FTC Staff Comment Before the Federal Aviation Administration Concerning the High Density Traffic Airports Rule

UNITED STATES OF AMERICA
FEDERAL TRADE COMMISSION
WASHINGTON, D.C. 20580
BEFORE THE
FEDERAL AVIATION ADMINISTRATION
DEPARTMENT OF TRANSPORTATION
WASHINGTON, D.C.
In The Matter of
Docket No. 27664
Study of the High Density Rule
Comment of the Staff of the
Bureau of Economics
of the
Federal Trade Commission(1)
November 23, 1994
I. Introduction and Summary
The staff of the Bureau of Economics of the Federal Trade Commission (FTC) appreciates this opportunity to respond
to the Federal Aviation Administration’s Notice of Study and Request for Comments (NSRC). The FAA is conducting
a comprehensive study of the High Density Traffic Airports Rule (HDR) and requests comments on the effectiveness
and viability of the HDR and any potential alternatives to the rule.(2)
The FAA adopted the HDR to help alleviate delays caused by congestion at certain high density traffic airports
(HDTAs). The HDR set the total number of operations—takeoffs and landings—allowed during certain restricted time
periods at the HDTAs and distributed this quota of operating privileges to incumbent carriers. The HDR currently
affects operations at four airports: Kennedy, LaGuardia, O’Hare, and Washington National. The number of operations
allowed under the HDR is based on the estimated capacity of each airport. The HDR requires carriers wishing to land
or take off during restricted periods to obtain the privilege to do so. These landing and takeoff privileges, commonly
referred to as “slots,” can be traded, sold, or leased.
The HDR serves an important economic role. By establishing a market for rights to land or take off at capacity-
constrained airports, the HDR helps ensure that a scarce resource, airport capacity, is allocated to its most highly
valued use. The FAA may wish to consider extending the HDR to other airports that might be prone to congestion
and delays due to excess demand for limited capacity during peak time periods.
The HDR has been criticized for setting artificial constraints on airport operations; however, as long as the number of
slots reflects the airports’ actual capacity, this criticism is inaccurate. Another criticism has been that the HDR, by
limiting entry, fosters the exercise of market power by incumbent carriers.
Several observations are relevant to the above criticisms. The binding constraint on net increases in operations has
been the HDTAs’ physical capacity, not the use of the “slot” system. If the slot system were eliminated, the constraint
on net operations would remain. And though increases in operations are constrained by the HDTAs’ physical
capacity, recent (1992) amendments to the HDR created preferences in slot lotteries that made it easier for new

carriers to obtain slots at HDTAs and for incumbent small carriers to expand the number of slots they control. These
slot reallocations do not increase the total number of operations, but they may act to reduce concentration somewhat.
Fundamentally, the HDR promotes, rather than limits, new entry, because it creates a market in which potential new
entrants can obtain operating privileges. Before such a market was created in 1986, large incumbent carriers could
unilaterally prevent entry at the HDTAs by vetoing grants of operating privileges to carriers wishing to provide new
service. Moreover, the findings of empirical assessments conducted by Bureau of Economics staff of slot usage in
May-June 1990 (prior to the 1992 amendments) and in September-October 1993 (after the 1992 amendments) do not
support the hypothesized exercise of market power.
The use of peak and off-peak landing and takeoff fees may be an alternative to the present form of slot-based
regulation. The FAA may wish to study this alternative. The information requirements needed to implement peak-load
pricing to allocate capacity efficiently could exceed the information requirements needed to implement slot-based
regulation. The benefits of peak-load pricing over slot-based allocation are not apparent.
II. Expertise of the Staff of the Federal Trade Commission
The Federal Trade Commission is responsible for enforcing the Federal Trade Commission Act,(3) which, among
other things, prohibits “unfair methods of competition.” The staff of the Federal Trade Commission, upon request from
federal, state, or local governmental bodies, comments on regulatory proposals that may affect competition,
consumers, or economic efficiency. In the course of this work, as well as in antitrust and consumer protection
research, nonpublic investigations, and litigation, the staff applies established principles and recent developments in
economic theory to competition and consumer protection issues.
The staff of the Bureau of Economics has a longstanding interest in slot allocation and transfer methods, as well as
more general interest in issues involving competition and regulation in the airline industry. This interest has been
reflected in comments submitted by the Bureau of Economics staff in previous FAA administrative proceedings on
slot allocation and transfer methods, and in previous FAA proceedings involving other aspects of airline competition
and regulation.(4) In addition, the staff has issued research reports on slot allocation and airline deregulation.(5)
III. Background
Effective April 27, 1969, the FAA designated five airports—Kennedy, LaGuardia, Newark, O’Hare, and Washington
National—as HDTAs and established the High Density Rule (HDR) to govern operations there.(6) The HDR, originally
a temporary measure to reduce delays caused by congestion, established limits on the total number of hourly
“slots”—takeoffs and landings—during certain hours of the day. The hourly slot quotas were based on the operating
capacity of each airport as determined by the FAA’s Engineering Performance Standards (“EPS”). Slot quotas at
Newark were suspended indefinitely in 1970, although the airport is still classified as a HDTA. Slot quotas at the four
other HDTAs were made permanent in 1973.
Before 1986, the incumbent airlines at each HDTA decided how slots would be allocated, with any reallocation
requiring their unanimous consent. The incumbent airlines could trade slots among themselves, one-for-one.(7)
Increasing competition in the post-1977 deregulated era strained this system severely;(8) in many cases it broke
down completely, freezing the previous allocation in place and denying slots to new entrants.(9) The FAA’s summary
of the situation in 1985 stated:
As a result of the unanimity requirement and the lack of deadlock-breaking provisions, the air carrier scheduling
committees have found it difficult in recent years to reach agreement on a schedule in full compliance with the High
Density Rule … . Overall, the scheduling committees are not currently functioning in a manner which provides for the
efficient allocation of slots, for rapid adjustment to market conditions and shifting carrier needs and preferences, for
adequate opportunity for expansion of operations, or for new carriers to serve high density airports.(10)

Starting in 1986, revised FAA regulations have encouraged development of a market-based slot transfer system.(11)
The initial allocation of slots was determined by “grandfathering”—that is, giving the right to a slot to the carrier that
had been operating it.(12) Subject to certain exceptions and procedures, slot holders are permitted to sell, trade, or
lease their slots. The FAA retains the right to repossess slots, which the regulations describe as operating privileges,
not property rights.(13) Slots may be withdrawn for such reasons as making necessary allocations for international
flights and implementing competition goals.(14) Further, slots will be recalled for reallocation if used less than a
minimum specified percent of the time.(15)
Amendments to the slot allocation and transfer regulations became effective November 1, 1992.(16) These
amendments were adopted, in part, “to promote the availability of slots to new entrant and limited incumbent carriers
at the high density airports, and thereby enhance competition.(17) Among other things, these amendments created
the classification of “limited incumbent carriers,” defined as carriers and commuter operators holding fewer than 12
slots. New entrants and limited incumbents benefit from preferences in the lotteries of unallocated slots. The 1992
amendments also restrict the transfer of slots obtained in a lottery. During the 24 month period following a lottery,
lottery-assigned slots can only be traded for others at the same HDTA and cannot be sold to other slot-holders (or
obtained through sale or merger) if the carrier obtaining the slots would, as a result, hold more than 12 slots. The
1992 amendments also raised the minimum slot usage requirement to 80 percent.
IV. The Benefits of Market-based Slot Reallocation
The HDR was originally adopted as a solution to the problem of congestion and backup delays at five (now four) of
the busiest airports in the country.(18) Airport congestion can result from a common- property resource problem,
which has the essential element that no one controls access to a resource that is available in a fixed supply. In the
absence of property rights for the fixed resource, competing users overuse it. Here, the resource that is available in
fixed supply is the capacity of the airport. Physical and technological constraints, such as the number of runways and
terminals and the capability of the air traffic control system, limit the density and frequency of operations. Overuse of
the scarce resource manifests itself in congestion and delay as the number of airplanes attempting to land or take off
during the same period exceed the airport’s physical capacity.
An economically efficient solution to a common-property resource problem would limit the use of the resource and
allocate rights of use to those who value them highest. The HDR, as amended in the FAA 1985 Final Rule,(19)
largely accomplished this by creating a slot market. Slot quotas in place since 1969 had already set limits on landing
and takeoff privileges at the HDTAs. By creating a largely unfettered market for slots, the FAA allowed slots to be
transferred to carriers with the most highly valued flights. Other things equal, the value of a given flight rises as
consumer demand for the flight rises. Thus, the slot market—that is, the ability of carriers to buy, sell and lease slots
freely—helps ensure that the flights offered are those that consumers value the most.
The FAA’s 1992 Final Rule(20) places some restrictions on the transfer of slots, which may reduce the economic
efficiency of the slot market. The 1992 Final Rule limits for a two-year period the transfer of the slots obtained through
a lottery. The rationale for adopting this two-year mandatory operating period was “to assure that slots obtained in a
lottery are likely to be used for operations by new entrants and limited incumbents and not sold back to other slot-
holders.(21) But because slot transfers likely represent efficient transfers of resources, absent any antitrust
concerns,(22) the restrictions on transfers following a lottery would perpetuate for two years allocations that might be
inefficient. The staff of the Bureau of Economics suggests that the FAA consider amending the HDR in order to
rescind the two-year restriction on the sale of newly-acquired slots following a lottery.
Critics of the HDR have described it as artificially constraining capacity at the HDTAs. For example, the National
Commission To Ensure a Strong Competitive Airline Industry(23) recommended in its report, Change, Challenge and
Competition: A Report to the President and Congress, that the “FAA review the rule that limits operations at ?high
density’ airports with the aim of either removing these artificial constraints or raising them to the highest practicable
level consistent with safety requirements.(24) As noted above, the hourly quotas at each of the HDTAs have been set
by the FAA based on the hourly capacity of each airport as determined by the EPS. According to the FAA.

