07350015%2E2013%2E799998

Journal of Business & Economic Statistics

ISSN: 0735-0015 (Print) 1537-2707 (Online) Journal homepage: http://www.tandfonline.com/loi/ubes20

Quantifying Consumer Perception of a Financially
Distressed Company
Robert G. Hammond
To cite this article: Robert G. Hammond (2013) Quantifying Consumer Perception of a
Financially Distressed Company, Journal of Business & Economic Statistics, 31:4, 398-411, DOI:
10.1080/07350015.2013.799998
To link to this article: http://dx.doi.org/10.1080/07350015.2013.799998

Accepted author version posted online: 06
May 2013.

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Date: 11 January 2016, At: 22:17

Quantifying Consumer Perception
of a Financially Distressed Company
Robert G. HAMMOND
Department of Economics, North Carolina State University, Raleigh, NC 27607 (robert hammond@ncsu.edu)

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To measure how consumers respond to negative information about the financial health of a durable-goods
producer, I use the prices at which vehicles sell in secondary markets to quantify consumer perception of
the Chrysler Corporation during the period surrounding the Chrysler Loan Guarantee Act of 1979. I focus
on Chrysler’s July 31, 1979 announcement of financial distress and request for assistance from the U.S.
government. The trend in the prices of used Chrysler vehicles relative to those of its American competitors
provides strong support for the claim that consumers reduce their willingness to pay for the goods of a
financially distressed company.

KEY WORDS: Chrysler; Corporate bailout; Used-vehicle prices.

I am changing my name to Chrysler,
I am going down to Washington, D.C.
I will tell some power broker,
“What they did for Iacocca
Will be perfectly acceptable to me.”
I am changing my name to Chrysler,
I am leaving for that great receiving line.
When they hand a million grand out,
I’ll be standing with my hand out,
Yes sir, I’ll get mine.
—Tom Paxton “I’m Changing My Name to Chrysler” (1980)

1.

INTRODUCTION

How do consumers perceive negative information about the
financial health of a company that produces a durable good? The

purchase of a durable good involves the formation of a relationship between a buyer and a seller. A vehicle purchase involves
the physical product exchanged at the date of sale as well as future parts, service, and warranty. Similarly, goods such as electronics (e.g., computers) and services (e.g., insurance) are sold
by companies who form a relationship with their customers that
can last for decades. When a purchase involves such a relationship, the producer’s financial health matters because consumers
value the certainty that a company will survive for the duration
of the relationship. As a result, negative information about the
company’s financial health may have a meaningful effect on
consumer perception of the company.
The particular durable-goods producer of interest here is the
Chrysler Corporation, the third-largest U.S. automobile manufacturer and the smallest of the “Big Three.” On December
21, 1979, the Chrysler Loan Guarantee Act (LGA) passed the
U.S. Congress, guaranteeing $1.2 billion in loans to the struggling company. I study this episode in American history to analyze how consumers perceive financially distressed companies,
where consumer perception in automotive markets is quantified by the prices at which a manufacturer’s vehicles sell in
secondary markets (i.e., used-vehicle prices). Using differencein-differences estimation, I measure consumer perception of two
events: (1) How do consumers perceive the announcement that
a company is in financial distress, a term that I use broadly to

refer to the state that precedes insolvency? (2) How do consumers perceive the government assisting a financially distressed company?
In quantifying consumer perception of events during the
period surrounding the Chrysler LGA of 1979, I find that

Chrysler’s announcement of financial distress substantially
harms its consumer perception. The results suggest that willingness to pay for Chrysler products fell by at least 6.1% due
to its financial distress and likely by much more. In addition
to a sharp, negative reaction to Chrysler’s distress announcement, Chrysler’s relative prices continue to suffer until the end
of 1981. Further, there is no evidence that the government’s announcement of financial assistance helps Chrysler’s consumer
perception in that the average price at which Chrysler vehicles
resell does not respond to Congress passing the Chrysler LGA
in December of 1979. Analyzing why Chrysler vehicles faced
these losses finds a strong role for positive consumption externalities in automobile markets, with the availability of parts and
service as a specific example of such externalities.
The contribution of this article is a novel estimation strategy
to measure consumer perception of financial distress. The difficulty of quantifying the treatment effect of financial distress has
been understood for some time: “Of course, it is extremely difficult to separate the proportion of the decline in sales and earnings experienced by a firm in receivership which is attributable
to the state of bankruptcy from that associated with the factors
that forced bankruptcy in the first place” (Baxter 1967, 399).
I overcome the empirical problem of disentangling these effects by using the prices at which a company’s products sell
in secondary markets. Used-good prices are useful in the measurement of financial distress because factors that are specific to
the good are present both before and after the announcement of
financial distress. This allows me to separately identify vehiclespecific effects to isolate the effect of interest: the change in
how Chrysler’s consumers perceived the company solely due to

its financial distress.

