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Can’t Pay or Won’t Pay? Unemployment,
Negative Equity, and Strategic Default
ONLINE APPENDIX
Kristopher Gerardi∗
FRB Atlanta
Kyle Herkenhoff†
University of Minnesota
Paul Willen§
FRB Boston
May 2017
∗
kristopher.gerardi@atl.frb.org
kfh@umn.edu
‡
ohanian@econ.ucla.edu
§
paul.willen@bos.frb.org
†
Lee Ohanian‡
UCLA
This appendix supplements the empirical analysis in “Can’t Pay or Won’t Pay? Unemployment, Negative Equity, and Strategic Default” by Gerardi, Herkenhoff, Ohanian, and Willen.
Below is a list of the sections contained in this appendix.
Contents
A.1
Comparison of Existing Measures of Strategic Default
2
A.2
PSID Consumption Data and TAXSIM
3
A.3
List of Control Variables
5
A.4
IV Details
6
A.5
Strategic Default with Assets
8
A.6
QRM Definitions of Strategic Default
9
A.7
Baseline Regressions with DTI
10
A.8
Income Changes and Non-Linearities
11
A.9
Robustness
17
A.10
Comparison of Default Rates in PSID and McDash/Equifax
21
A.11
Strategic Default Estimates using PSID-McDash/Equifax Weights
24
A.12
Unweighted Strategic Default Table
26
1
A.1
Comparison of Existing Measures of Strategic Default
Table A.1 below compares our estimates of strategic default to those of the existing literature’s. We find a somewhat larger share of strategic defaults, 38%, relative to other studies
whose estimates range from 19% to 35% depending on the year and method of measurement.
Table A.1: Existing Measures of Strategic Default
Study
Experian/Oliver Wyman (2009)
Bradley, Cutts, Liu (2015)
Data Source:
Experian (Number of obs. undisclosed)
Equifax Merged w/ Payroll Data (EFX Chicago Booth Kellogg School Finan- PSID (N=7k)
TWN) (N= 130k)
cial Trust Index, Q4-2008 to Q3-2010,
(N= 1k)
Coverage:
2004Q4–2009Q2
June 2008–June 2011
2008Q4–2010Q3
2009–2013
Definition of Strategic “[b]orrowers who rolled straight from
Default:
60 dpd to 180+ dpd, while staying less
than 60 dpd on their auto loans and less
than 90 dpd on their bank cards, retail
cards, and other personal loans, for 6
months after they first went 60 dpd on
their mortgage.”
Individuals with negative equity who
transition from Current to 180+ Days
Late with No Income Loss of 20% or
More
Of the people you know who have defaulted on their mortgage, how many
do you think walked away even if they
could afford to pay the monthly mortgage?
Budget constraint definition: ”What fraction of defaulters ‘can
pay’, i.e. what fraction satisfy c+m<y”
Fraction strategic:
7% to 14.6%
25% to 35%
38%
19% in 2009, 18% in 2008
2
Guiso, Sapienza, Zingales (2013)
Present Paper
A.2
PSID Consumption Data and TAXSIM
In Table A.2 we compare the entire PSID weighted sample of household heads (including
renters and mortgagors) to the Consumer Expenditure Survey (CEX) as tabulated by the
BEA.1 The PSID data are treated as follows: each category is annualized, then aggregated
to the line items below, and then the top 1% of positive values is winsorized, and only observations with annual food expenditure of at least $500 are counted. The numbers below
are reported at the family unit level in nominal terms. The main measures of food consumption align almost perfectly in both levels and trends. The expenditure on housing is quite
different due to the fact that the PSID includes a category called ‘additions’ and this is a
significant expenditure by many households. Most other line-items line up and follow similar
trends, however healthcare was recoded in the 2013 PSID and falls significantly in 2013.
Table A.2: PSID vs. CEX Expenditures Data (Source: PSID 2009-2013 Weighted)
Item
2009 2011 2013 CEX
CEX CEX CEX Notes
2009 2011 2013 PSID Notes
PSID PSID PSID
Avg. Annual Expenditures
Avg. Annual Expenditures
(Excluding
Pension/Cash
Contributions)
Food + Alcoholic Beveridges
49,067 49,705 51,100
41,873 42,560 43,738
41,768 41,319 41,176
6,807
6,647
Housing
Apparel and services
Transportation
16,895 16,803 17,148 Does not
include
additions
to home
1,725 1,740 1,604
7,658 8,293 9,004
Health care
3,126
3,313
3,631
Entertainment
Education
2,693
1,068
2,572
1,051
2,482
1,138
1,902
1,875
1,685
Other Non-Aligned
sumption
Con-
6,914
7,047
Reading,
Tobacco,
Misc.
1
6,909
7,190
Food at home, away, delivered, and food stamps
19,593 18,619 18,259 Mortgage Payments, Rent,
Additions,
Furnishings,
Property Taxes and Insurance,Utilities
1,307 1,153 1,144 Clothing Consumption
7,032 7,503 7,555 Car repair, Gas, Parking, Trains, Cabs, Other
Transp.
Expenses, Car
Insurance, Lease Outlays,
Down payments, Loan payments, Outright Car Purchases
2,999 2,987 2,679 Health Insurance, Doctor,
Hospital, Prescriptions
2,411 2,307 2,402 Trips and Recreation
1,372 1,442 1,440 School Expenses, and Other
School Exp.
407
399
507
Child Care, Alimony
Our measures come from the ”Multi-year CEX Tables” entitled ‘Average annual expenditures and characteristics of all consumer units, Consumer Expenditure Survey, 2006-2012’ as well as the 2013-2014 version
of the table. See http://www.bls.gov/cex/tables.htm for more details on the CEX tabulations.
3
For TAXSIM computations, we base our code on the NBER TAXSIM code provided by Erick
Zwick.2 Table A.3 summarizes PSID income per family compared to the comparable measure
from the Census. Our Census measure is mean family income, Table H-6.3 Table A.3 shows
that our measures of family income broadly align in levels with the Census measures, and
our average tax burden per family is about 22% over this time period.
Table A.3: PSID vs. Census Family Income and After-Tax Family Income (Source: PSID
2009-2013 Weighted)
Average Family Income
After TAXSIM Taxes
N
2009 Census
2011 Census
2013 Census
67976
117,538
69677
121,084
72641
122,952
2
2009 PSID 2011 PSID 2013 PSID
72660
55700
9005
69000
54220
9235
73580
58020
9398
The spouses pension variables were added later in the sample. For consistency we only focus on the
head’s pension variables.
3
‘Table H-6.
Regions–All Races by Median and Mean Income:
1975 to 2014’
https://www.census.gov/hhes/www/income/data/historical/household/
4
A.3
List of Control Variables
Table A.4 below lists, and provides sample summary statistics for the baseline set of controls
that are included in all of the main tables in the text.
Table A.4: Controls
Mean
Std.