The hourly quotas are set at the predominant IFR [Instrument Flight Rule] capacity, as determined by the FAA. The
predominant IFR capacity for each airport is the airport’s capacity under circumstances and configurations most
frequently encountered when weather conditions preclude Visual Flight Rule (VFR) operation. The limitations in the
rule are predominately determined by groundside constraints.[emphasis added](25)
If there is evidence that the FAA underestimated the capacity of the HDTAs or if capacity at the HDTAs has increased
since the HDR was adopted, then the FAA may wish to consider raising the slot quotas.(26) As long as the slot
quotas reflect each airport’s capacity, the slots are not “artificial” constraints on airport operations. As long as the
HDR slot quotas reflect the capacities of the HDTAs, the HDR promotes an efficient allocation of this limited capacity
by creating a market for slots.
In general, market determined prices provide important signals about resource reallocation. In the context of the
HDR, the prices at which slots trade inform decision makers of the social benefits of additional investments in
capacity. Slots for peak demand time periods, when capacity will be fully utilized, ordinarily will be more expensive
than slots for off-peak periods. By encouraging the shifting of lower-valued flights to off-peak times, this cost
differential delays the need to make expensive additions to capacity.
These benefits of market-determined slot prices suggest two implications for the FAA’s present proceeding. First,
rules restricting the transfer of slots, by constraining this market, may diminish its efficiency. Second, if airports other
than the four now covered may regularly experience delays due to congestion, then extending the HDR to cover
these additional airports may be a more efficient method of rationing their capacity than the current method, of
rationing by the queue (that is, by requiring airplanes to wait in line for long periods, whenever the demand for landing
or takeoff privileges exceeds an airport’s capacity).
V. The Competitive Implications of Slot Usage
The 1992 amendments to the HDR were adopted to promote competition at the HDTAs by facilitating the entry of
new carriers and by fostering the growth of smaller “limited incumbent” carriers.(27) The rulemaking proceeding that
led to the 1992 amendments was initiated under the Aviation Safety and Capacity Expansion Act of 1990, which
directed the Secretary of Transportation to initiate rulemaking to consider more efficient methods of allocating
capacity at HDTAs in order to provide improved opportunities for operations by “new entrant carriers.(28)
Critics of the HDR had suggested that it restrained competition at the HDTAs by impeding the entry of new carriers
and by limiting the ability of smaller carriers to expand and become more formidable competitors to the larger
carriers.(29) “Since there are only a few carriers offering slots for sale at any given time, securing the necessary
operating rights might be extremely difficult.(30) As evidence of the anticompetitive effects of the HDR, critics have
pointed to the higher concentration(31) and higher ticket prices at HDTAs relative to other airports.(32)
Although markets that are noncompetitive may show high concentration and high prices, these characteristics do not,
by themselves, necessarily imply the existence of anticompetitive behavior. Even if markets were perfectly
competitive, fares at capacity-constrained airports would be higher, all other things equal, than at airports without
capacity constraints. Equilibrium competitive prices for air travel at capacity-constrained airports would reflect the
“scarcity rents” associated with the constraint on capacity.(33) Indeed, the absence of higher airfares at slot
constrained airports would imply (again, all other things equal) that landing rights at HDTAs were not a valuable
resource, and slots would not trade at positive prices.(34) Thus, higher fares at HDTAs may merely reflect scarcity
rents, not monopoly rents.(35)
Finally, the concentration levels at the HDTAs may overstate the potential for anticompetitive behavior because the
HDTAs are not necessarily relevant antitrust markets. In antitrust analysis, a relevant market must be defined before
concentration can be measured. For each of the HDTAs, the relevant market may include more than the capacity-
constrained airport. For example, each of the four airports governed by the HDR is near one or more regional airports
that are not slot constrained: Newark in the New York metropolitan area, Midway in the Chicago area, and Dulles and

Baltimore-Washington International in the Washington, D. C. area. If flights using other local airports are in the same
antitrust market as flights from an HDTA (for example, because consumers can choose to fly from Dulles, National, or
BWI),(36) the competitive presence of the other airports can constrain the prices of flights using the HDTA.(37) In that
case, the relevant concentration levels would be those for the larger airport market, not for the HDTA alone.
Thus, higher concentration and higher fares at HDTAs relative to other airports could be consistent with either
competitive or anticompetitive behavior. When the FAA was considering the proposals that ultimately became the
1992 HDR amendments, the Bureau of Economics submitted comments on those proposals that tested a number of
the implications of anticompetitive theories of the effects of the HDR.(38) Using slot usage data supplied by the FAA’s
Office of Slot Administration, the 1991 Comments tested two principal hypotheses. The first hypothesis was based on
the standard analysis of market power in which dominant firms have a greater incentive to reduce output than do
fringe firms. Standard theory predicts that smaller fringe firms will increase output in response to a contraction of
output by the dominant firm.(39) It has been suggested that larger carriers will attempt to restrict output by reducing
the number of days on which a slot is used.(40) In the context of an airport slot market, this implies that larger carriers
would use their slots less intensively than the smaller fringe carriers would. If larger carriers have market power and
exercise it in this manner, slot usage would be negatively related to the market share of the carrier using the slot.
The second hypothesis that was examined in the 1991 Comments was that dominant carriers would be unwilling to
sell slots to potential new entrants at competitive rates but instead would lease their slots to selected other carriers,
with the purpose of deterring entry by carriers likely to increase overall slot use. In this scenario, dominant airlines
would choose their competitors by leasing slots to them. In this way, the dominant firm(s) could both ensure that less
efficient firms will be competing with them for the same passengers and simultaneously deter the entry of more
efficient firms.(41) The testable implications of this hypothesis are: (1) that the dominant air carriers will be net lessors
of slots; and (2) that these leased slots will be used relatively less intensively than slots held and operated by the
dominant carriers.
The empirical analyses contained in the 1991 Comments were largely inconsistent with these two anticompetitive
theories. Slot usage for all carriers at the four HDTAs tended to be very high—typically above 90 percent—and the
data indicated no relationship between market share and slot usage. That is, the negative relationship between
market share and slot usage predicted by the anticompetitive hypothesis did not emerge from actual slot usage data.
Further, rather than being net lessors of slots, the largest firms at each of the four HDTAs were consistently net
lessees of slots. This result, together with the high degree of slot usage, is inconsistent with the implications of the
anticompetitive theories.
Modelling slot usage as a function of multiple factors, the 1991 Comments also used multiple regression analysis to
test the anticompetitive hypothesis.(42) At O’Hare, the most concentrated of the four HDTAs, regression analysis
indicated a positive and statistically significant relationship between slot share and rate of use. Further, slots leased
by large carriers to others were used at a higher rate than large carriers’ owned and operated slots. Both of these
results are inconsistent with the anticompetitive hypotheses.(43)
At National and LaGuardia, the 1991 Comments found no discernible relationship between market share and usage
rate in May-June 1990. The relationship between leasing and slot usage was also inconsistent with the
anticompetitive leasing hypothesis. An exception was Kennedy, where the 1991 Comments found that firms with
larger market shares tended to show lower slot usage. This result would be consistent with the anticompetitive usage
hypothesis; however, it could also be explained by special circumstances at Kennedy. The largest slot holder at
Kennedy, with 31 percent of the slots, was a bank that had entered a purchase-lease arrangement with TWA. The
second largest slot holder was Pan Am. Both TWA and Pan Am had been experiencing financial difficulties on
transatlantic flights, which ultimately led to bankruptcy.(44) Thus, the negative coefficients on market share may have
simply reflected these largest carriers’ reduced output due to operating difficulties, rather than a monopolistic output
reduction.

For O’Hare, National, and LaGuardia, the May-June 1990 slot usage data are generally inconsistent with hypotheses
that the HDR allows carriers to exert market power. The only result that might support the anticompetitive
hypotheses, the negative and significant relationship between slot share and slot use at Kennedy, is probably
explained by the operating problems experienced by the largest carriers at that airport during the study period. In
addition, the data did not suggest that slots leased by large carriers to others were used at a significantly lower rate,
but suggested instead the opposite, that the largest slot holders at HDTAs tended to be net lessees.
VI. Slot Usage at the HDTAs in 1993
A. Slot Usage Rates and Leasing Patterns
In this section, we reexamine the relationship between market shares and slot usage using slot usage data for the
period September-October 1993.(45) Since the May-June 1990 period analyzed in our 1991 Comments, patterns of
slot usage at the HDTAs may have been affected by the bankruptcies of a number of different carriers or by other
factors.(46) As in our 1991 Comments, the statistical analysis is based on the set of slots meeting three criteria: (1)
the slots were operated by a domestic airline on a domestic route; (2) use of the slots was not restricted by the FAA;
and (3) the slots were not exempt from the FAA's “use or lose” rules.
Table 1(47) lists the slot holdings and the Herfindahl-Hirschmann Index (HHI) of concentration at the four HDTAs as
of October 31, 1993.(48) All remain either moderately or highly concentrated under the standards contained in the
Department of Justice and Federal Trade Commission Horizontal Merger Guidelines;(49) however, the level of
concentration has risen considerably at all four airports. Both LaGuardia and National were moderately concentrated
as of October 1993, just as they were in June 1990. LaGuardia's 1993 HHI of 1747 exceeds its 1990 HHI (1241) by
506, and National's HHI of 1657 exceeds its 1990 HHI (1282) by 375. Kennedy and O'Hare were highly concentrated
as before; Kennedy's 1993 HHI of 2233 exceeds its 1990 HHI (1816) by 417, and O'Hare's 1993 HHI of 3731
exceeds its 1990 HHI (3122) by 609.
TABLE 1
DOMESTIC SLOT HOLDINGS AND
CONCENTRATION
O'HARE
(ORD)
NATIONAL
(DCA)
HOLDER 	CODE
NUMBER OF
SLOTS
SHARE 	NUMBER OF
SLOTS
SHARE
FAA 	0001 	0 	0 	0 	0
SHAWMUT BANK N.A. 	0004 	0 	0 	32 	5.9

STATE ST. BANK & TRUST. 	0020 	0 	0 	0 	0
WILMINGTON TRUST CO. 	0023 	14 	1.0 	0 	0
BT COMMERCIAL CORP 	0026 	5 	0.4 	0 	0
CITIBANK 	0030 	0 	0 	26 	4.8
FIRST BANK NATIONAL ASSOC. 	0034 	39 	2.8 	0 	0
CONTINENTAL SLOT TRUST 	0035 	33 	2.3 	43 	8.0
AMERICAN AIRLINES 	AAL 	533 	37.7 	67 	12.4
DELTA AIRLINES 	DAL 	57 	4.0 	88 	16.3
FEDERAL EXPRESS 	FDX 	5 	0.4 	0 	0
BUSINESS EXPRESS 	GAA 	0 	0 	0 	0
MIDWEST EXPRESS 	MEP 	0 	0 	1 	0.2
NORTHWEST AIRLINES 	NWA 	19 	1.3 	87 	16.1
TRANS WORLD AIRLINES 	TWA 	0 	0 	1 	0.2
UNITED AIRLINES 	UAL 	674 	47.7 	42 	7.8
USAIR 	USA 	35 	2.5 	152 	28.2
TOTAL 	1414 	539

HHI 	3731 	1657
TABLE 1 CONTINUED
KENNEDY
(JFK)
LAGUARDIA
(LGA)
ALL HDTAs
HOLDER 	CODE
NUMBER OF
SLOTS
SHARE 	NUMBER OF
SLOTS
SHARE 	NUMBER OF
SLOTS
SHARE
FAA 	0001 	0 	0 	13 	2.5 	13 	0.5
SHAWMUT BANK 	0004 	44 	39.3 	20 	3.9 	96 	3.7
STATE ST BANK & TRUST 0020 	3 	2.7 	0 	0 	3 	0.1
WILMINGTON TRUST 	0023 	0 	0 	0 	0 	14 	0.5
BT COM'L CORP 	0026 	1 	0.9 	0 	0 	6 	0.2
CITIBANK 	0030 	0 	0 	53 	10.4 	79 	3.1
FIRST BANK NATIONAL
ASSOC.
0034 	0 	0 	0 	0 	39 	1.5
CONTINENTAL SLOT
TRUST
0035 	0 	0 	23 	4.5 	99 	3.8
AMERICAN AIRLINES 	AAL 	22 	19.6 	67 	13.1 	689 	26.7
DELTA AIRLINES 	DAL 	12 	10.7 	112 	21.9 	269 	10.4

