policy

Other Controls

5-Year
Horizon

10-Year
Horizon

5-Year
Horizon

10-Year
Horizon

5-Year Horizon

10-Year Horizon

Cohort

Zip
Code

Parent
Age

2
None (Benchmark)
Sch. Spending per Pupil
Financial Literacy
Economic Literacy
Math Curr. Improvement
None (Benchmark)
Sch. Spending per Pupil
Financial Literacy
Economic Literacy
Math Curr. Improvement
None (Benchmark)
Sch. Spending per Pupil
Financial Literacy
Economic Literacy
Math Curr. Improvement
None (Benchmark)

3
0.17***
0.14***
0.17***
0.17***
0.17***
-0.025***
-0.071***
-0.026***
-0.024***
-0.023***
-71.4****
-55.5***
-71.0***
-72.5***
-70.2***
-0.070***

4
0.11***
0.072***
0.11***
0.11***
0.10***
-0.12***
-0.18***
-0.12***
-0.12***
-0.12***
-67.4***
-53.3***
-67.2***
-68.6***
-66.1***
-0.11***

5
x
x
-0.0041
0.017***
0.0031
x
x
-0.0018
0.0013
-0.0085
x
x
2.43*
-1.72
-1.36
x

6
x
x
0.0052
0.051***
-0.0088*
x
x
-0.012***
-0.00016
-0.001
x
x
-3.56***
-4.54*
0.71
x

7
x
0.0000035**
0.0053
-0.0051
0.0074
x
0.0000059***
0.016***
-0.0022
-0.0073
x
-0.0020***
-4.92**
3.17
-4.96**
x

8
x
0.0000043***
-0.00094
-0.016***
0.011*
x
0.0000075***
0.029***
0.0033
-0.013
x
-0.0018***
-2.1
3.53
-5.21**
x

9
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes

10
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes

11
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes

Sch. Spending per Pupil
Financial Literacy
Economic Literacy
Math Curr. Improvement

-0.12***
-0.070***
-0.066***
-0.068***

-0.16***
-0.11***
-0.10***
-0.11***

x
-0.012**
0.041***
0.0056

x
-0.0015
0.024***
-0.020***

0.0000069***
0.0081*
-0.011
-0.0073

0.0000064***
0.0049
-0.016
0.00026

Yes
Yes
Yes
Yes

Note: Results using data from the New York Federal Reserve Bank Consumer Credit Panel/Equifax dataset and educational policy variables from Brown et al (2014).

Table 11: Educational Policies and Strength of Intergenerational Linkages between Child Credit Outcomes and Parental Credit Risk Score

Child Outcome

Delinquency

Homeownership

Date T Equifax
Risk Score

Has Credit Card

Covariate Specification

Coef. On Parent Equifax Risk
Score

Coef. On Educational
Policy

Coef. On (Parent Equifax Risk Score) *
Educational Policy

Other Controls

5-Year
Horizon

10-Year
Horizon

5-Year
Horizon

10-Year
Horizon

5-Year Horizon

10-Year Horizon

Cohort

Zip
Code

Parent
Age

3
-0.00086***
-0.00064***
-0.00086***
-0.00089***
-0.00086***
0.00012***
0.00038***
0.00013***
0.00012***
0.00011***
0.38***
0.31***
0.37***
0.38***
0.37***
0.00050***

4
-0.00046***
-0.00030***
-0.00046***
-0.00049***
-0.00045***
0.00062***
0.00095***
0.00062***
0.00063***
0.00059***
0.34***
0.30***
0.34***
0.35***
0.34***
0.00073***

5
x
x
0.017
-0.028
0.019
x
x
0.050***
-0.011
-0.036
x
x
-17.2***
14.0**
-20.6***
x

6
x
x
0.027
0.0037
0.027*
x
x
0.048
0.026
-0.079**
x
x
-12.3**
9.88
-20.5***
x

7
x
-0.000000029***
-0.000028
0.000063**
-0.000035
x
-0.000000033***
-0.000067**
0.000013
0.000037
x
0.0000084***
0.026***
-0.021**
0.026***
x