Min
Max
0.12
0.17
0.17
0.20
0.15
0.04
0.04
0.10
0.32
0.37
0.37
0.40
0.36
0.19
0.20
0.30
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
Black
0.21
0.41
0
1
American Indian
Asian
Pacific Islander
Other
Missing Race
Age
Male Dummy
Married Dummy
Less Than HS
HS
Some College
College and More
Number of Children
2009 Dummy
2011 Dummy
2013 Dummy
State House Price Growth
State Unemployment Rate
Change
# Observations
0.00
0.01
0.00
0.03
0.01
44.00
0.85
0.74
0.25
0.27
0.40
0.01
1.01
0.36
0.33
0.31
-0.02
0.08
0.06
0.12
0.02
0.16
0.08
10.50
0.36
0.44
0.43
0.45
0.49
0.10
1.17
0.48
0.47
0.46
0.08
0.16
0
0
0
0
0
24
0
0
0
0
0
0
0
0
0
0
-0.30523
-0.21622
1
1
1
1
1
65
1
1
1
1
1
1
9
1
1
1
0.22237
0.636364
NAICS
NAICS
NAICS
NAICS
NAICS
NAICS
NAICS
NAICS
Dummy
Dummy
Dummy
Dummy
Dummy
Dummy
Dummy
Dummy
2
3
4
5
6
7
8
9
5
Mean
Std.
Min
Max
Second Mortgage Dummy
Refi Dummy
Refi Missing Dummy
ARM Dummy
ARM Missing Dummy
0.16
0.47
0.00
0.08
0.00
0.37
0.50
0.04
0.28
0.06
0
0
0
0
0
1
1
1
1
1
Mortgage Interest Rate
Mortgage Interest Rate
Missing
15+ Year Remaining on
Mortgage Term Missing
Origination Year 1992
Origination Year 1993
Origination Year 1994
Origination Year 1995
Origination Year 1996
Origination Year 1997
Origination Year 1998
Origination Year 1999
Origination Year 2000
Origination Year 2001
Origination Year 2002
Origination Year 2003
Origination Year 2004
Origination Year 2005
Origination Year 2006
Recourse Dummy
Judicial Dummy
Sand States (CA, FL, AZ,
NV)
7,404
4.81
0.05
1.98
0.21
0
0
23
1
0.02
0.13
0
1
0.00
0.00
0.01
0.01
0.01
0.01
0.01
0.01
0.03
0.06
0.08
0.09
0.11
0.06
0.03
0.24
0.40
0.14
0.06
0.06
0.08
0.08
0.09
0.10
0.11
0.12
0.16
0.24
0.27
0.29
0.31
0.24
0.17
0.42
0.49
0.34
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
A.4
IV Details
In this section we provide details on how the disability and employment instruments are
constructed.
A.4.1
Disability Shocks
We follow the methods of Low and Pistaferri (2015) in identifying a household in which
the head or the spouse has suffered a disability. Specifically, we use information from the
following three PSID survey questions posed to both household heads and spouses: (i) Do
you have any physical or nervous condition that limits the type of work or the amount of work
you can do? If the respondent answers “Yes” the interviewer asks: (ii) Does this condition
keep you from doing some types of work? where the possible answers are: “Yes”, “No”, or
“Can do nothing”. Respondents that answer either “Yes” or “No” are then asked: (iii) For
work you can do, how much does it limit the amount of work you can do? where the possible
answers are given by: “A lot”, “Somewhat”, “Just a little”, or “Not at all”. If the answer to
question (i) is “No” or the answer to question (iii) is “Not at all” then we assume that the
respondent does not have a disability that limits her ability to work. We assume that the
respondent has a severe disability if her response to question (i) is “Yes” and her response
to question (ii) is “Can do nothing” or her response to question (iii) is “A lot”. We assume
that the remainder of respondents have a moderate disability (i.e. they answer “Yes” to
question (i) and either “Somewhat” or “Just a little” to question (iii)).
A.4.2
Bartik Shocks
The Bartik shock is meant to identify exogenous changes in employment status that influence
residual income. The instrument is based on aggregate sectoral employment flows at the
national level and industry shares at the state-level. Specifically, we use data from the
Bureau of Labor Statistics (BLS) to construct the following Bartik state-level employment
shock:
Bartikit =
X
shareempl
i,j,t−k ∗ ∆emplj,t−k,t
(1)
j
where i indexes the state, j indexes the 1-digit NAICs industry code, t indexes the current
survey year (2009, 2011, or 2013), and k indexes the number of years over which the growth
rates are computed. The Bartik shock is constructed by interacting national-level industry
growth in employment, ∆emplj,t−k,t , with the state-level initial composition of employment in
6
industry j, shareempl
i,j,t−k . Calculation of the national-level industry growth rates is performed
using data from all states excluding i. Bartik shocks are used frequently in the labor literature
to instrument for local aggregate demand shocks. The idea behind the Bartik shock is that
employment in all states in all industries is affected by national industry-level employment
movements, but movements in a given industry have a higher impact in a state where the
industry employs a greater share of the population. For example, the Bartik shock calculation
for Florida would place a lower weight on national employment changes in the financial
activities industries than the Bartik shock calculation for New York. In our context, the
Bartik variable is a natural choice for an instrument as state-level, labor demand shocks are
unlikely to be correlated with individual default decisions except through their impact on
the likelihood of job loss and, in turn, income loss. Our measures of employment by industry
and state are taken from the BLS. In particular, we use State and Area Employment, Hours,
and Earnings from the CES.
We construct the Bartik variable over a two-year horizon to maintain consistency with the
biennial frequency of the PSID. (i.e. k = 2). We also estimated specifications using Bartik
shocks constructed over a four-year horizon and found similar results. Finally, we also tried
interacting the Bartik variable with indicator variables corresponding to the industry in
which the household head was employed at the beginning of the horizon. Interacting the
Bartik variable with industry indicators allows the sensitivity of income loss to the exogenous,
state-level, labor demand shocks to differ depending on the particular industry in which the
individual is employed.4 . The results from this richer specification proved to be quite similar.
4
We included a full set of industry fixed effects among the control variables (not in the instrument set)
7
A.5
Strategic Default with Assets
Table A.5 replicates Table 4 in the main text including information on assets in the PSID.
Household assets, a are computed as the net financial assets of a household: the sum of
checking, saving, money market accounts, government bonds, stocks, and other bonds, less
an imputed 12.73% debt burden on all other unsecured debt obligations. 12.73% is the
average credit card interest rate from 2009-2013 according to the Board of Governors. So in
some cases, ability to pay of households may fall if they have negative net financial assets.
