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Fourth Quarter 2015

Volume 98, Issue 4

Why Ask? The Role of Asking Prices in Transactions
Regional Spotlight: Regions Defined and Dissected
Introducing: Banking Policy Review:
Over-the-Counter Swaps – Before and After Reform
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FOURTH QUARTER 2015

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Why Ask? The Role of Asking Prices in Transactions

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Ben Lester explores why sellers sometimes use negotiable asking prices,
how this pricing method affects how long it takes to sell something and
the ultimate selling price, and why this method can lead to more efficient
outcomes.

Regional Spotlight: Regions Defined and Dissected

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Why Ask? The Role of Asking Prices
in Transactions
BY BENJAMIN LESTER
“2009 Mercedes C300 Sport 4MATIC —
$19,000 or Best Offer” –Craigslist
Many goods are offered for sale with an asking price.
When a seller posts an asking price, it’s typically implied
that this is the price he is willing to accept in exchange for
his good but that he would also entertain offers below the
asking price. For example, when potential buyers read the
advertisement above, they understand that they can either
offer $19,000 and be sure of getting the car, as long as it
hasn’t sold yet, or they can offer less than $19,000, in which
case they may not get the car, depending on how much the
seller values it and whether any other buyers offer more.
This method of selling a good or service appears in a
variety of markets and goes by many names. For example,
it’s often called the listing price in the housing market or
the sticker price in the market for new cars. Companies are
typically listed for sale with an offer price. In the classified section, sellers will often announce a price followed by
the phrase “or best offer,” while Internet auction sites like
eBay allow sellers the option of posting a “buy-it-now” price.
While each of these markets may work slightly differently,
they all share the feature that sellers post some price, and
buyers can either pay that price or try to buy it for less.
As consumers, we often take it for granted that sellers
use different conventions to sell different types of goods and
services. However, the methods that sellers use to determine
whom they trade with and at what price are more than a
matter of habit or tradition. These different methods can
lead to very different outcomes, both for potential buyers
and the seller, and even for the economy as whole. For example, if the seller of the car above doesn’t include an asking
price, some potential buyers might not contact him because
they think the car is out of their price range. On the other

hand, if the seller chooses only one price at which he will
trade, and specifies that he will accept nothing less, he
might miss out on a buyer who would have been willing to
pay just a little bit less than the chosen price. Now, suppose
all the cars for sale in the economy were being sold to the
“wrong” buyer at the “wrong” price. Suddenly these small
mismatches at the microeconomic level would aggregate up
to a big problem at the macroeconomic level!
For this reason, a fundamental task of economic theory
is to understand why different goods are sold using different
pricing mechanisms and how these mechanisms determine
both what types of buyers end up buying a particular good
or service and how much they end up paying. While certain
methods of price determination have been studied extensively, the reason why a seller would benefit by using an asking
price remains an open question. This article explores the
most common explanations for why sellers use asking prices,
how the asking price a seller chooses affects the ultimate
selling price and time on the market, and why this method of
selling a good can lead to more efficient trading outcomes.
ASKING PRICES: A MIX OF POSTED PRICES, AUCTIONS
Before we explore the reasons why a seller might choose
to sell a good with an asking price, it’s helpful to note that
an asking price combines elements of two popular methods
of price determination: posted (or “take-it-or-leave-it”) prices
and auctions.
Benjamin Lester is a
A posted price is one that a
senior economic advisor
seller sets as nonnegotiable, and
and economist at the
customers can either buy at that price Federal Reserve Bank of
Philadelphia. The views
or not buy at all. Most transactions
expressed in this article
take place with posted prices: milk at
are not necessarily those
of the Federal Reserve.
the supermarket, meals at a restau-

Fourth Quarter 2015 | Federal R eserve Bank of Philadelphia R esearch Department | 1

rant, shoes at a department store, and so on. Auctions, on
the other hand, are less ubiquitous. In a typical first-price
auction, each would-be buyer places a bid, and the one who
places the highest bid wins, so long as it exceeds the seller’s
own value for his good. Auctions are more common when
the good for sale is unique, like a piece of art, a company, or
an oil field. They are also commonly used when a good is
expected to elicit a wide range of bids.
When a seller posts an asking price, a buyer can pay
that price and trade with certainty, as is the case with a
posted price. However, if nobody offers
the asking price, then the good is sold to
the highest bidder, as in an auction.1 In
this sense, an asking price is a hybrid of a
posted price and an auction. Therefore, in
order to understand why asking prices are
an attractive selling method, it’s helpful
to explore the advantages of posted prices
versus auctions.

picking out paint colors, tiles, and custom-made cabinets. It
would be pretty frustrating if, at the end of all this, there was
uncertainty over whether the appliances that fit in just right
would be available at a price within your budget. Again, in
this situation, you would naturally be attracted to a vendor
who posted fixed prices; indeed, you might even be willing to
pay him a little more for the certainty of getting your appliances when your kitchen was ready!3
A second advantage of posted prices is that they
provide a seller with the opportunity to send a signal that

In order to understand why asking prices are an
attractive selling method, it’s helpful to explore the
advantages of posted prices versus auctions.

THE ADVANTAGES OF POSTED PRICES
While there are many reasons why a seller might find it
profitable to use posted prices, here we will focus on two of
the most well-established explanations. First, buyers tend to
like posted prices because they provide them with certainty:
As long as the good is available, buyers know they can buy
it at the posted price. Hence, by using a selling method
that is appealing to buyers, a seller can attract more buyers.
Second, a posted price can signal important information to
buyers, either about the good for sale or about the seller’s
motivation to sell the good. In this case, the posted price
can help sellers attract the right buyers. Let’s explore both
of these explanations in greater detail.
When a seller posts a price at which he is always willing to sell, potential buyers can be assured that, as long as
the good is still available, they can buy it with certainty.
This can be especially important to a buyer who is either
averse to risk or impatient. For example, suppose you waited
until the day before your anniversary to buy your spouse a
present. The prospect of bidding for a gift and finding out
at midnight that you didn’t get it isn’t terribly appealing. Instead, you would naturally seek out a store where you could
be certain to walk out with a gift in hand.2
This element of certainty can also be important to buyers who have made a significant investment before making
a purchase, either in terms of money or time. For example,
suppose you are remodeling your kitchen. You spend months

contains information relevant to buyers. For example,
sometimes it’s difficult for buyers to discern the quality of
a good from an advertisement, or even from looking at the
good. In these cases, the price itself can convey information about the quality of the good, such as how well it was
manufactured, the types of materials that were used, or how
long it’s expected to last.4 When prices serve this signaling
function, they steer buyers toward the right sellers. That is,
buyers looking for higher-quality goods are drawn to sellers
of high-priced goods, while those willing to accept lower
quality in exchange for a lower price seek out sellers of
lower-priced goods.5
Prices can also provide a channel for sellers to signal
something about their own motivation to sell, which can
be completely unrelated to the quality of the product. For
example, a store that is going out of business might drop
its prices, as in the typical slogan “Everything must go!” A
store that did not have the same sense of urgency would
have no incentive to drop its prices as low.6 As a result,
prices can again play a valuable signaling role and help ensure that buyers who are more price-sensitive end up trading
with sellers who are more motivated to sell.
THE ADVANTAGES OF AUCTIONS
The main advantage to a seller of using an auction is
that it offers a way to price discriminate — that is, to charge
different buyers different prices, depending on how much
each buyer is willing to pay. In other words, auctions offer

2 | Federal R eserve Bank of Philadelphia R esearch Department | Fourth Quarter 2015

sellers greater flexibility than posted prices do, since posted
prices place certain limitations on a seller. One way to think
about this inflexibility is to realize that a posted price acts
as both a ceiling and a floor on the possible price that the
seller can charge.
For example, when it turns out that there are buyers who are willing to pay a lot — that is, when demand is
high — posted prices act as a ceiling on the price the seller
can get. Auctions, on the other hand, place no such upper
bound on the eventual transaction price. Posted prices are
also limiting when demand is low, as they serve as a floor
on prices. This can have important consequences: When
the seller has committed not to sell below his posted price,
it’s possible that a sale may not occur even if there is a buyer
who values the good more than the seller. This would not
happen if the good were sold via an auction instead.
In short, auctions offer two advantages over posted
prices. First, they allow sellers to sell their goods to the buyers who value them the most. Second, they do not rule out
profitable sales in cases when no big spenders make offers.
ASKING PRICES: A MIDDLE GROUND
Asking prices are a way to capture some aspects of all
the advantages discussed above. Because an asking price offers the buyer some degree of certainty, using this mechanism
could stimulate demand and thus increase profits. Moreover,
as we noted earlier, asking prices can serve as a signal to
would-be buyers about the quality of the good being sold or
the seller’s eagerness to sell. Yet, asking prices also allow sell-