8
x
-0.000000021**
-0.000026
0.000058***
-0.000042*
x
-0.000000042***
-0.000071
-0.00004
0.00010*
x
0.0000061*
0.011*
-0.020*
0.026***
x

9
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes

10
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes

11
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes

Sch. Spending per Pupil
Financial Literacy
Economic Literacy
Math Curr. Improvement

0.00094***
0.00051***
0.00048***
0.00049***

0.0011***
0.00073***
0.00071***
0.00072***

x
0.062**
0.00024
-0.033

x
0.032
-0.038
-0.050*

-0.000000057***
-0.00010***
0.000047
0.000055

-0.000000043***
-0.000043
0.000051
0.000034

Yes
Yes
Yes
Yes

Note: Results using data from the New York Federal Reserve Bank Consumer Credit Panel/Equifax dataset and educational policy variables from Brown et al (2014).

Table A1: Summary Statistics for 5-year subsample of the original 10-year sample
Mean
2

Std. Dev.
3

Min
4

25th %
5

Median
6

75th %
7

Max
8

Obs.
9

Child Forms Household
Child Forms Household First Age

0.55
21.5

0.50
1.4

0
20

1
21

1
23

1
24

417,601
228,739

Child Has Credit Card
Child Has Credit Card First Age

0.84
19.7

0.37
1.5

0
15

1
19

1
20

1
24

417,601
350,481

Child Homeowner
Child Homeowner First Age

0.11
22.5

0.31
1.6

0
15

0
22

0
23

0
24

1
24

417,601
46,247

Child Bankrupt

0.019

0.136

417,601

Child Foreclosure
Child Other Serious Default

0.066
0.33

0.249
0.47

0
0

0
1

1
1

417,601
399,357

Child Delinquency
Child Equifax Risk Score Average
Child Equifax Risk Score End
Child Credit Constrained

0.25
630.6
633.5
0.632

0.44
77.7
99.0
0.339

0
295
288
0

0
566.8
561
0.337

0
645.7
644
0.739

1
695
720
0.945

1
827
834
1

399,357
408,162
364,045
354,460

Note: Authors’ calculations using the New York Federal Reserve Bank Consumer Credit Panel/Equifax dataset.

Table A2: Benchmark Regressions of the Intergenerational Linkages between Child Credit Outcomes and Parental Credit Constraints, with an Indicator for Cosigned
Credit Cards
Coef. On Parent
Constrained

Coef. On Parent Age

Child Outcome

Coef. On Indicator of
Child Cosign Credit
Card

Coef. On (Indicator of
Child Cosign Credit
Card) * (Parent
Constrained)

