The full text on this page is automatically extracted from the file linked above and may contain errors and inconsistencies.
Economic Quarterly—Volume 96, Number 3—Third Quarter 2010—Pages 229–258
Earned Income Tax Credit
Recipients: Income,
Marginal Tax Rates, Wealth,
and Credit Constraints
Kartik B. Athreya, Devin Reilly, and Nicole B. Simpson
T
he Earned Income Tax Credit (EITC) has become the federal government’s largest cash-assistance program for low-income families, making it the centerpiece of anti-poverty programs in the United States.
Approximately 15 percent of households nationwide now qualify for the EITC
(Hoffman and Seidman 2002). Moreover, unlike other government programs,
the EITC is administered through the income tax filing process, which reduces any potential stigma associated with the program, and aids in ensuring
high participation rates (Smeeding, Phillips, and O’Connor 2000). According
to Eissa and Hoynes (2009), approximately $43 billion was allocated to 22
million families in the United States in 2007 through the federal EITC. This
compares to $16.5 billion that was spent on more traditional welfare programs,
such as Temporary Assistance for Needy Children (TANF).
The EITC is designed to augment income while encouraging work: The
tax credit increases with earnings for low levels of household income. The
size of the credit is such that, for low-income households that qualify, the
EITC is a negative tax on earnings that often constitutes a significant portion
of after-tax wage income. The EITC does appear to have been successful
in both helping the working poor get out of poverty and encouraging work.
Neumark and Wascher (2001), Ziliak (2006), and Simpson, Tiefenthaler, and
Hyde (2009) provide evidence that the combined federal and state EITC helps
Athreya is a senior economist at the Federal Reserve Bank of Richmond. Reilly is a former
research associate at the Federal Reserve Bank of Richmond. Simpson is an associate professor
of economics at Colgate University. The views expressed in this article are those of the authors
and not necessarily those of the Federal Reserve Bank of Richmond or the Federal Reserve
System. E-mails: kartik.athreya@rich.frb.org; nsimpson@colgate.edu.
230
Federal Reserve Bank of Richmond Economic Quarterly
families rise above the poverty line. In fact, the EITC has been estimated to
have helped five million people out of poverty in 2005, including 2.6 million
children.1 Hotz and Scholz (2000) find that, compared to other povertyreduction programs, the EITC is effective in raising the standard of living for
low-income households, while keeping administrative costs relatively low.
However, the EITC phases out with earnings, until eventually a household
no longer qualifies for it. The structure of the phase-out means that families
earning more than $41,000 in 2008 will not qualify for the EITC, while all
those earning less will. In addition, the credit targets families with children,
and increases in generosity with the number of children in the household. For
example, households with two or more children (in tax year 2008) earning
$15,000 could qualify for up to $4,824 in federal earned income credits. In
contrast, a childless single filer can receive only one-tenth of this amount, or at
most $438. Thus, for those households with children and low earned income,
the full refundability of the EITC ensures that it will represent a substantial
addition to income.
In this article, we summarize the details of the EITC and describe the
population of EITC recipients. Using Current Population Survey data, we estimate earnings and EITC benefits received by EITC recipients at various ages.
Naturally, we find that because of the eligibility requirements, the earnings
of EITC recipients are relatively similar across the age of recipients, which
makes them differ systematically from non-recipients of the same age—whose
earnings show a more pronounced “hump shape” with age. We then discuss
how the EITC affects marginal taxes in the United States and summarize its
theoretical and empirical effects on household labor supply decisions. Finally, we compare wealth levels of EITC recipients with non-recipients using
data from the Survey of Consumer Finances (SCF), and find significant differences in their wealth distributions, with EITC recipients being substantially
poorer. The fact that EITC recipients have relatively low wealth levels and
low earnings relative to others in their age group suggests that they may be
more likely to be borrowing-constrained than non-recipients. In fact, we find
some evidence for this in our analysis of SCF data.
1.
HISTORY OF THE EITC
In Table 1, we briefly summarize the history of EITC legislation. The EITC
started as a modest program as part of the Tax Reduction Act of 1975.2 The
program was unique among tax credits as it was refundable so that poor families could utilize its benefits even if they owed little or no taxes. Unlike welfare
programs such as Aid to Families with Dependent Children (AFDC), single
1 Center on Budget and Policy Priorities: www.cbpp.org/cms/?fa=view&id=2505.
2 For a more detailed history of the EITC, refer to Hotz and Scholz (2003).
K. B. Athreya, D. Reilly, and N. B. Simpson: EITC Recipients
231
Table 1 History of EITC Legislation
Year
1975
1978
1986
1990
1993
1997
2001
2009
Changes to the EITC
Introduced temporary “work bonus” called the EITC
Made EITC permanent
General expansion (largest increase since its inception) and indexed for
inflation; part of the Tax Reform Act
General expansion by doubling the maximum credit and increased
eligibility; added separate schedule for families with two or more
children; part of OBRA
General expansion (larger expansion for families with two or more
children); added EITC for childless filers; part of OBRA
Provisions made to improve compliance; part of Taxpayer Relief Act
Changes to provide marriage penalty relief and promoted simplification;
part of EGTRRA
Expansion for families with three or more children and expanded eligibility
for married couples; part of the American Recovery and Reinvestment Act
Sources: Hotz and Scholz (2003); Holt (2006); Tax Policy Center (2009).
parents as well as married couples were eligible for the program. The EITC
went through minor changes in subsequent years, the most important being
when it became a permanent provision of the Internal Revenue Code in 1978.
The Tax Reform Act of 1986 indexed the EITC to inflation and liberalized
the EITC, helping, by some estimates, to remove over six million Americans
from poverty (Ventry 2000). The Omnibus Reconciliation Act (OBRA) of
1990 increased the credit and added separate schedules for families with two
or more children. The largest expansion of the EITC occurred in 1993, as part
of the OBRA, in which the EITC was increased by an additional 25 percent.
Families with two or more children experienced the largest increase in the
credit, and childless filers could now qualify for the EITC. Both the size of the
credit and the eligible population have grown over time, and were fueled by the
passage of the Personal Responsibility and Work Opportunity Reconciliation
Act of 1996, which replaced AFDC with Temporary Assistance for Needy
Families (TANF). The United States experienced a 50 percent reduction in
welfare rolls between 1993 and 2000, and Grogger (2004) finds that much of
the drop is attributed to the EITC and reduction in welfare benefits.
Until 2001, the structure of the EITC was identical for single and married
filers. However, as part of the Economic Growth and Tax Relief Reconciliation
Act (EGTRRA) of 2001, married couples received larger benefits for larger
ranges of income levels than single filers. The success of the federal EITC has
led to the development of similar programs in 23 U.S. states and the District
of Columbia, totaling an additional $2 billion (Levitis and Koulish 2008).
232
Table 2 EITC Parameters, Tax Year 2008
Single, One
Qualifying
Child
34.00%
$8,580
$2,917
$15,740
15.98%
$33,995
Source: Minnesota House Research Department.
Single, Two+
Qualifying
Children
40.00%
$12,060
$4,824
$15,740
21.06%
$38,646
Married, No
Qualifying
Children
7.65%
$5,720
$438
$10,160
7.65%
$15,880
Married,
One
Qualifying
Child
34.00%
$8,580
$2,917
$18,740
15.98%
$36,995
Married,
Two+
Qualifying
Children
40.00%
$12,060
$4,824
$18,740
21.06%
$41,646
Federal Reserve Bank of Richmond Economic Quarterly
Phase-In Rate
Phase-In Ends
Maximum Credit
Phase-Out Begins
Phase-Out Rate
Eligibility Ceiling
Single, No
Qualifying
Children
7.65%
$5,720
$438
$7,160
7.65%
$12,880
K. B. Athreya, D. Reilly, and N. B. Simpson: EITC Recipients
233
Table 3 EITC Calculation by Phase
Phase
Phase-In
Plateau
Phase-Out
EITC
= Phase-In Rate * Income
= Maximum Credit
= Maximum Credit − Phase-Out Rate * (Income − Income Where
Phase-Out Begins)
Finally, the American Recovery and Reinvestment Act of 2009 increased
the credit for families with three or more children and expanded eligibility for
married couples. Families making up to $48,250 in annual earnings can now
qualify for the tax credit, with the maximum credit as high as $5,657 for a
family with three or more children. This EITC expansion is expected to help
an additional 650,000 households and 1.4 million children.3
2.
STRUCTURE OF THE EITC
The EITC acts as an after-tax wage subsidy for low-income workers and depends on earned income, number of children, and marital status.4 Earned income includes wages, salaries, tips, and other employee compensation; union
long-term disability benefits received prior to minimum retirement age; and
net earnings from self-employment. However, it does not include social security benefits, unemployment compensation, welfare benefits, scholarships,
worker’s compensation benefits, or pension/annuity income.
The EITC is structured in three phases: In the phase-in period, the credit
increases with earnings; in the plateau period, the credit reaches a maximum
and levels off; and in the phase-out period, the credit falls as the claimant’s
earnings rise. At the eligibility limit, the household earns no EITC. The EITC
is separated into different levels for claimants with no children, those with
one child, and those with two or more children. There are also different
tax credits for different types of filers: Married couples filing jointly are
eligible for slightly higher credit amounts in the phase-out period than single
filers and have slightly larger income eligibility ranges. Table 2 presents
the details of the EITC for tax year 2008 for different filing statuses (single
or married) and number of children, and includes the maximum credits and
earnings limitations. In Figure 1, we plot the amount of federal EITC that
single and married households receive across various income levels: single
filers are depicted by the solid lines, whereas married filers are depicted by
3 Tax Policy Center (2009).
4 Many of the poorest families are ineligible for the EITC since their earnings are too low
to qualify and/or they do not have children (Hoffman and Seidman 2002).
234
Federal Reserve Bank of Richmond Economic Quarterly
Figure 1 EITC Structure by Income, Tax Year 2008
5,000
$4,824
4,500
No Children
One Child
4,000
EITC (2008$)
Two or More
3,500
3,000
$2,917
2,500
2,000
1,500
1,000
$438
500
0
0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
40,000
Income
Note: Solid line represents single/head of household filers; dashed line represents married
filers.
the dashed lines. To calculate the EITC in each phase, we use the equations
in Table 3 along with the EITC parameters in Table 2.
As seen in Figure 1, the EITC significantly varies with the number of
children present in a household. Childless filers receive less than one-eighth
of the EITC than filers with one child and one-twelfth of filers with two or more
children. The federal credit can represent up to 34 percent and 40 percent of
income for filers with one and two or more children, respectively. In addition
to the federal EITC, many states supplement, or match, with additional credits.
As a result, if the taxpayer lives in a state that offers a state EITC, the total EITC
(federal plus state) could be much larger; for example, New York residents
receive an additional 30 percent of the federal credit. Also interesting is that
the slope of the EITC function is steeper in the phase-in range than in the phaseout range. That is, an additional dollar of earned income rewards households
in the phase-in range by giving them a credit, which can range from $0.07
(for childless singles) to $0.40 (for married couples with two children). In the
phase-out range, an additional dollar of income results in a reduction in the
K. B. Athreya, D. Reilly, and N. B. Simpson: EITC Recipients
235
credit, from $0.07 (for childless singles) to $0.21 (for married couples with
two children).
The range of eligible income is much larger as the number of dependent
children rises. As of 2008, married households with two children earning
less than $41,626 qualify for the EITC, compared to $15,880 for childless
couples. The maximum EITC does not vary with marital status, but the income
eligibility ranges are slightly larger for married couples. In addition, the
range of eligible income is much larger in the phase-out range so that more
households are in the phase-out range than in the phase-in range. In fact,
recent evidence suggests that married households are more likely to be in
the phase-out range than singles, since they are more likely to have higher
household income.
3.
LABOR MARKET CHARACTERISTICS OF
EITC RECIPIENTS
Using Current Population Survey (CPS) data from 2008, we analyze the labor
market characteristics of EITC recipients and compare them to non-EITC
recipients. We create household-level observations by matching individuals
who are married to each other, and we restrict the sample to households where
the household head is between the ages of 16 and 64 years. Households are
classified into six different types, based on marital status (married or single)
and number of children (no children, one child, two or more children). This
classification is consistent with the structure of the EITC, as discussed in
Section 2. We find that approximately 12.8 percent of households in our
sample receive the EITC. Table 4a reports the mean annual wage and salary
income, education level, and EITC amount for each household type, while
Table 4b reports the fraction of each type in the sample. All of the means
represent weighted averages using the household weights supplied by the CPS.
It is important to note that 2008 CPS data corresponds to the 2007 tax year
and that the CPS only reports estimated federal EITC and does not include
any state EITCs.
Approximately 60 percent of EITC recipient households are single, with
an equal distribution of single households having zero, one, and two or more
children. This contrasts to married couple households, where the majority
of EITC recipient households have two or more children. The amount of
EITC varies significantly across household types. Single households with
two children receive the most EITC ($2,728), which constitutes the largest
share of their annual income, at 15 percent. Households without children
receive much less EITC, constituting only 6 percent of their annual income.
236
Table 4 EITC Recipients
4b: Distribution of Households in the CPS
Married,
No Kids
EITC Recipients:
Percent of All Households
0.59%
Percent of EITC Recipients
3.9%
Non-EITC Recipients
Percent of All Households
14.60%
Percent of Non-EITC Recipients
14.77%
Single,
No Kids
Single,
One Kid
Single,
Two+ Kids
$7,024
$5,894
60.0%
n/a
$423
6.0%
$15,761
$9,739
54.1%
n/a
$1,808
11.5%
$17,421
$10,409
58.3%
n/a
$2,728
15.7%
$23,696
$32,305
43.4%
n/a
$32,125
$47,998
46.6%
n/a
$31,723
$51,997
48.3%
n/a
Married,
One Kid
Married,
Two+ Kids
Single,
No Kids
Single,
One Kid
Single,
Two+ Kids
Sum
1.21%
9.6%
3.12%
26.8%
3.15%
19.8%
2.32%
19.4%
2.45%
20.6%
12.83%
100.00%
7.91%
10.57%
12.40%
18.61%
46.26%
48.16%
3.81%
4.91%
2.19%
2.99%
87.17%
100.00%
Notes: Household data constructed using 2008 CPS; 16–64-year-olds, 2008 dollars. Means are weighted using the household
weight “hhwt.”
Federal Reserve Bank of Richmond Economic Quarterly
4a: Labor Market Characteristics of EITC Recipient vs. Non-Recipient Households
Married,
Married,
Married,
All
No Kids
One Kid
Two+ Kids
EITC Recipients:
Mean Household Income
$15,194
$8,325
$18,700
$21,212
St. Dev. of Household Income
$16,132
$8,100
$10,590
$11,225
Percent of High School or Less
61.5%
70.5%
64.1%
68.5%
Percent with Two Earners
26.3%
9.6%
24.6%
30.1%
Average EITC
$1,782
$495
$1,812
$2,623
EITC as Percent of Income
11.7%
5.9%
9.7%
12.4%
Non-EITC Recipients:
Mean Household Income
$47,235
$68,549
$83,372
$94,271
St. Dev. of Household Income
$49,653
$67,884
$71,052
$79,822
Percent of High School or Less
39.7%
36.1%
34.2%
29.6%
Percent with Two Earners
65.1%
56.8%
70.5%
71.3%
K. B. Athreya, D. Reilly, and N. B. Simpson: EITC Recipients
237
Much of the variation in the EITC across household types is because
of differences in annual income. Not surprisingly, married households earn
more than single households since there is the potential for two earners. It is
interesting to note, however, that the share of married households that have
two earners is quite low for EITC recipients, compared to non-recipients. For
example, approximately 30 percent of married households with two children
who receive the EITC have two earners, while 71 percent of non-recipients
have two earners. This could be due to the fact that the majority of two-earner
households surpass the income qualifications of the EITC. Or, it could be that
EITC-recipient households choose not to have a second income since they
receive the EITC.
Another interesting feature is that household earnings for EITC recipients
increase with the number of children, and this occurs for both married couple
households and single parent households. The difference in annual income
between childless households and households with children is much larger for
EITC-recipient households than for non-recipient households.
Even though single households that receive the EITC earn less than married households, they tend to be more educated (for married households, we
use the education level of the household head). Approximately 10 percent
fewer single households have a high school degree or less compared to married households and this is independent of the number of children. This is not
the case for non-recipient households: Single households that do not receive
the EITC are more likely to only have a high school education than married
households.
Thus, the EITC likely has the largest impact on households with children
since the EITC is much larger for these households as a share of their annual
income and more than 75 percent of EITC recipient households have children. Single households represent the majority (60 percent) of EITC recipient
households, and tend to be more educated than married EITC households,
which contrasts with the general population. EITC recipient households are
much less likely to have two earners than non-recipient households.
4.
EITC AND INCOME BY AGE
We now analyze how the EITC changes across recipients of different ages.
Since the EITC targets low-income families, it will disproportionately affect
younger households of child-rearing age. However, households may qualify
for the EITC at any stage of their life, as long as they have earned income
that is below the income limit. Importantly, there is no limit to the amount of
benefits received over a lifetime nor is there a time limit.
We analyze the pool of EITC recipients between 1992–2008 and catalog
how the EITC varies across households of different ages in a shortened panel.
Specifically, we estimate the average income/EITC (in 2008 dollars) for each
238
Federal Reserve Bank of Richmond Economic Quarterly
household at each age in each year of the CPS (using the household weights
supplied by the CPS). Then, we calculate the average income/EITC across the
panel by age; to do this, we account for the distribution of households at each
age across the panel. This yields an estimate for income/EITC, conditional
on receiving EITC, at each stage in the lifecycle for the typical household in
the CPS.
While the preceding is useful, it is an imperfect measure of the effect of
EITC on lifetime earnings. It abstracts from any cyclical effects that individuals experience in earnings (such as business cycles, changes in skill premium,
or occupational transitions) that occurred prior to 1992 for older cohorts (for
example, changes in earnings profiles for individuals born before 1974 are
not accounted for prior to 1992). However, our method accurately accounts
for the drastic changes that occurred in the EITC during this period. In addition, our estimates provide a sense of how the EITC changes by age and what
households can expect as they age, should they qualify at later dates.
In Figure 2a, we plot the average EITC for households that receive the
EITC at each age between 18–64 using 1992–2008 CPS data (the age of the
household head is used); we also plot the EITC as a percent of earnings (labor
earnings and EITC) in the same figure. A few interesting findings emerge.
The EITC is high for households headed by very young adults (age 18–25),
relatively constant for households in their thirties (at approximately $2,000 in
2008 dollars), and then declines precipitously as we look at households in their
late thirties and beyond. By the time households are in their fifties and sixties,
the average amount of EITC is just over $500. Thus, the amount of EITC that
households receive declines over the course of their lifetimes. However, the
interaction of the qualification requirements and the structure of benefits ensure
that the EITC remains a relatively constant fraction of recipients’ earnings,
at approximately 15 percent, for most of their lives. While the typical EITC
transfer is largest for the youngest recipients in our sample, the EITC represents
a significant fraction of annual earnings (at least 15 percent) throughout most
of a recipient’s working life. In addition, the EITC represents an even larger
proportion of the income of older EITC recipient households. For example, for
EITC recipients in their late fifties, the EITC increases as a percent of earnings
to approximately 18 percent. This is likely due to the fact that households that
qualify for the EITC at this age have very low incomes since they likely face
the income thresholds applicable to those with no children.
The patterns in EITC receipt across different age groups arise from two
factors: child-rearing stages and fluctuations in income over the lifetime. A
typical lifetime earnings profile exhibits a hump shape, where earnings are
low early in life, increase dramatically through the twenties and thirties, level
off through the forties, and start to decline in the fifties and sixties. This
is exactly what we observe for non-EITC recipient households in the CPS
sample. In Figure 2b, we plot household earnings (wages and salary) profiles
K. B. Athreya, D. Reilly, and N. B. Simpson: EITC Recipients
239
Figure 2 EITC Recipients and Non-Recipients Across Ages
25.00%
2,000
20.00%
1,500
15.00%
1,000
10.00%
500
5.00%
EITC
EITC as a Percent of Income
EITC (2008$)
Panel A: EITC over the Lifecycle for Recipient Households
2,500
EITC as a Percent of Income
0.00%
0
18
23
28
33
38
43
48
53
58
63
Age
Panel B: Lifecycle Income with and without the EITC
80,000
Income (2008$)
70,000
60,000
50,000
40,000
30,000
20,000
Income for EITC Recipients
Income for Non-EITC Recipients
Income + EITC for EITC Recipients
10,000
0
18
23
28
33
38
43
48
53
58
63
Age
Notes: Household data constructed using 1992–2008 CPS; 16–64-year-olds, 2008 dollars.
Means are weighted using the CPS household weight “hhwt.”
for non-EITC recipients and EITC recipients. By construction of the eligibility
requirements for EITC, however, those receiving it at various ages are much
more similar to each other than are non-recipients of differing ages. Amongst
recipients, the highest levels of benefits accrue to the young, typically around
age 25. Older recipients generally earn smaller amounts, primarily as the
number of dependents they may claim falls.
240
5.
Federal Reserve Bank of Richmond Economic Quarterly
MARGINAL INCOME TAX RATES
The EITC represents a negative income tax for households that qualify for
it. Thus, for low income levels, marginal income tax rates are negative. Using data from TAXSIM version 9.0 from the National Bureau of Economic
Research,5 we calculate the marginal income tax rates for all single and married households with no children, one child, and two children (i.e., dependents
exemptions) for tax year 2008.6 The marginal income tax rate is for adjusted
gross income only and does not include Federal Insurance Contributions Act
(FICA) contributions (i.e., Social Security and Medicaid).
In Figure 3, we plot the marginal tax rates across income levels for single
and married filing status earning up to $100,000 and differentiate households
based on the number of children they claim as dependents. As you can see
in the first panel for married households with two or more children, for low
levels of income, the marginal tax rate is −40 percent for both single and married filers, which represents the phase-in rate for the EITC. As incomes reach
$13,000, the marginal rate is 0 percent (in the plateau region). For households
with income above $13,000, the marginal tax rate becomes positive and gets
quite large quickly. For married households with incomes between approximately $19,000–$25,000, the marginal tax rate jumps to 21 percent, which
represents the EITC phase-out rate. That is, at the margin, these households
are experiencing a 21 percent reduction in their EITC for any additional income they earn in this range. For married households with incomes between
approximately $25,000–$40,000, the marginal income tax rate increases to
31 percent, which represents the EITC phase-out rate plus the lowest income
tax bracket of 10 percent. For married households with two children earning
$41,000, they face the phase-out rate and the next highest tax bracket of 15
percent, making their marginal tax rate 36 percent. Thus, the phasing out
of the EITC leads to dramatic increases in the marginal income tax rates for
these households. For married households above $41,000, they no longer
qualify for the EITC; hence, they face significant reduction in their marginal
tax rates, at 15 percent (in the second income tax bracket). As household
income approaches $90,000, the marginal tax rate increases to 25 percent for
married filers.7,8 Single taxpayers with two children experience similar jumps
in the marginal income tax rates, but for lower levels of income than married
households.
5 www.nber.org/˜taxsim/taxsim-calc9/index.html.
6 We follow the methodology of Hotz and Scholz (2003), Romich (2006), and Eissa and
Hoynes (2009) in generating the marginal tax rate schedule.
7 Marginal tax rates in the United States increase up to 35 percent for household incomes
up to $357,000 (in 2008). However, we focus on income tax rates for low- and middle-income
households.
8 If we were to include FICA contributions, the entire marginal tax curve would shift upward
by 7.65 percentage points across all income levels.