ing posted prices, auctions, bargaining, and any other way
one could imagine.7 Consider situations in which buyers
have to incur a cost in order to learn how much they value a
good. For example, in the real estate market, this cost can
be interpreted as the time and energy spent going to see a
house, researching the quality of the school district, finding
out how long it would take to commute to work, and so on.
When buyers face such hurdles, sellers will often choose to
use an asking price, as it provides the best balance between
the flexibility of an auction, which helps the seller get a
good price, and the certainty of a posted price, which helps
attract buyers. However, if the buyers’ cost of learning their
valuation is small enough, this balance shifts and an auction
is the optimal way to sell a good. On the other hand, for
goods that are similar and more or less interchangeable —
so that there is nothing to learn by going to inspect any one
particular seller or store — posted prices are optimal.
DISCUSSION AND CONCLUDING REMARKS
Why do certain types of buyers end up buying certain
goods or services? Why do they end up paying what they
do? Basic economic theory predicts that a good or service
should sell for the price that equates supply with demand:
Those willing to pay at least that price will buy; those willing to accept that price or less will sell.
Yet, anyone who has ever bought a house or car, walked
through a bazaar, or perused Craigslist knows that some
goods aren’t sold at a single price, and they are not always
acquired by the buyer who is willing to pay the most. To
understand these types of markets,
economists have to dig deeper into the
details of how prices and allocations
are determined. In this article, we
have explored one particular method
of price determination: asking prices.
We have proposed several reasons why
sellers might find it profitable to sell
their goods or services with an asking
price and how this pricing mechanism
can lead to the “right” buyer ultimately getting a particular
good or service.
What, then, does the theory tell us about how asking
prices affect actual sale prices and how long it takes to sell
a good? When sellers use asking prices, economists expect
to see certain patterns in the data. First, there should be a
particular type of price dispersion, with some sales taking
place at the asking price and then other sales taking place

In some situations, using an asking price can be the seller’s
best, or most profitable, way of selling a good among all
possible methods for determining prices.
ers to engage in some price discrimination. They leave open
the possibility of getting top dollar from a high-valuation
buyer. However, since the asking price is not a take-it-orleave-it offer, price-conscious buyers still have a chance. As a
result, the seller does not have to forgo profitable sales.
Indeed, in some situations, using an asking price can
be the seller’s best, or most profitable, way of selling a good
among all possible methods for determining prices, includ-

Fourth Quarter 2015 | Federal R eserve Bank of Philadelphia R esearch Department | 3

at various prices below the asking price. Second, we should
expect a relationship between the asking price that a seller
chooses and the amount of time the good spends on the
market, though this relationship depends on the reason for
using an asking price to begin with. If the asking price is
being used to offer buyers certainty or if it is a signal of the
seller’s motivation to sell, then lower asking prices should attract more buyers and hence shorten the good’s time on the
market. If, however, the asking price is a signal of quality,
then it’s unclear whether a low asking price will be associated with a long or short time on the market.

With a workable theory such as this, economists can
begin to identify the underlying causes of differences in
prices and allocations in these markets and forecast changes. In the housing market, for example, the ratio of asking
prices to actual sale prices varies widely from one location
to another and can change significantly over time. A theory
of asking price mechanisms offers a means to interpret such
variations in a way that standard pricing theory can’t. And
given the housing market’s impact on economic conditions,
interpreting house price movements is a vital part of understanding the overall economy.

NOTES

REFERENCES

Of course, there is also a third possibility: that two buyers end up offering the
asking price at the same time and a bidding war ensues. In this case, the good
can end up selling for more than the asking price. This is more common in some
markets than it is in others — houses will sometimes sell above the listing price,
while new cars almost never sell for more than the sticker price. To learn more about
the relationship between asking prices and bidding wars, see the paper by James
Albrecht and his coauthors, along with my own work with Ludo Visschers and Ronald
Wolthoff.

Albrecht, J.W., P.A. Gautier, and S.B. Vroman. “Directed Search in the Housing
Market,” forthcoming in Review of Economic Dynamics.

To read more about how this type of certainty can be attractive when buyers are
risk averse, see, for example, the articles by Eric Budish and Lisa Takeyama or
Timothy Mathews.

Chen, Y., and R.W. Rosenthal. “Asking Prices as Commitment Devices,” International
Economic Review, 37:1 (1996), pp. 129–155.

1

2

In the 1990s, Yongmin Chen and Robert Rosenthal, along with Michael Arnold,
were among the first to note that buyers would appreciate a cap on the maximum
price they would have to pay before they made a significant investment.

3

The idea that prices may provide a signal about quality has been around for quite
some time. See Asher Wolinsky’s 1983 article for an early formalization of this idea
and Alain Delacroix and Shouyong Shi’s 2013 article for a more recent contribution.

Arnold, M.A. “Search, Bargaining and Optimal Asking Prices,” Real Estate
Economics, 27: 3 (1999), pp. 453–481.
Budish, E.B., and L.N. Takeyama. “Buy Prices in Online Auctions: Irrationality on the
Internet?” Economics Letters, 72 (2001), pp. 325–333.

Delacroix, A., and S. Shi. “Pricing and Signaling with Frictions,” Journal of Economic
Theory, 148 (2013), pp. 1,301–1,332.
Lester, B., L. Visschers, and R.P. Wolthoff. “Competing with Asking Prices,” Federal
Reserve Bank of Philadelphia Working Paper 13-7 (2013).

4

You might ask, “Why doesn’t a seller just say that he is selling a higher- or lowerquality good?” Economists would call this cheap talk, since any seller could (and
would like to) make such a claim. However, when a seller who must pay a lot to
make a high-quality good commits to accepting no less than a certain price, he is
taking an action that a seller who produces lower-quality products at a lower cost
wouldn’t take. Hence, setting and committing to this posted price is informative
about the quality (and cost) of the good that a seller produces. Economists call this
outcome a separating equilibrium.

Mathews, T. “The Impact of Discounting on an Auction with a Buyout Option: A
Theoretical Analysis Motivated by eBay’s Buy-It-Now Feature,” Journal of Economics,
81:1 (2004), pp. 25–52.

5

Wolinsky, A. “Prices as Signals of Product Quality,” Review of Economic Studies, 50:4
(1983), pp. 647–658.

Albrecht and his coauthors use this explanation to try to understand how housing
prices can sometimes signal the urgency with which sellers want to sell their houses.

6

I describe and analyze these situations more formally in my paper with Visschers
and Wolthoff.

7

4 | Federal R eserve Bank of Philadelphia R esearch Department | Fourth Quarter 2015

REGIONAL SPOTLIGHT
Regions Defined and Dissected
BY PAUL R. FLORA
In 2013, the federal government confirmed what every
kid from Waynesboro, PA, had understood 50 years earlier — that Franklin County was inextricably tied to the
Washington–Baltimore region. Forsaking the Phillies and
Pirates, Little Leaguers from south-central Pennsylvania
traveled instead to watch Frank Robinson in the Orioles’
outfield. In the fall, local families jeered the Eagles and
Steelers, and cheered as Johnny Unitas led the Baltimore
Colts to victories. Good-paying jobs beckoned and TV
signals emanated from over the Blue Ridge mountains and
inside the dual beltways.
Franklin County is one of several new metropolitan statistical areas (MSAs) in the Federal Reserve’s Third District
and one of many small MSAs that have been drawn into the
much larger statistical constellations of Philadelphia, New
York, and Washington–Baltimore (Figure 1).1 How are these
delineations drawn? And what do they reveal about economic vitality and policy challenges in the tristate region?
This report describes how population levels and commuting
patterns define the Third District’s economic regions using
U.S. Office of Management and Budget (OMB) standards.
Specifically, how did Franklin County, PA, become tied to
the Washington–Baltimore region? Why did a largely rural,
four-county region on the Delmarva Peninsula become an
MSA? Are Trenton’s ties to New York stronger than its ties
to Philadelphia?
The Franklin County example highlights how successful the federal criteria are at capturing the economic and
cultural relationships among geographic areas. For researchers, the MSA classification provides a valuable common
basis on which to group and study economic regions as

distinct labor markets. However, some economic development patterns will always pose a challenge to a necessarily
rigid classification system. Indeed, some of the expansion of
these statistical areas has resulted from localized commuting
patterns that don’t appear to create the economic benefits
one would anticipate from a resilient MSA. This report
analyzes the census data to distinguish between commuting
generated by adjacent counties and commuting generated by
competition from larger, more distant labor markets.
COMMUTERSHEDS DEFINE REGIONS
Numerous criteria may be used to define regions.
Watersheds and river basins are a pragmatic choice for
environmental planning purposes. Marketing areas were
once defined primarily by the strength of television and
radio signals. Sports affinities can define a region culturally.2 Each of these definitions has some relevance for
regional economics, and not surprisingly, fan affiliation is
closely aligned with the OMB’s larger combined statistical
areas (CSAs). However, commuting patterns are a prime
way for economists to define and understand regional
economies, and the OMB’s more
rigorous approach, which focuses
Paul R. Flora is a research
on the strength of commuting patand policy support
terns among adjacent population
manager and senior
economic analyst in the
centers, sometimes called comResearch Department of
mutersheds, is of most interest for
the Federal Reserve Bank
of Philadelphia. The views
regional economists.3
expressed in this article
A commutershed is the broad
are not necessarily those
geographic area from which a
of the Federal Reserve.