Fixed effects

Adjusted R2

5-Year
Horizon

10-Year
Horizon

5-Year
Horizon

10-Year
Horizon

5-Year
Horizon

10-Year
Horizon

5-Year
Horizon

10-Year
Horizon

Cohort

Zip
Code

5Year

10Year

0.014***

0.031***

-0.0000021

-0.00022**

-0.000078

-0.010***

0.018***

0.020***

Yes

0.54%

0.65%

0.012***
0.20***
0.16***
-0.021***

0.025***
0.12***
0.10***
-0.13***

0.000061*
-0.00056***
-0.00038**
-0.0013***

-0.000020
-0.00043**
-0.00025*
-0.0028***

-0.00056
-0.029***
-0.027***
0.098***

-0.012***
-0.025***
-0.021***
0.24***

0.018***
0.032***
0.033***
0.034***

0.021***
0.019**
0.023***
0.053***

Yes
Yes
Yes
Yes

Yes
No
Yes
No

1.7%
2.8%
4.6%
2%

2.5%
1.6%
2.9%
5.7%

-0.021***

-0.11***

-0.00077***

-0.0019***

0.092***

0.22***

0.032***

0.052***

Yes

4.7%

8.2%

0.32***

0.31***

-0.0000070

-0.00043

-0.069***

-0.095***

-0.023***

-0.086***

Yes

6.5%

6.1%

0.26***

0.24***

0.000025

-0.00023

-0.064***

-0.086***

-0.018**

-0.073***

Yes

12%

11%

-77.1***

-84.4***

0.057

0.079

21.1***

30.5***

-5.81***

12.4***

Yes

14%

12%

-62.8***

-68.2***

0.010

0.020

20.0***

28.9***

-6.89***

8.10***

Yes

23%

19%

-85.5***
-69.2***
-0.094***

-83.3***
-66.8***
-0.15***

0.15**
0.077**
-0.00037**

0.062
0.0013
-0.00027

21.1***
19.8***
0.10***

28.7***
27.0***
0.11***

-3.99**
-5.31***
0.062***

12.4***
8.56***
0.13***

Yes
Yes
Yes

No
Yes
No

10%
17%
2.7%

9.5%
16%
4.4%

-0.069***
-0.12***
-0.00055*** -0.00049*** 0.099***
0.11***
0.058***
Note: Results using data from the New York Federal Reserve Bank Consumer Credit Panel/Equifax dataset.

0.11***

Yes

6.4%

1
Bankruptcy
Delinquency
Homeowner
Other Serious
Default (Ex.
Foreclos. and
Bankruptcy)
Average
Equifax Risk
Score
Date T Equifax
Risk Score
Has Credit Card

Table A3: Benchmark Regressions of the Intergenerational Linkages between Child Credit Outcomes and Parental Serious Default, with an Indicator for Cosigned
Credit Cards
Coef. On (Indicator of
Coef. On Parent
Coef. On Indicator of
Child Cosign Credit
Coef. On Parent Age
Fixed effects
Adjusted R2
Serious Default
Child Cosign Credit Card Card) * (Parent Serious
Child Outcome
Default)
5-Year
Horizon

10-Year
Horizon

5-Year
Horizon

10-Year
Horizon

5-Year
Horizon

10-Year
Horizon

5-Year
Horizon

10-Year
Horizon

Cohort

Zip
Code

5Year

10Year

0.013***
0.011***
0.13***
0.11***

0.025***
0.022***
0.075***
0.053***

-0.000024
0.000048
-0.00097***
-0.00071***

-0.00021***
-0.0000097
-0.00071***
-0.00049***

0.0050***
0.0045***
-0.028***
-0.024***

-0.0050**
-0.0067***
-0.023***
-0.017***

0.016***
0.016***
0.039***
0.038***

0.022***
0.023***
0.025***
0.027***

Yes
Yes
Yes
Yes

No
Yes
No
Yes

0.58%
1.6%
2.1%
3.9%

0.65%
2.6%
1.1%
2.4%

-0.017***
-0.013***

-0.095***
-0.075***

-0.00085***
-0.00038***

-0.0016***
-0.00095***

0.11***
0.11***

0.26***
0.25***

0.021***
0.020***

0.024***
0.023***

Yes
Yes

No
Yes

2.1%
4.6%

5.9%
8.3%

0.29***

0.24***

-0.0010***

-0.0014***

-0.093***

-0.13***

0.00076

-0.051***

Yes

7.8%

6.1%

0.23***

0.18***

-0.00084***

-0.00099***

-0.082***

-0.12***

0.0026

-0.043***

Yes

14%

11%

-62.3***

-60.9***

0.31***

0.36***

24.0***

38.5***

-6.05***

6.61***

Yes

14%

11%

-48.3***

-45.6***

0.22***

0.25***

21.5***

34.6***

-6.62***

3.86**

Yes

24%

20%

-65.2***
-49.6***
-0.092***

-60.8***
-45.1***
-0.14***

0.41***
0.30***
0.00036***

0.34***
0.24***
0.00049***

24.6***
21.7***
0.14***

36.5***
32.8***
0.17***

-4.64**
-5.42***
0.065***

6.63***
3.75*
0.11***

Yes
Yes
Yes

No
Yes
No

9.4%
17%
3.4%

8.7%
16%
5.2%

-0.065*** -0.10***
0.000095
0.00015
0.13***
0.16***
0.061***
Note: Results using data from the New York Federal Reserve Bank Consumer Credit Panel/Equifax dataset.