K. B. Athreya, D. Reilly, and N. B. Simpson: EITC Recipients
241
Figure 3 Marginal Income Tax Rates
Panel 2: Households with One Child
40
30
30
Marginal Tax Rate (%)
20
10
0
-10
-20
Married
-30
20
10
0
-10
-20
Married
-30
Single
Single
90,000
100,000
80,000
70,000
60,000
50,000
40,000
30,000
0
100,000
90,000
80,000
70,000
60,000
50,000
40,000
30,000
20,000
0
10,000
20,000
-40
-40
10,000
Marginal Tax Rate (%)
Panel 1: Households with Two Children
40
Income
Income
Panel 3: Households with No Children
40
Marginal Tax Rate (%)
30
20
10
0
-10
-20
Married
-30
Single
100,000
90,000
80,000
70,000
60,000
50,000
40,000
30,000
20,000
10,000
0
-40
Income
Source: TAXSIM 9.0, 2008 tax year.
The second panel in Figure 3 shows the marginal income tax schedule
for married and single households with one child. The figure is similar for
those with two or more children, however, the marginal rates are slightly lower
across all income levels. For example, the poorest households with one child
face a marginal tax rate of −34 percent (compared to 40 percent for households
with two or more children). In addition, marginal tax rates for those earning
between $20,000–$40,000 are approximately 5 percentage points lower for
those with one child, because of differences in the slope of the phase-out rate
(the phase-out rate is steeper for those with more children, as documented
in Table 2). As households go beyond EITC eligibility, the marginal income
tax schedule does not vary with the number of children. Once again, these
242
Federal Reserve Bank of Richmond Economic Quarterly
households experience significant reductions in their marginal tax rates as
soon as they are ineligible for the EITC.
In the last panel of Figure 3, the income tax schedule is quite different for
those with no children compared to those with children. Recall that the EITC
is much less generous for childless households. Thus, the negative marginal
rates are quite low (in absolute value terms) for the poorest households. Also
notice that the increases in the marginal rates are not as extreme for childless
singles; as a result, these households do not experience significant reductions
in their marginal tax rates as they become ineligible for the EITC (for incomes
above $15,800 for married households). Beyond EITC eligibility, they face
the same marginal income tax rates as households with children.
Our analysis of the marginal income tax schedule for EITC recipients
uncovers a few interesting points. First, the very poorest households with
children (those earning below $12,000) experience large negative income tax
rates (in absolute value terms) because of the EITC. Second, single parent
households that receive the EITC face some of the highest (positive) marginal
income tax rates in the United States (Ellwood and Liebman 2000); for example, a single mother with two children earning $35,000 pays a marginal
income tax rate of 36 percent (in 2008). These high marginal tax rates can
be attributed to the phasing out of the EITC and the progressive income tax
schedule (Romich 2006). Married households with children face slightly
lower marginal tax rates than single households with children. Third, once
households with children no longer qualify for the EITC, their marginal income
tax rates drop significantly, and once they surpass EITC eligibility, marginal
income tax rates no longer depend on the number of children in the household.
6.
LABOR SUPPLY RESPONSE TO EITC
As a wage subsidy, the EITC has the potential to affect both the decision
to work (i.e., the extensive margin) and the number of hours worked (the
intensive margin). In a static labor-leisure model, the EITC increases the
marginal value of working (i.e., the after-tax wage rate). Thus, in theory,
the EITC will increase labor market participation because of the substitution
of work for leisure. However, the effects of the EITC on hours worked are
theoretically ambiguous. We follow the formulation in Eissa and Hoynes
(2009) in extending the labor-leisure model to include the EITC.
Consider a representative household within the traditional labor-leisure
model, where the household unit decides how much to work. The household could constitute one or more workers, where the tradeoff to working is
household leisure. The budget constraint (without the EITC) is depicted by:
c = w̃ ∗ n, where c represents consumption, w̃ represents after-tax wages,
and n represents labor hours. Households have T units of time to devote to
labor (n) and leisure (l); T = n + l. The slope of the budget constraint, and
K. B. Athreya, D. Reilly, and N. B. Simpson: EITC Recipients
243
hence the cost of pursuing an additional unit of leisure, is w̃ units of consumption. In Figure 4a, we plot the budget constraint with leisure on the x-axis
and consumption on the y-axis (BLA ). Plotting an indifference curve on this
graph (with all of the standard assumptions for utility) provides the equilibrium quantity of leisure (lA ) and consumption (cA ), at point A. If after-tax
wages rise because of a reduction in the marginal income tax rate, the budget
line gets steeper (rotates to BLB ). For the same household, the equilibrium
quantity of leisure/labor may rise or fall because of the tax cut. The substitution effect reduces leisure, and hence raises labor supply. The income effect
raises leisure and lowers labor. The net effect depends on the relative size
of each effect. In the diagram, the income effect dominates such that labor
supply falls (leisure increases) in response to a tax cut (lB > lA ).
The EITC changes the after-tax wage rate (w̃) for different levels of
leisure/labor. For low levels of labor, when the household receives a tax
credit (i.e., a negative tax) for each additional unit of labor, the after-tax wage
is w̃ = w (1 + ts ), where ts > 0 is the phase-in rate. For higher levels of labor
in the plateau region, the after-tax wage is simply w since the EITC is constant
in this range; that is, w̃ = w where households receive
a transfer,
T r. During
the phase-out region, the after-tax wage is w̃ = w 1 − tp ; the EITC falls for
each additional unit of labor at the rate tp > 0. For very high levels of labor,
the after-tax wage
returns
to w once again. Thus, the budget constraint is as
follows: c = w 1 + τ p ∗ n for n ∈ (0, n1 ); c = w ∗ n + T r for n ∈ [n1 , n2 );
c = w (1 − ts ) ∗ n for n ∈ [n2 , n3 ); c = w ∗ n for n ∈ [n3 , T ); where T r is
the maximum EITC and ni represents different quantities of labor. The EITC
budget constraint, as plotted in Figure 4b, is kinked at each quantity of labor
ni in which w̃ changes.
By comparing the budget constraint with and without the EITC in the
various ranges of labor supply, we can determine the theoretical effects of
the EITC on hours worked. First notice that for households that do not work
(l = T ), the EITC is 0 and has no effect on the household’s budget constraint.
However, for those households that choose to work very little (i.e., n = ε,
where ε ∈ (0, n1 )), the slope of the budget line gets steeper. Here, there
is a positive substitution effect and no income effect. Thus, the EITC may
influence some households to enter the labor force, leading to a positive effect
on the extensive margin.
However, the effects of the EITC on the intensive margin are more complicated. In the phase-in range, the slope of the budget constraint is higher
with the EITC (w̃ > w since ts > 0); thus, a negative income effect and a positive substitution effect are both at play, making the effects on hours worked
ambiguous. Those in the plateau region receive the same amount of credit if
they earn more income, and hence a pure income effect occurs in which higher
income reduces the incentive to work. In the phase-out range, the slope of
the budget constraint is flatter than without the EITC (w̃ < w since tp > 0).
244
Federal Reserve Bank of Richmond Economic Quarterly
Figure 4 EITC and Labor Supply
Panel A: Labor-Leisure Model without the EITC
Consumption (c)
cB
B
A
cA
IC B
BL B
BL A
lA
lB
ICA
Leisure (l)
Panel B: Budget Constraints with and without the EITC
Consumption (c)
Budget constraint without EITC
Budget constraint with EITC
n3
n2
n1
Leisure (l)
Here, a negative substitution effect influences households to substitute leisure
for hours worked. In addition, a negative income effect may reduce hours
worked even more. Thus, households in the phase-out region unambiguously
reduce hours worked. Since a majority of EITC recipient households fall in
the flat or phase-out region, it is likely that the overall effects of the EITC on
hours worked are negative (Hotz and Scholz 2003). For those with income
beyond the phase-out region (n ∈ [n3 , T )), their return to an additional hour
of work is w, so that some of them may choose to restrict labor hours to be
eligible for the EITC, once again leading to a negative extensive margin effect.
K. B. Athreya, D. Reilly, and N. B. Simpson: EITC Recipients
245
Of course, the magnitude of these responses depends on the elasticities
of labor supply. High elasticities lead to larger labor supply responses, and
labor supply elasticities vary across different types of people. For example,
the uncompensated elasticity of labor supply is higher for women than for men
and the elasticity on labor force participation is larger than the elasticity of
hours (Evers, Mooij, and Van Vuuren 2008). Thus, the quantitative effects of
the EITC on both the extensive and intensive margins of labor supply decisions
depend critically on the presumed elasticities of labor supply.
There is a large empirical literature that examines the effects of the EITC
on labor supply, with most of the work focusing on single mothers. For a more
detailed summary of this literature, refer to Holt (2006) and Hotz and Scholz
(2003). The evidence indicates that the EITC does in fact increase labor force
participation, especially for single mothers (Meyer 2001), leading to positive
effects on the extensive margin. In fact, the EITC has led to a dramatic
increase in employment rates for single mothers during the 1980s and 1990s
(Eissa and Leibman 1996; Meyer 2001; Grogger 2004). However, the effects
of the EITC on the intensive margin are less clear in the data, with most studies
not finding a significant change in hours worked because of the EITC. The
most relevant work here is that of Cancian and Levinson (2005), who study
a natural experiment arising from the fact that one U.S. state (Wisconsin)
altered the generosity of its matching of the federal EITC. They argue that
there is essentially zero effect on hours. There is some evidence, however,
suggesting that single mothers may work more in response to the EITC since
they are likely to be in the phase-in region where marginal income tax rates are
negative (Eissa and Liebman 1996). Married women, however, who typically
fall in the phase-out range, may work fewer hours as a result of the EITC rates
(Ellwood 2000; Eissa and Hoynes 2004).
Very few studies analyze the labor market effects of the EITC on married
couples; notable exceptions include Eissa and Hoynes (2004, 2009). They find
that the EITC has small negative effects on both the extensive and intensive
margins for married couples. However, the EITC has differential effects on
primary and secondary earners. For example, increases in the EITC lower
both the participation rates and hours worked for secondary earners since
these households are usually being phased out of the EITC, where the returns
to working more are relatively low.
There seems to be some consensus in the empirical literature that the
EITC has positive effects on the extensive margin for households and little to
no effect on the intensive margin. Studies have shown that the labor supply of
low-income households is generally unresponsive to high marginal tax rates
(Keane and Moffitt 1998; Gruber and Saez 2002); this compares to highincome workers who are quite responsive to tax rates. Perhaps low-income
workers cannot adjust their work hours because of their job structure (Romich
2006). Or perhaps these workers do not realize the high marginal tax rates
246
Federal Reserve Bank of Richmond Economic Quarterly
because of the complexity of the income tax and benefits structure in the United
States. Recent theoretical work in a separate but related context suggests that
a central force may be that low-income households are typically low-wealth
households. As a result, these households will often be close to a borrowing
constraint. Consumption theory predicts that such households will work in a
manner insensitive to current wages, as the value of lowering the likelihood of a
binding borrowing limit (by working and reducing consumption) will be high.
The work of Pijoan-Mas (2006) suggests that this may be exactly the case, as he
is able to rationalize a relatively high willingness of households to substitute
labor intertemporally, with a low aggregate correlation between wages and
hours. In ongoing work, Athreya, Reilly, and Simpson (2010) utilize this
insight and embed households into a setting in which they face uninsurable
risks and liquidity constraints, and find that, indeed, the disincentives to labor
supply arising from the EITC are not strong.
7. WEALTH DISTRIBUTION OF EITC RECIPIENTS
As documented above, EITC recipients earn much less over their lifetimes
than the general population. This will have important effects on their wealth
holdings. In addition, their wealth level may affect their labor supply decision,
as discussed above. In this section, we use the 2007 SCF to compare the
distribution of wealth for EITC recipients and non-recipients, and then analyze
differences across the six different types of households. Wealth is defined as
household net worth, which is the difference between total assets and total
debt.9 The SCF does not report anything related to the EITC. However, we
calculate the imputed EITC level that households would have received in tax
year 2006 using the household structure and wage/salary income reported by
the SCF. That is, we feed the parameters of the federal EITC program into the
SCF to generate a proxy for the amount of EITC each household is eligible
to receive. However, it should be made clear that we cannot observe directly
if each household received the EITC—we know only whether or not they
qualified for the EITC and, if they qualified, how much EITC they should
have received.
All of the usual caveats apply when using the SCF data, in that it is a small
sample and is not representative of the U.S. population at large. Our sample
of the 2007 SCF contains 3,458 households compared to 86,259 households
in the 2008 CPS (recall that we restrict the analysis to household heads between 16 and 64 years old and use the individual-level data in the CPS to
create household-level observations). It is well-known that the SCF oversamples wealthy and married households. For example, when comparing the
9 We use the SCF definition of net worth, as used in various Federal Reserve Bulletin articles,
including Bucks et al. (2009).
K. B. Athreya, D. Reilly, and N. B. Simpson: EITC Recipients
247
distribution of household types between the CPS (reported in Table 4b) and
the SCF (in Table 5b), it is evident that married households are oversampled
in the SCF compared to the CPS and that single households are undersampled
(and especially childless singles and single parents with one child). Surprisingly, the SCF just slightly oversamples households that are eligible for the
EITC; they represent 12.8 percent of the CPS sample and 16.4 percent of the
SCF sample. Also, the SCF does surprising well in capturing an accurate
distribution of EITC recipients across household types and their mean income
and EITC levels, compared to the CPS. This provides support to our use of
the SCF to analyze EITC recipients. All of the reported means are reported
in 2007 dollars and are weighted using the replicate weights produced by the
SCF.10
In Table 5a, we report mean net worth (i.e., wealth), assets, debt, and
income across household types. Not surprisingly, households that qualify for
the EITC have much less net worth, assets, and debt than non-recipient households, and the difference is astounding. Mean net worth of EITC recipients is
$103,753 (in 2007 dollars) compared to $580,245 for non-recipients. Some of
the difference in net worth between EITC and non-EITC recipients can be explained by differences in income and age: EITC recipients earn 23 percent of
what non-recipients earn, on average, and are almost six years younger. Somewhat interesting is that mean debt level for EITC recipients is $45,755, which
represents 2.6 times their annual salary, compared to non-recipients whose
debt-to-income ratio is approximately 1.7. Thus, debt-to-income ratios are
quite high for households that qualify for the EITC.
In Table 6, we report mean wealth by quartiles for both EITC and nonEITC recipients. First, notice that households in the lowest quartile of EITC
recipients have average negative wealth of −$16,617. In fact, 18.4 percent
of households in the EITC sample have negative net worth. However, there
is a significant amount of heterogeneity in the first quartile, as evidenced by
the large standard deviation. This compares to the lowest quartile of nonEITC recipients, whose mean wealth level is $1,899 and standard deviation is
$324. Second, notice that the wealth distribution for EITC recipients is much
tighter than for non-recipients. The ranges of wealth in each quartile are much
smaller and the standard deviations are generally lower (with the exception of
the first quartile of EITC recipients). Third, the majority of EITC recipients
hold very little wealth; those in the third quartile of wealth hold on average
only $24,038 in net worth, compared to non-recipients in the third quartile
who hold more than $250,000. Only the top quartile of EITC recipients has a
significant amount of wealth. In fact, only 20.3 percent of EITC recipients
10 For a full discussion of the importance of weights in the SCF, refer to Kennickell (1999).
248
Table 5 Balance Sheets of EITC Recipients and Non-Recipients
5b: Distribution of Households in the SCF
Married,
No Kids
EITC Recipients:
Percent of All Households
0.97%
Percent of EITC Recipients
5.9%
Non-EITC Recipients
Percent of All Households
25.47%
Percent of Non-EITC Recipients
30.47%
Single,
No Kids
Single,
One Kid
Single,
Two+ Kids
$67,574
$86,545
$18,971
$6,990
$277
37.1
$56,102
$89,365
$33,263
$18,903
$1,720
40.4
$49,837
$96,465
$46,628
$19,070
$2,726
38.1
$275,437
$334,930
$59,493
$38,071
44.8
$351,416
$448,206
$96,790
$50,373
47.2
$223,309
$296,280
$72,971
$35,849
41.5
Married,
One Kid
Married,
Two+ Kids
Single,
No Kids
Single,
One Kid
Single,
Two+ Kids
Sum
2.20%
13.4%
4.88%
29.7%
2.53%
15.4%
2.46%
15.0%
3.38%
20.6%
16.42%
100.00%
12.66%
15.15%
22.68%
27.13%
17.42%
20.84%
2.83%
3.39%
2.52%
3.02%
83.58%
100.00%
Source: Authors’ calculations using the 2007 SCF. Means are weighted, in 2007 $.
Federal Reserve Bank of Richmond Economic Quarterly
5a: Assets, Debt, and Net Worth of EITC Recipient vs. Non-Recipient Households
Married,
Married,
Married,
All
No Kids
One Kid
Two+ Kids
EITC Recipients:
Mean Net Worth
$103,753 $284,403
$204,918
$118,468
Mean Assets
$149,507 $359,963
$255,239
$179,050
Mean Debt
$45,755
$75,560
$50,321
$60,582
Mean Household Income
$17,593
$6,199
$21,818
$22,502
Mean (Imputed) EITC
$1,778
$231
$1,440
$2,409
Mean Age
38.5
46.6
37.2
37.5
Non-EITC Recipients:
Mean Net Worth
$580,245 $803,447
$621,345
$737,654
Mean Assets
$708,564 $929,270
$790,176
$933,762
Mean Debt
$128,319 $125,823
$168,830
$196,108
Mean Household Income
$76,686
$87,916
$95,962
$105,640
Mean Age
44.3
46.9
43.6
41.3
K. B. Athreya, D. Reilly, and N. B. Simpson: EITC Recipients
249
hold more than the average wealth level for EITC recipients ($103,753). This
compares to non-recipients, where 41 percent hold more than the average
wealth level of $580,245 and 69 percent have more wealth than the average
EITC recipient.
There is significant variation in wealth across household types, as illustrated in Table 5a. Married households have three times as much wealth as
single households, with the largest difference for households with no children. It is likely that most of the wealth held by married households with no
children is comprised of housing wealth since this group is relatively old. In
addition, mean household wealth is smaller for households with more children
despite higher earnings, and this effect is particularly large for married households. Thus, mean wealth levels for single households are quite low but are
not that different for those with and without children. For married households,
households with children have higher earnings but significantly less wealth
compared to those without children. This is partially explained by age differences across married households—those without children are approximately
nine years older than those with children. In addition, single households without children earn the least income of any group, but are not the poorest type of
household in terms of net worth. Single households with two or more children
have the lowest net worth in both the EITC and non-recipient samples.
Our analysis documents several interesting findings about the wealth holdings of EITC recipients. Not surprisingly, we find that EITC recipients hold
very little wealth: EITC recipients, on average, hold only one-fifth of the
wealth of non-EITC recipients. In fact, the bottom quartile of EITC recipients
hold negative wealth on average, while the bottom quartile of non-recipient
households have small, positive wealth holdings. However, debt-to-income
ratios of EITC households are significantly higher than those of non-recipients
(2.6 compared to 1.7 on average). We find that married households that are
eligible for the EITC hold more wealth than single households, and wealth
holdings decrease with the number of children in the household.
8.
EITC AND CREDIT CONSTRAINTS
Based on the data presented in Figure 2b, the EITC increases earnings for
recipients during every year of their working life and more so in early life.
In a typical lifecycle model of savings and consumption, a household would
save in periods when income is high, and borrow when income is low. As
a result, the EITC allows low-income families to smooth consumption over
their lifetimes. At higher frequencies, such as within a given year, the EITC
can help, even though most families receive the EITC in lump sum when they
250
Table 6 Wealth Distributions: EITC Recipients vs. Non-Recipients
St. Dev.
Min.
Max.
Mean
Income
Mean
Age
−$16,617
$3,489
$24,038
$404,272
$1,860
$85
$531
$24,215
−$473,700
$190
$7,630
$52,120
$170
$7,560
$51,400
$615,000,000
$14,938
$15,919
$20,507
$19,014
34.0
33.7
38.6
47.7
$1,899
$75,329
$253,637
$1,991,197
$324
$697
$1,467
$33,646
−$118,999
$24,130
$141,520
$396,300
$24,120
$141,500
$396,210
$806,000,000
$34,055
$51,829
$76,599
$144,308
37.9
42.6
46.5
50.4
Source: Authors’ calculations using the 2007 SCF. Means are weighted, in 2007 dollars.
Federal Reserve Bank of Richmond Economic Quarterly
EITC Recipients:
Bottom Quartile
Lower Middle Quartile
Upper Middle Quartile
Upper Quartile
Non-EITC Recipients:
Bottom Quartile
Lower Middle Quartile
Upper Middle Quartile
Upper Quartile
Mean
K. B. Athreya, D. Reilly, and N. B. Simpson: EITC Recipients
251
file their tax returns.11 In addition, households may borrow against their EITC,
knowing that they will be receiving it later. Alternatively, households may save
their EITC for future consumption.
The ability of households to smooth (bring forward) an expected EITC
lump-sum payment that is made at the time of one’s annual income tax payment depends on the household’s ability to borrow. For those who can borrow,
the EITC may act as insurance against income, employment, or health shocks,
for example. If, on the other hand, households face significant borrowing constraints, they may not be able to borrow against their EITC, and so, while the
EITC still provides low frequency smoothing, it may not assist consumption
smoothing efforts within shorter periods, for example one calendar year.
Direct evidence on the extent to which EITC recipients are credit constrained is not possible, given current data limitations. Moreover, credit constraints are generally very difficult to identify. Typically, the measurement of
credit constraints in any given study relies on a particular theory of consumption to identify consumption or savings movements that appear “anomalous,”
such as the large “excess sensitivity” literature on the 1980s for the path of
aggregate consumption (see Deaton 1992). A handful of articles find evidence that suggests that those who share demographic characteristics with the
EITC recipients are likely to be credit constrained. For example, the results
of Jappelli (1990) indicate that lower income, wealth, and age are all associated with higher likelihoods of being credit constrained, all key features of the
EITC population as documented above. Souleles (1999) finds that households
that receive tax refunds and are liquidity constrained experience significant
increases in nondurable consumption at the time of refund receipt. Barrow
and McGranahan (2000) discover a seasonality of consumption behavior that
is consistent with the timing of the receipt of the EITC, especially for durable
goods. Berube et al. (2002) discuss the proliferation of paid tax preparation
services and refund loans (at relatively high interest rates) for EITC recipients,
suggesting that these households lack financial services and, hence, access to
credit. Finally, Elliehausen (2005) analyzes survey data from households that
use refund anticipation loans (RALs). He finds that EITC recipients who
use RALs are less likely to use various types of credit (including car loans,
bank and retail credit cards, and mortgages) than other RAL households. In
addition, Elliehausen (2005, 52) reports that:
Nearly half of EITC recipients that obtained RALs reported being turned
down or limited by a lender in the last five years, and a little more
than half said that they had thought about applying for credit but did
not because they thought that they would have been turned down. These
11 The advance EITC allows them to receive their EITC throughout the year in their paycheck,
but very few households participate in this option (Romich and Weisner 2000).
252
Federal Reserve Bank of Richmond Economic Quarterly
percentages are more than two times the percentage of all households
experiencing turndowns or limitations and more than three times the
percentage of all households perceiving limitations in credit availability.
However, no study has provided direct evidence of the extent to which EITC
recipients are credit constrained.
Using 2007 SCF data and following Jappelli (1990), we use a set of questions from the SCF that provide a sense of the severity of credit constraints
that EITC recipient households face. We use the following four measures:
1. Bad credit: For households that do not have a checking account, the
SCF asked why. If the response was because of credit problems,
bankruptcy, and/or does not meet qualifications for an account, then
a value of 1 was assigned.