FEDERAL EXPRESS 	FDX 	0 	0 	0 	0 	5 	0.2
BUSINESS EXPRESS 	GAA 	1 	0.9 	0 	0 	1 	0.0
MIDWEST EXPRESS 	MEP 	0 	0 	0 	0 	1 	0.0
NORTHWEST AIRLINES 	NWA 	9 	8.0 	41 	8.0 	156 	6.1
TRANS WORLD AIRLINES TWA 	4 	3.6 	0 	0 	5 	0.2
UNITED AIRLINES 	UAL 	7 	6.3 	35 	6.8 	758 	29.4
USAIR 	USA 	9 	8.0 	148 	28.9 	344 	13.3
TOTAL 	112 	512 	2577
HHI 	2233 	1747
One factor that makes the analysis of the 1993 slot usage data somewhat more complicated than the analysis of the
1990 slot usage is the increased prevalence of noncarrier slot holders. The growth in noncarrier holders of slots since
1990 is due, in large part, to the severe financial difficulties faced by many carriers in recent years. In the May-June
1990 period, only one noncarrier institution, the Shawmut Bank, held any slots. The Shawmut Bank held slots in trust
that were formerly held by TWA, and Shawmut leased these slots back to TWA on a long-term basis.
Table 1 indicates that six financial institutions in addition to the Shawmut Bank held slots during the September-
October 1993 period. The slots held by these institutions as part of bankruptcy proceedings were generally leased
back to the airline that previously held the slots. Other slots held by noncarriers represented collateral for secured
obligations and, again, these slots were generally leased back to the airline that previously held the slots. Because
slots held by noncarriers were typically leased back to carriers on a long term basis, we have recalculated slot market
shares and concentration under the assumption that slots held by noncarriers should, in most cases, be treated as
being held by the relevant carrier.(50) Table 1A contains the results of this analysis. This adjustment raises further
the HHI at each of the HDTAs, moving each into the “highly concentrated” range as defined in the 1992 Merger
Guidelines. In our analyses that follow, we examine whether, in these conditions of higher concentration, carriers
utilized their slots in an anticompetitive manner.
TABLE 1A
DOMESTIC SLOT HOLDINGS AND

CONCENTRATION
WITH NONCARRIER SLOTS ASSIGNED TO
CARRIERS
O'HARE
(ORD)
NATIONAL
(DCA)
HOLDER 	CODE
NUMBER OF
SLOTS
SHARE 	NUMBER OF
SLOTS
SHARE
FAA 	0001 	0 	0 	0 	0
WILMINGTON TRUST CO. 	0023 	14 	1.0 	0 	0
AMERICAN AIRLINES 	AAL 	533 	37.7 	67 	12.4
AMERICA WEST AIRLINES 	AMW 	5 	0.4 	0 	0
CONTINENTAL AIRLINES 	COA 	33 	2.3 	43 	8.0
DELTA AIRLINES 	DAL 	57 	4.0 	88 	16.3
FEDERAL EXPRESS 	FDX 	5 	0.4 	0 	0
BUSINESS EXPRESS 	GAA 	0 	0 	0 	0
MIDWEST EXPRESS 	MEP 	0 	0 	1 	0.2
NORTHWEST AIRLINES 	NWA 	58 	4.1 	87 	16.1
TRANS WORLD AIRLINES 	TWA 	0 	0 	33 	6.1

UNITED AIRLINES 	UAL 	674 	47.7 	42 	7.8
USAIR 	USA 	35 	2.5 	178 	33.0
TOTAL 	1414 	539
HHI 	3741 	1930
TABLE 1A CONTINUED
KENNEDY
(JFK)
LAGUARDIA
(LGA)
ALL HDTAs
HOLDER 	CODE
NUMBER OF
SLOTS
SHARE 	NUMBER OF
SLOTS
SHARE 	NUMBER OF
SLOTS
SHARE
FAA 	0001 	0 	0 	13 	2.5 	13 	0.5
WILMINGTON TRUST 	0023 	0 	0 	0 	0 	14 	0.5
AMERICAN AIRLINES 	AAL 	22 	19.6 	67 	13.1 	689 	26.7
AMERICA WEST
AIRLINES
AMW 	1 	0.9 	0 	0 	6 	0.2
CONTINENTAL
AIRLINES
COA 	0 	0 	23 	4.5 	99 	3.8
DELTA AIRLINES 	DAL 	12 	10.7 	112 	21.9 	269 	10.4
FEDERAL EXPRESS 	FDX 	0 	0 	0 	0 	5 	0.2

BUSINESS EXPRESS 	GAA 	4 	3.6 	0 	0 	4 	0.2
MIDWEST EXPRESS 	MEP 	0 	0 	0 	0 	1 	0.0
NORTHWEST
AIRLINES
NWA 	9 	8.0 	41 	8.0 	195 	7.6
TRANS WORLD
AIRLINES
TWA 	48 	42.9 	20 	3.9 	101 	3.9
UNITED AIRLINES 	UAL 	7 	6.3 	35 	6.8 	758 	29.4
USAIR 	USA 	9 	8.0 	201 	39.3 	423 	16.4
TOTAL 	112 	512 	2577
HHI 	2520 	2347
Table 2 examines the hypothesis that carriers with large slot holdings will attempt to restrict output by reducing the
number of days on which those slots are used. The table shows for each slot holder and each HDTA the proportion of
total days that the holder's slots were used (TPCT) and the proportion of weekdays (WPCT) that the slots were used
during the September-October 1993 period.(51) As was the case in the 1991 Comments, slots are typically utilized at
very high rates.(52) Virtually all of these slots were used at rates exceeding 90%, and the highest rate of slot
utilization actually occurred at O'Hare, the most concentrated of the four HDTAs. Further, the pattern of slot use does
not support the anticompetitive hypothesis. As a general matter, slots held by firms with larger slot market shares
appear to be used no less intensively than slots held by firms with smaller shares.
Looking at each airport individually, the lowest rates of total use (TPCT) at O'Hare were for slots held by the carriers
with the smallest shares, and weekday use rates (WPCT) were all very high and appear unrelated to share. A similar
pattern occurred at National: usage rates are typically very high, especially on weekdays, and slot usage appears
unrelated to share. At LaGuardia, neither the values of TPCT nor the values of WPCT suggest a relationship between
slot use and the number of slots held. At Kennedy, the holders with the fewest slots have the highest values of both
TPCT and WPCT; however, these firms hold only one slot each, so it is difficult to infer much from this result. Among
holders with multiple slots at Kennedy, no particular pattern relating slot usage to the number of slots held emerges.
TABLE 2
AVERAGE SLOT USE

O'HARE
SLOT HOLDER 	CODE
NUMBER OF SLOTS
HELD
TPCT WPCT
UNITED AIRLINES 	UAL 	674 	.95 	.98
AMERICAN AIRLINES 	AAL 	533 	.96 	.98
DELTA AIRLINES 	DAL 	57 	.97 	.99
FIRST BANK NATIONAL ASSOC. 0034 	39 	.93 	.96
USAIR 	USA 	35 	.97 	.98
CONTINENTAL SLOT TRUST 	0035 	33 	.95 	.98
NORTHWEST AIRLINES 	NWA 	19 	.95 	.97
WILMINGTON TRUST CO. 	0023 	14 	.97 	.98
BT COMMERCIAL CORP 	0026 	5 	.91 	.94
FEDERAL EXPRESS 	FDX 	5 	.91 	.98
NATIONAL
SLOT HOLDER 	CODE NUMBER OF SLOTS HELD TPCT WPCT
USAIR 	USA 	152 	.91 	.97

DELTA AIRLINES 	DAL 	88 	.95 	.97
NORTHWEST AIRLINES 	NWA 	87 	.92 	.96
AMERICAN AIRLINES 	AAL 	67 	.94 	.97
CONTINENTAL SLOT TRUST 	0035 	43 	.90 	.96
UNITED AIRLINES 	UAL 	42 	.92 	.96
SHAWMUT BANK 	0004 	32 	.95 	.98
CITIBANK 	0030 	26 	1.00 	1.00
MIDWEST EXPRESS 	MEP 	1 	.80 	.98
TRANS WORLD AIRLINES 	TWA 	1 	.89 	.98
TABLE 2 CONTINUED
LAGUARDIA
SLOT HOLDER 	CODE 	NUMBER OF SLOTS
HELD
TPCT WPCT
USAIR 	USA 	148 	.93 	.98
DELTA AIRLINES 	DAL 	112 	.95 	.98
AMERICAN AIRLINES 	AAL 	67 	.93 	.98

CITIBANK 	0030 	53 	1.00 	1.00
NORTHWEST AIRLINES 	NWA 	41 	.91 	.95
UNITED AIRLINES 	UAL 	35 	.93 	.98
CONTINENTAL SLOT TRUST 0035 	23 	.91 	.97
SHAWMUT BANK 	0004 	20 	.95 	.97
FAA 	0001 	13 	.94 	.98
KENNEDY
SLOT HOLDER 	CODE NUMBER OF SLOTS HELD TPCT WPCT
SHAWMUT BANK 	0004 	44 	.93 	.95
AMERICAN AIRLINES 	AAL 	22 	.97 	.98
DELTA AIRLINES 	DAL 	12 	.93 	.92
NORTHWEST AIRLINES 	NWA 	9 	.95 	.96
USAIR 	USA 	9 	.96 	.95
UNITED AIRLINES 	UAL 	7 	.94 	.93
TRANS WORLD AIRLINES 	TWA 	4 	.98 	.98
STATE ST BANK & TRUST 	0020 	3 	.92 	.94

BT COMMERCIAL CORP 	0026 	1 	.98 	1.00
BUSINESS EXPRESS 	GAA 	1 	1.00 	1.00
As in our 1991 Comments, our primary interest is to test the implications of hypotheses predicting anticompetitive
behavior of carriers controlling large shares of slots at the HDTAs. Table 2A contains average slot use for the slots
operated by each carrier. Particularly notable is the generally high level of slot usage. Only one carrier (Business
Express at National) operated its slots less than 90% of the time, and weekday usage is commonly as high as 98%.
As in Table 2, there does not appear to be an obvious pattern supporting the anticompetitive hypothesis. Slot usage
rates among the carriers operating at each of the HDTAs does not appear to be inversely related to the number of
slots operated by each carrier.
TABLE 2A
AVERAGE SLOT USE BY OPERATOR
O'HARE
CARRIER 	CODE
NUMBER OF SLOTS
OPERATED
TPCT WPCT
UNITED AIRLINES 	UAL 	712 	.95 	.98
AMERICAN AIRLINES 	AAL 	540 	.96 	.98
DELTA AIRLINES 	DAL 	48 	.97 	.99
NORTHWEST AIRLINES 	NWA 	45 	.93 	.96
USAIR 	USA 	30 	.98 	.98
CONTINENTAL AIRLINES 	COA 	30 	.94 	.98
TRANS WORLD AIRLINES 	TWA 	6 	.99 	1.00

SIMMONS AVIATION 	SYM 	3 	.98 	.98
NATIONAL
CARRIER 	CODE NUMBER OF SLOTS OPERATED TPCT WPCT
USAIR 	USA 	211 	.93 	.98
DELTA AIRLINES 	DAL 	86 	.95 	.98
AMERICAN AIRLINES 	AAL 	68 	.94 	.97
NORTHWEST AIRLINES 	NWA 	56 	.92 	.96
CONTINENTAL AIRLINES 	COA 	49 	.90 	.96
UNITED AIRLINES 	UAL 	31 	.94 	.97
TRANS WORLD AIRLINES 	TWA 	31 	.93 	.97
BUSINESS EXPRESS 	GAA 	7 	.88 	.92
TABLE 2A CONTINUED
AVERAGE SLOT USE BY OPERATOR
LAGUARDIA
CARRIER 	CODE 	NUMBER OF SLOTS
OPERATED
TPCT WPCT
USAIR 	USA 	197 	.94 	.98

DELTA AIRLINES 	DAL 	112 	.96 	.98
AMERICAN AIRLINES 	AAL 	67 	.93 	.98
UNITED AIRLINES 	UAL 	37 	.93 	.97
NORTHWEST AIRLINES 	NWA 	34 	.92 	.95
CONTINENTAL AIRLINES 	COA 	26 	.91 	.96
TRANS WORLD AIRLINES 	TWA 	19 	.97 	.99
PENN COMM 	PCA 	11 	.94 	.96
BUSINESS EXPRESS 	GAA 	7 	.92 	.96
TRANS WORLD EXPRESS 	RBD 	1 	.95 	.95
NORTHEAST EXPRESS 	NEE 	1 	.92 	.95
KENNEDY
CARRIER 	CODE NUMBER OF SLOTS OPERATED TPCT WPCT
TRANS WORLD AIRLINES 	TWA 	43 	.93 	.95
AMERICAN AIRLINES 	AAL 	22 	.98 	.99
DELTA AIRLINES 	DAL 	12 	.93 	.92
TRANS WORLD EXPRESS 	RBD 	10 	.94 	.94