Fourth Quarter 2015 | Federal R eserve Bank of Philadelphia R esearch Department | 5

city’s labor force is drawn. A regional economy’s resilience
is greatly improved by having an extensive commutershed
that can provide easy access to good jobs for the region’s
residents and access to skilled workers for the region’s firms.
Small, isolated regions have less diversity in the types of
jobs and skills found there than do large, integrated urban
areas. The quality of a region’s transportation infrastructure
can greatly enhance or impair accessibility within the commutershed, as can natural features such as waterways that
require bridges or tunnels. Residents in the Trenton metro
area benefit from the proximity and convenient rail access
into both New York and Philadelphia; Wilmington residents
can easily reach Philadelphia and Baltimore.
However, much of the expansion of urbanized areas is
a product of highways and sprawl, not of enhanced transit
infrastructure and compact development. One key to why
the Salisbury, DE–MD MSA expanded from two counties to
four was the suburban growth of rural areas along the Route
13A corridor and outward from each small town. (See the
accompanying discussion, How Are MSA Boundaries Decided?) Similar forces help explain the emergence of Cham-

bersburg–Waynesboro and East Stroudsburg as MSAs and of
their absorption into larger CSAs.
SPRAWLOPOLIS: NEW YORK AND PHILADELPHIA VIE
FOR TERRITORY
In contrast to a megalopolis made up of a chain of large
metro areas such as the Northeast corridor of Boston, New
York, Philadelphia, Baltimore, and Washington, sprawlopolis
may be a better term for CSAs. The commuting threshold to combine two statistical areas into a CSA is lower
than it is to merge counties and statistical areas — requiring only that the combined percentage of out-commuters
from and in-commuters to the smaller statistical area be
15 percent or greater.4 CSAs form and expand as long as
smaller adjacent metro areas meet the threshold requirement. Their expansion stops when metro areas give way to
adjacent rural counties. For example, rural Fulton County,
PA, stops the Washington–Baltimore CSA from expanding beyond Franklin County. Alternatively, when the next
adjacent metro area has a stronger commuting relationship

FIGURE 1

Tristate MSAs

Source: U.S. Office of Management and Budget.
* Part or all of these MSAs lie outside the boundaries of the Third District.
† Part of this MSA lies outside the three-state region.

6 | Federal R eserve Bank of Philadelphia R esearch Department | Fourth Quarter 2015

How Are MSA Boundaries Decided?
The Office of Management and Budget periodically reviews its criteria for
delineating metropolitan statistical areas and then realigns areas based on
population levels from the decennial census and county-to-county commuting
flows from the Census Bureau’s American Community Survey.
The OMB’s latest delineation of the Salisbury, MD–DE MSA offers an
illuminating glimpse into the rationale. Previously consisting of Somerset and
Wicomico counties, MD, it was expanded to include Worcester County, MD, and
Sussex County, DE (Figure 2) — creating an MSA with a total of 373,802 people,
a larger population than Trenton’s.5 And Sussex County contained over half of
that total population.
Since the 2000 census, Sussex County had been designated as the Seaford, DE,
micropolitan statistical area. That determination had been made based on three
characteristics of the census-designated Seaford urban cluster: First, Seaford’s
population exceeded the 10,000-person threshold that qualified the county as a
core-based statistical area (CBSA). Second, Seaford was the largest urban area
in the county, which identified the CBSA with Seaford. Third, the Seaford cluster
had fewer than 50,000 people, which meant it would be a micropolitan statistical
area and not an MSA.
Over the next 10 years, Sussex County would add over 40,000 people — a growth
rate of 26 percent.6 Yet, the county remained largely rural; 60 percent of its 197,145
people were scattered among nine urban clusters (ranging from 2,556 people
in Milton to 24,129 in Lewes) and one urbanized area (24,588 in the Delaware
portion of Salisbury — primarily consisting of the former Seaford cluster). Still,
none of those urban areas met the 50,000 population threshold to qualify Sussex
County as an MSA; however, the Salisbury urbanized area also contained 73,254
people in a portion of Wicomico County, and therein lies the key.7

TABLE 1

A Common Urban Area Tied Sussex to Salisbury
Salisbury MD-DE
MSA by County
Sussex, DE

Largest Qualifying Urban Area

2010
Population
197,145

Name

Population

Part of Salisbury Urbanized Area

24,588

Somerset, MD

26,470

Princess Anne Urban Cluster

10,396

Wicomico, MD

98,733

Part of Salisbury Urbanized Area

73,254

Worcester, MD

51,454

Ocean Pines Urban Cluster

28,959

Source: U.S. Census Bureau.

In addition, census designations can (under yet more arcane criteria) utilize
combinations of half-mile “hops” and 2.5-mile “jumps” to connect urban areas
interrupted by farmland.
Finally, Somerset County, which had been part of the prior Salisbury MSA, still
qualifies as an outlying county to the new MSA, as nearly 30 percent of its 9,180
residents commute to work in Sussex and Wicomico counties, exceeding the
25 percent threshold of residents who commute out or workers who commute
in. Worcester County, which had not previously been included in the Salisbury
MSA, draws just over 25 percent of its workforce from Sussex (9.0 percent) and
Wicomico (16.8 percent). Although it is adjacent to both counties, Worcester
would not qualify as part of either Sussex or Wicomico if they were not
considered a single cluster of central counties.
Individually, Somerset and Worcester are too small and rural to be considered
independent MSAs. However, due to the strength of their commuting ties with
the two central counties (out-commuting from Somerset and in-commuting
to Worcester), they are both delineated as outlying counties to the Salisbury
MSA (Figure 2). Even so, had the census not hopped and skipped across miles
of farmland, Sussex and Worcester would still be independent micropolitan
statistical areas — taking two-thirds of the present MSA’s population with them.

Because each county’s portion of Salisbury is its largest urban area, Sussex and
Wicomico counties are jointly considered the central counties of a single CBSA.
Furthermore, Salisbury qualifies as an MSA, since its urbanized area has more
than 50,000 residents. Interestingly, had the Lewes cluster not had 459 fewer
people than the Sussex portion of the
Salisbury urbanized area, then Sussex
would have remained a micropolitan
FIGURE 2
statistical area (Table 1).
Salisbury MSA Expanded Mainly from a Sliver of Farmland
But with 22 miles of mostly farmland
separating their downtowns, how did
the former Seaford cluster become
part of the Salisbury urbanized area?
By 2000, Seaford’s development had
sprawled about seven miles southward
along the Route 13A corridor as far as
Laurel, DE, and Salisbury had sprawled
about seven miles northward to Delmar
on the state line.8 Since then, the
remaining distance appears to have
been spanned, in part, with a single,
large housing development sprouting
up midway between Laurel and Delmar.

Commuting flows for Salisbury’s outlying counties.

Source: U.S. Census Bureau.

Fourth Quarter 2015 | Federal R eserve Bank of Philadelphia R esearch Department | 7

with a second large metro area, then expansion of the first
is blocked in that direction. An example, as we will see, is
the Trenton MSA, sandwiched between the New York and
Philadelphia MSAs. The upshot is that CSAs along the
Northeast corridor abut one another but do not merge or
combine into a megalopolis.
On the basis of population levels and commuting
patterns, the Chambersburg–Waynesboro, PA, MSA was
combined with the Hagerstown, MD, MSA, and thus with
the CSA known as Washington–Baltimore–Arlington, DC–
MD–VA–WV–PA. While over 4,000 residents of Franklin
County made their way to jobs in the Baltimore–Washington area (more than to adjacent Cumberland County to the
north), the strongest tie was driven by the 9,284 Franklin
County residents commuting south to adjacent Washington County, MD. The stream of commuters to the Baltimore–Washington area has long existed and recently grown,
but the greatest increase in commuting is the local back
and forth across the Mason–Dixon line between Franklin
County and Washington County.
The Trenton MSA (Mercer County) benefits greatly
from its location along the primary rail corridor between
New York and Philadelphia. Yet, localized commuting
patterns among its adjacent counties continue to play a
dominant role. Trenton has been combined with various
incarnations of the New York CSA since 1993, when it was
plucked from an earlier Philadelphia CSA on the basis of
1990 census data. Before that, Trenton had been partnered
with Philadelphia since 1981 on the basis of 1980 census
data, and before 1981 and significant suburban expansion,
Trenton had been a standalone MSA dating back to 1950,
when such designations were first made.
This tug of war between New York and Philadelphia
began in the early 17th century with border disputes and
multiple survey efforts to distinguish the colonial provinces
of East Jersey and West Jersey. In 1687, surveyor George
Keith established a 70-mile boundary between the provinces
that was disputed before it was finished. The Keith line was
eventually invalidated but not before municipal boundaries
were established on its basis. To this day, team allegiances
and other cultural references — is it called a hoagie or a sub?
— shift along this line (Figure 1).
What drives Trenton to New York today? Annual
census surveys averaged over 2006–2010 show significant
cross-commuting patterns between Trenton and its much
larger neighbors. Commuters from the New York and Philadelphia MSAs supply nearly half of Trenton’s workforce: 21.8
percent and 23.9 percent, respectively (Figure 3). Most of