2. Credit card balances: This is the total value of credit card balances held
by households. Credit card balances consist of the amount outstanding
on all credit cards and revolving store accounts after the last payment.
Balances do not include purchases made since the last account statement.
3. Late payment for 60+ days: This was assigned a value of 1 if the
household had any debt payments more than 60 days past due in the
last year.
4. Has no checking account: This was assigned a value of 1 if the household did not have a checking account.
Certainly, these four measures are not perfect predictors of being credit
constrained. For example, some households choose not to have a checking
account for reasons that are unrelated to their credit status. However, not
having a checking account will undoubtedly lead them to have less access to
credit in the future; without a checking account, many banks are not willing
to issue personal loans and/or mortgages. That is, the causality between these
measures and the likelihood of being credit constrained is unclear; however,
if we find some correlation between these measures and the EITC, it may
shed some light on the extent to which EITC households are or will be able
to borrow. Similarly, credit card balances are an imperfect measure of credit
constraints; lower balances may imply less willingness to use credit cards
and/or acquire debt, and not less ability to borrow. But it may also indicate
that they have lower credit limits, suggesting tighter borrowing constraints.
Of the four measures above, having bad credit and late payments are perhaps
the most accurate measures of credit constraints since both will lead to lower
credit scores and, hence, worse credit terms.
In the analysis that follows, we compare these four measures for households that receive the EITC versus non-recipient households. As we document
K. B. Athreya, D. Reilly, and N. B. Simpson: EITC Recipients
253
in Section 1, EITC-recipient households are younger, less educated, and have
more children than non-recipient households; as a result, they are poorer.
Obviously, having fewer current and, especially, future resources to borrow
against will make it more difficult for EITC-recipient households to borrow.
Nonetheless, it is useful to know the extent to which any household is likely
to be constrained as suggested by the criteria above. We therefore do not
condition on all possible household characteristics since they would likely
explain away any differences between EITC recipients and non-recipients.
Instead, we attempt to document the extent to which households that fit the
EITC profile face borrowing constraints.
In Table 7, we report the means and standard deviations of these four
measures for EITC recipients, non-recipients, and across household types.
(Recall that EITC recipients in this context are defined as those who qualify for
the EITC.) EITC recipient households report being denied a checking account
because of bad credit more frequently than non-recipients (2.3 percent versus
0.5 percent for non-recipients). They also have lower credit card balances
($2,131 compared to $4,174); this could indicate that these households have
lower credit limits, or are less willing to use acquire debt, or are less willing
to use credit cards. EITC households are twice as likely to have late debt
payments as non-recipients (11.2 percent compared to 5.4 percent), which
would lead to having less access to credit. In addition, EITC households are
three times more likely to not have a checking account (28 percent versus 7
percent).
When looking across households types, we can see that several interesting
facts emerge. First, single households have lower credit card balances; they
are generally more likely to have late payments; and they are less likely to have
a checking account than married households (holding constant the number of
children). However, the differences between single and married households
are larger for non-recipients than for EITC recipients. For example, married
households have much larger credit card balances than single households in
the non-EITC sample, but the difference is smaller for married and single
EITC recipients.
Second, married households with children that qualify for the EITC report
very high late payment frequencies compared to their non-recipient counterparts. Approximately 13 percent of married households with one child have a
late repayment, compared to just 5 percent of non-recipients. We do not observe significant differences between single-parent EITC recipients and nonrecipients. Thus, EITC recipient households that are married with children
will undoubtedly have worse credit statuses and lower borrowing limits than
their non-recipient counterparts.
Third, for married households, credit does not seem to be more restricted
for those with more children. However, single households seem to be more
credit constrained as the number of children increases, and this is true for both
254
Table 7 Measures of Being Credit Constrained
7b: Measures of Being Credit Constrained by Household Type
Married,
Married,
Married,
No Kids
One Kid
Two+ Kids
EITC Recipients:
Bad Credit
0.0%
0.0%
2.2%
Credit Card Balance (2007$)
$2,541
$2,966
$2,092
Late Payment for 60+ Days
1.9%
13.6%
13.1%
Has No Checking Account
28.7%
25.6%
25.7%
Non-EITC Recipients:
Bad Credit
0.2%
0.3%
0.0%
Credit Card Balance (2007$)
$4,497
$4,946
$5,502
Late Payment for 60+ Days
3.7%
3.9%
5.3%
Has No Checking Account
5.0%
4.1%
2.1%
Source: Authors’ calculations using the 2007 SCF. Means are weighted.
Single,
No Kids
Single,
One Kid
Single,
Two+ Kids
3.0%
$2,419
7.0%
31.6%
3.3%
$1,456
10.8%
27.3%
3.1%
$1,933
13.5%
29.3%
1.3%
$2,693
6.5%
11.7%
0.0%
$3,509
11.7%
13.2%
1.3%
$1,401
8.5%
25.7%
Federal Reserve Bank of Richmond Economic Quarterly
7a: Measures of Being Credit Constrained: EITC Recipients vs. Non-Recipients
Mean
St. Dev.
EITC Recipients:
Bad Credit
2.3%
0.3%
Credit Card Balance (2007$)
$2,131
$140
Late Payment for 60+ Days
11.2%
0.6%
Has No Checking Account
27.9%
0.9%
Non-EITC Recipients:
Bad Credit
0.5%
0.1%
Credit Card Balance (2007$)
$4,174
$91
Late Payment for 60+ Days
5.4%
0.2%
Has No Checking Account
7.0%
0.3%
K. B. Athreya, D. Reilly, and N. B. Simpson: EITC Recipients
255
EITC recipients and non-recipients. As documented above, the net worth of
single households falls as the number of children increases (from Table 5a).
Our analysis suggests that EITC recipients use credit markets differently
than non-recipients, possibly as a direct consequence of their income being
currently and perhaps temporarily low, and this may have important implications on their ability to borrow. For example, EITC recipients are less likely to
have a checking account and have lower credit card balances. They also more
frequently have late debt repayments and are denied checking accounts than
non-EITC recipients. Thus, it seems that at the time of receipt of the EITC,
households are closer to limits on their ability to borrow than households that
do not receive the EITC, and much of this is because of differences in income
and household structure between the two groups.
9.
CONCLUSION
In this article, we have studied several aspects of the Earned Income Tax
Credit (EITC) that have been previously overlooked, including the income
of EITC recipients at various ages, their wealth holdings, and the extent to
which they are credit constrained. Naturally, we find that average annual
earnings for those who receive the EITC are much lower than for non-EITC
recipients at every age. In addition, younger households receive more EITC,
and the amount of EITC received by these households suggests that the EITC
increases lifetime earnings non-negligibly. The EITC in all likelihood provides a nontrivial mechanism for young, working households to smooth their
consumption over their lifetimes.
The EITC acts as a negative income tax for recipient households. Specifically, we show that it has important implications on the marginal tax rate that
low-income households face at various levels of earned income. Because of
the phasing out of tax credits and income-support programs (such as TANF,
food stamps, etc.), marginal income tax rates are much higher for low-income
households than for middle- and high-income households in the United States.
In particular, the marginal tax rate is negative for low levels of income, very
high for those with moderate incomes that still qualify for the EITC, and then
falls once households no longer qualify. We find that single-parent households
that receive the EITC face some of the highest marginal income tax rates in
the United States.
We then consider the theoretical and empirical effects of the EITC on
the extensive and intensive margins of household labor supply. The EITC
has undoubtedly increased labor force participation, but the effects on hours
worked are ambiguous. This can be partly explained by the fact that lowincome/low-wealth households that face borrowing constraints are insensitive
to changes in the returns to working. Existing empirical work supports this
conclusion.
256
Federal Reserve Bank of Richmond Economic Quarterly
Lastly, using data from the Survey of Consumer Finances, we estimate
the wealth distribution of EITC households and measure the extent to which
EITC households are credit constrained. Not surprisingly, we find that EITCrecipient households are very poor in terms of net worth: The average household has less than 20 percent of the average wealth of the average non-recipient
household. In addition, EITC recipients are more likely to have bad credit and
are more likely to have late debt payments than the average U.S. household,
suggesting that they are more credit constrained.
REFERENCES
Athreya, Kartik, Devin Reilly, and Nicole Simpson. 2010. “The Earned
Income Tax Credit: Insurance Without Disincentives?” Manuscript.
Barrow, Lisa, and Leslie McGranahan. 2000. “The Effects of the Earned
Income Credit on the Seasonality of Household Expenditures.” National
Tax Journal 53 (December): 1,211–44.
Berube, Alan, Anne Kim, Benjamin Forman, and Megan Burns. 2002. The
Price of Paying Taxes: How Tax Preparation and Refund Loan Fees
Erode the Benefits of the EITC. Washington, D.C.: The Brookings
Institution, Center on Urban & Metropolitan Policy, and the Progressive
Policy Institute (May).
Bucks, Brian K., Arthur B. Kennickell, Traci L. Mach, and Kevin B. Moore.
2009. “Changes in U.S. Family Finances from 2004 to 2007: Evidence
from the Survey of Consumer Finances.” Federal Reserve Bulletin,
www.federalreserve.gov/pubs/bulletin/2009/pdf/scf09.pdf (March).
Cancian, Maria, and Arik Levinson. 2005. “Labor Supply Effects of the
Earned Income Tax Credit: Evidence from Wisconsin Supplemental
Benefit for Families with Three Children.” Working Paper 11454.
Cambridge, Mass.: National Bureau of Economic Research (July).
Deaton, Angus. 1992. Understanding Consumption. Oxford: Oxford
University Press.
Eissa, Nada, and Hilary Hoynes. 2004. “Taxes and the Labor Market
Participation of Married Couples: The Earned Income Tax Credit.”
Journal of Public Economics 88 (August): 1,931–58.
Eissa, Nada, and Hilary Hoynes. 2009. “Redistribution and Tax
Expenditures: The Earned Income Tax Credit.” www.econ.ucdavis.edu/
faculty/hoynes/publications/Eissa-Hoynes-NTJ-revision.pdf.
K. B. Athreya, D. Reilly, and N. B. Simpson: EITC Recipients
257
Eissa, Nada, and Jeffrey B. Liebman. 1996. “Labor Supply Response to the
Earned Income Tax Credit.” Quarterly Journal of Economics (May):
605–37.
Elliehausen, Gregory. 2005. “Consumer Use of Tax Refund Anticipation
Loans.” Georgetown University McDonough School of Business Credit
Research Center Monograph 37 (April).
Ellwood, David T. 2000. “The Impact of the Earned Income Tax Credit and
Social Policy Reforms on Work, Marriage, and Living Arrangements.”
National Tax Journal 53 (December): 1,063–106.
Ellwood, David T., and Jeffrey B. Liebman. 2000. “The Middle Class Parent
Penalty: Child Benefits in the U.S. Tax Code.” Working Paper 8031.
Cambridge, Mass.: National Bureau of Economic Research (December).
Evers, Michiel, Ruud De Mooij, and Daniel Van Vuuren. 2008. “The Wage
Elasticity of Labour Supply: A Synthesis of Empirical Estimates.” De
Economist 156 (March): 25–43.
Grogger, Jeffrey. 2004. “Welfare Transitions in the 1990s: The Economy,
Welfare Policy, and the EITC.” Journal of Policy Analysis and
Management 23: 671–95.
Gruber, Jon, and Emmanuel Saez. 2002. “The Elasticity of Taxable Income:
Evidence and Implications.” Journal of Public Economics 84 (April):
1–32.
Hoffman, Saul D., and Laurence S. Seidman. 2002. Helping Working
Families: The Earned Income Tax Credit. Kalamazoo, Mich.: W. E.
Upjohn Institute for Employment Research.
Holt, Steve. 2006. “The Earned Income Tax Credit at Age 30: What We
Know.” The Brookings Institution. www.brookings.edu/reports/
2006/02childrenfamilies holt.aspx (February).
Hotz, V. Joseph, and John Karl Scholz. 2000. “Not Perfect, but Still Pretty
Good: The EITC and Other Policies to Support the U.S. Low-Wage
Labour Market.” OECD Economic Studies 31: 25–42.
Hotz, V. Joseph, and John Karl Scholz. 2003. “The Earned Income Tax
Credit.” In Means-Tested Transfer Programs in the United States, edited
by Robert A. Moffitt. Chicago: The University of Chicago Press and
NBER, pp. 141–98.
Jappelli, Tullio. 1990. “Who Is Credit Constrained in the U.S. Economy?”
The Quarterly Journal of Economics 105 (February): 219–34.
Keane, Michael, and Robert Moffitt. 1998. “A Structural Model of Multiple
Welfare Program Participation and Labor Supply.” International
Economic Review 39 (August): 553–89.
258
Federal Reserve Bank of Richmond Economic Quarterly
Kennickell, Arthur B. 1999. “Revisions to the SCF Weighting Methodology:
Accounting for Race/Ethnicity and Homeownership.”
www.federalreserve.gov/pubs/oss/oss2/papers/weight.revision.pdf
(January).
Levitis, Jason, and Jeremy Koulish. 2008. “State Earned Income Tax Credits:
2008 Legislative Update.” Center on Budget and Policy Priorities.
www.cbpp.org/6-6-08sfp.pdf (October 8).
Meyer, Bruce D. 2001. “Taxes, Welfare, and Work by Single Mothers.”
NBER Reporter Online. Cambridge, Mass.: National Bureau of
Economic Research, www.nber.org/reporter/fall01/meyer.html (Fall).
Neumark, David, and William Wascher. 2001. “Using the EITC to Help Poor
Families: New Evidence and a Comparison with the Minimum Wage.”
National Tax Journal 54 (June): 281–318.
Pijoan-Mas, Josep. 2006. “Precautionary Savings or Working Longer
Hours?” Review of Economic Dynamics 9 (April): 326–52.
Romich, Jennifer L. 2006. “Difficult Calculations: Low–Income Workers
and Marginal Tax Rates.” Social Service Review 80 (March): 27–66.
Romich, Jennifer L., and Thomas Weisner. 2000. “How Families View and
Use the EITC: Advance Payment Versus Lump Sum Delivery.” National
Tax Journal 53 (December): 1,245–66.
Simpson, Nicole B., Jill Tiefenthaler, and Jameson Hyde. 2009. “The Impact
of the Earned Income Tax Credit on Economic Well-being: A
Comparison across Household Types.” Population Research and Policy
Review 29: 843–64.
Smeeding, Timothy M., Katherin Ross Phillips, and Michael O’Connor.
2000. “The EITC: Expectation, Knowledge, Use, and Economic and
Social Mobility.” National Tax Journal 53 (December): 1,187–210.
Tax Policy Center. 2009. “The American Recovery and Reinvestment Tax
Act of 2009: Expansion of the Earned Income Tax Credit, Conference
Report, Distribution of Federal Tax Change by Cash Income Percentile,
2009.” T09-0106 (February).
Ventry, Dennis J., Jr. 2000. “The Collision of Tax and Welfare Politics: The
Political History of the Earned Income Tax Credit, 1969–99.” National
Tax Journal 53 (December): 983–1,026.
Ziliak, James P. 2006. “Understanding Poverty Rates and Gaps: Concepts,
Trends, and Challenges.” Foundations and Trends in Microeconomics 1:
127–99.
Economic Quarterly—Volume 96, Number 3—Third Quarter 2010—Pages 259–272
Instability and
Indeterminacy in a Simple
Search and Matching Model
Michael U. Krause and Thomas A. Lubik
T
he search and matching model of Mortensen and Pissarides (1994)
has become a popular and successful framework for analyzing labor
market dynamics in dynamic stochastic general equilibrium (DSGE)
models.1 In this article, we point out a potentially problematic feature of
this framework. We show that the solution to the dynamic model can be
nonexistent or indeterminate. In particular, uniqueness problems arise when
endogenous matching in response to labor market pressures is not elastic
enough. In such a scenario, extraneous uncertainty, “sunspots,” can lead to
business cycle fluctuations even in the absence of any other disturbances.
However, a solution does not exist when matching is too elastic. While these
determinacy problems are plausible outcomes, we argue that they are not
likely, as they are associated with regions of the parameter space that are at
the extremes of typical calibrations.
Indeterminacy in search and matching models has previously been addressed by Giammarioli (2003). Her article differs from ours in that it introduces increasing returns in the matching function, which is a well-known
mechanism to generate multiplicity in DSGE models (see Farmer and Guo
[1994]). We show that indeterminacy in the search and matching model can
arise even under constant returns. Our paper is similar to Burda and Weder
We are grateful to Andreas Hornstein, Anne Davlin, Marianna Kudlyak, and Alex Wolman
for useful comments. The views expressed in this paper are those of the authors and should
not necessarily be interpreted as those of the Federal Reserve Bank of Richmond or the
Federal Reserve System. Krause is with the Economic Research Centre of the Deutsche
Bundesbank in Frankfurt, Germany. Lubik is a senior economist with the Richmond Fed.
E-mails: michael.u.krause@bundesbank.de; thomas.lubik@rich.frb.org.
1 A nonexhaustive list of references includes Merz (1995); Andolfatto (1996); Cooley and
Quadrini (1999); den Haan, Ramey, and Watson (2000); Krause and Lubik (2007); and Trigari
(2009).
260
Federal Reserve Bank of Richmond Economic Quarterly
(2002) in this respect. Their indeterminacy results are driven, however, by
the existence of labor market distortions, such as taxes, and the associated
fiscal policy functions, and not by the features of the matching process per
se. More recently, Hashimzade and Ortigueira (2005) analyzed the determinacy properties of a real search and matching model with capital. They show
numerically how, for a given parameterization, the model admits sunspot equilibria. Zanetti (2006) incorporates the standard search and matching model
into a New Keynesian DSGE model, where monetary policy is governed by
an interest rate feedback rule. He shows that this expands the region of the
parameter space where the Taylor principle, and thus equilibrium uniqueness,
is violated. However, his paper focuses on the monetary policy rule as a
source of indeterminacy. Labor market search and matching only provides
a transmission mechanism, but is not analyzed as an independent factor of
determinacy problems.
This article proceeds as follows. We present a canonical DSGE model
with search and matching frictions in the next section. This is a bare-bones
version of the model that does not rely on any increasing returns to scale in the
functional forms. Our model specification has the advantage that the determinacy regions can be characterized largely analytically. Section 2 discusses
issues related to the calibration of this model, while Section 3 derives its determinacy properties, both analytically and numerically. The final section briefly
summarizes and concludes.
1. A CANONICAL DSGE MODEL OF LABOR MARKET
SEARCH AND MATCHING
We develop a simple version of a discrete-time DSGE model with search
and matching frictions in the labor market.2 Key to the search and matching
model is that new employment relationships are the result of time-consuming
searches, both by firms and potential workers. In order to hire workers, firms
first have to advertise open positions; they have to post vacancies, which is
assumed to be costly. Existing matches between workers and firms are subject
to job destruction, which leads to a flow of workers into the unemployment
pool. The behavior of the aggregate economy is governed by the choices of
a representative household, which engages in consumption smoothing. The
household engages in perfect risk-sharing between its employed and unemployed members. The latter enjoy unemployment benefits while searching for
a job. We employ some simplifying assumptions later on that lead to steadystate and dynamic equations that can be solved analytically. The properties of
the full model are then analyzed numerically.
2 The model is similar to Lubik (2009), to which we refer the reader for additional discussion
and references.
M. U. Krause and T. A. Lubik: Indeterminacy and Search and Matching 261
Time is discrete. One period in the model is assumed to be a quarter. There
is a continuum of identical firms that employ workers, who each inelastically
supply one unit of labor.3 Output, y, of a typical firm is linear in employment,
n:
yt = nt .
(1)
The matching process is represented by a constant-returns matching function,
m(ut , vt ) = muξt vt1−ξ , of unemployment, u, and vacancies, v, with parameters
m > 0 and 0 < ξ < 1. It captures the number of newly formed employment
relationships that arise out of the contacts of unemployed workers and firms
seeking to fill open positions. Unemployment is defined as
ut = 1 − nt ,
(2)
which is the measure of all potential workers in the economy who are not
employed at the beginning of the period and are thus available for job search
activities.
Inflows to unemployment arise from exogenous job destruction at rate
0 < ρ < 1. Employment therefore evolves according to
nt = (1 − ρ)[nt−1 + m(ut−1 , vt−1 )].
(3)
Note that newly matching workers who are separated from their job within
the period reenter the matching pool immediately. We can define q(θ t ) as
the probability of filling a vacancy, or the firm-matching rate, where θ t =
vt /ut . We refer to θ as the degree of labor market tightness. In terms of
the matching function, we can write this as q(θ t ) = m(ut , vt )/vt = mθ −ξ
t .
Similarly, the probability of finding a job, the worker-matching rate, is p(θ t ) =
m(ut , vt )/ut = mθ 1−ξ
. An individual firm is atomistic in the sense that it takes
t
the aggregate matching rate, q(θ t ), as given. The employment constraint on
the firm’s decision problem is therefore
nt = (1 − ρ)[nt−1 + vt−1 q(θ t−1 )],
(4)
that is, it is linear in vacancy postings.
Firms maximize profits using the discount factor β t λλ0t (to be determined
below):
∞
λt
β t [nt − wt nt − κvt ] +
max∞
{vt ,nt }t=0
λ0
t=0
+
∞
t=0
βt
λt
μt (1 − ρ)[nt−1 + vt−1 q(θ t−1 )] − nt .
λ0
(5)
3 For expositional convenience, we present the problem of a representative firm only, and
abstract from indexing the individual form and aggregation issues.
262
Federal Reserve Bank of Richmond Economic Quarterly
Wages paid to the workers are w, while κ > 0 is a firm’s cost of opening a
vacancy. μ is the Lagrange multiplier on the firm’s employment constraint. It
can be interpreted as the marginal value of a filled position. Firms decide how
many vacancies to post (which can be turned into employment relationships)
and how many workers to hire. The first-order conditions are
λt+1
μt = 1 − wt + β(1 − ρ)
μ ,
(6)
nt :
λt t+1
λt+1
κ = β(1 − ρ)
μ q(θ t ),
(7)
vt :
λt t+1
which imply a job-creation condition
λt+1
κ
κ
= (1 − ρ)β
.
(8)
1 − wt+1 +
q(θ t )
λt
q(θ t+1 )
This optimality condition trades off the expected hiring cost (which depends
on the success probability q(θ t )) against the benefits of a productive match
(which consists of the output accruing to the firms net of wage payments and
the future savings on hiring costs when the current match is successful).
We assume that the economy is populated by a representative household.
The household is composed of workers who are either unemployed or employed. If they are unemployed they are compelled to search for a job, but
they can draw unemployment benefits, b. Employed members of the household receive pay, w, but share this with the unemployed. They do not suffer
disutility from working and supply a fixed number of hours.4 The household’s
only choice variable is consumption, so that its optimization problem is trivial:
∞
1−σ
−1
t Ct
max
β
,
(9)
∞
{Ct }t=0
1
−
σ
t=0
subject to
C t = Yt ,
(10)
where C is consumption and Y is income earned from labor and residual profits
from the firms; 0 < β < 1 is the discount factor, and σ −1 is the intertemporal
elasticity of substitution. From the household’s (trivial) first-order condition
we find that λt = Ct−σ , where λ is the multiplier on the household’s budget
constraint. In equilibrium, total income accruing to the household equals net
output in the economy, which is composed of production less real resources
lost in the search process:
Yt = yt − κvt .