Table A.5: Strategic Default with Assets
Can Pay
c<y−m+a
#
share
(1) (2)=(1)/(7)
A. All
Default
Population
95
6184
0.485
0.835
Default Rate
0.015
B. LTV>90
Default 61
0.525
Population 1219
0.724
Default Rate
0.050
C. LTV<90
Default 35
0.429
Population 4965
0.868
Default Rate
c > y − m + a > c(V A)
#
share
(3)
(4)=(3)/(7)
Can’t Pay
y − m + a < c(V A)
#
share
(5)
(6)=(5)/(7)
Total
#
(7)
40
570
61
655
196
7404
0.205
0.077
0.071
24
197
0.210
0.117
0.093
31
270
0.123
16
373
0.199
0.065
0.007
0.043
8
0.309
0.088
0.265
0.161
0.113
30
384
0.372
0.067
0.078
0.027
115
1684
0.069
81
5720
0.014
A.6
QRM Definitions of Strategic Default
Table A.6 computes default rates among those who meet the QRM definition of affordability,
and those who do not. We use the QRM guidelines to adjust income for taxes, insurance,
alimony, and other debt obligations. If the ratio of combined mortgage payments to adjusted
income is below 43%, the mortgage is deemed affordable. Applying this definition to our
sample, Table A.6 shows that there is a 5x difference in default propensities between those
who meet the QRM definition of affordability (1.6%), and those who don’t (9.2%). Among
those with high LTVs (>90), the default rate among those who do not meet QRM affordability criteria is 17.9% relative to 4.0% for those who do. For those with positive equity,
the level of default drops significantly for both groups.
Table A.6: QRM Based Definitions of Strategic Default
A. All
Default
Population
Can Pay
Debt to Income<43%
#
share
(1)
(2)=(1)/(5)
Can’t Pay
Debt to Income>43%
#
share
(3)
(4)=(3)/(5)
Total
#
(5)
100
6359
97
1045
196
7404
Default Rate
B. LTV>90
Default
Population
0.016
54
1338
Default Rate
C. LTV<90
Default
Population
Default Rate
0.508
0.859
0.092
0.465
0.794
62
346
0.040
46
5021
0.492
0.141
0.535
0.206
0.179
0.571
0.878
35
699
0.009
0.429
0.122
0.050
9
0.027
115
1684
0.069
81
5720
0.014
A.7
Baseline Regressions with DTI
Table A.7 reproduces Table 5 in the main text using the logarithm of the debt-to-income
ratio, or DTI, (i.e. log( m
)) as the main independent regressor instead of the logarithm of
y
residual income. Columns (1)–(3) report OLS coefficients, and columns (4)–(6) report logit
coefficients with average marginal effects in square parentheses. As in Table 5, the interaction
term is computed at the interquartile range for the logit specification. The coefficients can
be interpreted as semi-elasticities. For example, the point estimate in column (1) implies
that a 10% increase in DTI is associated with a 0.39 percentage point higher default rate.
Table A.7: Debt to Income Ratio Results: Linear Probability Model Cols (1) to (3), Logit
Coefficients Cols (4) to (6) (with AME in square brackets, interaction at interquartile range
of residual income), Dependent Variable is 60+ Days Late Indicator.
(1)
(2)
(3)
(4)
(5)
(6)
Loan to Value Ratio
0.058***
(6.09)
0.071***
(6.06)
0.259***
(7.17)
Log of DTI
0.039***
(8.47)
0.030***
(6.64)
-0.034***
(-3.48)
1.568***
(8.51)
[0.047***]
1.406***
(10.93)
[0.043***]
1.548***
(7.56)
[0.045***]
1.110***
(7.61)
[0.032***]
0.066***
(5.30)
-0.019
(-0.69)
-0.134***
(-3.88)
-2.318***
(-8.45)
-4.024***
(-3.28)
2.341***
(4.57)
[0.043***]
0.630**
(2.02)
[0.033***]
0.563*
(1.71)
[0.029***]
-4.630***
(-3.61)
7,402
0.036
N
N
N
7,402
0.077
Y
Y
Y
7,402
0.093
Y
Y
Y
7,402
N
N
N
7,402
Y
Y
Y
7,402
Y
Y
Y
Log of DTI * LTV
Constant
Observations
R-squared
Demographic Controls?
Mortgage Controls?
State Controls?
0.103***
(6.39)
10
A.8
Income Changes and Non-Linearities
This section reproduces the main analysis in the text, but rather than using residual income,
we focus on income shocks. In particular, we consider 2-year changes in gross family income
between the PSID survey dates. Columns (1)–(4) of Table A.8 below show the non-linear
impact of varying degrees of income loss on default. In columns (1) and (2) we include a series
of indicator variables corresponding to various intervals in the income growth distribution:
(−∞,−30%], (−30%, −15%], (−15%, −5%], and (−5%, 0%], with the omitted interval
corresponding to any positive growth. The results reported in columns (1) and (2) show
that income declines of more than 5% are significantly associated with increased mortgage
default. Households that experienced negative income growth between 15% and 30% are
2–3 percentage points more likely to default compared to households that experienced flat
or positive income growth, while households that suffered at least a 30% decline in income
are more than 4 percentage points more likely to default. Smaller declines in income (less
than 5%) are not statistically significant predictors of mortgage default.
In column (3), and for the remainder of this analysis, we simplify the specification and
include a single indicator variable for households that experienced a negative income shock
of at least -15%.5 Borrowers that saw their incomes decline by more than 15% were about
3 percentage points more likely to default compared to those that did not.
Table A.9 illustrates the corresponding logit specifications which are comparable in sign,
significance, and magnitude to our OLS estimates. Table A.10 displays the results of an IV
analysis. Column (1) in the table corresponds to the simple OLS estimates, which are replicated from Table A.8 (column (3)) for ease of comparison. Column (2) in the table displays
the estimation results when we use the unemployment shock and recent divorce shock to
instrument for income loss and cumulative house price appreciation to instrument for LTV
ratios (all columns in the table use the same instrument for LTV ratios). There is a sizeable
increase in the magnitude of the coefficient associated with income loss in the IV specification
compared to the OLS regression. Households that experience a significant income loss that
is caused by unemployment or divorce are approximately 26 percentage points more likely
to default on their mortgages. The huge increase in the estimated impact of income loss
on mortgage default in the IV specification is both plausible and consistent with economic
theory. The permanent income hypothesis predicts that permanent (or persistent) shocks to
income have a significantly larger effect on consumption decisions compared to more transitory income shocks. The IV specification isolates income losses due to unemployment and
5
We chose this threshold based on the estimates reported in columns (1) and (2), where it appears that
income growth becomes a significant predictor of mortgage default for declines between 5% and 15%. We
do report results for alternative income growth thresholds of -5% and -30% in our analysis below.
11
divorce shocks, which are both significant life events and thus, are likely to have persistent
effects. In other words, the IV specification is isolating more permanent income shocks,
which theory predicts should lead to a much larger impact on the propensity to default.
Column (3) shows the reduced form of Column (2) where the default indicator is directly
regressed on job loss and divorce indicators. In Column (4) of Table A.10 we modify the
instrument set by substituting for the unemployment variables with indicators of involuntary
unemployment spells only (for both the head and spouse). In addition, we include a set of
indicator variables corresponding to the number of prior unemployment spells as additional
controls. The income loss coefficient decreases slightly (from 0.26 to 0.20), but is still very
large in magnitude and statistically significant (at the 5 percent level). An income loss of
at least 15% (between surveys) caused by an involuntary unemployment spell or divorce is
estimated to increase the likelihood of default by 20 percentage points. Column (5) displays
the reduced form regression results, where the default indicator is regressed directly on the
involuntary unemployment shocks. The estimates are of comparable magnitudes with those
in column (3).