398

© 2013 American Statistical Association
Journal of Business & Economic Statistics
October 2013, Vol. 31, No. 4
DOI: 10.1080/07350015.2013.799998

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Hammond: Quantifying Consumer Perception of a Financially Distressed Company

This article is closely related to the work of Hortaçsu
et al. (2010) (HMSV), who also found a large, negative effect
of financial distress in a study of the 2008 automobile industry
crisis. My work complements theirs but makes an independent
contribution because I study financial distress during an earlier
episode in history that has two advantages over the late 2000s
period. First, the LGA period is advantageous because it allows me to separately measure the effect of the announcement

of financial distress from the effect of government support because there was a considerable lag between the announcement
and support with Chrysler in the 1970s whereas government
support came more quickly to the automobile industry in the
more-recent period (153 versus 30 days). Second, my period
is advantageous because it allows me to see a fuller picture of
the long-run consequences of financial distress simply because
it happened decades earlier, which provides a longer time series. Finally, my work is complementary to HMSV because our
identification strategies are quite different, providing independent insights.
Existing work (surveyed by Riordan 2003) has studied
the causal effect of a company’s financial structure on various product-market outcomes. Prominent in this literature,
Brander and Lewis (1986) analyzed an oligopoly market in
which the limited-liability provisions of debt give firms an incentive to pursue more aggressive output strategies. Empirical
papers also document the connection between financial structure
and product markets. Borenstein and Rose (1995) found some
evidence that soon-to-be-bankrupt airlines set lower prices but
the magnitudes of the decreases are small. Chevalier (1995)
showed that increased leverage in the supermarket industry encouraged local entry and expansion by rival supermarket chains.
The present article helps to clarify the link between financial and
product markets by shifting the focus from changes in firms’
product-market behavior following financial distress to changes

in consumers’ purchasing behavior following financial distress.
My objective is to understand how a firm’s product-market outcomes change in the absence of strategic adaptation by the firm.
This approach is instructive for how firms should alter their
pricing behavior following a financial hardship because it carefully isolates the way in which consumers respond to the firm’s
financial distress by using data from secondary markets.
Next, I offer a theoretical foundation for an analysis of the
consumer perception of financially distressed companies. I then
overview the historical background of the Chrysler LGA. The
methodology and data are detailed, followed by empirical results. To ensure that my findings are not driven by Chrysler’s
pricing or advertising strategies, I then review Chrysler’s strategic behavior in its primary market. A discussion of the article’s
implications concludes.
2.

DOES A COMPANY’S FINANCIAL HEALTH
MATTER TO ITS CUSTOMERS?

Andrade and Kaplan (1998) demonstrated the importance of
financial distress, showing that the direct and indirect costs of
distress are 10–20% of firm value in a sample of highly leveraged transactions that subsequently experienced distress. The
financial health of a company matters to investors (Opler and

Titman 1994), lenders (Dahiya, Saunders, and Srinivasan 2003),

399

suppliers (Hertzel et al. 2008), and managers (Sutton and Callahan 1987), but why should it matter to consumers? A literature
that is directly related has developed around the relationship
between product markets and a company’s capital structure.
Titman (1984) developed a theoretical model to determine the
capital structure of a durable-goods producer given the fact that
the “the price a consumer is willing to pay for a durable good
declines as the probability of the firm’s liquidation increases
reflecting the increase in expected maintenance costs” (Titman
1984, 139).
If the producer’s financial health is an important component
of the demand for durable goods, then positive consumption externalities can explain a negative response to the announcement
of financial distress. Katz and Shapiro (1985) defined positive
consumption externalities in durable goods as a situation “when
the quality and availability of postpurchase service for the good
depend on the experience and size of the service network, which
may in turn vary with the number of units of the good that

have been sold.” A particular example of such an externality is
parts availability, which has been called into question in some
cases of failed durable-goods producers (e.g., Daewoo Motors;
Holstein 2002). In the discussion that follows, I focus on positive consumption externalities as driving any potential consumer
reaction to financial distress and later test for the importance of
externalities versus alternative explanations.
To illustrate the role of externalities in the demand for a
durable good, I sketch out a version of the model provided in
Jeitschko and Taylor (2001), adapted to fit my setting. They introduced a stochastic, dynamic coordination (Stag-Hunt) game,
where each consumer in the market must decide in each period whether to own a Chrysler (denoted by action C) or own
another/no vehicle (action N). Action C is assumed to entail
a risk that, with probability 1 − p, the company will default
at some point in the future. Further, C is assumed to exhibit
positive consumption externalities in that its payoff depends
on the number of other consumers choosing C, that is, the
network size. In contrast, action N is assumed to have a certain payoff that is normalized to zero. While consumers do
not know the probability of nondefault p, they share a common prior under which all consumers find it optimal to initially
choose C.
In each period, each consumer receives private information
about p and updates her beliefs accordingly. After each period,

all consumers observe whether Chrysler has defaulted but not
whether it will default in some future period. In the next period,
consumers again choose between C (i.e., remain a Chrysler consumer) and N (i.e., switch) based on the expected payoff given
their beliefs about p and their beliefs about the future network
size. In my setting, beliefs about p are updated idiosyncratically
because each consumer has her own perception of the information she receives about Chrysler’s financial health. Jeitschko
and Taylor (2001) showed that this information leads some consumers to switch from C to N. However, the focus of their article
is on the consumers who switch, not based on their beliefs regarding default, but instead on their beliefs regarding the future
network size. They define a coordination avalanche as occurring when the network collapses to a size of zero despite the
fact that default is sufficiently unlikely to ensure that remaining
a Chrysler consumer is optimal.

400

Journal of Business & Economic Statistics, October 2013

Jeitschko and Taylor’s results imply that consumption externalities introduce feedback from the initial consumers who
leave Chrysler’s network; it is this feedback that leads to further reductions in the network size. A full-blown avalanche will
not occur as long as consumers are sufficiently patient (future
periods are completely discounted in their baseline model) or

consumption externalities are sufficiently small that they do not
overwhelm the direct utility from owning the good. While this
modified version of the Jeitschko and Taylor (2001) model highlights how positive consumption externalities affect demand for
durable goods, the remainder of the article will focus on an
empirical investigation of Chrysler vehicles using data from the
period surrounding its financial distress in the late 1970s.