(11)
4 We thus assume income pooling between employed and unemployed households and abstract
from potential incentive problems concerning labor market search. This allows us to treat the
labor market separate from the consumption choice. See Merz (1995) and Andolfatto (1996) for
discussion of these issues.
M. U. Krause and T. A. Lubik: Indeterminacy and Search and Matching 263
Finally, we need to derive how wages are determined. We assume that
wages are set according to the Nash bargaining solution.5 Firms and workers
maximize the bargaining function
max (Wt )η (Jt )1−η ,
(12)
wt
with respect to the variable over which the two parties bargain, namely the
wage, wt . This results in the sharing rule:
ηJt = (1 − η) Wt .
(13)
Wt denotes the match surplus accruing to the worker, while Jt is the firm’s
surplus, that is, the value of a filled job. The latter can be found from the
firm’s optimization problem. It is equal to the Lagrange multiplier on the
employment constraint, μt , and is the shadow value of a filled position; to wit,
Jt = μt . From the first-order condition with respect to employment we find
that
Jt = 1 − wt + β(1 − ρ)
λt+1
Jt+1 .
λt
(14)
The expression states that the value of a filled job is its marginal product, 1,
net of wage payments, wt ; but it also has a continuation value Jt+1 , which is
discounted at the time preference rate, β, and assuming that the filled job is
still there next period. The latter is captured by the survival rate (1 − ρ).
We can derive the worker’s surplus as follows. The worker receives payment in the form of the wage, wt . But while he is working, he loses the value
of being unemployed, b. The latter can be interpreted as the money value of
enjoying leisure, engaging in household production, or simply unemployment
benefits. Therefore, the current period net return is wt − b. In the next period,
the worker receives the continuation value Wt+1 , which is discounted at rate
β. The worker has to take into account that he might not be employed next
period, which is captured by the survival rate (1 − ρ), adjusted for the fact that
a separated worker might not find a job again with probability [1 − p(θ t )].
Putting it all together, we have
Wt = wt − b + β(1 − ρ) [1 − p(θ t )]
λt+1
Wt+1 .
λt
(15)
The two marginal values can now be substituted into the sharing rule and,
after some algebra using the firm’s first-order conditions, we can find the
Nash-bargained wage:
wt = η (1 + κθ t ) + (1 − η)b.
(16)
5 This is a standard assumption in the literature. Shimer (2005) provides further discussion.
264
Federal Reserve Bank of Richmond Economic Quarterly
We can now use this wage equation to derive the job-creation condition:
Yσ
κ
κ
= (1 − ρ)β σt
,
(1 − η) (1 − b) − ηκθ t+1 +
q(θ t )
Yt+1
q(θ t+1 )
(17)
where we have used the first-order conditions of the household to eliminate
the Lagrange multiplier, λ, from the discount factor. The dynamics of the
model are given by the five equations in five unknowns: (2), (3), (11), (17),
and the definition of labor market tightness, θ t .
2.
STEADY STATE AND CALIBRATION
We first compute the deterministic steady state of the model. We then linearize
the dynamic system around the steady state and analyze the local determinacy
properties of the economy. The equations describing the steady state are
u = 1 − n,
(18)
v
θ =
,
(19)
u
1 − ρ 1−ξ ξ
(20)
mv u ,
n =
ρ
Y = n − κv,
(21)
1 − β(1 − ρ) κ ξ
θ + cηθ .
(22)
(1 − η)(1 − b) =
β(1 − ρ) m
(20) is the employment accumulation equation. It stipulates that inflows and
outflows of the unemployment pool have to be equal. In a steady-state equilibrium, the number of separated workers, ρn, has to equal newly hired workers.
Equation (22) is the job-creation condition, while the other equations are
definitions.
There are five endogenous variables (u, n, v, θ , y) and seven structural
parameters (ρ, m, ξ , κ, β, η, b). Because of the nonlinearity in the last
equation, there is no analytical solution to this system. Given values for the
parameters, however, we can compute a numerical solution. Using a nonlinear
equation solver we determine θ from equation (22).6 From equation (20) we
−1
mθ 1−ξ
, and the solution for the other variables
can find u = 1 + 1−ρ
ρ
follows immediately.
We find it more convenient, however, to calibrate the model by fixing
the steady-state unemployment rate, u = u. This implies that one parameter
has to be determined endogenously. Additionally, we can fix the endogenous
6 Since the function in θ is monotonically increasing for nonnegative θ , there is a unique
solution to this equation as long as 0 ≤ b < 1. This reflects the fact that the outside option of
the worker, namely staying unemployed, cannot be larger than the worker’s marginal product, i.e.,
the maximum rent that the worker can extract from the firm.
M. U. Krause and T. A. Lubik: Indeterminacy and Search and Matching 265
matching rate, q = mθ −ξ , by using evidence on the rates at which firms fill
vacancies. Hence, another parameter has to be determined endogenously.
Using n = 1 − u in (20), we find that the match efficiency parameter is
m=
ρ 1−u
1−ρ u
ξ
q 1−ξ and labor market tightness is θ =
θξ
m
q
1/ξ
. From (22)
η
1 1−β(1−ρ)
we can then also compute 1−b
= 1−η
θ + 1−η
. Note, however, that
κ
β(1−ρ) m
this condition does not pin down b and κ independently, nor does any other
restriction in the model. Equation (21) helps only insofar as it restricts κ such
that y remains positive. We chose to fix the vacancy cost parameter, κ, and let
the benefit parameter, b, be determined endogenously.
For our calibration exercise we set the discount factor as β = 0.99. We
chose a separation rate of ρ = 0.1. This is consistent with the evidence
reported in Shimer (2005) and Lubik (2010), who use various econometric
methods to estimate this parameter from U.S. labor market data. We agnostically set the bargaining parameter as η = 0.5 and follow most of the literature
in this respect. Similarly, the match elasticity is ξ = 0.5, which is on the
low end of estimates in the literature. Note that this benchmark calibration
implements the Hosios condition, under which the market allocation in the
model is socially efficient. The value for the match elasticity is at the low end
of the plausible range as reported in the empirical study by Petrongolo and
Pissarides (2001). We set the intertemporal substitution elasticity as σ = 1.
Finally, the two steady-state values are chosen as follows. We fix the
unemployment rate, u, at 12 percent. Our idea is to capture both measured
unemployment in terms of recipients of unemployment benefits and potential
job searchers that are only marginally attached to the labor force, but are open
to job search. Since we do not model labor force participation decisions, this
is a shortcut to capturing effective labor market search. This approach has
been taken by Cooley and Quadrini (1999) and Trigari (2009). In choosing
the steady-state job-matching rate, we follow den Haan, Ramey, and Watson
(2000) who set q = 0.7. In the numerical determinacy analysis below we
conduct robustness checks for selected parameters and the calibrated steadystate values by varying them over their admissible range.
3.
INDETERMINACY AND NONEXISTENCE
We now proceed by linearizing the dynamic equilibrium conditions around the
steady state. It is a well-known feature of linear rational expectations models
that they can have multiple equilibria, or that the solution may not even exist.
We show that both scenarios are possible outcomes in the standard search
and matching model, but they are associated with regions at the fringes of the
parameter space. The linearized system is as follows (where xt = log xt −log x
266
Federal Reserve Bank of Richmond Economic Quarterly
denotes the percentage deviation of the variable xt from its steady state x):
u ut = −n nt ,
θ t = vt − ut ,
nt+1 = (1 − ρ)nt + ρ(1 − ξ )vt + ρξ ut ,
n
κv
nt −
vt ,
Yt =
y
y
κξ ξ
θ − ηκθ X −1 θ t+1 − σ Yt+1 ,
ξ θ t − σ Yt =
m
(23)
(24)
(25)
(26)
(27)
κ ξ
1
θ .
where X = β(1−ρ)
m
It is straightforward to substitute out ut , vt , and Yt , so that we are left with
ξ +σ κv
y
α2
α2 ρ
θt
θ t+1
+
ρ(1
−
ξ
)
−
α1
α1
α1 u
=
,
(28)
u−ρ
nt+1
n
t
ρ(1 − ξ )
u
where α 1 = β(1 − ρ)(ξ − ηmθ 1−ξ ) + σ κv
and α 2 = σ yn (1 + κθ ). This
y
reduced form is expressed in terms of the state (or predetermined) variable,
nt , and the jump variable, θ t , which is a function of vacancy postings, vt . The
stability properties of the solution depend on the eigenvalues of the coefficient
matrix. A unique solution requires that one root be inside the unit circle and
the other root outside. Indeterminacy arises when both roots are inside the
unit circle, while nonexistence occurs with both roots being explosive. In the
former case, both equations are dynamically stable and an infinite number
of paths (starting from arbitrary initial conditions) toward the unique steady
state exist. In the latter case, both equations are explosive, which implies that,
from any arbitrary initial condition, employment and vacancies would grow
without bounds. This violates transversality or boundary conditions and can
therefore not be an equilibrium.
The coefficient matrix is sufficiently complicated to prevent simple analytical derivations of the equilibrium regions. For illustrative purposes and
for gaining intuition, we therefore make the simplifying assumption that the
representative household is risk neutral, σ = 0. Later on, we discuss the
general case using simulation results. Under risk neutrality, the coefficient
matrix reduces to
ξ
β(1−ρ)(ξ −ηp)
ρ(1 − ξ )
0
u−ρ
u
.
(29)
Since the coefficient matrix is triangular, the eigenvalues can be read off the
principal diagonal. Recall that the worker matching rate is p = mθ 1−ξ ,
ρ 1−u
which is equal to 1−ρ
. Since we are treating the unemployment rate as a
u
parameter to be calibrated, the determinacy conditions therefore only depend
on structural parameters.
We establish the determinacy properties in the following proposition.
M. U. Krause and T. A. Lubik: Indeterminacy and Search and Matching 267
Proposition 1
1. The model solution is indeterminate if and only if
(a) 0 < ρ < 2u,
β(1−ρ)
ηp.
(b) 0 < ξ < 1+β(1−ρ)
2. The model solution is nonexistent if and only if
(a) ρ > 2u > 0,
β(1−ρ)
(b) 1+β(1−ρ)
ηp < ξ < 1.
3. The model solution is unique if and only if either
(a) 0 < ρ < 2u,
β(1−ρ)
ηp < ξ < 1,
(b) 1+β(1−ρ)
or
(c) ρ > 2u > 0,
β(1−ρ)
(d) 0 < ξ < 1+β(1−ρ)
ηp.
Proof. Indeterminacy requires both roots inside the unit circle. Call λ2 =
u−ρ
. It is straightforward to verify that |λ2 | < 1 over the permissible range iff
u
ξ
. We have to distinguish two
0 < ρ < 2u. Call the other root λ1 = β(1−ρ)(ξ
−ηp)
cases: if ξ > ηp, no parameter combination can be found such that |λ1 | < 1.
If ξ < ηp, we can write −β(1−ρ)(ξ −ηp) > ξ > β(1−ρ)(ξ −ηp). Simple
algebra in combination with ξ > 0 then yields 1(b). Nonexistence requires
that both roots be outside the unit circle. This is just the opposite scenario
discussed before. Part 2 of the proposition follows immediately. Uniqueness
requires one stable and one unstable eigenvalue. The parameter regions are
consequently implied by those not considered in part 1 and 2.
The proposition shows that indeterminacy is a potential outcome in this
model. It arises when the job destruction rate is less than twice the (calibrated)
unemployment rate. For instance, at a separation rate of 10 percent, the unemployment rate would have to be less than 5 percent to definitely rule out
indeterminacy on account of condition 1(a). This value is not implausible,
given historical data for the United States where the average post-war unemployment rate is 4.8 percent. However, it has been argued (e.g., Trigari [2009])
that the proper corresponding concept for model unemployment includes not
only the registered unemployed but also all workers potentially available for
employment, such as discouraged workers or workers loosely attached to the
labor force. Consequently, u should be assigned a much higher value (for
instance, 26 percent as in Trigari [2009]), which raises the possibility of equilibrium indeterminacy.7
7 Calibrating u to a different value implies that benefits, b, and match efficiency, m, would
have to change, too, since they are computed endogenously from the steady-state conditions. Higher
268
Federal Reserve Bank of Richmond Economic Quarterly
Condition 1(b) imposes an upper bound on the match elasticity, ξ . In
the benchmark calibration, this upper bound is 0.147. Since ξ is typically
calibrated to be above 0.5, this would rule out indeterminacy. However, this
observation comes with the caveat that values for the match elasticity below
0.5 have some support in the literature. For instance, Cooley and Quadrini
(1999) argue that a low elasticity in the range of ξ = 0.1 is necessary to match
labor market cyclicality. Using likelihood-based econometric methods, Lubik
(2010) finds that there is, in fact, substantial probability mass on low values
of ξ . We also note that the upper bound is increasing in the Nash bargaining
parameter. But even if η → 1, indeterminacy would not occur for the typical
parameter choices in the literature. Suppose, however, that the unemployment rate were set to u = 0.06. In this case, the upper bound increases to
0.816, which would imply indeterminacy for typical search elasticity choices.
Clearly, the interpretation of the pool of searchers in the matching model
matters for determinacy questions.
Intuitively, we can think about a sunspot equilibrium in the following way.
Firms are willing to incur vacancy posting costs if they expect to recoup them
through the proceeds from production net of wages and the savings on future hiring, as captured by the job-creation condition (17). The equilibrating
mechanism is the behavior of the matching rate, q(θ ). An increase in vacancy
posting raises labor market tightness and lowers the probability that an individual firm is successful in finding an employee. This, in turn, raises effective
hiring costs, κ/q(θ ), which would have to be offset by higher expected returns.
It is this externality, namely the fact that firms do not internalize the effect of
their posting decisions on aggregate match probabilities, that is at the heart of
the determinacy issue.8
Now suppose that a firm believes that future profits will be higher than
is warranted by the fundamentals, such as the level of productivity. Beliefs
of this kind can be triggered by sunspot shocks, as in the interpretation by
Lubik and Schorfheide (2003). This belief would compel the firm to post
more vacancies. If other firms were to do the same, aggregate tightness would
increase and match probability would fall, raising effective hiring cost. What
tends to rule out a sunspot equilibrium is that expected future benefits are not
consistent with the higher posting costs. Consequently, rational firms do not
act on sunspot beliefs. This argument breaks down in an environment where
future benefits rise to accommodate higher current costs. The proposition
steady-state unemployment corresponds to a higher value of b and lower m. This can be interpreted
as an implication of different labor market institutions.
8 This has similar characteristics to the notion of an upward-sloping labor demand schedule
in Farmer and Guo (1994). In their model, production exhibits constant returns to scale at the individual firm level, but increasing returns in the aggregate. An individual firm hiring more workers
raises the marginal product of workers in the aggregate, thereby stimulating more labor demand.
The job-creation condition can be thought of as a vacancy-demand curve.
M. U. Krause and T. A. Lubik: Indeterminacy and Search and Matching 269
Figure 1 Determinacy Regions
0.9
1.0
0.8
0.8
0.7
Non-Existence
0.5
0.4
0.6
u
ρ
0.6
Indeterminacy
0.4
0.3
Indeterminacy
0.2
Non-Existence
0.2
0.1
0.0
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.0
0.2
0.4
0.6
0.8
1.0
0.6
0.8
1.0
ξ
ξ
1.0
0.8
0.8
0.6
0.6
Indeterminacy
η
q
1.0
0.4
0.4
Indeterminacy
0.2
0.0
0.0
0.2
0.2
0.4
0.6
ξ
0.8
1.0
0.0
0.0
0.2
0.4
ξ
stipulates that indeterminacy arises when both the separation, ρ, and the match
elasticity, ξ , are too small. When the former applies, the unemployment pool
is small, while the latter makes new matches, and thereby future employment,
highly elastic to vacancy postings. Consequently, the savings on future hiring
costs react more than current effective costs, which helps validate sunspot
beliefs.
A similar argument applies for the case of nonexistence of equilibrium. In
general, nonexistence problems would arise for unemployment rates that are
too low for given separation rates, in combination with excessively high match
elasticities. In more technical terms, this combination makes the employment
equation explosive. Any disturbance to a steady-state equilibrium would result
in excessive job destruction (due to high separation rates) and matching that
is inconsistent with the job-creation condition.
We now turn to the full model solution with risk-averse households (σ >
0). We compute the determinacy regions numerically for combinations of
the match elasticity, ξ , and various other structural parameters. The results
are presented in Figure 1, where we have plotted determinacy regions for
different subsets of the parameter space. The parameters are calibrated at the
benchmark values discussed above. In each panel we vary two parameters over
270
Federal Reserve Bank of Richmond Economic Quarterly
their admissible range while keeping the other parameters at their benchmark
values.
As a general conclusion, determinacy problems tend to arise when the
match elasticity, ξ , is either too small or too big. For small ξ , the equilibrium
is indeterminate when the job destruction rate, ρ, or the unemployment rate, u,
is too small. This is related to the analytical condition found in Proposition 1.
Furthermore, firm-matching rates, q, above 0.2 and a Nash parameter that puts
more weight on workers also lead to multiplicity. No equilibrium exists for
large ξ and either a small unemployment rate or ρ above 0.2. We also analyze
the sensitivity of the regions with respect to σ (not reported). As σ → 0, the
indeterminacy regions expand. In particular, any q implies multiple equilibria
when ξ < 0.2. In the limit the boundaries between regions are given in the
proposition. As the household becomes more risk averse, however, regions of
indeterminacy disappear entirely.
An interesting special case to consider is a calibration with the Hosios
condition, where η = ξ . This can be represented by a 45-degree line in the
lower-right panel of Figure 1. In the absence of outside information on the
value of the bargaining parameter, η, the Hosios calibration is often chosen
in the literature. In this case, indeterminacy and nonexistence are ruled out
and become highly unlikely for other parameter combinations. For instance,
equilibrium nonexistence requires a separation rate of ρ = 0.81. Moreover, if
η = ξ , we can rule out indeterminacy in the case of σ = 0 because condition
1(b) of the proposition never holds. The equilibrium could still be nonexistent,
but this would require very high separation rates. In principle, these could
obtain when the model period is much longer than a quarter since eventually
all workers turn over within a long enough time horizon.9
Interpreting these results in light of standard calibrations used in the literature, we would argue that indeterminacy and nonexistence do not present
serious problems for the search and matching framework. Hence, it is unlikely that sunspot equilibria would be helpful in explaining labor market
dynamics (as claimed in Hashimzade and Ortigueira [2005]). This is not to
say that labor search and matching frameworks cannot support indeterminate equilibria. Mildly increasing returns to scale in the matching function
(Giammarioli 2003) lead to widely expanded indeterminacy regions, while a
New Keynesian model with search and matching frictions in the labor market
has broader indeterminacy properties than the standard New Keynesian model
(Zanetti 2006).
9 Incidentally, the continuous-time version of this simple search and matching model always
has a unique solution (see Shimer [2005]), as the separation-relevant time horizon is infinitesimally
small. We are grateful to Andreas Hornstein for pointing this out.
M. U. Krause and T. A. Lubik: Indeterminacy and Search and Matching 271
4.
CONCLUSION
We show in this article that for most plausible parameterizations the simple search and matching model does not suffer from determinacy problems.
Specifically, we argue that it is unlikely that the model has multiple equilibria
so that extraneous uncertainty, i.e., animal spirits, can cause business cycles.
Parameterizations that lead to indeterminacy can be found, but they lie at the
boundaries of the region that the empirical literature would consider plausible.
We identify the match elasticity and the separate rate as crucial parameters in
that respect.
These properties are obviously model specific, but our conclusions are
likely robust to modifications such as endogenous job destruction. While the
boundaries of the determinacy regions are likely to shift, the dynamic mechanism stays unaffected. The main caveat to our study is that our analysis applies
to a local equilibrium in the neighborhood of the steady state. However, the
underlying model is nonlinear and local results may therefore not adequately
describe the global equilibrium properties. Naturally, this is a topic for further
investigation. Moreover, researchers may actually be interested in the business cycle implications of indeterminacy that do not depend on policy rules
or externalities. It appears plausible that actual labor market decisions are
characterized to some extent by animal spirits. Further research should shed
some light on this issue.
REFERENCES
Andolfatto, David. 1996. “Business Cycles and Labor Market Search.”
American Economic Review 86 (March): 112–32.
Burda, Michael C., and Mark Weder. 2002. “Complementarity of Labor
Market Institutions, Equilibrium Unemployment and the Propagation of
Business Cycles.” German Economic Review 3 (February): 1–24.
Cooley, Thomas F., and Vincenzo Quadrini. 1999. “A Neoclassical Model of
the Phillips Curve Relation.” Journal of Monetary Economics 44
(October): 165–93.
den Haan, Wouter, Garey Ramey, and Joel Watson. 2000. “Job Destruction
and the Propagation of Shocks.” American Economic Review 90 (June):
482–98.
Farmer, Roger E. A., and Jang-Ting Guo. 1994. “Real Business Cycles and
the Animal Spirits Hypothesis.” Journal of Economic Theory 63 (June):
42–72.
272
Federal Reserve Bank of Richmond Economic Quarterly
Giammarioli, Nicola. 2003. “Indeterminacy and Search Theory.” European
Central Bank Working Paper 271 (September).
Hashimzade, Nigar, and Salvador Ortigueira. 2005. “Endogenous Business
Cycles with Frictional Labor Markets.” The Economic Journal 115
(March): 161–75.
Krause, Michael U., and Thomas A. Lubik. 2007. “The (Ir)relevance of Real
Wage Rigidity in the New Keynesian Model with Search Frictions.”
Journal of Monetary Economics 54 (April): 706–27.
Lubik, Thomas A. 2009. “Estimating a Search and Matching Model of the
Aggregate Labor Market.” Federal Reserve Bank of Richmond
Economic Quarterly 95 (Spring): 101–20.
Lubik, Thomas A. 2010. “Identifying the Search and Matching Model of the
Labor Market.” Manuscript.
Lubik, Thomas A., and Frank Schorfheide. 2003. “Computing Sunspot
Equilibria in Linear Rational Expectations Models.” Journal of
Economic Dynamics and Control 28 (November): 273–85.
Merz, Monika. 1995. “Search in the Labor Market and the Real Business
Cycle.” Journal of Monetary Economics 36 (November): 269–300.
Mortensen, Dale T., and Christopher A. Pissarides. 1994. “Job Creation and
Job Destruction in the Theory of Unemployment.” Review of Economic
Studies 61 (July): 397–415.
Petrongolo, Barbara, and Christopher Pissarides. 2001. “Looking Into the
Black Box: A Survey of the Matching Function.” Journal of Economic
Literature 39 (June): 390–431.
Shimer, Robert. 2005. “The Cyclical Behavior of Equilibrium
Unemployment and Vacancies.” American Economic Review 95
(March): 25–49.
Trigari, Antonella. 2009. “Equilibrium Unemployment, Job Flows, and
Inflation Dynamics.” Journal of Money, Credit and Banking 41
(February): 1–33.
Zanetti, Francesco. 2006. “Labor Market Frictions, Indeterminacy, and
Interest Rate Rules.” Journal of Money, Credit and Banking 38
(October): 1,959–70.
Economic Quarterly—Volume 96, Number 3—Third Quarter 2010—Pages 273–290
How Large Has the Federal
Financial Safety Net
Become?
Nadezhda Malysheva and John R. Walter
I
n 2002, Walter and Weinberg examined the federal financial safety net
as it stood at the end of 1999 (Walter and Weinberg 2002). At the time,
the authors estimated that approximately 45 percent of all financial firm
liabilities were protected by the safety net. As one would expect in this article,
the current estimate indicates that the size of the net has grown, as the financial
market turmoil that began in 2007 led federal government agencies to expand
the range of institutions and the types of liabilities protected by the safety net.
