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Download by: [Universitas Maritim Raja Ali Haji] Date: 11 January 2016, At: 22:17

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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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)

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 relation-ship between a buyer and a seller. A vehicle purchase involves the physical product exchanged at the date of sale as well as fu-ture parts, service, and warranty. Similarly, goods such as elec-tronics (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 relation-ship, 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 man-ufacturer 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 strug-gling company. I study this episode in American history to ana-lyze how consumers perceive financially distressed companies, where consumer perception in automotive markets is quanti-fied by the prices at which a manufacturer’s vehicles sell in secondary markets (i.e., used-vehicle prices). Using difference-in-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 con-sumers perceive the government assisting a financially dis-tressed 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 will-ingness 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 announce-ment, Chrysler’s relative prices continue to suffer until the end of 1981. Further, there is no evidence that the government’s an-nouncement 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 exter-nalities 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 diffi-culty of quantifying the treatment effect of financial distress has been understood for some time: “Of course, it is extremely dif-ficult to separate the proportion of the decline in sales and earn-ings 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” (Baxter1967, 399). I overcome the empirical problem of disentangling these ef-fects by using the prices at which a company’s products sell in secondary markets. Used-good prices are useful in the mea-surement 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 vehicle-specific effects to isolate the effect of interest: the change in how Chrysler’s consumers perceived the company solely due to its financial distress.

© 2013American Statistical Association Journal of Business & Economic Statistics

October 2013, Vol. 31, No. 4 DOI:10.1080/07350015.2013.799998

398

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This article is closely related to the work of Hortac¸su 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 al-lows me to separately measure the effect of the announcement of financial distress from the effect of government support be-cause 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 se-ries. Finally, my work is complementary to HMSV because our identification strategies are quite different, providing indepen-dent insights.

Existing work (surveyed by Riordan 2003) has studied the causal effect of a company’s financial structure on var-ious 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 in-centive 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 en-couraged 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 out-comes 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 care-fully 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 re-sults. To ensure that my findings are not driven by Chrysler’s pricing or advertising strategies, I then review Chrysler’s strate-gic 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 lever-aged transactions that subsequently experienced distress. The financial health of a company matters to investors (Opler and Titman1994), lenders (Dahiya, Saunders, and Srinivasan2003),

suppliers (Hertzel et al.2008), and managers (Sutton and Calla-han1987), 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 ex-ternalities 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; Holstein2002). In the discussion that follows, I focus on posi-tive 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 in-troduced a stochastic, dynamic coordination (Stag-Hunt) game, where each consumer in the market must decide in each pe-riod 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 cer-tain payoff that is normalized to zero. While consumers do not know the probability of nondefault p, they share a com-mon prior under which all consumers find it optimal to initially choose C.

In each period, each consumer receives private information aboutpand 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 con-sumer) and N (i.e., switch) based on the expected payoff given their beliefs aboutpand their beliefs about the future network size. In my setting, beliefs aboutpare updated idiosyncratically because each consumer has her own perception of the infor-mation she receives about Chrysler’s financial health. Jeitschko and Taylor (2001) showed that this information leads some con-sumers to switch from C to N. However, the focus of their article is on the consumers who switch, not based on their beliefs re-garding default, but instead on their beliefs rere-garding the future network size. They define a coordination avalanche as occur-ring 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.

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Jeitschko and Taylor’s results imply that consumption ex-ternalities introduce feedback from the initial consumers who leave Chrysler’s network; it is this feedback that leads to fur-ther 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 high-lights 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.

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 prof-its driven by “an extremely weak management team, an un-appealing lineup of cars and trucks, and production costs much higher than at Ford or General Motors” (Hyde2003, 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 av-erage gasoline mileage for each company’s fleet must rise to 27.5 miles per gallon for the 1985 model year, when the aver-age for all American manufacturers was 13 miles per gallon in 1973. Because of their smaller size, it is argued that macroeco-nomic shocks and CAFE compliance hurt manufacturers such as Chrysler more than it hurt the market leader, General Motors (Hyde2003, 230). Finally, foreign manufacturers were increas-ing their presence in the U.S. market durincreas-ing 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 theWashington Post(Egan1978)]. Despite this, public attention peaked in the summer of 1979; seeTable 1for 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 per-formance. 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 regula-tory relief and federal tax credits from the Carter administration. The move sent “shock waves through the banking and financial communities” (Hyde2003, 242). Shortly thereafter, Secretary of the Treasury G. William Miller rejected the idea of tax cred-its and instead proposed loan guarantees of up to$750 million dollars. After months of intense negotiation, lobbying, and pub-lic 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 Chrysler chief executive officer, 1975–1979 Iacocca, Lee Chrysler chief executive officer, 1979–1992 Miller, William Carter administration treasury secretary,