those commuting into Trenton are from suburbs throughout
the adjacent counties of Bucks, Burlington, and Middlesex.
However, Trenton’s out-commuting ties are far stronger to
New York — 23.0 percent of Mercer County residents commute into New York versus only 7.9 percent into Philadelphia. Jobs are more plentiful and wages are higher around
New York than around Philadelphia.
Since none of the four possible one-way commutes met
the 25 percent threshold, Trenton remained an independent MSA. However, the lower 15 percent threshold for
in-commuters and out-commuters combined is easily met
by both large MSAs; New York absorbs Trenton into its
CSA with a combined 44.8 percent compared with Philadelphia’s 31.8 percent. Interestingly, the share of commuters
coming from the Pennsylvania side of the Delaware River
would have had to increase only 1.1 percent in order for the
Philadelphia MSA to have regained Trenton in a merger of
statistical areas, as was the case in the 1980s. One could
easily imagine that happening if a sizeable, well-placed
transit-oriented development were built across the river
from downtown Trenton.
A tug of war for Trenton has little value if only for
bragging rights. However, transit-oriented developments
represent wiser, more sustainable development for urban
areas. Creating a transit-oriented development adjacent to

FIGURE 3

Trenton’s Commuting Ties Much Stronger to New York
Mercer County residents commuting to New York and Philadelphia MSAs;
commuters from New York and Philadelphia MSAs into Mercer County.

Source: U.S. Census Bureau.

8 | Federal R eserve Bank of Philadelphia R esearch Department | Fourth Quarter 2015

Trenton would benefit its residents
and businesses while providing
a larger workforce with easier
access to jobs. Strengthening
the existing transit connections
with faster, more frequent trains
between Trenton and Philadelphia
would also benefit Trenton’s
— and Philadelphia’s — urban
core. Downtown development
would increase as households and
businesses seek to locate near
the transit stops. The growth of
downtown housing in turn would
attract more retail shops and other
commerce. Compact development
with workforce housing would
also offer the benefit of walkable
commutes to local jobs.
RISE OF THE LONG-DISTANCE
COMMUTER
East Stroudsburg stands out as
an exception to the dominance of

Criteria for Delineating Core-Based Statistical Areas
Each region delineated by the OMB is considered a core-based statistical area (CBSA). These are
divided by size into metropolitan statistical areas (MSAs) and smaller micropolitan statistical
areas. Two or more adjacent CBSAs may form a combined statistical area (CSA) on the basis of
commuting patterns.
To qualify as a CBSA, a county (or group of counties) must have:
• A Census Bureau-delineated urbanized area of at least 50,000 residents, or
• A Census Bureau-delineated urban cluster of at least 10,000 residents.
Urbanized areas and urban clusters are generally referred to as urban areas.
A CBSA is categorized as an MSA if its largest urban area has 50,000 people or more. Otherwise, it is a
micropolitan statistical area.
To qualify as a central county of a CBSA, the county must have:
• At least 50 percent of its population residing in urban areas of at least 10,000 residents, or
• Within its boundaries at least 5,000 people residing in a single urban area of at least 10,000 people.
To qualify as an outlying county of a CBSA, a county must have:
• At least 25 percent of its employed residents working in the central county or counties of the CBSA, or
• At least 25 percent of its workforce residing in the central county or counties of the CBSA.
Two adjacent CBSAs will merge to form one CBSA if the central county or counties (as a group)
qualify as outlying to the central county or counties (as a group) of the other CBSA, using the criteria above.
Source: U.S. Office of Management and Budget, https://www.whitehouse.gov/sites/default/files/omb/assets/fedreg_2010/06282010_metro_standards-Complete.pdf.

TABLE 2

Rise in Treks to New York City Gives East Stroudsburg Longest Average Commute Time
			
Average one-way
Percent commuting
Number commuting		
Rank* Metropolitan Statistical Area
commute time
45 minutes or more each way
45 minutes or more each way		
							
			
1980
2013
1980
2013
1980
2013
1
2
3
4
5
6
7
—
8
—
9
13
14
22

East Stroudsburg, PA
Philadelphia–Camden–Wilmington, PA–NJ–DE–MD**
Dover, DE
Gettysburg, PA
Trenton, NJ
York–Hanover, PA
Allentown–Bethlehem–Easton, PA–NJ
Third District MSAs**
Reading, PA
United States
Lebanon, PA
Chambersburg–Waynesboro, PA
Salisbury, MD–DE**
Williamsport, PA

20.1
25.1
17.8
20.2
21.7
19.6
18.8
22.1
17.9
21.7
17.0
18.5
18.7
17.6

37.9
28.6
27.6
27.2
27.6
26.8
27.0
26.6
25.6
25.8
23.8
23.6
22.6
20.0

10.4
16.6
8.0
11.4
9.8
7.3
7.1
12.0
5.1
11.6
5.1
6.5
8.3
4.8

30.5
20.8
20.1
19.7
19.0
18.9
18.3
17.6
16.7
16.2
14.3
12.4
12.3
7.4

2,989
357,405
3,377
3,432
13,440
10,417
19,437
494,434
7,026
10,923,652
2,443
3,182
7,263
2,228

22,102
558,425
14,672
9,350
31,721
39,179
67,438
1,111,355
31,035
22,150,805
8,656
8,505
19,323
3,719

Source: U.S. Census Bureau.
*Rank among 22 Third District MSAs by percent of residents commuting 45 minutes or more each way.
**Includes counties outside the three-state region.

Fourth Quarter 2015 | Federal R eserve Bank of Philadelphia R esearch Department | 9

FIGURE 4

A Surge in Super-Commuting from East Stroudsburg

Monroe County commuters bypass closer counties in favor of New York.

Source: U.S. Census Bureau.

local commuters over those to distant urban cores. As with
Chambersburg–Waynesboro, the East Stroudsburg MSA
was promoted from its prior categorization as a stand-alone
micropolitan statistical area. It was then combined into the
New York CSA. However, in contrast to Chambersburg–
Waynesboro, and despite population growth of 22 percent
and a pattern of residential sprawl, East Stroudsburg’s commuting linkages have grown much stronger to the distant
urban core of New York even as they have also grown
among adjacent counties.
Long-distance commuting from Monroe County in the
Poconos along the Interstate 80 corridor to Manhattan and
the other four boroughs of New York rose dramatically in recent decades (Figure 4). Long-distance commuting from East
Stroudsburg tripled from 1980 to 2013 — from 10.4 percent
to 30.5 percent.9 The increase was much smaller for the nation, from 11.6 percent to 16.2 percent. In 2013, Philadelphia
and Dover commuters were distant seconds, with 20.8 percent and 20.1 percent, respectively. The average travel time
for East Stroudsburg residents was a hefty 37.9 minutes, compared with the next-longest time of 28.6 minutes for Philadelphia residents and a national average of 25.8 minutes.

Numerous reasons have been cited for the increase in
long-distance commuting from East Stroudsburg:
• Rising home prices in and near New York (the “drive
till you qualify” rationale);
• The pull of starry night skies and other rural amenities;
• A desire for less risk after the 9/11 tragedy;
• Limited job opportunities in Monroe County.
Commutes reflect a tradeoff of one’s time for higher
wages, lower housing costs, or a preferred lifestyle. East
Stroudsburg’s caravan of commuters who depart before
dawn and return after dusk reflect one of several extreme
responses to the hard choices faced by workers in the highcost New York metropolitan area.10 Rail service could reduce
the time, improve the schedule, and alleviate the stress of
East Stroudsburg’s road warriors. Indeed, for decades, longrange transportation plans for East Stroudsburg, the Lehigh
Valley, and other regions have expressed great enthusiasm for transit to larger cities. However, these plans have
languished, as federal law requires that they demonstrate
reasonable expectations of available funding. 11
POLICY IMPLICATIONS
The simple examples described in this article illustrate
the potential for creating significantly more robust regional
economies by strategically improving transit or encouraging more compact urban development. Households can
benefit from greater mobility — easier access to more jobs
with shorter commute times and less congestion. Firms can
benefit from a larger skilled labor pool and by the boost to
productivity that tends to accompany the growth of metropolitan areas.
What compact urban design can accomplish for cities
can be mirrored by better rural planning practices, as well.12
Compact development in rural areas preserves open space
and retains the lifestyle that prior residents enjoyed and new
residents seek. Consolidating new growth within existing rural villages and towns could reduce the number and scale of
MSAs, such as Salisbury, and could increase the efficiencies
(and thus lower the cost) of providing fixed-rail transit from
East Stroudsburg into New York City.