1. THE SAFETY NET: ITS DEFINITION, COSTS,
AND BENEFITS
Walter and Weinberg defined the federal financial safety net as consisting of
all explicit or implicit government guarantees of private financial liabilities.
Private financial liabilities are those owed by one private market participant to
another. As used by Walter and Weinberg, the phrase government guarantee
means a federal government commitment to protect lenders from losses due to
a borrower’s default (Walter and Weinberg 2002).1 Following this definition,
we include in our estimate of the safety net, insured bank and thrift deposits,
certain other banking company liabilities, some government-sponsored enterprise (GSE) liabilities, selected private employer pension liabilities, as well as
The authors would like to thank Jason Annis, Marc Chumney, Tim Pudner, and Deanna
West for providing data and valuable advice, as well as Huberto Ennis, Robert Hetzel,
Sabrina Pellerin, and John Weinberg for their insightful comments on an earlier draft. The
views expressed in this article are those of the authors and do not necessarily reflect
those of the Federal Reserve Bank of Richmond or the Federal Reserve System. E-mail:
john.walter@rich.frb.org.
1 In addition to estimating the proportion of financial firm liabilities backed by the federal
government, Walter and Weinberg also estimated the proportion of nonfinancial firm and household
liabilities with such backing.
274
Federal Reserve Bank of Richmond Economic Quarterly
a subset of the liabilities of other financial firms. The details of why we chose
to include these liabilities are provided below.
Effect of a Safety Net on Economic Efficiency
Government actions in the form of subsidies, taxes, or regulations change
market outcomes, and in competitive markets such changes distort allocations
and can reduce economic efficiency. Does the financial safety net cause distortions? As discussed in Walter and Weinberg, in principle, the government
could design guarantees that mimic market outcomes. Typically, however,
government intervention arises from a desire to alter market outcomes. In
the case of guarantees, this means either expanding coverage or underpricing
relative to private market guarantees. Underpricing means that the guarantor collects fees that are less than the expected value of its obligations. This
underpricing subsidizes risk taking.
Underpriced guarantees tend to shift resources away from activities that
are not covered toward those that are. In that way, a government guarantee
is similar to a direct subsidy paid to those engaged in a particular activity. A
guarantee is different, however, in the way it affects attitudes toward risk. By
assigning to the government part of the risk in the activities being financed, the
safety net reduces market participants’ willingness to control risk. Overprovision of guarantees, while not necessarily drawing resources into an activity,
does shift risk preferences in a way similar to underpricing. In short, guarantees lead to expanded risk taking.
Our calculation of the size of the safety net does not represent a measure
of the size of the distortions to the allocation of resources and risk taking.
Such a measure would require knowledge of the extent of underpricing or
overprovision of government guarantees. Those would be difficult to measure,
especially the latter, since government provision often preempts private market
activity. We nevertheless believe that the extent of distortions is directly related
to the size of the safety net. Other things being equal, the greater the share of
private liabilities protected by the government safety net, the more likely it is
that government guarantees are extending beyond the level of protection that
would be provided in a private market.
Why Have a Safety Net?
If the safety net is distortionary, why have one? Proponents of the financial
safety net, especially as it applies to banks, often argue that private risksharing arrangements tend to disregard the systemic consequences of large
losses borne by an individual or a small group of institutions. The idea here is
that such losses might spill over and generate further losses caused, for example, by a contagious loss of investor confidence. Under such a view, govern-
N. Malysheva and J. R. Walter: The Federal Financial Safety Net
275
ment protection for certain investors could prevent widespread financial panic
or distress. While the potential systemic consequences of a large financial
failure are difficult to assess, when faced with the possibility of widespread
failures of financial firms, policymakers are likely to conclude that preventing
such failures by protecting creditors of financial firms (providing safety net
protection) is prudent.
Similarly, some observers maintain that the safety net protections can
lower the costs of, and therefore encourage, certain highly beneficial financial
arrangements. For example, Diamond and Dybvig (1983) argue that banks’
performance of the maturity transformation function is highly beneficial to the
economy but is more costly without government-provided deposit insurance.
Banks perform maturity transformation by gathering money from numerous
short-term depositors (those bank customers whose deposits mature soon after
deposited—especially checking deposits, which are available, meaning that
they mature, immediately after being deposited) to fund long-term loans to
businesses and individuals. Without deposit insurance, which only the government has sufficient resources to provide, bank runs are likely to occur.
A bank run happens when many depositors attempt to withdraw their funds
simultaneously. Since banks make long-term loans, they cannot recover sufficient money from borrowers to meet a run and, therefore, fail. To protect
themselves from runs, banks can undertake costly private measures, but Diamond and Dybvig argue that government deposit insurance is likely to be less
expensive and therefore preferable to such measures.
2.
LEGISLATIVE AND REGULATORY CHANGES THAT
EXPANDED THE SAFETY NET
As shown in Table 1, we estimated the proportion of financial firm liabilities
protected as of the end of 2009. By the end of 2009, a number of government programs had been established to address turmoil in financial markets.
Employing methods similar to those used by Walter and Weinberg when they
measured the size of the safety net for the end of 1999, we find that as of the
end of 2009 about 59 percent of financial firm liabilities were protected by the
federal safety net.
One of the most important reasons for the increase from 1999 to 2009
is the enlarged portion of banking firm liabilities that market participants are
likely to consider protected: banking and savings firm liabilities with an implicit backing. In 1999, implicitly guaranteed liabilities of banks and savings
institutions amounted to about 13 percent of all of these firms’ liabilities (15.9
percent for commercial banks and 4.2 percent for savings institutions), or $820
Table 1 Estimated Federal Financial Safety Net
Banking and Savings Firms
(Includes BHCs)
Credit Unions
Implicitly Guaranteed
Liabilities
Explicitly and Implicitly
Guaranteed Liabilities
Total Liabilities
6,536
40.2%
7,276
44.8%
13,812
85.0%
16,249
725
88.7%
817
3,345
2,333
188
973
6,838
100%
3,345
2,333
188
973
6,838
2,799
85.5%
3,273
748
4.9%
748
4.9%
15,158
14,862
35.1%
24,921
58.9%
42,335
725
88.7%
Government-Sponsored Enterprises
Fannie Mae
Freddie Mac
Farm Credit System
Federal Home Loan Banks
Total
Private Employer Pension Funds
3,345
2,333
188
973
6,838
100%
2,799
85.5%
Other Financial Firms
Total for Financial Firms
10,059
23.8%
Notes: Data from December 2009, in billions of dollars. Figures may not sum exactly due to rounding. The figures in the
column “Explicitly and Implicitly Guaranteed Liabilities” are the sum of the numbers in the first two columns, “Explicitly
Guaranteed Liabilities” and “Implicitly Guaranteed Liabilities.” See Appendix for table legend.
Federal Reserve Bank of Richmond Economic Quarterly
Explicitly Guaranteed
Liabilities
276
Financial Firms
N. Malysheva and J. R. Walter: The Federal Financial Safety Net
277
billion; in 2009, about 45 percent of banking and savings firm liabilities were
implicitly guaranteed, by our estimate, amounting to $7.3 billion.2
How did Walter and Weinberg determine which institutions to include as
having an implicit guarantee and which liabilities issued by these institutions
might be covered? As the authors noted, the critical question is whether
market participants believe that a given institution will be protected, even
though official policy may not state explicitly that all of these liabilities are
protected. As of 1999, Walter and Weinberg argued that market participants
were likely to assume that certain holders of liabilities in the largest 21 banking
companies and the two largest thrift companies would be protected in the event
that these firms became troubled. These 21 banking companies and two thrifts
all had assets (in 1999 dollars) of more than $50 billion, which was greater
than the smallest of the 11 institutions identified by the Comptroller of the
Currency in 1984 as potentially too big to fail (Walter and Weinberg 2002,
p. 381). The liabilities that Walter and Weinberg assumed the market would
be highly likely to view as protected were deposits of more than $100,000
(deposits of less than $100,000 are included in the “Explicitly Guaranteed
Liabilities” column in the tables), federal funds loans made to the 21 banks
and two thrifts, and repo transactions with these banks and thrifts. Though
we intend to use a similar methodology for estimating the size of implicit
guarantees for banking companies in 2009, events during the recent financial
crisis required some adjustments.
Support for Stress-Tested Financial Companies
Given that the government had responded aggressively to problems in financial
firms during the financial turmoil of 2008–2009, our challenge is to decide
which institutions have implicit guarantees. Here we maintain that market
participants were very likely to assume that the liabilities of the financial firms
that were stress tested early in 2009 (participants in the Supervisory Capital
Assessment Program—SCAP) had a strong likelihood of receiving federal
backing if they suffered financial distress. Indeed, the announcement of the
stress tests in February 2009 came with a promise of government-provided
capital for stress-tested institutions that were shown to be in need of additional
capital:
Under [the Treasury’s Capital Assistance Program] CAP, federal banking
supervisors will conduct forward-looking assessments [SCAP stress tests]
to evaluate the capital needs of the major U.S. banking institutions under a more challenging economic environment. Should that assessment
indicate that an additional capital buffer is warranted, banks will have
2 An explanation of the factors underlying the large increase is provided below.
278
Federal Reserve Bank of Richmond Economic Quarterly
an opportunity to turn first to private sources of capital. In light of the
current challenging market environment, the Treasury is making government capital available immediately through the CAP to eligible banking
institutions to provide this buffer. (FinancialStability.gov 2009)
Additionally, a number of these firms did, in fact, receive government aid
in the form of capital injections in 2008 and early 2009 through the Treasury’s
Capital Purchase Program or in response to the stress tests (FinancialStability.gov 2010, pp. 21, 27, 67–80). This aid, both the aid promised under the
CAP and aid received through the Capital Purchase Program, reduced the likelihood that all liabilityholders of the protected firms would suffer losses, so
here we include all liabilities of the stress-tested banking institutions in our
safety net calculation.
While some observers in 2009 may have viewed the likely passage of
financial reform legislation as diminishing federal backing, we nevertheless
count the liabilities of the stress-tested firms. Legislation that was intended
to limit the chance that financial institutions would receive federal aid was
being considered in the U.S. Congress during 2009. If market participants
were convinced that such legislation would forestall any opportunity for the
creditors of the largest financial institutions to be protected by the federal
government, then our calculation might appropriately exclude the liabilities
of stress-tested banking institutions. In fact, most of the legislative proposals
included language that called for the closure of troubled financial firms with
losses to equityholders and at least some creditors (though at least one leading
proposal contained protections for creditors of financial firms if the failure of
such a firm might create a systemic risk).3 Nevertheless, legislative proposals
contained provisions meant to establish a mechanism that could clearly identify “systemically important” financial firms. Such mechanisms seem likely
to encourage market participant expectations of federal aid to the creditors of
the largest (i.e., systemically important) firms. Given the ambiguous effect of
the reform proposals on the probability of federal aid to the largest banking
firms, and the clear protections provided for troubled firms and for their creditors during the financial turmoil, we retain their liabilities in our estimate of
liabilities protected by the safety net, in keeping with Walter and Weinberg
(2002). (In a later section we remove the liabilities of stress-tested institutions
and re-estimate the size of the safety net—see Table 2.)
As indicated earlier, the total liabilities of the 19 stress-tested bank holding
companies, less their liabilities that were explicitly covered by deposit insurance, summed to $7.3 trillion (“Implicitly Guaranteed Liabilities” column in
3 See H.R. 4173 as of December 2, 2009, p. 370, available at: http://www.house.gov/apps/
list/press/financialsvcs dem/presscfpa 121109.shtml.
N. Malysheva and J. R. Walter: The Federal Financial Safety Net
279
the tables). This sum equals about 45 percent of all banking and savings firm
liabilities.
Increased Ceiling on Insured Deposits
Several Federal Deposit Insurance Corporation (FDIC) programs expanded
the explicit portion of the safety net for banks and thrifts (“Explicitly Guaranteed Liabilities” column in the tables) beyond the long-standing $100,000
coverage for deposits (which are also included in the “Explicitly Guaranteed
Liabilities” column in the tables).4 For example, in October 2008 the Emergency Economic StabilizationAct of 2008 temporarily increased FDIC deposit
insurance coverage from $100,000 to $250,000, until December 31, 2009. In
May 2009, the $250,000 cap was extended to December 31, 2010, by the
Helping Families Save Their Homes Act. In July 2010, legislation made permanent the $250,000 coverage limit (Federal Deposit Insurance Corporation
2010a).
Transaction Account Guarantee Program
Further, in October 2008 the FDIC implemented a program to insure uninsured deposits (those deposits in accounts containing more than $250,000) in
noninterest-bearing transactions accounts for those insured banks and thrifts
wishing to participate. The program is temporary. At first it covered such
transactions accounts until December 31, 2009. Later the FDIC extended
the program’s coverage until June 30, 2010, and then extended it again until
December 31, 2010, with a pre-announced option to extend it an additional
12 months (Federal Deposit Insurance Corporation 2010a).5 This program,
the Transaction Account Guarantee Program (TAGP), added $834 billion to
our “Explicitly Guaranteed Liabilities” column in the tables for banking and
savings firms (Federal Deposit Insurance Corporation 2009c).
Debt Guarantee Program
Last, in October 2008 the FDIC offered, to banking and savings institutions
wishing to participate, the option to receive FDIC insurance coverage for senior
unsecured debt issued by such institutions. This Debt Guarantee Program
4 Since April 2006, deposits in certain retirement accounts at banks and thrifts have been
protected by the FDIC up to $250,000 (Federal Deposit Insurance Corporation 2006). Deposits in
such accounts, up to the $250,000 ceiling, are included in the “Explicitly Guaranteed Liabilities”
column of our tables.
5 The Dodd-Frank Wall Street Reform and Consumer Protection Act extended coverage for
noninterest-bearing transaction accounts through December 31, 2012 (Federal Deposit Insurance
Corporation 2010c).
280
Federal Reserve Bank of Richmond Economic Quarterly
(DGP) at first covered debt issued by June 30, 2009, and maturing by June 30,
2010. The DGP was later extended to cover debt issued by October 31, 2009,
and maturing by December 31, 2012. As of December 31, 2009, the program
was insuring $309 billion in debt (Federal Deposit Insurance Corporation
2009b).
3.
OTHER COMPONENTS OF THE SAFETY NET
As in 1999, we include for 2009 the liabilities of government-sponsored enterprises (direct GSE liabilities plus the dollar amount of mortgage-backed
security guarantees) in the “Implicitly Guaranteed Liabilities” column in the
tables. Earlier we noted that government guarantees can often modify market
prices. Though our article has made no attempt to measure the size of guarantees’ effect on market prices, in the case of the GSEs’ implicit guarantee,
the size of the effect on market prices has been estimated by Passmore (2005)
and others.6 Passmore (2005) estimates that the average homeowner saved
between 3 and 11 basis points on his or her mortgage because of the implicit
guarantee. The subsidy lowers the GSEs’ borrowing costs, and some of this
saved borrowing cost is passed on to homeowners by the GSE in the form of
lowered mortgage interest rates. Passmore calculates that about half of the
guarantee’s benefit flows to the shareholders of the GSEs. While the Treasury
made clear its support for Fannie Mae and Freddie Mac once these two financial firms were placed in conservatorship in September 2008, the support
was not as strongly stated as that given to insured deposits, so we leave these
liabilities in the implicit column in the tables.7
We estimate the amount of private pensions explicitly guaranteed in 2009
by the Pension Benefit Guarantee Corporation (PBGC) based on the latest
private pension data available, which are data for 2007 (Pension Benefit Guarantee Corporation 2010, pp. 83, 105). Our admittedly rough 2009 figure is
derived by simply adjusting the 2007 figure by twice the average annual growth
rate of private pension liabilities for the previous 10 years (1997–2007).
We also count all of the liabilities of American International Group (AIG)
as implicitly guaranteed in the “Other Financial Firms” row in the tables.8
6 Beyond Passmore, the Congressional Budget Office (2001) also developed estimates of the
GSEs’ guarantee on mortgage interest rates.
7 We treat Fannie Mae and Freddie Mac as private entities and therefore include their liabilities in our table, consistent with the way Walter and Weinberg treated these entities, even though
the status of Fannie Mae and Freddie Mac as privately owned firms is more ambiguous now than
in 1999.
8 The insured deposit liabilities of AIG’s savings bank are not included in the “Other Financial
Firms” row since these liabilities were included in the “Banking and Savings Firms” row. While
AIG owns a savings bank, it is not classified as a bank holding company (and does not file a bank
holding company report [Y9C] with federal regulators), so we do not include it in the Banking
and Savings Firms row.
N. Malysheva and J. R. Walter: The Federal Financial Safety Net
281
We count their liabilities as such because of the aid provided them by the
Federal Reserve and the U.S. Treasury following AIG’s financial problems in
September 2008. Because there were no clear signals about whether aid might
be forthcoming for other large, nonbank financial firms (beyond the stress test
firms), we did not include the liabilities of any firms other than AIG in the
“Other Financial Firms” row in tables.
4. ALTERNATIVE ESTIMATES OF THE SIZE OF THE
SAFETY NET
As has been noted, Table 1 is based on several assumptions similar to those
made by Walter and Weinberg in 2002. For example, we assumed that all
liabilities of stress-tested bank holding companies would be protected, not
just the liabilities representing FDIC-insured bank deposits. What would be
the size of the safety net if these assumptions were changed?
Contrary to our assumption about the likely protection of liabilityholders
of stress-tested companies, one can imagine circumstances under which such
liabilityholders might be left unprotected. If one of these companies were to
fail at a time when financial markets were broadly healthy, policymakers could
more easily allow the company to be handled as a bankruptcy so that no government funds are employed to protect liabilityholders (of course, the holders
of FDIC-insured deposits would still be covered given that such deposits are
protected regardless of the circumstances surrounding the failure). In times
of general financial market strength, the failure of a large holding company
could perhaps be absorbed without worries of a cascade of additional failures.
And at such times, if the firm were handled through the Dodd-Frank Act’s orderly liquidation process, it is possible that neither the government nor other
financial firms would provide funds to protect liabilityholders.9
While investors might expect large financial firm failures to typically occur in times of widespread financial weakness, and therefore anticipate that
their investments would be protected, some large firms have failed in times of
financial market health. One such example was London-based Barings Bank,
which failed when financial markets were broadly strong in 1995. Its failure
was because of the huge trading losses generated by one unchecked Barings
trader who took large, unauthorized futures positions. Given that there are circumstances under which the holders of stress-tested company liabilities might
be left unprotected, dropping the assumption of their coverage and recalculating our estimate of implicitly guaranteed liabilities seems worthwhile.
9 The Orderly Liquidation Authority section of the Dodd-Frank Wall Street Reform and Con-
sumer Protection Act of 2010 contains provisions that allow funds gathered from assessments on
the largest financial firms to be used to protect liabilityholders.
Table 2 Estimated Federal Financial Safety Net, Narrowly Defined
Banking and Savings Firms
(Includes BHCs)
Credit Unions
Explicitly Guaranteed
Liabilities
Total Liabilities
5,392
33.2%
5,392
33.2%
16,249
725
88.7%
725
88.7%
817
3,345
2,333
188
973
6,838
100%
3,345
2,333
188
973
6,838
Private Employer Pension Funds
2,799
85.5%
3,273
Other Financial Firms
Total for Financial Firms
3,345
2,333
188
973
6,838
100%
2,799
85.5%
8,915
21.1%
6,838
16.2%
15,753
37.2%
15,158
42,335
Notes: Data from December 2009, in billions of dollars. Figures may not sum exactly due to rounding. The figures in the
column “Explicitly and Implicitly Guaranteed Liabilities” are the sum of the numbers in the first two columns, “Explicitly
Guaranteed Liabilities” and “Implicitly Guaranteed Liabilities.” See Appendix for table legend.
Federal Reserve Bank of Richmond Economic Quarterly
Explicitly and Implicitly
Guaranteed Liabilities
Government-Sponsored Enterprises
Fannie Mae
Freddie Mac
Farm Credit System
Federal Home Loan Banks
Total
Implicitly Guaranteed
Liabilities
282
Financial Firms
N. Malysheva and J. R. Walter: The Federal Financial Safety Net
283
Large financial firms that are not bank holding companies might receive no
protection in such instances, so we also drop liabilities of AIG from those
liabilities with implicit backing.
Also, we included in our explicitly insured deposits category those deposits
covered by the FDIC’s temporary guarantee programs, since these programs
were in place in 2009. But under the debt guarantee program no new debt
issues were covered after October 31, 2009 (Federal Deposit Insurance Corporation 2010b). The TAGP was set to expire as of the end of 2010, though
the Dodd-Frank Act extended it to December 31, 2012. In the case of future
financial firm failures, such programs may not be in place, and might not be
reinstated. Therefore, re-estimating our measure of the size of the safety net
without considering these deposits as protected also seems worthwhile.
Table 2 contains our estimate of the size of the safety net without including
the liabilities of the stress-tested bank holding companies, AIG, and the FDIC
temporary insurance program deposits. These changes mean that, compared to
Table 1, the proportion of liabilities receiving explicit and implicit guarantees
falls to 37.2 percent.
Additionally, while we assume that the liabilityholders of the housing and
farm credit GSEs will be protected from loss, as were such holders of Fannie
Mae and Freddie Mac debt during the 2007–2009 financial crisis, under some
circumstances such holders might be left unprotected. As in the case of the
stress-tested companies, if a GSE were to fail during a period in which financial markets were healthy, policymakers might leave debtholders unprotected.
Therefore, it is possible that one might want to exclude the liabilities of the
GSEs from the calculation of the safety net. If the $6.8 trillion in liabilities of
the GSEs were removed (which are the only implicitly guaranteed liabilities
in Table 2), then our measure of the safety net would shrink to 21 percent of
total liabilities in Table 2, the amount of explicit liabilities shown in Table 2.
Some readers might contend that one category of liabilities, which we have
excluded from our safety net estimate, could legitimately be added: money
market mutual fund liabilities. In the creation of our tables, and in Walter
and Weinberg (2002), mutual fund liabilities are excluded because the principal value of mutual fund investments, including money market mutual fund
investments, can decline, without the mutual fund defaulting, if the entity in
which the funds are invested defaults. As a result, these investments are akin
to equity and unlike private liabilities—the focus of our estimates—which
typically must pay back full principal (or else be in default). For example, an
investor in a money market mutual fund, which in turn invested in financial
firm commercial paper, could lose principal if the commercial paper was not
repaid, but the mutual fund can continue to operate (i.e., not default).10 This
10 Money market mutual funds are loath to pay back less than full principal (“break the
buck” in mutual fund parlance), and few have done so over time. Instead, the money market
284
Federal Reserve Bank of Richmond Economic Quarterly
view of money market mutual fund investments as equity must be tempered,
however, by events in 2008. Specifically, the Treasury stepped in and protected investors in mutual funds from losses, thereby treating investments in
the funds like other guaranteed liabilities, in which losses are prevented by
government assistance or guarantees. As a result, one might argue that our
estimates of the fraction of total liabilities carrying a government guarantee—
both the numerator and denominator—should include money market mutual
funds. If one adds the amount of such fund balances outstanding at the end of
2009 ($3.3 trillion [Investment Company Institute 2010]) to our estimates in
the column “Explicitly and Implicitly Guaranteed Liabilities” in Table 1, the
proportion would increase to 62 percent. The Table 2 figure would increase
to 42 percent.
5.