1979–1981

1978

November 2 Iacocca announced as President and Chief Operating Officer

1979

July 31 Riccardo delivers the second-quarter financial report

August 1 Numerous newspapers report Chrysler’s financial woes

August 9 Chrysler requests assistance from the Carter administration

September 17 Miller rejects Chrysler’s initial request for federal aid

September 18 Riccardo resigns as Chairman of the Board September 20 Iacocca named Chairman of the Board and Chief

Executive Officer

November 2 Miller announces aid package with$1.2 billion in loan guarantees

December 21 United States Congress passes the Loan Guarantee Act

1980

January 6 President Carter signs the Loan Guarantee Act June 24 Chrysler receives first$500 million of aid

1983

August 15 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 difference-in-differences estimation approach that is described in the next section. Changes in sales are company-specific and model year-specific, making inference based on intertemporal variation dif-ficult. Data on used-vehicle prices avoid several of these compli-cations in that factors specific to a particular make-model-model year are present both pre- and post-treatment, allowing identi-fication 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 theAutomobile Red Book, the nation’s oldest (since 1911) used-vehicle price guide (National Market Reports, Inc.1978–1983). TheAutomobile Red Book collects its price data on the basis of reports of used-vehicle dealers’ resale prices. Several other companies provide competing val-uation guides and methodologies differ slightly. (For example,

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theBlack Bookbases 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 trade-in prices), there is less reason to suspect that theAutomobile Red Bookpresents 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 cov-ering 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 vehi-cle prices: retail, wholesale, and loan value. Retail (wholesale) is the average price at which the vehicle sold in retail (whole-sale) 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 approxi-mate consumer perception.Table 2provides 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

Make Model year Price Percentile

January 1, 1978;N =108

Chevrolet Vega 1976 $3794.64 0

Dodge Coronet 1976 $5281.20 25

Mercury Marquis 1976 $6102.72 50

Mercury Cougar 1976 $7550.16 75

Cadillac Seville 1977 $17,056.32 100

January 1, 1981;N =113

Plymouth Fury 1976 $1686.21 0

Plymouth Volare 1976 $2726.03 25

AMC Hornet 1977 $3091.38 50

Ford Thunderbird 1976 $3737.76 75

Chevrolet Corvette 1977 $9077.41 100

November 15, 1983;N=113

AMC Matador 1976 $1038.88 0

Dodge Aspen 1976 $1787.85 25

Dodge Aspen 1977 $2198.57 50

Oldsmobile Starfire 1977 $2633.45 75

Chevrolet Corvette 1977 $7803.71 100

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

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 12-month/12,000-mile warranties, all vehicles that I study were already out of the manufacturer’s warranty prior to the mid-1979 period of interest (Hyde2003, 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 man-ufacturers, leaving data on models manufactured by the fol-lowing 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, Cadil-lac, 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 be-longed to Japanese manufacturers (8.3%) and European man-ufacturers (4.3%). Foreign manman-ufacturers are not included be-cause 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. En-ergy 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 to-ward 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 dis-tress announcement, Table 3gives 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 com-petitors, this fact is driven entirely by its lack of a subcompact vehicle offering in the 1976 or 1977 model years; the first sub-compacts 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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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. ESTIMATION OF TIME-VARYING TREATMENT EFFECTS

I modify the standard difference-in-differences estimation ap-proach by replacing the typical step-function treatment indicator with a flexible set of time-fixed effects (Laporte and Windmei-jer2005). Estimating the treatment effect given a treatment date of July 31, 1979 is misleading because it compounds all prior

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 manufac-turers in these data to have their own time-fixed effects according to the following specification:

Log(Priceit)= F

f=2

T

s=2

θf sF

(f)

i T

(s)

t +ci+ǫit, (1)

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

Table 3. Fuel economy by manufacturer/segment

Firm Subcompact Compact Midsize Fullsize Sports All

AMC 18.122 17.500 15.000 17.165

(0.119) (0.053) (0.000) (0.091)