Column (6) in Table A.10 displays the results when we instrument for income loss using
the disability shock and the Bartik employment shocks. We construct the Bartik variable
over a two-year horizon (i.e. k = 2)6 to maintain consistency with the biennial frequency
of the PSID and our other results. We interact the Bartik variable with indicator variables
corresponding to the industry in which the household head was employed at the beginning
of the horizon.7 In column (6) of Table A.10, the coefficient estimate is 0.26 (statistically
significant at the 5% level), which is very similar in magnitude to the estimates we obtained
using unemployment spells and recent divorces as instruments (columns (2) and (4)). The
first stage results displayed in Table A.11, (column (6) in Panel B) show that the disability
indicator is a strong predictor of severe income loss, which is consistent with the findings in
Low and Pistaferri (2015). For space considerations we report the first stage estimates for
the Bartik variables in the Appendix instead of Table A.11.8 The reduced form specification
results reported in column (7) of Table A.10 show that the disability variable has a slightly
6
We also estimated specifications using Bartik shocks constructed over a four-year horizon and found
similar results.
7
Interacting the Bartik variable with industry indicators allows the sensitivity of income loss to the
exogenous, state-level, labor demand shocks to differ depending on the particular industry in which the
individual is employed. We include a full set of industry fixed effects among the control variables (not in the
instrument set).
8
Virtually all of the Bartik coefficients have the expected negative sign, so that positive state-level, labor
demand shocks (i.e. increases in employment) are associated with a lower likelihood of significant income loss,
however, they are not statistically significant, which suggests that they are not especially strong instruments
for income loss at the household-level. However, it is clear from the weak instrument test p-values reported in
Table A.10 that the combination of the disability and Bartik variables constitute a strong set of instruments.
12
smaller direct impact on mortgage default compared to the the unemployment and divorce
variables.
In column (8) of Table A.10 we substitute the severe disability shock into the instrument
set. Households that experience severe disability shocks are more likely to suffer more persistent income losses compared to households that suffer more moderate disability shocks,
and thus we would expect the effect of income loss on default to increase as a result of this
substitution. This is exactly what we find as the point estimate of the effect of income loss on
mortgage default increases from 0.26 to 0.32.9 In addition, the first stage results show that
households that experience a severe disability shock are about twice as likely to experience
an income loss of at least 15%, and the reduced form estimates (column (9)) show that they
are also much more likely to default on their mortgage debt.
9
The difference between the two point estimates is not statistically significant however.
13
Table A.8: Baseline Results: Linear Probability Model, Dependent Variable is 60+ Days
Late Indicator.
(1)
(2)
(3)
(4)
Loan to Value Ratio
0.082
(8.60)
0.082
(7.06)
∗∗∗
0.083
(7.10)
0.062∗∗∗
(5.38)
Percent Income Change ∈ (−∞, −30] (d)
0.054∗∗∗
(5.49)
0.041∗∗∗
(4.39)
Percent Income Change ∈ (−30, −15] (d)
0.026∗∗∗
(3.21)
0.020∗∗
(2.45)
Percent Income Change ∈ (−15, −5] (d)
0.022∗∗∗
(2.82)
0.019∗∗
(2.46)
0.007
(1.03)
0.004
(0.70)
0.029∗∗∗
(4.47)
-0.045∗∗
(-2.58)
∗∗∗
Percent Income Change ∈ (−5, 0] (d)
∗∗∗
Percent Income Change <-15% (d)
LTV * Percent Income Change <-15%
Constant
-0.035***
(-5.42)
-0.076***
(-3.02)
-0.074***
(-2.92)
0.105∗∗∗
(3.79)
-0.057**
(-2.28)
7,404
0.031
N
N
N
7,404
0.075
Y
Y
Y
7,404
0.074
Y
Y
Y
7,404
0.080
Y
Y
Y
Observations
R2
Demographic Controls
Mortgage Controls
State Controls
Notes: This table displays OLS estimation results of regressions of default on LTV ratios and income growth.
Income is defined as gross family income and growth in income is calculated between consecutive survey dates.
Default is defined as 60+ days late as of survey date (at least two missed payments). The sample includes
all household heads in the PSID who are mortgagors, aged 24–65, and labor force participants (including
those who are disabled) with combined LTV ratios less than 250 percent. Robust t-statistics are reported
in parentheses and dummy variables are signified by (d). Level of statistical significance: ∗∗∗ p < 0.01,
∗∗
p < 0.05, ∗ p < 0.10.
14
Table A.9: Baseline Results: Logit, Dependent Variable is 60+ Days Late Indicator. Average
Marginal Effects Reported.
Percent Income Change ∈ (−∞, −30] (d)
Percent Income Change ∈ (−30, −15] (d)
Percent Income Change ∈ (−15, −5] (d)
Percent Income Change ∈ (−5, 0] (d)
Loan to Value Ratio
(1)
(2)
0.063***
(5.15)
0.032***
(3.05)
0.028***
(2.71)
0.007
(0.79)
0.062***
(10.38)
0.042***
(4.38)
0.020**
(2.37)
0.025***
(2.61)
0.006
(0.66)
0.049***
(8.33)
7,404
N
N
N
7,404
Y
Y
Y
Percent Income Change <-15% (d)
(3)
(4)
0.050***
(8.36)
0.025***
(4.40)
0.050***
(8.34)
0.025***
(4.42)
0.051***
(3.48)
7,404
Y
Y
Y
7,404
Y
Y
Y
LTV * % Income Ch. <-15% (d)
Observations
Demographic Controls
Mortgage Controls
State Controls
Notes: This table displays average marginal effects from logit regressions of default on LTV ratios and income
growth. Income is defined as gross family income and growth in income is calculated between consecutive
survey dates. Default is defined as 60+ days late as of survey date (at least two missed payments). The
sample includes all household heads in the PSID who are mortgagors, aged 24–65, and labor force participants
(including those who are disabled) with combined LTV ratios less than 250 percent. Robust t-statistics
are reported in parentheses and dummy variables are signified by (d). Level of statistical significance:
∗∗∗
p < 0.01, ∗∗ p < 0.05, ∗ p < 0.10.
15
Table A.10: IV Results: Dependent Variable is 60+ DL Indicator, 1st Endogenous Variable is 2-Year Income Change, 2nd
Endogenous Variable is LTV. Col (1) is OLS, Cols (2) and (3) use unemployment and divorce as IVs for income. Cols (4) and
(5) use invol. unemployment and divorce. Cols (6) and (7) use disability and Bartik shocks, and Cols (8) and (9) use severe
disability and Bartik shocks. Cumulative HP growth is IV for LTV in all Columns.
