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Do African Americans Prefer to Live
in Segregated Communities?
BY ROBERT DeFINA
F
ollowing Hurricane Katrina, many people
were shocked by the extent of racial
segregation in the New Orleans housing
market. And yet, New Orleans is far from
an isolated case. Forty years after passage of the Fair
Housing Act, racially segregated neighborhoods are all too
common in the United States. The reasons usually offered
for this continued segregation include discrimination
in the real estate and housing markets. Recently, these
reasons have been challenged by a theory claiming that
segregation exists because African Americans prefer to
live together for positive reasons, such as to share and
support a common heritage. In this article, Bob DeFina
examines the evidence and notes that it casts doubt on
the viability of the so-called self-segregation hypothesis.
The devastation caused by Hurricane Katrina shocked the country
and revealed glaring inadequacies in
the infrastructure of New Orleans.
Images of homes and stores inundated
by floods, residents trapped on roofs,
and stories of lost children gripped the
nation and left many asking how such
outcomes were possible.
Bob DeFina
is a professor
at Villanova
University and a
visiting scholar in
the Philadelphia
Fed’s Research
Department. This
article is available
free of charge at
www.philadelphiafed.org/econ/br/.
www.philadelphiafed.org
Perhaps just as surprising was
another fact the storm laid bare. New
Orleans, the country was to see, had
a housing market sharply segregated
by race. News stories of the storm’s
impact uncovered neighborhood after
neighborhood overwhelmingly composed of African Americans. While
the Crescent City obviously had white
residents, they appeared to live in areas
largely separate from African Americans.
New Orleans, it turns out, is not
an isolated case. Forty years after the
civil rights movement and the Fair
Housing Act of 1968, racially segregated housing continues to be widespread. By most measures the extent of
segregation has moderated somewhat
during the past several decades. Yet
analysts, such as Douglas Massey, find
that two-thirds of African Americans
currently live in metro areas racially divided enough to be classified as “highly
segregated” or “hyper-segregated.”
The fact that housing segregation
has persisted into the 21st century is
not disputed. But the reasons it has endured are less clear. Beginning in the
1970s and continuing into the 1990s,
there seemed to be broad agreement
that racial segregation was mainly due
to past and ongoing discrimination in
the real estate and lending markets.
This view was buttressed by the careful work of scholars such as Douglas
Massey and Nancy Denton and John
Yinger.1
That thinking, however, has
been challenged by an idea called the
self-segregation hypothesis. Proponents, including Stephen and Abigail
Thernstrom, and Orlando Patterson,
argue that race relations have improved markedly over time. While
discrimination may have underpinned
housing segregation in the past, it no
1
See especially Yinger’s 1995 study. To a lesser
extent, racial differences in wealth and income
have also been implicated. That is, African
Americans have fewer financial resources, on
average, and so might not be able to afford
to live in the same neighborhoods as more
affluent white families. Some researchers, such
as Charles Leven, James Little, Hugh Nourse,
Robert Read, and David Harris, have also
suggested that whites avoid living near African
Americans for nonracial reasons, such as a
desire to avoid the crime and high poverty rates
correlated with a neighborhood’s percentage of
African Americans. Still, racial discrimination
was widely considered the main driving force.
Camille Charles’s 2003 study contains a
comprehensive review of theories and evidence
related to housing segregation.
Business Review Q4 2007 1
longer plays an important role. Rather,
according to this hypothesis, current
levels of segregation reflect the preferences of African Americans to live
together. These researchers also assert
that desires for same-race neighbors
stem from positive and natural inclinations to live with one’s own racial or
ethnic group and to preserve and support a shared and unique culture. Put
simply, segregation continues because
birds of a feather flock together.
The self-segregation hypothesis
portrays housing segregation in a relatively positive light. From an economic
perspective, voluntary choices in
any market lead to the most efficient
outcomes for society unless individual
decisions affect others who are not part
of the transaction. That is, if everyone
is already doing what they want, it is
not possible to make anyone better
off. So it is when African Americans
voluntarily choose to live in segregated communities. Far from being a
problem, segregation would represent a
set of choices to be respected. Nothing
can be done to improve matters, nor
should anyone try. In fact, economists
have pointed out that segregated
neighborhoods might provide some
social benefits, as well as social costs.2
2
David Cutler and Edward Glaeser have
identified some possible benefits to African
Americans from living in racially segregated
communities. For example, they note that
segregation might keep high-income and lowincome African Americans together, thus
providing low-income residents with better role
models and more effective social networks that
can lead to better jobs and other services. At
the same time, the authors suggest that racial
segregation can impose external costs on those
who live in segregated communities. Indeed,
they present empirical evidence that racial
segregation per se has led to less educational
attainment and more out-of-wedlock births
among African Americans than otherwise
would have occurred. Segregation can also
lead to a spatial mismatch in which residents of
segregated communities are separated from jobs.
On net, they conclude that the external costs of
racial segregation exceed the benefits to African
Americans. Under such circumstances, some
policy response might be warranted even with
self-segregation.
2 Q4 2007 Business Review
The process of self-segregation can
be contrasted with one in which racial
discrimination underpins segregation.
With active discrimination, groups of
individuals are unwillingly excluded
from full participation in the market.
This might result, for example, from
racial “steering,” whereby African
Americans purposely are not shown
properties in certain areas. It could
also occur if African Americans are
refused mortgage loans for reasons
unrelated to their creditworthiness.
In these cases, the prices and quantities transacted in the market will not
Initial statements of
the self-segregation
hypothesis provided
little in the way of
supporting empirical
evidence.
fully incorporate the true demands
for housing. The market will then be
inefficient, and at least in theory, some
people could be made better off by
actions that eliminate the discrimination. Interventions would also be warranted since housing discrimination
based on race is illegal.
Initial statements of the self-segregation hypothesis provided little
in the way of supporting empirical
evidence. But given the importance
of understanding the sources of racial
segregation and the different policy
implications, researchers have spent
considerable effort examining the
theory. Their endeavors have included attempts at measuring African
American preferences for same-race
neighbors, explorations of the links
between racial preferences and actual
location decisions, and studies of the
factors that underlie any preference for
self-segregation. Taken together, the
evidence casts serious doubt on the
self-segregation hypothesis. It appears
that the sources of racial housing
segregation lie elsewhere.
RECENT TRENDS IN RACIAL
HOUSING SEGREGATION
Housing segregation refers to
a situation where different racial
groups are concentrated in particular
neighborhoods within a metropolitan
area. The uneven distribution could
take various forms. For instance, one
racial group might be overrepresented
in certain neighborhoods that are
scattered throughout a city, forming a
sort of checkerboard pattern. Or the
neighborhoods in which we see overrepresentation could be packed closely
together in the center of the city.
Economists use numerical indexes
to summarize the extent of segregation. Segregation index values are
normally calculated for metropolitan
statistical areas (MSAs), since such
areas are thought to constitute housing markets. An MSA contains a
city with at least 50,000 people along
with surrounding counties that are
thought to be economically integrated.
The Philadelphia MSA, for example,
includes the city of Philadelphia and
eight other counties, including three
in New Jersey. Index calculations
require detailed information on the
racial compositions of neighborhoods
within each MSA that is available only
from the decennial census. As a result,
index estimates are available only once
a decade.
Several alternative indexes are
available.3 Perhaps the one most fre3
Different indexes emphasize different
dimensions of segregation, such as the racial
composition of neighborhoods and their spatial
pattern, as just mentioned. Many tend to be
quite correlated in practice. In their 1988 study,
Douglas Massey and Nancy Denton describe
the calculation of more than 20 possible
segregation indexes and analyze the degree to
which they are correlated.
www.philadelphiafed.org
quently used is the so-called dissimilarity index. The index varies between
0 and 1, with higher values indicating
a higher degree of segregation (see the
appendix: Calculating the Dissimilarity
Index). Estimates of the dissimilarity
index for U.S. MSAs show that African American segregation has generally declined since 1980 (see the Table).
For example, between 1980 and 2000,
values of the dissimilarity index fell in
97 percent of MSAs.4 Furthermore,
the decrease was at least 5 percent in
81 percent of the cases.
But despite the declines, the
degree of segregation remains high.
Researchers use a rule of thumb that
dissimilarity index values greater
than 0.6 indicate highly segregated
MSAs. As explained in the appendix,
this means that 60 percent of African Americans or whites would have
to change neighborhoods to create
an even distribution of races across
neighborhoods. In 2000, two-thirds
of all African Americans lived in an
MSA in which the dissimilarity index
had a value of at least 0.6. Indeed, the
average value for all MSAs, weighted
by their respective African American
populations, was 0.64. Segregation
tended to be higher in the Northeast
and Midwest and lower in the South
and West. Certain localities, such as
the city of Philadelphia, had dissimilarity index values that approached 0.8.
According to the self-segregation
hypothesis, these segregated housing
patterns are best explained by people’s
preferences for same-race neighbors.
This is a strong claim and one that has
been investigated in a variety of ways.
4
David Cutler, Edward Glaeser, and Jacob
Vigdor present historical estimates of the
dissimilarity index from 1890 to 1990. Their
data show that the average dissimilarity index
for cities, weighted by their African American
population, climbed from 1890 to 1970, after
which it declined.
www.philadelphiafed.org
DO AFRICAN AMERICANS
PREFER SEGREGATION?
Assessing the validity of the selfsegregation hypothesis begins with an
understanding of African American
racial housing preferences. That is, do
African Americans prefer to live in
communities with a high percentage
of same-race neighbors? Researchers
have examined the question using surveys to elicit attitudes about the racial
composition of neighborhoods.
One approach involves what has
been termed a “show card” experiment. These experiments were first
conducted in Detroit in 1976 and then
again in Atlanta, Boston, Detroit,
and Los Angeles in the 1990s. The
procedure entails showing participants
five cards. Each card contains 15
houses meant to represent a neighborhood (see Survey Data on Racial
Housing Preferences). The houses are
pre-colored to indicate a particular
mix of African American and white
homeowners. Neighborhood configurations range from having one African
American neighbor out of 14 to having
all 14 African American neighbors.
Participants are told they have found
an attractive, affordable home that
they like and are asked to rank the five
hypothetical neighborhoods from most
to least desired.5
The results from these experiments consistently indicate that the
neighborhood composition most fre-
5
The show-card approach has its critics. For
example, in his 1978 study, John Yinger argues
that it is hard to separate African Americans’
attitudes about living in neighborhoods
with different racial compositions from their
preconceptions of the types and levels of
public services in those neighborhoods. Thus,
uncovering a person’s pure preferences about
the racial composition of neighborhoods using
surveys is difficult. Proponents counter that
the problem is adequately handled by telling
respondents that they have found an “affordable
and attractive home that they like.” Doing so,
in their minds, eliminates concerns about the
different quality of services in the different
neighborhoods that residents might encounter.
quently chosen by African American
participants is one containing seven
African American neighbors and
seven white neighbors.6 A 50-50 split
can be interpreted as considerable sentiment among African Americans for
integrated neighborhoods. However,
because African Americans comprised
only about 13 percent of the population at the time, the desire for 50
percent African Americans required a
sizable overrepresentation of same-race
neighbors. Consequently, the preference for a 50-50 split might also be
interpreted as an inclination toward
self-segregation.
Also telling is that a fair number of African Americans specified
a preference for either a mostly black
neighborhood or one that is completely
black. Keith Ihlanfeldt and Benjamin Scafidi, for example, found that
between 35 percent and 45 percent
of African Americans desired mostly
black or all black neighborhoods.
These data suggest that a desire for
self-segregation, while not necessarily
the whole story, might be a significant
factor in observed patterns of housing
segregation.
A shortcoming of the show card
experiments is that participants face
restricted choices. They are allowed
to choose only among five different
neighborhood configurations. The limited choices could force respondents
to choose either more or fewer African
American neighbors than they would
ideally want. For example, a respondent might prefer to have 40 percent
of neighbors be African American but
might indicate that 50 percent is the
most preferred ratio because the 40
6
Examples of studies include those by Reynolds
Farley, Charlotte Steeh, Tara Jackson, Maria
Krysan, and Keith Reeves; Lawrence Bobo and
Camille Zubrinsky; Reynolds Farley, Elaine
Fielding, and Maria Krysan; Keith Ihlanfeldt
and Benjamin Scafidi; and Maria Krysan and
Reynolds Farley.
Business Review Q4 2007 3
TABLE
Trends in the Dissimilarity Index*
(African Americans versus Non-Hispanic Whites)
Dissimilarity Index
Area
Number of MSAs
1980
1990
2000
All MSAs
330
0.727
0.678
0.640
Selected Areas
220
0.730
0.682
0.645
Northeast
31
0.779
0.766
0.739
Midwest
53
0.822
0.788
0.741
South
114
0.660
0.605
0.581
West
22
0.714
0.625
0.559
Philadelphia MSA
1
0.781
0.768
0.720
Philadelphia City
--
0.839
0.829
0.767
Region
* Data for all areas except the city of Philadelphia are from John Iceland, Daniel H. Weinberg, and Erica Steinmetz, “Racial and Ethnic Residential Segregation in the United States: 1980-2000,” U.S. Census Bureau, mimeo. Selected MSAs are those 220 with at least 10 census tracts and 3
percent or 20,000 or more blacks in 1980. Averages are weighted by the size of the African American population. Data for the city of Philadelphia
come from the Lewis Mumford Center’s website: http://mumford.albany.edu.
percent choice is not available. Choice
is also restricted in that the hypothetical neighborhoods contain only
African American and white families.
Other racial and ethnic groups, such as
Latino and Asian households, are excluded, and this too can skew conclusions about preferences for self-segregation. Even if African Americans do
prefer to live apart from whites, they
might want to live in neighborhoods
with members of other racial and
ethnic groups. Knowing about these
preferences can shed additional light
on the desire for self-segregation. This
is especially true in the United States,
4 Q4 2007 Business Review
where the population has become
increasingly diverse along racial and
ethnic lines.