[4] [2] [2] [0] [0] [8]

Chrysler 19.000 16.600 13.000 15.500 15.912

(0.050) (0.151) (0.104) (0.264) (0.104)

[0] [6] [4] [7] [2] [19]

Ford 27.750 20.500 17.648 12.875 17.600 18.112

(0.403) (0.154) (0.213) (0.049) (0.326) (0.164)

[4] [4] [8] [8] [5] [29]

GM 22.333 18.318 15.700 14.053 15.840 17.019

(0.140) (0.142) (0.047) (0.070) (0.108) (0.077)

[12] [10] [10] [19] [6] [57]

All 22.614 18.829 16.435 13.559 16.466 17.124

(0.157) (0.077) (0.083) (0.048) (0.146) (0.061)

[20] [22] [24] [34] [13] [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 manufac-turer/segment are in brackets, where a vehicle refers to a particular make-model-model year.

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that equals 1 if the observation is manufactured by companyf (i.e.,Fi(f) =1 ifi∈f, 0 otherwise). Tt is a dummy variable that equals 1 if the observation falls in periods(i.e.,Tt(s)=1 if

t=s, 0 otherwise). For each companyf,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).ciis a vehicle fixed effect, controlling for unobserved characteristics that affect a vehicle’s price. By analyzing the log of prices, I mea-sure the Chrysler treatment effect vectorθas (approximately) a percentage change in decimal form because the marginal effect is equal to exp(θt)−1, which is approximately equal toθt in decimal form whenθtis 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 ef-ficiency. One approach to estimating Equation (1) is to ignore the unobserved fixed effectsci, include only observed covari-ates, 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 effectsci. 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$1 worth of leaded gasoline). In particular, I estimate the effects of fuel efficiency separately for each segment from each manufacturer by creating a company-segment vectorSthat indicates into which company-segment a vehicle falls. There are four manufacturers in these data and five segments of the automobile market but three company-segments are not present (fullsize and sports vehicles were not tured by AMC, while subcompact vehicles were not manufac-tured by Chrysler). Interacting Log(MP$) with the S vector shows how consumers respond to increased fuel efficiency dif-ferently across vehicle segments and across manufacturers.

The way in which used-vehicle prices fall over time im-plies that serial correlation of the error term is a concern. The Wooldridge (2002) test for serial correlation in fixed-effects 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 (Wooldridge2002). This approach is reason-able because estimating a panel-data model with AR(1) errors provides an estimated autoregressive parameterρ=0.995. The number of observations is 5060−113=4947. Of the 5060 to-tal 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 stan-dard errors that are clustered at the vehicle level.

To summarize, in the next section’s main results, the depen-dent 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 time-fixed effects, implying that I estimate aθ parameter for each manufacturer in each period. The only covariate included is Log(MP$it), which is interacted with a company-segment vectorS.

6. RESULTS AND DISCUSSION

6.1 Aggregate Prices

Before following the estimation approach outlined in the pre-vious 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 graph-ically in Figure 3 and partial output of the regression results through the middle of 1982 is shown inTable 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 sec-ondary 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 used-vehicle prices for Chrysler and its competitors and instead focus on changes in the trends in used-vehicle prices.

Figure 3shows 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 post-announcement 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 competi-tors is the main goal of the analysis. FromFigure 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 ef-fects, 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. FromFigure 4, Chrysler begins the sample pe-riod at a small but statistically insignificant advantage relative to its competitors of approximately 1%. The causal interpreta-tion 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

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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 postan-nouncement 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 ef-ficiency. 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 Zettelmeyer2009).

6.3 Robustness Checks

Equation (1) allows each manufacturer to have its own time-fixed effects. As a robustness check, I also present results that estimate the treatment effects relative to all non-Chrysler

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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 ve-hicles are “treated” negatively. Since consumers who decide not to purchase a Chrysler vehicle may still choose to make a vehi-cle purchase, the non-Chrysler vehivehi-cles may be “treated” posi-tively. 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 manu-facturing any subcompact vehicles prior to or during the 1976

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

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

(1) (2) (3) (4)