10 | Federal R eserve Bank of Philadelphia R esearch Department | Fourth Quarter 2015

NOTES

REFERENCES

On the basis of its 2010 population estimate, Franklin County was delineated
by the OMB as the Chambersburg–Waynesboro, PA, MSA — a promotion from
its prior status as a micropolitan statistical area. In turn, commuting patterns
tied Franklin County to the Hagerstown, MD, MSA, and thus with the combined
statistical area (CSA) designated as the Washington–Baltimore–Arlington, DC–
MD–VA–WV–PA CSA.

Arendt, Randall, and Elizabeth A. Brabec. Rural by Design: Maintaining Small Town
Character. Chicago: Planners Press, American Planning Association, 1994.

1

For a look at how baseball allegiances often mirror commuting ties, see the
fascinating New York Times interactive graphic.

2

In its 2010 notice of new criteria for delineating metropolitan statistical areas,
the OMB states that “the general concept of a metropolitan statistical area is that
of an area containing a large population nucleus and adjacent communities that
have a high degree of integration with that nucleus.”

3

Other significant “acquisitions” by large CSAs of far-flung Third District MSAs
included Atlantic City, MD; Ocean City, MD; and Dover, DE, by the Philadelphia–
Reading–Camden, PA–NJ–DE–MD CSA. Reading, PA, and Vineland, NJ,
continued to be included. Both Atlantic City and Ocean City have been part of
prior incarnations of the Philadelphia CSA. East Stroudsburg and the Allentown–
Bethlehem–Easton MSAs were absorbed by the New York–Newark, NY–NJ–CT–
PA CSA.

4

Brinkman, Jeffrey. “Location Dynamics: A Key Consideration for Urban Policy,”
Federal Reserve Bank of Philadelphia Business Review (First Quarter 2014).
Feeney, Sheila Anne. “They Take the Early Bus: Meet New York’s Super Commuters,”
am New York, April 15, 2012. http://www.amny.com/they-take-the-early-bus-meetnew-york-s-super-commuters-1.7046274.
Giratikanon, Tom, Josh Katz, David Leonhardt, and Kevin Quealy. “Up Close on
Baseball’s Borders,” The Upshot, New York Times, April 24, 2014. http://www.nytimes.
com/interactive/2014/04/23/upshot/24-upshot-baseball.html?abt=0002&abg=1.
Haas, Anette, and Liv Osland. “Commuting, Migration, Housing and Labour Markets:
Complex Interactions,” Urban Studies, 51:3 (February 2014) pp. 463–476.
Koslowsky, M., A.N. Kluger, and M. Reich. Commuting Stress: Causes, Effects, and
Methods of Coping. New York: Plenum Press, 1995.
McKenzie, Brian. “Out-of-State and Long Commutes: 2011,” U.S. Census Bureau
American Community Survey Reports (February 2013). http://www.census.gov/hhes/
commuting/files/2012/ACS-20.pdf.

After the 2000 census, Wicomico qualified as an MSA, Sussex and Worcester
qualified as micropolitan statistical areas, and Somerset was ineligible as a CBSA
on its own. However, Somerset had enough residents commuting into Wicomico to
be considered an outlying county of the small, largely rural MSA.

McKenzie, Brian. “County-to-County Commuting Flows: 2006–10,” U.S. Census
Bureau Working Paper (January 2013). http://www.census.gov/hhes/commuting/
files/2010/2006-10 Commuting Flows Paper.doc.

6

Wicomico’s population grew by over 14,000 people (17 percent) to reach a total
population of 98,733 in 2010.

Sandow, Erika. “Till Work Do Us Part: The Social Fallacy of Long-Distance
Commuting,” Urban Studies, 51:3 (February 2014) pp. 526–543.

The Salisbury urbanized area includes an additional 239 people in neighboring
Somerset County, MD.

Slater, Philip E. The Pursuit of Loneliness: American Culture at the Breaking Point.
Boston: Beacon Press, 1970.

5

7

8

Delmar’s motto is “The little town too big for one state.”

Long-distance commuters are defined here as those who commute 45 minutes
or more one way. This metric allows historical comparisons with 1980 and 1990,
when commutes were shorter. In 2013, 22.5 percent of East Stroudsburg residents
commuted 60 minutes or more, compared with 8.4 percent for the nation.

9

In addition to the time spent commuting, researchers have associated long
commutes with greater incidences of neck and back pain, obesity, worry, even
divorce. See the articles by Anette Haas and Liv Osland, and Erika Sandow.
Alois Stutzer and Bruno Frey found “a large negative effect of commuting time
on people’s satisfaction with life” after compensating for offsetting benefits,
such as higher wages and lower housing costs. The paradox persisted even after
accounting for potential frictions, benefits to other household members, and
other explanations. Whether their extreme commute reflects a voluntary choice
or one imposed by life events, these commuters represent one manifestation of
the victims of a culture that Philip Slater cautioned against in his 1960s book, The
Pursuit of Loneliness.

Stirling, Stephen. “How a Man Named Keith Took a Long Walk and Defined N.J.
Forever,” NJ Advance Media for NJ.com, June 12, 2015. http://www.nj.com/news/
index.ssf/2015/06/how_a_man_named_keith_took_a_long_walk_and_defined.
html#incart_river.
Stutzer, Alois, and Bruno S. Frey. “Stress That Doesn’t Pay: The Commuting Paradox,”
Scandinavian Journal of Economics, 2008.

10

Passenger rail service from the Poconos to New York City and to Philadelphia
operated for nearly 100 years before ending in the 1960s. The Lackawanna
Railroad established eight stations in Monroe County. In 1881, five trains
departed from New York City daily for the Poconos.
11

12

See Randall Arendt’s Rural by Design.

Tatu, Christina. “Pocono Road Warriors Shrug Off Killer Commutes,” Pocono Record,
May 4, 2014. http://www.poconorecord.com/article/20140504/NEWS/405040346.
U.S. Office of Management and Budget. “Revised Delineations of Metropolitan
Statistical Areas, Micropolitan Statistical Areas, and Combined Statistical Areas,
and Guidance on Uses of the Delineations of These Areas,” OMB Bulletin, 13–01
(February 28, 2013). https://www.whitehouse.gov/sites/default/files/omb/
bulletins/2013/b-13-01.pdf.
U.S. Office of Management and Budget. “2010 Standards for Delineating Metropolitan
and Micropolitan Statistical Areas; Notice, Federal Register, a75:123 (June 28, 2010).
https://www.whitehouse.gov/sites/default/files/omb/assets/fedreg_2010/06282010_
metro_standards-Complete.pdf.
U.S. Census Bureau. “Urban Area Criteria for the 2010 Census; Notice,” Federal
Register, 76:164 (August 24, 2011). http://www2.census.gov/geo/pdfs/reference/
fedreg/fedregv76n164.pdf.

Fourth Quarter 2015 | Federal R eserve Bank of Philadelphia R esearch Department | 11

BANKING POLICY REVIEW
Over-the-Counter Swaps – Before and After Reform
BY MICHAEL SLONKOSKY
One of the landmark events of the financial crisis was
the collapse and bailout of insurer AIG and the bailout of
many large banks to which it had sold credit default swaps
(CDS), including Goldman Sachs ($12.9 billion in swaps),
Société Générale ($12 billion), and Deutsche Bank ($12 billion).1 One lesson policymakers drew from this crisis was that
financial firms could build up huge risk exposures essentially
hidden from the view of regulators in over-the-counter (OTC)
derivatives markets. The Dodd–Frank Act sought to shift
most derivatives trading from an unregulated and opaque
chain of bilateral deals to trades carried out in transparent,
central marketplaces under the watchful eye of regulators. As
a result, U.S. regulators have spent nearly five years writing
and revising regulations governing OTC derivatives. In the
U.S., the rulemaking is nearly complete, and market participants have moved a significant share of their business toward
centralized trading and settlement.2 European regulators’ rulemaking process should be substantially completed by 2016.
Now that the main elements of the new regulations can
be described, let’s see how a simplified trade would be typically carried out by a fictional set of institutions both before
and after the reform.3 First Bank is a large dealer bank that
buys and sells securities and derivatives. High Yield (HY) is
a mutual fund that has a large portfolio of junk bonds. HY
wants to hedge against the risk of a downturn in the junk
bond market.
BEFORE THE REFORM
First Bank sells HY a swap based on an index that is
dependent on the value of a basket of junk bonds. The terms