To get at this issue, researchers
devised an alternative to the show card
experiment, called the ideal neighborhood design approach (see Survey Data
on Racial Housing Preferences). In this
methodology, participants are given a
card with 15 blank houses. They are
asked to design their ideal neighborhood by indicating which of four racial
and ethnic groups they would like to
see in the neighborhoods’ houses. The
four groups are African Americans,
whites, Latinos, and Asians. This
approach allows more complex and
varied neighborhood compositions
than does the show card experiment.
It can also help decrease any pressure
participants in the show card experiment might feel to identify what they
believe are socially acceptable neighborhood configurations.7
As with the show card results, the
ideal neighborhood design evidence
reveals an openness to integration
with a desire for an overrepresentation
7
The 2001 study by David Harris and the one
by Maria Krysan and Reynolds Farley discuss
this concern.
www.philadelphiafed.org
Survey Data on Racial Housing Preferences
S
urvey data on preferences or the desired racial composition of neighborhoods come primarily from the
Multi-City Study of Urban Inequality (MCSUI). The MCSUI was conducted during the 1990s in four
cities: Atlanta, Boston, Detroit, and Los Angeles. Questions elicited information about the socio-demographic attributes of the respondents and also their preferences and perceptions about neighborhood
characteristics.
Two types of information on preferences about the racial composition of neighborhoods were obtained. The first
is commonly referred to as a show card study. Here, respondents are shown five cards, each containing 15 houses. On
each card, a certain number of houses are white and others black, indicating a particular proportion of African American and white households. Respondents are told that they are looking for a home and have found one they like and can
afford in each neighborhood. They are then asked to rank the five neighborhood choices from most to least preferred.
Respondents are also asked about their willingness to move into each of the neighborhoods regardless of their rankings.
The five neighborhood choices shown to African American respondents are displayed below.
Neighborhood A
Neighborhood B
Neighborhood C
Your
House
Your
House
Your
House
Neighborhood D
Neighborhood E
Your
House
Your
House
A second type of information comes from a variant of the show card strategy. Instead of being shown pre-designed
neighborhoods, respondents are shown a single card with 15 blank houses and asked to place a letter in each. The letters
stand for four racial/ethnic groups: A for Asian, B for Black, L for Latino, and W for White. The combination would
then give the racial/ethnic composition of the neighborhood in which the respondent would most like to live. The “ideal
neighborhood” approach was used in the Los Angeles phase of the MCSUI. The card shown to respondents is displayed
below.
Your
House
www.philadelphiafed.org
Business Review Q4 2007 5
of African Americans (i.e., a fraction
in the neighborhood greater than the
MSA average). Camille Charles, a
pioneer in using this approach, found
that African Americans in Los Angeles prefer neighborhoods composed
of 37 percent African Americans (see
her 2000 study). Only 2.8 percent of
African Americans wanted all African
Americans in their neighborhoods.
Of the four racial and ethnic groups
that participated in the study, African Americans were most amenable
to integration. That is, their desired
own-group percentage was the lowest
of the four groups. National data from
the 2000 General Social Survey are
broadly consistent: African Americans
prefer 42 percent same-race neighbors,
while about 6.5 percent prefer all samerace neighbors (see the 2003 study by
Charles). 8
Taken together, the survey evidence shows that African Americans
tend to express a desire for integrated
communities at levels that would
coincide with an overrepresentation
of same-race neighbors. For a nonnegligible amount of respondents, the
desired fraction of African American
neighbors is high. Based on the diversity of preferences, it would be hard to
conclude that desires for self-segregation can fully explain the extent of
segregation that currently exists. But
it would be likewise unreasonable to
dismiss the possibility that they play
some significant role.
Even if preferences for self-segregation are reflected in housing decisions
to some degree, the question still
remains as to what underlies them. A
8
The General Social Survey is taken by the
National Opinion Research Center at the
University of Chicago. The survey, which has
been conducted almost every year for the past
several decades, asks respondents questions
about their attitudes concerning numerous
social, economic, and political issues.
6 Q4 2007 Business Review
key part of the self-segregation hypothesis is that preferences for predominantly black communities stem from
warm feelings toward other African
Americans in general — what has
been called positive in-group feelings
or neutral ethnocentrism.
Economists have had little to
neighborhood’s various attributes. In
the end, racial preferences might take
a back seat to the others. It is also possible that racial discrimination might
prevent individuals from living where
they would most like. Communities
with higher fractions of white families
might not be fully available to African
Economists have presented two types of
evidence on the extent to which the racial mix
of neighborhoods reflects housing preferences.
say about this issue thus far, although
other social scientists, such as sociologists, have provided some evidence
(see What Do the Racial Preferences
of African Americans Reflect?). What
economists have examined in-depth is
the extent to which racial preferences
influence individual location decisions.
DO PREFERENCES FOR SAMERACE NEIGHBORS DRIVE
LOCATION DECISIONS?
If self-segregation does play an
important role, we would expect to
see people distributed across neighborhoods of different racial compositions
in ways that mirror their racial preferences. Segregated communities would
be composed primarily of individuals
with a preference for lots of same-race
neighbors, while integrated communities would be home to those wanting a
more even split.
A correspondence between racial
preferences and neighborhood racial mix might occur, but there is no
guarantee. Racial preferences could
be a concern, but perhaps only one of
many. Other neighborhood characteristics, such as school quality, closeness
to work, crime rates, and local taxes,
can also matter. Location decisions
will likely reflect trade-offs among a
Americans who prefer such places.
If such areas are not available, they
might be forced to live in neighborhoods with a higher than ideal fraction
of same-race families. Oddly enough,
African Americans could end up in
neighborhoods with racial mixes very
different from their preferences even
if preferences matter a lot and even if
they can freely choose among different
communities (see Racial Tipping and
Neighborhood Change).
Economists have presented two
types of evidence on the extent to
which the racial mix of neighborhoods
reflects housing preferences. One is
indirect and uses market prices to
infer the role of preferences in home
purchases. The strategy is to examine
home purchases and rentals by African
Americans and to measure whether
they paid more to live in predominately
African American neighborhoods than
in other, more integrated areas. This is
done after accounting for other factors
that might cause prices and rents to
differ among neighborhoods. Again,
those other factors can include things
like school quality and the amount of
public services. If, after controlling
for other factors, they were willing to
pay more, the logic goes, one can infer
both that they had preferences for
www.philadelphiafed.org
What Do the Racial Preferences of
African Americans Reflect?
E
thnocentrism
might explain
racial housing
preferences to some
degree, but it need
not be the only underlying factor.
A desire for segregation could also
arise from fears of hostility and
ill-treatment by those in other racial
groups. That is, segregation could
reflect a “circling of the wagons”
and not “birds of a feather flocking together.” If so, the idea of
voluntary choice about same-race
neighbors would be seen in a different light, one at odds with the
self-segregation hypothesis.
As one way to illuminate the
issue, several sociologists have
modeled the preferences of African
Americans concerning neighborhood racial composition. In one set
of studies, Charles (her 2000 article)
and Krysan and Farley used results
from the show card studies. In addition to the question asked about
their most preferred neighborhood
configuration, participants were
also queried about how important
racial group membership is to them
and their future. Specifically, they
were asked: “Do you think what
happens to (respondent’s group) in
this country will have something to
do with your life?” If a respondent
answered “yes,” he or she was asked:
“A lot”? “Some”? Or “not very
much”? Answers to this “common
fate identity” question are taken to
measure the strength of a respondent’s solidarity and identification
with his or her own racial or ethnic
group.
www.philadelphiafed.org
The researchers then investigated
whether a respondent’s attitude about
common fate identity was statistically
linked to his or her preferences about
neighborhood racial composition. That
is, do those respondents who prefer
the most African American neighbors
also have the strongest in-group feelings? As always, the statistical models
control for other factors that could
influence those preferences. Neither
Charles nor Krysan and Farley found
any significant link.
A related study by Bobo and
Zubrinsky came to the same conclusion using a different survey and an
alternative measure of in-group affiliation. They conducted a telephone
survey in Los Angeles that elicited
information about African Americans’
willingness to live in neighborhoods
that were 50 percent white. They also
asked respondents to rate their feelings
toward other racial groups, including
their own. A statistical model that
linked the strength of in-group feelings
to preferences about neighborhood
composition found no significant relationship, again accounting for other
possible influences on preferences.
Finally, Krysan and Farley analyzed answers to open-ended questions
about why respondents chose their
most preferred racial composition in
the show card studies. The answers
were varied but only infrequently reflected ethnocentrism. Moreover, such
concerns were voiced almost exclusively by respondents preferring completely
segregated neighborhoods. But even for
that select group (about 20 percent of
the respondents), ethnocentrism was
mentioned less than half the time.
more segregated communities and that
they acted on those preferences.9
An early study by Thomas King
and Peter Mieszkowski examined data
on rental housing in New Haven, Connecticut. The authors determined that
African Americans were willing to pay
more to live in highly segregated areas
compared with more integrated neighborhoods. Thus, segregation did seem
to reflect racial preferences. However,
subsequent work challenged that conclusion. In his 1978 study, John Yinger
pointed out that King and Mieszkowski
did not adequately control for the
possibility that discrimination, not
preferences, caused African Americans to pay more for housing in more
segregated areas.10 After adjusting
King and Mieszkowski’s model to fix
the shortcoming, Yinger applied it to
data on African American home buy-
9
Ideally, one would want to measure how
a person’s willingness to pay changed as
a neighborhood’s actual racial mix varied
from the person’s preferred mix. If the actual
percentage of African Americans was less
than the preferred fraction, willingness to
pay should increase as the actual percentage
increases, since the neighborhood mix is
moving closer to the person’s preferences. But
if the actual fraction exceeds the preferred
mix, further increases in the percentage of
African Americans should decrease willingness
to pay, since the neighborhood mix is moving
further away from the person’s preferences.
This means that the true relationship between
a person’s willingness to pay for housing
and a neighborhood’s percentage of African
Americans could be nonlinear. The studies
discussed in the following paragraph did
not factor in the possibility of a nonlinear
relationship, although it is not clear how the
conclusions would change if they had.
10
Yinger suggests, for example, that there could
be significant barriers facing African Americans
who want to live in integrated areas, thus
restricting them to segregated neighborhoods.
As the population of African Americans grows,
home prices in the segregated areas would rise
as the restricted supply of houses confronted
a rising demand. African Americans would
have to pay these higher prices because they
were prevented from moving into lower cost,
more integrated areas. So higher prices need
not reflect stronger preferences but rather
discrimination and the resulting restricted
choice.
Business Review Q4 2007 7
Racial Tipping and Neighborhood Change
E
ven if individuals give high weight to a
neighborhood’s racial composition and
freely make decisions based on their preferences, they could end up in neighborhoods with racial compositions that are
far from their desired mix. This possibility was raised by
economist Thomas Schelling in a famous and influential
article.
Schelling assumes that African Americans and
whites each prefer a slight majority of same-race neighbors. He then posits that one type of family, say, African
American, moves into a neighborhood that satisfies its
preferences. Doing so tips the racial mix more toward an
African American majority and away from a white majority. This causes a white family to move out, since the
racial composition is now too different from its preferred
ers in St. Louis. He found no evidence
that they were willing to pay more for
housing in areas with higher fractions
of same-race neighbors. George Galster
further modified Yinger’s approach
to measure the relationship between
house prices and racial composition
even more precisely. His analysis confirmed Yinger’s findings. More recently,
Cutler, Glaeser, and Vigdor explored
the issue using housing price data for a
broader selection of cities and MSAs.
Like Yinger and Galster, they conclude
that African Americans have not been
willing to pay relatively more to live in
more segregated areas.
Other researchers have employed
more direct tests that use responses
from the show card experiments. Keith
Ihlanfeldt and Benjamin Scafidi developed a statistical model of the actual
percentage of African Americans
in the respondents’ neighborhood.
Among the explanatory variables
was each respondent’s most preferred
neighborhood configuration. If the
self-segregation hypothesis is valid, the
8 Q4 2007 Business Review
mix. The white family that moved out is then replaced by
an African American family, since the neighborhood is
now more consistent with African American preferences.
This once again causes the fraction of African Americans
to rise and leads yet another white family to move out.
The process is repeated until the neighborhood ends up
overwhelmingly African American. This happens even
though each African American family preferred only a
slight majority of same-race neighbors!
Schelling’s message is that segregation could occur
for a wide range of preferences concerning neighborhood
racial composition as long as the preferences of African
Americans and whites differ. His model has been studied
and modified through the years, but the basic insight has
held up. The study by Rajiv Sethi and Rohini Somanathan is a good example of recent work on the topic.
correlation between preferences and
the percentage of African Americans
in the neighborhood should be positive. That is, respondents who prefer
more same-race neighbors should live
in more segregated areas and those
who prefer fewer same-race neighbors
should tend to live in less segregated
areas. Their model takes account of
numerous other variables that conceivably might affect a respondent’s
neighborhood selection, including
the respondent’s income, occupation,
education level, and perceptions of
white hostility, among others. Models
were estimated using data for Atlanta,
Detroit, and Los Angeles.
Ihlanfeldt and Scafidi determined
that racial preferences of respondents
were indeed positively correlated with
the percentage of African Americans
in their neighborhood of residence.
They found some differences among
the cities. For instance, the estimated
links were stronger in Atlanta and Detroit than in Los Angeles. Nonetheless, the positive relationships between
preferred and actual percentage of
African Americans in neighborhoods
lend some support to the self-segregation hypothesis.
Of course, statistical significance
is only one part of the story. Statistical significance means only that a
researcher is reasonably sure that the
impact of a variable is not zero. Also
important is the amount by which
preferences affect each city’s racial
composition. That is, a relationship
could be statistically significant but
have little practical importance.
To quantify the specific impact
of preferences, Ihlanfeldt and Scafidi
used their estimates to simulate what
the racial composition of neighborhoods would be if all respondents preferred complete integration. Complete
integration would occur if all neighborhoods had a percentage of African
Americans that matched the percentage for the MSA as a whole. For the
sample period studied, this would
mean that each Atlanta neighborhood
would have an African American
www.philadelphiafed.org
population share equal to 27 percent;
the shares in Detroit and Los Angeles
would be 25 percent and 22 percent,
respectively.