BUSINESS EXPRESS 	GAA 	9 	.96 	.97
USAIR 	USA 	7 	.96 	.95
UNITED AIRLINES 	UAL 	6 	.93 	.92
CARNIVAL AIR LINES 	CAA 	1 	.98 	1.00
NORTHEAST EXPRESS 	NE 	1 	.92 	.93
PENN COMM AIRLINE 	PCA 	1 	.93 	.93
Table 3 compares the usage rates of slots held and operated by each carrier with slots leased by the carrier to other
carriers.(53) The anticompetitive leasing hypothesis suggests that slot holders' owned and operated (O&O) slots will
be used at a higher rate than the slots leased to other firms. Accordingly a positive difference between the usage
rates of O&O slots and leased slots would be consistent with this hypothesis. Table 3 lists for each HDTA (1) the
number of O&O slots for each carrier that owns and operates at least one slot; (2) the number of slots that each
carrier leased to other carriers; (3) the total and weekday usage rates for the O&O slots and the leased slots; (4) the
difference (D) between the O&O usage rate and the leased usage rate for total days and weekdays; and (5) two
measures of the statistical significance of this difference, tu and te. tu is a t-statistic calculated under the assumption
that the variances of O&O and leased usage rates are different. te is a t-statistic calculated under the assumption that
the two distributions have the same variance.
TABLE 3
RATES OF
USE IN
OWNED AND
OPERATED
VERSUS
LEASED
SLOTS
O'HARE
OWNED
AND
OPERATE
D
LEASED
TO
OTHER
S

SLOT
HOLDER
COD
E
N 	TPC
T
WPC
T
N 	TPC
T
WPC
T
D 	tu 	te 	D 	tu 	te
TPCTO&
O -
TPCTL
WPCTO&
O -
WPCTL
AMERICAN
AIRLINES
AAL 	459 	.956 	.980 	74 	.957 	.977 	-.0004 	-.09 -.11 	.003 	1.0
0
1.0
7
CONTINENTA
L AIRLINES
COA 	13 	.951 	.977 	20 	.956 	.978 	-.0049 	-.28 -.32 	-.0012 	-.13 -.15
DELTA
AIRLINES
DAL 	33 	.969 	.992 	24 	.961 	.984 	.0076 	.70 	.69 	.009 	1.8
4
1.8
8
NORTHWEST
AIRLINES
NWA 	32 	.928 	.962 	26 	.948 	.972 	-.021 	-
1.39
-
1.34
-.0010 	-.99 	-
1.0
1
UNITED
AIRLINES
UAL 	613 	.952 	.977 	61 	.956 	.979 	-.004 	-.54 -.70 	-.001 	-.32 -.36
USAIR 	USA 	19 	.981 	.982 	16 	.949 	.980 	.0322 	2.93
*
3.04
*
.002 	.26 .25
* Statistically significant at the .05 level.
TABLE 3
CONTINUED
RATES OF
USE IN
OWNED AND
OPERATED
VERSUS
LEASED
SLOTS

NATIONAL
OWNED
AND
OPERATE
D
LEASED
TO
OTHER
S
SLOT
HOLDER
COD
E
N 	TPC
T
WPC
T
N 	TPC
T
WPC
T
D 	tu 	te 	D 	tu 	te
TPCTO&
O -
TPCTL
WPCTO&
O -
WPCTL
AMERICAN
AIRLINES
AAL 	55 	.941 	.976 	12 	.929 	.961 	.0120 	.61 	.77 	.015 	1.36 1.30
CONTINENTA
L AIRLINES
COA 	32 	.888 	.980 	11 	.940 	.928 	-.052 	-
2.87
*
-
2.41
*
-.023 	-
2.56
*
-
2.14
*
DELTA
AIRLINES
DAL 	78 	.951 	.980 	10 	.903 	.928 	.0482 	2.21
*
2.89
*
.052 	2.14 4.55
*
NORTHWEST
AIRLINES
NWA 	35 	.912 	.956 	52 	.922 	.966 	-.010 	-.58 -.58 	-.010 	-.82 -.87
TRANS
WORLD
AIRLINES
TWA 	19 	.951 	.979 	14 	.950 	.975 	.0012 	.07 	.06 	.004 	.27 	.30
UNITED
AIRLINES
UAL 	20 	.931 	.966 	22 	.918 	.961 	.013 	.82 	.80 	.005 	.44 	.43
USAIR 	USA 	162 	.924 	.979 	16 	.943 	.967 	-.018 	-
1.13
-
0.88
.013 	1.08 1.11
* Statistically significant at the .05 level.

TABLE 3
CONTINUED
RATES OF
USE IN
OWNED AND
OPERATED
VERSUS
LEASED
SLOTS
LAGUARDIA
OWNED
AND
OPERATE
D
LEASED
TO
OTHER
S
SLOT
HOLDER
COD
E
N 	TPC
T
WPC
T
N 	TPC
T
WPC
T
D 	tu 	te 	D 	tu 	te
TPCTO&
O -
TPCTL
WPCTO&
O -
WPCTL
AMERICAN
AIRLINES
AAL 	38 	.931 	.987 	29 	.925 	.974 	.0057 	.38 	.37 	.013 	2.41
*
2.36
*
CONTINENTA
L
COA 	12 	.904 	.961 	11 	.912 	.970 	-.008 	-.25 -.25 	-.009 	-.47 -.48
DELTA
AIRLINES
DAL 	83 	.961 	.981 	29 	.921 	.971 	.0404 	3.05
*
3.87
*
.010 	1.49 1.61
NORTHWEST
AIRLINES
NWA 	14 	.906 	.939 	27 	.905 	.952 	.0016 	.07 	.07 	-.013 	-.57 -.65
TRANS
WORLD
TWA 	6 	.967 	.977 	14 	.940 	.967 	.0269 	1.18 1.06 	.0100 	.56 	.55

UNITED
AIRLINES
UAL 	16 	.940 	.974 	19 	.921 	.977 	.0189 	1.17 1.14 	-.003 	-.29 -.28
USAIR 	USA 	161 	.948 	.985 	40 	.950 	.970 	-.002 	-.18 -.14 	.014 	2.56
*
2.82
*
* Statistically significant at the .05 level.
TABLE 3
CONTINUED
RATES OF
USE IN
OWNED
AND
OPERATED
VERSUS
LEASED
SLOTS
KENNEDY
OWNED
AND
OPERATED
LEASED
TO
OTHERS
SLOT
HOLDER 	CODE
N
TPCT
WPCT 	N 	TPCT WPCT 	D 	tu 	te 	D 	tu 	te
TPCTO&O
- TPCTL
WPCTO&O
- WPCTL
AMERICAN
AIRLINES
AAL 	17 	.975 	.988 	5 	.974 	.967 	.0012 	.06 	.06 	.0202 	1.13 1.74
DELTA
AIRLINES
DAL 	12 	.929 	922 	0 	. 	. 	. 	. 	. 	. 	. 	.

BUSINESS
EXPRESS
GAA 	4 	.943 	.946 	0 	. 	. 	. 	. 	. 	. 	. 	.
TRANS
WORLD
AIRLINES
TWA 	35 	.930 	.948 	13 	.948 	.948 	-.019 	-
1.32
-
1.21
.0001 	.00 	.00
UNITED
AIRLINES
UAL 	6 	.932 	.919 	1 	.984 	.977 	-.052 	. 	-
1.27
-.058 	. 	-.92
USAIR 	USA 	7 	.96 	.95 	2 	.959 	.965 	-.001 	-.04 -.04 	-.015 	-.37 -.36
* Statistically significant at the .05 level.
At O'Hare, the two largest airlines (in terms of slot share), United (47.7% share) and American (37.7% share) used
their O&O slots just as intensively as other carriers utilized slots that these two carriers leased to them. The
differences are very small and in all cases statistically insignificant. In one case — USAir in the all days analysis —
the difference between a carrier's usage of its O&O slots and lessees' usage of its leased slots is positive and
significant. Although this finding is consistent with the anticompetitive hypothesis, USAir's modest share at O'Hare
(2.5%) indicates that its practices would be unlikely to raise competitive concerns.
At National, one carrier, Delta, leased slots to carriers that used the slots a smaller percentage of the time than it
used its O&O slots. Delta was the second largest carrier at National (16.3% share). By contrast, for the largest carrier
at National, USAir (28.2% share), there is essentially no difference between its usage of its O&O slots and lessees'
usage of its leased slots (the differences are either very small or negative, and not statistically different from zero at
the five percent level of significance.) Thus, the carrier with the largest share of slots at National does not lease its
slots in a manner consistent with the anticompetitive hypothesis.(54)
The findings at LaGuardia are somewhat more problematic. USAir also is the largest carrier at LaGuardia (28.9%
share of slots). Based on total-days usage, USAir leased slots to carriers that used the slots just as intensively as
USAir used its O&O slots; however, based on weekdays-only usage, USAir leased slots to carriers that used the slots
significantly less often than USAir used its O&O slots. This result is consistent with the anticompetitive leasing
hypothesis. Delta, the second largest carrier at LaGuardia (21.9% share), and American, the third largest carrier at
LaGuardia (13.1%), also appear to lease slots to carriers that use the slots less often than Delta or American use
their O&O slots. Thus, at LaGuardia some leasing patterns appear consistent with the anticompetitive leasing
hypothesis.(55) At Kennedy, the differences in slot use of O&O slots and leased slots are all statistically insignificant.
Overall, the patterns of use of O&O slots and leased slots are somewhat mixed with regard to the anticompetitive
leasing hypothesis. At O'Hare, the most highly concentrated of the four HDTAs, and at Kennedy, the differences in
slot usage are inconsistent with anticompetitive leasing hypothesis. At National and LaGuardia, the competitive
implications of the differences in O&O and leased slot usage rates are less clear. At National, the largest carrier,
USAir, appears to lease slots to carriers that use them at least as intensively as USAir uses its O&O slots. This
finding is contrary to the predictions of the anticompetitive leasing hypothesis. Nonetheless, Delta, the second largest
carrier at National, tends to use its O&O slots at a significantly higher rate than the carriers that lease slots from it. At
LaGuardia, where USAir is again the largest carrier, USAir, Delta, and American lease slots to carriers that use them

less intensively than these carriers use their O&O slots. This result, which passes standard tests of statistical
significance, is consistent with the anticompetitive leasing hypothesis.
Table 4 lists the net leasing positions of the carriers holding slots at each HDTA in September-October 1993. The
anticompetitive leasing hypothesis predicts that carriers with a large share of the slots at an HDTA will tend to be net
lessors. Accordingly, this anticompetitive hypothesis would be supported if the difference between the number of slots
operated and the number of slots held is negative.(56) The results in Table 4 are somewhat mixed, but do not
generally support the anticompetitive hypothesis. At O'Hare and National, the largest carriers are net lessees—they
operate more slots than they hold, counter to the prediction of the anticompetitive hypothesis. At LaGuardia, the
largest carrier, USAir, operates four fewer slots than it holds, while the next two largest carriers, Delta and American,
operate the same number of slots that they hold. At Kennedy, because Shawmut Bank's 44 slots are treated as being
held by TWA, TWA holds 48 slots and operates 43, making TWA the largest holder at Kennedy and a net lessor of
slots. The next two largest carriers at Kennedy, however, American and Delta, are not net lessors of slots.
TABLE 4
SLOTS OWNED, OPERATED, AND NET LEASING POSITION
O'HARE
SLOT HOLDER 	CODE SLOTS 	SLOTS 	SLOTS OPERATED -
HELD OPERATED 	SLOTS HELD
UNITED AIRLINES 	UAL 	674 	712 	38
AMERICAN AIRLINES 	AAL 	533 	540 	7
DELTA AIRLINES 	DEL 	57 	48 	-9
USAIR 	USA 	35 	30 	-5
NORTHWEST AIRLINES 	NWA 	38 	45 	-7
FEDERAL EXPRESS 	FDX 	5 	0 	-5
NATIONAL