0.10***

Yes

8.3%

9.7%

1
Bankruptcy
Delinquency
Homeowner
Other Serious
Default (Ex.
Foreclos. and
Bankruptcy)
Average
Equifax Risk
Score
Date T Equifax
Risk Score
Has Credit Card

Table A4: Benchmark Regressions of the Intergenerational Linkages between Child Credit Outcomes and Parental Credit Risk Score, with an Indicator for Cosigned
Credit Cards

Child
Outcome

1
Bankruptcy

Delinquency

Homeowner
Other Serious
Default (Ex.
Foreclosure
and
Bankruptcy)
Average
Equifax Risk
Score
Date T
Equifax Risk
Score
Has Credit
Card

Coef. On Parent Equifax
Risk Score

Coef. On Parent Age

Coef. On Indicator of
Child Cosign Credit
Card

Coef. On (Indicator of Child
Cosign Credit Card) *
(Parent Equifax Risk Score)

Fixed effects

Adjusted R2

5-Year
Horizon

10-Year
Horizon

5-Year
Horizon

10-Year
Horizon

5-Year
Horizon

10-Year
Horizon

5-Year
Horizon

10-Year
Horizon

Cohort

Zip
Code

5Year

10Year

-0.000062***

-0.00012***

0.000056

-0.000085

0.077***

0.087***

-0.000097***

-0.00012***

Yes

0.7%

0.77%

-0.000055***

-0.00011***

0.00012***

0.000089

0.074***

0.084***

-0.000095***

-0.00012***

Yes

1.7%

2.6%

-0.00094***

-0.00054***

0.00047***

0.00014

0.18***

0.12***

-0.00026***

-0.00019***

Yes

5.4%

2.3%

-0.00083***

-0.00043***

0.00052***

0.00017

0.17***

0.13***

-0.00025***

-0.00020***

Yes

6.3%

3.2%

0.000098***

0.00061***

-0.0011***

-0.0027***

0.24***

0.40***

-0.00019***

-0.00020***

Yes

2.1%

6.9%

0.00010***

0.00056***

-0.00061***

-0.0019***

0.23***

0.38***

-0.00017***

-0.00020***

Yes

4.6%

-0.0018***

-0.0016***

0.0017***

0.00097***

-0.11***

-0.36***

0.000065

0.00035***

Yes

16%

12%

-0.0016***

-0.0013***

0.0013***

0.00082***

-0.094***

-0.33***

0.000048

0.00031***

Yes

19%

15%

0.41***

0.42***

-0.32***

-0.28***

-5.82

60.5***

0.031***

-0.041***

Yes

31%

22%

0.35***

0.34***

-0.29***

-0.26***

-8.42*

50.6***

0.034***

-0.029***

Yes

35%

26%

0.44***

0.41***

-0.26***

-0.28***

-2.05

61.0***

0.026***

-0.043***

Yes

22%

18%

0.37***

0.34***

-0.24***

-0.25***

-6.00

50.6***

0.030***

-0.031***

Yes

25%

22%

0.00067***

0.00096***

-0.00067***

-0.00088***

0.47***

0.70***

-0.00049***

-0.00077***

Yes

5.3%

8.5%

0.00051***

0.00076***

-0.00067***

-0.00090***

0.45***

0.67***

-0.00045***

-0.00072***

Yes

8.9%

11%

Note: Results using data from the New York Federal Reserve Bank Consumer Credit Panel/Equifax dataset.