of the swap say that First Bank makes payments to HY if the
value of the index falls and vice versa if the index rises. The
offer that First Bank makes to HY for the swap includes the
price that HY must pay to First Bank for this deal as well as
what collateral HY must post in case HY were to default on
its obligations. In the OTC market, collateral is referred to
as margin, which may take the form of cash or other types
of securities.4 By contrast, a large dealer bank such as First
Bank might post no margin at all. The terms of this agreement are completely private, as the counterparties — the
participants in this deal — do not announce the terms of
their deal in any public forum.
Now suppose that First Bank does not want to take on
all of the risk of junk bond prices falling and being forced to
make payments to its customer HY. So, First Bank finds another customer, say dealer bank Second Bank, which is bullish on the likelihood of junk bond prices skyrocketing and
is willing to buy the swap. As is common in trades between
dealer banks, neither party posts margin.
Let’s stop here and follow the money: If junk bond
prices fall, Second Bank makes payments to First Bank, and
First Bank makes payments to HY.5 What do we notice
about these transactions?
First, all terms of the agreement, including margin
requirements, are negotiated bilaterally, and the risks to all counMichael Slonkosky is a
terparties depend on First Bank
banking research associate
and Second Bank’s risk controls.
at the Federal Reserve Bank
of Philadelphia. The views
What happens if junk bond prices
expressed in this article are
collapse? Second Bank’s bet on a
not necessarily those of the
price boom has not panned out,
Federal Reserve.

12 | Federal R eserve Bank of Philadelphia R esearch Department | Fourth Quarter 2015

so it is contractually required to make payments to First
Bank. But Second Bank has not posted any margin that can
be seized by First Bank. Thus, First Bank may be unable to
make its contractual payments to HY, which is also out of
luck because First Bank has not posted any margin, either.
This knock-on chain of defaults is one type of what financial economists call contagion. Of course, this example is
too simple. Large dealer banks are engaged in thousands of
transactions and, typically, no single pair of trades will really
count for much. But if lots of financial firms are either hedging or taking large bets on the junk bond market, then we
are talking about real money!
Second, the market is opaque. Other market participants — let alone regulators — have no straightforward way
to learn the terms of the deals that First Bank or HY have
made, or even to know that First Bank and HY have actually made a deal. This information could be important to potential customers deciding whether they want to
do business with First Bank or HY.6 Furthermore,
HY itself has no way of knowing that by buying
protection against a decline in the junk bond
market, it has become exposed to the risk of Second Bank defaulting. But HY’s ignorance of the
risk it is taking on when it trades with First Bank
is not the only problem. When junk bond prices
plummet, other market participants start worrying about who is exposed to the shock. If market
participants suspect that Second Bank is exposed, they may
pull back from doing business with First Bank or HY.

Bank. Standardized swaps must be centrally cleared. This
means that to execute these trades, First Bank and Second
Bank must be members of a central counterparty that clears
high-yield index swaps.9
Let’s call our central counterparty Counterparty California (“Risk checks in, but it never leaves”), or CC. In this
type of arrangement, CC guarantees the trades of each of
its members. First Bank and Second Bank do not actually
contract with each other. Instead, First Bank sells the swap
to CC, and CC sells the swap to Second Bank. CC becomes
the counterparty to every trade.
How can adding another link in the chain help? The
most important way is that CC is designed to manage risk.10
CC requires all of its members to post margin and typically
requires members to contribute to a reserve fund that can
be used in the event that a member defaults. In addition,
CC limits the total risk exposure of its members. It imposes

There are actually two sets of regulations, one for
standardized swaps and another for nonstandardized
(or customized) swaps.

AFTER THE REFORM
The regulations impose changes in how swaps are
cleared, traded, and reported.7 There are actually two sets
of regulations, one for standardized swaps and another for
nonstandardized (or customized) swaps. An example of a
nonstandardized swap is a CDS on a particular firm, a socalled bespoke CDS, or any swap traded by only a few market
participants. Standardized swaps are ones used by many
firms — for example, our CDS on a high-yield bond index.
These types of swaps will be moved to central platforms,
which include well-known exchanges such as the Chicago
Mercantile Exchange, Inc.8
How standardized swaps are regulated. Clearing. Let’s
return to our initial example. For a moment, put aside considering precisely how prices are determined and how parties
are matched to each other and just assume that HY trades
with First Bank and that First Bank trades with Second

position limits on its members, such as a limit on the total
dollar value of high-yield swaps that First Bank can sell. CC
also nets offsetting contracts among its members, thereby
reducing each member’s exposure to others.11 Third, CC has
formal procedures to handle defaults by its members. For
example, if junk bond prices fall and Second Bank is unable
to meet its contractual payments, CC may auction off the
contract to its other members and reimburse First Bank for
any losses, first from Second Bank’s margin account and
second from the reserve fund. Finally, CC is regulated by the
Commodity Futures Trading Commission (CFTC). Indeed,
it may receive special regulatory attention as a systemically
important financial institution.12
Trading. Now let’s go back a step and ask about how
counterparties are matched and how prices are determined.
The regulations require that the swap be executed via one
of two types of trading platforms. One type is a centralized
exchange called a designated contract market. A real-world
example of such an exchange is Bloomberg. Exchanges execute trades through a central limit order book, which publicly
lists bids and offers and uses some well-defined mechanism
to match them.13 For example, First Bank posts the price at

Fourth Quarter 2015 | Federal R eserve Bank of Philadelphia R esearch Department | 13

which it agrees to buy the swap, and Second Bank posts the
price at which it agrees to sell, and they are matched electronically according to some well-defined rule. In this way,
HY can see whether it is getting a good deal from First Bank
— if Second Bank is willing to buy the contract from First
Bank at a much better price than First Bank was willing to
pay, HY will not be happy — and other market participants
(and the CFTC) can learn a lot about supply and demand
conditions in this market.
A second possibility is that First Bank and Second
Bank are members of a new type of entity called a swap
execution facility (SEF) along with a number of other dealer
banks and other large participants. Although swaps may be
traded using a central order limit book, the SEF is permitted
to use an alternate mechanism to ensure that HY can learn
whether it got a good deal from First Bank. In addition to
its own offer price, First Bank must quote HY offers from at
least two other members of the SEF.14
Reporting. First Bank and Second Bank must report the
initial primary terms of the trade and continue to provide
information about any changes in the contract over time.
These terms must be reported to a swaps data repository
(SDR), which makes some of this information public (sometimes with a delay) and some of this information available
only to the CFTC.15 So, the CFTC has extensive information
on the derivatives exposures of individual firms and sectors.
How nonstandardized swaps are regulated. Now
consider that HY has taken a particularly large position in
a single firm and wishes to hedge against the possibility of
a ratings downgrade or a default by that firm. Unlike the
index swap, this bespoke CDS contract would be regulated
by the Securities and Exchange Commission (SEC) as a
security-based swap. More generally, if a swap is not sufficiently standardized or not in high enough demand to be
centrally cleared, it can still be traded bilaterally. The trade
is executed much as it was before the new regulations, but
with some important differences.16
Most important, under the proposed rules, HY and
First Bank are not free to choose their own margin requirements. Unlike margin requirements for standardized swaps,
which are set by the clearing houses, the proposed margin
requirements for nonstandardized swaps are written into the
regulations under the SEC’s regulatory purview. The proposed regulatory requirements are quite detailed, imposing
minimum amounts for particular classes of swaps, and they

are designed to be conservative. For example, both dealer
First Bank and mutual fund HY would have to post margin,
since both swaps dealers and financial firms must post margin.17 However, if HY were a large agribusiness firm seeking
to hedge the risk of default by a supplier, regulations require
only First Bank to post margin — although First Bank itself
might require HY to post margin for it to be willing to do
business with HY.
Furthermore, the types of securities that can be posted
as margin are restricted. The firms can post cash or U.S.
Treasury securities freely, but less liquid securities, such as
corporate bonds, would require a haircut. That is, per dollar,
a corporate bond would contribute only 80 cents toward
the margin requirement. In addition to the margin requirements, First Bank will have to report information about the
trade to an SDR.
SOME CRITICISMS OF THE REGULATIONS
The main goal of this article is to be descriptive, but
let me conclude with some of the more significant criticisms that economists and other analysts have leveled at the
new regulatory framework. Probably most fundamentally,
some critics view the regulations as a costly response to a
problem that doesn’t exist. For example, Peter Wallison has
argued that OTC derivatives played only a minor role in the
financial crisis. Many commentators have noted that central
clearing concentrates risk at large clearing organizations.
This concentration of risk poses a challenge for regulators
such as the CFTC and SEC, which have not traditionally
focused on safety and soundness concerns. As a result, the
concentration of risk at a few institutions raises concerns for
critics of Dodd–Frank’s resolution mechanism for systemically important financial institutions.18
In addition, the Dodd–Frank Act exempts foreign exchange swaps and forwards from the new regulatory framework.19 Darrell Duffie has argued persuasively against the
decision to exempt foreign exchange derivatives from the
regulation, and John Hull argues that nearly all derivatives,
not just standardized derivatives, can be centrally cleared.
Also, some view the introduction of new futures contracts
that are close substitutes for swaps as an example of regulatory arbitrage, in which traders innovate to avoid costly regulations in swaps markets and shift transactions to less closely
regulated venues.20

14 | Federal R eserve Bank of Philadelphia R esearch Department | Fourth Quarter 2015

NOTES
A CDS is a type of insurance contract in which the seller pays the buyer
when the credit risk of a security or group of securities rises. It is just one
type of a wide range of derivative contracts grouped under the general
term swaps for regulatory purposes.