The researchers found that even
if all respondents preferred complete
integration, the percentage of African
Americans in the respondents’ neighborhoods predicted by their models
would remain high. Specifically, the
average African American in Atlanta,
Detroit, and Los Angeles would still
live in a neighborhood where the African American population shares equal
65 percent, 83 percent, and 76 percent.
So even if African Americans had
housing preferences that were neutral
with regard to race, the cities would
continue to be marked by substantial
segregation.
Lance Freeman took a somewhat
similar approach. He first used the information from show card experiments
to construct an index that indicated
how receptive African Americans
were to integration with whites. He
then estimated models, comparable to
those of Ihlanfeldt and Scafidi, which
predicted the percentage of whites
in the neighborhoods of the African
American respondents. Consistent
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with Ihlanfeldt and Scafidi, he found
that preferences mattered in a statistical sense. However, he also determined
that respondents’ preferences had a
relatively small impact on the actual
racial compositions of their neighborhoods.
In sum, indirect evidence based
on market prices fails to support the
idea that racial preferences drive housing location decisions. More direct evi-
sults from preferences to live together
based on positive feelings. If these
preferences are important, the significance of racially separated neighborhoods would be less bothersome and
the case for policy intervention much
weaker. Researchers have examined
the idea from numerous angles using
different techniques and data sets.
The evidence provided suggests that
self-segregation, especially for positive
To the extent that preferences do get
reflected in housing decisions, they do not
appear capable of explaining anything close
to current levels of segregation.
dence that uses survey responses about
preferences indicates that they play at
most a limited role. To the extent that
preferences do get reflected in housing
decisions, they do not appear capable
of explaining anything close to current
levels of segregation.
CONCLUSION
The self-segregation hypothesis
suggests that the persistence of racial
segregation of African Americans re-
reasons, helps little in understanding
racial housing segregation. The sources
appear to lie elsewhere, and unfortunately, the other possibilities can be far
from benign. These include ongoing
discrimination in real estate markets
and racial stereotyping (see Yinger’s
1998 study). Forty years after the civil
rights movement, it appears that much
work remains to be done. BR
Business Review Q4 2007 9
APPENDIX
Calculating the Dissimilarity Index
Housing segregation refers to the residential patterns of different racial and ethnic groups across neighborhoods
within a larger area, usually a metropolitan statistical area (MSA). MSAs are the focus of segregation measurement,
since they are generally thought to comprise a housing market. A commonly used measure of the degree of housing
segregation is the dissimilarity index, although others exist.* The index is generally applied to two groups — say, African
Americans and whites — and measures the fraction of African Americans that would have to move to achieve a perfectly even distribution across neighborhoods. The index ranges from 0 to 1, with 0 indicating perfect integration. So if
an MSA was 20 percent African American, the dissimilarity index would be 0 if the population of each neighborhood
within the MSA was 20 percent African American. An index value of 0.25 would indicate that 25 percent of African
Americans or 25 percent of whites would have to move to a different neighborhood in order to be evenly spread across
neighborhoods in the MSA. An MSA with a value of 0.6 or greater is generally classified as “highly segregated.”
The formula for the index is:
N
Dissimilarity = 0.5
i=l
Black population in area i
Black population in MSA
White population in area i
,
White population in MSA
for the N areas within the MSA. When the index is calculated, the areas within the MSA are often taken to be official
census tracts, which usually contain about 4,000 people and are meant to represent neighborhoods.
As an example of how the dissimilarity index is calculated and interpreted, suppose that an MSA has 40 African
Americans and 160 whites, for a total population of 200. So 20 percent of the population is African American and 80
percent is white. Also suppose that there are two neighborhoods. In the first, there are 20 African Americans and 40
whites. In the second, there are 20 African Americans and 120 whites. In this case, the dissimilarity index equals:
0.5 * {|(20/40) – (40/160)| + |(20/40) – (120/160)|} = 0.25.
Thus, segregation is low in the example. As mentioned, the dissimilarity value of 0.25 means that 25 percent of the
African American population or 25 percent of the white population has to change neighborhoods to achieve an even
distribution in which dissimilarity equals 0. The total African American population is 40, so 25 percent is 10 people. If
10 left neighborhood 1 and went to neighborhood 2, neighborhood 1 would have 10 African Americans and neighborhood 2 would have 30. The dissimilarity index would then equal:
0.5 * {|(10/40) – (40/160)| + |(30/40) – (120/160)|} = 0.
That is, there would be complete integration because the fraction of African Americans and whites in each neighborhood — 20 percent and 80 percent — equals their fractions for the population as a whole. A similar outcome would
obtain if 25 percent of the white population, or 40 people, moved from neighborhood 2 to neighborhood 1:
0.5 * {|(20/40) – (80/160)| + |(20/40) – (80/160)|} = 0.
*
For a thorough discussion of numerous segregation measures, see Douglas S. Massey and Nancy A. Denton, “The Dimensions of Residential Segregation,” Social Forces, 67 (December 1988), pp. 281-315.
10 Q4 2007 Business Review
www.philadelphiafed.org
REFERENCES
Galster, George. “Black and White
Preferences for Neighborhood Racial
Composition,” AREUEA Journal, 10
(1982), pp. 39-66.
Massey, Douglas S., and Nancy A. Denton.
American Apartheid: Segregation and the
Making of the Underclass. Cambridge, MA:
Harvard University Press, 1993.
Charles, Camille Z. “Neighborhood RacialComposition Preferences: Evidence from a
Multiethnic Metropolis,” Social Problems,
47 (2000), pp. 379-407.
Harris, David R. “’Property Values Drop
When Blacks Move in Because...’ Racial
and Socioeconomic Determinants of
Neighborhood Desirability,” American
Sociological Review, 64 (1999), pp. 461-79.
Massey, Douglas S., and Nancy A.
Denton. “The Dimensions of Residential
Segregation,” Social Forces, 67 (1988), pp.
281-315.
Charles, Camille Z. “The Dynamics of
Racial Residential Segregation,” American
Review of Sociology, 29 (2003), pp. 167-207.
Harris, David R. “Why Are Whites and
Blacks Averse to Black Neighbors?,” Social
Science Review, 30 (2001), pp. 100-16.
Clark, William A.V. “Residential
Preferences and Residential Choices in
a Multiethnic Context,” Demography, 29
(1992), pp. 451-66.
Ihlanfeldt, Keith R., and Benjamin Scafidi.
“Black Self-Segregation as a Cause of
Housing Segregation: Evidence from the
Multi-City Study of Urban Inequality,”
Journal of Urban Economics, 51 (2002), pp.
366-90.
Bobo, Lawrence, and Camille Zubrinsky.
“Attitudes on Racial Integration: Perceived
Status Differences, Mere In-Group
Preference or Racial Prejudice?,” Social
Forces, 74 (1996), pp. 883-909.
Cutler, David M., and Edward L. Glaeser.
“Are Ghettos Good or Bad?,” Quarterly
Journal of Economics, 112 (1997), pp. 82772.
Cutler, David M., Edward L. Glaeser, and
Jacob L. Vigdor. “The Rise and Decline of
the American Ghetto,” Journal of Political
Economy, 107 (1999), pp. 455-506.
Farley, Reynolds, Charlotte Steeh,
Tara Jackson, Maria Krysan, and Keith
Reeves. “Stereotypes and Segregation:
Neighborhoods in the Detroit Area,”
American Journal of Sociology, 100 (1994),
pp. 750-78.
Farley, Reynolds, Elaine Fielding,
and Maria Krysan. “The Residential
Preferences of Blacks and Whites: A
Four Metropolis Analysis,” Housing Policy
Debate, 8 (1997), pp. 763-800.
King, Thomas A., and Peter Mieszkowski.
“Racial Discrimination, Segregation and
the Price of Housing,” Journal of Political
Economy, 81 (1973), pp. 590-606.
Krysan, Maria, and Reynolds Farley. “The
Residential Preferences of Blacks: Do They
Explain Persistent Segregation?,” Social
Forces, 80 (2002), pp. 937-80.
Leven, Charles L., James T. Little, Hugh
O. Nourse, and Robert Read. Neighborhood
Change: Lessons in the Dynamics of Urban
Decay. Cambridge, MA: Ballinger, 1976.
Massey, Douglas. “Why Housing
Segregation Still Matters,” Journal of
Catholic Social Thought, 3 (Winter 2006),
pp. 97-114.
Patterson, Orlando. The Ordeal of
Integration: Progress and Resentment in
America’s ‘Racial’ Crisis. New York: Civitas/
Counterpoint, 1997.
Schelling, Thomas, C. “Dynamic Models
of Segregation,” Journal of Mathematical
Sociology, 1 (1971), pp.143-86.
Sethi, Rajiv, and Rohini Somanathan.
“Inequality and Segregation,” Journal of
Political Economy, 112 (2004), pp. 12961321.
Thernstrom, Stephen, and Abigail
Thernstrom. America in Black and White:
One Nation, Indivisible. New York: Simon
and Schuster, 1997.
Yinger, John. “The Black-White Price
Differential in Housing: Some Further
Evidence,” Land Economics, 54 (1978),
pp. 187-206.
Yinger, John. Closed Doors, Opportunities
Lost. New York: Russell Sage Foundation,
1995.
Yinger, John. “Housing Discrimination
Is Still Worth Worrying About,” Housing
Policy Debate, 9 (1998), pp. 893-927.
Freeman, Lance. “Minority Housing
Segregation: A Test of Three Perspectives,”
Journal of Urban Affairs, 22 (2000), pp.
15-35.
www.philadelphiafed.org
Business Review Q4 2007 11
Stock Prices and Business Investment
BY YARON LEITNER
I
s there a link between the stock market and
business investment? Empirical evidence
indicates that there is. A firm tends to invest
more when its stock price increases, and it
tends to invest less when the price falls. In this article,
Yaron Leitner discusses existing research that explains
this relationship. One question under consideration is
whether the stock market actually improves investment
decisions.
Empirical evidence points to a
link between the stock market and the
amount of money firms spend on investment. A firm tends to invest more
after the price of its stock increases,
and it tends to invest less after the
price falls. Investment could be in capital (for example, buying machines or
buying a new plant) or in research and
development (for example, developing
a new drug).
Recent research has tried to come
up with theoretical explanations and
test them empirically. One important
issue is whether the stock market actually improves investment decisions.
This might be the case, for example,
Yaron Leitner is a
senior economist
in the Research
Department of
the Philadelphia
Fed. This article
is available free
of charge at www.
philadelphiafed.
org/econ/br/.
12 Q4 2007 Business Review
if the firm’s stock price tells the firm
something about the profitability of
its investments — which might be
the case if market participants have
useful information or knowledge that
the firm does not have. Interestingly,
recent research has also suggested
that while informed participants make
prices more informative and therefore
improve the firm’s investment decisions, informed participants might also
attempt to manipulate a firm’s investment policies.
THE STOCK MARKET
CAN GUIDE INVESTMENT
DECISIONS
Stock Prices Reflect Investors’
Information About the Firm. Investors hold stocks because they expect to
obtain dividends and/or make capital
gains. When investors expect future
profits to be high, they pay more to
hold the stock; when investors expect
profits to be low, they pay less. Investors do not know what future profits
will be, but they can collect pieces of
information that may help them assess
the firm’s value. For example, investors
can look at the firm’s financial statements as well as the financial statements of other firms in the industry.
They can collect information about
the firm’s technology, the demand
for its products, and its competitive
environment. They can also look at
other macroeconomic indicators; for
example, a strong GDP report might
strengthen investors’ beliefs that demand for the firm’s products is going to
be solid. Using these pieces of information, each investor can come up with
his own assessment of the firm’s value.
The stock price reflects these assessments.
When new information arrives,
prices adjust. For example, the stock
price of a biotech firm will rise after
it announces that it passed the initial
tests for approval of a new drug, and
the price is likely to fall if the firm gets
involved in a lawsuit. Passing the initial tests means that the firm is likely
to generate more profits, and therefore, investors are willing to pay more
to hold the stock. In contrast, being
involved in a lawsuit means that the
firm is likely to generate less profits,
and therefore, investors are willing to
pay less.
Investors May Have Information the Firm Does Not Have. Some
of the information that investors have
may be publicly available (for example,
the firm’s financial statements). However, some investors may have information no one else has.
Consider the following example:
A large hedge fund, Short-Term Management (STM), hires a group of analysts whose job is to help choose which
www.philadelphiafed.org
stocks to buy. These analysts carefully
study the demand for a firm’s products
(for example, who will use a new drug)
as well as the firm’s position relative to
its competitors’. The firm can also hire
its own analysts, but since the firm is
not in the business of choosing stocks,
the cost of having its own group of
analysts may outweigh the benefits.
STM may have a better assessment than the firm (as well as other
investors) of the future demand for the
firm’s products and the firm’s position
relative to its competitors’. This assessment is called private information. In
other words, private information refers
to the data that STM’s analysts gather
as well as to their analysis of these
data. The private information STM
has allows it to evaluate the firm better
than anyone else.1
How could STM use its private
information to make a profit? Very
simple: If STM thinks the firm’s stock
is undervalued (that is, the firm’s prospects are better than those reflected in
the current price), it will buy the stock;
if the stock is overvalued, STM will
sell it. STM may not be correct all the
time. After all, no one can fully predict
the future. But STM may be correct on
average; that is, the number of times it
makes a correct decision (buy an undervalued stock or sell an overvalued
stock) will be higher than the number
of times it makes mistakes. This will
allow STM to make a profit even after
paying its analysts’ wages.
To keep its information advantage, STM will try to hide its information. However, once STM trades,
its information (or at least part of it)
1
The fact that some investors (like STM)
have better information than the firm in some
respects does not mean that they have better
information in all respects. For example, STM
may know more about the demand for the firm’s
products, but the firm may know more about
the technology it uses. In other words, the firm
may also have some private information.
