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The Economics of Student Loan Borrowing and Repayment
Clusters of Knowledge: R&D Proximity and the Spillover Effect
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THIRD QUARTER 2013
The Economics of Student Loan Borrowing and Repayment
1
Media reports and policymakers’ concerns about rising student loan
balances and defaults have greatly intensified in recent years. Wenli
Li sheds light on the economics behind these trends and discusses the
implications for the broader economy.
Clusters of Knowledge: R&D Proximity and the Spillover Effect 11
Innovation, a key to economic growth, does not happen in a vacuum.
Economists have studied the knowledge spillovers that occur when
firms locate near one another. Yet, these spillovers have proved hard to
empirically verify. Gerald A. Carlino and Jake K. Carr explain what
they found through a more accurate way to measure the geographic
concentration of research and development labs.
The Promise and Challenges of Bank Capital Reform
23
The failure and bailout of prominent financial institutions amid the crisis
of 2007-09, and the effect these events had on the economy as a whole,
have led policymakers to rethink how the global financial system is
regulated. Ronel Elul explains the history behind bank capital regulation
and how ongoing regulatory changes might help prevent future crises.
Research Rap
Abstracts of the latest working papers produced by the Research
Department of the Federal Reserve Bank of Philadelphia.
31
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The Economics of Student Loan
Borrowing and Repayment
BY WENLI LI
eports in the popular press and
policymakers’ concerns about student loans
have greatly intensified in recent years
because of rising student loan balances
and defaults. Even greater cause for concern arose
as student loans outstanding passed credit card debt
to become the single largest nonmortgage household
debt in 2012. Worries about the risk of massive default
have even prompted a comparison with the subprime
mortgage crisis.1
R
Existing theoretical and empirical work by economists on student
loans can shed light on the economics behind this trend and, therefore,
help provide answers to a number of
important questions: What determines
whether and how much a household
borrows for student loans, and what
determines whether and when a household repays these loans? What factors
account for the widely noted increase
in student loans outstanding and
For example, Steven Eisman titled his presentation on student loans at the Ira Sohn Conference “Subprime Goes to College.”
1
defaults? What are the implications of
the trend for households’ consumption
and for the broader economy?
A SIMPLE THEORY OF
STUDENT BORROWING AND
REPAYMENT
What Makes Student Loans Different? Student loans are made solely
for the purpose of financing higher
education; that is, they are designed to
help students pay for college tuition,
books, and living expenses. They are
different from other consumer loans,
including credit card debt, auto loans,
or mortgages; for those types of loans,
households borrow to purchase goods
Wenli Li is a senior economic advisor and economist at the Federal
Reserve Bank of Philadelphia. The views expressed in this article
are not necessarily those of the Federal Reserve. This article and
other Philadelphia Fed research and reports are available at www.
philadelphiafed.org/research-and-data/publications.
www.philadelphiafed.org
they consume immediately, such as
clothes, a car, or a house. Economists
often view student loans as a means of
financing investment in human capital. In other words, student loans help
borrowers, through their college experience, to acquire knowledge as well as
social and personal attributes that may
enhance their ability to later perform
in the economy and, thus, gain higher
earnings.2 It is in this sense that student loans are analogous to investment
in physical capital such as an MRI
machine purchased by a clinic. Unlike
a pill given to a patient, the machine
is not consumed immediately; rather it
is used for future production (scanning
patients), and with each use, it generates income from the fee a patient pays
for each test.
Both Supply and Demand Factors Affect Student Borrowing. A
household’s decision to take out a
student loan — the demand side — is
obviously tied to its decision about
whether to attend college. The majority of people in the U.S. go to college
shortly, if not immediately, after high
school. These people are often in their
late teens or early 20s and lack the
financial resources to pay for college,
even with the help of their parents.
Therefore, they need to borrow to
cover the cost. Put simply, for a large
fraction of the U.S. population, the
decision about whether and when to
take out a student loan is closely tied
to the decision of whether, when, and
2
Of course, education serves other important
purposes that are not captured by a narrow look
at graduates’ earning power, but in this article I
focus solely on the economics of student loans.
Business Review Q3 2013 1
where to attend college. As a matter
of fact, according to the Chronicle of
Higher Education, about 60 percent of
Americans who attend college borrow
annually to cover costs.
As with any other economic decision, the decision of whether, when,
and where to attend college depends
on the difference between the benefits
and the costs. The economic benefits
of going to college are captured by the
gain in future earnings, and the costs
include the earnings a student forgoes
while in school, in addition to tuition,
books, and living expenses. Described
this way, the prospective student’s decision sounds very simple. But even if
we imagine, as most economic analyses
do, that the student has the ability to
rationally calculate costs and benefits,
the decision is actually fraught with
uncertainty.
First, think about costs. While
some of the costs — tuition, books,
and living expenses — are immediately observable and are relatively
easier to calculate and predict over,
say, a two- or four-year period, real borrowing costs may fluctuate as interest
rates and inflation rates fluctuate. In
addition, students’ forgone earnings
may be very difficult to measure with
any precision. The income gains from
a college education are entirely in the
future and need to be estimated and,
thus, can be very imprecise. For example, a computer science major not only
needs to figure out job prospects and
prevailing salaries in four years’ time,
but he must also project job prospects
and wages over the rest of his working
life. To complicate the matter further,
he also needs to factor in the possibility that he may end up disliking the
field and taking up a different career
with lower potential earnings.
The lender’s decision — the supply side — would be relatively simple if
students borrowed in a perfect capital
market. The concept of a perfect capital market is an ideal benchmark used
2 Q3 2013 Business Review
by economists, in which many realworld difficulties are assumed away.
The concept is useful because it forces
us to think carefully about the factors
that may limit a student’s capacity to
borrow. In a perfect capital market,
lenders can sign a contract that makes
the payments conditional on borrowers’ future earnings and can at no cost
to themselves compel borrowers to
work and earn enough to repay the
loan. The factors that affect a lender’s
decision about whether to extend a
student loan will thus be the opportunity cost of the funding (the interest
the lender could have earned on other
loans) and the riskiness of the gains
(mainly due to the uncertainty about
the borrower’s income).
Two factors complicate our ideal
world. First, human beings, not machines, are the ones producing earnings. In a civilized society, humans
cannot serve as collateral because
lenders cannot enslave borrowers, nor
can they buy and sell them.3 Second,
although lenders can garnish borrowers’ earnings when borrowers do not
make payments, borrowers’ earnings
also depend on their effort. This is
very different from machines, whose
value depends mainly on their resale
value, which is largely outside the
control of the owners who use it as collateral. For example, a computer software engineer living in New Jersey can
go to work for an investment house in
New York City and make $60,000 a
year with a commuting cost of $8,000
a year, or she can work for $50,000 for
a local firm that has better work schedules and does not require any commute. Suppose the engineer has to give
half of her income to the lender to ser-
vice student loans. In the first case, it
means that the engineer pays $30,000
to the lender and has $22,000 for herself after taking out commuting costs.
In the second case, it means that the
engineer pays $25,000 to the lender
and the same amount to herself. The
engineer will choose to work locally,
since she makes the same amount of
money in either case, but the lender
will lose $5,000 if the engineer chooses
to work in New Jersey rather than in
New York City.
Over the years, the federal government has become the dominant
supplier of student loans, first through
its loan guarantee programs and more
recently through direct loans.4 The
Structure of the Student Loan Market
provides a brief discussion of the role of
government in the student loan market. Therefore, a full account of the
supply side of the market would require
us to discuss the underlying political
forces, since the total loan amount and
interest rates are set by Congress. That
is beyond the scope of this article.
The Repayment Decision. The
student loan payment decision, like all
other consumer loan payment decisions, depends on the borrower’s ability to pay and the costs and benefits
associated with default. The ability to
pay depends on the borrower’s income and assets. If a borrower loses
his job or suffers a big loss in the stock
market or a decline in the value of his
primary residence, he may not be able
to service his debt. The benefits of not
paying one’s student loans are the resources that are freed and that can be
used for consumption purposes or to
service other debt. Felicia Ionescu and
Marius Ionescu show that households
Prior to the mid-19th century, debtors’ prisons
were a common way to deal with unpaid debt.
The father of the British writer Charles Dickens
was sent to Marshalsea debtors’ prison. As a
result, Dickens used Marshalsea as the model
for debtors’ prison in his novels.
4
Prominent arguments for government involvement are that social returns to education are
greater than private returns. Furthermore,
employers tend to underinvest in generalized
training, since they do not fully capture the
returns in the event the trained employees leave
the firm.
3
www.philadelphiafed.org
The Structure of the Student Loan Market
T
here are three types of student loans: federally guaranteed loans
made by banks and other lenders; federal loans made directly
by the government; and private loans, which are essentially the
same as other consumer loans from banks and companies. In
the case of guaranteed loans, the government pays a subsidy to
lenders that make the loans and also guarantees the amount
loaned.*
Effective July 2010, in response to the changing market and the debate
about the federal government’s role in supporting student financing, Congress
expanded federal aid to college students while ending federal subsidies to private
lenders through loan guarantees.
The interest rate paid by students on both guaranteed loans and direct loans
is fixed and set by Congress. The government pays the interest that accrues
while the borrower is in school. Congress in 2007 temporarily reduced interest
rates for low- and middle-income undergraduate borrowers to 3.4 percent from
6.8 percent until July 1, 2012. Congress then extended the freeze in interest rates
until July 2013, at which time it pegged rates to the 10-year Treasury yield.
Private loans usually have worse terms than either type of federal loan, and
interest rates on private loans can change over time. Because most students have
limited credit histories, private lenders often require cosigners. The borrower is
responsible for paying the interest that accrues.
* The top 10 holders of government guaranteed loans (FFELP loans) in the third quarter of 2010
were SLM Corporation, Nelnet, Wells Fargo, Brazos Group, JPMorgan Chase Bank, the Pennsylvania Higher Education Assistance Agency, College Loan Corporation, CIT, PNC, and Goal
Financial. SLM Corporation had the largest market share (close to 60 percent), and each of the
other institutions had under 10 percent of the market share.
have incentives to default on student
loans first, before defaulting on credit
card debt. By keeping their credit card
account current, they can continue to
use it as a transaction account or for
borrowing purposes. Economists call
this phenomenon “preserving liquidity.”
The benefits from defaulting on
student loans are, by contrast, limited.
Unlike credit card debt, car loans, and
other consumer loans, student loans
cannot be discharged or reduced by a
judge (known as “cramming down”)
under personal bankruptcy. Instead,
borrowers who are late with their
federal student loan payments have to
enter into a repayment plan that can
last 10 to 15 years, and during that
time, a fraction of their earnings will be
garnished, similar to what occurs in a
Chapter 13 repayment plan under personal bankruptcy. The government can
www.philadelphiafed.org
also garnish the borrower’s tax returns
and benefits. Other costs of defaulting
on one’s student loans include limited
future access to the credit market, since
the borrower’s decision to default will
affect his credit score from the credit
bureau. Evidence from bankruptcy filers may give some sense of the order of
magnitude of these costs. For instance,
using data from the Federal Reserve’s
triennial Survey of Consumer Finances,
Song Han and Geng Li find that bankruptcy filers are more than 40 percent
less likely to have credit cards than
comparable households that did not file
for bankruptcy. If they do have cards,
their lines of credit have far lower limits
(by $12,000) compared with those who
did not file for bankruptcy. Moreover,
bankruptcy filers pay higher interest
rates (1.2 percentage points higher)
than people who did not file.5
With this theory in mind, we can
now turn to the empirical evidence
and discuss how and why student
loans outstanding and defaults have
increased sharply and the implications
for the broader economy.
MORE TREND THAN CYCLE
Rising Student Loan Balances.
The analysis here draws on the Federal
Reserve Bank of New York (FRBNY)/
Equifax Consumer Credit Panel dataset, a nationally representative random
sample of anonymized credit reports
from Equifax, one of three major
consumer credit reporting agencies in
the U.S., containing borrowers’ ages,
amounts borrowed, and repayment
histories for bank and department
store credit cards, car loans, mortgages,
home equity loans, etc.6
Figure 1 shows the outstanding
balances for various consumer loans,
credit card debt, auto loans, home
equity loans, and student loans. Note
that I omit first mortgages because,
unlike the other loans discussed here,
first mortgages are of much larger value
and collateralized.7 Two observations
are worth noting. Student loans have
been trending up since the beginning
of our sample period (the first quarter
of 2003), and they did not come down
until very recently. By comparison,
credit card debt and auto loans did not
5
It is likely that those who default on student
loans will suffer a larger effect related to access
to credit than bankruptcy filers. Bankruptcy
wipes out some or all of a borrower’s existing debts, a situation that is attractive to new
lenders, who will not have to compete with old
lenders to be repaid. But default does not wipe
out student loans.
6
The calculation is based on a 1 percent
random sample of the FRBNY Consumer Credit
Panel, while the panel accounts for about 5
percent of all households that have files with
the credit bureau.
7
Although car loans are also collateralized, cars
depreciate much faster than houses. For most
car loans, the resale value of the car is not the
primary determinant of the loan terms.
Business Review Q3 2013 3
FIGURE 1
Trend of Student Loan Balances vs.
Other Loan Balances
Source: Federal Reserve Bank of New York/Equifax Consumer Credit Panel
exhibit a comparable long-run trend,
and their acceleration and deceleration
coincided with the crisis. Home equity
loans also experienced a long boom
prior to the crisis. But balances came
down immediately after the crisis, an
immediate effect of the significant decline in house prices and the decline in
households’ equity in their homes.
The rise in student loan balances comes from the rise in both the
number of people who borrowed and
the amount each person borrowed. In
contrast to other loans, the fraction of
people with student loans has been increasing steadily over time and is now
about 15 percent of the total population (Figure 2). The average student
loan balance has also been moving up
over the years for all age groups (Figure 3). In the first quarter of 2012, the
average student loan balance for a 40year-old was $30,000!
The Effects of Supply and Demand Factors. Although we cannot completely separate the effects of
demand-side factors from supply-side
4 Q3 2013 Business Review
factors, there are reasons to believe
that both have contributed to the
phenomenal rise in total student loans
outstanding. On the demand side,
estimates of the difference in lifetime
earnings for those with college degrees
versus only high school diplomas range
from $650,000 to $1 million.8 This
is because a shift in the production
technology over the past decade or two
has favored skilled labor over unskilled
labor by increasing skilled labor’s relative productivity and hence its relative
demand. For instance, the adoption of
computers in the workplace has posed
challenges for many workers. However,
it is less costly for more educated, able,
or experienced workers to learn to
use computers and thus adapt to the
new technology. The wage differential for educated workers has certainly
See the paper by Anthony P. Carnevale,
Stephen J. Rose, and Ban Cheah, Keith Sill’s
Business Review article on the skill premium,
and http://www.pewsocialtrends.org/2011/05/16/
lifetime-earnings-of-college-graduates/.
8
not gone unnoticed by high school
students deciding whether to enter the
labor force. Indeed, more students are
now accessing higher education than
before. According to the Census Bureau, college enrollment as a fraction
of the population between ages 16 and
25 rose from 34 percent in 1990 to 51
percent in 2010.
The rise in student loan borrowing per person reflects to a large extent
the rising cost of higher education that
has been going on for over a decade.