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3.

THE CHRYSLER LOAN GUARANTEE ACT OF 1979

Before discussing the data, I provide a brief overview of the
period at hand; for a more detailed account, see Hyde (2003)
and Stuart (1980). The basic story of Chrysler’s fall during the
second half of the 1970s is one of declining sales and profits driven by “an extremely weak management team, an unappealing lineup of cars and trucks, and production costs much
higher than at Ford or General Motors” (Hyde 2003, 236). These
problems left the company ill-equipped to withstand financial
shocks brought on by a number of external factors that affected
the entire automobile market. These factors included inflation
exceeding 10%, interest rates on automobile loans exceeding
20%, and rising gasoline prices. Further, corporate average fuel
efficiency (CAFE) standards passed in 1975 mandated that average gasoline mileage for each company’s fleet must rise to
27.5 miles per gallon for the 1985 model year, when the average for all American manufacturers was 13 miles per gallon in
1973. Because of their smaller size, it is argued that macroeconomic shocks and CAFE compliance hurt manufacturers such
as Chrysler more than it hurt the market leader, General Motors
(Hyde 2003, 230). Finally, foreign manufacturers were increasing their presence in the U.S. market during the second half of
the 1970s, eroding the sales of domestic manufacturers.
As a result of these factors, Chrysler’s financial problems had
received press coverage dating back to 1978 [e.g., a June 14,
1978 article in the Washington Post (Egan 1978)]. Despite this,
public attention peaked in the summer of 1979; see Table 1 for
a summary of the key events during this period. As chairman of
the Chrysler Corporation from 1975 to 1979, John J. Riccardo
held quarterly press conferences to discuss the company’s performance. On July 31, 1979, Riccardo held his second-quarter
press conference to deliver the financial report. The unexpected
severity of the financial outlook led to widely reported news
(e.g., Dewar and Rowe 1979) that Chrysler intended to seek
some form of government assistance. Indeed, on August 9, 1979,
Chrysler first publicly declared its intentions, requesting regulatory relief and federal tax credits from the Carter administration.
The move sent “shock waves through the banking and financial
communities” (Hyde 2003, 242). Shortly thereafter, Secretary
of the Treasury G. William Miller rejected the idea of tax credits and instead proposed loan guarantees of up to $750 million
dollars. After months of intense negotiation, lobbying, and public relations, the Chrysler LGA of 1979 passed Congress on
December 21, 1979. This chapter in history closed on August

Table 1. Timeline of the Chrysler Loan Guarantee Act period
Chief actors
Riccardo, John
Iacocca, Lee
Miller, William
1978
November 2

Chrysler chief executive officer, 1975–1979
Chrysler chief executive officer, 1979–1992
Carter administration treasury secretary,
1979–1981
Iacocca announced as President and Chief
Operating Officer

1979
July 31

Riccardo delivers the second-quarter financial
report
Numerous newspapers report Chrysler’s financial
woes
Chrysler requests assistance from the Carter
administration
Miller rejects Chrysler’s initial request for federal
aid
Riccardo resigns as Chairman of the Board
Iacocca named Chairman of the Board and Chief
Executive Officer
Miller announces aid package with $1.2 billion in
loan guarantees
United States Congress passes the Loan
Guarantee Act

August 1
August 9
September 17
September 18
September 20
November 2
December 21
1980
January 6
June 24
1983
August 15

President Carter signs the Loan Guarantee Act
Chrysler receives first $500 million of aid
Chrysler repays the final loan

15, 1983, when Chrysler CEO Lee Iacocca presented a check to
pay off the final loan. The repayment was in full and seven years
ahead of schedule. I use the LGA period to measure changes in
public perception using data on used-vehicle prices as described
in the next section.

4.

USED-VEHICLE PRICE DATA

I quantify consumer perception of automobile manufacturers
with used-vehicle prices. I do not use data on new-vehicle sales
because changes in sales are influenced by a number of factors
that cannot be completely controlled for with the differencein-differences estimation approach that is described in the next
section. Changes in sales are company-specific and model yearspecific, making inference based on intertemporal variation difficult. Data on used-vehicle prices avoid several of these complications in that factors specific to a particular make-model-model
year are present both pre- and post-treatment, allowing identification of their effect separately from the treatment effect of
interest. I refer to make-model-model years as vehicles, which
separates the 1976 Chrysler Cordoba from the 1977 Chrysler
Cordoba.
The data come from the Automobile Red Book, the nation’s
oldest (since 1911) used-vehicle price guide (National Market
Reports, Inc. 1978–1983). The Automobile Red Book collects
its price data on the basis of reports of used-vehicle dealers’
resale prices. Several other companies provide competing valuation guides and methodologies differ slightly. (For example,