Dependent Variable:
60+ Days Delinquent
(1)
LTV Ratio
0.083***
(7.10)
Percent Income Change <-15% (d) 0.029***
(4.47)
Unemployed Head Last Year (d)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
0.167***
(3.15)
0.264***
(4.26)
0.147***
(3.06)
0.175***
(3.46)
0.199**
(2.45)
0.146***
(3.04)
0.172***
(3.35)
0.233**
(2.27)
0.150***
(3.14)
0.178***
(3.35)
0.266**
(2.13)
0.149***
(3.13)
0.053***
(4.12)
0.031**
(2.36)
0.034
(1.40)
Unemployed Spouse Last Year (d)
Recent Divorce (d)
0.034
(1.43)
0.035**
(2.04)
0.054*
(1.88)
Involuntary Layoff (d)
16
Involuntary Layoff, Spouse (d)
Disability Shock (d)
0.018*
(1.80)
Severe Disability Shock (d)
0.051*
(1.75)
IV for LTV Ratio:
.
.
HPA Since
Purchase
IV for Income:
.
.
Job Loss,
Recent Divorce
7,404
0.074
Y
Y
Y
N
7,404
.
Y
Y
Y
N
7,404
0.069
Y
Y
Y
N
7,404
.
Y
Y
Y
Y
7,404
0.067
Y
Y
Y
Y
7,404
.
Y
Y
Y
N
7,404
0.061
Y
Y
Y
N
7,404
.
Y
Y
Y
N
7,404
0.062
Y
Y
Y
N
.
.
0.271
0
.
0
0.237
3.49e-10
.
0
0.916
0.00252
.
0
0.923
0.00317
.
0
Observations
R2
Demographic Controls
Mortgage Controls
State Controls
Control for Prior Unempl Spells
IV Diagnostics
Over ID Pval, Null Valid
Weak ID Pval, Null Weak
HPA Since
Purchase
HPA Since
Purchase
HPA Since
Purchase
Invol. Job Loss,
Recent Divorce
HPA Since
Purchase
HPA Since
Purchase
Disability,
Bartik Shock
HPA Since
Purchase
HPA Since
Purchase
Severe Disability,
Bartik Shock
Notes: This table displays a set of estimation from regressions of default on LTV ratios and income loss. Default is defined as 60+ days late as of
survey date (at least two missed payments). Income loss is defined as a drop in household income of at least 15% from the previous interview. The
sample includes all household heads in the PSID who are mortgagors, aged 24–65, and labor force participants (including those who are disabled)
with combined LTV ratios less than 250 percent in 2009, 2011, and 2013. Robust t-statistics are reported in parentheses and dummy variables are
signified by (d). Level of statistical significance: ∗∗∗ p < 0.01, ∗∗ p < 0.05, ∗ p < 0.10.
A.9
Robustness
Table A.12 below displays robustness results for our main specifications in Table 7 in the
text. Columns (1) and (2) include state fixed effects. These specifications yield consistent,
although somewhat stronger, parameter estimates when compared to columns (4) and (6),
respectively, of Table 7. Columns (3) and (4) of Table A.12 use Bartik shocks that are
constructed with 4 year and 1 year CES employment changes by state and industry, respectively. Our estimates are very close to Columns (6) and (8) in Table 7. Columns (5) and
(6) include a dummy for negative equity instead of a continuous variable, and for low LTVs,
the dummy on negative equity implies a stronger effect of house price changes on default.
An LTV of 1 in column (1) is associated with a 28% likelihood of default versus a 34%
likelihood of default in column (5). On the other hand, for higher LTVs, the relationship is
reversed: an LTV of 1.2 in column (1) is associated with a 33% likelihood of default versus a
34% likelihood of default in column (5). Columns (7) and (8) combine the head and spouse
disability shocks to obtain more power, and again, we see similar results to the main table
in the text. Additionally, in every case, the model passes over-identification tests at the 1%,
5%, and 10% statistical levels.
Table A.13 displays the first stages of the various regressions in Table A.12, where each
specification has two first stages corresponding to LTV and residual income. Panel A shows
that cumulative house price growth is a strong instrument for LTV, and Panel B shows
that the alternate instruments for income yield strong first stage results. In every case, the
alternate sets of instruments pass weak identification tests.
17
Table A.11: First Stage IV Results: Col (1) is OLS, Cols (2) and (3) use unemployment
and divorce as IVs for income. Cols (4) and (5) use invol. unemployment and divorce. Cols
(6) and (7) use disability and Bartik shocks, and Cols (8) and (9) use severe disability and
Bartik shocks. Cumulative HP growth is IV for LTV in all Columns.
Panel A: LTV Ratio
Table 5 Column:
(2)
Cumulative HPA (Since Purchase) -0.080***
(-14.44)
Unemployed Head Last Year (d)
0.019
(1.44)
Unemployed Spouse Last Year (d)
0.026
(1.54)
Recent Divorce (d)
0.053**
(2.26)
Involuntary Layoff (d)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
-0.080***
(-14.44)
-0.080***
(-14.55)
-0.080***
(-14.55)
-0.081***
(-14.53)
-0.081***
(-14.53)
-0.081***
(-14.52)
-0.081***
(-14.52)
0.053**
(2.29)
0.007
(0.41)
0.062
(1.41)
Involuntary Layoff, Spouse (d)
Disability Shock (d)
0.012
(0.89)
Severe Disability Shock (d)
0.091***
(2.94)
Panel B: Income Loss
Cumulative HPA (Since Purchase)
Unemployed Head Last Year (d)
Unemployed Spouse Last Year (d)
Recent Divorce (d)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
0.009
(1.09)
0.139***
(6.48)
0.122***
(4.98)
0.235***
(5.69)
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
0.008
(0.94)
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
0.007
(0.82)
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
0.007
(0.80)
0.149***
(3.37)
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
7,404
Y
Y
Y
7,404
Y
Y
Y
7,404
Y
Y
Y
Involuntary Layoff (d)
Involuntary Layoff, Spouse (d)
Disability Shock (d)
Severe Disability Shock (d)
Observations
Demographic Controls
Mortgage Controls
State Controls
7,404
Y
Y
Y
0.235***
(5.70)
0.125***
(4.15)
0.042
(0.95)
7,404
Y
Y
Y
7,404
Y
Y
Y
7,404
Y
Y
Y
0.065***
(3.33)
7,404
Y
Y
Y
Notes: This table displays the first stage estimation results for IV specifications reported in columns (2) - (9)
in Table A.10. The sample includes all household heads in the PSID who are mortgagors, aged 24–65, and
labor force participants (including those who are disabled) with combined LTV ratios less than 250 percent
in 2009, 2011, and 2013. Robust t-statistics are reported in parentheses and dummy variables are signified
by (d). Level of statistical significance: ∗∗∗ p < 0.01, ∗∗ p < 0.05, ∗ p < 0.10.
18
Table A.12: Robustness Results for Table 7.