CONCLUSION
Recent government actions by legislators and financial regulators expanded
the federal financial safety net. Such actions include augmentation of deposit
insurance, debt guarantees for banking companies, aid to stress-tested financial firms, and, perhaps, various regulatory reform legislative proposals. As
discussed in Walter and Weinberg (2002), this expansion has likely encouraged a view that liabilityholders will be protected by the federal government
in times of financial difficulty in the future. As a result of this expectation of
government protection, liabilityholders will exercise less oversight over financial firm risk taking then they would without this expectation, financial firms
will undertake more risk, and financial market decisions will be distorted and
inefficient.
mutual fund’s parent typically injects funds to allow the fund to pay back full principal. This
behavior by mutual fund parent companies indicates that parent companies and investors may well
view money market mutual fund investments more as liabilities than equity, regardless of the fact
that money market mutual funds can break the buck without defaulting.
N. Malysheva and J. R. Walter: The Federal Financial Safety Net
APPENDIX A:
285
LEGEND TO TABLE 1
• Banking and Savings Firms11
– Explicitly Guaranteed Liabilities
∗ FDIC-insured deposits of all commercial banks and savings institutions including transaction accounts covered by the FDIC’s
TAGP, plus debt guaranteed by the FDIC’s DGP
– Implicitly Guaranteed Liabilities
∗ Total liabilities of the 19 stress-tested institutions, less FDICinsured deposits and accounts covered by TAGP and debt covered by DGP for the 19 stress-tested institutions
• Credit Unions
– Explicitly Guaranteed Liabilities
∗ National Credit Union Administration-insured shares and
deposits
• Government Sponsored Enterprises
– Implicitly Guaranteed Liabilities of:
∗ Fannie Mae
· Total liabilities
· Fannie Mae mortgage-backed securities held by third
parties
· Other guarantees
∗ Freddie Mac
· Total liabilities
· Freddie Mac participation certificates and structured
securities held by third parties
∗ Farm Credit System
· Total liabilities
· Farmer Mac guarantees
∗ Federal Home Loan Banks
· Total liabilities
11 See Section 4 for a description of the differences between Table 1 and Table 2 estimates.
286
Federal Reserve Bank of Richmond Economic Quarterly
• Private Employer Pension Funds
– Explicitly Guaranteed Liabilities
∗ Pension liabilities backed by the PBGC
• Other Financial Firms
– Explicitly Guaranteed Liabilities
∗ Total liabilities of AIG, less FDIC-insured deposits of AIG
Federal Savings Bank
APPENDIX B:
DATA APPENDIX TO TABLE 1
Banking and Savings Firms—Explicitly Guaranteed Liabilities:
“Estimated FDIC-insured deposits” of commercial banks, savings
institutions, and U.S. branches of foreign banks (Federal Deposit
Insurance Corporation 2009a), plus “Amount Guaranteed” in the
Transaction Account Guarantee Program (Federal Deposit Insurance Corporation 2009c), plus “Debt Outstanding” in the Debt
Guarantee Program (Federal Deposit Insurance Corporation 2009b).
Banking and Savings Firms—Implicitly Guaranteed Liabilities:
Total liabilities of the 19 stress-tested institutions found in theY9C (quarterly bank holding company financial reports), less 1) the explicitly
guaranteed deposits of the banks and savings institutions owned
by these 19 firms, and 2) the FDIC-insured debt (insured under the
DGP) of each of these institutions. The estimated FDIC-insured
deposits and the guaranteed amount in noninterest-bearing transaction accounts for each bank can be found on the FDIC’s website
in the “Institution Directory” (www2.fdic.gov/idasp). The amount
of DGP debt of each firm can be found on the firms’ 10Ks.
Banking and Savings Firms—Total Liabilities:
Total liabilities from the following sources: For large (consolidated assets of over $500 million) bank holding companies, Consolidated
Financial Statements for Bank Holding Companies (FR Y9C); for
small (consolidated assets less than $500 million) bank holding
N. Malysheva and J. R. Walter: The Federal Financial Safety Net
287
companies, Parent Company Only Financial Statements for Small
Bank Holding Companies (FR Y9SP)—from which consolidated
total liabilities can be derived; for banks not owned by a bank holding company, Consolidated Reports of Condition and Income for
a Bank (FFIEC 031 and FFIEC 041); and for all thrift liabilities,
Thrift Financial Reports.
Credit Unions—Explicitly Guaranteed Liabilities:
Total insured shares at the $250,000 limit (National Credit Union
Administration 2009).
Credit Unions—Total Liabilities:
Board of Governors (2010), Table L.115—Credit Unions, “Total
liabilities.”
Government-Sponsored Enterprises:
Fannie Mae:
Total liabilities, plus Fannie Mae MBS held by third parties, plus
other guarantees found in the Fannie Mae 10K, “Item 6.
Selected Financial Data” (p. 70).
Freddie Mac:
10K report of Freddie Mac, “Total liabilities” (“Consolidated Balance Sheets,” p. 209), plus “Total PCs and Structured Securities issued” (“Item 6. Selected Financial Data,” p. 57),
less “Total Freddie Mac PCs and Structured Securities held”
in Freddie Mac portfolio (Table 28, p. 104).
Farm Credit System:
Farm Credit System (2010), “Total liabilities” (“Combined Statement
of Condition Data,” p. 3), plus “Farmer Mac guarantees” (p.
12).
Federal Home Loan Banks:
Federal Home Loan Banks (2010), “Total liabilities” (“Combined
Statement of Condition,” p. 194).
Private Employer Pension Funds—Explicitly Guaranteed Liabilities:
Liabilities of all pension funds insured by the PBGC (which insures only
defined benefit plans) were $2,559 billion in 2007, the latest date for
which data are reported (Pension Benefit Guarantee Corporation
288
Federal Reserve Bank of Richmond Economic Quarterly
2010, pp. 83, 105). This figure is inflated by twice (because 2007–
2009 involves two years of growth) the average annual growth rate
of PBGC-insured pension liabilities from 1997–2007 to obtain our
estimate of all liabilities in pension funds insured by the PBGC as
of December 31, 2009 ($2,946 billion). Since PBGC covers pensions only up to a specified maximum payment per year, a portion
of beneficiaries’ pensions in guaranteed plans—those with pensions paying above this maximum—are not insured. According
to the PBGC, this portion is estimated to be 4–5 percent (Pension
Benefit Guarantee Corporation 2007, p. 24; Pension Benefit Guarantee Corporation 1997, footnote to Table B-5). To arrive at the
guaranteed portion of PBGC guaranteed pension fund liabilities,
we multiplied total 2009 fund liabilities ($2,946 billion) by 0.95 to
yield $2,799 billion.
Private Employer Pension Funds—Total Liabilities:
There appears to be no data on the total liabilities of all private employerdefined benefit pension funds. Therefore, we estimate our total
liability figure based on PBGC data. To derive our figure, we begin
with our previously determined estimate of all private pension fund
liabilities that are included in PBGC ($2,946) and then divide it by
0.9 to arrive at our total liability figure of $3,273 billion. The PBGC
insures only about two-thirds of private sector single-employerdefined benefit plans, but almost all multi-employer plans (Pension
Benefit Guarantee Corporation 2009, p. 5). Among the types
of defined benefit plans PBGC does not insure are small (fewer
than 25 employees) plans maintained by small professional service
employers like doctors, lawyers, and accountants. Since the PBGC
excludes only the smaller single-employer plans, and includes most
multi-employer plans, we assume that it covers well more than 66
percent (i.e., two-thirds) of all liabilities, setting our estimate at 90
percent.
Other Financial Firms—Implicitly Guaranteed Liabilities:
“Total liabilities of AIG” found in its 10K report, less “estimated insured
deposits” of AIG Federal Savings Bank found on the FDIC’s website in the “Institution Directory” (http://www2.fdic.gov/idasp).
Other Financial Firms—Total Liabilities:
Board of Governors (2010), Tables L.116—Property-Casualty Insurance
Companies; L.117—Life Insurance Companies; L.126—Issuers
N. Malysheva and J. R. Walter: The Federal Financial Safety Net
289
of Asset-Backed Securities; L.127—Finance Companies; L.128—
Real Estate Investment Trusts; L.129—Security Brokers and Dealers; L.131—Funding Corporations, less taxes payable whenever a
figure for taxes was reported on these tables.
REFERENCES
Board of Governors of the Federal Reserve System. 2010. Level Tables in
Federal Reserve statistical release Z.1, “Flow of Funds Accounts of the
United States.” www.federalreserve.gov/releases/z1/20100311/z1r-4.pdf
(11 March).
Congressional Budget Office. 2001. Federal Subsidies and the Housing
GSEs. Washington, D.C.: Government Printing Office.
Diamond, Douglas, and Philip Dybvig. 1983. “Bank Runs, Deposit
Insurance, and Liquidity.” Journal of Political Economy 91 (June):
401–19.
Farm Credit System. 2010. “2009 Annual Information Statement of the Farm
Credit System.” www.farmcredit-ffcb.com/farmcredit/serve/public/finin/
annin/report.pdf?assetId=150693&uniq=1277320695095 (1 March).
Federal Deposit Insurance Corporation. 2006. “FDIC Insurance for
Retirement Accounts Increased to $250,000.” www.fdic.gov/news/
news/press/2006/pr06029.html (14 March).
Federal Deposit Insurance Corporation. 2009a. “Table III-B: Estimated
FDIC-Insured Deposits by Type of Institution.” www2.fdic.gov/qbp/
2009dec/qbp.pdf (31 December).
Federal Deposit Insurance Corporation. 2009b. “Table IV-C: Debt Issuance
Under Guarantee Program.” www2.fdic.gov/qbp/2009dec/qbp.pdf (31
December).
Federal Deposit Insurance Corporation. 2009c. “Table III-C: Transaction
Account Guarantee Program.” www2.fdic.gov/qbp/2009dec/qbp.pdf (31
December).
Federal Deposit Insurance Corporation. 2010a. “Changes in FDIC Deposit
Insurance Coverage.” www.fdic.gov/deposit/deposits/changes.html.
Federal Deposit Insurance Corporation. 2010b. “Temporary Liquidity
Guarantee Program, Second Quarter 2010.” www2.fdic.gov/qbp/
2010jun/qbptlgp.html (31 August).
290
Federal Reserve Bank of Richmond Economic Quarterly
Federal Deposit Insurance Corporation. 2010c. “FDIC Board Proposes Rules
on Temporary Unlimited Deposit Insurance Coverage for
Noninterest-Bearing Transaction Accounts.” www.fdic.gov/news/
news/press/2010/pr10217.html (27 September).
Federal Home Loan Banks. 2010. “2009 Combined Financial Report.”
www.fhlb-of.com/ofweb userWeb/resources/09yrend.pdf (30 March).
FinancialStability.gov. 2009. “U.S. Treasury Releases Terms of Capital
Assistance Program.” www.financialstability.gov/latest/tg40.html (25
February).
FinancialStability.gov. 2010. “Troubled Assets Relief Program (TARP):
Monthly 105(a) Report–July 2010.” www.financialstability.gov/docs/
105CongressionalReports/July%202010%20105(a)%20Report Final.pdf
(10 August).
Investment Company Institute. 2010. “Weekly Total Net Assets (TNA) and
Number of Money Market Mutual Funds.” www.ici.org/pdf/
mm data 2010.pdf.
National Credit Union Administration. 2009. “2009 Yearend Statistics for
Federally Insured Credit Unions.” www.ncua.gov/Resources/
Reports/statistics/Yearend2009.pdf.
Passmore, Wayne. 2005. “The GSE Implicit Subsidy and the Value of
Government Ambiguity.” Real Estate Economics 33 (July): 465–86.
Pension Benefit Guarantee Corporation. 1997. “Pension Insurance Data
Book 1996.” www.pbgc.gov/docs/1996databook.pdf.
Pension Benefit Guarantee Corporation. 2007. “Pension Insurance Data
Book 2006.” www.pbgc.gov/docs/2006databook.pdf.
Pension Benefit Guarantee Corporation. 2009. “Pension Insurance Data
Book 2008.” www.pbgc.gov/docs/2008databook.pdf.
Pension Benefit Guarantee Corporation. 2010. “Pension Insurance Data
Book 2009.” www.pbgc.gov/docs/2009databook.pdf.
Walter, John R., and John A. Weinberg. 2002. “How Large Is the Federal
Financial Safety Net?” Cato Journal 21 (Winter): 369–93.
Economic Quarterly—Volume 96, Number 3—Third Quarter 2010—Pages 291–317
The Politics of Sovereign
Defaults
Juan Carlos Hatchondo and Leonardo Martinez
S
overeign debt issuance and repayment decisions are determined by public officials and may thus be affected by issues such as the proximity of
elections; conflicts between the executive branch and the parliament;
institutional breakdowns such as military coups; etc. This article first discusses theoretical and empirical studies about the role of political factors in
sovereign default episodes. Before concluding, the article also discusses the
role of political factors in five recent default episodes.1
The preferences of public officials and the environment in which they
must act affect their perceived costs and benefits of defaulting. This has been
recognized by several authors. For instance, in discussing the role of political factors as determinants of defaults, Sturzenegger and Zettelmeyer (2006)
conclude that “a solvency crisis could be triggered by a shift in the parameters that govern the country’s willingness to make sacrifices in order to repay,
because of changes in the domestic political economy (a revolution, a coup,
an election, etc.). . . ” Similarly, Rijckeghem and Weder (2009) argue that a
country’s willingness to pay is influenced by politics, i.e., by the distribution
of political power and of benefits and costs of defaulting across voters. The
heterogeneity of public officials’ preferences is also highlighted by Santiso
(2003) who writes, “One basic rule of the confidence game [in international
financial markets] is then to be very careful when nominating the official government voicer. For investors it is mainly the ministry of economics or finance
or the governor of the central bank.”
Hatchondo is an economist with the Federal Reserve Bank of Richmond. Martinez is with
the International Monetary Fund. For helpful comments, we thank Kartik Athreya, Anne
Davlin, Andreas Hornstein, Jorge Roldos, and Alex Wolman. The views expressed herein
are those of the authors and should not be attributed to the IMF, its Executive Board, or
its management; the Federal Reserve Bank of Richmond; or the Federal Reserve System.
E-mail: juancarlos.hatchondo@rich.frb.org.
1 Hatchondo, Martinez, and Sapriza (2007a) present a brief discussion of political costs of
defaulting. In this article, we extend their analysis and present a more thorough discussion of
theoretical and empirical results.
292
Federal Reserve Bank of Richmond Economic Quarterly
We first describe theoretical studies that illustrate how the risk of losing
elections may induce a sovereign to avoid a default even when creditors have
no access to legal procedures that would allow them to force the sovereign
to pay. This risk would be present when sovereign debt is at least partially
held by local creditors with political power to deny support to political groups
that advocate for a default. Note that, since it is difficult to declare a selective
default on foreign bondholders only, the presence of these local creditors could
also explain why foreign investors are willing to buy sovereign debt.
Second, we describe theoretical work that studies how political turnover,
i.e., the alternation in office of policymakers with different objectives, affects
incentives to borrow from foreign lenders and to default on debt held by
foreigners. Policymakers may differ in the weights they assign to different
constituencies of domestic residents when allocating fiscal resources and they
may differ in their willingness to pay the debt. Studies that assume differences
in policymakers’ spending preferences find that a higher frequency of political
turnover tends to generate higher debt levels and higher default probabilities.
In contrast, studies that assume that policymakers differ in their willingness
to repay debt find that the relationship between the default probability and the
frequency of political turnover may be nonmonotonic.
Studies that assume that policymakers differ in their willingness to repay
make possible the existence of defaults triggered by political turnover.2 We
refer to such default episodes as “political defaults.” Political defaults occur
when a “creditor-friendly” government (with a higher willingness to pay)
is replaced by a “debtor-friendly” government (with a lower willingness to
pay). It should be mentioned that while political turnover may explain the
timing of the default decision, poor economic conditions are likely to play a
key role in political defaults. In fact, in Hatchondo, Martinez, and Sapriza’s
(2009) model of political defaults, political defaults are only likely to occur
after a creditor-friendly government encounters poor economic conditions
that lead it to choose high borrowing levels. These studies also find that after
political defaults, debt and interest rate spread levels are lower than the levels
observed after defaults caused by negative income shocks only, and are lower
than the pre-default levels.3 Recall that a political default is triggered when
a creditor-friendly government is replaced by a debtor-friendly government.
These studies argue that in a political default, post-default debt levels are
2 Using a historical data set with 169 sovereign default episodes, Tomz and Wright (2007)
find that 38 percent of default episodes in their sample occurred in years when the output level in
the defaulting country was above the trend value. Thus, it is unlikely that these episodes were
triggered by difficult economic conditions. Tomz and Wright argue that some of these episodes
may have been triggered by political turnover.
3 The interest rate spread corresponds to the difference between the yield of sovereign bonds
and the risk-free rate. When we contrast theoretical predictions with data, we use the yield on
90-day U.S. Treasury bills as the risk-free rate.
J. C. Hatchondo and L. Martinez: The Politics of Defaults
293
lower than pre-default levels because investors are less willing to lend to
debtor-friendly governments. This contrasts with alternative explanations that
rely on a boycott against a government in default that is not explained by
characteristics of the government but by its past behavior.4
Third, we review empirical studies that have tested the existence of statistical relationships between political factors and default decisions. These
studies have found that the proximity of elections, the turnover of government
officials, increases in political instability, and the presence of a presidential
democratic regime instead of a parliamentary democratic regime are statistically associated with a higher default probability.
We conclude with a brief description of the role of political turnover in
five recent default episodes: Argentina 2001, Ecuador 1998, Pakistan 1998,
Russia 1998, and Uruguay 2003. First, we attempt to identify whether these
default episodes occur after a creditor-friendly government was replaced by
a debtor-friendly government. In order to do so, we look at a measure of
political risk computed by the International Country Risk Guide (ICRG). We
argue that if a creditor-friendly government was replaced by a debtor-friendly
government at the time of the default, the level of political risk computed by
the ICRG should be lower in the years before the default than in the years
after the default. We find that only in Argentina is the level of political risk
systematically lower in the years before the default than in the years after the
default. We also present anecdotal evidence indicating that political turnover
was important in determining the timing of the Argentine default. The role
of political factors in the Argentine crisis has also been highlighted in previous studies. IMF (2004) argues that in Argentina “economic, social, and
political dislocation occurred simultaneously, leading to the resignation of
the President, default on Argentina’s sovereign debt, and the abandonment of
convertibility. . . ” Similarly, IMF (2003) finds that in Argentina “the inability
to mount a policy response stemmed from a combination of economic constraints and political factors. . . ” In addition, we show that the behavior of
interest rate and debt levels before and after the Argentine default is broadly
aligned with the predictions of theoretical studies.
1. THEORETICAL LITERATURE
In this section, we summarize lessons that can be extracted from theoretical
studies that analyze the role of political factors in sovereign default episodes.
4 For instance, it is often argued that creditors may punish a defaulting government by excluding it from capital markets. This is assumed in Eaton and Gersovitz’s (1981) seminal model
of sovereign default and in extensions of their work (Hatchondo, Martinez, and Sapriza [2007b]
discuss the role of this assumption).
294
Federal Reserve Bank of Richmond Economic Quarterly
First, we discuss political costs of sovereign defaults. Second, we describe
how political turnover affects debt issuance and repayment decisions.
Political Costs of Defaults
In a hypothetical scenario in which sovereign defaults were costless, governments would always default and, in anticipation of that, investors would not
purchase public debt to begin with. Yet, we observe that governments are able
to borrow significant amounts in spite of the weak legal protection enjoyed by
bondholders. This observation can be taken as evidence of costs associated
with sovereign defaults.5 The literature has debated the ability of foreign creditors to impose explicit sanctions on governments that have reneged on their
debts (see Hatchondo, Martinez, and Sapriza [2007a]). This section reviews
a number of studies that emphasize that sovereign defaults may be politically
costly primarily because a fraction of sovereign debt is held by local voters.
For a government, an alternative to defaulting is to raise taxes in order to be
able to pay its debt. In any society, people are likely to have different exposures
to the debt of their government and to a tax increase. In general, we can expect
that a sovereign default will not occur as long as debtholders have sufficient
political power. Dixit and Londregan (2000) formalize this idea. They argue
that when making the decision to raise taxes to pay the interest or repay the
principal on its debt, the government will pay due attention to the relative
political power of the debtholders and other taxpayers. They consider a twoperiod model in which debt is issued in the first period and two political parties
compete to win an election. Voters differ in their learning abilities for human
capital accumulation, initial wealth, and in their preferences over “position
issues” such as gun control, abortion, etc. In the first period, voters invest
by accumulating human capital or by buying government bonds. Government
debt revenues are allocated to build infrastructure capital. Elections are held at
the end of the first period. Before the elections, each party presents a platform
of income taxes, debt repayment, and their stance on position issues. In the
second period, production takes place and the party in office levies taxes and
decides the fraction of debt that is repaid. There are no punishments to a
defaulting government. Dixit and Londregan (2000) show that under some
distributional assumptions, the number of bondholders who are indifferent to
voting for any of the two parties on the basis of position issues alone may
be larger than the number of nonbondholders—voters that decided to invest
in human capital instead of buying government bonds. Consequently, an
5 That is, for sovereign debt to exist, it is necessary that at least in some circumstances it
would be more costly for a sovereign to default than to pay back its debt. Similarly, for sovereign
defaults to exist, it is necessary that at least in some circumstances it would be more costly for
a sovereign to pay back its debt than to default.
J. C. Hatchondo and L. Martinez: The Politics of Defaults
295
equilibrium with positive debt issuance and no default can be sustained. In
that equilibrium, the presence of a larger number of swing voters who would
favor the repayment of debt ensures that the party that proposes to pay back
the debt wins the election.
In general, citizens who are wealthier may hold more government debt and
may suffer more in the event of a sovereign default. Furthermore, those who
are older tend to be wealthier while those who are younger tend to generate
more income and thus are more exposed to an increase in income taxes. Consequently, as long as wealthier and older citizens impose their will, sovereign
defaults may be prevented. Tabellini (1991) emphasizes these ideas. He
presents a two-period economy that is inhabited in period 1 by a generation
of young agents who live for two periods. There are two generations active in
period 2: young and old. The young in period 1 differ in their initial endowment of goods. The old in period 2 care about the welfare of their offspring,
so they may leave bequests. There is political competition and the government’s decisions are determined by majority voting. Debt is issued in the first
period and the repayment decision is made in the second period. Tabellini
(1991) assumes that individuals cannot punish a defaulting government. The
young in period 1 vote on how much debt to issue and individually decide how
much to save for the next period. They can only save in government bonds, so
aggregate savings must equal total debt issuance (there is no external debt).
At the beginning of period 2, the young and old vote on how much debt is
going to be repaid. Old agents are the debtholders and young agents are the
only ones being taxed in period 2. Tabellini (1991) shows that a coalition
composed of old agents and those young agents who are the children of the
wealthy (old) bondholders may vote to repay the debt. The second group in
the coalition may enjoy a net benefit from repaying the debt because the taxes
they pay to honor the debt are offset by the bequests they plan to receive from
their parents.