SLOT HOLDER 	CODE SLOTS 	SLOTS 	SLOTS OPERATED -
HELD OPERATED 	SLOTS HELD
USAIR 	USA 	178 	211 	33
DELTA AIRLINES 	DAL 	88 	86 	-2
NORTHWEST AIRLINES 	NWA 	87 	56 	-31
AMERICAN AIRLINES 	AAL 	67 	68 	1
UNITED AIRLINES 	UAL 	42 	31 	-11
MIDWEST EXPRESS 	MEP 	1 	0 	-1
TRANS WORLD AIRLINES TWA 	33 	31 	-2
TABLE 4 CONTINUED
SLOTS OWNED, OPERATED, AND NET LEASING POSITION
LAGUARDIA
SLOT HOLDER 	CODE SLOTS 	SLOTS 	SLOTS OPERATED -
HELD OPERATED 	SLOTS HELD
USAIR 	USA 	201 	197 	-4
DELTA AIRLINES 	DAL 	112 	112 	0

AMERICAN AIRLINES 	AAL 	67 	67 	0
NORTHWEST AIRLINES 	NWA 	41 	34 	-7
UNITED AIRLINES 	UAL 	35 	37 	2
KENNEDY
OWNED AND OPERATED
SLOT HOLDER 	CODE SLOTS 	SLOTS 	SLOTS OPERATED -
HELD OPERATED 	SLOTS HELD
TRANS WORLD AIRLINES TWA 	48 	43 	-5
AMERICAN AIRLINES 	AAL 	22 	22 	0
DELTA AIRLINES 	DAL 	12 	12 	0
USAIR 	USA 	9 	7 	-2
NORTHWEST AIRLINES 	NWA 	9 	0 	-9
UNITED AIRLINES 	UAL 	7 	6 	-1
BUSINESS EXPRESS 	GAA 	1 	9 	8
B. Regression Analysis
As in our 1991 Comments, we further examine the anticompetitive hypotheses regarding slots using multiple
regression analysis to analyze the more recent (September-October 1993) slot usage data. The dependent and
independent variables used in these regressions are generally the same as those used in the 1991 Comments.(57) In

the regressions, the dependent variable is the number of days a slot was used, NDOPER. The dependent variable is
measured by using either all 61 days in the sample period or only the 43 weekdays in the period.
The independent variables in the regressions consist of (1) SLMS, the slot holder's share of the total number of
nonrestricted and nonexempt slots at the HDTA at the end of the two-month period; (2) NAMS, the slot holder's
national market share (based on Revenue Passenger Miles) for the two month period; (3) WITHD, the FAA
withdrawal priority number; (4) LEASE, a dummy variable equal to one if the slot is operated by a single operator
other than the slot holder and zero otherwise; (5) MULTO, a dummy variable equal to one if the slot is operated by
more than one operator, and was not traded in the two month period and zero otherwise; and (6) Pt, a dummy
variable equal to one if the slot's hour or half-hour period starts at time t. The dummy variable PT is dropped, where T
is the last hour or half-hour period of the day for which slots are allocated at each airport.
The inclusion of SLMS allows us to test the anticompetitive hypothesis that slots held by carriers with a large share of
the slots at the HDTA will be used less intensively than slots held by carriers with smaller shares.(58) The variables
LEASE and LEASE*SLMS (which interacts these two variables) allow us to test the anticompetitive leasing
hypothesis, which predicts that slots leased by large holders of slots to other carriers will be used at lower rates. The
other variables in the regressions control for other factors that might affect usage rates, but which do not necessarily
imply anticompetitive behavior. NAMS controls for differences in slot use resulting from the degree to which different
carriers have more or less extensive route structures.(59) All other things equal, the more extensive a carrier's route
network, the more flexibility the carrier will have to adjust routes and, consequently, the more efficiently a carrier will
be able to utilize slots. The slot withdrawal number, WITHD, controls for possible differences in the strength of a
holder's “property right” associated with a slot and any influence that this may have on usage rates. The dummy
variables Pt are included in order to control for differences in slot use arising from differences in the time of day over
which a slot is defined. A slot covering a period of the day in which air travel tends to be high will, all other things
equal, be used at a higher rate than a slot covering a less busy time of day.
The larger number of noncarrier slot holders in the 1993 data makes the analysis here somewhat more complicated
than in the 1991 Comments. In September-October 1993, as in May-June 1990, the Shawmut Bank held TWA's
former slots in trust and TWA, in turn, had a long-term lease with Shawmut for use of the slots. Thus, Shawmut's slots
were treated as held by TWA in the regression analysis. The slots held by the other noncarrier holders have been
treated similarly.(60)
The results from the 1993 regressions are reported in Tables 5.1 through 5.4.(61) As explained below, they do not
tend to support the anticompetitive hypotheses.
TABLE 5.1
REGRESSION RESULTS
O'HARE
DEPENDENT VARIABLE: NUMBER OF DAYS SLOT OPERATED, SEPT -
OCT 1993
(t-statistics in parentheses)

VARIABLE 	COEFFICIENTS
ALL DAYS 	WEEKDAYS
ONLY
CONSTANT 	52.58
(51.94)*
40.49
(70.65)*
SLMS 	-0.91
(-1.65)*
-0.52
(-1.66)*
NAMS 	7.39
(3.18)*
4.66
(3.54)*
WITHD 	.18 x 10-4
(0.17)
.77 x 10-4
(1.22)
LEASE 	0.24
(0.77)
0.23
(1.25)
LEASE*SLMS 	-0.40
(-0.45)
-0.37
(-0.74)
MULTO 	-0.20
(-0.46)
-0.02
(-0.07)
MULTO*SLMS 	0.42
(0.37)
-0.40
(-0.61)
N 	1414 	1414

MEAN DEP. VAR. 	58.20 	42.07
R-SQUARED 	.325 	.122
ADJ R-SQUARED 	.308 	.100
F 	18.96* 	5.4842*
LOG LIKELIHOOD 	-2881.62 	-2077.27
* Significant at less than the .05 level (one-tail test)
TABLE 5.2
REGRESSION RESULTS
NATIONAL
DEPENDENT VARIABLE: NUMBER OF DAYS SLOT OPERATED, SEPT -
OCT 1993
(t-statistics in parentheses)
VARIABLE 	COEFFICIENTS
ALL DAYS 	WEEKDAYS
ONLY
CONSTANT 	61.10
(24.85)*
43.64
(37.86)*
SLMS 	-0.84
(-0.41)
2.03
(2.12)*

NAMS 	6.47
(1.66)*
1.14
(0.62)
WITHD 	-0.48 x 10-2
(-5.35)*
-0.12 x 10-2
(-2.83)*
LEASE 	-0.97
(-1.02)
0.37
(0.83)
LEASE*SLMS 	0.58
(0.11)
-4.71
(-1.87)*
MULTO 	-1.30
(-1.31)
-0.21
(-0.46)
MULTO*SLMS 	3.91
(0.70)
-1.11
(-0.42)
N 	539 	539
MEAN DEP. VAR. 	56.65 	41.80
R-SQUARED 	.232 	.144
ADJ R-SQUARED 	.196 	.104
F 	6.48* 	3.61*
LOG LIKELIHOOD 	-1461.69 	-1053.23

* Significant at less than the .05 level (one-tail test)
TABLE 5.3
REGRESSION RESULTS
KENNEDY
DEPENDENT VARIABLE: NUMBER OF DAYS SLOT OPERATED, SEPT -
OCT 1993
(t-statistics in parentheses)
VARIABLE 	COEFFICIENTS
ALL DAYS 	WEEKDAYS
ONLY
CONSTANT 	76.39
(4.24)*
48.99
(3.80)*
SLMS 	-0.52
(-0.16)
2.57
(1.14)
NAMS 	10.57
(1.92)*
4.81
(1.22)
WITHD 	-0.38 x 10-2
(-1.08)
-0.13 x 10-2
(-0.54)
LEASE 	0.98
(0.48)
1.67
(1.14)

LEASE*SLMS 	1.92
(0.32)
-3.47
(-0.80)
MULTO 	0.12
(0.11)
0.13
(0.16)
MULTO*SLMS 	0.37
(0.10)
-3.38
(-1.21)
N 	112 	112
MEAN DEP. VAR. 	57.70 	40.93
R-SQUARED 	.181 	.174
ADJ R-SQUARED 	.043 	.035
F 	1.31 	1.25
LOG LIKELIHOOD 	-266.24 	-228.68
* Significant at less than the .05 level (one-tail test)
TABLE 5.4
REGRESSION RESULTS
LAGUARDIA
DEPENDENT VARIABLE: NUMBER OF DAYS SLOT OPERATED, SEPT -
OCT 1993

(t-statistics in parentheses)
VARIABLE 	COEFFICIENTS
ALL DAYS 	WEEKDAYS
ONLY
CONSTANT 	63.06
(20.72)*
43.29
(37.64)*
SLMS 	2.24
(1.11)
2.47
(3.23)*
NAMS 	-1.35
(-0.34)
4.06
(2.71)*
WITHD 	-0.13 x 10-2
(-1.92)*
-0.37 x 10-3
(-1.42)
LEASE 	-0.57
(-0.67)
0.53
(1.65)*
LEASE*SLMS 	-1.22
(-0.35)
-4.50
(-3.41)*
MULTO 	-1.07
(-1.25)
-0.23
(-0.70)
MULTO*SLMS 	2.34
(0.71)
0.01
(0.01)

N 	512 	512
MEAN DEP. VAR. 	57.30 	42.00
R-SQUARED 	.163 	.179
ADJ R-SQUARED 	.089 	.105
F 	2.18* 	2.43*
LOG LIKELIHOOD 	-1375.75 	-887.44
* Significant at less than the .05 level (one-tail test)
1. O'Hare
O'Hare is the most concentrated of the four HDTAs. The high level of concentration might suggest that the potential
for anticompetitive behavior would be greatest here. In fact, the two coefficients on SLMS, the slot holder's market
share, are negative and statistically significant at the .05 level using a one-tail test. While these findings are
consistent with the anticompetitive hypotheses, these coefficients are very small.(62) The coefficients on LEASE and
LEASE*SLMS also are statistically insignificant, as are the coefficients on the control variables except NAMS,
national market share. Thus, this regression appears to indicate that slot usage at O'Hare is primarily a function of the
extensiveness of a carrier's route structure.(63)
2. National
At National, the coefficient on SLMS is negative in the all-days regression, but it is statistically insignificant. In the
weekdays-only regressions, the coefficient on slot market share is both positive and significant, which is inconsistent
with the anticompetitive usage hypothesis. Both coefficients on LEASE are statistically significant a the .05 level using
a one-tail test, and the coefficient on LEASE*SLMS is statistically significant at the .05 level in the weekdays only
regression. For weekday use, the largest slot holder at National, USAir, leased slots to carriers who used these slots
significantly less intensively than USAir utilized its owned and operated slots.(64) This finding is consistent with the
anticompetitive leasing hypothesis, although the effect is offset by the positive, significant coefficient on SLMS in the
weekdays-only regression.
The coefficient on WITHD is significant in both the all-days and weekdays-only regressions. In both regressions, this
coefficient is negative, indicating that slots with higher withdrawal numbers (i.e., those least likely to be withdrawn)
are used less intensively—a somewhat surprising result.
3. Kennedy