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gradually becomes reflected in prices.
STM’s buy orders (positive information) will tend to push the price up,
and its sell orders (negative information) will push the price down.
In particular, suppose someone
had to guess whether STM has positive
information or negative information by looking at aggregate buy and
sell orders. Any order could come
either from STM or from some other
investors who do not have private
information. The other investors buy
for its products will be high or low, but
it knows that if the demand is high,
the investment will yield a gross return
of $6 million (that is, a profit of $5
million), and if the demand is low, the
investment will yield a gross return
of zero (that is, a loss of $1 million).
Should the firm make this investment?
If the firm knew for sure that
demand was going to be high, it would
make the investment; if it knew for
sure that demand was going to be low,
it would not. However, the firm does
When some investors have better information
than the firm, the firm can use the stock price
as a guide in its investment decisions.
and sell not because they have private
information but for other reasons; for
example, they need to rebalance their
portfolio or buy a new house. Now
suppose you see that there are many
more buy orders than sell orders. A buy
order increases the chance that STM
has positive information; after all,
STM buys only in this case. Similarly,
a sell order increases the chance that
STM has negative information. Thus,
buy orders move the price up, and sell
orders move the price down.2
The Information in Prices Can
Help the Firm Make Investment
Decisions. When some investors have
better information than the firm, the
firm can use the stock price as a guide
in its investment decisions.
Consider the following example.
Suppose a firm wants to expand its
business overseas, which requires an
upfront investment of $1 million. The
firm does not know whether demand
2
There is an extensive literature that studies
the way prices adjust to information. Two of the
earlier theoretical contributions are the paper
by Albert Kyle and the paper by Lawrence Glosten and Paul Milgrom.
not have that information. Suppose
that the only thing the firm knows is
that there is a 50-50 chance for high
or low demand. This means that if the
firm invests, on average, it would earn
a profit of $2 million (½*5 - ½*1=2).
Therefore, without further information, the firm will make the investment — and this will be the right
decision, given the information the
firm had at the time it invested.
Now go back to STM and its team
of analysts. Once they learn that the
firm is considering expanding its business overseas (say, the firm announced
it), they work day and night and eventually conclude that the investment is
not likely to generate anything. They
advise STM’s senior management to
sell the stock, and when STM does so,
the price goes down.
The firm does not have STM’s
information, but when the firm sees
that its price goes down, it may infer
that STM does not think that the investment is likely to succeed. The firm
can use this information and forgo
the investment. Assuming that STM’s
analysts are correct, the firm saves $1
million.
Business Review Q4 2007 13
The Value of Information. The
fact that the firm can use the information in stock prices increases its value.
In the example above, the firm can
avoid making a bad investment if it
learns that demand is low. The firm
will invest only if it learns that demand
is high. This strategy gives an expected
profit of $2.5 million (1/2*5+½*0
=2.5). Remember, if the firm makes
the investment without knowing what
demand will be (that is, without looking at the price), its expected profit
is only $2 million. Therefore, STM’s
trading activities increase the value of
the firm by $1/2 million. The information is valuable because it helps the
firm make better investment decisions.
Empirical Evidence. If firms learn
from stock prices, changes in stock
prices are more likely to affect investment when the stock price contains
more private information, that is,
when prices are more likely to reflect
the trading activities of investors like
STM. The logic is simple: If investors
like STM trade based on their private
information, the firm can learn from
prices, and price changes affect future
investment decisions. On the other
hand, if there are no investors like
STM who trade based on private information, the firm cannot learn from
prices, and price changes do not affect
investment.3
Qi Chen, Itay Goldstein, and Wei
Jiang provide empirical evidence that
supports the view that firms learn from
stock prices when they make their
investment decisions. They show that
a firm’s investment is indeed more
sensitive to its stock price when the
price reflects more private information.
A key to their analysis is determining when stock prices contain more
private information. Chen, Goldstein,
and Jiang use two measures and find
that the implication holds for both.
To learn more, see Measures of Private
Information.
STOCK MARKET AFFECTS
FIRM’S ABILITY TO FINANCE
INVESTMENTS
In the previous section, we focused on a firm that was considering
an investment opportunity (a business
expansion). The problem was that the
firm did not know whether the investment was profitable. In this section,
we consider a similar situation but
assume that the firm knows whether
its investment is profitable. Now the
problem is that the firm may find it
too expensive to finance its investment
because the stock price does not reflect
the investment’s true prospects.
Stock Prices May Not Reflect
the Firm’s True Value. A firm’s stock
price reflects two things. The first is
the firm’s (true) prospects, that is, the
expected cash flows the firm is going
to generate from its operations. The
value of these cash flows in today’s
terms is the firm’s fundamental value.
The second — called the nonfundamental component — reflects factors
that affect the price but that have
nothing to do with the firm’s prospects.
An example is investor sentiment (that
is, the market mood): Low sentiment
pushes prices down; high sentiment
pushes prices up.4 In a world without
3
There may be a relationship between price
changes and investment even when the price
contains no private information. For example,
a strong GDP report may move up prices as well
as investment. In this case, the firm does not
need to rely on prices for its investment; it can
look directly at the GDP report. But when the
price contains private information, the relationship between prices and investment is likely to
be stronger.
14 Q4 2007 Business Review
4
In 1996, former Federal Reserve Chairman
Alan Greenspan used the phrase “irrational
exuberance” to describe the market mood at
that time. This phrase was also the title of a
2000 book by Yale economics professor Robert
Shiller, who argued that the stock market had
indeed become dangerously overvalued.
frictions — for example, all investors
have the same information and same
assessments of the firm’s profitability
— the stock price would equal the
fundamental value because otherwise
investors could make “free money” by
buying undervalued stocks and selling
overvalued stocks. But when there are
frictions, as happens in reality, the
stock price may sometimes deviate
from its fundamental value.
When Prices Do Not Reflect
Fundamentals, Equity Financing
May Be Too Costly. Consider a firm
with a profitable investment opportunity. How can the firm finance its
investment? If the firm has a lot of
cash, it can finance its new investment
using internal funds. For example, if
the firm keeps most of its profits rather
than distributing them as dividends,
the firm is likely to have enough cash
to finance profitable investment opportunities that come its way. However,
when the firm does not have enough
cash at hand, it needs to raise money
from an external source. It can do so
either by borrowing (issuing debt) or
selling more shares of stock (issuing
equity).
Issuing equity is sometimes the
only option. In particular, lenders, who
want to get their money back, may be
willing to lend only to the point where
the risk of default is not too high. In
addition, lenders often require collateral, and the firm may not have enough
of it. Therefore, a firm that has already
borrowed a lot (up to its limit) and that
has no stockpile of cash can finance a
new investment only if it issues equity.
We will refer to such a firm as “equity dependent” because its ability to
finance a new investment depends on
its ability to issue a new equity.
Before making the investment, an
equity-dependent firm must consider
two things. First, it needs to consider the “stand-alone” value of the
investment, that is, the value of the
www.philadelphiafed.org
Measures of Private Information
T
he finance literature has come up with
two measures to assess the amount of
private information in stock prices. Qi
Chen, Itay Goldstein, and Wei Jiang
showed that their results hold for both
measures.
The first measure, developed by Richard Roll, is
based on what economists call firm-specific variation.
The idea is as follows. The price of a given stock often
changes because of market-related and industry-related
events. For example, release of a GDP report is likely to
affect the prices of most stocks. But a stock’s price also
moves because of events unique to the firm, for example,
the firm’s plans to acquire a new plant. Roll’s measure
calculates how much of the overall variation in the firm’s
stock price is attributable to firm-specific rather than
economy- or industry-wide factors. The measure is higher
when the firm’s stock price is more likely to move because
of firm-specific events, rather than economy-wide or
industry-wide events.*
Focusing on firm-specific variation as a measure of
trade based on private information makes sense because
market- and industry-related price movements are likelier
to reflect public information, that is, information available
to all. Indeed, Roll showed that firm-specific variation is
largely unassociated with public news releases and argued
that firm-specific variation mainly reflects trading by
investors with private information (for example, STM).
Roll mentioned that there might be another explanation,
namely, that firm-specific variation simply reflects noise,
for example, factors unrelated to fundamentals. However,
empirical evidence documented since then provides strong
support to the hypothesis that firm-specific variation
reflects more private information than noise. For example,
Artyom Durnev, Randall Morck, Bernard Yeung, and
Paul Zarowin showed that firm-specific variation is highly
correlated with stock prices’ ability to predict firms’ future
earnings.
The second measure, developed by David Easley,
Nicholas Kiefer, and Maureen O’Hara, captures the
probability that a trade will come from a trader who has
private information. The measure is based on a model
where some individuals have private information and some
do not. The first group of traders is called informed and
the second uninformed. Informed individuals trade only
on days on which they receive private information (that
is, they privately learn something about the firm). They
trade in order to profit from their private information;
they buy if they receive good news about the firm and sell
if they receive bad news. The uninformed trade every day,
and their trading activity does not reflect any information
regarding the firm; for example, they buy and sell to
rebalance their portfolios.
To calculate the probability of a trade by an informed
investor, we first need to fit the model to the data. In
particular, we can look at daily order flows over some
period (say, a year) and then use statistical methods to
estimate the probability that a given order comes from an
informed trader.
The estimated probability (of informed trading)
is low when the number of buy and sell orders does not
fluctuate much from one day to another. In contrast, when
there are large fluctuations in order flows, the estimated
probability of informed trading is high. Intuitively, if the
number of uninformed investors is high (so the probability
of informed trading is low), there is no reason to expect
that all of them will decide to buy or that all of them will
decide to sell on the same day. Instead, we can expect
that the number of uninformed investors who decide to
buy will be roughly the same on any given day and so will
the number of investors who decide to sell. Therefore,
we will not see large fluctuations in order flows, and the
estimated probability of informed trading will indeed be
low. In contrast, when there are large fluctuations in order
flows, the estimated probability of informed trading is high
because under the model above, large deviations from the
“normal” order flow indicate that it is likely that trades
are coming from investors who have received private
information; for example, on a day on which informed
investors receive good news about the firm, they will all
buy, and the number of buy orders on that day will be
larger than normal.
Finally, note that, in principle, the two measures
above may reflect not only the trading activity of
investors like STM but also the trading activity of the
firm’s managers, who may also have superior information
regarding some aspects of the firm. If this were the case,
the measure above may capture information that the firm
already knew, which is not consistent with the idea that
the firm learns from prices. Chen, Goldstein, and Jiang
validate their results by performing some tests that suggest
that while the two measures may reflect some information
the firm already knew, it also reflects information the firm
did not know.
* To calculate this measure, one needs to run a regression where a firm’s return is explained by the return on the market and by the return on the industry to which the firm belongs. The measure is estimated by 1-R 2, where R2 is R-square from the regression. In other words, R 2 is the share of variation in
stock returns that can be explained by general (market) or industry-wide factors, and what’s left over (1-R 2) measures private information.
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Business Review Q4 2007 15
investment if the firm had the cash to
finance it. Second, given that the firm
is equity dependent, it needs to take
into account the cost of issuing equity.
In particular, if the stock price equals
the firm’s fundamental value, the
firm knows that it is selling the stock
for what it is worth. But if the firm
believes that its stock is undervalued
(its price is less than the fundamental
value), the firm knows that it is losing
money when it sells its stock. In other
words, the firm receives less than what
the stock is really worth. In this case, a
firm may decide to forgo some investments, even though the firm would
make the investments if it had its own
money. In other words, an equity-dependent firm may decide to forgo its
investment because the cost of issuing
new shares is too high compared with
the revenues the firm expects to obtain
from the new investment.5
Empirical Evidence. The discussion above implies that the investment
of equity-dependent firms will be
more sensitive to the nonfundamental
component in stock prices than the
investment of firms that are less equity
dependent. In particular, an equitydependent firm will tend to invest
less when its stock price is below the
fundamental value, that is, when the
nonfundamental component is negative. This occurs not because investment opportunities change but because
5
Issuing equity may raise another problem: If
the firm knows more than its investors, investors may fear that the firm is selling equity not
because it needs to finance a profitable investment but because the firm thinks that its stock
is overvalued. Therefore, once the firm decides
to sell more shares, investors may pay even less
than what the initial price was. According to
the pecking order theory, the firm will issue
equity only as a last resort. In particular, a firm
that needs to raise money will do it in the following order: First, the firm will use its internal
funds, then it will borrow; only after it has borrowed as much as it can will it issue equity. To
learn more about the pecking order theory, read
the paper by Stewart Myers.
16 Q4 2007 Business Review
an undervalued stock increases the
cost of obtaining the money the firm
needs for its investment.
Malcolm Baker, Jeremy Stein,
and Jeffrey Wurgler found empirical
evidence consistent with the implication above. A challenging issue in
their analysis was how to measure the
nonfundamental component in stock
prices. Baker, Stein, and Wurgler
tried to tackle this issue by looking at
the actual return on the stock in the
long term; specifically, they looked at
returns over the three years subsequent
to the investment. Their idea is that
the firm expected these returns when
it considered its investment and that
the firm used these returns to determine whether its stock was under- or
overvalued. Of course, the firm did not
and could not know for sure how future returns would turn out. However,
using future returns as a proxy for the
firm’s expected returns is a way for the
authors (and us) to have a reasonable
estimate of what the firm might have
had in mind. Using this logic they find
that the investment of equity-dependent firms is indeed more sensitive to
the nonfundamental component in
stock prices than the investment of
firms that are less equity dependent.6
Lenders Also Look at Stock
Prices. Stock prices may also affect
the cost of borrowing. In particular,
potential lenders (banks) can learn
from stock prices just as the firm in
the previous section did. Banks can
then use the information in stock
prices to evaluate a loan.7 When stock
prices reflect fundamentals, there is no
problem: Banks have correct information about the firm, and a firm with a
profitable investment opportunity can
raise money because the stock price
reflects that. But if the price does not
reflect fundamentals, a firm with a
good investment opportunity may need
to forgo it. In particular, when banks
see that the stock price is low, they
may wrongly conclude that the firm’s
prospects are not so good, and therefore, they may be unwilling to lend, or
they may agree to lend only at a very
high interest rate.8
TRADERS CAN MANIPULATE
INVESTMENT DECISIONS
We have seen that the stock
market may affect investment decisions
because it provides information both
to the firm that makes the investment
and to those who provide the money
for the investment. Itay Goldstein
and Alexander Guembel developed
a model to show that while this may
improve investment decisions, it may
also open the door for manipulation.