According to the College Board,
over the period 1997-98 to 2007-08,
published tuition and fees for full-time
in-state students at public four-year
colleges and universities rose 54 percent in inflation-adjusted dollars — an
average of 4.4 percent per year;9 those
for full-time students at two-year colleges and universities rose 17 percent
in real terms — 1.5 percent annually;
published tuition and fees for full-time
students at public two-year colleges
and universities rose 33 percent in real
terms, 2.9 percent annually. Reduced
funding from government is partially
responsible for the rise in tuition and
fees. According to the annual Grapevine Study, conducted by Illinois State
University’s Center for the Study of
Education Policy with the cooperation of the State Higher Education
Executive Officers, state appropriations for colleges and students sank by
7.6 percent in 2011-12, the largest such
decline in at least half a century.
Finally, declines in family resources following the recent financial
crisis have also driven up demand for
student loans in the past five years.
According to the Survey of Consumer
Finances, between 1998 and 2007,
9
In economics, the nominal value of something
is its money value in different years. By contrast,
real values adjust for differences in price levels
of those years. As a result of the adjustment, any
differences in real values are then attributed to
differences in the amount of goods that money
income could buy in each year.
www.philadelphiafed.org
while real median household income
fell 3.9 percent, real median household net worth went up by 10 percent.
Between 2007 and 2010, however, real
median household income fell 11 per-
cent, and median household net worth
fell 39 percent over that same period.
On the supply side, the U.S. government has played an increasingly important role in extending student loans
FIGURE 2
Percent of Indebted Households by Loan Type
Note: Households includes those with credit histories on file.
Source: Federal Reserve Bank of New York/Equifax Consumer Credit Panel
FIGURE 3
Average Student Loan Balance by Age
that are cheaper than those the private
market would offer, thus crowding out
banks from the lending market (Figure
4). Furthermore, starting in July 2010,
the government replaced loan guarantees with direct loans and effectively
ended all subsidies to private lenders. According to the Department of
Education, Federal Student Aid, an
office of the department, managed or
oversaw $713 billion in student loans
in 2011, which accounts for close to
90 percent of the market. Most college
students qualify for federal student
loans. Students can borrow the same
amount of money, at the same loan
rate, regardless of their own income
or their parents’, regardless of their
expected future income, and regardless
of their credit history. Only students
who have defaulted on federal student
loans or have been convicted of drug
offenses are excluded.
Trends in Past Due and Delinquent Loans. The trend in loans past
due closely mirrors the rise in loans
outstanding (Figure 5).10 The total
amount of past dues has been trending
up since the beginning of our sample
period, although the increase in past
dues accelerated after 2007. This is
again in contrast to the total amount
of past dues of other consumer loans,
which exhibit more of a cyclical pattern; that is, the amount of past dues
for all other consumer loans was more
or less flat until right around the crisis.
Moreover, after 2009, the past due
amount came down for all consumer
debt except student loans.
The movement of delinquency
rates tells a similar story (Figure 6). In
terms of population, the delinquency
rate on student loans has exceeded
the delinquency rates on all three
other types of consumer loans. My
For private student loans, past dues are those
with one missed payment. For government
loans, past dues may include those with multiple
missed payments because of their 270-day grace
period.
10
Source: Federal Reserve Bank of New York/Equifax Consumer Credit Panel
www.philadelphiafed.org
Business Review Q3 2013 5
FIGURE 4
Federal and Nonfederal Student Loans
and Grants
Source: The College Board
FIGURE 5
Past Due Balances on Consumer Loans
estimate of a 14 percent to 15 percent
student loan delinquency rate that we
observed in 2012 is probably a lower
bound for the actual delinquency rates
for student loans. Other estimates by
economists at the New York Fed put
the delinquency rate as high as 26
percent.11 Data limitations require the
analyst to make assumptions, which I
discuss further in the adjacent explanation, Calculating Student Loan Delinquency Rates.
Given the long-run factors that
have increased the demand for higher
education and the factors driving
up college costs, in tandem with the
slower rise in household incomes, it
is not surprising that we saw a rise in
student loan defaults long before the
start of the crisis. The ensuing economic recession, in particular the weak
labor market, nevertheless further
drove up the defaults in student loans,
as it did with most other consumer
loans. For younger adults, particularly
those in their 20s, who often hold
student loans, the unemployment rates
have been especially high (about 16
percent). Finally, part of the rise in
student loan delinquency rates may
also stem from portfolio adjustments as
borrowers stop their student loan payments in order to keep their credit card
payments current to preserve liquidity,
as I discussed earlier.
BROAD ECONOMIC IMPACT
Aggregate statistics and averages
often mask substantial differences at
the individual level. To gain further
insight, it is often necessary to examine the differences among individuals
in a more disaggregated way. These
individual differences can lead to
very different policy prescriptions. For
example, suppose we find that very
young people owe all of the loans and
Note: Includes loans 30 days or more delinquent or charged off.
Source: Federal Reserve Bank of New York/Equifax Consumer Credit Panel
6 Q3 2013 Business Review
11
See the article by Meta Brown, Andrew
Haughwout, Donghoon Lee, Maricar Mabutas,
and Wilbert van der Klaauw.
www.philadelphiafed.org
FIGURE 6
Consumer Loan Delinquency Rates
Note: Includes charged-off loans.
Source: Federal Reserve Bank of New York/Equifax Consumer Credit Panel
Calculating Student Loan Delinquency Rates
T
he calculation of student loan delinquency rates is somewhat
involved due to the unique market structure of student loans.
The key difficulty lies in the fact that the credit bureau data do
not have information on whether a household needs to make
student loan payments in the current quarter. The reason is
that with federal loans, there is typically a six- or nine-month
grace period, depending on the type of loan, after a borrower
leaves school during which the borrower does not have to make payments on
his loans. We do not want to count these borrowers in the denominator when
calculating the default rate, which is defined as the ratio of the number of borrowers who are behind on their student loan payments over the number of borrowers who need to make student loan payments.
One way to get around this issue is to follow the New York Fed’s approach*
and exclude individuals who owed as much as or more than they did in the
previous quarter while maintaining a zero past-due balance. The rationale
behind this approach is that presumably those whose balance did not change
across two quarters and who did not have student loan past dues do not need
to make payments on their student loans yet. If I use this strategy, then the
delinquency rates are much higher. For instance, 26 percent of borrowers would
have past-due balances in the first quarter of 2012 by this calculation as opposed to 14 percent. However, this method is not perfect. For example, it might
miss borrowers who negotiated smaller payments with their lenders through an
income-based repayment plan. If their new payments are too low to cover accruing interest, their balances would be higher rather than lower. We wouldn’t
count these borrowers as being in delinquency using the proposed method even
though they clearly need to be there.
* See the article by Brown and coauthors.
www.philadelphiafed.org
that they are the ones defaulting. In
this case, we might argue that there is
less cause for concern because young
people have a long horizon over which
to work out their situation. And the
policy prescription may be to design
programs to help these people find
jobs or find better jobs. On the other
hand, suppose a large fraction of loans
are held by 50-year-olds and that these
older households are defaulting in
significant numbers. In this case, we
might be much more concerned, since
these people have much shorter horizons over which to recover from their
financial difficulty. The corresponding
policy prescription may require some
degree of loan forgiveness.
To address questions like these, I
reexamine student loan balances, past
dues, and default rates by borrowers’
age using the FRBNY/Equifax Consumer Credit Panel. Two main observations emerge from the analysis.
First, over time, average student
loan balances have increased for all
age groups, but more for those between
ages 30 and 55. Furthermore, it appears to take longer to pay off loans
than in the past. For example, in the
first quarter of 2012 the decline in
average balances really started after
age 32, as opposed to the late 20s in
the first quarter of 2003 (Figure 7).
Balances didn’t stabilize until age 45
in the first quarter of 2012, as opposed
to the late 30s in the first quarter of
2003 (Figure 7).12 Second, the trend toward older households with significant
amounts of student debt is confirmed
if we look at the fraction of people
who have student loans by age. Those
between ages 25 and 45 had the larg-
A small part of the balance is accounted for
by cosigned loans, and, as expected, cosigned
student loans have two peaks: at age 25 (less
than 10 percent of the total balance at that
age) and at age 55 (less than 20 percent of the
total balance). At age 25, borrowers have their
parents as cosigners. At age 55, they most likely
act as cosigners for their children.
12
Business Review Q3 2013 7
est increase. These two observations
are striking, since they indicate that
student loans are not just an issue for
young borrowers as conventional wisdom perceives, but that the middle-aged
(those 40 and above) actually shoulder
a lot of the burden.13
An examination of the total
amount of past dues by age confirms
that it is indeed the middle-aged who
are struggling with their student loan
repayments (Figure 8).14 To some
extent, this trend is not surprising,
since the growth in student loans has
outstripped the growth in income for
some time, as discussed earlier. The
housing crisis obviously exacerbated
the situation by further reducing
households’ net worth.15
Looking just at average borrowings obscures the fact that there are
also substantial differences in the
amount they borrowed. A high average balance might mean that the typical individual’s balance is high. At the
same time, it could mean that most
individuals have very low balances,
while a relatively small number of
individuals have very burdensome debt
levels. One way to think about this is
to consider the difference between the
mean and the median. The mean is
simply the average: the total amount
divided by the number of people. The
median is the amount at which half of
the population has more and half has
less. A classic example to illustrate the
FIGURE 7
Student Loan Balances by Age of Borrowers
Source: Federal Reserve Bank of New York/Equifax Consumer Credit Panel
FIGURE 8
Past Due Student Loan Balances
by Age of Borrowers
This may be due to a trend in the proportion
of parents cosigning on loans while they are still
paying down their own. Identifying this would
require analyzing the individual trade lines,
which appears to be out of scope for this paper.
13
Brown and coauthors have also documented
similar findings in their 2012 article.
14
The harder question that we cannot pinpoint
with the data is why so many people are still
borrowing so much to finance their education.
It could be that individuals are slowly learning
about the change (lower) in expected income.
Or it could simply be that receiving an education is a decision that involves a lot more than
just having a higher income in the future.
15
8 Q3 2013 Business Review
Source: Federal Reserve Bank of New York/Equifax Consumer Credit Panel
difference between mean and median
is that after Bill Gates walks into a
bar that already has four unemployed
people whose income is zero, everyone
in the bar is, on average, a millionaire,
since the mean income is over $1 mil-
lion but the median is still zero (since
half of them are still unemployed).16
16
See a different version of the story at http://
introductorystats.wordpress.com/2011/09/04/
when-bill-gates-walks-into-a-bar/.
www.philadelphiafed.org
Although not as extreme, in our data,
in the first quarter of 2012, the median
balance at age 35 is $14,000, while
the mean is close to $25,000. About
10 percent of borrowers have balances
over $56,000, and 5 percent of the
households have student loan balances
over $81,000, suggesting that a relatively small number of households are
seriously burdened by their debt level.
The Broad Economic Implications. One of the major concerns
about ballooning student loans and
student loan defaults is that these
loans will have a negative impact on
borrowers’ consumption, since the
borrowers need to devote a large fraction of their income to making loan
payments. Furthermore, those who
default on student loans will have more
restricted access to credit because
their credit scores will be lower. For
credit-constrained families, such as
those who need to borrow to buy a car,
repair a roof, etc., this drop in credit
scores may make all of this additional
consumption infeasible. Indeed, the
credit card utilization rate (credit card
balance divided by credit limit) for
those with student loan balances over
$56,000 is 55 percent, compared with
39 percent for the general population
in the first quarter of 2012. Economists
have found that high credit card utilization rates are indicators of liquidity
or income shocks.17
Andrew Glover, Jonathan Heathcote, Dirk Krueger, and Jose-Victor
Rios-Rull show that older people will
fare worse than the young after the
recent financial crisis, since they do
not have as long a horizon as the
young to recover from the losses they
have suffered: loss in income, loss in
stock market investment and, more
important, loss in their housing asset.
My finding that middle-aged and older
17
See the article by Ronel Elul, Nicholas
Souleles, Souphala Chomsisengphet, Dennis
Glennon, and Bob Hunt.
www.philadelphiafed.org
households are much more indebted by
student loans than they used to be (the
mean age of those with student loan
balances over $56,000 is 38 years old,
and the median age is 36 years old)
and to a surprising extent before the
crisis suggests that if we take student
loan borrowing into consideration,
middle-aged and older people may be
even worse off.
Aside from these immediate
economic concerns, researchers have
found some longer-term social concerns. For example, researchers have
found evidence that high debt burdens
make students less likely to choose
lower-paying careers such as teaching. Jesse Rothstein and Cecilia Rouse
study a “natural experiment” generated
by a change in financial aid policy by a
highly selective university. The university introduced a “no loans” policy, in
which it replaced the loan component
of financial aid awards with grants.
Interestingly, they find that debt causes
graduates to choose jobs with substantially higher salaries, such as those in
finance and consulting, and reduces
the probability that students choose
low-paid “public interest” jobs such as
grade-school teacher or social worker.18
Additionally, Dora Gicheva suggests that each $10,000 in additional
student debt decreases the borrower’s
long-term probability of marriage by 7
percentage points.19 A 2010 poll found
that 85 percent of college graduates
Two features of the policy change make this
a natural experiment. First, the change was
unexpected. This means that any change in
students’ employment choices was not affected
by some expected change in financing policies.
In addition, the change in a student’s debt load
was caused by a decision by the university,
rather than a decision by the student. This
means that it was the change in debt load that
induced the change in students’ employment
choices, rather than the other way around. As
with most natural experiments, though, the
precise answers come at some cost to generality. Among other questions, it is natural to ask
whether the behavior of students at a highly
selective university is indicative of the behavior
of students more generally.
18
were planning to move back home
after graduation (Dickler 2010). The
high unemployment rates and low
income of new graduates are the leading causes behind these survey results.
But having large student loans can
certainly make things worse. Although
currently there are more open questions than settled answers regarding
the extent to which student loans hurt
the formation of households, there
is no doubt that reduced household
formation has obviously hurt the recovery of the nation’s housing market.
According to the Census Bureau, the
homeownership rate of those under
age 35 declined from its 2006 peak
of 42.6 percent to 36.8 percent in the
first quarter of 2012. By comparison,
the overall homeownership rate came
down only 3.4 percentage points,
from 68.8 percent to 65.4 percent. Of
course, the reduced homeownership
rates for the young also reflect their
increased credit constraints that are
not related to household formation.
Further research is called for.
CONCLUSION
The substantial increase in student loans in recent years is a continuation of a trend that started a decade
ago due to technological innovation.
But the trend was exacerbated by the
Great Recession. As households experienced significant contractions in
income and wealth, housing wealth in
particular, and as jobs became scarce,
more students had to borrow increasingly large amounts to fund their
educations. Moreover, student loans
became delinquent as borrowers’ payment ability declined. This article suggests that any policy to address student
loans needs to target both secular and
cyclical factors.
To deal with the issue that those with high
student loan balances may be those who have
less intention of forming a household in the first
place, Gicheva uses exogenous changes in limits
and eligibility of federal loans as instruments.
19
Business Review Q3 2013 9
REFERENCES
Brown, Meta, Andrew Haughwout, Donghoon Lee, Maricar Mabutas, and Wilbert
van der Klaauw. “Grading Student Loans,”
Federal Reserve Bank of New York, Liberty
Street Economics (March 2012).
Elul, Ronel, Nicholas Souleles, Souphala
Chomsisengphet, Dennis Glennon,
and Robert M. Hunt. “What Triggers
Mortgage Default?” American Economic
Review 100:2 (2010), pp. 490-494.