Loan to Value Ratio
Log Residual Income
(1)
(2)
(3)
(4)
0.287***
(3.65)
-0.242**
(-2.30)
0.255***
(3.67)
-0.178*
(-1.94)
0.184***
(3.64)
-0.099*
(-1.91)
0.190***
(3.62)
-0.124*
(-1.95)
HPA Since
Purchase
Invol. Job Loss,
Head & Spouse
7,404
Y
Y
Y
Y
Y
0.305
0.000225
HPA Since
Purchase
Disability,
Bartik Shock
7,404
Y
Y
Y
Y
N
0.329
0.00415
HPA Since
Purchase
Disability,
Bartik Shock (4yr)
7,404
Y
Y
Y
N
N
0.155
1.22e-07
HPA Since
Purchase
Disability,
Bartik Shock (1yr)
7,404
Y
Y
Y
N
N
0.214
1.83e-05
LTV>100 (d)
IV for LTV:
IV for Income:
19
Observations
Demographic Controls?
Mortgage Controls?
State Controls?
State FEs?
Job Loss FEs?
Jtest Pval Null Valid
Weak ID Pval Null Weak
(5)
(6)
-0.289**
(-2.53)
0.346***
(2.93)
-0.098*
(-1.76)
0.297***
(3.30)
HPA Since
Purchase
Invol. Job Loss,
Head & Spouse
7,339
Y
Y
Y
N
Y
0.313
0.000425
HPA Since
Purchase
Disability,
Bartik Shock
7,339
Y
Y
Y
N
N
0.107
6.81e-07
(7)
(8)
0.181***
(3.60)
-0.094*
(-1.85)
0.194***
(3.77)
-0.116*
(-1.96)
HPA Since
Purchase
Combined Disability,
Bartik Shock
7,404
Y
Y
Y
N
N
0.420
4.66e-08
HPA Since
Purchase
Combined Severe Disability,
Bartik Shock
7,404
Y
Y
Y
N
N
0.333
2.90e-08
Notes: See Table 7 for additional notes. Col. 1 and Col. 2 include state FEs. Col. 3 and Col. 4 construct Bartik shocks using 4 year and 1 year
employment changes by state and industry, respectively. Col. 5 and Col. 6 use a dummy for negative equity instead of a continuous variable. Col. 7
and Col. 8 combined the head and spouse disability shocks. Level of statistical significance: ∗∗∗ p < 0.01, ∗∗ p < 0.05, ∗ p < 0.10.
Table A.13: First Stages of the Robustness Results for Table 7.
A. LTV First Stage
Cumulative State HP Growth from Purchase Date
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
-0.076***
(-13.59)
-0.076***
(-13.42)
0.933
(0.74)
0.004
(0.20)
0.012
(0.65)
-0.081***
(-14.46)
-0.081***
(-14.53)
-0.081***
(-14.53)
-0.081***
(-14.53)
-0.081***
(-14.53)
0.424
(0.50)
-0.081***
(-14.52)
0.470
(0.55)
0.003
(0.16)
0.014
(0.80)
0.003
(0.16)
0.014
(0.80)
Bartik Instrument (2 Yr. Ch.)
Transition into Disability, Head (d)
Transition into Disability, Spouse (d)
Involuntary Unemployment, Head (d)
Involuntary Unemployment, Spouse (d)
0.003
(0.16)
0.014
(0.80)
0.025
(1.21)
0.000
(0.00)
Bartik Instrument (4 Yr. Ch.)
-0.070
(-0.14)
Bartik Instrument (1 Yr. Ch.)
0.164
(0.11)
Transition into Disability Head or Spouse (d)
0.012
(0.89)
Transition into Severe Disability Head or Spouse (d)
Observations
R-squared
Demographic Controls?
Mortgage Controls?
State Controls?
State FEs?
Job Loss FEs?
20
B. Income First Stage
Cumulative State HP Growth from Purchase Date
0.091***
(2.96)
7,404
0.372
Y
Y
Y
Y
Y
7,404
0.370
Y
Y
Y
Y
N
7,404
0.351
Y
Y
Y
N
N
7,404
0.351
Y
Y
Y
N
N
7,404
0.351
Y
Y
Y
N
Y
7,404
0.351
Y
Y
Y
N
N
7,404
0.351
Y
Y
Y
N
N
7,404
0.352
Y
Y
Y
N
N
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
-0.035***
(-2.65)
-0.034**
(-2.53)
5.605*
(1.66)
-0.134***
(-2.58)
-0.087**
(-2.06)
-0.026*
(-1.92)
-0.023*
(-1.71)
-0.019
(-1.46)
-0.025*
(-1.83)
10.328***
(4.61)
-0.024*
(-1.81)
10.092***
(4.49)
-0.145***
(-2.78)
-0.092**
(-2.17)
-0.146***
(-2.81)
-0.091**
(-2.15)
-0.022
(-1.61)
10.463***
(4.64)
-0.151***
(-2.88)
-0.084**
(-2.01)
Bartik Instrument (2 Yr. Ch.)
Transition into Disability, Head (d)
Transition into Disability, Spouse (d)
Involuntary Unemployment, Head (d)
Involuntary Unemployment, Spouse (d)
-0.198***
(-3.85)
0.094
(1.54)
-0.217***
(-4.16)
0.081
(1.29)
Bartik Instrument (4 Yr. Ch.)
6.240***
(4.85)
Bartik Instrument (1 Yr. Ch.)
14.649***
(3.66)
Transition into Disability Head or Spouse (d)
-0.126***
(-3.69)
Transition into Severe Disability Head or Spouse (d)
Observations
R-squared
Demographic Controls?
Mortgage Controls?
State Controls?
State FEs?
Job Loss FEs?
Notes: See Table 7 and Table A.12 for additional notes.
-0.281***
(-3.83)
7,404
0.347
Y
Y
Y
Y
Y
7,404
0.339
Y
Y
Y
Y
N
7,404
0.321
Y
Y
Y
N
N
7,404
0.320
Y
Y
Y
N
N
7,339
0.325
Y
Y
Y
N
Y
7,339
0.319
Y
Y
Y
N
N
7,404
0.321
Y
Y
Y
N
N
7,404
0.321
Y
Y
Y
N
N
A.10
Comparison of Default Rates in PSID and McDash/Equifax
Table A.14 expands the comparison of default rates and LTV ratio distributions between
the PSID and McDash/Equifax (CRISM) datasets performed in Section 2.2 of the paper.
Specifically, it includes results for 2011 and 2013 along with 2009.
For both PSID and CRISM, we break out the LTV ratio distribution into three intervals,
LTV ≤ 80, 80 < LTV < 100, and LTV ≥ 100, and show the fraction of the sample in each
interval and the default rate within each interval. We calculate LTV shares and default rates
for three different CRISM samples. The first sample includes all active first lien mortgages,
and is most comparable to aggregate default rates commonly reported by the Mortgage
Bankers Association and McDash.10 The second sample includes only first liens associated
with owner-occupant properties. The PSID only asks respondents for information on the
loans associated with their principal residence, so this sample of mortgages should be more
comparable to the PSID sample. Finally, the third sample also includes only first lien,
owner-occupants, but also eliminates mortgages that are reported by the servicer as being in
the foreclosure process where the borrower appears to have vacated the property and moved
elsewhere.11 This additional restriction brings the CRISM sample closer to the PSID sample
for comparison purposes because, again, the PSID only asks questions about mortgages
associated with the respondents’ current, principal residence. For example, a respondent
who has moved out of a property that is still in the foreclosure process and is now renting,
would be considered to be a renter in the PSID, and no information on the delinquent
mortgage would be collected.