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Hammond: Quantifying Consumer Perception of a Financially Distressed Company

the Black Book bases its prices on winning bids in automobile
auctions.) While any differences in methodologies may affect
the level of prices (e.g., the Kelley Blue Book is purportedly
biased in favor of dealers with high retail prices and low tradein prices), there is less reason to suspect that the Automobile
Red Book presents prices that are unrepresentative in terms of
changes over time. Price changes are of interest in the panel data
used here.
For a particular vehicle, I record the price at which it sold on
average in used-vehicle markets for the years 1978–1983. Price
data are available in eight volumes per year (each volume covering 45 days). Volumes are not readily available for February
15, 1980, October 1, 1980, or July 1, 1981, leaving data in 45
periods. The prices are taken for Region B, which includes the
Southeastern, Southern, and Midwestern U.S. states, but there
is little regional variation among Regions A, B, and C. Each
price volume provides data on the three different average vehicle prices: retail, wholesale, and loan value. Retail (wholesale)
is the average price at which the vehicle sold in retail (wholesale) secondary markets, while loan value is the average amount
for which the vehicle may be financed for a loan. I use the
average retail price because these data most closely approximate consumer perception. Table 2 provides the distribution of
used-vehicle prices for three periods in the sample.
I study the 1976 and 1977 model years. Including earlier
model years would prevent full coverage through 1983 given
that the price data are available for eight years in these price
guides. Including later model years does not allow time for
vehicle-specific factors to become incorporated into the level
of prices. That is, by 1978, information about vehicles in the
1977 model year has already been priced into the used-vehicle
market and therefore does not confound identification of the
treatment effect of interest. Further, using 1976 and 1977 model

Table 2. Distribution of used-vehicle prices over time
Model
year

Price

Percentile

January 1, 1978; N = 108
Chevrolet
Vega
Dodge
Coronet
Mercury
Marquis
Mercury
Cougar
Cadillac
Seville

1976
1976
1976
1976
1977

$3794.64
$5281.20
$6102.72
$7550.16
$17,056.32

0
25
50
75
100

January 1, 1981; N = 113
Plymouth
Fury
Plymouth
Volare
AMC
Hornet
Ford
Thunderbird
Chevrolet
Corvette

1976
1976
1977
1976
1977

$1686.21
$2726.03
$3091.38
$3737.76
$9077.41

0
25
50
75
100

November 15, 1983; N = 113
AMC
Matador
Dodge
Aspen
Dodge
Aspen
Oldsmobile
Starfire
Chevrolet
Corvette

1976
1976
1977
1977
1977

$1038.88
$1787.85
$2198.57
$2633.45
$7803.71

0
25
50
75
100

Make

Model

NOTES: The three panels show the vehicles at each quartile of the constant-dollar usedprice distribution for the first, middle, and last period in the sample, respectively. The
sample sizes shown are the number of vehicles in each period.

401

years eliminates warranties as a potential explanation if the
empirical results suggest that consumers respond negatively to
Chrysler’s announcement of financial distress. Because Chrysler
followed the existing industry standard by offering only 12month/12,000-mile warranties, all vehicles that I study were
already out of the manufacturer’s warranty prior to the mid1979 period of interest (Hyde 2003, 211).
The sample of vehicles for study was selected by including
those make-models available in Berry, Levinsohn, and Pakes
(1995) (BLP) for the 1976 and 1977 model years. The BLP
dataset includes “information on (essentially) all models” but
excludes trucks. From the BLP sample, I exclude foreign manufacturers, leaving data on models manufactured by the following four companies: American Motors (AMC), Chrysler,
Ford, and General Motors (GM). During this period, AMC
manufactured vehicles under its own name; Chrysler’s brands
were Chrysler, Dodge, and Plymouth; Ford’s brands were Ford,
Lincoln, and Mercury; and GM’s brands were Buick, Cadillac, Chevrolet, Oldsmobile, and Pontiac. From Ward’s Auto
(http://wardsauto.com/public-data), shares of the United States
market for 1976 were as follows: GM (46.5%), Ford (24.6%),
Chrysler (14.4%), and AMC (1.9%). The remaining share belonged to Japanese manufacturers (8.3%) and European manufacturers (4.3%). Foreign manufacturers are not included because the Japanese share of the market is undergoing such rapid
growth over this period (from 8.3% in 1976 to 19.0% in 1983)
that their inclusion may add more noise than identifying power.
The final sample contains 5060 observations, with 59 models
observed in the 1976 model year and 54 models observed in the
1977 model year, 113 vehicles in total.
Data are included on gasoline prices and the fuel economy of
each vehicle under study. In particular, I construct a measure of
each vehicle’s cost of driving (MP$) by dividing its EPA miles
per gallon rating by the dollar price of leaded gasoline. Monthly
gasoline prices (including taxes) are reported by the U.S. Energy Information Administration (http://www.eia.doe.gov). It is
important to control for changes in gasoline prices because
large price increases have been shown to shift purchases toward more fuel-efficient new vehicles and accelerate scrappage
of less fuel-efficient used vehicles (Li, Timmins, and von Haefen
2009). The monthly trend in leaded gasoline prices is shown in
Figure 1. To mitigate concerns about the run up in gasoline prices
(driven by the 1979 Iranian Revolution) prior to Chrysler’s distress announcement, Table 3 gives the average fuel economy
of each manufacturer’s vehicles, broken down by automobile
segment: subcompact, compact, midsize, fullsize, and sports.
While Chrysler’s overall fuel efficiency is lower than its competitors, this fact is driven entirely by its lack of a subcompact
vehicle offering in the 1976 or 1977 model years; the first subcompacts from Chrysler brands were introduced in the 1978
model year (the Dodge Omni and Plymouth Horizon). Table 3
provides some evidence that rising gasoline prices should not
have affected Chrysler to a larger extent than its competitors.
Finally, for this period, data are not available on the stock
or average quality of used vehicles in the market. While the
fixed-effects model that I outline in the next section makes this
omission less problematic, the importance of the stock of used
vehicles and their quality is highlighted in Gavazza, Lizzeri,
and Roketskiy (2012) and Schiraldi (2011); see those papers for