Carnevale, Anthony P., Stephen J. Rose,
and Ban Cheah. “The College Payoff:
Education, Occupations, and Lifetime
Earnings,” Georgetown University Center
on Education and the Workforce, Washington, D.C. (2011).
Gicheva, Dora. “In Debt and Alone?
Examining the Causal Link Between
Student Loans and Marriage” (2013),
manuscript, University of North Carolina at Greensboro.
Dickler, Jessica. “Boomerang Kids: 85% of
College Grads Move Home,” CNNMoney
(November 15, 2010); http://money.cnn.
com/2010/10/14/pf/boomerang_kids_
move_home/index.htm.
Eisman, Steven. “Subprime Goes to College,” (2010); http://www.scribd.com/
doc/32066986/Steve-Eisman-Ira-SohnConference-May-2010.
10 Q2 2012 Business Review
Glover, Andrew, Jonathan Heathcote,
Dirk Krueger, and Jose-Victor Rios-Rull.
“Intergenerational Redistribution in the
Great Recession,” University of Pennsylvania Working Paper (2012).
Han Song, and Geng Li. “Household
Borrowing After Personal Bankruptcy,”
Journal of Money, Credit and Banking, 43
(2011), pp. 491-517.
Illinois State University. “Fiscal Year 20112012,” Grapevine, ISU Center for the Study
of Education Policy; http://grapevine.
illinoisstate.edu/.
Ionescu, Felicia, and Marius Ionescu. “The
Interplay between Student Loans and
Credit Cards and Amplification of Consumer Default,” Colgate University Working Paper (2011).
Rothstein, Jesse, and Cecilia Rouse.
“Constrained after College: Student Loans
and Early Career Occupational Choices,”
Journal of Public Economics, 95:1-2 (2011),
pp. 149-63.
Sill, Keith. “Widening the Wage Gap: The
Skill Premium and Technology,” Federal
Reserve Bank of Philadelphia Business Review (Fourth Quarter 2002), pp. 25-32.
www.philadelphiafed.org
Clusters of Knowledge:
R&D Proximity and the Spillover Effect
BY GERALD A. CARLINO AND JAKE K. CARR
he United States is home to some of the most
innovative companies in the world, such as
Apple, Facebook, and Google, to name a few.
Inventive activity depends on research and
development, and R&D depends on, among other things,
the exchange of ideas among individuals. People’s physical
proximity is a key ingredient in the innovation process.
Steve Jobs understood this when he helped to design
the layout of Pixar Animation Studios. The original
plan called for three buildings, with separate offices
for animators, scientists, and executives. Jobs instead
opted for a single building with a vast atrium at its core.
To ensure that animators, scientists, and executives
frequently interacted and exchanged ideas, Jobs moved
the mailboxes, the cafeteria, and the meeting rooms to
the center of the building.
T
There is nothing really new in the
recognition that face-to-face contact
among individuals is one key to innovation. Mervin Kelly, who for a time
ran AT&T’s legendary Bell Labs, was,
according to a New York Times article,
“convinced that physical proximity was
everything.”1 According to the article,
Kelly personally helped to design a
building that opened in 1941 “where
everyone would interact with one another.” Hallways were designed to be so
long that when walking a hall’s length
1
Jon Gertner, “True Innovation,” New York
Times, February 25, 2012.
Gerald A. Carlino is a senior economic advisor and economist specializing
in regional analysis at the Federal Reserve Bank of Philadelphia. Jake
K. Carr is a former economic analyst in the Research Department of the
Federal Reserve Bank of Philadelphia. The views expressed in this article
are not necessarily those of the Federal Reserve. This article and other
Philadelphia Fed research and reports are available at www.philadelphiafed.
org/research-and-data/publications.
www.philadelphiafed.org
one would encounter “a number of acquaintances, problems, diversions and
ideas. A physicist on his way to lunch
in the cafeteria was like a magnet
rolling past iron filings.” Within this
unique culture, Bell Labs’ employees
developed some of the most important
inventions of the 20th century, including the transistor, the laser, and the
solar cell.
Most American companies are
small in size, and they obviously lack
the resources of companies such as Apple, Facebook, and Google. Does their
small size deprive these firms of the
benefits of knowledge spillovers — the
continuing exchange of ideas among
individuals and firms — that physical proximity provides? The answer
appears to be no. There is an exceptionally high spatial concentration of
individual R&D labs in the Northeast
corridor, around the Great Lakes, in
Southern California, and in California’s Bay Area. The high geographic
concentration of R&D labs creates an
environment similar to that found at
Bell Labs, in which ideas move quickly
from person to person and from lab to
lab.2 This exchange of ideas underlies
the creation of new goods and new
ways of producing existing goods.
In this article, we will discuss a
recent study that we coauthored with
Robert Hunt and Tony Smith. That
2
Knowledge spillovers are the unintended
transmission of knowledge that occurs among
individuals and organizations. For example, as
pointed out by AnnaLee Saxenian, although
there is intense competition in California’s
Silicon Valley, a remarkable level of knowledge
spillovers occurs.
Business Review Q3 2013 11
study has two main goals. First, our
study introduces a more accurate way
to measure the extent of the spatial
concentration of R&D activity. This
new approach allows us to document
the spatial concentration of more than
1,000 R&D labs in the Northeast corridor of the U.S. An important finding
that emerged from this approach is
that the clustering of labs is by far most
significantly concentrated at very small
spatial scales, such as distances of
about one-quarter of a mile, with significant clustering attenuating rapidly
during the first half-mile. The rapid
attenuation of significant clustering is
consistent with the view that knowledge spillovers are highly localized.
We also observe a secondary node
of significant clustering at a scale of
about 40 miles. This secondary node
of clustering is interesting because its
spatial scale is roughly the same as
that of the local labor market. That is,
firms will draw most of their workers and most residents will commute
to jobs within 40 miles. Hence, this
scale is consistent with the view that
the efficiency gains and cost savings
at the labor market level (e.g., better matching of workers’ skills to the
needs of labs) are important for innovative activity.
A second goal of our study is to
provide evidence on the extent to
which knowledge spillovers are geographically localized within the R&D
clusters we identify. Data on patent
citations have been used to track
knowledge spillovers. Patents contain
detailed geographic information about
the inventors as well as citations to
prior patents on which the inventions
were built. If knowledge spillovers
are localized within the clusters that
we identify, then citations of patents
generated within a cluster should come
disproportionately from within the
same cluster as previous patents. We
find that citations are a little over four
times more likely to come from the
12 Q3 2013 Business Review
same cluster as earlier patents than one
would expect based on the preexisting concentration of technologically
related activities.
LEARNING IN CLUSTERS
An enormous increase in the
material well-being of individuals has
been achieved over the past 200 to 300
years. We not only have more of the
same goods and services but also a variety of new goods and services — such
as the personal computer, the Internet,
and cellular phones — whose specific
characteristics could not have been
imagined just 50 years ago. It took an
diminishes the farther one gets from
the source of that knowledge. Looking at innovative activity, Adam Jaffe,
Manuel Trajtenberg, and Rebecca
Henderson and, more recently, Ajay
Agrawal, Devesh Kapur, and John
McHale find that nearby inventors are
much more likely to cite each other’s
inventions in their patents, suggesting
that knowledge spillovers are indeed
localized. Mohammad Arzaghi and
Vernon Henderson look at the location
pattern of firms in the advertising industry in Manhattan. They show that
for an ad agency, knowledge spillovers
and the benefits of networking with
An important finding that emerged from our
new approach is that the clustering of labs is
by far most significantly concentrated at very
small spatial scales, such as about one-quarter
of a mile.
accumulation of knowledge to design
and build these goods and services and
bring them to market. Inventions or
innovations do not happen in a vacuum but instead are created by individuals working together to solve common
problems. Often, new knowledge is
tacit knowledge, that is, knowledge
that is highly contextual and difficult
or even impossible to codify or electronically transmit.
Beginning with Alfred Marshall,
economists have studied the benefits
that individuals and firms gain from
locating near one another, in what are
referred to as agglomeration economies.
Knowledge spillovers, an important
aspect of agglomeration economies,
have proved hard to empirically verify.
The empirical evidence on knowledge
spillovers is rather sparse. What the
limited research suggests is that the
transmission of knowledge rapidly
nearby agencies are extensive, but the
benefits dissipate quickly with distance
from other ad agencies and are gone
after roughly one-half mile.
More than most economic activity, innovative activity such as R&D
depends on knowledge spillovers.
R&D labs will have an incentive to
locate near one another if knowledge
spillovers tend to dissipate rapidly with
increasing distance from the source of
that knowledge.
A map of the spatial distribution
of R&D labs reveals a striking clustering of R&D activity (Figure 1). In places that have little R&D activity, each
dot on the map represents the location of a single R&D lab. For example,
there is only one lab in Montana, represented by the single dot. In counties
with a dense clustering of labs, the
dots tend to sit on top of one another,
representing a concentration of labs.
www.philadelphiafed.org
FIGURE 1
Location of R&D Labs
Each dot on the map represents the location of a single R&D lab in 1998. In areas with dense clusters of labs, the
dots tend to sit on top of one another.
Sources: Directory of American Research and Technology and authors’ calculations
A prominent feature of the map is the
high concentration of R&D activity
in the Northeast corridor, stretching
from northern Virginia to Massachusetts. There are other concentrations,
such as the cluster around the Great
Lakes and the concentration of labs in
California’s Bay Area and in Southern
California.
The high geographic concentration of R&D labs creates an environment in which ideas move quickly from
person to person and from lab to lab.
Locations that are dense in R&D acwww.philadelphiafed.org
tivity encourage knowledge spillovers,
thus facilitating the exchange of ideas
that underlie the creation of new goods
and new ways of producing existing
goods. The tendency for innovative
activity to cluster raises a number of
interesting and important questions.
How strong is the tendency for R&D
labs to cluster? Where in space do
these labs cluster, and what are the
geographic sizes of these clusters? How
rapidly does the mutual attraction
among labs attenuate with distance?
Providing answers to these questions
is an important objective of our study
with Hunt and Smith.
MEASURING CLUSTERING OF
ECONOMIC ACTIVITY
Although R&D labs tend to
be spatially concentrated, a similar
pattern of geographic concentration
would be found for either population or
employment. Thus, studies that look at
the concentration of R&D labs need
to control for the general tendency
for economic activity and population
to cluster spatially. In a 1996 study,
Business Review Q3 2013 13
David Audretsch and Maryann Feldman introduced the “locational Gini
coefficient” to show that innovative
activity at the state level tends to be
considerably more concentrated than
is manufacturing employment and that
industries that stress R&D activity
also tend to be more spatially concentrated.3
Glenn Ellison and Edward Glaeser
have identified a potential problem
with the Audretsch and Feldman
study. They argue that an industry may
appear to be spatially concentrated if
that industry consists of a few large
firms. In this instance, the industry
would be classified as industrially concentrated but not necessarily spatially
concentrated. Ellison and Glaeser
developed an alternative measure of
spatial concentration — called the
EG index — that controls both for
the overall concentration of economic
activity and for the industrial organization of the industry. Typically, the
EG index has been used to gauge the
geographic concentration of various
manufacturing industries with fixed
spatial boundaries, such as states, metropolitan areas, and counties.4
3
A locational Gini coefficient shows how similar (or dissimilar) the location pattern of employment (or innovative activity, in Audretsch
and Feldman’s case) in a particular manufacturing industry is to the location pattern of overall
manufacturing employment. The larger the
value found for the locational Gini, the more
concentrated is employment (or innovative activity) in a particular industry relative to overall
manufacturing employment. See the Business
Review article by Kristy Buzard and Gerald
Carlino for a discussion of the construction of
the locational Gini coefficient. The study by
Audretsch and Feldman looked at the spatial
concentration of innovative activity by industry.
Their analysis, which is at the state level, uses
1982 census data provided by the United States
Small Business Administration. They construct
a data set on innovations by state and industry
that is culled from information on new product
announcements in over 100 scientific and trade
journals.
For examples of studies that use the EG index,
see the studies by Ellison and Glaeser; Stuart
Rosenthal and William Strange; and Glenn Elli4
14 Q3 2013 Business Review
The EG index suffers from a
number of important aggregation
issues that result from using fixed
spatial boundaries. For example, when
calculating EG indexes at the county
level, researchers will not take into account any activity that crosses county
borders. As a result, measures of spatial
concentration will be underestimated
for counties. For example, Philadelphia
County shares a border with Montgomery County. One stretch of City
Avenue divides these two counties.
Economic activity on the Philadelphia side of City Avenue is allocated
to Philadelphia County, while activity on the Montgomery County side is
assigned to that county. But this partition of economic activity is artificial,
since this activity is really part of the
same cluster. As a result, concentration will be underestimated for both
counties. To avoid problems associated
with fixed spatial boundaries, authors
of several recent studies have used
geocoded data to identify the exact
location of establishments. These studies base their approach on the actual
distance between establishments and
are, therefore, not bound by a fixed
geographical classification.5
MEASURING THE CLUSTERING
OF R&D LABS
In our study, we used 1998 data
from the Directory of American Research and Technology to electronical-
son, Edward Glaeser, and William Kerr. See the
Business Review article by Buzard and Carlino
for a discussion of the EG index.
Another problem is that authors of studies based on the EG index often provide only
indexes of localization, without any indication
of the statistical significance of their results.
Without such statistical analyses, it is unclear
whether the concentrations found differ from
concentrations that would have been found
if the locations of economic activity were
randomly chosen. See the article by Gilles
Duranton and Henry Overman for a discussion
of statistical issues with the EG index.
5
ly code the R&D labs’ addresses and
other information. Since the directory
lists the complete address for each
establishment, we were able to assign a
geographic identifier (using geocoding
techniques) to more than 3,100 R&D
labs in the U.S. in 1998. We limited
our analysis to 1,035 R&D labs in the
10 states (Connecticut, Delaware,
Maryland, Massachusetts, New Hampshire, New York, New Jersey, Pennsylvania, Rhode Island, and Virginia) and
the District of Columbia that make up
the Northeast corridor of the United
States.
A key question we need to determine is whether an observed spatial
collection of labs in this corridor is
somehow unusual; that is, is it different
from what we would expect based on
the spatial concentration of manufacturing employment? We used manufacturing employment instead of manufacturing firms as our benchmark.6
In our study, we start with a “global”
measure of concentration that is based
on the observed concentration of
R&D labs at various distances, ranging
from a quarter-mile to 100 miles. For
example, suppose we want to calculate
the average number of labs that are
located within a quarter-mile radius of
6
The concentration of R&D establishments is
measured relative to a baseline of economic activity as reflected by the amount of manufacturing employment in the Zip code, as reported in
the 1998 vintage of Zip Code Business Patterns.
Since one of our objectives is to describe the
localization of total R&D labs, manufacturing employment represents a good benchmark
because most R&D labs are owned by manufacturing firms. We elected to use manufacturing
employment as our benchmark rather than the
number of manufacturing establishments in a
Zip code, since past studies (such as the study
by Audretsch and Feldman) use manufacturing employment as their benchmark. When we
look at the clustering of R&D labs in specific
industries relative to the location of all R&D
labs in our data set, we find that the patterns
of clustering in specific industries are highly
similar to the overall clustering of labs that we
found when we used manufacturing employment as the benchmark.
www.philadelphiafed.org
one another. We start by choosing one
of the labs and drawing a ring with a
quarter-mile radius around that lab.