If a sovereign cannot perfectly discriminate between domestic and foreign
creditors, then the political cost of defaulting described above also allows
the sovereign to issue debt to foreign lenders. This is argued by Guembel
and Sussman (2009). While the setups presented in Tabellini (1991) and
Dixit and Londregan (2000) do not consider the possibility of a government
borrowing from foreign lenders, Guembel and Sussman (2009) study this
possibility. They propose an environment in which the government cannot
perfectly discriminate between domestic and foreign creditors who cannot
impose sanctions to a government in default. The authors assume electoral
competition between two political parties that compete with a platform that
specifies the repayment strategy to be implemented once in power. The two
parties are identical and their only objective is to win elections. A default
entails a redistribution of wealth toward local individuals who hold an amount
of debt that is low enough to make the loss caused by not being compensated for
296
Federal Reserve Bank of Richmond Economic Quarterly
the defaulted debt smaller than the benefit from avoiding the taxes they would
have paid to service the debt. Like Tabellini (1991) and Dixit and Londregan
(2000), Guembel and Sussman (2009) show that, under certain circumstances,
the government repays its debt in spite of the fact that no creditor can punish
defaulting governments. The reason is that the median voter would favor a
platform that proposes to pay back the debt. The authors also show that when
there is imperfect information about the characteristics of the median voter,
“lending booms” (high foreign demand for bonds) can price the median voter
out of the market and thus increase the probability of default. With imperfect
information, investors may mistakenly interpret high bond prices that can be
caused by an increase in foreign demand for bonds as evidence of a strong
willingness to pay by the future government.
Drazen (1998) focuses on analyzing the influence of political factors on
a government’s decision to finance its expenditures by issuing debt to domestic or foreign lenders. Like Guembel and Sussman (2009), Drazen (1998)
considers a setup in which the government issues debt to local and foreign
residents. Unlike Guembel and Sussman (2009), Drazen (1998) studies the
case in which the government can selectively default on local or foreign debt
and foreign debtholders can punish a defaulting government. He argues that
governments can, in fact, exert some control over whether debt is held by
domestic or foreign residents. In particular, he mentions that the government
can affect the allocation of debt among domestic and foreign agents through
capital controls that restrict the ability of domestic (foreign) residents to buy
debt issued abroad (locally), through the currency denomination of public debt
(debt denominated in domestic currency may be more attractive to domestic
residents), through differential tax treatment, etc. He proposes then a political
economy model in which domestic debtholders vote on repayment decisions.
Thus, as in Tabellini (1991) and Dixit and Londregan (2000), debt held by
local agents can be sustained in equilibrium. Since Drazen (1998) assumes
that foreign debtholders can punish a defaulting government, debt held by
foreigners can also be sustained in equilibrium. Drazen (1998) argues that
countries where debtholders have more political power should tend to finance
a higher proportion of public expenditures by issuing domestic debt. In his
setup, as the income distribution becomes less concentrated, more agents can
save and buy domestic debt and thus benefit from interest payments on public
debt. Those agents would vote for a political platform that proposes to issue
more domestic debt and to honor this debt. Consequently, countries with relatively less concentrated income distributions (higher median income for the
same mean) may tend to finance a higher proportion of public expenditures
by issuing domestic debt.
In summary, the main lessons from the literature on the political costs of
sovereign defaults described above are (i) a sovereign default will not occur
as long as local debtholders have sufficient political power; (ii) a default is
J. C. Hatchondo and L. Martinez: The Politics of Defaults
297
likely to be prevented as long as wealthier and older citizens impose their will;
(iii) as long as a sovereign cannot default solely on foreign creditors without
affecting local creditors, political costs of defaulting also allow the sovereign
to issue debt to foreign lenders; and (iv) countries where debtholders have
more political power (for instance, because of a more even distribution of
income) will tend to finance a higher proportion of public expenditures by
issuing domestic debt.
Political Turnover and Sovereign Default Risk
In this section, we summarize lessons from studies that focus on the role
of political turnover, which is defined as the alternation in power of groups
with different preferences. These studies typically lack a deep theory that
links the objectives of citizens and policymakers, and links policy choices to
election outcomes. This modeling strategy may be useful to clarify causality
relationships from political variables to sovereign debt issuance and default
decisions.
An economy is said to have more political stability when political turnover
is less frequent. What is the relationship between political stability and default
risk? Amador (2003) and Cuadra and Sapriza (2008) contribute to answering
this question. They study models of sovereign default in which policymakers disagree on the optimal allocation of fiscal resources within each period
because they want to please different constituencies. They show that an increase in political stability reduces the risk of a sovereign default, which in
turn reduces the interest rate spread on sovereign bonds. The intuition for their
results is as follows. The current government knows that future resource allocations may be decided by a government that would make different choices
from the ones the current government would make. Consequently, the current
government would like to transfer resources from the future (when decisions
may not be made following its preferred criteria) to the present (when it can decide where to allocate those resources). With less political stability it is more
likely that the current government will disagree with the choices of future
governments and, therefore, the current government is more eager to transfer
resources from the future to the present. Thus, political stability affects the
effective discount factor of the incumbent government. One instrument that
the government can use to bring resources from the future is to issue more
debt. Higher debt levels increase the default probability—when defaulting,
the government benefits from not paying back its debt and these benefits are
larger if debt levels are larger. Another strategy is defaulting in situations in
which the government would have to pay large amounts in the present while
a substantial fraction of the cost of defaulting appears in the future. In contrast, if there is more political stability, the government is less eager to transfer
resources to the present, it wants to borrow less, and it is less willing to default.
298
Federal Reserve Bank of Richmond Economic Quarterly
The analysis in Amador (2003) and Cuadra and Sapriza (2008) assumes
that policymakers do not differ in their willingness to pay back sovereign
debt and, therefore, receive the same treatment from international investors.
This implies that lenders do not care about the type of policymaker in office
or about which type of policymaker may be in office in the future. This
seems unrealistic. Examples abound in which politicians disagree about the
benefits of maintaining a good credit standing. As explained in the next section,
in the proximity of elections, default risk may be influenced by poll data.
This suggests that a better understanding of the relationship between political
turnover and default risk could be achieved by allowing for the existence of
policymakers with different preferences for debt repayment.
Aghion and Bolton (1990) study a setup in which policymakers differ in
their willingness to pay. Unlike other studies described in this section, they
present a model with endogenous turnover. They show how the government
may want to overaccumulate debt to affect the result of elections. They consider a two-period closed economy inhabited by a continuum of agents who
live for two periods (there are no intergenerational transfers). At the beginning
of each period, elections are held to appoint government authorities. Agents
only differ in the endowment they receive in every period and derive utility from private consumption and a publicly provided good. The first-period
government determines the level of the public good provided in that period
and the proportion of expenditures that is financed through a uniform tax and
through debt issuances. The second-period government determines the level
of the public good provided in that period, the uniform tax, and the repayment
of debt. Aghion and Bolton (1990) assume that there are two political parties. The “right-wing” (“left-wing”) party is assumed to maximize the utility
of a group of agents with an above-average (below-average) income level.
Given that debtholdings increase with income, the right-wing party displays
a stronger preference to pay back debt than the left-wing party. By issuing
more debt in the first period, the right-wing party increases the size of the constituency that prefers the debt to be paid back and, through that, it increases the
likelihood of winning the election held at the beginning of the second period.
Thus, electoral concerns induce the right-wing party to issue a larger amount
of debt in the first period.
Cole, Dow, and English (1995), Alfaro and Kanczuk (2005), and
Hatchondo, Martinez, and Sapriza (2009) also study models of sovereign
default with two types of policymakers that differ in their willingness to pay.
Unlike Aghion and Bolton (1990), these studies assume that the two types
alternate stochastically in power. In their setups, policymakers who assign
more weight to the future (for example, because they are more likely to win
elections) are more willing to pay because they are more concerned about the
costs of defaulting that appear in the future.
J. C. Hatchondo and L. Martinez: The Politics of Defaults
299
Cole, Dow, and English (1995) and Alfaro and Kanczuk (2005) study
setups with asymmetric information about the type of policymaker in office. Thus, a cost of defaulting is that lenders update their beliefs about the
government’s type, which in turn may affect future borrowing opportunities.
Cole, Dow, and English (1995) show that an equilibrium exists in which the
patient policymaker always repays, the impatient policymaker always defaults,
and, in the period where there is a type change from impatient to patient, the
patient policymaker is able to perfectly signal its type by making a settlement
payment because the impatient type would not find it optimal to do the same.
Their model can explain cycles of borrowing and exclusion from credit markets that finish when the government pays part of the debt in default. They
argue that this pattern is consistent with the aftermath of many 19th century
default episodes in Latin America and in the United States.
In the framework proposed by Alfaro and Kanczuk (2005), there are equilibria in which lenders do not know the type of policymaker in office. They
allow for a publicly observable aggregate productivity shock and show the
existence of equilibria in which, for moderately negative productivity shocks,
the patient type does not default in order to avoid damaging the government’s
reputation—i.e., the probability that lenders assign to the patient type being
in office.
In order to simplify the learning process faced by lenders and make their
models tractable, Cole, Dow, and English (1995) and Alfaro and Kanczuk
(2005) limit the set of borrowing levels available to the government. In general,
in models with asymmetric information, equilibrium borrowing levels may be
distorted by the desire of the borrower who is less willing to default to reveal his
type through his borrowing choice. In particular, when borrowing less would
allow a patient government to distinguish itself from impatient governments,
the patient government may not want to borrow as much as it would if its
type was public information. The drawback of restricting the set of borrowing
levels is that it limits the usefulness of the models for studying macroeconomic
fluctuations.
Hatchondo, Martinez, and Sapriza (2009) consider a political process
similar to the one used by Cole, Dow, and English (1995) and Alfaro and
Kanczuk (2005), but do not assume asymmetric information about the government type and, therefore, do not need to restrict the set of borrowing levels
available to the government. Moreover, the framework used by Hatchondo,
Martinez, and Sapriza (2009) follows closely the one used in recent quantitative models of sovereign default (see, for example, Aguiar and Gopinath
[2006], Arellano [2008], Hatchondo and Martinez [2009], and Hatchondo,
Martinez, and Sapriza [2010]).
Hatchondo, Martinez, and Sapriza (2009) identify two channels through
which political stability may influence default risk in addition to the channel
outlined in Amador (2003) and Cuadra and Sapriza (2008). On the one hand,
300
Federal Reserve Bank of Richmond Economic Quarterly
if political turnover is expected to trigger a default, the default risk premium
charged on bond issuances is higher when the probability of political turnover
is higher (which corresponds to lower political stability). Thus, this channel
predicts a negative relationship between political stability and default risk, as
in Amador (2003) and Cuadra and Sapriza (2008). On the other hand, if less
political stability were to imply more default risk, the default risk premium
charged on bond issuances would be higher. In turn, a higher borrowing cost
would make the government less willing to borrow. In particular, it could
make the government unwilling to choose debt levels for which a political
default—defined as a default that would occur because of political turnover—
would be likely. Therefore, less political stability could reduce default risk.
The possibility of a positive relationship between political stability and default
risk is not present in Amador (2003) and Cuadra and Sapriza (2008).
Based on their findings on the relationship between political stability and
borrowing costs, Hatchondo, Martinez, and Sapriza (2009) argue that political
defaults are only likely to occur in economies where there is enough political stability. If the current government chooses borrowing levels that would
lead to a default after political turnover, it has to compensate lenders for this
contingency, i.e., for the contingency of another government becoming the decisionmaker in the future. If the probability of this contingency is high enough
(political stability is low), it is too expensive for the current government to
choose borrowing levels that would lead to a political default. In this scenario,
the current government does not borrow so heavily and, therefore, political
turnover would not trigger a default.
In addition, Hatchondo, Martinez, and Sapriza (2009) show that, in economies with enough political stability, political turnover may weaken the correlation between default and output. Thus, introducing political turnover may
bring the predictions of the baseline quantitative model of sovereign default
closer to the data. Using a historical data set with 169 sovereign default
episodes, Tomz and Wright (2007) report a weak correlation between economic conditions and default decisions. They find that 38 percent of default
episodes in their sample occurred in years when the output level in the defaulting country was above the trend value.
The model presented by Hatchondo, Martinez, and Sapriza (2009) also
highlights distinctive features of political defaults. In their model, if a default is not preceded by political turnover, post-default debt levels tend to
return to pre-default levels relatively fast. In contrast, if a default is caused
by political turnover, post-default debt levels tend to be lower than pre-default
levels. Recall that a default is caused by political turnover when a government is replaced by another government that is more willing to default. In
a political default, post-default debt levels are lower than pre-default levels
because the cost of borrowing is higher for governments that are more willing to default and, consequently, post-default governments borrow less than
J. C. Hatchondo and L. Martinez: The Politics of Defaults
301
pre-default governments. This contrasts with alternative explanations for low
post-default borrowing levels that rely on a boycott against a government in
default that is not explained by characteristics of the government but by its
previous default decision (for instance, creditors could agree to exclude a defaulting government from capital markets independently of the likelihood of
future government repayments). The mechanism that generates lower postdefault debt levels illustrated by Hatchondo, Martinez, and Sapriza (2009) is
similar to one presented by Cole, Dow, and English (1995). In Cole, Dow, and
English (1995), post-default governments cannot borrow because they would
always default. In Hatchondo, Martinez, and Sapriza (2009), post-default
governments can borrow but at a higher interest rate than pre-default governments. In equilibrium, post-default governments choose to borrow less than
pre-default governments.
The second distinctive feature of political defaults highlighted by
Hatchondo, Martinez, and Sapriza (2009) is that post-default equilibrium
spreads tend to be lower than pre-default spreads. That is, high-willingness-topay governments pay higher spreads than do low-willingness-to-pay governments. Before a political default, when the government has a high willingness
to pay, bondholders require a compensation for the possibility that the current government is replaced by a government with a lower willingness to pay.
In contrast, after a political default, the low-willingness-to-pay government
does not need to compensate lenders for the risk of political turnover. This is
because political turnover would actually mean good news to bondholders.
To further illustrate the relationship between pre- and post-political default
levels of debt and spread, consider the case in which two types of governments,
creditor-friendly and debtor-friendly, alternate in power, and a political default
occurs when the first type is replaced by the second type. In addition, suppose that the type of policymaker currently in charge of the government is
likely to be in charge of the government at the time the debt it issues has
to be paid back (but a change in the type of policymaker is possible). Figure 1 presents the interest rate spread each of these two types of government
would have to pay as a function of the borrowing level they choose. The functions in the figure resemble the equilibrium functions derived in Hatchondo,
Martinez, and Sapriza (2009). The functions presented in Figure 1 display
three steps. The first step corresponds to “low” issuance volumes. At these
volumes, the debt issued is sufficiently low that the government will almost
surely pay it back, regardless of the type in power. The second step corresponds to “intermediate” issuance levels. These are the issuance values such
that a debtor-friendly policymaker would default in the next period whereas
a creditor-friendly policymaker would pay. When a creditor-friendly policymaker is in office, the spread charged by lenders for these issuance volumes
is increasing in the probability of political turnover. When a debtor-friendly
policymaker is in office, the spread charged by lenders for these issuance
302
Federal Reserve Bank of Richmond Economic Quarterly
Figure 1 Interest Rate Spread
Spread
CreditorFriendly's
Choice
Debtor-Friendly's Choice
Debt
volumes goes to infinity because a debtor-friendly government would choose
to default on these volumes (this is the case when the recovery rate on defaulted debt is zero, as in Hatchondo, Martinez, and Sapriza [2009]). Finally,
the third step corresponds to “high” issuance volumes. At these volumes,
investors realize that the government will almost surely default tomorrow, regardless of the type in power and, therefore, spreads go to infinity. Hatchondo,
Martinez, and Sapriza (2009) show that, when facing such options, creditorfriendly governments may choose to issue intermediate debt levels and to pay
intermediate spreads while debtor-friendly governments may choose to issue
low debt levels and to pay low spreads. Thus, the levels of debt and spread
are typically higher before a political default than after the default. Figure 1
also presents the typical government’s choices according to the equilibrium
studied by Hatchondo, Martinez, and Sapriza (2009).
In summary, the literature studying the relationship between political
turnover and default risk shows us that: (i) governments may want to overaccumulate debt to affect the result of elections; (ii) more political stability
may imply a lower default risk if it makes the government less eager to transfer resources to the present; (iii) political defaults are only likely to occur in
economies where there is enough political stability; (iv) political turnover may
weaken the correlation between default and output; (v) around political defaults, post-default debt levels may be lower than pre-default levels; and (vi)
J. C. Hatchondo and L. Martinez: The Politics of Defaults
303
creditor-friendly governments may pay higher spreads than debtor-friendly
governments and, consequently, post-political-default spreads may be lower
than pre-political-default spreads.
2.
EMPIRICAL LITERATURE
In Section 1, we discussed insights from theoretical studies that show how
political factors may influence sovereign default risk. In this section, we
summarize the findings of empirical studies that have investigated statistical
relationships between political factors and default risk. These studies have
found that the proximity of elections, changes in the finance minister or central
bank governors, increases in indicators of political instability, and the presence
of a presidential democratic regime instead of a parliamentary democratic
regime are statistically associated with a higher default probability. These
studies include controls such as the debt over gross domestic product ratio,
the level of reserves, or output growth. This attenuates the criticism that
political indicators may be significant only because of their correlation with
policy choices (such as the accumulation of debt).
Political Stability
In Section 1, we discussed how an increase in political instability may increase
default risk. We discuss next studies that propose measures of political stability
and use these measures to evaluate whether political stability affects default
risk. Citron and Nickelsburg (1987), Balkan (1992), and Brewer and Rivoli
(1990) find that this seems to be the case.
Citron and Nickelsburg (1987) use a logit model to estimate the probability
of default using data from Argentina, Brazil, Mexico, Spain, and Sweden for
the 1960–1983 period. They construct an indicator of political instability that
measures the number of changes in government—that were accompanied by
changes in policy—that took place within the previous five years. They find
that, on top of various macroeconomic indicators, their measure of political
instability has a significantly positive effect on the default probability.
The results in Balkan (1992) are consistent with the ones in Citron and
Nickelsburg (1987). Balkan (1992) uses an index of political instability that
“measures the amount of social unrest that occurred in a given year.” He
estimates the probability of default using a sample larger than the one used by
Citron and Nickelsburg (1987): 31 countries from 1971–1984. Controlling
for 10 economic indicators and an index of democratization, he finds that a
higher index of political instability increases the probability of observing a
debt rescheduling in the subsequent year.
Brewer and Rivoli (1990) also find that political instability has a significant
negative effect on a country’s perceived creditworthiness. In particular, they
304
Federal Reserve Bank of Richmond Economic Quarterly
argue that the frequency of regime change appears to be at least as important
as economic variables in explaining lenders’ risk perceptions. They use two
indexes of regime stability. One index represents the frequency of change in
the head of government and the other represents the frequency of change in
the governing group (political party or military government). Instead of using
data on defaults or interest rate spread, Brewer and Rivoli (1990) use credit
ratings from Institutional Investor and Euromoney (two private credit-rating
consultants). They use a sample of 30 countries from 1967–1986. They do not
find evidence in favor of other political indicators such as the existence of an
armed conflict or the democratic nature of the government having significant
effects on credit ratings.
Bussiere and Mulder (2000) use a sample of 44 developing countries to test
the contribution of political variables to the severity of the financial crises that
took place between 1994–1997 (not all crises are linked to a default episode).
They find that indicators about the uncertainty of election outcomes amplify
the magnitudes of subsequent crises. Those indicators consist of an index
of volatility of the electorate (the change in the proportion of seats held by
each party from one election to the other) and a dummy variable that captures
the presence of elections during the sample period. (They also find that an
index of political polarization based on the number of political parties and an
index of the fragility of the ruling coalition do not have statistically significant
effects.)
Political Turnover and Default Risk
In Section 1 we also discussed theoretical studies that assume that policymakers differ in their willingness to default, which allows for political defaults—
i.e., defaults triggered by political turnover—to occur. Figure 2 illustrates a
notable example of how the probability of default (reflected in sovereign bond
spreads) may be influenced by changes in the probability of political turnover.
This should happen when policymakers differ in their willingness to default.
The figure shows the behavior of the sovereign spread in Brazil before and after the election of 2002. The concerns raised by the possible electoral victory
of the left-wing candidate Luiz Inacio “Lula” Da Silva because of his previous
declarations in favor of a debt repudiation is the most accepted explanation
for the sharp increase in the spread on sovereign bonds preceding the 2002
Brazilian election. Goretti (2005) finds further evidence in favor of that hypothesis. She uses a nonlinear econometric model to account for the behavior
of the sovereign spread in Brazil between November 2001 and October 2002.
She finds that a measure of the perceived probability of Lula’s victory (based
on opinion polls) has a statistically significant effect on spread levels. In the
event, Brazil did not default on its debt.
J. C. Hatchondo and L. Martinez: The Politics of Defaults
305
Figure 2 Elections and Sovereign Bond Spread in Brazil
Interest Rate Spread (in %)
22
Elections: Second Round
17
Argentine Default
Elections: First Round
12
7
2
12/3/2001
4/26/2002
9/17/2002
2/8/2003
7/2/2003
__
2002 2003
Source: J.P. Morgan (EMBI Global).
Notes: The Emerging Market Bond Indices (EMBI) track the return on traded debt instruments nominated in a foreign currency. J.P. Morgan computes various indices that
differ in the countries included, the weights assigned to countries, and the liquidity of
the debt instruments included.
The results in Block and Vaaler (2004) and Manasse, Roubini, and
Schimmelpfennig (2003) suggest that the Brazilian example illustrated in
Figure 2 is not an exception. Close to elections, the possibility of political
turnover seems to increase the level of default risk. Block and Vaaler (2004)
find that election years are associated with an average downgrade of sovereign
debt. They also report that bond spreads are higher in the 60 days before an
election compared to spreads in the 60 days after an election. They study a
sample of 19 developing countries from 1987–1998. The sample includes 18
presidential elections. Similarly, Manasse, Roubini, and Schimmelpfennig
(2003) find that the probability of a debt crisis increases in years with presidential elections. They define a debt crisis as either an episode classified as
a default by Standard & Poor’s, or the acceptance of an IMF loan in excess
of 100 percent of the country’s quota. They use a sample of 37 developing
306
Federal Reserve Bank of Richmond Economic Quarterly
countries from 1976–2001 and estimate the probability of a debt crisis one
year ahead.
The equilibrium behavior predicted by Hatchondo, Martinez, and Sapriza
(2009) may help us understand why an increase in the probability of political
turnover, on average, increases default risk, as found by Block and Vaaler
(2004) and Manasse, Roubini, and Schimmelpfennig (2003). Hatchondo,
Martinez, and Sapriza (2009) show that the effect of political turnover on
the default probability may depend on the type of the current government.
In their model, when a debtor-friendly government is in office, the level of
default risk does not depend on the probability of political turnover because
political turnover would not trigger a political default. In contrast, when a
creditor-friendly government is in office, the level of default risk increases
with respect to the probability of political turnover because political turnover
could trigger a political default. Thus, on average, one can expect that the
possibility of political turnover close to elections would increase the level of
default risk, as found in empirical studies.
It should be stressed that a change in the type of government in power does
not need to be preceded by an election. For instance, the turnover of high rank
government officials could signal changes in a government’s willingness to
default. Moser (2007) and Moser and Dreher (2007) find evidence suggesting
that this may be the case. Moser (2007) finds that changes in the finance
minister generate an average increase of 100 basis points of the sovereign
spread on the day of the announcement. This is based on a sample of 12
Latin American countries from 1992–2007. Moser (2007) documents that
around one third of the announcements of a change in the finance minister
during that time led to a decrease in the sovereign spread, which implies that
the increase in the spread of the negative announcements is larger than 100
basis points. Similarly, based on a sample of 20 emerging countries from
1992–2006, Moser and Dreher (2007) find that bond spreads increase and
local currencies depreciate as a result of changes in central bank governors.6
As discussed in Section 1, policymakers may differ in their willingness
to default because they represent constituencies with different exposures to
sovereign debt. The political power of debtholders may vary with the characteristics of the political system. Consequently, these characteristics could
affect default decisions. The findings in Saiegh (2009), Kohlscheen (2009),
and Rijckeghem and Weder (2009) suggest that this is the case.