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402

Journal of Business & Economic Statistics, October 2013

Figure 1. Leaded regular gasoline, cents/gallon including taxes, constant dollars.

further discussion. Before moving to the econometric model,
I note that each vehicle’s used price and its costs of driving
(MP$) are reported in constant 1983 dollars using the monthly
Consumer Price Index.
5.

periods and does not allow for consumers to have incorporated
information about Chrysler’s financial health prior to the July
1979 announcement. Further, I allow each of the four manufacturers in these data to have their own time-fixed effects according
to the following specification:

ESTIMATION OF TIME-VARYING TREATMENT
EFFECTS

Log(Priceit ) =

F 
T


(f )

θf s Fi Tt(s) + ci + ǫit ,

(1)

f =2 s=2

I modify the standard difference-in-differences estimation approach by replacing the typical step-function treatment indicator
with a flexible set of time-fixed effects (Laporte and Windmeijer 2005). Estimating the treatment effect given a treatment date
of July 31, 1979 is misleading because it compounds all prior

where i denotes a particular vehicle and t denotes a particular month-and-a-half period. The arguments of summation are
f for each manufacturer (AMC, Chrysler, Ford, or GM) and
s for each period (of the 45 periods). Fi is a dummy variable

Table 3. Fuel economy by manufacturer/segment
Firm

Subcompact

Compact

Midsize

AMC

18.122
(0.119)
[4]

17.500
(0.053)
[2]
19.000
(0.050)
[6]
20.500
(0.154)
[4]
18.318
(0.142)
[10]
18.829
(0.077)
[22]

15.000
(0.000)
[2]
16.600
(0.151)
[4]
17.648
(0.213)
[8]
15.700
(0.047)
[10]
16.435
(0.083)
[24]

Chrysler

Ford

GM

All

[0]
27.750
(0.403)
[4]
22.333
(0.140)
[12]
22.614
(0.157)
[20]

Fullsize

[0]
13.000
(0.104)
[7]
12.875
(0.049)
[8]
14.053
(0.070)
[19]
13.559
(0.048)
[34]

Sports

All

[0]
15.500
(0.264)
[2]
17.600
(0.326)
[5]
15.840
(0.108)
[6]
16.466
(0.146)
[13]

17.165
(0.091)
[8]
15.912
(0.104)
[19]
18.112
(0.164)
[29]
17.019
(0.077)
[57]
17.124
(0.061)
[113]

NOTES: Shown are the average EPA miles per gallon ratings for the vehicles of each manufacturer. Standard errors are in parentheses. The number of vehicles within each manufacturer/segment are in brackets, where a vehicle refers to a particular make-model-model year.

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Hammond: Quantifying Consumer Perception of a Financially Distressed Company

that equals 1 if the observation is manufactured by company f
(f )
(i.e., Fi = 1 if i ∈ f , 0 otherwise). Tt is a dummy variable
that equals 1 if the observation falls in period s (i.e., Tt(s) = 1 if
t = s, 0 otherwise). For each company f , θf s ∀s ∈ [2, . . . , T ] is
a manufacturer-specific time-fixed effect, which improves over
a single treatment indicator because it exploits the fact that these
data have multiple control groups (AMC, Ford, and GM). ci is
a vehicle fixed effect, controlling for unobserved characteristics
that affect a vehicle’s price. By analyzing the log of prices, I measure the Chrysler treatment effect vector θ as (approximately) a
percentage change in decimal form because the marginal effect
is equal to exp(θt ) − 1, which is approximately equal to θt in
decimal form when θt is close to zero (Halvorsen and Palmquist
1980).
Observed vehicle covariates that are available in the BLP
data include engine size (horsepower and number of cylinders),
vehicle size (interior room and number of doors), and fuel efficiency. One approach to estimating Equation (1) is to ignore
the unobserved fixed effects ci , include only observed covariates, and estimate by ordinary least squares. Such an approach
is not appropriate in these data because tests for the presence of
unobserved fixed effects reject the null hypothesis of no such
unobservables (F (112, 4748) = 16.51, p-value = 0.00). As a
result, I use panel-data techniques to control for the unobserved
fixed effects ci . The only continuous covariate included in these
regressions is the log of a vehicle’s fuel efficiency (the number
of miles one could drive for $1 worth of leaded gasoline). In
particular, I estimate the effects of fuel efficiency separately for
each segment from each manufacturer by creating a companysegment vector S that indicates into which company-segment a
vehicle falls. There are four manufacturers in these data and five
segments of the automobile market but three company-segments
are not present (fullsize and sports vehicles were not manufactured by AMC, while subcompact vehicles were not manufactured by Chrysler). Interacting Log(MP$) with the S vector
shows how consumers respond to increased fuel efficiency differently across vehicle segments and across manufacturers.
The way in which used-vehicle prices fall over time implies that serial correlation of the error term is a concern.
The Wooldridge (2002) test for serial correlation in fixedeffects models rejects the null hypothesis of no serial correlation
(F (1, 112) = 151.65, p-value = 0.00). As a result, I conduct the
estimation with the first difference of Log(Priceit ) to eliminate
serial correlation (Wooldridge 2002). This approach is reasonable because estimating a panel-data model with AR(1) errors
provides an estimated autoregressive parameter ρ = 0.995. The
number of observations is 5060 − 113 = 4947. Of the 5060 total number of points in time at which the 113 vehicles were
observed, the first observation for each vehicle is lost from first
differencing. Finally, note that the estimation uses robust standard errors that are clustered at the vehicle level.
To summarize, in the next section’s main results, the dependent variable is first differenced to eliminate serial correlation,
then the equation is demeaned to eliminate the time-invariant
fixed effects (i.e., subtracting the mean over time for each
vehicle). Each of the four manufacturers has their own timefixed effects, implying that I estimate a θ parameter for each
manufacturer in each period. The only covariate included is
Log(MP$it ), which is interacted with a company-segment
vector S.