Chrysler Chrysler FE relative to

Period Fixed effect AMC Ford GM

1-Apr-78 0.011 −0.0040.004 0.000

15-May-78 0.046 0.003 0.015∗ 0.013

1-Jul-78 0.044 0.020 0.020∗∗∗ 0.009

15-Aug-78 0.053 0.024∗∗ 0.008 0.013

1-Oct-78 0.044 0.010 0.016∗∗ 0.010

15-Nov-78 0.025 0.015 0.026∗∗∗ 0.012

1-Jan-79 0.017 0.013 0.008 0.007

15-Feb-79 0.009 0.015 0.011∗ 0.014∗∗

1-Apr-79 −0.023 0.014∗∗ 0.016∗∗ 0.008

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 3displays graphically each manufacturer’s time-fixed 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 the following notation from Section5:Ff=2

T

s=2θf sFi(f)T

(s)

t ) and Log(MP$it) interacted

with a company-segment vectorS.

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 mea-sure consumer perception of Chrysler’s financial distress. These results are shown inFigure 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. BecauseFigure 5does not suggest the results in

Figure 4are 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 inFigure 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 dis-entangle 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 con-sumers value a vehicle’s existing manufacturer’s warranty, fi-nancial distress may reduce a vehicle’s price by casting doubt on whether the warranty will be honored in the case of liquida-tion. 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 consump-tion externalities can explain the experiences of Chrysler that I document. Positive consumption externalities are a type of net-work effect and imply that durable goods with smaller netnet-works should be more sensitive to financial distress, which has the po-tential 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 neg-ative 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 dis-tress because these goods have a larger quality premium that a negative signal could erode. This section presents results disag-gregated 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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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 Reportsreliability 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 Reportsreliability rating should have a less-negative

treatment effect. One could also argue that the change in reliabil-ity 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 becauseConsumer Re-ports 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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Table 5. Disaggregated results by Chrysler models for the August 15, 1979 treatment effect

Quantity sold new Reliability rating

Make Model Treatment effect Standard error Wheelbase 1976 1977 1978 1976 1977 1978

Dodge Monaco −0.074 0.018 117.4 38,937 91,807 37,594 3 1 1

Chrysler Newport −0.071 0.009 123.9 102,353 67,892 3 2

Plymouth Gran fury −0.055 0.013 121.4 48,951 31,692 3 1

Plymouth Fury −0.049 0.010 117.4 97,063 92,056 61,358 1 1 1

Dodge Charger −0.048 0.030 115.0 53,770 29,099 1 2

Chrysler LaBaron −0.046 0.010 112.7 70,037 125,558 2 2

Chrysler Cordoba −0.031 0.013 115.0 175,456 142,619 105,442 2 1 2

Plymouth Volare −0.025 0.010 112.7 311,259 304,305 210,125 2 1 2

Dodge Aspen −0.018 0.012 112.7 232,742 242,111 157,308 1 1 1

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 fromConsumer Reportsby 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 inTable 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 inTable 5are from nine separate regressions where the control group remains the same but the treatment group changes to a different Chrysler model for each regression).

The results suggest that, in keeping with the externalities hypothesis, models with smaller networks experienced a nega-tive effect from the distress announcement that was larger than that of models with larger networks. To illustrate, compare the quantities of the three models with the largest effects to those of the three models with the smallest effects. The former group includes the Dodge Monaco, Chrysler Newport, and Plymouth Gran Fury with effects between−5.5% and−7.4% and quan-tities that are generally below 70,000 units per year. The latter group includes the Dodge Aspen, Plymouth Volare, and Chrysler Cordoba with effects between−1.8% and−3.1% and quantities that are generally above 175,000 units per year. However, in con-trast to the quality-signaling hypothesis, there is no discernible pattern in the size of the treatment effect across more and less reliable models or across upward or downward trending models. Also note that there is no pattern across brand (Chrysler, Dodge, or Plymouth). Finally, each model’s wheelbase is shown to point out that the models with the largest effect tend to be larger

ve-hicles but I argue that this pattern is capturing the network-size effect, where the best-selling Chryslers were the smallest.

In conclusion, I find a statistically significant and econom-ically meaningful erosion of Chrysler’s public perception (as measured by the prices at which its used vehicles sell) in the periods surrounding its announcement of financial distress. The timing of the decline is consistent with the view that consumers value the financial health of the companies with whom they do business and that an announcement of financial distress has a meaningful impact on consumer perception, even when negative information about the company’s financial state was previously available (e.g., Egan1978). Because all vehicles in the sample were already out of the manufacturer’s warranty, warranty con-cerns are not driving these results. I find supportive evidence for positive consumption externalities (where consumers worry that financial distress will reduce the size of their vehicle network) but no evidence for quality signaling (where consumers worry that financial distress implies that products are of poor quality). This suggests that positive consumption externalities are driv-ing my finddriv-ings, with the availability of parts and service as a specific example of such externalities.