Let’s go back to the example
where a firm was considering an
investment opportunity ($1 million
payment upfront, which results in either a $5 million profit or a $1 million
loss). Suppose the firm does not know
whether the investment will succeed or
fail, but STM does. As we saw earlier,
STM can use its private information to
7
Indeed, widely used measures of default risk
(for example, Altman’s Z-Score) include the
firm’s stock price. The Z-score was developed
in 1968 by Edward Altman for forecasting the
probability that a company will enter bankruptcy within a two-year period. The Z-score combines five common business ratios, one of which
is the ratio between the market value of equity
and the book value of debt. (The market value
of equity is the stock price times the number of
shares outstanding.) Banks and industrial companies regularly use updated and refined proprietary versions of Altman’s Z-score model.
6
To determine how equity dependent a firm is,
Baker, Stein, and Wurgler construct an index.
According to the index, a firm is more equity
dependent if it has borrowed a lot; it is less
equity dependent if it has higher operating cash
flows or higher cash balances or if it pays higher
dividends.
8
In this section we focused on the case where
prices that do not reflect fundamentals make it
hard for a firm to finance its project. Prices that
do not reflect fundamentals would also make it
hard for the firm in the previous section to learn
from prices.
www.philadelphiafed.org
make a profit by buying undervalued
stock and selling overvalued stock. If
STM does so, the stock price reflects
STM’s private information and can
help the firm make better investment
decisions. In particular, a price decline
indicates to the firm that STM thinks
the investment is a failure, and the
firm can save money by not investing.
Goldstein and Guembel show that
an investor like STM may choose to
trade even if it has no information at
all.9 In this case, the only purpose of
STM’s trade is to manipulate the firm’s
investment decisions and make money
out of it. In particular, they assume
that sometimes STM has private information about the firm and sometimes
it does not. They show that STM may
choose to trade not only in the first
case but also in the second case.
Manipulation Is Possible
Through Short Sales. When STM
has no information, it can make a
profit by short selling the stock. Short
selling means that an investor (in our
case, STM) borrows the stock from
someone else and sells it. Then, at a
later date, the investor buys the stock
and returns it to whomever he borrowed it from. In other words, a short
seller sells a stock that he does not
own. Short selling might be a good
strategy if one expects prices to fall.
In this case, the short seller can make
a profit by buying the stock at a lower
price than the price at which he sold
the stock.
But why should STM expect to
be able to buy the stock at a lower
price? The main idea is as follows: By
selling the stock, STM drives down
the price. The firm infers that the
lower price may indicate that STM
thinks the firm’s investment is likely
9
Goldstein and Guembel use the word speculator to refer to an investor like STM, which may
or may not have private information.
www.philadelphiafed.org
to fail. Therefore, the firm does not
invest. This by itself reduces the value
of the firm and the price of the stock
even further, thereby allowing STM
to buy the stock at a lower price than
it initially sold it for. In other words,
initially, investors thought the firm
had an investment expected to yield
a profit of $2.5 million, so they were
willing to pay more to hold the stock.
Once they learn the firm is not making the investment, they are willing to
pay less and the price of the stock falls.
You might ask: What’s so special
about STM? Why can’t anyone follow
the same strategy and make a profit?
The logic is as follows: For the average investor, who never has private
information, short selling is a recipe
for losing money because the average investor competes with investors,
like STM, who are likely to be better
informed. Since the more informed investors make money, the less informed
lose. Remember, there must be an
investor on the other side of each of
STM’s trades. However, for an investor
like STM, short selling can be a winning strategy even when it has no private
information about the stock. The reason
is that only STM knows whether it
does or does not have information
— and this by itself is a very important
piece of information. In other words,
STM has an information advantage
not only when it has private information about the firm but also when it
does not. In the first case, it knows
whether the investment will succeed
or fail. In the second case, it does not
know that, but it knows that no one
else knows. In contrast, the average
investor, who never obtains private
information, always needs to take into
account the possibility that he or she is
trading with another investor (STM)
with better information.
To summarize, by short selling,
an investor can manipulate the stock
price and the firm’s investment deci-
sions. Indeed, many firms complain
about short sales, arguing that they
may be manipulative and therefore
costly to shareholders. For example, in
a letter to the Securities and Exchange
Commission (SEC), Medizone International Inc. claims that “short-selling…
and other actions that have served to
limit our access to capital, diminished
or suppressed the value of our shares…
This short selling has proven extremely
detrimental to our company and our
shareholders.”10
One of the interesting features of
the model above is that manipulation
is profitable only through short sales.
In particular, STM can profit by selling
the stock initially and buying it later,
but STM cannot profit from doing
the opposite, that is, buying first and
selling later. The reason is that if STM
trades when it has no information,
the trades distort prices as well as the
firm’s investment decisions. In particular, STM’s selling the stock leads
to a price decline and an inefficient
decrease in investment; STM’s buying
the stock drives the price up, leading
to an inefficient rise in investment. In
both cases, the firm makes a wrong
investment decision, and the stock
price falls at a later time to reflect that.
In other words, regardless of whether
STM manipulates by buying or selling,
the price eventually drops. This means
that STM can profit only if it sells
initially.
Finally, note that even though
manipulation distorts investment
10
This example is provided by Goldstein and
Guembel. The letter can be found at http://
www.sec.gov/rules/concept/s72499/marshal2.txt.
Regulatory bodies (for example, the SEC in the
United States) have introduced restrictions such
as the “up-tick” rule on short sales. According
to the up-tick rule, established by the SEC,
every short-sale transaction must be entered at a
price that is higher than the price of the previous trade. The up-tick rule prevents short sellers
from adding to the downward momentum when
the price of an asset is already experiencing
sharp declines.
Business Review Q4 2007 17
decisions, which is bad for the firm,
overall, the stock market produces
better decisions, which is good for the
firm. Otherwise, the firm would have
ignored the information in the stock
price. In other words, if the firm (or
other investors) knew that the stock
market reflects wrong information too
often, they would have ignored it when
they made their decisions. However,
if the price usually reflects correct
information and only seldom reflects
incorrect information (which is the
case if STM is likely to have private information), the firm as well as investors
would consider the price when they
make their decisions.
CONCLUSION
Stock prices may affect investment decisions because they provide
information to firms about the profitability of their investment opportunities. Stock prices may also affect firms’
ability to finance new investments. In
particular, when prices do not reflect
fundamentals, a firm with a profitable
investment opportunity may need to
forgo it.
We have also seen that while
short selling may make stock prices
more informative about the firm’s
prospects and therefore may improve
the firm’s investment decisions, the
ability to short sell may also open the
door to manipulation. In particular, by
short selling a stock, an investor with
no information may cause a firm to
believe that its investment is likely to
fail. This may cause the firm to forgo
some profitable investment opportunities. The SEC administers regulations
concerning short sales. For example,
the SEC does not permit short sales
when a stock price is falling. Much of
the discussion about regulation of short
sales centers on the tradeoff between
making stock prices more informative
and the danger of manipulation. The
work discussed in this article can help
clarify the terms of this tradeoff. BR
Easley, David, Nicholas M. Kiefer, and
Maureen O’Hara. “Cream-Skimming
or Profit-Sharing? The Curious Role of
Purchased Order Flow,” Journal of Finance,
51 (1996), pp. 811-33.
Goldstein, Itay, and Alexander Guembel.
“Manipulation and the Allocation Role
of Prices,” Review of Economic Studies
(forthcoming).
REFERENCES
Baker Malcolm, Jeremy C. Stein, and
Jeffrey Wurgler. “When Does the Market
Matter? Stock Prices and the Investment
of Equity-Dependent Firms,” Quarterly
Journal of Economics, 118 (2003), pp. 9691005.
Chen, Qi, Itay Goldstein, and Wei Jiang.
“Price Informativeness and Investment
Sensitivity to Stock Price,” Review of
Financial Studies, 20:3 (2007), pp. 619-50.
Durnev, Artyom, Randall Morck, Bernard
Yeung, and Paul Zarowin. “Does Greater
Firm-Specific Return Variation Mean
More or Less Informed Stock Pricing?,”
Journal of Accounting Research, 41 (2003),
pp. 797-836.
18 Q4 2007 Business Review
Easley, David, Nicholas M. Kiefer, and
Maureen O’Hara. “One Day in the Life
of a Very Common Stock,” Review of
Financial Studies, 10 (1997), pp. 805-35.
Glosten, Lawrence R., and Paul R.
Milgrom. “Bid, Ask and Transaction
Prices in a Specialist Market with
Heterogeneously Informed Traders,”
Journal of Financial Economics, 14 (1985),
pp. 71-100.
Kyle, Albert S. “Continuous Auctions and
Insider Trading,” Econometrica, 53 (1985),
pp. 1315-35.
Myers, Stewart C. “The Capital Structure
Puzzle,” Journal of Finance, 39 (1984), pp.
575-92.
Roll, Richard, “R2,” Journal of Finance, 43
(1988), pp. 541-66.
Shiller, Robert J. Irrational Exuberance, 2nd
ed. Princeton, NJ: Princeton University
Press, 2005.
www.philadelphiafed.org
What Do We Know About Chapter 13
Personal Bankruptcy Filings?
BY WENLI LI
S
ince 1980, the number of households filing
for bankruptcy has more than tripled. This
drastic increase in personal bankruptcy
filings led to substantial debate among
economists and policymakers. That debate subsequently
resulted in the enactment of extensive reforms in 2005
when Congress passed the Bankruptcy Abuse Prevention
and Consumer Protection Act. Ultimately, the rationale
for this legislation is the presumption that Chapter
13 leads to more appropriate outcomes compared
with either Chapter 7 filings or other options outside
bankruptcy. In this article, Wenli Li outlines the results
of two recent studies that have taken a more detailed
look at actual outcomes in Chapter 13.
The U.S. personal bankruptcy
filing rate has gone up dramatically
for the past two decades. In 1980, for
every 1,000 households, only four filed
for bankruptcy. Today, the number
has more than tripled. About onethird of the bankruptcies were filed
under Chapter 13 (Figure 1). The U.S
personal bankruptcy code has two key
Wenli Li is an
economic advisor
and economist
in the Research
Department of the
Philadelphia Fed.
This article is
available free of
charge at www.
philadelphiafed.org/econ/br/.
www.philadelphiafed.org
features: Chapter 7 and Chapter 13.
Under Chapter 7, debtors sacrifice part
of their assets in exchange for a discharge of their debts. Under Chapter
13, debtors sacrifice part of their future
earnings in exchange for a partial
discharge of their debts. This drastic
increase in personal bankruptcy filing
rates led to substantial debate, academic as well as legislative, and finally
resulted in the enactment of extensive
bankruptcy reforms in 2005 with the
passage of the Bankruptcy Abuse
Prevention and Consumer Protection
Act. The core of the legal reform is
to further restrict debtors’ access to
Chapter 7 personal bankruptcy and to
force some debtors to file under Chapter 13 or not at all, so that debtors with
sufficient income would be forced to
repay at least part of their debt through
their future earnings.1
Ultimately, the rationale for this
legislation is the presumption that
Chapter 13 leads to more appropriate
outcomes (for some debtors) compared with either Chapter 7 or options
outside of bankruptcy. But what do
debtors and creditors really achieve
under Chapter 13? Or more important,
how does the Chapter 13 bankruptcy
system serve its two conflicting objectives: to provide debtors with a partial
financial fresh start by discharging
some of their debt, and to help creditors collect their defaulted loans by
enforcing debtors’ obligation to repay?
Two recent studies have taken a
more detailed empirical look at actual
outcomes in Chapter 13. One is my
study with Hülya Eraslan and PierreDaniel Sarte, and the other is a study
by Scott Norberg and Andrew Velkey.
FEATURES OF U.S.
BANKRUPTCY LAW
The key feature of U.S. personal
bankruptcy law, both before and after
passage of the 2005 reform act, is that
it contains two basic types of bankruptcy proceedings: Chapter 7 and
Chapter 13. Before passage of the 2005
reform act, a debtor’s bankruptcy decision and choice between chapters were
1
Some of the other significant changes
the Bankruptcy Reform Act introduced to
bankruptcy doctrine include increasing the
amount of paperwork that must be filed by every
debtor; requiring pre-filing counseling and
post-filing financial education for debtors whose
debts are primarily consumer debts; and making
Chapter 13 less attractive by, among other
things, requiring five-year payment plans (for
above-median debtors) rather than the threeyear plans that were previously the norm.
Business Review Q4 2007 19
FIGURE 1
Annual Household Bankruptcy Filing Rate
Total Filings/Total Households
mostly voluntary. The 2005 reform act
abolished some debtors’ right to choose
between chapters. To file under Chapter 7, debtors whose incomes are above
their state median family income must
now pass a “means test” that requires
that (i) their monthly income net of
allowable expenses calculated according to IRS rules be less than $166.67
per month and (ii) their net monthly
income multiplied by 60 be less than
25 percent of their unsecured debt.2 If
their incomes are above the median
level and they fail the means test, debtors must file under Chapter 13 if they
file for bankruptcy at all.
Chapter 7 is often called liquida-
2
The state median income divides the higher
half of the population in the state from the
lower half in terms of income level. In other
words, half of the population in the state has
income greater than the median, and half have
income less than the median.