403

6.
6.1

RESULTS AND DISCUSSION

Aggregate Prices

Before following the estimation approach outlined in the previous section, I present average prices for all four American
manufacturers separately for vehicles in each model year in
Figure 2. I observe no clear evidence of a meaningful change
in the prices of Chrysler vehicles relative to its competitors’
prices. The only obvious change around the period in question
is the flattening out of the price trend for AMC vehicles in
both model years. Further investigation shows that this break
occurs because the AMC Gremlin and AMC Hornet experience
an anomalous one-period price increase from July 1 to August
15, 1979. I proceed by estimating Chrysler’s period-by-period
treatment effects to more carefully control for vehicle-specific
factors that are ignored in these aggregate prices.

6.2

Treatment Effects With Manufacturer-Specific
Time Effects

Each manufacturers’ time-fixed effects are displayed graphically in Figure 3 and partial output of the regression results
through the middle of 1982 is shown in Table 4. Recall that I
quantify consumer perception with the price at which a given
vehicle sells in secondary markets at different points in time
to measure how much of a vehicle’s value is retained in secondary markets. The fixed-effects regression analysis identifies
the treatment effects of interest from within-vehicle variation,
implying that these results ignore changes in the levels of usedvehicle prices for Chrysler and its competitors and instead focus
on changes in the trends in used-vehicle prices.
Figure 3 shows that all four manufacturers follow the same
basic trend over the sample period but that Chrysler breaks from
this common trend coincident with its announcement of financial
distress on July 31, 1979 (making August 15, 1979 the first postannouncement period). The basic trend starts around 4%, which
coincides with the positive bump seen in the aggregate 1977
model year prices and the leveling off in aggregate 1976 model
year prices. The Chrysler fixed effects then decline through the
middle of 1980, reaching around –12%. Finally, the Chrysler
fixed effects begin to rise, where less negative numbers reflect
less frequent price adjustments for vehicles that have been traded
in the market for years.
Measuring Chrysler’s position relative to that of its competitors is the main goal of the analysis. From Figure 3, Chrysler
appears to diverge from all three other American manufacturers
coincidently with its announcement of financial distress. This
is more clearly seen in Figure 4, which shows treatment effects, that is, the Chrysler fixed effects net of the GM fixed
effects. GM is used because it is the market leader but Table
4 makes it clear that using another control group would give
similar results. From Figure 4, Chrysler begins the sample period at a small but statistically insignificant advantage relative
to its competitors of approximately 1%. The causal interpretation of this positive treatment effect is that a vehicle loses its
value 1% slower because it was manufactured by Chrysler. The
treatment effect oscillates around 1% until the May 15, 1979
period, when it falls to approximately −1%, and remains at that
level for one period. In the first period after its announcement of

Journal of Business & Economic Statistics, October 2013

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404

Figure 2. (a) 1976 Model year average used-vehicle prices by manufacturer. (b) 1977 Model year average used-vehicle prices by manufacturer.

financial distress, the treatment effect drops to −6%. The postannouncement treatment effect is the first effect that is statistically
different from zero. The effects then jump to the −3% range,
where they stay until February 15, 1982, with a distinct but
slight positive trend through the end of 1981. Beginning April
1, 1982, the effects fluctuate but are close to 0%. Before more
fully discussing the main results, I provide several robustness
checks.
Finally, recall that the estimation controls for each vehicle’s
fuel efficiency, which helps to ensure that the results I find are
not driven by adjustments to rising gasoline prices. The results

suggest that consumers respond positively to increased fuel efficiency. Because this is intuitive and of secondary interest, I
do not discuss specifics and instead refer the reader to the fuller
discussion in the relevant literature (for example, Busse, Knittel,
and Zettelmeyer 2009).
6.3 Robustness Checks
Equation (1) allows each manufacturer to have its own timefixed effects. As a robustness check, I also present results
that estimate the treatment effects relative to all non-Chrysler

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Hammond: Quantifying Consumer Perception of a Financially Distressed Company

405

Figure 3. Time-fixed effects for all vehicles by manufacturer.

subcompact vehicles. This approach addresses the following
concern: all four manufacturers in these data are potentially
“treated” by Chrysler’s financial distress. If consumers care
about a durable-goods producer’s financial health, Chrysler vehicles are “treated” negatively. Since consumers who decide not
to purchase a Chrysler vehicle may still choose to make a vehicle purchase, the non-Chrysler vehicles may be “treated” positively. Therefore, is the post-announcement drop in Chrysler’s

relative prices amplified by consumers substituting away from
its vehicles and toward the vehicles of other manufacturers? Or
does it reflect solely the direct effect on Chrysler? In the former
case, the previous results would overestimate the true effect of
Chrysler’s financial distress.
A control group that includes only subcompacts provides a
check against overestimation because Chrysler was not manufacturing any subcompact vehicles prior to or during the 1976

Figure 4. Time-fixed effects of Chrysler relative to GM.