6.5 Interpretation of Results

I find consistent evidence that consumers reduce their willing-ness to pay for the products of a financially distressed company. FromFigure 4, Chrysler vehicles have a−6.1% treatment effect in the period following its announcement of financial distress. When evaluated at the median Chrysler used price on August 15, 1979, this decline translates to a one-period loss of$216.24 (= −0.061∗$3,544.92). Accounting for Chrysler’s prolonged losses implies that the full effect of its financial distress is sub-stantial in size. In particular, as a conservative estimate, the sum of the upper bounds for each treatment effect (i.e., the up-per dashed line in Figure 4) between the announcement date and the end of 1981 is −16.1%, which is a cumulative loss of$570.32. The second question of interest from these results concerns the effects of the U.S. government’s offer of financial assistance to Chrysler during this period, which would be seen at the end of 1979 if it exists. InFigure 4, there is no statistically

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meaningful or economically large change in the Chrysler treat-ment effects following the passage of the Chrysler LGA by the U.S. Congress on December 21, 1979. As a result, there is no evidence that government support helped to improve Chrysler’s public perception during this period.

To understand these responses by consumers to Chrysler’s announcement of financial distress, I analyze several Chrysler models separately, finding evidence that positive consumption externalities are an important component of the demand for durable goods. According to the model that was sketched out in Section2, consumer perception of the future network size plays a crucial role. This implies that the initial decrease in the Chrysler treatment effects reflects two factors: an increase in the expected probability of default and a decrease in the expected network size. Further, the prolonged losses that Chrysler vehi-cles faced reflect the feedback effect from additional decreases in the expected network size, as seen in Chrysler’s share of the new-vehicle market: 11.1%, 9.1%, 9.5%, and 9.9% from 1979 to 1982, respectively. Only when Chrysler’s market share lev-eled off in 1981 do we see a reverse in consumers’ expectation of the future network size, as inferred from the treatment effects inFigure 4.

In the context of these interpretations, it is useful to com-pare my results with those in HMSV, who studied the 2008 financial distress of Ford and GM but excluded Chrysler from their analysis because the company was sold to private equity firm Cerberus prior to the onset of the automobile industry’s financial distress. They found that, relative to a control group of Honda vehicles, Ford and GM’s financial distress leads to$417 in losses per vehicle on average. To make this finding compara-ble to mine, a loss of$417 in 2010 dollars is equivalent to a loss of$190 in 1983 dollars (http://data.bls.gov/cgi-bin/cpicalc.pl). Therefore, the HMSV result is quite similar to what I find (a loss of $216 in 1983 dollars). Using GM as an example, the authors showed that this translates to an 11.1% drop in GM vehicle profit margins. Further, the importance of positive con-sumption externalities in automobile markets is also consistent with HMSV, who found that vehicles with longer expected ser-vice lives face larger price declines than vehicles with shorter remaining lives. The similarities between my findings and theirs are reassuring because the two papers use very different data and identification strategies. Those authors had data on wholesale automobile auction prices and identified the effect of financial distress by relating intraweek variation in prices to intraweek variation in daily credit default swap spreads with 5-year matu-rity, where spreads proxy for the manufacturer’s likelihood of bankruptcy.

6.6 Chrysler’s Strategic Behavior in Its Primary Market

I show that consumers reduced their willingness to pay for used Chrysler vehicles relative to used non-Chrysler vehicles. This happens because the supply of used Chrysler vehicles in-creases and/or the demand for used Chrysler vehicles falls. I take the results as evidence of a decline in Chrysler’s public perception; in the next section’s concluding remarks, I relate this decline to the literature that links financial and product markets. An alternative explanation is that changes in the sec-ondary market for Chrysler vehicles are due to changes in

Chrysler’s strategic behavior in its primary market. Here, I pro-vide epro-vidence that my results are not driven by Chrysler’s pricing or advertising strategies.