1

Viral Acharya and Alberto Bisin demonstrate theoretically that from society’s
standpoint, bilateral trading can lead to too much risk.

10

2

Imagine that First Bank and Second Bank have a second deal in which Second
Bank’s customer is hedging against the decline in junk bond prices and subsequently
sells the swap to First Bank. If the contracts are for the same dollar amount, then
while First Bank and Second Bank’s gross exposure to each other is doubled, their
net exposure to each other is actually zero.

In this article, I can go over only the basics, as no single rulemaking
document gives a complete account of the U.S. regulations. The
Commodity Futures Trading Commission’s website provides links to all of
its rulemaking. Davis Polk’s memorandum is a readable account of the
regulations as of March 2013.

For example, CFTC regulations require CC to have risk mitigation techniques
sufficient to withstand the failure of one or two clearing members and their affiliates,
depending on how risky CC’s profile is and on whether CC is designated systemically
important in multiple jurisdictions. Currently, eight institutions are designated
financial market utilities that are systemically important. For example, the CFTC
is the primary regulator of Chicago Mercantile Exchange, Inc. and ICE Clear Credit,
L.L.C.

In this article, I gloss over a lot of details about margin. For those
interested, the Bank for International Settlements defines margin at
http://www.bis.org/cpmi/glossary_030301.pdf.

13

The regulations do not prescribe a particular method for matching orders.

14

This system is called a request for quote system.

In the U.S., mandatory centralized trading for one group of swaps began
in February 2014. At the end of 2014, over half of interest rate swaps
and over 80 percent of credit default swaps were trading on centralized
platforms.

11

12

3

4

Actually, Second Bank is unlikely to wish to take a large bet like this and
will search for customers willing to bet that junk bond prices rise, but that
would only complicate the example.

5

Knowing the terms of the deal might also be important to market
participants who want to engage in similar trades but don’t want to get a
bad deal.

6

The Securities and Exchange Commission (SEC) has jurisdiction over
security-based swaps, or swaps based on individual stocks or bonds or
narrowly focused indexes. The Commodity Futures Trading Commission
(CFTC) has jurisdiction over all other swaps such as those based on broad
securities indexes and government securities. The CFTC and SEC will share
rulemaking authority over mixed swaps, or swaps that could fit into either
category.

7

The reporting delay and the division between public and nonpublic information
are intended to balance the benefits of transparency and the need to monitor any
abusive practices against ensuring that traders have an opportunity to keep trades
secret long enough to make a profit. For example, large trades, known as block
trades, are reported with a lag to give traders a chance to make some profit on the
trade.
15

The rules for nonstandardized swaps have not yet been finalized. Here, I describe
the proposed rules as of September 2015. The most recent version of the SEC’s
proposed margin requirements for nonstandardized swaps can be found at https://
www.sec.gov/rules/proposed/2012/34-68071.pdf.

16

Alternatively, First Bank may use its own model for determining margin
requirements, but this model must meet the specifications of the SEC.
17

As I will make clear, the regulations are written so that platforms might
use a wide variety of trading mechanisms, although regulators expect
most standardized transactions to migrate to the existing exchanges. But
whatever the precise trading mechanism, the central platforms must clear
all trades according to standardized rules.

David Skeel critically examines Dodd–Frank’s resolution scheme for systemically
important institutions and proposes an alternative.

The regulation refers to central counterparties as derivatives clearing
organizations (DCOs). The Chicago Mercantile Exchange is an example of
a real-world DCO. The regulation refers to members of a DCO as a futures
commission merchant. In this article, I assume that all customer swaps are
intermediated by dealer banks. In fact, larger customers may be granted
direct access to central clearing and trading mechanisms via certain types
of agency agreements with dealer banks.

See the illuminating exchange between Robert Litan and Darrell Duffie about
futurization of swaps, in which traders have designed futures contracts that are
essentially identical to the regulated swaps contracts.

8

9

18

A forward is a nonstandardized contract between two parties to buy or sell an asset
at a specified future time at a price agreed upon beforehand.

19

20

Fourth Quarter 2015 | Federal R eserve Bank of Philadelphia R esearch Department | 15

REFERENCES
Acharya, Viral, and Alberto Bisin. “Counterparty Risk Externality: Centralized Versus
Over-the-Counter Markets,” Journal of Economic Theory, 114 (2014), pp. 153–182.
Davis Polk & Wardwell, L.L.P. “An Asset Manager’s Guide to Swap Trading in the
New Regulatory World,” client memorandum, March 11, 2013.
Duffie, Darrell. “Futurization of Swaps: Stanford’s Duffie Offers Another Viewpoint
on This Emerging Trend,” Bloomberg BGOV Analysis, January 28, 2013, http://www.
darrellduffie.com/uploads/policy/DuffieBGOV_FuturizationOfSwaps.pdf.
Duffie, Darrell. “On the Clearing of Foreign Exchange Derivatives,”
manuscript, May 2011, http://www.darrellduffie.com/uploads/policy/
DuffieClearingFXDerivatives2011.pdf.
Litan, Robert. “Futurization of Swaps: A Clever Innovation Raises Novel Policy
Issues for Regulators,” Bloomberg BGOV Analysis, January 14, 2013, http://www.
darrellduffie.com/uploads/policy/BGOV_FuturizationOfSwaps.pdf.
Skeel, David. The New Financial Deal: Understanding the Dodd–Frank Act and Its
(Unintended) Consequences, Hoboken, NJ: Wiley, 2010.
U.S. Commodity Futures Trading Commission. “Dodd–Frank Final Rules, Final
Guidance, Final Exemptive Orders, and Other Final Actions,” http://www.cftc.gov/
LawRegulation/DoddFrankAct/Dodd-FrankFinalRules/index.htm.
Wallison, Peter. Comment on Lynn Stout, “Regulate OTC Derivatives by Deregulating
Them,” Regulation, (Fall 2009), pp. 35–38, http://object.cato.org/sites/cato.org/
files/serials/files/regulation/2009/9/v32n3-1.pdf#page=6.

16 | Federal R eserve Bank of Philadelphia R esearch Department | Fourth Quarter 2015

RESEARCH RAP
Visit our website for more abstracts and papers of interest to the professional researcher produced by economists and
visiting scholars at the Philadelphia Fed.

IMPACT OF UNCONVENTIONAL MONETARY POLICY
ON FIRM FINANCING CONSTRAINTS: EVIDENCE FROM
THE MATURITY EXTENSION PROGRAM
This paper investigates the impact of unconventional
monetary policy on firm financing constraints. It focuses on
the Federal Reserve’s maturity extension program (MEP),
which was intended to lower longer-term rates and flatten
the yield curve by reducing the supply of long-term government debt. Consistent with those models that emphasize
bond market segmentation and limits to arbitrage, around
the MEP’s announcement, stock prices rose most sharply for
those firms that are more dependent on longer-term debt.
These firms also issued more long-term debt during the MEP
and expanded employment and investment. These responses
are most pronounced for those firms with stronger balance
sheets. There is also evidence of “reach for yield” behavior
among some institutional investors, as the demand for riskier
debt also rose during the MEP. The authors’ results suggest
that unconventional monetary policy may have helped to relax financing constraints and stimulate economic activity in
part by affecting the pricing of risk in the bond market.
Working Paper 15–30. Nathan Foley-Fisher, Federal
Reserve Board; Rodney Ramcharan, University of Southern
California; Edison Yu, Federal Reserve Bank of Philadelphia.
DISCLOSURE OF STRESS TEST RESULTS
Should regulatory bank examinations be made public? Regulators have argued that the confidentiality of the
examination process promotes frank exchanges between
bankers and examiners and that public disclosure of examination results could have a chilling effect. The author
examines the tradeoffs in a world in which examination
results can be kept confidential, but regulatory interventions
are observable by market participants, as they typically are
for stress tests. Inducing banks to communicate truthfully
requires regulators to engage in forbearance, which is priced

into banks’ uninsured debt and raises the costs of inducing truthful communication. Regulators that disclose exam
results bear higher monitoring costs and impose excessive
capital requirements because interventions are not as sensitive to underlying risks. My model predicts that disclosure is
optimal when the regulator’s model is relatively inaccurate.
Working Paper 15–31. Mitchell Berlin, Federal Reserve
Bank of Philadelphia.
THE SYSTEM OF NATIONAL ACCOUNTS AND
ALTERNATIVE ECONOMIC PERSPECTIVES
Brent Moulton and Nicole Mayerhauser (2015) point out
that, for more than 50 years, economists have featured the
concept of human capital in their models of labor, growth,
productivity, and distribution of income. The authors recommend the addition to the System of National Accounts
(SNA) of supplemental person-level accounts: i.e., a System
of Person Accounts (SPA). They see this as the best way of
recognizing the processes of human capital creation as well
as related issues of how income is distributed among individuals and families. The authors argue that this change would
support three different perspectives from which economic activity can be viewed: (1) a current period outcomes perspective, (2) a risky possibilities perspective, and (3) a resources
perspective. Moreover, these gains could be realized without
changing the SNA in any substantial respects.
Working Paper 15–32. Alice O. Nakamura, University
of Alberta; Leonard I. Nakamura, Federal Reserve Bank of
Philadelphia.
FOREIGN COMPETITION AND BANKING INDUSTRY
DYNAMICS: AN APPLICATION TO MEXICO
The authors develop a simple general equilibrium
framework to study the effects of global competition on
banking industry dynamics and welfare. They apply the
framework to the Mexican banking industry, which under-