We then count the number of other
labs in that quarter-mile ring and enter
that number in a spreadsheet. Next,
we move to another lab and draw a
quarter-mile ring around it; then we
count the number of other labs in its
quarter-mile ring and enter that number in the spreadsheet. We repeat this
procedure for all of the 1,035 labs in
the corridor. Finally, we can compute
the global measure of concentration at
the level of a quarter-mile by averaging
the 1,035 entries in the spreadsheet.
This gives us the average number of
labs that are located within a quartermile of one another.
We computed the global measures
of the concentration of R&D labs for
distances ranging from a quarter-mile
to 100 miles. Finally, R&D clusters
for a given distance, such as a quartermile, are identified as “significant”
only when they contain more R&D
labs than would be expected at that
distance based on manufacturing
employment (see Appendix: Measuring
Concentration Based on K-Functions).
We show that for every distance we
considered, the spatial concentration
of R&D labs is much more pronounced
than it is for manufacturing employment. As we have noted, physical
proximity is a key ingredient in order
for firms and individuals to maximize
the benefits from knowledge spillovers.
This suggests that we should expect
to see evidence that the benefits from
such spillovers decline rapidly with
increasing distance among the labs.
More important, we find that the concentration of labs is most significant
when labs are located within a quartermile radius of one another and that the
significance of clustering of labs relative to manufacturing falls off rapidly
as the distance among labs increases.
The rapid attenuation of significant
clustering at small spatial scales is conwww.philadelphiafed.org
sistent with the view that knowledge
spillovers are highly localized.
We also found evidence of a secondary node of statistically significant
clustering at a distance of about 40
miles. This scale is roughly comparable
to that of a local labor market, suggesting that such markets may provide
additional spillovers that improve the
efficiency of labs. One way dense locations improve efficiency is through the
better quality of matches among labs
and workers that occurs in large and
dense labor markets. Workers and labs
in larger, denser labor markets can be
PLOTTING THE CLUSTERING
OF R&D LABS
The discussion to this point has
revealed at what distances the clustering of labs is most significant, but it
does not tell us where this clustering
takes place. Therefore, we use a second
approach, referred to as a “local” measure of clustering, to identify specific
geographic areas within the corridor
with high concentrations of R&D labs.
Thus, a novel feature of our study is
the use of a local measure of clustering to identify specific R&D clusters
as well as the labs that belong to them.
R&D clusters for a given distance, such as
a quarter-mile, are identified as “significant”
only when they contain more R&D labs than
would be expected at that distance based on
manufacturing employment.
much more selective in their matches
because the opportunity costs (the lost
wages or profits when the worker or
firm has not made a successful match)
of waiting for a prospective partner are
lower. That is because even though
workers and labs are more selective, on
average they form better matches and
tend to match more quickly. As a result, the average output from matches
(such as new ideas that lead to innovation) is higher, and a higher share of
the workforce and labs is engaged in
productive matches. Another possibility is that labs in larger and denser
locations may share critical inputs into
the production process. For example,
Robert Helsley and William Strange
argue that the necessary inputs into
the process of innovation are more
plentiful and more readily available in
an area with a dense network of input
suppliers. The dense network of input
suppliers facilitates innovation by making it cheaper to bring new ideas to
fruition.
This approach allows us to show on
a map the exact locations where the
clustering of labs is occurring. For example, suppose we want to know how
many other labs are located within a
half-mile radius of a given lab. To find
this, as we did for the global measure
of clustering, we draw a circle with a
radius of a half-mile around a particular lab and count the number of other
labs that fall within that half-mile
circle. Before, to get the global measure
of clustering, we computed the average
number of other labs across all 1,035
labs at a half-mile distance. To get the
local measure of clustering, we are interested in the number of other labs in
the individual clusters themselves. The
local measures of clustering focus on
the size and locations of specific R&D
clusters.
Once again, we are confronted
with the issue of whether the count of
the labs in each of these half-mile circles is greater than would be expected
based on the spatial concentration of
Business Review Q3 2013 15
manufacturing employment. Figure 2
shows the strength of the clustering of
labs relative to manufacturing employment for labs located south of Central
Park in New York City. The 11 black
dots indicate that the data strongly support the concentration of labs relative
to the concentration of manufacturing
employment, while the grey dots indicate somewhat less support.
To identify a half-mile cluster in
New York City, we start by drawing
rings with a half-mile radius around
each of the 11 black dots shown in
Figure 2. Figure 3 shows the pattern
resulting from the construction of
these half-mile rings. Notice that these
rings tend to overlap one another, indicating a mutual influence among these
labs. Next, we take the union of these
rings to form the “half-mile” cluster in
New York City (Figure 4). An important thing to note about this half-mile
cluster is that its actual geographic
distance is greater than a half-mile.
Figure 5 shows the locations of the
four half-mile clusters we identified in
the Boston area. The largest (both spatially and by number of labs) is found
in Cambridge, MA, shown roughly at
the center of the map. We also found
two half-mile buffer clusters located
along Route 128 and one such cluster
located along Route 495.
We repeated the procedure used
to create half-mile clusters, but this
time we constructed one-mile rings
around each of the 1,035 labs. We
identified eight one-mile clusters in
the Boston area, which are shown in
Figure 6. Notice that all four half-mile
clusters are each contained within a
unique one-mile cluster. Next, we followed the same procedure to first create a five-mile cluster (of which there
are two in Boston) and then a 10-mile
cluster (of which there is one in Boston). Figure 7 shows the two five-mile
clusters (solid black line) and the 10mile cluster (dotted black line).
There are 187 R&D labs within
16 Q3 2013 Business Review
FIGURE 2
R&D Labs in New York City
Each dot represents the location of a
single R&D lab. The black dots strongly
indicate a local cluster of labs relative
to manufacturing employment. The
grey dots indicate a less significant
concentration of labs relative to
manufacturing employment.
FIGURE 3
Constructing Half-Mile Buffer Rings
This half-mile cluster in New York City
was created by constructing rings
with a half-mile radius around each
black dot. These rings tend to overlap
one another, indicating a mutual
influence among these labs.
FIGURE 4
Half-Mile Cluster in New York City
To identify New York City’s half-mile
cluster, we drew a line around the
perimeter of the rings in Figure 3. It
is important to note, however, that
the actual geographic distance of this
cluster is greater than a half-mile.
Sources: Directory of American Research and Technology and authors’ calculations
www.philadelphiafed.org
FIGURE 5
Half-Mile Clusters in Boston
Lexington
Cambridge
Newton
Westborough
Franklin
This figure shows four half-mile clusters of labs in Boston, the largest of which is in
Cambridge at the junction of Route 90 and Route 93.
FIGURE 6
One-Mile Clusters in Boston
Lexington
Cambridge
Newton
Westborough
Franklin
Eight one-mile clusters of labs in Boston are indicated by dotted brown rings. Notice
that all four half-mile clusters, which are indicated by solid brown rings, are situated
within one-mile clusters.
Boston’s single 10-mile cluster. Most of
these labs conduct R&D in five industries: computer programming and data
processing, drugs, lab apparatus and
analytical equipment, communications
equipment, and electronic equipment.
The largest five-mile cluster, which is
www.philadelphiafed.org
shown in Figure 7, contains 108 labs,
which account for 58 percent of all labs
in the larger 10-mile cluster. At the onemile scale, Boston has eight clusters,
six of which are centered in the largest
five-mile cluster. The largest of these
one-mile clusters contains 30 labs, half
of which conduct research on drugs.
Figure 8 shows the clusters of
R&D labs we identified in the Philadelphia region, where there are a total
of 49 labs. The city of Philadelphia
is shown by the darker grey area east
of the center of the figure. The dotted black ring depicts Philadelphia’s
10-mile cluster. Of the 49 labs in this
broad cluster, 16 conduct research on
drugs, and another 16 perform research
in the plastics materials and synthetic
resins industry. The Philadelphia
region contains two five-mile clusters,
shown by the solid black boundaries in
Figure 8. The most prominent subcluster is centered in the King of Prussia
area, directly west of the city of Philadelphia, and contains 30 labs, of which
40 percent conduct research on drugs.
Within this subcluster, there is a much
tighter concentration of labs (indicated
by the dotted brown ring in Figure 8)
located near Routes 76 and 276.
The second subcluster is centered
in the city of Wilmington, DE, where
about 25 percent of the labs are also
engaged in research on drugs, but most
(almost 60 percent) are conducting
research on plastics materials and synthetic resins.
THE EFFECTS OF KNOWLEDGE
SPILLOVERS
Innovation is important because it
can directly affect a nation’s productivity growth and the economic welfare
of society through the introduction of
new or improved goods and lower prices. In addition to these direct benefits,
as we have argued in this article, the
innovative activity of one person can
also influence the innovative activity
of others through knowledge spillovers.
Paul Krugman has argued, however,
that knowledge spillovers are impossible to measure empirically because
they “are invisible; they leave no paper
trail by which they may be measured
and tracked.” However, as Jaffe and coauthors have noted, “Knowledge flows
Business Review Q3 2013 17
FIGURE 7
Ten-Mile Cluster in Boston
Lexington
Cambridge
Newton
Westborough
Franklin
This figure shows the two five-mile clusters of labs in Boston (solid black lines) and
the single 10-mile cluster (dotted black line). Notice that all four half-mile clusters
(solid brown) identified for Boston are situated within one-mile clusters (dotted
brown). Similarly, most of the one-mile clusters lay within the two five-mile clusters,
and the two five-mile clusters are contained within the 10-mile cluster.
FIGURE 8
Ten-Mile Cluster in Philadelphia
King of
Prussia
Philadelphia
Wilmington
In the Philadelphia region, we identified a single one-mile cluster that is located west
of the city (the city is shown in dark grey) approximately in the King of Prussia, PA,
area. The Philadelphia region has two five-mile clusters (solid black lines) and one 10mile cluster (dotted black line). The second five-mile cluster is centered in the city of
Wilmington, DE.
do sometimes leave a paper trail in the
form of patent citations to prior art.”
Jaffe and coauthors pioneered a
18 Q3 2013 Business Review
method for studying the geographic extent of knowledge spillovers using patent citations. Every patent contains the
names, hometowns, and Zip codes of
the inventors named in the patent. A
patent can be assigned to a location by
using the Zip code of one of its inventors (usually the first person named).
Patent citations are similar to citations
received by academic articles in that
patent citations reference prior technology or prior art on which the citing
patent builds. Therefore, Jaffe and coauthors hold that patent citations are a
useful proxy for measuring knowledge
flows among inventors. If knowledge
spillovers are localized within a given
metropolitan area, then citations to
patents within a given metropolitan
area should disproportionately come
from other inventors who are located
within that metropolitan area.
However, Jaffe and coauthors
point out that just because we observe
a geographic clustering of technologically related activities, such as the
clustering of the semiconductor industry in Silicon Valley, this clustering
is not necessarily evidence of knowledge spillovers among these related
activities. There are other sources of
agglomeration economies in metropolitan areas, such as better matching and
sharing, that could explain the spatial
clustering of activities in the semiconductor industry. Jaffe and coauthors
deal with the spatial clustering of
related activities by constructing a set
of control patents designed to match
the existing geographic concentration
of technologically related activities.
To test for localized knowledge spillovers, Jaffe and coauthors construct
three patent samples. The first sample
consists of a set of originating patents.
The second sample consists of a set of
patents that cite one of the originating
patents (referred to as citing patents).
The final sample consists of a control
patent chosen to match each of the
citing patents. To qualify as a control
patent, the patent must be as similar as
possible (in terms of being in the same
technology class and having an appliwww.philadelphiafed.org
cation date as close as possible) to the
matched citing patent, but the control
patent must not cite the matched
originating patent. Jaffe and coauthors
compute two geographic matching
frequencies: one between the citing
patents and the originating patents
and one between the control patents
and the originating patents. Their test
for the localization of knowledge spillovers is whether the citation matching frequency for a given geographic
definition (states and metropolitan
areas) is significantly greater than that
associated with the control matching
frequency. Jaffe and coauthors find
that patent citations are two times
more likely to come from the same
state and about six times more likely
to come from the same metropolitan
area as earlier patents than one would
expect based on the control patents.
In our study, we adopt Jaffe and
coauthors’ methodology to look for evidence of localized knowledge spillovers,
except that we use the boundaries determined by the nine five-mile clusters
identified in our research instead of using state and metropolitan area boundaries.7 State boundaries are politically
determined, rather than economically
justified, and states are too big to adequately capture knowledge spillovers,
which are highly localized. In addition,
the boundaries of metropolitan areas
are determined by labor market flows;
therefore, they are not well suited for
analysis of spillovers among individuals
engaged in innovative activity. Instead,
we use the boundaries determined by
our nine five-mile clusters as our basic
geography, since these boundaries are
determined by interrelationships among
7
We identified two five-mile clusters in Boston
(Figure 7), three such clusters in New York, two
in Philadelphia (Figure 8), and two in Washington, D.C. In this article, we present only the
findings averaged across the nine clusters. See
our working paper for details on the individual
clusters.
www.philadelphiafed.org
the R&D labs and more accurately
reflect the appropriate boundaries in
which knowledge spillovers are most
likely to occur.
The patent citation counts that
we use are constructed from the NBER
Patent Citations Database. Patents are
assigned to locations according to the
Zip code of the first inventor named on
the patent.8 There were 9,105 patents
applied for in the nine five-mile buffer clusters we identified in our study
during the period 1996–1997. After
removing self-citations, these originating patents received 90,159 forward
citations during the period 1996–
2006.9 But we were able to find control
patents for only about 55,000 of the
citing patents. This limits our analysis to those citing patents for which
we have controls.10 We find that, on
average, a patent that falls within one
of our five-mile clusters is 4.3 times
more likely to cite an earlier patent in
the same five-mile cluster compared
with a control patent (a finding that is
highly statistically significant). Despite
the fact that knowledge spillovers are
not directly observable, they do leave
a paper trail in the form of patent cita-
The patent and citation data we use from the
National Bureau of Economic Research (NBER)
Patent Data Project provide the name, town,
and Zip code of the principal (or first named)
inventor on each patent. As is standard when
assigning patents to areas, we assign patents to
our clusters using the Zip code of the first inventor named on the patent. Knowledge spillovers
can occur among individuals who meet because
they are part of either local technical or social
networks. For example, AnnaLee Saxenian
describes how Walker’s Wagon Wheel bar in
Mountain View, CA, became a popular place
for engineers who lived in Silicon Valley to
exchange ideas.
8
9
Since self-citations may not result from knowledge spillovers, we excluded not only inventor
self-citations but also citing patents owned
by the same organizations as the originating
patent.
tions. We find that these paper trails
provide evidence consistent with the
geographic concentration of knowledge
spillovers.
CONCLUSION
In this article, we summarize
the findings from our study that uses
distance-based measures to analyze
the spatial concentration of over 1,000
R&D labs in the Northeast corridor of
the United States. Rather than using
a fixed spatial scale, such as counties
and metropolitan areas, we attempt to
describe the spatial concentration of
R&D labs more precisely by considering the spatial structure at different
scales. We find that the clustering of
labs is by far most significant at very
small spatial scales, such as distances
of about one-quarter of a mile, with
significance attenuating rapidly during
the first half-mile. The rapid attenuation of significant clustering at small
spatial scales is consistent with the
view that knowledge spillovers are
highly localized.