20 Q4 2007 Business Review
tion. Under Chapter 7, a debtor gives
up all of his assets above a certain
exemption level. In exchange, the
debtor gets almost all of his unsecured
debt discharged. The exemption level
varies with states. A debtor cannot file
for bankruptcy for six years after the
last filing.
Chapter 13 is also called a wage
earner’s plan. Under Chapter 13, a
debtor gets to keep all of his assets.
However, he must repay some of the
unsecured debt out of future earnings
through a repayment plan over three
to five years. Only after the completion
of the repayment plan will the debtor
obtain a legal discharge of his remaining debts. In principle, a debtor can
file for Chapter 13 repeatedly without
a time limit between the two adjacent
filings. In practice, bankruptcy courts
often require a 180-day gap.
A debtor can also choose to
remain delinquent on his loans with-
out filing for bankruptcy, something
known as informal bankruptcy. In that
case, if the loan is secured by a house
or a car, lenders can seize the house or
the car, a process legally called foreclosure. If the loan is unsecured, such as
credit card debt, lenders will immediately start adding finance charges
and late fees to the amount owed.
They will also likely make phone calls
and write letters soliciting payments.
Shortly after that, unsecured lenders
typically sell their debts to collection
agencies. Unsecured creditors as well
as collection agencies can also sue the
debtor and obtain a court judgment
against the debtor. They collect the
judgment by garnishing the debtor’s
wages.3
Individuals who choose informal
bankruptcy over formal bankruptcy
and debt payment are often those
who do not have regular jobs, assets,
or bank accounts. This means that
even if a creditor obtained a judgment
against a debtor, it would be nearly
impossible for the creditor to collect on
it. In their study of informal bankruptcy, Amanda Dawsey and Lawrence
Ausubel point out that high bankruptcy costs also contribute to informal
bankruptcy.
HOW DOES CHAPTER 13
BANKRUPTCY WORK?
Figure 2 lists the basic steps of
a typical Chapter 13 case. The case
starts with the debtor’s submitting a
petition and a repayment plan. Prior to
April 2006, the filing fee for a Chapter
13 case was $185; it’s now $235 plus a
$39 miscellaneous administrative fee.
In general, the filing fee is due at the
time of petition. The court sometimes
may allow the debtor to pay this filing
fee in installments if the debtor cannot
See Robert Hunt’s Business Review article for
more details.
3
www.philadelphiafed.org
FIGURE 2
Chapter 13 Bankruptcy Procedure
Debtor files petition
Proposes a repayment plan
confirm
reject
Judge’s
decision
dismiss
Debtor exits
bankruptcy,
case closed
Debtor carries
out the plan
no
yes
Continue the
payments?
Plan
finished?
no
yes
Debtor
discharged,
case closed
Debtor exits
bankruptcy,
case closed
pay all at once.4 If the debtor hires a
private attorney, he will also have to
pay the attorney’s fees. The attorney’s
fees can be anywhere from a couple of
hundred dollars to a few thousand, depending on the complexity of the case
and the experience of the attorney.
As soon as a debtor files for bankruptcy, something called the “automatic stay” goes into effect. The automatic
stay prohibits virtually all creditors
4
The filing fee may be waived entirely only for
individuals who qualify under very strict feewaiver provisions.
www.philadelphiafed.org
from taking any action directed at collecting the debts the debtor owes them
until the court says otherwise. These
actions include foreclosures, termination of contracts for deed, repossession
actions, and lawsuits to obtain judgments on debts and pressure to sell off
equipment, crops, and livestock.
The petition contains schedules
A to J, which detail the debtor’s assets
(real estate assets such as housing,
and personal assets such as furniture
and jewelry); income, expense, and
debts (secured, unsecured priority,
and unsecured nonpriority); pend-
ing lawsuits, including foreclosures;
and past income.5 Together with the
5
Types of unsecured priority claims include,
among others, alimony, maintenance and
support, taxes and certain other debts owed
to government entities, and money owed to
employee benefit plans for services rendered
within the 180 days immediately preceding
filing of the original petition. Unsecured
nonpriority claims are mostly credit card debt.
The plan must pay priority claims in full before
unsecured nonpriority creditors receive any
money unless a particular priority creditor
agrees to different treatment of the claim or,
in the case of a domestic support obligation,
unless the debtor contributes all “disposable
income”— discussed below — to a five-year
plan.
Business Review Q4 2007 21
petition, the debtor must also submit
a repayment plan that devotes all of
his disposable income – income net of
necessary expenses – to the payment
of claims.
For a proposed payment plan to
be confirmed, it must extend for at
least three years, but it cannot exceed
five years. It must also be filed in good
faith. In particular, the plan must
propose to pay at least as much as the
value of the assets creditors would
have received under Chapter 7. Finally,
the plan must make up all missed payments on secured debt before submitting payments to unsecured creditors.
Within a few days after the debtor
files the bankruptcy petition, the
bankruptcy court assigns a Chapter 13
trustee to oversee the case. The trustee
may be a local bankruptcy attorney,
who will be very knowledgeable about
Chapter 13 bankruptcy generally,
as well as the local court’s rules and
procedures specifically. In some courts,
trustees are not attorneys but business
people with specialized knowledge of
finance or personal bankruptcy. The
trustee serves primarily as a mediator
between the debtor and his creditors.
In almost all cases, the debtor deals
mostly with the trustee, and a bankruptcy judge follows the recommendations of the trustee.
Shortly after his appointment, the
trustee schedules a section 341 meeting for creditors to attend. This is the
first court appearance for the debtor.
At the meeting, creditors will be given
an opportunity to ask any questions regarding the debtor’s financial situation
that may affect the plan. Although
they can raise objections, creditors do
not actively vote on a repayment plan.
After the meeting, the judge decides
whether to dismiss the case, reject the
plan, or confirm the plan.
The plan can be dismissed either
because it was not filed in good faith
or because it is not viewed as feasible.
22 Q4 2007 Business Review
When the repayment plan is dismissed,
the case ends. But several important
consequences remain. First, all liens
on the debtor’s property are reinstated.
The automatic stay is lifted. Creditors
can resume their legal remedies outside
of bankruptcy to pursue the payment
of their debts. Interest (and in some
cases penalties) that stopped accruing
during the bankruptcy will be added to
the debts. In other words, interest and
penalties are retroactive from the time
of the stay.
Although they can
raise objections,
creditors do not
actively vote on a
repayment plan.
Sometimes, the court does not
dismiss the case outright. Instead, the
plan is simply rejected and the debtor
is given a chance to propose a modified plan. After modification, the plan
will again be subject to court decision.
If the plan is confirmed, the debtor starts making payments according
to the confirmed plan.6 The debtor
will be discharged only upon completion of the plan. A confirmed plan
can be renegotiated. For example, the
debtor can prepay in the event that his
assets appreciate or he receives additional income from other sources, such
as an inheritance. The debtor can also
convert the case into Chapter 7 with
the court’s agreement or simply default
on the confirmed plan and then have
the plan dismissed. The trustee can
6
Often, debtors start making payments to the
trustee as soon as they submit their proposed
plans. The payment minus court expenses
will be refunded to debtors if their cases
are dismissed. This requirement militates
against the possibility of debtors’ lingering in
bankruptcy court, reaping all the gains without
making any payments.
also force the debtor to alter the plan
when he observes that the debtor has
had a substantial increase in income.
CHAPTER 13 BY THE NUMBERS
In my research project with Hülya
Eraslan and Pierre-Daniel Sarte on the
realities and dynamics of Chapter 13
personal bankruptcies, we collected all
Chapter 13 bankruptcy filings between
August 1, 2001, and August 1, 2002, in
the federal bankruptcy court, district
of Delaware. About 10 percent of the
cases were excluded from the sample
because of incomplete information
resulting either from a filing error
(deficient filing) or a court recording
error. Almost all of these excluded
cases were dismissed subsequently. The
final sample contained 904 cases.7 At
the time of the writing of this article,
about 190 cases remain open.
In another study, Scott Norberg
and Andrew Velkey examined a sample made up of 795 Chapter 13 cases
filed in 1994 in seven federal judicial
districts, which comprise 14 Chapter
13 trusteeships. The seven federal judicial districts are Northern District of
Georgia, Southern District of Georgia,
Middle District of North Carolina,
Middle District of Tennessee, Western
District of Tennessee, District of Maryland, and Western District of Pennsylvania. In each district, a quota sample
of roughly 1 percent of the Chapter 13
cases filed in 1994, but not fewer than
100 cases, was pulled.8
Each sampling approach has its
merits. The two benefits of my study
7
For the purposes of this article, we do not
include the 72 cases filed initially under
Chapter 13 but converted to Chapter 7. Since
this article was written, more cases have closed.
See our Working Paper for updated information.
8
The data source for both studies is the U.S.
Public Access to Court Electronic Filing Service
Center, the federal judiciary’s centralized
registration, billing, and technical support
center for electronic access to U.S. district,
bankruptcy, and appellate court records.
www.philadelphiafed.org
with Eraslan and Sarte are: (i) The
data are recent. This is important,
since there was a significant increase
in personal bankruptcy in the 1990s.
(ii) For further analysis, it is helpful to look at a more homogeneous
population. For example, if we want to
examine the effect of family income
on bankruptcy outcomes, we prefer
that unobserved differences between
families in different states not affect
our results. The benefit of Norbert and
Velkey’s study is that their sample is
more representative of the nation as a
whole.
WHO FILES FOR CHAPTER 13
PERSONAL BANKRUPTCIES?
Table 1 presents profiles of the
Chapter 13 filers in the two studies.
To draw a comparison with an average
household, I’ve also included, when
available, information derived from the
2001 Survey of Consumer Finances.
As can be seen, Chapter 13 filers are
far from being the most destitute of
the general population. Both studies
indicate that these people tend to have
regular jobs, and the unemployment
rate among filers is far lower than the
state or national unemployment rate.
Thus, they all receive regular incomes,
although their incomes fall short of the
national average by 30 to 60 percent.
The majority of the debtors also
own their homes, and the homeownership rate among the debtors is
substantially higher than the national
average in our more recent sample.
The homeownership rate is lower
among debtors than among the general
population by about 10 percentage
points in Norberg and Velkey’s sample.
But the rates among debtors vary quite
a bit among the seven districts in their
sample, ranging from 33 percent in
the Middle District of Tennessee to
79 percent in the Western District of
Pennsylvania.
Not surprisingly, despite their
www.philadelphiafed.org
TABLE 1
Profiles of Chapter 13 Filers
Eraslan, Li,
and Sarte
Norberg and
Velkey
Male
29.8%
36.7%
Female
35.1%
36.3%
Joint filing
35.1%
National Data
(SCF)
27.0%
Marriage
41%
40%
Average household size
2.67
2.69
2.50
Homeownership rate
87%
54%
72%
Average monthly income ($)
1646
946
2297
Debt excluding mortgagesannual income ratio
1.36
1.29
0.28
With previous filing history
22%
32%
Note: Monthly income is real income constructed by deflating nominal income by the consumer
price index, setting 1982-84 to 100.
income and assets, the Chapter 13
filers are heavily indebted. The debt
to income ratio, excluding mortgages,
averages 1.36, with a median of 1.02.
According to the 2001 Survey of Consumer Finances, the average debt to
income ratio, excluding mortgages, is
0.28 and the median is 0.06 for the nation. Norberg and Velkey found similar
numbers for their 1994 sample.
Another remarkable finding is
that a substantial portion of filers, over
20 percent in our sample and nearly 32
percent in Norberg and Velkey’s, have
filed for bankruptcy previous to the
case under study.
In terms of other demographics,
Chapter 13 filers in both studies do not
differ much from the general population in terms of marital status and
household size.9
The profiles of Chapter 13 filers
uncovered in the two studies are in
contrast to those of Chapter 7 filers documented by other studies. For
example, in their study, Scott Fay, Erik
9
We also report filing status by gender, and we
infer debtors’ gender from their first names.
10
Fay, Hurst, and White’s sample consists of
both Chapter 7 and Chapter 13 filers. Given
the relatively small number of Chapter 13 filers
in their sample, the reported sample statistics
reflect mostly those of Chapter 7 filers.
Business Review Q4 2007 23
Furst, and Michelle White find that
Chapter 7 filers have the same rate of
unemployment as the general population.10 The homeownership rate in
their study is far lower than the general
population’s. The average monthly
income is about 50 percent below the
nation’s average. Most important, filers
in this study experienced, on average,
a much higher income drop at the time
of filing.
HOW SUCCESSFUL HAS THE
CHAPTER 13 SYSTEM BEEN?
The success of the bankruptcy
system depends on how well it serves
its dual goals: maximizing return to
creditors by enforcing debtors’ obligation to repay their debts and providing
debtors with a financial fresh start by
discharging some of their debt. The
two goals are obviously at conflict. Unfortunately, the law does not explicitly
specify how the two goals should be
balanced.
Even without a precise way of
evaluating the success or failure of
Chapter 13, we can make headway
by thinking about some features of a
desirable bankruptcy procedure. First,
all confirmed cases should eventually
be discharged. Remember, a case that
is not discharged shifts the debtor
and his creditors back into a private
collection procedure. Second, recovery
rates for unsecured creditors should
not be lower than those gained from
other solutions to borrower default.
From the creditors’ standpoint, a
higher recovery for unsecured debt is
the primary advantage of Chapter 13
over Chapter 7 and other remedies
outside of bankruptcy. Finally, multiple
filings should be the exception, not
the rule, especially for those who
had successfully obtained a previous
discharge.
The Grim Realities of Chapter
13 Personal Bankruptcy. I summarize the performance measures in the
24 Q4 2007 Business Review
two studies in Table 2. Several findings
emerge from the two studies. First,
although a large percentage of Chapter
13 filers do have their proposed plans
confirmed, the success rate measured
by the percentage of cases discharged
is low.11 In our sample, about 18 percent of the cases remained open as of
October 30, 2006. Even if we assume
that all of the cases still open will be
ultimately discharged, the maximum
rate of discharge would be 51 percent,
about half of the cases. In 1994, according to Norberg and Velkey, only
33 percent of the cases obtained a
discharge. This strongly suggests that a
substantial fraction of repayment plans
were unrealistic in the first place, either because the debtors were “forced”
to agree on a plan that demands an
“unrealistic” amount of repayment or
because the debtors did not fully take
11
Recall that a Chapter 13 bankruptcy case
is ultimately either dismissed or discharged.