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Journal of Business & Economic Statistics, October 2013

Table 4. Partial output of separate time fixed effects regression results

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Period

(1)
Chrysler

(2)

Fixed effect

AMC

1-Apr-78
0.011
15-May-78
0.046
1-Jul-78
0.044
15-Aug-78
0.053
1-Oct-78
0.044
15-Nov-78
0.025
1-Jan-79
0.017
15-Feb-79
0.009
1-Apr-79
−0.023
15-May-79
−0.041
1-Jul-79
−0.069
15-Aug-79
−0.099
1-Oct-79
−0.100
15-Nov-79
−0.086
1-Jan-80
−0.123
1-Apr-80
−0.144
15-May-80
−0.141
1-Jul-80
−0.128
15-Aug-80
−0.090
15-Nov-80
−0.066
1-Jan-81
−0.094
15-Feb-81
−0.123
1-Apr-81
−0.107
15-May-81
−0.102
15-Aug-81
−0.088
1-Oct-81
−0.077
15-Nov-81
−0.054
1-Jan-82
−0.090
15-Feb-82
−0.085
1-Apr-82
−0.052
15-May-82
−0.030
1-Jul-82
−0.049
Observations
Number of Vehicles
Adjusted R-squared

(3)
(4)
Chrysler FE relative to
Ford

−0.004
−0.004
0.003
0.015∗
0.020
0.020∗∗∗
0.008
0.024∗∗
0.010
0.016∗∗
0.015
0.026∗∗∗
0.013
0.008
0.015
0.011∗
0.014∗∗
0.016∗∗
−0.010∗∗
−0.001
−0.037∗∗∗
−0.026∗∗
−0.080∗∗∗
−0.038∗∗
−0.048∗∗∗
−0.028∗∗
−0.037∗∗∗
−0.032∗∗∗
∗∗∗
−0.037
−0.017
−0.021
−0.029∗
−0.051∗∗∗
−0.039∗∗
−0.049∗∗∗
−0.031∗
−0.015
−0.024∗
−0.006
−0.035∗∗∗
−0.013
−0.025∗∗∗
−0.012
−0.035∗∗∗
−0.023
−0.026∗∗
−0.019
−0.032∗∗∗
−0.004
−0.023∗∗∗
∗∗
−0.037
−0.027∗∗∗
−0.018
−0.019∗∗
0.030∗∗
−0.020∗∗
−0.010
−0.017∗∗
0.009
−0.007
−0.012∗∗∗
−0.007∗
−0.001
−0.003
4947
113
0.391

GM
0.000
0.013
0.009
0.013∗
0.010
0.012∗
0.007
0.014∗∗
0.008∗
−0.009∗
−0.010
−0.061∗∗∗
−0.033∗∗∗
−0.030∗∗∗
−0.026∗∗∗
−0.033∗∗
−0.035∗∗
−0.032∗∗
−0.025∗∗
−0.030∗∗∗
−0.025∗∗∗
−0.040∗∗∗
−0.024∗∗
−0.030∗∗∗
−0.024∗∗∗
−0.021∗∗∗
−0.018∗∗
−0.016∗∗
−0.027∗∗∗
−0.005
−0.007∗∗∗
−0.007

NOTES: The dependent variable is Log(Pricet ) − Log(Pricet−1 ). Column (1) displays the
Chrysler time-fixed effects. Columns (2)–(4) display the difference between the Chrysler
fixed effect and the AMC, Ford, and GM fixed effects, respectively. Significance stars
indicates those periods in which a manufacturer’s fixed effect is statistically different than
Chrysler’s fixed effect; ∗ , ∗∗ , and ∗∗∗ denote significance at the 10%, 5%, and 1% level,
respectively. Standard errors (clustered at the vehicle level) are suppressed for ease of
presentation. Time dummies for February 15, 1980; October 1, 1980; and July 1, 1981 are
not included because these data are not readily available. While this table shows only partial
output through the middle of 1982, Figure 3 displays graphically each manufacturer’s timefixed effects for the entire sample period. The highlighted periods are those after Chrysler’s
announcement of financial distress and before the Loan Guarantee Act passed the U.S.
Congress. The regressors that are included in the estimation are as follows: a separate vector
of time dummies for each company (AMC, Chrysler, Ford, and GM) (which corresponds to


(f )
the following notation from Section 5: Ff =2 Ts=2 θf s Fi Tt(s) ) and Log(MP$it ) interacted
with a company-segment vector S.

and 1977 model years. Since Chrysler vehicle owners would be
less likely to substitute away from Chryslers to subcompacts, I
can use subcompact vehicles as a control group to cleanly measure consumer perception of Chrysler’s financial distress. These
results are shown in Figure 5. The key difference from earlier is
that the fall in the treatment effects is larger and appears to begin
earlier, from −1% on May 15, 1979 to −6% on July 1 to −14%
on August 15. Because Figure 5 does not suggest the results in