Virtually all coefficients estimated by both regressions are statistically insignificant at standard levels.(65) Accordingly,
no relationships between slot shares and slot usage or leasing and slot usage are discernable. In fact, the F-statistic
testing the joint significance of all the explanatory variables is statistically insignificant, which indicates that the
explanatory variables, taken together, cannot explain the observed variation in the dependent variable.
4. LaGuardia
For the all-days regression, the constant term and WITHD are statistically significant at standard levels.(66) The
weekdays-only regression appears somewhat more informative. The coefficient on SLMS, slot market share, is
positive and significant, a result that is inconsistent with the anticompetitive hypothesis. On the other hand, the
negative, significant coefficient on LEASE*SLMS indicates that slots leased by larger carriers to other carriers tend to
be used relatively less intensively, a result consistent with the anticompetitive hypothesis (and with the results
reported in Table 3).(67) According to this analysis, the two carriers with the largest slot shares at LaGuardia, USAir
and Delta, leased slots to other carriers who utilized them less intensively, other things equal, than these two carriers
utilized their owned and operated slots.(68)
5. Summary of Regression Results
Generally, the regressions using the September-October 1993 data do not indicate any consistent relationships
between slot usage and either holders' slot market shares or holders' leasing behavior. The results, therefore, do not
generally support the two anticompetitive hypotheses examined.(69) Many of the findings do not meet standard tests
of statistical significance; in these cases, the hypothesis that the variables to which they apply had no effect on slot
usage one way or the other cannot be rejected. The negative, significant coefficients on slot market share at O'Hare
do support the anticompetitive hypothesis, but would account only for very small effects. The analyses that provide
statistically significant results supporting the anticompetitive leasing hypotheses also contain offsetting results with
regard to slot market share. At National and LaGuardia, the weekdays-only regressions produce negative, statistically
significant results that are consistent with the anticompetitive leasing hypothesis, but these same analyses contain
positive, statistically significant coefficients on market share that are inconsistent with the anticompetitive slot usage
hypothesis.
VII. Peak and Off-peak Pricing as an Alternative to the HDR
Some critics of the HDR have suggested that the use of peak and off-peak landing fees may be a preferred
alternative to the HDR.(70) In a previous analysis of the use of peak and off-peak aircraft operations fee
structures,(71) the staff of the FTC concluded that properly structured takeoff and landing fees can be an important
step toward more efficient pricing of operating rights.(72)
The marginal cost of an aircraft operation consists of: (1) the resource costs imposed on the airport; (2) the delay
costs imposed on aircraft operations and passengers, including the delay costs imposed on other flights; and (3) the
noise costs imposed on residents living near the airport. During peak periods, the first two, and possibly all three, of
these marginal costs increase. Increased congestion during peak periods increases delay costs imposed on
passengers and airlines using the airport during these times.(73) The resource costs imposed on the airport also
increase (e.g., additional costs are imposed on the air traffic control system).
Economically efficient prices would cover all of these costs. Because the marginal costs of a landing will increase
during peak periods, the economically efficient price of a landing also will increase during peak periods. Thus, using a
peak/off-peak pricing structure would be consistent with economically efficient pricing.
Although economically efficient pricing will have a peak/off-peak structure, whether or not a regulatory body or airport
administrator would (or could) choose the appropriate levels of peak and off-peak prices both to avoid congestion and
to utilize capacity optimally is not clear. Some critics promote the use of price rather than quantity, that is, slot
regulation, as the appropriate regulatory instrument. The underlying reason for this preference is not always clear,

and the relative merits of a quantity-based versus price-based system are not clear ex ante. While market prices are
superior economizers of information compared to administratively set output levels, administratively set prices do not
necessarily possess the same advantage.(74)
The amount of information required to implement peak and off-peak pricing may be formidable. An airport’s capacity
is fixed, but the demand for air travel from a particular airport can fluctuate substantially. Not only does demand
fluctuate between peak and off-peak periods during each day, but peak and off-peak demand can fluctuate from day
to day and month to month. The demand for air travel (both at peak and off-peak times) will fluctuate with changes in
the business cycle, seasonally, and as a result of purely random events such as terrorist threats and the weather. If
administratively determined peak and off-peak prices cannot respond quickly to cyclical and random changes in peak
and off-peak demand, then the welfare of air travelers may be reduced. If prices are set too low, travelers may be
faced with congestion and congestion related delays; if prices are set too high, airport capacity may be under-utilized
as the number of operations falls below levels necessary to control congestion.
The HDR was adopted in order to allocate existing capacity. If that capacity is known and reflected in the hourly slot
quotas, the prices at which slots trade will fluctuate as demand fluctuates: higher prices during high- demand peak
periods, and lower prices during low-demand off-peak periods. Thus, given the level of capacity, market forces will
lead naturally to peak and off-peak prices. To implement slot-based regulation, the only information that regulators or
airport administrators need is an estimate of the capacity of the airport. Since airport capacity does not generally
change from day to day or hour to hour, the degree of regulatory oversight necessary to implement slot-based
regulation is modest.
Thus, while using peak/off-peak pricing of takeoff and landing fees would likely enhance efficiency compared to
rationing airport capacity by the queue, it appears likely that such a pricing structure is not superior to slot-based
regulation. The information required to set peak-load prices at levels that adequately discourage congestion may be
considerable given the degree of cyclical and random fluctuations in the demand for air travel.
VIII. Suggested Areas for Study
To the extent the FAA might be concerned about the effects, if any, of slot market regulation on other dimensions of
competition in the airline industry, the FAA might consider expanding its slot usage data base to include such
information as the size and destination of the airplane using a particular slot, the prices at which carriers sell slots to
one another, and rates at which slots are leased. Such data would allow a more refined analysis of how slot usage
might affect or evidence competition among airlines.(75) If collecting this additional data is burdensome, the FAA
might consider collecting the data for a limited study period or for a limited, representative sample of the slots. In
addition, the FAA might want to consider, as part of its study of the HDR, a reexamination of the operating capacity at
each of the HDTAs. Such a study would help to ensure that slot quotas still accurately reflect capacity and do not
inappropriately constrain airport operations.
The staff of the Bureau of Economics supports the FAA’s efforts to encourage the use of market-based systems to
allocate scarce airport resources, including the use of price-based and quantity-based allocation schemes. We
suggest that the FAA consider under what conditions use of quantity-based regulation systems, such as the HDR,
may be more efficient than price-based regulation systems.
IX. Conclusions
The high density rule was created in reaction to congestion and delays at the high density traffic airports. Although
the rule has been criticized as imposing artificial constraints on operations at the four HDTAs, it is the limited capacity
of these airports, not the HDR, that ultimately constrains their operations. The HDR is basically a mechanism for
allocating the limited capacity of the HDTAs to competing carriers. By creating a market for slots, the HDR helps
ensure that slots are allocated to the flights that are valued most highly by consumers.(76)

We concluded from our empirical examination of slot usage at the four HDTAs both before and after the 1992
amendments to the HDR were adopted that slot usage was not consistent with the exercise of market power by
incumbent carriers. The 1992 amendments were designed specifically to promote entry by new carriers and foster the
growth of smaller incumbent carriers at the HDTAs. Given the important economic function of the HDR as a
mechanism for efficiently allocating limited capacity, the FAA may wish to consider rescinding one part of those
amendments, the two-year restriction on the sale of slots obtained through a lottery. Further, the FAA may wish to
study the feasibility of extending the HDR to include additional airports regularly prone to congestion and delays
attributed to excess demand at peak time periods. Alternatively, the FAA may wish to consider allowing airports that
are not slot-regulated to implement peak-load pricing of landing and takeoff fees as a means of allocating scarce
capacity more efficiently than by simple rationing by the queue. As we have emphasized in our comments, the HDR
is not the problem—rather, the problems are the limited capacity, high demand, and resulting congestion and
congestion-related delays at the HDTAs. The HDR is an attempt to solve these problems in an economically efficient
manner.
(1) This comment represents the views of the staff of the Bureau of Economics of the Federal Trade Commission.
They are not necessarily the views of the Commission or any individual Commissioner. Inquires regarding this
comment should be directed to Laurence Schumann (202-326-3359) of the FTC’s Bureau of Economics.
(2) See “Study of High Density Rule,” FAA Docket No. 27664, 59 Fed. Reg. 15332 (April 1, 1994), and “Notice of
Public Meeting,” FAA Docket No. 27664, 59 Fed. Reg. 48165 (September 30, 1994).
(3) 15 U.S.C. 41 et seq.
(4) See the comments of the staff of the Bureau of Economics of the Federal Trade Commission, High Density Traffic
Airports: Slot Allocation and Transfer Methods, FAA Docket No. 25758 (November 15, 1991); Slot Allocation
Alternative Methods, FAA Docket No. 24110 (July 27, 1984). Comments on other aspects of airline competition and
regulation include, Elimination of Airport Delays, FAA Docket No. 24206 (August 30, 1994); Discussion Authority for
Agreement to Shift Schedules, Department of Transportation, Docket No. 44634 (February 17, 1987); and Charges
for the Use of Metropolitan Washington Airports, FAA Docket No. 25204 (April 13, 1987). See also Comments of the
Bureaus of Economics, Competition, and Consumer Protection of the Federal Trade Commission, Massport Program
for Airport Capacity Efficiency (February 29, 1988).
(5) See D. Koran and J. Ogur, Airport Access Problems: Lessons Learned from Slot Regulation by the FAA, Bureau
of Economics Staff Report to the Federal Trade Commission (May 1983), and J. Ogur, M. Vita, and C. Wagner, The
Deregulated Airline Industry: A Review of the Evidence, Bureau of Economics Staff Report to the Federal Trade
Commission (January 1988).
(6) Federal Aviation Regulations Amendment No. 93-13, 33 Fed. Reg. 17896 (December 3, 1968).
(7) See Department of Transportation, Federal Aviation Administration, High Density Traffic Airports: Slot Allocation
and Transfer Methods, Final Rule, 14 C.F.R. Parts 11 and 93, 50 Fed. Reg. 52180, 52185 (1985) (“FAA 1985 Final
Rule”).
(8) See D. M. Grether, R.M. Issac, and C.R. Plott, “The Allocation of Landing Rights by Unanimity Among
Competitors,” 71 American Economic Review 166 (1981).
(9) See FAA 1985 Final Rule, supra note 7. See also Comments of the Department of Justice, Slot Allocation:
Alternative Methods and Slot Transfer Methods, Notice of Proposed Rulemaking, FAA Docket No. 24110 and 24105
(August 6, 1984), pp. 11-12.
(10) Supra note 7 at 52181.