We introduce a novel way to identify the location of clusters and number
of labs in these clusters. For example,
this approach identified a number of
clusters of R&D labs in the Boston,
New York–Northern New Jersey, Philadelphia–Wilmington, and Washington,
D.C., areas. We also found that each
of these clusters has distinct characteristics, especially in terms of the mix of
industries the R&D labs serve.
Using patent data, we are able to
provide evidence that knowledge spillovers are highly localized within the
clusters of R&D labs that we identify.
We find that patent citations are a little over four times more likely to come
from the same cluster as earlier patents
than one would expect based on the
preexisting geographic concentration
of technologically related activities.
10
There was an insufficient number of control
patents to confidently conduct the analysis for
the one-mile or half-mile clusters.
Business Review Q3 2013 19
Appendix: Measuring Concentration Based on K-Functions
The Global K-Function
A popular measure of concentration is Ripley’s K-function, which we use to test for clustering at differing distances:
1 n
Kˆ O (d ) = ∑ Ci (d )
n i =1
where Ci (d ) is the count of additional labs within distance d from lab (location) i and n is the total number of locations
in the study (n = 1,035 in our study). To see how this works, set d equal to one mile. Take the first lab and draw a one-mile
circle around that lab. Count the number of other labs in that one-mile circle and enter the resulting count of other labs
into a spreadsheet. Go to the next lab and construct a one-mile circle around that lab. Count the number of other labs in
that one-mile circle and enter the resulting number into the spreadsheet. Repeat these steps for all 1,035 labs. Sum over
the 1,035 observations and divide by 1,035 labs. This is the average value of concentration of labs at a distance of one mile,
denoted by Kˆ O (1) . We calculate the average observed value of concentration, beginning at a quarter-mile and increasing
at quarter-mile increments below one mile and at one-mile increments from one mile to 100 miles.
The key question of interest is whether the overall pattern of R&D locations in the 10 states and the District of Columbia exhibits more clustering than would be expected from the spatial concentration of manufacturing in those areas. To
address this question statistically, our null hypothesis is that R&D locations are determined entirely by the distribution of
manufacturing employment.
We use a two-step procedure for generating counterfactual observations that are used to test the null hypothesis. In the
simulations, we randomly allocated labs to Zip codes based on a probability proportional to manufacturing employment in
that Zip code so that Zip codes containing a large share of employment are more likely to be assigned labs. For each distance,
we compute a simulated distribution of labs. We compared the observed value for their K-functions (the Kˆ O (d ) ) with
values obtained from a simulated distribution of R&D labs. If the observed value for the K-function for a given distance is
large relative to the simulated distribution, this is taken as evidence of significant clustering of labs relative to manufacturing
employment. P-values can be computed as:
The number of simulated values at distance d that are at least as large as the observed value
P(d ) =
Number of simulation performed
For example, if we performed 1,000 simulations and there are 10 simulated values at least as large as Kˆ (d ) , then
O
there is only a one-in-a-hundred chance of observing a value at least as large as Kˆ (d ). In this example, there is significant clustering of R&D locations at the 0.01 level of statistical significance at spatial scale d. However, we found that the
clustering of labs is so strong relative to manufacturing employment that the estimated p-values were uniformly 0.001 for
all the distances we considered. We obtained sharper discrimination by calculating the z-scores for each observed estimate,
Kˆ O (d ) , as given by
O
=
z (d )
Kˆ 0 (d ) − K d
=
, d {0.25, 0.5, 0.75,1, 2,...,99,100}
sd
where K d and sd are the corresponding sample means and standard deviations for the N + 1 sample K-values. These
z-scores are shown along the vertical axis in the figure, while the horizontal axis shows distances among R&D labs. The
higher the z-score for a given distance, the more spatially concentrated the R&D labs are at that distance relative to manufacturing employment. Notice that the highest z-score we found, which is more than 30 standard deviations away from the
mean, occurs at the shortest distance among labs we considered (one-quarter of a mile) and declines rapidly with distance
up to a distance of about five miles. The rapid decline in z-scores (significance of clustering of R&D labs) at short distances
is consistent with the view that knowledge spillovers are highly geographically localized. Notice that the lowest z-score
obtained, which occurs at a distance of about five miles, is still more than 7 standard deviations away from the mean,
indicating that R&D labs are significantly more concentrated than manufacturing employment over all the distances we
considered. We also observe a secondary mode of significance at a scale of about 40 miles, which is roughly associated with
metropolitan areas.
20 Q3 2013 Business Review
www.philadelphiafed.org
The Local K-Function
Basically, the local version of Ripley’s K-function for a lab at a given location is simply the count of all additional labs
within distance d of the given lab. In terms of the notation, the local K-function, Kˆ i ,(dat) location i is given for each distance,
d, by,
i
i
We use the same null hypothesis employed in the global K-function analysis that R&D labs are distributed in a manner
proportional to the distribution of manufacturing employment. The only substantive difference from the procedure used
in global K-function analysis is that the actual point associated with location i is held fixed when computing the simulated
values for the local K-function. That is, for a given distance, holding the location of the lab fixed, we compute a simulated
distribution of labs at that point. We compared the observed value for their K-functions (the Kˆ i (d ) ) with values obtained
from a simulated distribution of R&D labs. If the observed value for the K-function at a given point is large relative to the
simulated distribution, this is taken as evidence of significant clustering of labs relative to manufacturing employment at
that location. The set of radial distances (in miles) used for the local tests was D = {0.5, 0.75,1, 2,5,10,11,12..,100} .
In our global analysis, the p-values were essentially the same for nearly all spatial scales. That is not the case for the local
analysis. It is not surprising to find that many isolated R&D locations exhibit no local clustering whatsoever; therefore, wide
variations in significance levels are possible at any given spatial scale. Thus, p-values are used in the local K-function analysis.
An attractive feature of these local tests is that the resulting p-values for each point i in the observed pattern can be
mapped. This allows us to check visually for regions of significant clustering. In particular, groupings of very low p-values
serve to indicate not only the location but also the approximate size of possible clusters.
Because we conduct tests for local clustering over many locations and spatial scales, we need to address two aspects of
the “multiple testing” problem. First, suppose that there is, in fact, no local clustering of labs. In our simulations, we would
nonetheless expect to find that 5 percent of the
Clustering of Labs Attenuates Rapidly with Distance
observed values for the local K-functions for
a given distance are statistically significant at
the 5 percent level of significance. Therefore,
when many such tests are conducted (1,035 tests
for each distance considered), we are likely to
find some degree of significant clustering using
standard testing procedures. The incidence of
this type of “false positive” findings is mitigated
by reducing the threshold level of significance
(the p-value) deemed to be “significant.” That is,
we can minimize the incidence of false positives
due to the multiple testing problem by focusing on labs with very high levels of statistical
significance (p-values of 0.001 or lower). We
refer to these as core points — the black dots in
Figure 2 in the article.a A second condition of
a core point is that there must be at least four
other labs at a given distance. This condition
is imposed to exclude isolated labs that happen
to be in areas with little or no manufacturing
employment.
Kˆ (d ) = C (d )
The grey dots in Figure 2 are associated with p-values no
greater than 0.005.
a
*Z-scores are shown along the vertical axis, while the horizontal axis shows distances among R&D labs. The higher the z-score for a given distance,
the more spatially concentrated the R&D labs are at that distance relative to manufacturing employment. For example, a z-score of 10, occurring at
a distance of about two miles, indicates that the concentration of labs at that distance is 10 standard deviations away from the mean at that distance,
indicating that labs are significantly more concentrated at that distance relative to manufacturing employment.
b
www.philadelphiafed.org
Business Review Q3 2013 21
REFERENCES
Agrawal, Ajay, Devesh Kapur, and John
McHale. “How Do Spatial and Social
Proximity Influence Knowledge Flows?
Evidence from Patent Data,” Journal of Urban Economics, 64:2 (2008), pp. 258-69.
Arzaghi, Mohammad, and J. Vernon
Henderson. “Networking Off Madison Avenue,” unpublished manuscript (2005).
Audretsch, David B., and Maryann P. Feldman. “R&D Spillovers and the Geography
of Innovation and Production,” American
Economic Review, 86 (1996), pp. 630-40.
Buzard, Kristy, and Gerald A. Carlino.
“The Geography of Research and Development Activity in the U.S.,” Federal Reserve Bank of Philadelphia Business Review
(Third Quarter 2008), pp. 1-10.
Carlino, Gerald A., Jake K. Carr, Robert
M. Hunt, and Tony E. Smith. “The Agglomeration of R&D Labs,” Federal Reserve Bank of Philadelphia Working Paper
12-22 (September 2012).
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Directory of American Research and Technology, 23rd ed. New York: R.R. Bowker,
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Duranton, Gilles, and Henry G. Overman.
“Testing for Localization Using MicroGeographic Data,” Review of Economic
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Ellison, Glenn, and Edward L. Glaeser.
“Geographic Concentration in U.S. Manufacturing Industries: A Dartboard Approach,” Journal of Political Economy, 105
(1997), pp. 889-927.
Ellison, Glenn, Edward L. Glaeser, and
William Kerr. “What Causes Industry
Agglomeration? Evidence from Coagglomeration Patterns,” Discussion Paper 2133,
Harvard Institute of Economic Research
(April 2007).
Jaffe, Adam, M. M. Trajtenberg, and R.
Henderson. “Geographic Localization of
Knowledge Spillovers as Evidenced by
Patent Citations,” Quarterly Journal of Economics, 108 (1993), pp. 577-98.
Krugman, Paul R. Geography and Trade.
Cambridge, MA: MIT Press, 1991.
Rosenthal, Stuart, and William C. Strange.
“The Determinants of Agglomeration,”
Journal of Urban Economics, 50 (2001), pp.
191-229.
Saxenian, AnnaLee. Regional Advantage:
Culture and Competition in Silicon Valley
and Route 128, 2nd ed. Cambridge, MA:
Harvard University Press, 1996.
Helsley, Robert W., and William C.
Strange. “Innovation and Input Sharing,”
Journal of Urban Economics, 51:1 (2002),
pp. 25-45.
www.philadelphiafed.org
The Promise and Challenges
of Bank Capital Reform
BY RONEL ELUL
he failure and bailout of some prominent
financial institutions amid the crisis of 200709, and the effect these events had on the
economy as a whole, have led policymakers to
rethink how the global financial system is regulated.1
These changes, commonly known as the Basel III
Accords, will require banks to maintain more capital
in reserve, hold higher-quality capital, and assign
greater risk weights to certain types of assets.2
T
Why were these changes considered necessary? And how might the
new standards help prevent future
crises? To understand the rationale
behind the changes, it is helpful to
examine the history of bank capital
regulation and explore some reasons
why previous regulatory frameworks
may have proved inadequate during
the crisis.
1
The Federal Reserve Bank of St. Louis has
compiled a timeline of the financial crisis at
http://timeline.stlouisfed.org/index.
cfm?p=timeline.
2
The Basel Committee on Banking Supervision
provides an overview and details on Basel III at
http://www.bis.org/bcbs/index.htm.
HOW AND WHY WE
REGULATE BANKS
Why We Need to Regulate
Banks. Society may have a particular
interest in financial stability — and in
particular regulating financial institutions so as to reduce the incidence
of their failure — for several reasons.
One reason is the key role that banks
play in channeling funds to firms
throughout the economy. This means
that the impact of a bank failure, or
of a weak bank, can be greater than
that of other kinds of businesses.
Victoria Ivashina and David Scharfstein give an example of how a shock
to banks can affect other parts of the
economy. They show that banks that
were members of lending syndicates
Ronel Elul is an economic advisor and economist specializing
in microeconomics and financial market research at the Federal
Reserve Bank of Philadelphia. The views expressed in this article
are not necessarily those of the Federal Reserve. This article and
other Philadelphia Fed research and reports are available at www.
philadelphiafed.org/research-and-data/publications.
www.philadelphiafed.org
with Lehman Brothers reduced their
lending to a greater extent than other
banks following the Lehman bankruptcy in September 2008.3 Ivashina
and Scharfstein reason that these
banks expected to shoulder the commitments that Lehman could no longer honor, so they cut back on making
other loans. Similarly, Manju Puri,
Jorg Rocholl, and Sascha Steffen show
that German savings banks that had
significant exposure to U.S. subprime
mortgages were more likely to reject
loan applications.
Another reason why society is
concerned with regulating banks is
the interconnection among financial
institutions; the failure of one can
bring down others. This was cited, for
example, in the bailout of AIG, whose
failure would have led to significant
losses at Goldman Sachs and the large
French bank Société Générale, among
others. Yet another reason that bank
failures may be of social concern is
that because U.S. bank deposits are
guaranteed (through the FDIC), taxpayers may end up bearing the costs of
bank failures.4
Finally, the regulation of banks
may be important simply because they
are particularly fragile, as compared
with nonfinancial firms. Many financial firms are fragile because they tend
3
In a lending syndicate, a group of banks makes
a shared commitment to make loans to a particular borrower at the customer’s demand for
some fixed period of time.
4
Although the guarantee fund is paid for by an
assessment on banks, taxpayers are on the hook
to the extent that the funds needed to pay off
depositors turn out to be greater than the funds
available.
Business Review Q3 2013 23
to fund their assets with debt. Furthermore, this debt often has much shorter
maturity than the assets (for example,
using demand deposits to fund mortgage lending). Thus, they are subject to the risk of bank runs in which
lenders (including depositors) refuse to
continue financing the bank. At the
same time it may be difficult for the
bank to raise funds by selling its assets,
and so it is at risk of failure.
Capital Requirements Are an
Important Regulatory Tool. One
of the most important ways in which
banks are regulated is through capital
requirements. A financial institution’s
capital is its net worth: the difference between the values of its assets
and liabilities. A bank’s typical assets
would include loans to businesses and
households, and securities such as
municipal bonds or mortgage-backed
securities, while its liabilities would include deposits, loans from other banks
or the central bank, and other types
of debt.
But what’s the best way to measure net worth? One way would be
to consistently use market values for
assets and liabilities, a measure that
economists call “economic capital.”
But the capital measure used by regulators departs from this by relying more
on accounting book values. One
reason for this is that it may be hard
to determine market values for assets,
a particular problem during financial
crises, when markets shut down and
the number of trades falls to a trickle.
Thus, for regulatory purposes, loans
the bank made might be carried at historical cost until they reach a certain
level of delinquency, for example 90
days delinquent, at which point they
are written off.
A further reason book values are
used is that market values fluctuate
more often; this might create more
uncertainty about when regulators
would intervene. This uncertainty
might make it more difficult for the
24 Q3 2013 Business Review
bank to raise financing. The drawback
of relying on book values, however, is
that these tend to be backward-looking
and, thus, generally represent a less upto-date measure of the firm’s worth.
Capital regulation usually takes
the form of requiring the bank to hold
a minimum level of capital, relative
to the bank’s assets. A typical capital
ratio requirement would require the
bank’s equity financing to be at least
a certain fraction of the value of some
measure of its assets.5 Requiring banks
to hold capital has several benefits.
One is that holding capital helps to
there can be spillovers to other financial institutions and to society more
generally.