Table A5. Educational Policies (Urban and Schmeiser, 2015) and Strength of Intergenerational Linkages between Child Credit Outcomes and Parental
Credit Constraints
Coef. On (Parent
Coef. On Parent
Constrained) *
Fixed effects
Adjusted R2
Constrained
Educational Policy
Child Outcome
Covariate Specification
5-Year
10-Year
5-Year
10-Year
Zip
Parent
5-Year
10-Year
Cohort
Horizon
Horizon
Horizon
Horizon
Code
Age
Horizon Horizon
1
Delinquency

Homeownership

Date T Riskscore

None (Benchmark)
Personal Finance
Economic Education
None (Benchmark)
Personal Finance
Economic Education
None (Benchmark)
Personal Finance

0.17***
0.17***
0.17***
-0.025***
-0.027***
-0.027***
-71.4****
-70.5***

0.11***
0.10***
0.11***
-0.12***
-0.12***
-0.13***
-67.4***
-66.8***

x
0.0083**
-0.0069
x
0.013**
0.0071
x
-5.61***

x
0.0076*
-0.012**
x
0.016
0.018*
x
-3.72**

Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes

4.6%
4.6%
4.6%
3.1%
3.1%
3.1%
16%
16%

2.9%
2.9%
2.9%
4.5%
4.5%
4.5%
15%
15%

Economic Education
None (Benchmark)

-72.3***
-0.070***

-68.9***
-0.11***

2.36
x

4.05
x

Yes
Yes

16%
4.7%

15%
5.6%

Has Credit Card

Personal Finance
-0.071*** -0.11***
0.0090
0.013*
Yes
Yes
Yes
4.7%
5.6%
Economic Education
-0.072*** -0.11***
0.0066
0.0060
Yes
Yes
Yes
4.7%
5.6%
Note: Results using data from the New York Federal Reserve Bank Consumer Credit Panel/Equifax dataset and educational policy data from Urban and
Schmeiser (2015).

Table A6: Educational Policies (Urban and Schmeiser, 2015) and Strength of Intergenerational Linkages between Child Credit Outcomes and Parental Credit Risk
Score

Child Outcome

1
Delinquency

Homeownership

Date T Equifax
Risk Score

Covariate
Specification

Coef. On Parent Equifax Risk
Score

Coef. On (Parent Equifax
Risk Score) * Educational
Policy

Adjusted R2

Controls

5-Year
Horizon

10-Year
Horizon

5-Year
Horizon

10-Year
Horizon

Cohort

Zip
Code

Parent
Age

5-Year
Horizon

10-Year
Horizon

None (Benchmark)

-0.00086***

-0.00046***

Yes

6.3%

3.1%

Personal Finance
Economic Education
None (Benchmark)
Personal Finance
Economic Education

-0.00086***
-0.00086***
0.00012***
0.00013***
0.00012***

-0.00046***
-0.00046***
0.00062***
0.00062***
0.00063***

-0.000034***
-0.0000074
x
-0.000040**
-0.000011

-0.0000023
-0.0000039
x
-0.000042
-0.000032*

Yes
Yes
Yes
Yes
Yes

6.3%
6.3%
3%
3%
3%

3.1%
3.1%
5.4%
5.4%
5.4%

0.38***
0.38***

0.34***
0.34***

x
0.0062

x
0.0032

Yes
Yes

25%
25%

21%
21%

0.38***
0.00050***

0.34***
0.00073***

-0.0013
x

-0.0041
x

Yes
Yes

25%
7.1%

21%
8.8%

None (Benchmark)
Personal Finance
Economic Education
None (Benchmark)

Has Credit Card

Personal Finance
0.00051***
0.00073***
-0.000050
-0.000027
Yes
Yes
Yes
7.1%
8.8%
Economic Education
0.00051***
0.00074***
-0.000033*
-0.000026
Yes
Yes
Yes
7.1%
8.8%
Note: Results using data from the New York Federal Reserve Bank Consumer Credit Panel/Equifax dataset and educational policy data from Urban and
Schmeiser (2015).

Full text of Working Papers (Federal Reserve Bank of Richmond) : Intergenerational Linkages in Household Credit, Working Paper 15-14

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