(11) See FAA 1985 Final Rule, supra note 7, p. 52180, and 14 C.F.R. Parts 93.211 - 93.229.
(12) According to the 1985 Final Rule (supra note 7, p. 52190), air carriers and commuter operators holding
permanent slots on December 16, 1985 were allocated those slots subject to certain withdrawal provisions. See 14
C.F.R. Part 93.215.
(13) See 14 C.F.R. Part 93.223 (a).
(14) See 14 C.F.R. Part 93.223. For example, in March of 1986, the FAA withdrew 5 percent of the slots from
incumbent carriers for distribution via lottery to new entrant carriers.
(15) Originally, the minimum slot usage requirement was 65 percent.
(16) See Department of Transportation, Federal Aviation Administration, “High Density Traffic Airports: Slot Allocation
and Transfer Methods, Final Rule,” 14 C.F.R. 93, 57 Fed. Reg. 37308 (1992) (“FAA 1992 Final Rule”) .
(17)” See FAA 1992 Final Rule, supra note 16.
(18) At the time that the HDR was first proposed, the FAA noted that congestion related delays of varying magnitude
were also found at airports in Boston, Miami, Los Angeles, San Francisco, and Atlanta. The situations at New York,
Chicago, and Washington, however, were described as “the most
critical.” The FAA did leave open the possibility that if congestion and delay became more of a problem at these other
cities, the HDR could be extended as appropriate. See High Density Traffic Airports: Notice of Proposed Rule Making
and Notice of Public Hearing, 33 Fed. Reg. 12580 (1968).
(19) Supra note 7.
(20) Supra note 16.
(21)” Supra note 16 at 37309.
(22) Slot transfers that confer market power or otherwise lessen competition would, of course, not represent efficient
transfers of resources. Nonetheless, antitrust concerns would not be limited to the 2- year period following a slot
lottery. Slot transfers are already scrutinized by antitrust authorities (see, for example the discussion in footnote 36
below), and there is no reason to expect that transfers conducted in the 2-year period following a lottery are more
likely to be anticompetitive than slot transfers conducted at other times.
(23) The National Commission To Ensure a Strong Competitive Airline Industry, created by Congress (Pub. L. No.
103-13, April 7, 1993), consisted of 15 voting and 11 non-voting members appointed by the President, the Senate
and the House of Representatives. The bipartisan Commission issued its report in August, 1993.
(24)” Supra note 23, p. 9. The National Commission studied and made recommendations on a large number of issues
affecting airline competition and efficiency.
(25) FAA 1985 Final Rule, supra note 7 at 52181.
(26) We express no opinion about how accurately the slot quotas correspond to the HDTAs’ capacities.
(27) FAA 1992 Final Rule, supra note 16 at 37308.

(28)” The “Aviation Safety and Capacity Expansion Act of 1990” was enacted as Title IX, § 9126(a) of the Omnibus
Reconciliation Act of 1990, Public Law 101-508 (104 Stat. 1388-371).
(29) See, for example, Airline Competition: Effects of Airline Market Concentration and Barriers to Entry on Airfares
(General Accounting Office Report to Congressional Requesters, RCED-91-101). According to this report, “Since an
airline must obtain a slot to serve [the HDTAs], slot restrictions can be a barrier to entry at these airports.” (p. 22)
Economists do not typically consider the need to purchase an input, such as the ability to land or take off, to be a
barrier to entry. Nonetheless, government imposed constraints can be barriers to entry to the extent that they harm,
rather than promote, consumer welfare. See H. Demsetz, “Barriers To Entry,” 72 American Economic Review 47
(March 1982). Taxi medallions, for example, are often cited as barriers to entry since they place artificially low
constraints on the number of taxis operating in a city, causing harm to consumers (due to the inefficiently low number
of taxis) that far exceeds any benefits that they may confer. On the other hand, government- imposed constraints can
also promote entry by establishing property rights. For example, patents can prevent new entry in existing markets,
but they can also encourage new entry into newly developing markets. Since existing markets that are protected by
patents might not have been developed without this protection, patents can accurately be described as facilitating
rather than barring entry.
As long as the number of slots set at each of the HDTAs reflects each airport’s capacity, it is the limited capacity that
acts as a constraint, not the need to purchase a slot. When viewed in this light, the slot market does not constrain
entry, but instead it facilitates entry. If the slot market did not exist, then, given the limited capacity at the HDTAs, a
potential new entrant might be unable to obtain rights to land or take off. Indeed, prior to the adoption of the FAA’s
1985 Final Rule (supra note 5), incumbent carriers were often able to bar the entry of new carriers at the HDTAs by
denying them operating
privileges. See, for example, the discussion in Koran and Ogur, supra note 7, p. 16.
Some critics have suggested that, since slots were originally distributed for free, potential new entrants are at a
competitive disadvantage because they have to purchase slots in order to begin service. The relevant concept of cost
in determining economic decisions, however, is that of opportunity cost. The cost the current owner incurs to use a
slot is the revenue that the owner would receive if the slot were sold or leased to another carrier, because that
amount of money is foregone every time the owner uses it. This cost is identical to the cost that a new entrant must
pay in order to buy or lease it. Although the original distribution of slots increased the value of the firms that received
slots for free (and the wealth of their shareholders), now the relevant economic decisions concerning output, prices,
entry, or exit are based on the opportunity costs, which are the same whether a firm is currently operating a slot or
considering purchasing or leasing it.
(30)” Policies for the Deregulated Airline Industry, Congressional Budget Office (1988), p. 55.
(31) Based on slot holdings, each of the HDTAs were either moderately or highly concentrated in 1990, according to
the standards of the 1984 Department of Justice Merger Guidelines (although newly revised Merger Guidelines were
issued by the DOJ and FTC in 1992, the relevant classification of market concentration is the same as that in place in
1990). See Comment of the Staff of the Bureau of Economics of the Federal Trade Commission, High Density Traffic
Airports: Slot Allocation and Transfer Methods (“1991 Comments”), FAA Docket No. 25758 (November 15, 1991),
Table 1.
(32) The report Airline Competition: Effects of Airline Market Concentration and Barriers to Entry on Airfares (General
Accounting Office Report to Congressional Requesters, RCED-91-101), concludes that slot restrictions increase
average or median fares eleven percent on short haul routes and four percent from all routes to and from HDTAs.
(33) Scarcity rents arise when competition for a resource causes the price of that resource to rise above the marginal
cost of “inframarginal” units. Although at the margin the price of the scarce resource will equal its marginal cost, this
price will exceed the marginal cost of all other units of the resource. Hence, the inframarginal units will earn a “profit”

(or, more precisely, an economic rent) generated by the scarcity. Here, scarcity rents reflect the limited capacity and,
in turn, the limited number of slots available. In contrast, monopoly rents result from anticompetitive output
restrictions. Here, monopoly rents would be additional rents due to slot holders’ decisions to exercise market power
by restricting slot use in a way that prevents existing slots from moving to their highest valued uses.
(34) In a competitive market, a slot’s value would equal the net present value of its associated scarcity rents. Thus,
the GAO’s conclusion, that it is slot restrictions that cause higher fares at HDTAs, may be imprecise. Fares at HDTAs
will reflect the cost of purchasing slots just as they will reflect the costs of other inputs. Fares may be higher at
HDTAs simply because at other airports that are not capacity constrained, capacity is not a scarce, and consequently
costly, input. If there are airports other than the HDTAs that are capacity constrained, then scarcity rents will be
reflected in higher costs for air travel from these airports. Not all of the rents, however, will necessarily be reflected
explicitly in airfares. Some of the rents may be “paid” through diminishing the welfare of travelers who face added
congestion and delays at these airports.
(35) Several studies have concluded that high fares at concentrated hubs represent the exercise of market power, in
addition to whatever scarcity rents may be generated by capacity constraints. See, for example, S. Borenstein,
“Airline Mergers, Airport Dominance, and Market Power,” 80 American Economic Review 400 (1990) and W. Evans
and I. Kessides, “Localized Power in the U.S. Airline Industry,” 75 Review of Economics and Statistics 66 (1993).
Others have argued that higher fares at hub airports are consistent with competitive behavior. See Kleit and Maynes,
“Airline Networks as Joint Goods: Implications for Competition Policy,” 4 Journal of Regulatory Economics 175
(1992). Despite these differing interpretations, none of these authors identifies the system of marketable slots as a
source of competitive problems in airline markets.
(36) An illustration of how the HDTAs and their regional competitors could be considered in the same antitrust market
is provided by the positions the Department of Justice (“DOJ”) took in Eastern Airlines’ bankruptcy proceedings. The
DOJ opposed United’s proposed purchase of Eastern’s National Airport slots and facilities on the grounds that it
would “lessen competition” for flights between Washington and other cities since United was already the dominant
carrier at Dulles International Airport and the acquisition would have given it control of 20 percent of the slots at
National. See “Justice Dept. to Dispute Deal for Eastern Slots,” Washington Post, February 15, 1991, p. B2; 60
Antitrust & Trade Regulation Report 152 (January 31, 1991), 186 (February 7, 1991), and 272 (February 21, 1991).
The DOJ, however, did not object to Northwest Airlines’ purchase of Eastern’s slots and facilities at National even
though Northwest had more slots than United at National prior to the Eastern bankruptcy sale, because Northwest’s
share at Dulles was less significant than United’s. The DOJ analysis is consistent with a relevant antitrust market
consisting of at least both National and Dulles. What is pertinent, therefore, is how the sale of Eastern’s National
assets affected concentration in this antitrust market, not how it affected concentration at just National. Similarly, even
though United was the largest carrier at O’Hare, the DOJ did not object to United’s purchase of Eastern’s O’Hare
slots, a position consistent with considering flights to and from Midway Airport to be in the same antitrust market as
flights to and from O’Hare.
(37) The fact that slots are scarce at one airport and thus sell at positive prices, while they are not scarce at another
airport in the same region, is not alone an indication of whether or not the two airports are in the same antitrust
market. Two regional airports can be in the same antitrust market even if prices for slots vary between the airports.
The primary factor in determining market definition is not price, but the degree to which travelers will switch from one
airport to another in response to changes in relative prices.
(38) See 1991 Comments, supra note 31.
(39) See A.A. Alchian and W.R. Allen, Exchange and Production: Competition, Coordination and Control, 3d. ed.
(1983), pp. 266-268.
(40) See High Density Traffic Airports: Slot Allocation and Transfer Methods: Supplemental Notice of Proposed
Rulemaking, 56 Fed. Reg 4667 (September 13, 1991), at 46678.