INTERNATIONAL CAPITAL
REGULATION
Why Might We Want Regulatory Harmony? Since the 1970s, there
has also been an effort to harmonize international capital regulations
through the Basel Committee on
Banking Supervision (BCBS).7 Why
would we need international harmonization of capital regulations? One reason is that bank failures in one country
The first international agreement on capital
regulation was the 1988 Basel Accord,
commonly known as Basel I.
absorb unanticipated losses, thereby
inspiring confidence that the bank
can continue as a going concern. In
addition, it protects nonequity liability
holders, especially depositors, and deposit insurers (and thus, the taxpaying
public) against losses. Finally, it limits
risk by restraining asset growth; to
lend more, banks need to raise more
capital.
For several reasons many economists feel that banks would not hold
enough capital were they left to their
own devices, and thus they must be
regulated. One reason is that equity
financing tends to be more expensive than debt financing because debt
interest payments are tax deductible.6
Another important reason is that the
management team of a bank does not
bear the full cost of the bank’s failure;
I will discuss the various ways in which regulators measure assets for capital regulation below.
The most commonly used measure is riskweighted assets, in which the amount of capital
required per dollar of an asset depends on the
risk of the asset. As discussed below, the DoddFrank Act would require banks to maintain a 7
percent equity capital ratio by 2019.
5
can spill over to other countries. One
early example is the failure of the German Herstatt Bank in 1974. Herstatt
had agreed to exchange Deutsche
marks it received from its customers for
U.S. dollars, which were to be delivered
in New York, but the bank was shut
down by German regulators before it
could deliver the dollars (since New
York markets opened later in the day).
This led to turmoil in the interbank
markets that banks use to borrow
from each other. Another example is
Lehman Brothers; one of the biggest
creditors in its bankruptcy was the German Deposit Insurance Fund.
Another reason given for why we
need international harmonization is
the potential for a race-to-the bottom
6
Another reason equity financing is more
expensive than debt is that the value of equity
is more sensitive to private information that
insiders might have about the value of the bank,
as discussed by Stewart Myers and Nicholas
Majluf.
7
The BCBS provides a forum for international
cooperation on banking supervisory matters,
including the harmonization of regulations.
www.philadelphiafed.org
in bank regulation.8 That is, each national regulator will lower its standards
in order to lure business to its jurisdiction. But are there any drawbacks to
harmonization?
Giovanni Dell’Ariccia and Robert
Marquez develop a model that analyzes
the tradeoff between the benefits and
costs of international harmonization of
regulations. In their model, regulators
are interested not only in the profitability of their home banks but also in
financial stability. Competition among
regulators leads to standards that are
too lax because national regulators
want to benefit home bank shareholders and don’t fully take into account
the benefits to other countries’ banks
of imposing tighter standards on
their own banks. Specifically, tighter
standards set by regulators on banks
domiciled in that country lead to
fewer bank failures in other countries
in which the bank also does business.
On the other hand, there is a cost
to coordinating regulation: uniform
standards may not fit each country. In
Dell’Ariccia and Marquez’s model, this
is because the public in each country
places different weights on financial
stability versus the profitability of their
home banks. But one can also imagine
other salient differences, such as differences across countries in the concentration of the banking sector or in the
relative sophistication of nonbank financial markets. So when is it good to
harmonize regulations? In their model,
a regulatory union is beneficial when
countries are not too dissimilar, so that
the benefits outweigh the costs.
The First Basel Accord. The first
international agreement on capital
regulation was the 1988 Basel Accord,
commonly known as Basel I. Basel I
required banks to hold at least 8 percent capital relative to risk-weighted
assets. Asset classes perceived as less
risky received lower risk weights. For
example, sovereign debt was assigned
a zero risk weight (so no capital was
required), mortgages were given a 50
percent risk weight, and corporate
bonds a 100 percent risk weight. This
meant, for example, that the capital a
bank was required to hold per dollar
of mortgage loans made was only half
that for corporate loans. Each country
that was a party to Basel I agreed to
write its own regulations that implemented these principles, although, in
practice, the national authorities had
considerable discretion in how to interpret them.
What was the effect of the first
Basel Accord? Patricia Jackson and
her coauthors survey the literature and
find that this accord generally represented a tightening of regulations,
since it led banks in the G-10 countries
to raise their capital ratios, on average.9 There may have been some negative consequences to this, however.
First, some economists, such as Ben
Bernanke (who later became Chairman of the Federal Reserve Board)
and Cara Lown, have argued that this
led to a credit crunch, or a decline in
lending, during the 1990-91 recession
in the U.S.
In addition, Basel I may also have
encouraged regulatory arbitrage, that is,
a shift toward risky activities that are
not fully captured by the regulations.
The reason is that with higher capital
requirements, banks may have had an
increased motivation to evade regu-
lations in order to conserve capital.
Furthermore, setting uniform international standards required more formal
rules than had existed in the past,
which could make it easier for banks to
structure their activities in such a way
so as to evade these regulations.
In his study, David Jones gives
several examples of how banks could
use securitization to reduce their
regulatory capital requirements while
still effectively retaining all of the risk
of the loans. One way they can do
this is by selling the most senior, safest
parts of the assets to investors (thereby
removing them from their balance
sheets) while retaining the junior,
riskier portions. Basel I’s emphasis on
credit risk alone may also have encouraged banks to increase their profits
by taking on other risks. For example,
Linda Allen, Julapa Jagtiani, and
Yoram Landskroner find that, after the
introduction of the first Basel Accord,
some banks took on additional interest
rate risk without increasing their capital.10 In addition, Basel I did not distinguish between different risks within
categories. Since all corporate loans
received a 100 percent risk weight, for
example, banks might lend to riskier
customers, thereby increasing the risk
of distress — a risk partially borne by
other banks and taxpayers — without
being required to hold more capital
of its own. Finally, Basel I considered
the credit risk of assets individually,
rather than the riskiness of the bank’s
whole portfolio; thus, a well-diversified
portfolio could have the same required
capital as a poorly diversified portfolio. Notwithstanding these specific
examples, a survey of the literature by
By interest rate risk we mean holding assets
whose values fluctuate more in response to
variations in interest rates than do the values
of the liabilities used to fund the assets. In particular, a rise in interest rates can lead to a large
fall in assets with long maturities. While these
assets yield high returns because they are riskier,
they would not require more capital.
10
The risk of a “race to the bottom” in banking
regulation was cited as a reason that “standards
be implemented uniformly and in a timely fashion” by Stephen Cecchetti, head of the monetary and economic department at the Bank for
International Settlements, in an interview with
the Wall Street Journal on October 30, 2012.
8
www.philadelphiafed.org
9
The Group of Ten, or G-10, is composed of 11
nations that are members of the International
Monetary Fund: Belgium, France, Germany,
Italy, Japan, the Netherlands, Sweden, the
United Kingdom, the United States, Canada,
and Switzerland.
Business Review Q3 2013 25
Linda Allen finds no consensus that
banks increased their overall risk in
response to Basel I.
Basel II Made Capital Requirements More Sensitive to Risk. The
second Basel Accord (Basel II), published in 2004, was designed to address
some of the shortcomings of Basel I,
and its provisions remain in force in
some countries. Basel II makes the
standard framework more risk-sensitive
than Basel I, especially within asset
categories. It does this primarily by
relying on credit ratings to calibrate
risks. Thus, assets with a BBB rating
from Standard & Poor’s require less
capital than those with a BB rating.
Basel II also allows large banks to use
their own internally developed risk
models, the presumption being that
these models more accurately reflect
risk, particularly at the portfolio level.
Note, however, that countries differed
in how they implemented the accord.
For example, while European regulators allow banks to estimate their own
required capital using internal models,
U.S. regulators permit U.S. banks to
use their own internal models only for
assets held in their trading book, and
even then, they are more restricted
than banks in other countries.
Shortcomings of Basel II. There
are some shortcomings with the Basel
II framework, however, some of which
became apparent during the financial
crisis.
First, the heavy reliance on credit
ratings may have created problems.
For instance, Basel II treats ratings inconsistently, with sovereign debt often
receiving lower capital charges than
corporate bonds with the same ratings.
For example, a corporate bond with a
rating between A– and A+ receives a
50 percent risk weighting, whereas a
sovereign bond with the same rating
(such as Greek bonds in 2009) would
get only a 20 percent risk weighting.
This inconsistency may help to explain
the heavy holdings of risky sovereign
26 Q3 2013 Business Review
debt by some European banks.
Another shortcoming of the Basel
II capital accord is that it underweights
“tail risk.” That is, it arguably does
not assign sufficient capital to protect
against extreme events such as a nationwide collapse of the housing market or a financial crisis. Viral Acharya,
Thomas Cooley, Matthew Richardson,
and Ingo Walter have argued that in
the run-up to the financial crisis, this
aspect of the Basel II framework encouraged the biggest financial institutions to accumulate large amounts of
tail risk without holding a commensurate amount of capital. One example is
the most senior tranches of mortgagebacked securities (MBS), which had
AAA ratings (and thus very low capital charges) and were often retained
by large banks.11 Such securities were
considered safe, except in what was
then considered the unlikely event of
a large and widespread collapse in the
housing market.
Another instance of Basel II
underemphasizing tail risk is that, in
some circumstances, it allows banks to
use their own internal models and, in
particular, encourages the use of valueat-risk (VaR), an approach to measuring the risk of loss in a given portfolio
of assets.12 However, in most common
implementations of value-at-risk, the
behavior in the tails, that is, in the case
of extreme events, is not fully considered. That is, value-at-risk measures
losses that occur with a large enough
probability (for example, 99 percent
of the time) but does not consider the
potential severity of losses in the other
1 percent. Basel II may encourage tail
risk in another way. The regulations
have a similar impact across many
banks, and thus, they may all align
their portfolios in similar ways, thereby
further heightening systemic risk.
Another potential problem with
Basel II is that it tends to have a
procyclical effect on capital charges.
That is, capital requirements can go
down in booms and rise following a
period of financial instability. One
reason for this procyclical effect is that
the regulations rely on credit ratings,
which generally go up in good times
and down in bad times. Another factor
contributing to procyclicality arises
from the use of value-at-risk for setting capital requirements. Asset price
volatility is an important input into
value-at-risk calculations. Because
data from the recent past are generally
used to estimate volatility, following a
period of financial stability in which
asset volatilities are relatively low such
as 2001-06, a bank’s portfolio is likely
to appear less risky and thus require
less capital. Conversely, as can be seen
from Figure 1, (which plots the level of
the S&P 500 and stock market volatility as measured by the VIX index),
during bad times prices tend to be
more volatile, and so capital requirements increase.13 As the joint report
from the Financial Stability Forum14
and the BCBS points out, one potentially undesirable consequence of this
11
A tranche is a slice of a mortgage-backed
security that is sold as a separate bond. The
senior tranches of private MBS are those that
have first claim on cash flows in the case of
default and are thus less risky (and so obtain a
higher rating). However, as became apparent
during the financial crisis, they are by no means
risk-free.
13
For more on the use of value-at-risk by banks
in meeting capital requirements, see the article
by Mitchell Berlin and the book by Anthony
Saunders.
12
The VIX is an index disseminated by the
Chicago Board Options Exchange that uses
information from S&P 500 index options to
infer the market’s expectation of volatility over
the next 30 days.
14
The Financial Stability Forum was established
in 1999 to promote international financial stability through enhanced information exchange
and international cooperation in financial
market supervision and surveillance. In 2009, it
was replaced by the Financial Stability Board,
which has a broader membership.
www.philadelphiafed.org
FIGURE 1
The S&P 500 and the VIX Volatility Index
Sources: Standard & Poor’s, Chicago Board Options Exchange.
procyclicality is that it tends to encourage more lending during booms and,
conversely, requires banks to sell assets
when their prices have fallen, thus potentially amplifying these cycles.
Finally, although Basel II expands
the range of risks that are considered
in determining regulatory capital,
some, such as liquidity risk, are still
neglected.15 One example of this risk is
highlighted by the collapse of the British lender Northern Rock in September 2007. Hyun Song Shin shows that
Northern Rock had obtained an unusually small share of its funding from
traditional branch-based retail deposits.
On the other hand, it relied heavily on
deposits from offshore and Internet-
Liquidity risk refers to the problems of having
assets that are difficult to sell and liabilities that
have short maturities — for example, deposits.
With this asset-liability structure, banks can be
caught in a situation in which they must sell assets at fire-sale prices if liability holders such as
depositors refuse to roll over their claims.
15
www.philadelphiafed.org
based bank accounts and on “wholesale
funding,” in which short-term securities are sold to investors. And while
traditional retail depositors tend to
be slow to withdraw their funds from
a bank, this was not the case for the
other investors upon whom Northern
Rock relied too heavily, and the lender
was hurt when these investors fled risky
investments at the start of the financial crisis in the summer of 2007 and
refused to roll over their deposits at
institutions such as Northern Rock.
Similarly, a paper by Viral Acharya, Philipp Schnabl, and Gustavo
Suarez shows that Basel II was also
subject to regulatory arbitrage in the
run-up to the financial crisis because
of its inconsistent treatment of credit
and liquidity risk. Banks set up assetbacked commercial paper conduits
that were “off balance sheet” for
regulatory purposes. These conduits
purchased medium- to long-term assets (often mortgage-backed securi-
ties) and held them until maturity.
They were financed by issuing a type
of short-term debt called asset-backed
commercial paper (ABCP), with maturities of 30 days or less. Even though
the assets were formally off the banks’
balance sheets, in reality, the banks
were exposed to the risk that they
would be forced to take over the assets
if investors stopped purchasing the
ABCP. Banks were exposed to risk
because they typically offered “liquidity guarantees” — promises to pay off
maturing commercial paper as long as
assets were not actually in default — to
persuade investors to buy it. From the
bank’s perspective, this was an attractive deal because these liquidity guarantees carried lower capital charges
than would have been the case had the
assets been formally held on the bank’s
balance sheet. However, this structure
really left the risk with the issuing
bank because the short maturity of the
ABCP meant that it would need to be
paid off well before the assets were formally in default. Once investors, concerned about the risk of the underlying
assets, stopped buying new commercial
paper, the banks were forced to take
these assets back onto their balance
sheets, degrading their capital ratios.
REFORM OF BASEL II
Basel II.5. Recent revisions to
the Basel Accords have addressed
these concerns. Some of these revisions were proposed in 2009 and are
colloquially known as Basel II.5. One
area involves increasing capital requirements for certain assets, particularly for “resecuritizations” such as collateralized debt obligations (CDOs).16
16
A CDO is an asset-backed security in which
the underlying collateral is itself composed of
other debt securities. For example, during the
subprime bubble, low-rated, junior mortgagebacked security tranches were sometimes
packed into new securities. For more on CDOs
and the risk they can carry, see the paper by
Joshua Coval, Jakub Jurek, and Erik Stafford.
Business Review Q3 2013 27
These were often created from risky
tranches of mortgage-backed securities
and performed particularly badly once
mortgage defaults began to rise. In addition, liquidity guarantees offered by
banks as part of securitizations (such
as the ABCP discussed by Acharya
and his coauthors) now receive higher
risk weights and thus require more
capital.
These revisions to Basel also
introduced a “stressed VaR” calculation, in which banks would need to
calculate their potential losses under a
“period of significant financial stress.”17
This would address two issues raised
above: the procyclicality of capital requirements based on VaR, and the fact
that standard VaR implementations
tend to underemphasize tail risk. One
limitation of stress testing is that it is
tempting to use past crises to inform
the construction of the stress scenarios
(indeed, the Bank for International
Settlements explicitly refers to the period of 2007-08), but future crises are
likely to be quite different from past
ones. This is an intrinsic issue in all
systemic risk regulation; while markets
continue to evolve, regulators can be
trapped in fighting the last crisis.