Focusing on the 2009 statistics in the top panel of the table, the overall default rate in
the PSID is 3.9% while the default rate in the broadest CRISM sample is 8.6%. This is
a large discrepancy and on its face calls into question the representativeness of the PSID
sample on the dimension of mortgage performance. However, when we throw investors
and second homes out of the CRISM sample the aggregate default rate falls from 8.6% to
6.5%. Eliminating mortgages in foreclosure for which the borrower is no longer living in the
property further reduces the CRISM default rate to 5.4%. We see a very similar pattern
for 2011 and 2013. Thus, adjusting the CRISM sample to more closely align with the PSID
sample reduces the default rate discrepancy from 4–5 percentage points to 1.0–1.5 percentage
points.
10
Including second liens in the sample has almost no impact on the default rate, so for space considerations
we decided to begin with a sample of only first liens.
11
The CRISM data provide the zip codes of each mortgage borrower’s mailing address and the property
address. When the two zip codes differ, we assume that the borrower no longer resides in the property.
21
In addition to the sample differences, Table A.14 shows that there are material differences
between the LTV distributions in the PSID and CRISM. For example, in both 2009 and 2011,
the fraction of high LTV mortgages (≥ 100) is about 10 percentage points higher in CRISM
compared to the PSID. The default rates associated with high LTV mortgages are similarly
high in both datasets, which suggests that the composition of high LTV mortgages is similar
across the two datasets.12 Since the default rates associated with high LTV mortgages are
much higher than those associated with lower LTV loans, the smaller share of high LTV
loans in the PSID sample has a negative effect on the overall default rate and drives some of
the discrepancy in the aggregate default rates between the two datasets. To see this, in the
last column of the table, we recalculate the default rate in the PSID using LTV shares from
CRISM (the shares that correspond to Sample (3)). In both 2009 and 2011 this adjustment
almost completely closes the remaining gap between default rates,13 increasing the PSID
default rate to virtually the same level as the CRISM default rate (5.4% in 2009 and 4.8%
in 2011).
12
This is notable because the LTV ratios are calculated in very different ways. In the PSID, we calculate
LTV ratios using the self-reported remaining mortgage balance and the self-reported house value at the time
of the survey. In contrast, the LTV ratio in CRISM is based on the actual remaining mortgage balance
reported by the servicer and an estimate of the value of the house based on the cumulative change in the
zip code-level house price index since the month in which the mortgage was originated. The fact that the
default rates within each LTV interval are quite similar across both datasets suggests that composition of
loans in each interval is similar.
13
In 2013, the adjustment does not make a material difference because the LTV shares are very similar in
both datasets.
22
Table A.14: Comparison of Default Rates in the PSID and McDash/Equifax
2009
McDash/Equifax
LTV Category
LTV ≥ 100
80 < LTV < 100
LTV ≤ 80
Sample (1):
First Liens Only
Sample (2):
First Liens Only
No Investors
PSID
Sample (3):
First Liens Only
No Investors, Living in Home
Default Rate using
Share
Default Rate
Share
Default Rate
Share
Default Rate
Share
Default Rate
21.8%
24.2%
54.0%
23.1%
8.4%
2.8%
21.0%
24.1%
55.0%
17.4%
6.6%
2.2%
20.7%
24.2%
55.1%
14.0%
5.7%
2.0%
10.5%
18.8%
70.7%
16.0%
3.8%
2.2%
All
8.6%
6.5%
5.4%
CRISM Shares
3.9%
5.4%
2011
McDash/Equifax
23
LTV Category
LTV ≥ 100
80 < LTV < 100
LTV ≤ 80
Sample (1):
First Liens Only
Sample (2):
First Liens Only
No Investors
PSID
Sample (3):
First Liens Only
No Investors, Living in Home
Default Rate using
Share
Default Rate
Share
Default Rate
Share
Default Rate
Share
Default Rate
23.4%
26.3%
50.2%
22.0%
7.6%
3.1%
22.4%
27.1%
50.4%
14.3%
5.1%
2.1%
22.2%
27.5%
50.3%
12.4%
4.6%
1.9%
12.7%
22.1%
65.2%
12.6%
3.8%
2.0%
All
8.7%
5.7%
5.0%
CRISM Shares
3.8%
4.8%
2013
McDash/Equifax
LTV Category
LTV ≥ 100
80 < LTV < 100
LTV ≤ 80
All
Sample (1):
First Liens Only
Sample (2):
First Liens Only
No Investors
PSID
Sample (3):
First Liens Only
No Investors, Living in Home
Default Rate using
Share
Default Rate
Share
Default Rate
Share
Default Rate
Share
Default Rate
10.4%
24.2%
64.4%
24.2%
8.4%
3.3%
9.4%
26.6%
64.1%
17.8%
6.1%
2.4%
9.0%
26.5%
64.5%
16.0%
5.4%
2.2%
10.1%
24.2%
65.7%
12.6%
4.9%
1.1%
6.7%
4.8%
4.3%
3.2%
CRISM Shares
3.1%
Notes: This table compares mortgage default rates and LTV distributions in the PSID and McDash/Equifax (CRISM) datasets. CRISM is a
proprietary dataset that contains credit bureau data on individual consumers’ credit histories matched to LPS mortgage servicing data.
A.11
Strategic Default Estimates using PSID-McDash/Equifax
Weights
To further address concerns regarding representativeness of the PSID, we generate a set of
weights using McDash/Equifax (CRISM) data, which we have made available to the public.14 We do so using post-stratification. We split the restricted PSID sample (i.e. prime
age, LTV<2.5, single-family, owner-occupied, 2009-2013) into a set of 225 bins. We impose
the same mortgage criteria on CRISM and split it into the same 225 bins. We then compute
ratios of population shares in those bins. This allows us to produce an identical distribution
of individuals across bins between CRISM and the PSID. For whites, we use 5 LTV bins
{ LTV<.8, .8<LTV<.9, .9<LTV<1, 1<LTV<1.1, 1.1<LTV<2.5 }, 5 Age bins { 24-34,3540,41-47,48-55,56-65} (which correspond to age quintiles), and 5 Principal Remaining Bins
{Less than 59k, 59k-100k,101k-148k,149k-216k,216k and more} (which correspond to principal remaining quintiles). For non-whites, we collapse non-populated cells, which primarily
include minorities with severe negative equity. We use 4 LTV bins { LTV<.8, .8<LTV<.9,
.9<LTV<1, 1<LTV<2.5 }, 5 Age bins (same as above), and 5 Principal Remaining Bins
(same as above). Of the 225 possible bins for whites, all bins are populated. Of the 225 bins
for non-whites, 223 are populated. Therefore our weights allow us to almost exactly match
the 4-way joint distribution of age, LTV, principal, and race in CRISM.