(41) See U.S. General Accounting Office, Airline Competition: Industry Operating and Marketing Practices Limit
Market Entry, U.S. General Accounting Office Report to Congressional Requestors, RCED 90-147 (1990). For a
theoretical discussion of this effect, see K.E. Rockett, “Choosing the Competition and Patent Licensing,” 21 RAND
Journal of Economics 161 (1990), which examines a single incumbent (with a patent) facing a limited number of
potential entrants. For a general discussion of the fragility of entry deterrence models, see D. Malueg and M.
Schwartz, “Preemptive Investment, Toehold Entry, and the Mimicking Principle,” 22 RAND Journal of Economics 1
(1991), and M. Waldman, “The Role of Multiple Potential Entrants/Sequential Entry in Noncooperative Entry
Deterrence,” 22 RAND Journal of Economics 446 (1991).
(42) The dependent variable in these regressions was the number of days the slot was operated in the two month
study period. The explanatory variables were: (1) the slot holder’s share of slots at the HDTA; (2) the slot holder’s
national market share; (3) the FAA withdrawal priority number; (4) a dummy variable set equal to one if the slot was
operated by a single operator other than the slot holder, and zero otherwise; (5) a dummy variable set to one if the
slot was operated by more than one operator, and was not traded in the two month study period, and zero otherwise;
(6) a dummy variable set equal to one if the slot holder changed during the two month period, and zero otherwise;
and (7) a set of dummy variables each set equal to one for the particular hour or half-hour period for which the slot
existed, and zero otherwise.
(43) During the period covered by the 1991 Comments (May-June 1990), United Airlines controlled 658 slots at
O’Hare (44 percent). Later, United obtained more slots at O’Hare when it acquired Air Wisconsin, which had
controlled 44 slots (3 percent). This acquisition prompted a private antitrust suit by American Airlines, which had
controlled 491 slots (33 percent). To settle the case, United sold 12 of the Air Wisconsin slots to American. Since Air
Wisconsin already had a close operating relationship with United and since Air Wisconsin's market share had been
relatively modest, the likelihood that these
transactions had a significant impact on competition at O’Hare appears small, though this is an empirical matter.
Section VI below analyzes more recent slot data to assess whether the changes in shares of slots controlled at
O’Hare and the other HDTAs have had any effect on competitive behavior there.
(44) Both carriers faced problems at various times due to fears of terrorism and increased competition on
transatlantic flights. See “Secretary’s Task Force on Competition in the U.S. Domestic Airline Industry: International
Air Service,” U.S. Department of Transportation, February 1990. International flights use restricted international slots
that are not allocated in the same way as slots for domestic flights, and hence were not in the data used in this study.
Passengers from these flights were an important source of traffic for these airlines’ domestic flights, though, and slots
used for those domestic flights were included in the data.
(45) Ideally we would have preferred to examine May-June 1993 slot usage data for comparison to the results based
on May-June 1990 data reported in our 1991 comments. Unfortunately, this data had already been archived and was,
therefore, not readily available when we began work on this comment. The slot usage data that was available to us
was from the September-October 1993 period and the November- December 1993 period. We used the September-
October data for this study because we felt that it would represent more typical or average slot usage than would the
November-December data, which would include slot usage during the Thanksgiving and Christmas holiday periods.
(46) Carriers that held slots at one or more HDTA and filed for bankruptcy protection since June 1990 include
Continental, Pan Am, Midway, America West, and TWA. Midway, Pan Am, and Eastern, which was still operating
while in bankruptcy proceedings during the May-June 1990 period, have since been liquidated.
(47) The tables discussed in this section are generally numbered so as to correspond to similar tables in the 1991
Comments. That is, Table 1 is comparable to Table 1 in the 1991 Comments, Table 2 is comparable to Table 2 in the
1991 Comments, and so on. Tables that do not correspond to tables found in the 1991 Comments are denoted by
letters following the number of the preceding table (e.g., Table 1A, Table 2A, and so on).

(48) In Table 1, slots held by American Airlines include slots held by AMR Slot Holdings (FAA code 0033), a
subsidiary of American's parent firm, AMR. USAir's slots include slots held by USAir Shuttle, Inc. (FAA code USS), a
subsidiary of Shuttle, Inc. USAir manages and operates the USAir Shuttle for Shuttle Inc. Finally, United's slots
include slots held by Air Wisconsin Inc. (FAA code AWI), which was purchased by United in 1992.
(49) The Department of Justice and Federal Trade Commission jointly released horizontal merger guidelines on April
2, 1992. Under the Guidelines, markets are “moderately concentrated” if their HHIs are between 1000 and 1800 and
“highly concentrated” if their HHIs exceed 1800.
(50) Generally the carriers transferring slots to trusts have received long-term leases on the transferred slots.
Continental, for example, has ten-year leases on the slots held by the Continental Slot Trust; the USAir Shuttle has
ten year leases on the slots held by the Shuttle Slot Trust (Citibank); and Business Express has ten-year leases on
slots transferred to the State Street Bank. BT Commercial Corp., a subsidiary of Banker's Trust, held slots originally
held by America West and transferred as collateral for secured obligations. First Bank National Association holds
slots originally held by Northwest Airlines and transferred as collateral for certain secured obligations. The one
exception to this general rule is that the slots of the Wilmington Trust are not allocated to any carrier because many of
its slots were transferred from Pan Am, which no longer exists. Also, we have kept separate the slots held by the FAA
and Federal Express.
(51) The distinction between total and weekday use of slots was an issue pertinent to the proposed regulations that
gave rise to our 1991 Comments. The results reported here continue to distinguish between total and weekday slot
usage in order to facilitate comparisons with the slot usage patterns previously reported.
(52) A comparison of Table 2 in this comment with Table 2 in our 1991 Comments indicates that slot usage rates
were slightly higher in the September-October 1993 period than they were in the May-June 1990 period.
(53) For purposes of Table 3, a carrier's “held” slots include those leased to it from noncarrier slot holders (i.e.,
financial institutions) on a long term basis and held in trust by noncarrier slot holders as collateral for secured
obligations. See footnote 50.
(54) In our 1991 Comments, we found that USAir leased its slots at National in a manner consistent with the
anticompetitive leasing hypothesis.
(55) In our 1991 Comments, we found that leasing patterns at LaGuardia were not consistent with the anticompetitive
leasing hypothesis.
(56) As was the case with Table 3 (see footnote 53), in Table 4 a carrier's “held” slots include those leased to it from
noncarrier slot holders (i.e., financial institutions) on a long term basis and held in trust by noncarrier slot holders as
collateral for secured obligations. See footnote 50 above. The regression analysis in the next section allocates the
slots held by noncarriers in the same manner.
(57) One variable has been dropped because it is not applicable to the September-October 1993 data. MULTH, a
dummy variable equal to one if the slot holder changed during the two-month period and zero otherwise, was
dropped because no slot was transferred from one holder to another during the September-October 1993 period.
(58) The regressions include the interactions of SLMS with LEASE and MULTO; consequently, the coefficient on
SLMS measures changes in NDOPER as SLMS changes only in those cases in which LEASE and MULTO equal
zero. For cases in which LEASE equals 1 and MULTO equals 0, the effect of a change in SLMS on NODPER equals
the sum of the coefficients on SLMS and LEASE*SLMS; when MULTO equals 1 and LEASE equals 0, this effect is
the sum of the coefficients on SLMS and MULTO*SLMS; and when both LEASE and MULTO equal 1, this effect is
the sum of the coefficients on SLMS, LEASE*SLMS, and MULTO*SLMS. For the majority of slots in our sample, both
LEASE and MULTO are zero; consequently, our discussion of the effects of slot market share on the number of days

a slot was used (i.e., our dependent variable NDOPER) generally concerns the coefficient on SLMS. We have,
however, calculated the effects of changes in SLMS on NDOPER for those cases in which either LEASE, MULTO, or
both equal 1. Those cases in which these effects are significant are discussed in footnotes.
(59) The national market shares for the two slot holders that do not offer carrier service, Wilmington and Federal
Express, are set to zero.
(60) See footnote 50 supra.
(61) For expositional convenience, the coefficients on the time-period dummy variables are not reported. Estimates of
these coefficients are available upon request.
(62) In the ALL DAYS (WEEKDAYS ONLY) regression, the coefficient of -0.91 (-0.52) indicates that an increase in
slot market share of 50 percent would decrease slot usage by 0.46 (0.26) days over a two month period. In the
WEEKDAYS ONLY regression, the sum of the coefficents on SLMS and LEASE*SLMS equals - .89 (see footnote
58), which is statistically significant at the .05 level using a one-tail test (t-statistic equals -1.85). This result indicates
that an increase in market share of 50% would decrease slot usage by 0.45 days for those slots that were leased.
Approximately 14 percent of the 1414 O'Hare slots included in our sample were leased. In our 1991 Comments, we
found a positive and significant relationship between SLMS and slot usage at O’Hare, a result inconsistent with the
anticompetitive hypothesis.
(63) As indicated in Table 2, there is little variation in usage rates across the various airlines holding slots at O'Hare.
Since the two largest carriers at O'Hare are also the two largest carriers nationally, NAMS may be capturing some of
the relationship between slot use and market share. When NAMS is dropped from the regressions, the coefficient on
SLMS falls to -.199 (t-statistic -.39) in ALL DAYS regression and -.072 (t-statistic - .25) in the WEEKDAYS ONLY
regression.
(64) By combining the coefficients on the LEASE and LEASE*SLMS variables with USAir's slot market share at
National, the analysis indicates that the slots that USAir leased to other carriers were used 1.18 fewer days over this
two month period than USAir utilized its owned and operated slots. This difference is statistically significant.
(65) In the case of the all-days regression, the coefficient on national market share, NAMS, would be significant at the
.05 level using a one-tail test. This indicates that a carrier with a more extensive national route structure tend to use
slots at Kennedy somewhat more intensively.
(66) The negative coefficient on WITHD suggests that slots less likely to be withdrawn by the FAA (i.e., those with
higher withdrawal numbers) are less highly used—a somewhat counterintuitive result. The F-statistic for the
regression is statistically significant.
(67) For those cases in which slots were leased (i.e., LEASE equals 1), the sum of the coefficients on SLMS and
LEASE*SLMS is -2.02, which is significant at the .05 level using a one-tail test (t-statistic equals -1.86). This results
indicates that an increase in market share of 50% would decrease the usage of leased slots by 1 day. This result is
consistent with the results reported in Table 3. Approximately 22 percent of the 512 LaGuardia slots included in our
sample were leased.
(68) The analysis indicates that the slots leased by USAir (Delta) to other carriers were used 1.24 (0.45) fewer days
over this two month period than were the carrier's owned and operated slots. These differences are statistically
significant.
(69) These results, of course, depend on the data and the model that was estimated.

(70) See, for example, S. Morrison and C. Winston, “The Dynamics of Airline Pricing and Competition,” 80 American
Economic Review 389 (May 1990). They write, “Because slots limit the effect of competition on fares and the number
of competitors, they should be eliminated and replaced by congestion-based takeoff and landing fees. Congestion
pricing would reduce travel delays efficiently … and could enhance competition.” (p. 392).
(71) See Comments of the Bureaus of Economics, Competition, and Consumer Protection of the Federal Trade
Commission, Massport Program for Airport Capacity Efficiency (February 29, 1988).
(72) Both peak-load pricing and the HDR have the desirable attribute of being market-based methods of allocating
airport capacity. When resources are allocated by non-market methods, such as through administrative action, the
benefit of the good or service is awarded as an economic rent. Economic theory predicts that private parties will make
expenditures in pursuit of such gain until the gain is dissipated. See, e.g., Posner, “The Social Costs of Monopoly and
Regulation,” 83 Journal of Political Economy 807 (1975), for a discussion of the proposition that pursuit of economic
rents converts those rents into social costs. Thus, in order to obtain goods or services awarded by non-market
methods, firms expend real resources—such as for attorneys, lobbyists, and consultants—until the expected gains
are dissipated. These expenditures represent a social cost that is largely avoided by market-based allocation
methods.
(73) Examples of these costs include the extra fuel used by aircraft waiting to take off and land and the additional
time wasted by delayed passengers. For a more detailed discussion of this point, see the FTC Comments on the
Massport Program, supra note 71, Appendix A, and Alfred E. Kahn, The Economics of Regulation: Principles and
Institutions, Cambridge: MIT Press (1988), pp. 87-116.
(74) See F. A. Hayek, “The Use of Knowledge in Society,” 35 American Economic Review 519 (1945), and M. L.
Weitzman, “Prices and Quantities,” 41 Review of Economic Studies 477 (1974).
(75) The FAA may also wish to examine whether different airlines’ financial circumstances affected their slot usage
rates.
(76) Markets are being used to allocate scarce resources efficiently in a number of regulatory settings. The 1990
Clean Air Act, for example, allows pollution emission credits to be purchased and sold. By allowing a market for
pollution credits to exist, Congress created incentives that will help insure that air quality standards are met through
the cheapest and most efficient means. As described in the New York Times (October 23, 1990, p. A18), “The idea in
the legislation is that if one company can remove a ton of sulfur dioxide at its plant more cheaply than another
company can do it at another plant, then the ton should be removed in the cheapest place, and the utility doing the
extra work should be able to sell the credit to other utilities, thus reducing emissions at the lowest possible overall
cost.” More recently, the Federal Communications Commission has used an auction to allocate spectrum to firms
providing “personal communications services.”