Basel III. More extensive revisions, known as Basel III, have also
been adopted in principle, and individual countries are supposed to adopt
rules that would phase them in by the
beginning of 2019. In addition to the
reforms of international capital regulations undertaken by the Basel committee, there is also a parallel effort under
way in the United States. For more
details, see Dodd-Frank and Basel III.
Strengthened capital requirements.
First, capital requirements have been
increased in several respects. There is
a greater reliance on common equity
capital, since equity is a more stable
Dodd-Frank and Basel III
T
he Basel framework envisions that each country will adopt
the capital regulations at the national level. In the United
States, the three large regulators — the Office of the Comptroller of the Currency, the Federal Reserve, and the Federal
Deposit Insurance Corporation — adopted rules in July 2013
that detail how many of the revisions to Basel will be implemented.*
In addition, the Dodd-Frank Wall Street Reform and Consumer Protection Act, signed into law on July 21, 2010, also dramatically changes how
financial institutions are regulated in the United States. Many of these provisions are quite similar to those formalized in Basel II.5 and Basel III (for example, stress-testing of bank portfolios), and thus little conflict should arise as
Basel III is implemented. However, in some cases, Dodd-Frank envisions a very
different regulatory approach. One notable example is the use of credit ratings
for regulatory purposes: The Basel Accords continue to give these considerable
weight, while under Dodd-Frank, regulatory agencies’ reliance on credit ratings
is drastically curtailed. And indeed, the recently released rules do not incorporate credit ratings. However, some aspects of Basel III are not covered by these
rules, and considerable thought will have to be given to their implementation
in the U.S.
* For further detail on these rules, see the Federal Reserve Bank of Philadelphia’s Banking
Legislation and Policy, 32:2 (Second Quarter 2012). For an overview of the Dodd-Frank Act, see
Banking Legislation and Policy, 29:2 (Second Quarter 2010).
buffer against losses. By contrast, other
forms of regulatory capital, which
proved to be poor buffers during the financial crisis, now play a more limited
role in meeting regulatory capital requirements. For example, two forms of
capital used in the past — deferred tax
losses and mortgage servicing rights —
did not prove to be very good buffers
during the financial crisis and are now
more restricted.18 An example of a
security that previously was considered
as capital but must be phased out under Basel III is trust preferred securities
(TruPS). These are hybrid instruments
having characteristics of both debt and
equity. In particular, like equity, they
could count toward capital, but like
Deferred tax losses were not very valuable
when banks were suffering losses. And servicing rights declined in value when the securitized
mortgage market shrank dramatically during
the crisis.
debt, their dividend payments were
tax-deductible for the issuer, which
made them attractive to issuing banks.
Unfortunately, during the financial
crisis it became clear that the debtlike element of these securities meant
that they were not able to fully meet
their role in stabilizing the bank. For
example, TruPS have a fixed term and
need to be replaced at maturity (unlike
equity). Also, many of these securities
had dividends that accumulated if they
were not paid; this limited their ability
to absorb losses.19
In addition, Basel III will also require a capital conservation buffer. This
buffer consists of an additional 2.5 percent of risk-weighted assets that banks
can draw on during times of stress, but
doing so will place limits on earnings
18
The Basel committee gave the period from
2007 to 2008 as one example.
17
28 Q3 2013 Business Review
For further detail on trust preferred securities,
see the article by Jennifer Salutric and Joseph
Wilcox.
19
www.philadelphiafed.org
distributions. That is, if losses are large
enough that a bank needs to use the
buffer to meet its capital requirements,
the bank will be restricted in its dividend distributions, stock repurchases,
and discretionary executive compensation such as bonuses.20 Rafael Repullo
and Javier Suarez develop a model
in which they show that this type of
buffer can help mitigate the negative
effects resulting from the procyclicality
of the Basel II capital requirements.
Basel III will also introduce two
capital ratios to supplement the existing one based on risk-weighted assets.
The first is a leverage ratio, in this case
a minimum 3 percent of capital against
all assets, without any risk-weighting;
the other is the liquidity coverage
ratio, which is discussed below.21 In
addition to the leverage ratio adopted
in Basel III, in July 2013 U.S. regulators proposed that large institutions be
subject to stricter requirements, in particular 5 percent for the largest bank
holding companies and 6 percent for
their insured depository institutions.
Regulating leverage ratios has
several benefits. First, as Tobias Adrian
and Hyun Song Shin show, financial
institution leverage tends to be very
procyclical (rising during booms and
falling during busts) and so imposing a
maximum leverage ratio can help moderate these cycles. In addition, a simple
rule like a leverage ratio is harder to
manipulate by shifting portfolios away
from activities with high risk weights
toward risky activities with low risk
weights. That is, the leverage ratio
reduces the incentive for regulatory arbitrage. Finally, because it does not rely
Another proposed approach to providing
additional capital during times of stress is contingent capital. This is debt that automatically
converts into equity under certain conditions.
For further discussion of contingent capital, see
the article by Yaron Leitner.
20
21
Some countries, such as the United States and
Canada, already use leverage ratios for regulatory capital purposes.
www.philadelphiafed.org
on complex models to determine the
proper risk weight for assets, the leverage ratio may provide better protection
against loss even when modelers —
at both banks and regulatory agencies
— have relatively imprecise knowledge
about the true risks, as they inevitably do.22 However, as Katia D’Hulster
points out, the fact that it ignores the
risk of assets can also be a weakness;
thus, its proper place has typically been
viewed as part of a broader framework
for capital regulation, rather than as
a substitute for risk-sensitive capital
requirements.
Systemically important financial institutions (SIFIs). Finally, because of the
transmission of shocks from one bank
to another during the crisis, capital
reform has also focused on increasing
capital and supervisory measures for
institutions deemed to be “systemically
important.” Under the Dodd-Frank
Wall Street Reform and Consumer
Protection Act, U.S. bank holding
companies with assets of $50 billion or
more will be designated as systemically
important. These institutions will be
However, the leverage ratio is also subject to
manipulation. As documented in the report
of the examiner for the Lehman bankruptcy,
Lehman Brothers used various accounting maneuvers (such as Repo 105) to reduce the level
of debt on its balance sheet.
subject to additional regulation; for example, they will be required to develop
a “living will” to facilitate their orderly
liquidation.23 In addition, the act tasks
the newly established Financial Stability Oversight Council with determining
whether nonbanks should be designated as systemically important and subject to Federal Reserve oversight. For
example, in June 2013, AIG and GE
Capital disclosed that they had been
designated as systemically important.
The broadening of the SIFI category to
include nonbanks is natural, given the
key role that nonbank financial institutions — AIG in particular — played
in the crisis. In addition to the SIFIs
designated by U.S. regulators under
the Dodd-Frank Act, the Financial
Stability Board has published a list of
29 global systemically important financial institutions (G-SIFIs). Under Basel
III, these institutions will be subject to
additional capital requirements.
Finally, while I have focused on
reforms to international capital regulations, Basel III also adds measures to
reduce liquidity risk. See New Liquidity
Requirements Under Basel III.
22
For further details on how Dodd-Frank
changes the regulation of institutions deemed to
be systemically important, see the Federal Reserve Bank of Philadelphia’s Banking Legislation
and Policy, 30:4 (Fourth Quarter 2011).
23
New Liquidity Requirements Under Basel III
W
e have seen that Northern Rock failed in part because of illiquidity. Basel III adds liquidity requirements. One is the liquidity coverage ratio: the requirement that a bank have enough
liquid assets to withstand outflows under a 30-day stress scenario. One example would be a significant runoff of wholesale
deposits. Wholesale deposits are those obtained through nontraditional demand deposit accounts, such as from Internet accounts. Wholesale deposits tend to be much more mobile and typically evaporate when a bank
gets into trouble. Another liquidity requirement added by Basel III is the net
stable funding ratio, which requires that at least some fraction of long-term assets
(such as loans with maturities greater than one year) be funded with long-term
financing sources.
Business Review Q3 2013 29
CONCLUSION
Capital requirements play an
important role in regulating banks’
risk-taking and mitigating the consequences of bank failures. Since the
1970s, there has been an effort to
harmonize international regulation
of banks through the Basel Accords.
The financial crisis showed, however,
that these regulations still have room
for improvement, for example, in how
they treat liquidity risk, underweight
extreme or “tail” events, and continue
to allow scope for regulatory arbitrage.
The recent revisions to the Basel Accords are designed to address some of
these concerns. Integrating all of these
revisions with the Dodd-Frank Act
will be another challenge.
Dell’Ariccia, Giovanni, and Robert Marquez. “Competition Among Regulators
and Credit Market Integration,” Journal of
Financial Economics, 79:2 (2006), pp. 401430.
Puri, Manju, Jorg Rocholl, and Sascha
Steffen. “Global Retail Lending in the Aftermath of the U.S. Financial Crisis: Distinguishing Between Supply and Demand
Effects.” Journal of Financial Economics,
100.3 (2011), pp. 556-578.
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Jones, David. “Emerging Problems with the
Basel Capital Accord: Regulatory Capital
Arbitrage and Related Issues,” Journal of
Banking and Finance, 24 (2000), pp. 35-58.
Leitner, Yaron. “Contingent Capital,” Federal Reserve Bank of Philadelphia Business
Review (Second Quarter 2012).
Repullo, Rafael, and Javier Suarez. “The
Procyclical Effects of Bank Capital
Regulation,” Review of Financial Studies,
26:2 (2013), pp. 452-490.
Salutric, Jennifer, and Joseph Wilcox.
“Emerging Issues Regarding Trust
Preferred Securities,” Federal Reserve
Bank of Philadelphia, SRC Insights (First
Quarter 2009), available at http://www.
philadelphiafed.org/bank-resources/
publications/src-insights/2009/first-quarter/
q1si4_09.cfm.
Saunders, Anthony. Financial Institutions
Management: A Modern Perspective. Boston: Irwin McGraw Hill, 2000.
Shin, Hyun Song. “Reflections on Northern Rock: The Bank Run That Heralded
the Global Financial Crisis,” Journal of Economic Perspectives, 23:1 (2009), pp. 101-119.
Valukas, Anton R. “Report of Examiner
to the United States Bankruptcy Court,
Southern District of New York,” Chapter
11 Case No 08-13555 (March 2010).
Myers, Stewart C., and Nicholas S. Majluf.
“Corporate Financing and Investment
Decisions When Firms Have Information
That Investors Do Not Have,” Journal of
Financial Economics, 13:2, (1984), pp. 187221.
www.philadelphiafed.org
Research Rap
Abstracts of
research papers
produced by the
economists at
the Philadelphia
Fed
Economists and visiting scholars at the Philadelphia Fed produce papers of interest to the professional
researcher on banking, financial markets, economic forecasting, the housing market, consumer
finance, the regional economy, and more. More abstracts may be found at www.philadelphiafed.org/
research-and-data/publications/research-rap/. You can find their full working papers at http://www.
philadelphiafed.org/research-and-data/publications/working-papers/.
Modeling the Credit Card Revolution:
The Role of Debt Collection and
Informal Bankruptcy
In the data, most consumer defaults on
unsecured credit are informal, and the lending industry devotes significant resources
to debt collection. The authors develop a
new theory of credit card lending that takes
these two features into account. The two
key elements of their model are moral hazard and costly state verification that relies
on the use of information technology. They
show that the model gives rise to a novel
channel through which IT progress can
affect outcomes in the credit markets, and
argue that this channel can be critical to
understand the trends associated with the
rapid expansion of credit card borrowing
in the 1980s and over the 1990s. Independently, the mechanism of the model helps
reconcile high levels of defaults and indebtedness observed in the U.S. data.
Working Paper 13-12. Lukasz A. Drozd,
The Wharton School, University of Pennsylvania, Federal Reserve Bank of Philadelphia
Visiting Scholar; Ricardo Serrano-Padial,
University of Wisconsin.
Who Said Large Banks Don’t Experience
Scale Economies? Evidence from a RiskReturn-Driven Cost Function
The Great Recession focused attention
on large financial institutions and systemic
risk. The authors investigate whether large
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size provides any cost advantages to the
economy and, if so, whether these cost advantages are due to technological scale economies or too-big-to-fail subsidies. Estimating
scale economies is made more complex by
risk-taking. Better diversification resulting
from larger scale generates scale economies
but also incentives to take more risk. When
this additional risk-taking adds to cost, it can
obscure the underlying scale economies and
engender misleading econometric estimates of
them. Using data pre- and post-crisis, they estimate scale economies using two production
models. The standard model ignores endogenous risk-taking and finds little evidence of
scale economies. The model accounting for
managerial risk preferences and endogenous
risk-taking finds large scale economies, which
are not driven by too-big-to-fail considerations. The authors evaluate the costs and
competitive implications of breaking up the
largest banks into smaller banks.
Working Paper 13-13/R. Joseph P. Hughes,
Rutgers University; Loretta J. Mester, Federal
Reserve Bank of Philadelphia and The Wharton
School, University of Pennsylvania.
Market Run-Ups, Market Freezes,
Inventories, and Leverage
The authors study trade between an
informed seller and an uninformed buyer who
have existing inventories of assets similar to
those being traded. They show that these
inventories may lead to prices that increase
Business Review Q3 2013 31
even absent changes in fundamentals (a “run-up”),
but may also make trade impossible (a “freeze”) and
hamper information dissemination. Competition may
amplify the run-up by inducing buyers to enter lossmaking trades at high prices to prevent a competitor
from purchasing at a lower price and releasing bad news
about inventory values. Inventories also prevent seller
competition from delivering the Bertrand outcome,
in which prices match sellers’ valuations. The authors
discuss both empirical implications and implications for
regulatory intervention in illiquid markets.
Working Paper 13-14. Supersedes Working Paper 12-8.
Philip Bond, University of Minnesota; Yaron Leitner, Federal Reserve Bank of Philadelphia.
The Cost of Delay
In this study, the authors make use of a massive
database of mortgage defaults to estimate REO liquidation timelines and time-related costs resulting from the
recent post-crisis interventions in the mortgage market
and the freezing of foreclosures due to “robo-signing”
revelations. The cost of delay, estimated by comparing today’s time-related costs to those before the start
of the financial crisis, is eight percentage points, with
enormous variation among states. While costs are estimated to be four percentage points higher in statutory
foreclosure states, they are estimated to be 13 percentage points higher in judicial foreclosure states and 19
percentage points higher in the highest-cost state, New
York. They discuss the policy implications of these
extraordinary increases in time-related costs, including
recent actions by the GSEs to raise their guarantee fees
15-30 basis points in five high-cost judicial states. Combined with evidence that foreclosure delays do not improve outcomes for borrowers and that increased delays
can have large negative externalities in neighborhoods,
the weight of the evidence is that current foreclosure
practices merit the urgent attention of policymakers.
Working Paper 13-15. Larry Cordell, Federal Reserve
Bank of Philadelphia; Liang Geng, Federal Reserve Bank
of Philadelphia; Laurie Goodman, Lidan Yang, Amherst
Securities Group, LP.
Improving GDP Measurement:
A Measurement-Error Perspective
The authors provide a new and superior measure of
U.S. GDP, obtained by applying optimal signal-extraction
techniques to the (noisy) expenditure-side and incomeside estimates. Its properties — particularly as regards
32 Q3 2013 Business Review
serial correlation — differ markedly from those of the
standard expenditure-side measure and lead to substantially revised views regarding the properties of GDP.