Table A.15 summarizes the LTV distribution of the PSID under 3 sets of weights: CRISM
weights (Column (2)), raw PSID (Column (3)), family weights applied to the PSID (Column
(4)). By construction, the CRISM weights match the LTV distribution. We also match
the LTV distribution when we split by principal remaining, age, and race (subject to the
collapsed LTV bins for non-whites).
Table A.16 reproduces our main strategic default table using the CRISM shares. In
general, there are economically insignificant differences between the two tables. We find
that the share of strategic defaulters drops from 37.7% in Table A.1 to 37.2% in Table A.16.
Given that the two sets of results are so similar, there are strong reasons to use the PSID
family weights, rather than the CRISM weights, since the PSID weights use many more
post-stratum.
In our baseline OLS regressions, Table A.17, we see nearly identical point estimates to
the unweighted OLS regressions. The coefficient on LTV is 0.078 in Table 5 Column (2)
for the unweighted regressions, and the coefficient on LTV in Table A.17 Column (2) is
.085 for the weighted regression. The coefficient on log residual income is -0.025 in Table
14
The
data
and
the
code
to
build
the
(https://sites.google.com/site/kyleherkenhoff/research)
24
weights
are
available
here:
Table A.15: Weighted LTV Distribution, CRISM vs PSID (Years: Pooled 2009-2013)
PSID Shares under PSID
Sample Restrictions for Regressions, Raw
(1)
PSID Shares under PSID
Sample Restrictions for Regressions using CRISM
Weights
(2)
(3)
PSID Shares under PSID
Sample Restrictions for
Regressions using PSID
Family Weights
(4)
51.0
16.6
15.3
10.3
6.9
51.0
16.6
15.3
10.3
6.9
59.4
14.8
15.1
6.3
4.5
63.5
13.8
13.1
4.8
4.9
CRISM Shares under PSID
Sample Restrictions for Regressions
LTV<.8
.8<LTV<.9
.9<LTV<1
1<LTV<1.1
1.1<LTV<2.5
Notes: CRISM and PSID restricted samples (i.e. prime age, LTV<2.5, single-family, owner-occupied,
2009-2013).
Table A.16: Strategic Default, Weighted Using PSID-McDash/Equifax Weights
Can Pay
c<y−m+a
#
share
(1) (2)=(1)/(7)
A. All
Default
Population
87
5147
0.372
0.695
Default Rate
0.017
B. LTV>90
Default 62
0.382
Population 1573
0.656
Default Rate
0.039
C. LTV<90
Default 25
0.351
Population 3574
0.714
Default Rate
0.007
c > y − m + a > c(V A)
#
share
(3)
(4)=(3)/(7)
Can’t Pay
y − m + a < c(V A)
#
share
(5)
(6)=(5)/(7)
Total
#
(7)
90
1791
57
469
234
7404
0.386
0.242
0.050
61
647
0.377
0.270
0.122
40
180
0.095
29
1144
0.407
0.229
0.025
0.245
0.063
0.246
0.075
0.222
17
289
0.243
0.058
0.060
0.032
163
2398
0.068
71
5006
0.014
Notes: Weighted using PSID-CRISM weights described in the text. CRISM and PSID restricted samples
(i.e. prime age, LTV<2.5, single-family, owner-occupied, 2009-2013).
25
5 Column (1) for the unweighted regressions, and the coefficient on log residual income in
Table A.17 Column (2) is -.026 for the weighted regression. The weights do little to the
point estimates since the post-stratum are controls. In general, if the sample is random and
the post-stratum are controls, i.e. the weighting criteria are being “conditioned-on,” then
the weights are redundant and merely introduce noise.
Table A.17: Baseline Results: Linear Probability Model Cols (1) to (3), Logit Coefficients
Cols (4) to (6) (with AME in square brackets, interaction at interquartile range of residual
income), Dependent Variable is 60+ Days Late Indicator.
(1)
(2)
(3)
(4)
(5)
(6)
Loan to Value Ratio
0.084***
(7.13)
0.085***
(5.67)
1.042***
(4.80)
Log Residual Income
-0.036***
(-6.85)
-0.026***
(-4.73)
0.039***
(3.18)
2.141***
(10.97)
[0.059***]
-0.919***
(-10.92)
[-0.025***]
2.213***
(9.73)
[0.059***]
-0.814***
(-8.14)
[-0.022***]
0.372***
(6.22)
0.119*
(1.93)
-0.612***
(-4.20)
4.721***
(5.02)
0.616
(0.41)
1.574
(0.65)
[0.059***]
-0.874***
(-4.17)
[-0.022***]
0.060
(0.27)
[-0.0322]***
1.255
(0.50)
7,404
N
N
N
7,404
Y
Y
Y
7,404
Y
Y
Y
7,404
N
N
N
7,404
Y
Y
Y
7,404
Y
Y
Y
Log Residual Income x Loan to Value Ratio
Constant
Observations
Demographic Controls?
Mortgage Controls?
State Controls?
-0.086***
(-4.55)
Notes: Weighted using PSID-CRISM weights described in the text. This table displays OLS estimation
results of regressions of default on LTV ratios and residual income in Cols. (1) to (3). Cols (4) to (6)
report logit coefficients, and the square bracketed terms are the average marginal effects. To compute the
interaction we compute the difference in the LTV AME bewteen the interquartile range of residual income.
Residual Income is defined as gross family income less mortgage expenses. Default is defined as 60+ days late
as of survey date (at least two missed payments). The sample includes all household heads in the PSID who
are mortgagors, aged 24–65, and labor force participants (including those who are disabled) with combined
LTV ratios less than 250 percent. Robust t-statistics are reported in parentheses and dummy variables are
signified by (d). Level of statistical significance: ∗∗∗ p < 0.01, ∗∗ p < 0.05, ∗ p < 0.10.
A.12
Unweighted Strategic Default Table
Table A.18 is an unweighted version of the main strategic default table in the text (Table 4).
The number of observations in Table 4 is weighted (i.e. there are 196 weighted defaulters).
The number of defaulters in unweighted in Table A.18 (i.e. there are 248 unweighted
observations) , hence the total number of defaulters differs between the two tables.
26
Table A.18: Strategic Default, Unweighted
Can Pay
c<y−m+a
#
share
(1) (2)=(1)/(7)
A. All
Default
Population
103
5093
0.415
0.688
Default Rate
0.020
B. LTV>90
Default 59
0.444
Population 1257
0.657
Default Rate
0.047
C. LTV<90
Default 44
0.383
Population 3836
0.699
Default Rate
0.011
c > y − m + a > c(V A)
#
share
(3)
(4)=(3)/(7)
Can’t Pay
y − m + a < c(V A)
#
share
(5)
(6)=(5)/(7)
Total
#
(7)
82
1819
64
499
248
7404
0.331
0.246
0.045
43
508
0.323
0.266
0.128
32
151
0.085
39
1311
0.339
0.239
0.030
Notes: Unweighted.
27
0.258
0.067
0.241
0.079
0.212
32
348
0.278
0.063
0.092
0.033
133
1913
0.070
115
5491
0.021