Fourth Quarter 2015 | Federal R eserve Bank of Philadelphia R esearch Department | 17

went a major structural change in the 1990s as a consequence of both government policy and external shocks.
Given the high concentration in the Mexican banking
industry, domestic and foreign banks act strategically in the
authors’ framework. After calibrating the model to Mexican
data, the authors examine the welfare consequences of government policies that promote global competition. They find
relatively high economywide welfare gains from allowing
foreign bank entry.
Working Paper 15–33. Dean Corbae, University of Wisconsin–Madison, National Bureau of Economic Research;
Pablo D’Erasmo, Federal Reserve Bank of Philadelphia.
HEALTH-CARE REFORM OR LABOR MARKET REFORM?
A QUANTITATIVE ANALYSIS OF THE AFFORDABLE
CARE ACT
An equilibrium model with firm and worker heterogeneity is constructed to analyze labor market and welfare
implications of the Patient Protection and Affordable Care
Act, commonly called the Affordable Care Act (ACA).
The authors’ model implies a significant reduction in the
uninsured rate from 22.6 percent to 5.6 percent. The model
predicts a moderate positive welfare gain from the ACA
because of the redistribution of income through health
insurance subsidies at the exchange as well as the Medicaid
expansion. About 2.1 million more part-time jobs are created under the ACA at the expense of 1.6 million full-time
jobs, mainly because the link between full-time employment
and health insurance is weakened. The model predicts a
small negative effect on total hours worked (0.36 percent),
partly because of the general equilibrium effect.
Working Paper 15–34. Makoto Nakajima, Federal Reserve
Bank of Philadelphia; Didem Tüzemen, Federal Reserve Bank of
Kansas City.
EXCESS RESERVES AND MONETARY POLICY
NORMALIZATION
In response to the Great Recession, the Federal Reserve
resorted to several unconventional policies that drastically
altered the landscape of the federal funds market. The current environment, in which depository institutions are flush
with excess reserves, has forced policymakers to design a
new operational framework for monetary policy implementation. The authors provide a parsimonious model that captures the key features of the current federal funds market,
along with the instruments introduced by the Federal Re-

serve to implement its target for the federal funds rate. The
authors use this model to analyze the factors that determine
rates and volumes as well as to identify the conditions such
that monetary policy implementation will be successful.
They also calibrate the model and use it as a quantitative
benchmark for applied analysis, with a particular emphasis
on understanding how the market is likely to respond as
policymakers raise the target rate.
Working Paper 15–35. Roc Armenter, Federal Reserve
Bank of Philadelphia; Benjamin Lester, Federal Reserve Bank of
Philadelphia.
GENTRIFICATION AND RESIDENTIAL MOBILITY
IN PHILADELPHIA
Gentrification has provoked considerable debate and
controversy about its effects on neighborhoods and the
people residing in them. This paper draws on a unique
large-scale consumer credit database to examine the mobility patterns of residents in gentrifying neighborhoods in
the city of Philadelphia from 2002 to 2014. The authors
find significant heterogeneity in the effects of gentrification across neighborhoods and subpopulations. Residents in
gentrifying neighborhoods have slightly higher mobility rates
than those in nongentrifying neighborhoods, but they do
not have a higher risk of moving to a lower-income neighborhood. Moreover, gentrification is associated with some
positive changes in residents’ financial health as measured
by individuals’ credit scores. However, when more vulnerable residents (low-score, longer-term residents, or residents
without mortgages) move from gentrifying neighborhoods,
they are more likely to move to lower-income neighborhoods
and neighborhoods with lower values on quality-of-life
indicators. The results reveal the nuances of mobility in gentrifying neighborhoods and demonstrate how the positive
and negative consequences of gentrification are unevenly
distributed.
Working Paper 15–36. Lei Ding, Federal Reserve Bank
of Philadelphia; Jackelyn Hwang, Princeton University; Eileen
Divringi, Federal Reserve Bank of Philadelphia.
A TRACTABLE CITY MODEL FOR AGGREGATIVE
ANALYSIS
An analytically tractable city model with external increasing returns is presented. The equilibrium city structure
is either monocentric or decentralized. Regardless of which
structure prevails, intracity variation in endogenous vari-

18 | Federal R eserve Bank of Philadelphia R esearch Department | Fourth Quarter 2015

ables displays exponential decay from the city center, where
the decay rates depend only on parameters. Given population, the equilibrium of the model is generically unique.
Tractability permits explicit expressions for when a central
business district (CBD) will emerge in equilibrium, how
external increasing returns affect the steepness of downtown
rent gradients, and how wages and welfare vary with population. An application to urban growth boundary is presented.
Working Paper 15–37. Satyajit Chatterjee, Federal Reserve
Bank of Philadelphia; Burcu Eyigungor, Federal Reserve Bank
of Philadelphia.
AGENCY AND INCENTIVES: VERTICAL INTEGRATION
IN THE MORTGAGE FORECLOSURE INDUSTRY
In many U.S. states, the law firms that represent lenders
in foreclosure proceedings must hire auctioneers to carry
out the foreclosure auctions. The authors empirically test
whether processing times differ for law firms that integrate
the mortgage foreclosure auction process compared with law
firms that contract with independent auction companies.
They find that independent firms are able to initially schedule auctions more quickly, but when postponements occur,
they are no faster to adapt. Since firms schedule the initial
auction before contracting, independent auction companies
have an incentive to conform to the law firms’ schedules in
order to secure the contracts. The authors argue that this
is evidence of a cost of integration stemming from poorly
aligned incentives within the firm.
Working Paper 15–38. Lauren Lambie-Hanson, Federal
Reserve Bank of Philadelphia; Timothy Lambie-Hanson, Haverford College.

cal predictions are consistent with the observed disparity in
crisis-related output losses.
Working Paper 15–39. Daniel Sanches, Federal Reserve
Bank of Philadelphia.
WHO IS SCREENED OUT OF SOCIAL INSURANCE
PROGRAMS BY ENTRY BARRIERS? EVIDENCE FROM
CONSUMER BANKRUPTCIES
Entry barriers into social insurance programs will be
effective screening devices if they cause only those individuals receiving higher benefits from a program to participate
in that program. We find evidence for this by using plausibly
exogenous variations in travel-related entry costs into the
Canadian consumer bankruptcy system. Using detailed balance sheet and travel data, we find that higher travel-related
entry costs reduce bankruptcies from individuals with lower
financial benefits of bankruptcy (unsecured debt discharged,
minus secured assets forgone). When compared across filers, each extra kilometer traveled to access the bankruptcy
system requires approximately $11 more in financial benefits
from bankruptcy.
Working Paper 15–40. Vyacheslav Mikhed, Federal
Reserve Bank of Philadelphia; Barry Scholnick, University of
Alberta School of Business.

BANKING PANICS AND PROTRACTED RECESSIONS
This paper develops a dynamic model of bank liquidity provision to characterize the ex post efficient policy response to a banking panic and study its implications for the
behavior of output in the aftermath of a panic. It is shown
that the trajectory of real output following a panic episode
crucially depends on the cost of converting long-term assets
into liquid funds. For small values of this liquidation cost,
the recession associated with a banking panic is protracted.
For intermediate values, the recession is more severe but
short lived. For relatively large values, the contemporaneous
decline in real output in the event of a panic is substantial
but followed by a vigorous rebound in real activity above
the long-run level. The author argues that these theoreti-

Fourth Quarter 2015 | Federal R eserve Bank of Philadelphia R esearch Department | 19

20 | Federal R eserve Bank of Philadelphia R esearch Department | Fourth Quarter 2015

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