Working Paper 13-16. S. Boragan Aruoba, University
of Maryland, Federal Reserve Bank of Philadelphia Visiting
Scholar; Francis X. Diebold, University of Pennsylvania,
Federal Reserve Bank of Philadelphia Visiting Scholar;
Jeremy Nalewaik, Federal Reserve Board; Frank Schorfheide, University of Pennsylvania, Federal Reserve Bank of
Philadelphia Visiting Scholar; Dongho Song, University of
Pennsylvania.
Competition in Bank-Provided Payment Services
Banks supply payment services that underpin
the smooth operation of the economy. To ensure an
efficient payment system, it is important to maintain
competition among payment service providers, but
data available to gauge the degree of competition are
quite limited. The authors propose and implement a
frontier-based method to assess relative competition in
bank-provided payment services. Billion dollar banks
account for around 90 percent of assets in the U.S., and
those with around $4 to $7 billion in assets turn out to
be both the most and the least competitive in payment
services, not the very largest banks.
Working Paper 13-17. Wilko Bolt, De Nederlandsche
Bank; David Humphrey, Florida State University, Federal
Reserve Bank of Philadelphia Visiting Scholar.
Dynamics of Investment, Debt, and Default
How does physical capital accumulation affect the
decision to default in developing small open economies?
The authors find that, conditional on a level of foreign
indebtedness, more capital improves the sovereign’s
ability to meet its obligations, reducing the likelihood
of default and the risk premium. This effect, however, is
diminishing in the stock of capital because capital also
tames the severity of the contraction following default,
making autarky more appealing. Access to long-term
debt and costly capital adjustment are crucial for matching business cycles. Their quantitative model delivers
default episodes that mimic those observed in the data.
Working Paper 13-18. Grey Gordon, University of
Indiana; Pablo Guerrón-Quintana, Federal Reserve Bank
of Philadelphia.
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Estimating Dynamic Equilibrium Models with
Stochastic Volatility
The authors propose a novel method to estimate
dynamic equilibrium models with stochastic volatility.
First, they characterize the properties of the solution to
this class of models. Second, the authors take advantage of the results about the structure of the solution to
build a sequential Monte Carlo algorithm to evaluate
the likelihood function of the model. The approach,
which exploits the profusion of shocks in stochastic
volatility models, is versatile and computationally
tractable even in large-scale models, such as those often
employed by policy-making institutions. As an application, the authors use their algorithm and Bayesian
methods to estimate a business cycle model of the U.S.
economy with both stochastic volatility and parameter
drifting in monetary policy. Their application shows the
importance of stochastic volatility in accounting for the
dynamics of the data.
Working Paper 13-19. Jesús Fernandez-Villaverde,
University of Pennsylvania, Federal Reserve Bank of Philadelphia Visiting Scholar; Pablo Guerrón-Quintana, Federal
Reserve Bank of Philadelphia; Juan F. Rubio-Ramírez,
Duke University, Federal Reserve Bank of Philadelphia
Visiting Scholar.
Subsidizing Price Discovery
When markets freeze, not only are gains from trade
left unrealized, but the process of information production through prices, or price discovery, is disrupted as
well. Though this latter effect has received much less
attention than the former, it constitutes an important
source of inefficiency during times of crisis. The authors
provide a formal model of price discovery and use it
to study a government program designed explicitly to
restore the process of information production in frozen
markets. This program, which provided buyers with
partial insurance against acquiring low-quality assets,
reveals a fundamental trade-off for policymakers: while
some insurance encourages buyers to bid for assets
when they otherwise would not, thus promoting price
discovery, too much insurance erodes the informational
content of these bids, which hurts price discovery.
Working Paper 13-20. Braz Camargo, Sao Paulo
School of Economics – FGV; Kyungmin (Teddy) Kim,
University of Iowa; Benjamin Lester, Federal Reserve Bank
of Philadelphia.
www.philadelphiafed.org
Credit Ratings and Bank Monitoring Ability
In this paper the authors use credit rating data from
two large Swedish banks to elicit evidence on banks’
loan monitoring ability. For these banks, their tests
reveal that banks’ credit ratings indeed include valuable
private information from monitoring, as theory suggests.
However, their tests also reveal that publicly available
information from a credit bureau is not efficiently impounded in the bank ratings: The credit bureau ratings
not only predict future movements in the bank ratings but also improve forecasts of bankruptcy and loan
default. The authors investigate possible explanations
for these findings. Their results are consistent with
bank loan officers placing too much weight on their
private information, a form of overconfidence. To the
extent that overconfidence results in placing too much
weight on private information, risk analyses of the bank
loan portfolios in the authors’ data could be improved
by combining the bank credit ratings and public credit
bureau ratings. The methods the authors use represent
a new basket of straightforward techniques that enable
both financial institutions and regulators to assess the
performance of credit rating systems.
Working Paper 13-21. Supersedes Working Paper
10-21. Leonard I. Nakamura, Federal Reserve Bank of
Philadelphia; Kasper Roszbach, Sveriges Riksbank, University of Gronigen.
Trend-Cycle Decomposition: Implications from an
Exact Structural Identification
A well-documented property of the BeveridgeNelson trend-cycle decomposition is the perfect negative correlation between trend and cycle innovations.
The authors show how this may be consistent with a
structural model where trend shocks enter the cycle, or
cyclic shocks enter the trend and that identification restrictions are necessary to make this structural distinction. A reduced-form unrestricted version such as that
of Morley, Nelson and Zivot (2003) is compatible with
either option, but cannot distinguish which is relevant.
They discuss economic interpretations and implications
using U.S. real GDP data.
Working Paper 13-22. Mardi Dungey, University of
Tasmania, CFAP, University of Cambridge, CAMA; Jan
P.A.M. Jacobs, University of Groningen, University of
Tasmania, CAMA, CIRANO; Jing Tian, University of
Tasmania; Simon van Norden, HEC Montréal, CAMA,
CIRANO, CIREQ, Federal Reserve Bank of Philadelphia
Visiting Scholar.
Business Review Q3 2013 33
Large Capital Infusions, Investor Reactions, and
the Return and Risk-Performance of Financial
Institutions over the Business Cycle
The authors examine investors’ reactions to announcements of large capital infusions by U.S. financial
institutions (FIs) from 2000 to 2009. These infusions
include private market infusions (seasoned equity offerings (SEOs)) as well as injections of government capital
under the Troubled Asset Relief Program (TARP). The
sample period covers both business cycle expansions
and contractions, and the recent financial crisis. They
present evidence on the factors affecting FIs’ decisions
to raise capital, the determinants of investor reactions,
and post-infusion risk-taking of the recipients, as well as
a sample of matching FIs. Investors reacted negatively
to the news of private market SEOs by FIs, both in the
immediate term (e.g., the two days surrounding the announcement) and over the subsequent year, but positively to TARP injections. Reactions differed depending
on the characteristics of the FIs, and the stage of the
business cycle. More financially constrained institutions
were more likely to have raised capital through private
market offerings during the period prior to TARP, and
firms receiving a TARP injection tended to be riskier
and more levered. In the case of TARP recipients, they
appeared to finance an increase in lending (as a share
of assets) with more stable financing sources such as
core deposits, which lowered their liquidity risk. However, the authors find no evidence that banks’ capital
adequacy increased after the capital injections.
Working Paper 13-23. Supersedes Working Paper 1146. Elyas Elyasiani, Fox School of Business and Management, Temple University, and Fellow, Wharton Financial
Institution Center; Loretta J. Mester, Federal Reserve Bank
of Philadelphia, The Wharton School; Michael S. Pagano,
Villanova School of Business, Villanova University.
Credit Access and Credit Performance After
Consumer Bankruptcy Filing: New Evidence
This paper uses a unique data set to shed new light
on the credit availability and credit performance of
consumer bankruptcy filers. In particular, the authors’
data allow them to distinguish between Chapter 7 and
Chapter 13 bankruptcy filings, to observe changes in
credit demand and supply explicitly, to differentiate
existing and new credit accounts, and to observe the
performance of each credit account directly. The paper
has four main findings. First, despite speedy recovery in
their risk scores after bankruptcy filing, most filers have
34 Q3 2013 Business Review
much reduced access to credit in terms of credit limits,
and the impact seems to be long lasting. Second, the
reduction in credit access stems mainly from the supply
side as consumer inquiries recover significantly after the
filing, while credit limits remain low. Third, lenders do
not treat Chapter 13 filers more favorably than Chapter
7 filers. In fact, Chapter 13 filers are much less likely
to receive new credit cards than Chapter 7 filers even
after controlling for borrower characteristics and local
economic environment. Finally, the authors find that
Chapter 13 filers perform more poorly than Chapter 7
filers (after the filing) on all credit products (credit card
debt, auto loans, and first mortgages). Their results, in
contrast to prior studies, thus suggest that the current
bankruptcy system does not appear to provide much
relief to bankruptcy filers.
Working Paper 13-24. Julapa Jagtiani, Federal Reserve
Bank of Philadelphia; Wenli Li, Federal Reserve Bank of
Philadelphia.
Congestion, Agglomeration, and
the Structure of Cities
Congestion pricing has long been held up by economists as a panacea for the problems associated with ever
increasing traffic congestion in urban areas. In addition,
the concept has gained traction as a viable solution
among planners, policymakers, and the general public.
While congestion costs in urban areas are significant
and clearly represent a negative externality, economists
also recognize the advantages of density in the form
of positive agglomeration externalities. The long-run
equilibrium outcomes in economies with multiple
correlated, but offsetting, externalities have yet to be
fully explored in the literature. To this end, the author
develops a spatial equilibrium model of urban structure
that includes both congestion costs and agglomeration
externalities. The author then estimates the structural
parameters of the model by using a computational solution algorithm and matches the spatial distribution of
employment, population, land use, land rents, and commute times in the data. Policy simulations based on the
estimates suggest that naive optimal congestion pricing
can lead to net negative economic outcomes.
Working Paper 13-25. Jeffrey C. Brinkman, Federal
Reserve Bank of Philadelphia.
Stress Tests and Information Disclosure
The authors study an optimal disclosure policy of
a regulator who has information about banks’ ability
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to overcome future liquidity shocks. They focus on
the following trade-off: Disclosing some information
may be necessary to prevent a market breakdown, but
disclosing too much information destroys risk-sharing
opportunities (Hirshleifer effect). The authors find
that during normal times, no disclosure is optimal, but
during bad times, partial disclosure is optimal. They
characterize the optimal form of this partial disclosure.
The authors also relate their results to the debate on
the disclosure of stress test results.
Working Paper 13-26. Itay Goldstein, University of
Pennsylvania; Yaron Leitner, Federal Reserve Bank of
Philadelphia.
Reverse Mortgage Loans: A Quantitative Analysis
Reverse mortgage loans (RMLs) allow older
homeowners to borrow against housing wealth without moving. In spite of growth in this market, only 2.1
percent of eligible homeowners had RMLs in 2011. In
this paper, we analyze reverse mortgages in a life-cycle
model of retirement, calibrated to age-asset profiles.
The ex-ante welfare gain from RMLs is sizable at
$1,000 per household; ex-post, low-income, low-wealth
and poor-health households use them. Bequest motives, nursing-home moving risk, house price risk, and
interest and insurance costs all contribute to the low
take-up rate. The model predicts market potential for
RMLs to be 5.5 percent of households.
Working Paper 13-27. Makoto Nakajima, Federal Reserve Bank of Philadelphia; Irina A. Telyukova, University
of California, San Diego.
Banking Crises and the Role of Bank Coalitions
The goal of this paper is to provide a framework
to analyze the effectiveness of bank coalition formation in response to an external aggregate shock that
may cause disruption to the payment mechanism and
real economic activity. The author shows that the kind
of insurance mechanism provided by a specific type
of bank coalition allows society to completely prevent
any disruption to real activity that can be caused by a
temporary drop in the value of banking assets, at least
in the case of a shock that is not too big. If the shock
is relatively large, then a private bank coalition will be
unable to completely prevent a disruption in real activity even though it will be able to substantially mitigate
the effects on equilibrium quantities and prices. Thus,
the existence of a private bank coalition of the kind
described in this paper can be an effective means of
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preventing significant disruptions in trading activity.
Working Paper 13-28. Daniel Sanches, Federal Reserve
Bank of Philadelphia.
Macroeconomic Dynamics Near the ZLB: A Tale of
Two Equilibria
This paper studies the dynamics of a New Keynesian dynamic stochastic general equilibrium (DSGE)
model near the zero lower bound (ZLB) on nominal
interest rates. In addition to the standard targetedinflation equilibrium, the authors consider a deflation
equilibrium as well as a Markov sunspot equilibrium
that switches between a targeted-inflation and a
deflation regime. The authors use the particle filter to
estimate the state of the U.S. economy during and after
the 2008–09 recession under the assumptions that the
U.S. economy has been in either the targeted-inflation
or the sunspot equilibrium. The authors consider a
combination of fiscal policy (calibrated to the American Recovery and Reinvestment Act) and monetary
policy (that tries to keep interest rates near zero) and
compute government spending multipliers. Ex-ante multipliers (cumulative over one year) under the targetedinflation regime are around 0.9. A monetary policy that
keeps interest rates at zero can raise the multiplier to
1.7. The ex-post (conditioning on the realized shocks in
2009–11) multiplier is estimated to be 1.3. Conditional
on the sunspot equilibrium, the multipliers are generally
smaller and the scope for conventional expansionary
monetary policy is severely limited.
Working Paper 13-29. S. Borağan Aruoba, University
of Maryland, Federal Reserve Bank of Philadelphia Visiting
Scholar; Frank Schorfheide, University of Pennsylvania,
NBER, Federal Reserve Bank of Philadelphia Visiting
Scholar.
Business Review Q3 2013 35
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THIRD QUARTER 2013
The Economics of Student Loan Borrowing and Repayment
1
Media reports and policymakers’ concerns about rising student loan
balances and defaults have greatly intensified in recent years. Wenli
Li sheds light on the economics behind these trends and discusses the
implications for the broader economy.
Clusters of Knowledge: R&D Proximity and the Spillover Effect 11
Innovation, a key to economic growth, does not happen in a vacuum.
Economists have studied the knowledge spillovers that occur when
firms locate near one another. Yet, these spillovers have proved hard to
empirically verify. Gerald A. Carlino and Jake K. Carr explain what
they found through a more accurate way to measure the geographic
concentration of research and development labs.
The Promise and Challenges of Bank Capital Reform
23
The failure and bailout of prominent financial institutions amid the crisis
of 2007-09, and the effect these events had on the economy as a whole,
have led policymakers to rethink how the global financial system is
regulated. Ronel Elul explains the history behind bank capital regulation
and how ongoing regulatory changes might help prevent future crises.
Research Rap
Abstracts of the latest working papers produced by the Research
Department of the Federal Reserve Bank of Philadelphia.
31
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Reserve Act in 1913 and the opening of the Federal Reserve
Banks in 1914, the Fed is asking scholars, historians, and
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nation’s central bank. Do you know of materials that should
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The inventory will give researchers, academics, and others
interested in studying U.S. central banking a single point of
electronic access to documents, photographs, and audio and
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also being included about material not yet available online
On December 23, 1913, President Woodrow Wilson signed the
Federal Reserve Act, establishing the Federal Reserve System
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The Economics of Student Loan Borrowing and Repayment
Clusters of Knowledge: R&D Proximity and the Spillover Effect
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