A discharge is granted only after a debtor
successfully finishes his confirmed repayment
plan.
into account the possibility of future
adverse events that would affect their
ability to pay.12
Related to the low discharge rate
is the finding that creditors, secured
and unsecured, receive very little on
their debts. Specifically, on average,
secured creditors receive at most 36
cents on the dollar in our sample, assuming that the remaining open cases
will result in a 100 percent recovery
rate. In Norberg and Velkey’s sample,
they receive only 31 cents on the dollar, even though secured creditors are
supposed to receive full payments in a
successful Chapter 13 case, according
to the bankruptcy law.13
12
Of course, some consumers certainly did
experience adverse events subsequent to filing
a plan. But it seems unlikely that plans that are
unsuccessful between 50 to 70 percent of the
time can be ascribed to pure bad luck.
13
Because a trustee’s commission is proportional
to the amount of payments under Chapter
13, debtors often choose to have their regular
mortgage or car loan payment outside of their
repayment plans to reduce the payment amount
under bankruptcy. Arrears, however, have to be
paid through repayment plans.
TABLE 2
Performance of the Chapter 13
Bankruptcy System
Eraslan, Li, and
Sarte Study
Norberg and
Velkey Study
Confirmation rate
82%
77%
Discharge rate
33%
33%
Recovery rate of all debt
27%
30.1%
Recovery rate of secured debt
22%
30.6%
Recovery rate of unsecured debt
16%
19.5%
Subsequent refiling rate
30%
33%
Note: Eraslan, Li, and Sarte’s sample is as of October 30, 2006.
www.philadelphiafed.org
Unsecured creditors fare worse,
receiving, on average, at most 31 cents
on the dollar in our sample and 20
cents on the dollar in Norberg and
Velkey’s. Over half of the creditors in
our sample, secured as well as unsecured, receive absolutely nothing and
just a few cents on the dollar in Norberg and Velkey’s sample. Although it
is not directly comparable, according
to the 2001-2002 Reports of Income
and Financial Conditions from the
nation’s commercial banks, the recovery rate for overdue credit card loans is
23 cents per dollar.
The payoffs to the creditors are
strikingly low considering the substantial cost associated with Chapter 13
bankruptcy cases. In addition to the
filing fee and attorney’s fees, the debtor
pays the trustee 3 to 10 percent of each
payment he makes to his creditors
through the trustee. Thus, for every
dollar owed to creditors, it costs 0.6 to
3 cents in trustee fees alone to collect
20 to 30 cents.
Another striking finding that
emerges from both studies is the high
rate at which debtors file again after
the termination of the case under
study. Of the 726 debtors who have
exited bankruptcy through either
discharge or dismissal, 30 percent of
them filed again at least once. The refiling rate is as high as 33 percent for
Norberg and Velkey’s sample. Even for
those who emerged successfully from
their cases through discharge, the refiling rate exceeds 20 percent. These
numbers are very high considering
that from the mid-1990s to 2006, the
unconditional bankruptcy filing rate
for households in general is less than
1.4 percent in the U.S.
To sum up, the numbers uncovered from both studies show that
debtors did not succeed in completing
their plans in the majority of cases,
and when they did succeed, a substantial fraction of them were still at risk
www.philadelphiafed.org
of filing again. Furthermore, creditors
did not recover much under Chapter
13: median creditors received close
to nothing. Thus, the performance of
Chapter 13 poses a challenge to any
argument that it is an efficient mechanism for resolving the two objectives
of the bankruptcy law: debt relief and
debt collection. In particular, proponents of the 2005 law would instead
have to base their support for the law
narrowly, our evidence indicates that
Chapter 13 collection procedures are
unlikely to be effective against them.14
This suggests that the rationale for the
new bankruptcy act must be sought
in its other effects, such as deterring
bankruptcy altogether among those
who have the capacity to repay.
Of course, what we have discussed
so far concerns Chapter 13 bankruptcy
provisions from an efficiency stand-
Lenders in states with relatively more
generous bankruptcy laws take into account
the potentially higher personal bankruptcy
filing rate in those states and consequently
charge a higher rate to borrow.
on the possibility that Chapter 13 has
strong, desirable benefits in disciplining consumers, lenders, or both.
POSSIBLE CONSEQUENCES OF
THE 2005 REFORM ACT
As mentioned earlier, at the
center of the 2005 Bankruptcy Abuse
Prevention and Consumer Protection Act is a means test that intends
to move a potentially large number of
would-be Chapter 7 filers into Chapter 13. The purpose is to return more
money to general unsecured creditors
than the creditors would otherwise receive. Whether this purpose is served
depends on the actual effectiveness of
Chapter 13 bankruptcy as a means to
collect debts.
According to the two studies
reviewed here, however, Chapter 13
bankruptcy is an ineffective collection
device. Median creditors receive almost nothing after discharge and nearly
half of debtors do not get their debt
discharged. If those who end up in
Chapter 13 because of the new law are
mostly people who fail the means test
point after the fact. That is, we ask:
once a debtor has entered bankruptcy,
how well does Chapter 13 perform?
It should be kept in mind that bankruptcy law has broader effects. For
example, researchers Reint Gropp,
John Scholz, and Michelle White,
and Emily Lin and Michelle White
find that bankruptcy law affects the
supply of credit. Specifically, lenders
in states with relatively more generous
bankruptcy laws take into account the
potentially higher personal bankruptcy
filing rate in those states and consequently charge a higher rate to borrow.
Jeremy Berkowitz and Michelle
White and Wei Fan and Michelle
White find that bankruptcy law also
affects the incentive to take risks, in
particular, the decision to become
14
Recall that a large number of Chapter 13
filers have income less than their state median
income. We can’t make the same statement
for relatively high-income debtors who may be
forced to choose Chapter 13 instead of Chapter
7 under the new law because they differ in
fundamental ways from our sample of Chapter
13 filers.
Business Review Q4 2007 25
entrepreneurs. Both homeowners and
renters respond strongly to increases in
homestead exemptions in making their
decisions to be self-employed.
In light of these studies, an outcome that looks inefficient conditional
on the borrower’s entering bankruptcy
may have positive effects. For instance,
consumers or lenders may be more
prudent in their borrowing or lending decisions when they expect to
fare poorly in bankruptcy. Whether
Chapter 13 outcomes we observed can
be rationalized in a broader view of the
goals of bankruptcy will require further
research.
CONCLUSION
Two recent studies of Chapter 13
personal bankruptcy provide a detailed
picture of who enters Chapter 13 and
how well borrowers and creditors fare.
The two studies uncover evidence
that paints a rather grim picture of the
realities of Chapter 13 personal bankruptcy. Plans are seldom completed
successfully, creditors recover relatively
little, and borrowers are very likely
to re-enter bankruptcy. Thus, these
findings raise some flags about the
stated rationale for the reform, moving
more borrowers from Chapter 7 to
Chapter 13. To put it simply, despite
some caveats mentioned in the article,
based on our research, the Chapter
13 bankruptcy system has a long way
to go in terms of providing debt relief
for borrowers and debt collection for
creditors. BR
REFERENCES
Berkowitz, Jeremy, and Michelle White.
“Bankruptcy and Small Firms’ Access to
Credit,” Rand Journal of Economics, 35
(2004), pp. 69-84.
Fay, Scott, Erik Hurst, and Michelle
White. “The Household Bankruptcy
Decision,” American Economic Review, 92
(June 2002), pp. 708-18.
Dawsey, Amanda, and Lawrence Ausubel.
“Informal Bankruptcy,” manuscript, 2001.
Gropp, Reint, John Karl Scholz, and
Michelle White. “Personal Bankruptcy and
Credit Supply and Demand,” Quarterly
Journal of Economics, 112 (1997), pp. 21751.
Eraslan, Hülya, Wenli Li, and PierreDaniel Sarte. “The Anatomy of U.S.
Personal Bankruptcy Under Chapter 13,”
Working Paper 07-31 (September 2007).
Fan, Wei, and Michelle White.
“Personal Bankruptcy and the Level of
Entrepreneurial Activity,” Journal of Law
and Economics, 46 (October 2003), pp.
543-69.
26 Q4 2007 Business Review
Lin, Emily Y., and Michelle White.
“Bankruptcy and the Market for Mortgage
and Home Improvement Loans,” Journal
of Urban Economics, 50 (July 2001), pp.
138-63.
Norberg, Scott, and Andrew, Velkey.
“Debtor Discharge and Creditor
Repayment in Chapter 13,” manuscript.
Hunt, Robert. “Consumer Debt Collection
in America,” Federal Reserve Bank of
Philadelphia Business Review (Second
Quarter, 2007).
www.philadelphiafed.org
RESEARCH RAP
Abstracts of
research papers
produced by the
economists at
the Philadelphia
Fed
You can find more Research Rap abstracts on our website at: www.philadelphiafed.org/econ/resrap/index.
html. Or view our Working Papers at: www.philadelphiafed.org/econ/wps/index.html.
IMPLICATIONS OF MEANS-TESTING
IN CHAPTER 7 BANKRUPTCY
The authors study, theoretically and
quantitatively, the general equilibrium of
an economy in which households smooth
consumption by means of both a riskless
asset and unsecured loans with the option
to default. The default option resembles
a bankruptcy filing under Chapter 7 of
the U.S. Bankruptcy Code. Competitive
financial intermediaries offer a menu of loan
sizes and interest rates wherein each loan
makes zero profits. The authors prove the
existence of a steady-state equilibrium and
characterize the circumstances under which
a household defaults on its loans. They show
that their model accounts for the main statistics regarding bankruptcy and unsecured
credit while matching key macroeconomic
aggregates and the earnings and wealth distributions. They use this model to address
the implications of a recent policy change
that introduces a form of “means-testing”
for households contemplating a Chapter 7
bankruptcy filing. They find that this policy
change yields large welfare gains.
(Revision forthcoming in Econometrica)
Working Paper 07-16, “A Quantitative
Theory of Unsecured Consumer Credit with
Risk of Default,” Satyajit Chatterjee, Federal
Reserve Bank of Philadelphia; Dean Corbae,
University of Texas at Austin; Makoto Nakajima, University of Illinois; and Jose-Victor
Rios-Rull, University of Pennsylvania
www.philadelphiafed.org
EFFECTS OF TRADE LIBERALIZATION ON WELFARE, TRADE, AND
EXPORTS
The authors study a variation of the
Melitz (2003) model, a monopolistically
competitive model with heterogeneity in
productivity across establishments and fixed
costs of exporting. They calibrate the model
to match the employment size distribution of U.S. manufacturing establishments.
Export participation in the calibrated
model is then compared to the data on U.S.
manufacturing exporters. With fixed costs
of starting to export about 3.9 times as large
as the costs of continuing as an exporter,
the model can match both the size distribution of exporters and transition into and
out of exporting. The calibrated model is
then used to estimate the effect of reducing
tariffs on welfare, trade, and export participation. The authors find sizable gains to
moving to free trade. Contrary to the view
that the gains to lowering tariffs are larger
in models with export decisions, they find
that steady state consumption increases by
less in their benchmark model of exporting
than in a similar model without fixed costs.
However, they also find that comparisons
of steady state consumption understate the
welfare gains to trade reform in models
with fixed costs and overstate the welfare
gains in models without fixed costs. With
fixed costs, tariffs lead to an over-accumulation of product varieties that can be used
more effectively along the transition to the
Business Review Q4 2007 27
new steady state. Thus, following trade liberalizations,
economic activity overshoots its steady state, with the
peak in output coming 10 years after the trade reform.
Finally, the authors explore the impact of the key
modeling assumptions in the theoretical literature for
quantitative results.
Working Paper 07-17, “Establishment Heterogeneity,
Exporter Dynamics, and the Effects of Trade Liberalization,” George Alessandria, Federal Reserve Bank of Philadelphia, and Horag Choi, University of Auckland
THE RELATIONSHIP BETWEEN ESTABLISHMENT AGE AND EMPLOYMENT GROWTH
This paper presents new evidence on the relationship between a metropolitan area’s employment growth
and its establishment age distribution. The author
finds that cities with a relatively younger distribution
of establishments tend to have higher growth, as well
as higher job and establishment turnover. Geographic
variations in the age distribution account for 38 percent
of the geographic differences in growth, compared to
the 32 percent accounted for by variations in industry composition. Differences are disproportionately
accounted for by entrants and young (five years or
younger) establishments. Furthermore, the relationship
between age and growth is robust to controls for urban
diversity and education. Overall, the results support
a micro-foundations view of urban growth, where the
benefits of agglomeration affect firms not through some
production externality but through a process that determines which firms enter, exit, and thrive at a given
location.
Working Paper 07-18, “The Relationship Between the
Establishment Age Distribution and Urban Growth,” R.
Jason Faberman, Federal Reserve Bank of Philadelphia
FLUCTUATIONS IN SEPARATION RATES
AND UNEMPLOYMENT
This paper uses CPS gross flow data, adjusted for
margin error and time aggregation error, to analyze the
business cycle dynamics of separation and job finding rates and to quantify their contributions to overall
unemployment variability. Cyclical changes in the
separation rate lead those of unemployment, while the
job finding rate and unemployment move contemporaneously. Fluctuations in the separation rate explain
between 40 and 50 percent of fluctuations in unemployment, depending on how the data are detrended.
The authors’ results suggest an important role for the
separation rate in explaining the cyclical behavior of
28 Q4 2007 Business Review
unemployment.