Figure 4 are overestimated, I take the main specification to be
robust.
Next, I provide a check of how sensitive the results are to the
omission of data on the stock and quality of used vehicles in
the market. To do so, I incorporate manufacturer-specific and
model year-specific time-fixed effects into the analysis, which
is useful because the stock and quality of 1976 vehicles will be
different than that of 1977 vehicles. These results confirm the
main findings, suggesting that observing vehicle prices several
times per year allows me to parse out the effect of financial
distress separately from omitted factors such as the stock and
quality of vehicles.
Finally, I separate vehicles into 1976 and 1977 model years
and display the treatment effects for each model year separately
in Figure 6. For 1976 model year vehicles, the one-period decline
coincident with Chrysler’s distress announcement is −7.7%. For
1977 model year vehicles, the one-period postdistress decline
is meaningfully smaller at −2.2% but this is preceded by an
additional drop of −2.1%. Further, in unreported results, I disentangle this effect into segments of the automobile market.
There, I find that compact, midsize, and fullsize Chryslers face
5.9%, 4.0%, and 3.7% one-period declines, respectively. While
there is heterogeneity in the effects by model year and segment
of the automobile market, the basic story holds: there is a large
decline coincident with Chrysler’s announcement of financial
distress that holds for each model year and in each segment.
Given this robustness, I now attempt to reconcile the findings
with the existence of positive consumption externatilities.
6.4 Why Do Consumers Respond
to Financial Distress?
What explains the large decline in Chrysler’s used-vehicle
prices following its announcement of financial distress? If consumers value a vehicle’s existing manufacturer’s warranty, financial distress may reduce a vehicle’s price by casting doubt
on whether the warranty will be honored in the case of liquidation. But all vehicles in the sample are out of the manufacturer’s
warranty, implying that warranty concerns do not explain the
negative public perception of Chrysler’s financial distress. The
model discussed in Section 2 argues that positive consumption externalities can explain the experiences of Chrysler that I
document. Positive consumption externalities are a type of network effect and imply that durable goods with smaller networks
should be more sensitive to financial distress, which has the potential to reduce the size of the network. However, my findings
can also be explained by quality signaling, where consumers
take a company’s announcement of financial distress as a negative signal of the quality of its products. The quality-signaling
hypothesis implies that goods with a better preannouncement
quality perception should be more sensitive to financial distress because these goods have a larger quality premium that a
negative signal could erode. This section presents results disaggregated by Chrysler models to bring each explanation to the
data.
My proxy for the externalities hypothesis is the quantity of
new vehicles sold for each model year between 1976 and 1978,
where the prediction is that models with more new vehicles

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Hammond: Quantifying Consumer Perception of a Financially Distressed Company

407

Figure 5. Time-fixed effects of Chrysler relative to all non-Chrysler subcompact vehicles.

sold should have a less-negative treatment effect of financial
distress. My proxy for the quality-signaling hypothesis is the
Consumer Reports reliability ratings for the 1976 through 1978
model years, which are available in the BLP dataset. While
expert ratings of quality are not an ideal proxy for consumers’
perception of quality, the two should be correlated and thus the
quality-signaling hypothesis predicts that models with a lower
Consumer Reports reliability rating should have a less-negative

treatment effect. One could also argue that the change in reliability is what matters, which predicts that models with a downward
trend in reliability should have a less-negative treatment effect.
The reliability rating for 1978 is the best proxy for perception
just prior to the distress announcement because Consumer Reports releases its reviews of a particular model year in April
of the following year, implying that the 1978 issue was out a
few months before the LGA period began. The reliability rating

Figure 6. Time-fixed effects of Chrysler relative to GM, all vehicles, each model year separately.

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Journal of Business & Economic Statistics, October 2013

Table 5. Disaggregated results by Chrysler models for the August 15, 1979 treatment effect
Quantity sold new
Make
Dodge
Chrysler
Plymouth
Plymouth
Dodge
Chrysler
Chrysler
Plymouth
Dodge

Reliability rating

Model

Treatment effect

Standard error

Wheelbase

1976

1977

1978

1976

1977

1978

Monaco
Newport
Gran fury
Fury
Charger
LaBaron
Cordoba
Volare
Aspen

−0.074
−0.071
−0.055
−0.049
−0.048
−0.046
−0.031
−0.025
−0.018

0.018
0.009
0.013
0.010
0.030
0.010
0.013
0.010
0.012

117.4
123.9
121.4
117.4
115.0
112.7
115.0
112.7
112.7

38,937
102,353
48,951
97,063
53,770

91,807

37,594
67,892

3
3
3
1
1

1

1
2

175,456
311,259
232,742

31,692
92,056
29,099
70,037
142,619
304,305
242,111

61,358
125,558
105,442
210,125
157,308

2
2
1

1
1
2
2
1
1
1

1
2
2
2
1

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NOTES: These disaggregated results include only those Chrysler models (from the Chrysler, Dodge, and Plymouth brands) where I observe quantity and reliability data for at least two
of the three models year from 1976, 1977, and 1978. The treatment effect is the Chrysler fixed effect net of the GM fixed effect for the single post-announcement period of August 15,
1979; its standard error is also shown. The quantity of new vehicles sold and reliability ratings were taken from Consumer Reports by Berry, Levinsohn, and Pakes (1995). The reliability
rating is a relative index that ranges from 1 (much less than average reliability) to 5 (much better than average reliability).

is a relative index that ranges from 1 (much less than average
reliability) to 5 (much better than average reliability).
The disaggregated results are in Table 5, which includes only
those Chrysler models (from the Chrysler, Dodge, and Plymouth
brands) where I observe quantity and reliability data for at least
two of the three models year from 1976, 1977, and 1978 to give a
full picture of how the network size and reliability of the models
changed in the years prior to Chrysler’s distress announcement.
The table is sorted by the point estimate of the treatment effect
to provide a visual illustration of any patterns that might exist
in the models that experienced the largest treatment effects. As
before, the Chrysler treatment effect is the Chrysler fixed effect
net of the GM fixed effect. Here, I show only the treatment
effect for the single post-announcement period of August 15,
1979 but the full time series of treatment effects for each of
the nine models are available from the author upon request.
The econometric model used here is consistent with the main
specification, except that the Chrysler fixed effects are estimated
using a single Chrysler model (i.e., the results in Table 5 are from
nine separate re