Using a sample of 48 developing countries between 1971–1997,
Saiegh (2009) finds that countries governed by a coalition of parties are less
likely to default than those governed by single-party governments. Similarly, Kohlscheen (2009) finds that parliamentary democracies display a lower
6 A possible caveat of these results is that the political factors may reflect shocks to
fundamentals.
J. C. Hatchondo and L. Martinez: The Politics of Defaults
307
probability of default compared to that of presidential democracies. He estimates a probit model based on a sample covering 59 democracies from
1976–2003.
Rijckeghem and Weder (2009) classify regimes as democratic and nondemocratic according to the value of a democratization index, and differentiate
between defaults on external and domestic debt. They use a sample of 73 countries from 1974–2000. Rijckeghem and Weder (2009) find that the frequency
of defaults on external debt is larger than the frequency of defaults on domestic
debt, independent of whether the political regime is democratic. When they
restrict their estimations to samples with only democratic regimes, they find
that parliamentary systems and systems with a large number of veto players
deter external defaults as long as economic conditions are sufficiently good.
They do not find statistical evidence of other political factors deterring defaults
on domestic debt. For nondemocratic regimes, they find that the proximity to
elections and low polarization do deter defaults on domestic debt but they do
not find evidence that political indicators other than the type of regime deter
defaults on external debt.
Balkan (1992) constructs an index of democracy that “measures the extent
that the executive and legislative branches of government reflect the popular
will.” He estimates the probability of default using a sample of 31 countries
from 1971–1984. Controlling for 10 economic indicators and a measure of
political stability, he finds that a higher index of democracy decreases the
probability of observing a debt rescheduling in the subsequent year.
3.
RECENT SOVEREIGN DEFAULTS IN
EMERGING MARKETS
In this section, we discuss the influence of political factors on five recent default episodes: Argentina 2001, Ecuador 1999, Pakistan 1999, Russia 1998,
and Uruguay 2003. First, we attempt to identify whether these defaults were
political defaults. We do this with a commonly used index of political risk.
This index suggests that the Argentine default is the most likely to have been
political. Then, we present the behavior of the levels of sovereign debt and
spreads around the Argentine default and show that the Argentine data is consistent with the predictions of the theory developed by Hatchondo, Martinez,
and Sapriza (2009) for political defaults.
Political Turnover and the International Country
Risk Guide Aggregate Index of Political Risk
In Section 1, we explain how governments may differ in their willingness
to pay back sovereign debt because they represent different constituencies.
For instance, while some governments may be more concerned about the
308
Federal Reserve Bank of Richmond Economic Quarterly
well-being of debtholders, others are more concerned about the well-being of
taxpayers. We also explain that this implies that a default may occur when
a creditor-friendly government (with a lower willingness to default) is replaced by a debtor-friendly government (with a higher willingness to default)
and we refer to such a default as a political default. Having a measure of
governments’ willingness to default would allow us to conduct a systematic
analysis of whether default episodes were triggered by political turnover. We
will use as such a measure the index of political risk for investors included in
the International Country Risk Guide (ICRG). ICRG is a credit-rating publication published by The Political Risk Services Group. This index is commonly used in empirical studies (see, for example, Erb, Harvey, and Viskanta
[1996, 1999], Bilson, Brailsford, and Hooper [2002], Reinhart, Rogoff, and
Savastano [2003], and Bekaert, Harvey, and Lundblad [2007]).
Bilson, Brailsford, and Hooper (2002) define political risk as “the risk
that arises from the potential actions of governments and other influential domestic forces, which threaten expected returns on investment.” In the context
of sovereign debt, default is the government’s action that affects the return
obtained by lenders and, for a given debt level, political risk for investors is
lower (higher) when policymakers with a high (low) willingness to pay are
in power. Thus, political turnover could trigger a default when the level of
political risk changes from low to high.7
The ICRG index of political risk is one of the three components of the
overall ICRG country risk index. The other two indexes are the financial risk
index and the economic risk index. The index of political risk is supposed to
reflect political risk only, independent from economic risk and financial risk
(which are captured by the other two indexes). Thus, the index of political
risk does not necessarily mirror default risk. In fact, we will illustrate that the
default premium implied by Argentine bond prices (the spread) was higher
when political risk was lower.
The ICRG Index of Political Risk and Political
Turnover in Recent Default Episodes
Table 1 presents summary statistics of the behavior of political risk (100 minus
the ICRG index of political risk) before and after the default episodes in
Argentina 2001, Ecuador 1999, Pakistan 1999, Russia 1998, and Uruguay
2003. Since a political default occurs after a creditor-friendly government
is replaced by a debtor-friendly government, one should expect that in the
years before a political default political risk was lower than in the years after
7 It must be said that the ICRG index of political risk does not purely reflect an assessment
about the type of policymakers in office. It also depends on the perceived likelihood of observing
a change of the type in office and on institutional factors.
J. C. Hatchondo and L. Martinez: The Politics of Defaults
309
Table 1 Political Risk in Recent Default Episodes
Argentina
Ecuador
Pakistan
Russia
Uruguay
(1)
12-2001
07-1999
11-1999
08-1998
05-2003
(2)
29.1
40.3
49.7
44.6
27.8
(3)
25.6
39.3
48.6
43.3
27.9
(4)
38.4
42.9
52.9
47.5
27.5
(5)
0
11.5
27.1
34.2
36.5
(6)
0
0
8.3
22.2
61.1
(7)
124
28
4
22
1
(8)
n/a
n/a
18
23
13
Notes: (1) month of default; (2) average risk in the sample; (We consider data starting
eight years before the default and three years after the default. The exception is Russia.
The data for Russia starts in April 1992.) (3) average risk before the default; (4) average risk after the default; (5) percentage of months before the default with risk above
the after-default average; (6) percentage of months after the default with risk below the
before-default average; (7) number of consecutive months before the default with political
risk below the after-default average; (8) number of consecutive months after the default
with political risk above the before-default average; n/a indicates that political risk after
the default is always above the before-default average.
the default. One can see in Table 1 that this occurs in Argentina, Ecuador,
Pakistan, and Russia.
Among the four default episodes associated with an increase in political
risk, we identify the default episode in Argentina as the most likely to have
been political. Argentina exhibits the largest increase in political risk after the
default. Comparing columns (3) and (4) in Table 1, we can see that the postdefault level of political risk in Ecuador, Pakistan, and Russia is less than 10
percent higher than the pre-default level. In Argentina, the post-default level
of political risk is 39 percent higher than the pre-default level. In addition,
among these four countries, Argentina is the most likely to have experienced
the kind of political stability Hatchondo, Martinez, and Sapriza (2009) argue should precede a political default. Recall that Hatchondo, Martinez, and
Sapriza (2009) explain that pre-default creditor-friendly governments would
only choose debt levels for which a political default would occur in environments with high political stability. Table 1 shows that among the four countries
where default episodes marked an increase in political risk, Argentina is the
only one where the level of political risk was consistently lower before the
default (and consistently high after the default; see columns (5)–(8) in Table
1). In order to further support the view that the Argentine default was preceded
by political turnover, the next subsection describes political events around the
default.
Political Turnover Around the Argentine Default
A series of political events that occurred around the 2001 default seem to confirm that the default episode in Argentina was preceded by political turnover.
310
Federal Reserve Bank of Richmond Economic Quarterly
In the presidential campaign of 1999, the two main candidates expressed opposing positions as to whether the future government should declare a moratorium on its foreign debt. The Economist (1999) wrote that “while Eduardo
Duhalde, his Peronist opponent, has made rash public-spending promises, and
suggested that Argentina should default on its foreign debt, it has been Mr. de
la Rua who has responsibly promised to maintain the main thrust of current
economic policies, including convertibility.”
The creditor-friendly approach of Fernando de la Rua’s government is also
apparent from its attempt to impose drastic austerity to balance the budget—
including cuts of up to 13 percent in public sector wages and pensions. In the
face of a drying up of credit and an economy in its fourth year of recession,
this was perceived to be the only way to stave off default on Argentina’s $128
billion of public foreign debt and maintain the currency-board system that
pegs the peso, at par, to the dollar. This policy stance was reinforced by de
la Rua’s statement that “. . . there’ll be no default and no devaluation. Our
effort is to reactivate the internal market, which needs lower interest rates. It
could be necessary to lower the costs of the debt, but we will comply with our
obligations” (see The Economist [2001]).
Having lost political support even from members of his own party, de la
Rua left office on December 19, 2001, and was succeeded by governments
with a more debtor-friendly approach. The newly appointed president, the
PeronistAdolfo Rodriguez Saa, immediately declared a default that was widely
supported in Congress. He was replaced two weeks later and his successor, Eduardo Duhalde, confirmed the default decision by failing to serve a
USD 28 million interest payment due on an Italian lira bond. According to
Sturzenegger and Zettelmeyer (2006), it is estimated that around 60 percent
of the debt in default was held by domestic residents.
The Behavior of Spread and Debt Levels Around the
Argentine Default
Hatchondo, Martinez, and Sapriza (2009) predict that in a political default,
post-default debt levels are lower than pre-default levels and post-default
spread levels are lower than pre-default levels. We will contrast these predictions with the behavior of debt and spread levels around the 2001 Argentine
default, which we have argued has the characteristics of a political default.
Figure 3 shows that, in Argentina, spreads were lower after the 2005 debt
exchange, when (according the ICRG index of political risk) the government
was perceived as riskier to creditors, than before the default, when the government was perceived as less risky to creditors. Thus, the behavior of the
J. C. Hatchondo and L. Martinez: The Politics of Defaults
311
Figure 3 Argentina Sovereign Spread
20
18
Sovereign Spread (in %)
16
14
12
10
8
6
4
2
0
1991Q2
1994Q4
1998Q2
2001Q4
2005Q2
Notes: The vertical line marks the month of default. The line with dots (black) corresponds to the yield of government debt computed by Neumeyer and Perri (2005). The
solid gray line corresponds to the measure of the spread computed by J.P. Morgan using
foreign currency debt.
spread in Argentina is roughly in line with the one predicted by Hatchondo,
Martinez, and Sapriza (2009).8
Figure 4 shows that in Argentina, governments perceived to be riskier to
creditors have chosen relatively low debt levels after the default—the debt
level decreases sharply in 2005 when the defaulted debt is exchanged. This is
consistent with the decrease in the debt level after a political default predicted
by Hatchondo, Martinez, and Sapriza (2009). It is also consistent with the
difficulties in market access observed after a default episode (IMF [2002a] and
Gelos, Sahay, and Sandleris [2004] discuss evidence of a drainage in capital
flows to countries that defaulted).
8 In Hatchondo, Martinez, and Sapriza’s (2009) model, the recovery rate on defaulted bonds
is zero and, consequently, defaulted bonds have no value. Therefore, Hatchondo, Martinez, and
Sapriza (2009) do not present predictions that one could contrast with the spread data between
the default episode in 2001 and the debt exchange in 2005.
312
Federal Reserve Bank of Richmond Economic Quarterly
Figure 4 Face Value of Argentina’s Public Debt that is Denominated in
Foreign Currency
2,800
2006 $U.S. Per Capita
2,600
2,400
2,200
2,000
1,800
1,600
1,400
1,200
Dec-94
Oct-96
Aug-98
Jun-00
Apr-02
Feb-04
Nov-05
Notes: The series does not include arrears. The vertical line marks the month of default.
Of course, other factors besides political turnover may have affected
Argentina’s borrowing decisions and the market price of its debt. One way
of controlling for some of these factors is to compare the behavior of debt
and spread in Argentina with the one in Uruguay. Argentina and Uruguay are
neighboring countries with highly correlated business cycles. In fact, both
countries had experienced negative growth since 1999, after the Brazilian devaluation. Brazil was a major trading partner of Argentina and Uruguay and
both countries had pegged their exchange rate to the dollar, which may have
slowed down the adjustment of prices to that shock. Both countries defaulted
on their debt, but the 2003 Uruguayan default does not seem to have been
triggered by political turnover. According to Table 1, the pre- and post-default
levels of political risk in Uruguay are almost identical. There is also anecdotal evidence consistent with that. The Uruguayan president at that time,
Jorge Batlle, had previously campaigned in 1989 with a platform that proposed to swap the central banks’ gold reserves to pay off the debt in default.
In the midst of the 2002 crisis, he announced that the country would make
sacrifices in order to honor its debt contracts. Unlike in Argentina, the ruling coalition in Uruguay had control of Congress and managed to approve
several rounds of spending cuts and tax increases to reduce the budget deficit
(see The Economist [2002]). The Uruguayan government could avoid missing
J. C. Hatchondo and L. Martinez: The Politics of Defaults
313
Figure 5 Uruguay’s Foreign-Currency-Denominated Public Debt and
Sovereign Spread over U.S. Treasury Bills
3,200
Debt
Spread
20
2,800
15
2,600
2,400
10
2,200
5
Sovereign Spread (%)
Debt (2006 $U.S. Per Capita)
3,000
25
2,000
0
1,800
Dec-94
Oct-96
Aug-98
Jun-00
Apr-02
Feb-04
Nov-05
Notes: The vertical line marks the month of default.
debt payments and also stop a bank run thanks to a joint rescue package provided by the IMF, the World Bank, and the Inter-American Development Bank
(see IMF [2002c]).
In a press release, the IMF executive board
“. . . commended the Uruguayan authorities for their decisive policy action,
their commitment to maintaining a framework that will foster private sector
activity, and their continued close cooperation with the Fund. . . ” (see IMF
[2002b]). Sturzenegger and Zettelmeyer (2006) estimate that the bondholders
that participated in the Uruguayan exchange suffered a reduction in the net
present value of their claims within the range of 10–15 percent, substantially
lower than the loss experienced by holders of Argentine debt (more than 60
percent). In order to induce a higher participation rate in their debt exchange,
the Uruguayan authorities announced that the new bonds were going to receive
de facto seniority over the previously issued bonds. Ex post, bondholders that
did not participate in the exchange were fully paid back.
Figure 5 shows that the spread and debt levels in Uruguay were not lower
after the default episode than before the crisis (as they were in Argentina). The
figure also shows that the spread and debt levels were not particularly low in
Uruguay after 2005, at the time when they were low in Argentina. Thus, we
do not find that low post-default levels of spread and debt in Argentina may
be accounted for by shocks that also affected Uruguay during that time.
314
4.
Federal Reserve Bank of Richmond Economic Quarterly
CONCLUSIONS
This article discusses how political factors may influence sovereign default
risk. First, the article presents a summary of theoretical studies on this issue.
We survey studies that argue that a sovereign may be willing to repay its debt
because it is in the best interest of local agents with political power. We
also discuss theoretical studies that examine how changes in the government’s
willingness to pay and the frequency of these changes (political stability) affect
sovereign default risk. We then discuss a large body of empirical work that
finds evidence of the influence of political stability and other characteristics of
a political system on default risk. In addition, we study five recent sovereign
defaults and find that the 2001 Argentine default is the most likely to have been
triggered by political turnover, and that the behavior of spread and debt levels
around that default is broadly in line with the one predicted by Hatchondo,
Martinez, and Sapriza (2009).
REFERENCES
Aghion, Philippe, and Patrick Bolton. 1990. “Government Domestic Debt
and the Risk of Default: A Political-Economic Model of the Strategic
Role of Debt.” In Public Debt Management: Theory and History, edited
by Rudiger Dornbusch and Mario Draghi. New York: Cambridge
University Press, 315–44.
Aguiar, Mark, and Gita Gopinath. 2006. “Defaultable Debt, Interest Rates
and the Current Account.” Journal of International Economics 69
(June): 64–83.
Alfaro, Laura, and Fabio Kanczuk. 2005. “Sovereign Debt as a Contingent
Claim: A Quantitative Approach.” Journal of International Economics
65 (March): 297–314.
Amador, Manuel. 2003. “A Political Economy Model of Sovereign Debt
Repayment.” Stanford University mimeo.
Arellano, Cristina. 2008. “Default Risk and Income Fluctuations in
Emerging Economies.” American Economic Review 98 (June): 690–712.
Balkan, Erol M. 1992. “Political Instability, Country Risk and Probability of
Default.” Applied Economics 24 (September): 999–1,008.
Bekaert, Geert, Campbell Harvey, and Christian Lundblad. 2007. “Liquidity
and Expected Returns: Lessons from Emerging Markets.” Review of
Financial Studies 20 (November): 1,783–831.
J. C. Hatchondo and L. Martinez: The Politics of Defaults
315
Bilson, Christopher M., Timothy J. Brailsford, and Vincent C. Hooper. 2002.
“The Explanatory Power of Political Risk in Emerging Markets.”
International Review of Financial Analysis 11: 1–27.
Block, Steven A., and Paul M. Vaaler. 2004. “The Price of Democracy:
Sovereign Risk Ratings, Bond Spreads and Political Business Cycles in
Developing Countries.” Journal of International Money and Finance 23
(October): 917–46.
Brewer, Thomas L., and Pietra Rivoli. 1990. “Politics and Perceived Country
Creditworthiness in International Banking.” Journal of Money, Credit
and Banking 22 (August): 357–69.
Bussiere, Matthieu, and Christian Mulder. 2000. “Political Instability and
Economic Vulnerability.” International Journal of Finance &
Economics 5 (October): 309–30.
Citron, Joel-Tomas, and Gerald Nickelsburg. 1987. “Country Risk and
Political Instability.” Journal of Development Economics 25 (April):
385–92.
Cole, Harold L., James Dow, and William B. English. 1995. “Default,
Settlement, and Signalling: Lending Resumption in a Reputational
Model of Sovereign Debt.” International Economic Review 36 (May):
365–85.
Cuadra, Gabriel, and Horacio Sapriza. 2008. “Sovereign Default, Interest
Rates and Political Uncertainty in Emerging Markets.” Journal of
International Economics 76 (September): 78–88.
Dixit, Avinash, and John Londregan. 2000. “Political Power and the
Credibility of Government Debt.” Journal of Economic Theory 94
(September): 80–105.
Drazen, Allan. 1998. “Towards a Political-Economic Theory of Domestic
Debt.” In The Debt Burden and its Consequences for Monetary Policy,
edited by G. Calvo and M. King. London: Macmillan, 159–76.
Eaton, Jonathan, and Mark Gersovitz. 1981. “Debt with Potential
Repudiation: Theoretical and Empirical Analysis.” Review of Economic
Studies 48 (April): 289–09.
Erb, Claude B., Campbell R. Harvey, and Tadas E. Viskanta. 1996. “Political
Risk, Economic Risk, and Financial Risk.” Financial Analysts Journal
52: 29–46.
Erb, Claude B., Campbell R. Harvey, and Tadas E. Viskanta. 1999. “New
Perspectives on Emerging Market Bonds.” Journal of Portfolio
Management 25 (Winter): 83–92.
316
Federal Reserve Bank of Richmond Economic Quarterly
Gelos, Gaston, Ratna Sahay, and Guido Sandleris. 2004. “Sovereign
Borrowing by Developing Countries: What Determines Market Access?”
IMF Working Paper 04/221.
Goretti, Manuela. 2005. “The Brazilian Currency Turmoil of 2002: A
Nonlinear Analysis.” International Journal of Finance & Economics 10:
289–306.
Guembel, Alexander, and Oren Sussman. 2009. “Sovereign Debt without
Default Penalties.” Review of Economic Studies 76 (October):
1,297–320.
Hatchondo, Juan Carlos, and Leonardo Martinez. 2009. “Long-Duration
Bonds and Sovereign Defaults.” Journal of International Economics 79
(September): 117–25.
Hatchondo, Juan Carlos, Leonardo Martinez, and Horacio Sapriza. 2007a.
“The Economics of Sovereign Default.” Federal Reserve Bank of
Richmond Economic Quarterly 93 (Spring): 163–87.
Hatchondo, Juan Carlos, Leonardo Martinez, and Horacio Sapriza. 2007b.
“Quantitative Models of Sovereign Default and the Threat of Financial
Exclusion.” Federal Reserve Bank of Richmond Economic Quarterly 93
(Summer): 251–86.
Hatchondo, Juan Carlos, Leonardo Martinez, and Horacio Sapriza. 2009.
“Heterogeneous Borrowers in Quantitative Models of Sovereign
Default.” International Economic Review 50 (October): 1,129–51.
Hatchondo, Juan Carlos, Leonardo Martinez, and Horacio Sapriza. 2010.
“Quantitative Properties of Sovereign Default Models: Solution Methods
Matter.” Review of Economic Dynamics 13 (October): 919–33.
IMF. 2002a. “Sovereign Debt Restructurings and the Domestic Economy
Experience in Four Recent Cases.” Washington, D.C.: IMF (February
21).
IMF. 2002b. “Joint Statement by IMF, World Bank and Inter-American
Development Bank on Uruguay.” News Brief No. 02/82 (August 4).
IMF. 2002c. “IMF Completes First Review and Approves New Increase on
Uruguay Stand-By.” News Brief No. 02/87 (August 8).
IMF. 2003. “Lessons from the Crisis in Argentina.” Washington, D.C.: IMF
(March 24).
IMF. 2004. “The IMF and Argentina, 1991–2001.” Independent Evaluation
Office Report. Washington, D.C.: IMF.
Kohlscheen, Emanuel. 2009. “Sovereign Risk: Constitutions Rule.” Oxford
Economic Papers 62 (January): 62–85.
J. C. Hatchondo and L. Martinez: The Politics of Defaults
317
Manasse, Paolo, Nouriel Roubini, and Axel Schimmelpfennig. 2003.
“Predicting Sovereign Debt Crises.” IMF Working Paper 03-221
(November).
Moser, Christoph. 2007. “The Impact of Political Risk on Sovereign Bond
Spreads. Evidence from Latin America.” University of Mainz mimeo.
Moser, Christoph, and Axel Dreher. 2007. “Do Markets Care about Central
Bank Governor Changes? Evidence from Emerging Markets.” CESifo
Working Paper No. 2177.
Neumeyer, Pablo A., and Fabrizio Perri. 2005. “Business Cycles in
Emerging Economies: The Role of Interest Rates.” Journal of Monetary
Economics 52 (March): 345–80.
Reinhart, Carmen M., Kenneth S. Rogoff, and Miguel A. Savastano. 2003.
“Debt Intolerance.” Brookings Papers on Economic Activity 34: 1–74.
Rijckeghem, Caroline, and Beatrice Weder. 2009. “Political Institutions and
Debt Crises.” Public Choice 138 (March): 387–408.
Saiegh, Sebastian. 2009. “Coalition Governments and Sovereign Debt
Crises.” Economics and Politics 21 (July): 232–54.
Santiso, Javier. 2003. The Political Economy of Emerging Markets: Actors,
Institutions and Crises in Latin America. New York: Palgrave Macmillan.
Sturzenegger, Federico, and Jeromin Zettelmeyer. 2006. “Defaults in the
90s.” Universidad Torcuato Di Tella mimeo.
Tabellini, Guido. 1991. “The Politics of Intergenerational Redistribution.”
Journal of Political Economy 99 (April): 335–57.
The Economist. 1999. “Argentina’s next steps.” October 21.
The Economist. 2001. “Time to end the agony.” November 1.
The Economist. 2002. “Uruguay’s economy. Victim of contagion.” July 25.
The Economist. 2005. “Argentina’s debt restructuring. Tough deal.” May 3.
Tomz, Michael, and Mark L. J. Wright. 2007. “Do Countries Default in ‘Bad
Times’?” Journal of the European Economic Association 5: 352–60.
@FedFRASER