Working Paper 07-19, “The Cyclicality of Separation
and Job Finding Rates,” Shigeru Fujita, Federal Reserve
Bank of Philadelphia, and Garey Ramey, University of
California, San Diego
STANDARD SETTING, PATENTS,
INTELLECTUAL PROPERTY, AND
ELECTRONIC PAYMENT SYSTEMS
For many reasons, payment systems are subject to
strong network effects; one of those is the necessity
of interoperability among participants. This is often
accomplished via standard-setting organizations. The
goal of the Single European Payments Area (SEPA) is
to establish modern cross-boarder consumer payment
systems for Europe. This too will require a standardsetting arrangement. But patents are also becoming an
important feature of electronic payment systems, and
thus standard setting under SEPA should incorporate
a policy to address the ownership and licensing of essential intellectual property. Using examples from the
experience of European mobile telephony and financial
patenting in the United States, the authors argue that
the lack of a well-developed IP policy creates significant
risks for participants in the new SEPA payment systems.
Working Paper 07-20, “Intellectual Property Rights and
Standard Setting in Financial Services: The Case of the
Single European Payments Area,” Robert M. Hunt, Federal
Reserve Bank of Philadelphia; Samuli Simojoki, Attorneys
at Law Borenius and Kemppinen; and Tuomas Takalo,
Bank of Finland
PATENTS ON BUSINESS METHODS
Nearly a decade after the federal circuit decision in
State Street, patents on computer-implemented methods
of doing business have become commonplace. To date,
there is little evidence of any effect on the rate of innovation or R&D among firms in financial services. Indeed, measuring such effects presents difficult problems
for researchers. We do know that some of these patents
are successfully licensed and others are the subject of
ongoing litigation. Looking ahead, a number of recent
Supreme Court decisions are likely to have a significant
effect on how business method patents are enforced.
Congress is also considering significant reforms to U.S.
patent law.
Working Paper 07-21, “Business Method Patents for
U.S. Financial Services,” Robert M. Hunt, Federal Reserve
Bank of Philadelphia
www.philadelphiafed.org
DESIGNING AN EFFICIENT
PAYMENT SYSTEM
The authors study the design of efficient intertemporal payment arrangements when the ability of agents
to perform certain welfare-improving transactions is
subject to random and unobservable shocks. Efficiency
is achieved via a payment system that assigns balances
to participants, adjusts them based on the histories of
transactions, and periodically resets them through settlement. The authors’ analysis addresses two key issues
in the design of actual payment systems. First, efficient
use of information requires that agents participating
in transactions that do not involve monitoring frictions subsidize those that are subject to such frictions.
Second, the payment system should explore the tradeoff between higher liquidity costs from settlement and
the need to provide intertemporal incentives. In order
to counter a higher exposure to default, an increase in
settlement costs implies that the volume of transactions
must decrease but also that the frequency of settlement
must increase.
Working Paper 07-22, “A Dynamic Model of the Payment System,” Thorsten Koeppl, Queen’s University; Cyril
Monnet, Federal Reserve Bank of Philadelphia; and Ted
Temzelides, University of Pittsburgh
POPULATION DENSITY AND
OCCUPATIONAL CHANGES
Using U.S. census micro-data, the authors show
that, on average, workers change occupation and industry less in more densely populated areas. The result
is robust to standard demographic controls, as well as
to including aggregate measures of human capital and
sectoral mix. Analysis of the displaced worker surveys
shows that this effect is present in cases of involuntary
separation as well. On the other hand, the authors
actually find the opposite result (higher rates of occupational and industrial switching) for the sub-sample
of younger workers. These results provide evidence in
favor of increasing-returns-to-scale matching in labor
markets. Results from a back-of-the-envelope calibration suggest that this mechanism has an important
role in raising both wages and returns to experience in
denser areas.
Working Paper 07-23, “Thick-Market Effects and
Churning in the Labor Market: Evidence from U.S. Cities,” Hoyt Bleakley, University of Chicago, and Jeffrey Lin,
Federal Reserve Bank of Philadelphia
www.philadelphiafed.org
PLANT AND AGGREGATE
INVESTMENT DYNAMICS
The authors study a model of lumpy investment
wherein establishments face persistent shocks to common and plant-specific productivity and nonconvex
adjustment costs lead them to pursue generalized (S,s)
investment rules. They allow persistent heterogeneity
in both capital and total factor productivity alongside
low-level investments exempt from adjustment costs to
develop the first model consistent with the cross-sectional distribution of establishment investment rates.
Examining the implications of lumpy investment for
aggregate dynamics in this setting, the authors find that
they remain substantial when factor supply considerations are ignored but are quantitatively irrelevant in
general equilibrium.
The substantial implications of general equilibrium
extend beyond the dynamics of aggregate series. While
the presence of idiosyncratic shocks makes the time-averaged distribution of plant-level investment rates largely invariant to market-clearing movements in real wages
and interest rates, the authors show that the dynamics
of plants’ investments differ sharply in their presence.
Thus, model-based estimations of capital adjustment
costs involving panel data may be quite sensitive to the
assumption about equilibrium. The authors’ analysis
also offers new insights about how nonconvex adjustment costs influence investment at the plant. When
establishments face idiosyncratic productivity shocks
consistent with existing estimates, the authors find that
nonconvex costs do not cause lumpy investments but
act to eliminate them.
Working Paper 07-24, “Idiosyncratic Shocks and the
Role of Nonconvexities in Plant and Aggregate Investment Dynamics,” Aubhik Khan, Federal Reserve Bank of
Philadelphia, and Julia K. Thomas, Federal Reserve Bank
of Philadelphia and NBER
ADAPTING TO INNOVATION:
WHERE DOES NEW WORK GO?
Where does adaptation to innovation take place?
The supply of educated workers and local industry
structure matter for the subsequent location of new
work – that is, new types of labor-market activities that
closely follow innovation. Using census 2000 microdata, the author shows that regions with more college
graduates and a more diverse industrial base in 1990
are more likely to attract these new activities. Across
Business Review Q4 2007 29
metropolitan areas, initial college share and industrial diversity account for 50 percent and 20 percent,
respectively, of the variation in selection into new work
unexplained by worker characteristics. He uses a novel
measure of innovation output based on new activities
identified in decennial revisions to the U.S. occupation
classification system. New work follows innovation, but
unlike patents, it also represents subsequent adaptations
by production and labor to new technologies. Further,
workers in new activities are more skilled, consistent
with skill-biased technical change.
Working Paper 07-25, “Innovation, Cities, and New
Work,” Jeffrey Lin, Federal Reserve Bank of Philadelphia
DESIGNING AN OPTIMAL CARD-BASED
PAYMENT SYSTEM WHEN CASH IS
AN ALTERNATIVE
Payments are increasingly being made with payment cards rather than currency — this despite the
fact that the operational cost of clearing a card payment usually exceeds the cost of transferring cash. In
this paper, the authors examine this puzzle through
the lens of monetary theory. They consider the design
of an optimal card-based payment system when cash is
available as an alternative means of payment and derive
conditions under which cards will be preferred to cash.
The authors find that a feature akin to the controversial “no-surcharge rule” may be necessary to ensure the
viability of the card payment system. This rule, which
is part of the contract between a card provider and a
merchant, states that the merchant cannot charge a
customer who pays by card more than a customer who
pays by cash.
Working Paper 07-26, “Optimal Pricing of Payment
Services When Cash Is an Alternative,” Cyril Monnet, Federal Reserve Bank of Philadelphia, and William
Roberds, Federal Reserve Bank of Atlanta
IMPLEMENTATION ISSUES AND OPTIMAL
MONETARY POLICY
Currently there is a growing literature exploring the
features of optimal monetary policy in New Keynesian
models under both commitment and discretion. With
respect to time-consistent policy, the literature focuses
on solving for allocations. Recently, however, King
and Wolman (2004) have examined implementation
issues involved under time-consistent policy when the
monetary authority chooses nominal money balances.
Surprisingly, they find that equilibria are no longer
30 Q4 2007 Business Review
unique under a money stock regime. Indeed, there exist
multiple steady states. Dotsey and Hornstein find that
King and Wolman’s conclusion of nonuniqueness of
Markov-perfect equilibria is sensitive to the instrument
of choice. If, instead, the monetary authority chooses
the nominal interest rate rather than nominal money
balances, there exists a unique Markov-perfect steady
state and point-in-time equilibria are unique as well.
Thus, in King and Wolman’s language, monetary policy
is implementable using an interest rate instrument,
while it is not implementable using a money stock
instrument.
Working Paper 07-27, “Interest Rate Versus Money
Supply Instruments: On the Implementation of MarkovPerfect Optimal Monetary Policy, ”Michael Dotsey, Federal Reserve Bank of Philadelphia, and Andreas Hornstein,
Federal Reserve Bank of Richmond
INNOVATION AND LOCAL ECONOMIC
CHARACTERISTICS
This paper extends the research in Carlino, Chatterjee, and Hunt (2007) to examine the effects of local
economic characteristics on the rate of innovation (as
measured by patents) in more than a dozen industries.
The availability of human capital is perhaps the most
important factor explaining the invention rate for most
industries. The authors find some evidence that higher
job market density is associated with more patenting
in industries such as pharmaceuticals and computers.
They find evidence of increasing returns with respect
to city size (total jobs) for many industries and more
modest effects for increases in the size of an industry
in a city. This suggests that inter-industry spillovers are
often at least as important as intra-industry spillovers in
explaining local rates of innovation. A more competitive local market structure, characterized by smaller
establishments, contributes significantly to patenting in
nearly all industries. More often than not, specialization among manufacturing industries is not particularly
helpful, but the authors find the opposite for specialization among service industries. Industries benefit from
different local sources of R&D (academia, government
labs, and private labs) and to varying degrees.
Working Paper 07-28, “Innovation Across U.S. Industries: The Effects of Local Economic Characteristics,”
Gerald A. Carlino, Federal Reserve Bank of Philadelphia,
and Robert M. Hunt, Federal Reserve Bank of Philadelphia
www.philadelphiafed.org
VIOLATING PPP ACROSS COUNTRIES
The authors show that deviations from the law of
one price in tradable goods are an important source
of violations of absolute PPP across countries. Using
highly disaggregated export data, they document systematic international price discrimination: At the U.S.
dock, U.S. exporters ship the same good to low-income
countries at lower prices. This pricing-to-market is
about twice as important as any local nontraded inputs,
such as distribution costs, in explaining the differences
in tradable prices across countries. The authors propose a model of consumer search that generates pricing-to-market. In this model, consumers in low-income
countries have a comparative advantage in producing
nontraded, nonmarket search activities and therefore
are more price sensitive than consumers in high-income
countries. The authors present cross-country time-use
evidence and evidence from U.S. export prices that is
consistent with the model.
Working Paper 07-29, “Pricing-to-Market and the
Failure of Absolute PPP,” George Alessandria, Federal
Reserve Bank of Philadelphia, and Joseph Kaboski, Ohio
State University
CYCLICAL PROPERTIES OF THE PRIVATE
RISK PREMIUM
This paper studies cyclical properties of the private
risk premium in a model where a continuum of heterogeneous entrepreneurs are subject to aggregate as well
as idiosyncratic risks, both of which are assumed to be
highly persistent. The calibrated model matches highly
skewed wealth and income distributions of entrepreneurs found in the Survey of Consumer Finances. The
authors provide an accurate numerical solution to the
model, even though the model is shown to exhibit serious nonlinearities that are absent in incomplete market
models with idiosyncratic labor income risk. The model
is able to generate the aggregate private risk premium
of 2 to 3 percent and the low risk-free rate. However,
it generates very little variation in these variables over
the business cycle, suggesting that the model lacks the
ability to amplify aggregate shocks.
Working Paper 07-30, “Private Risk Premium and
Aggregate Uncertainty in the Model of Uninsurable
Investment Risk,” Francisco Covas, Board of Governors
of the Federal Reserve System, and Shigeru Fujita, Federal
Reserve Bank of Philadelphia
www.philadelphiafed.org
PERSONAL BANKRUPTCY FILINGS UNDER
CHAPTER 13
By compiling a novel data set from bankruptcy
court dockets recorded in Delaware between 2001 and
2002, the authors build and estimate a structural model
of Chapter 13 bankruptcy. This allows them to quantify
how key debtor characteristics, including whether they
are experiencing bankruptcy for the first time, their
past-due secured debt at the time of filing, and income
in excess of that required for basic maintenance, affect
the distribution of creditor recovery rates. The analysis further reveals that changes in debtors’ conditions
during bankruptcy play a nontrivial role in governing
Chapter 13 outcomes, including their ability to obtain
a financial fresh start. The authors’ model then predicts that the more stringent provisions of Chapter 13
recently adopted, in particular those that force subsets
of debtors to file for long-term plans, do not materially
raise creditor recovery rates but make discharge less
likely for that subset of debtors. This finding also arises
in the context of alternative policy experiments that
require bankruptcy plans to meet stricter standards in
order to be confirmed by the court.
Working Paper 07-31, “The Anatomy of U.S. Personal
Bankruptcy Under Chapter 13, ”Hülya Eraslan, University
of Pennsylvania; Wenli Li, Federal Reserve Bank of Philadelphia; and Pierre-Daniel Sarte, Federal Reserve Bank of
Richmond
ESTIMATING PAYMENT NETWORK SCALE
ECONOMIES FOR EUROPE
The goal of SEPA (Single Euro Payments Area) is
to facilitate the emergence of a competitive, intra-European market by making cross-border payments as easy
as domestic transactions. With cross-border interoperability for electronic payments, card transactions will
increasingly replace cash and checks for all types of
payments. Using different methods, the authors estimate card and other payment network scale economies
for Europe. These indicate substantial cost efficiency
gains if processing is consolidated across borders rather
than “piggybacked” onto existing national operations.
Cost reductions likely to induce greater replacement of
small value cash transactions are also illustrated.
Working Paper 07-32, “Payment Network Scale
Economies, SEPA, and Cash Replacement,” Wilko Bolt,
De Nederlandsche Bank, and David Humphrey, Florida
State University, and Visiting Scholar, Federal Reserve
Bank of Philadelphia
Business Review Q4 2007 31
@FedFRASER