First, if Chrysler reduced the prices of its new vehicles, the supply of used Chryslers may rise because current owners sell and buy new Chrysler vehicles and/or the demand for used Chryslers may fall because new and used Chrysler vehicles are substitutes. In this case, the results that I find may be explained by changes in Chrysler’s new-vehicle pricing strategy. In fact, Chryslerraisedsticker prices on its 1979 models on July 5, 1979; this was the last in a series of price increases for this model year that cumulatively made 1979 Chryslers 9.9% more expensive on average than 1978 Chryslers. Even considering the high U.S. inflation rate of 11.3% in 1979, a real price decrease of 1.4% is unlikely to explain my findings for used Chrylsers because this price cut is small and its timing does not matchFigure 4. Chrysler did introduce rebates of $400 on its vehicles during this period but the short life of the rebate program (August 18 through September 30) makes it unlikely that these temporary rebates completely explain the decline that I find in used-vehicle prices (Jones1979). Further, the rebate program excluded the Dodge Omni and Plymouth Horizon, two of the company’s most popular cars; these subcompacts (which were introduced in the 1978 model year) were in short supply as consumers downsized to adjust to rising fuel prices.

Second, if Chrysler reduced its level of advertising, the de-mand for used Chryslers may fall if advertising affects consumer perception. In this case, the results that I find may be explained by changes in Chrysler’s advertising strategy. In fact, Kelmen-son (1984) documents that Chrysler President and CEO Lee Ia-coccaraisedChrysler’s level of advertising following his arrival. On August 20, 1979, Chrysler introduced a$10 million televi-sion advertising campaign featuring baseball broadcaster Joe Garagiola. In addition, the company began a series of print advertisements signed by Iacocca directly addressing its re-quest for government assistance, such as an ad that appeared in theWall Street Journalin September 1979 with the headline “Chrysler’s Problems Ultimately Won’t Be Solved in Congress, the Treasury or the Banks. But in the Marketplace” (Hyde2003). Because Chrysler’s pricing and advertising strategies do not explain the decline in its used-vehicle prices, I conclude that consumers reduced their willingness to pay for the products of the Chrysler Corporation as a result of its financial distress, driven by positive consumption externalities in the demand for durable goods.

7. CONCLUSION

Quantifying consumer perception of the events during the 1979 Chrysler Loan Guarantee Act period implies the following results:

1. Chrysler vehicles face a 6.1% loss in the period immediately following its announcement of financial distress on July 31, 1979.

2. There is some evidence that Chrysler used-vehicle prices reflect anticipation of the company’s financial distress as early as two periods before its distress announcement but

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this effect is not entirely robust (e.g., occurs for the 1977 model year but not the 1976 model year).

3. Starting in the second period after the distress announcement, the Chrysler treatment effects continue on a lower path with treatment effects near−3% until late 1981.

4. There is no evidence of a response in consumer perception to the U.S. government’s financial support of Chrysler.

These findings suggest that the announcement of financial distress has a statistically and economically meaningful effect on how consumers perceive the distressed company. This intu-itive result is found by measuring the prices at which Chrysler vehicles sell in secondary markets and supports the notion that financial distress reduces a consumer’s willingness to pay for the products of a durable-goods producer whose financial health is in question. I find that willingness to pay for Chrysler products fell by at least 6.1% due to its financial distress and likely by much more. The policy implications of these findings are cen-tered around the ability of the government to arrest and possibly reverse the decline that I find in Chrysler’s public perception. But, there is no meaningful response in the Chrysler treatment effects to the U.S. government’s offer of financial assistance. These facts cast doubt on the notion that the government can improve the consumer perception of a financially distressed company.

Importantly, I am able to distinguish among the various hy-potheses that may explain the negative public perception of Chrysler’s financial distress. Because all vehicles that I study were already out of the manufacturer’s warranty, concerns about losing the existing warranty did not cause my results. Two lead-ing potential explanations are positive consumption externalities (e.g., availability of parts and service) and quality signaling (dis-tress implies that products are of poor quality). I test for each hypothesis by disaggregating the main effect for nine Chrysler models and find that the models with the largest negative effects are those with the smallest networks of existing vehicles in the market, which is consistent with the argument that consumption externalities play a large role in durable-goods markets. How-ever, there is no discernible pattern of the model-level treatment effects across more and less reliable models, which is inconsis-tent with the quality-signaling hypothesis. These results suggest that concerns about the availability of parts and service (along with other positive consumption externalities) are the driving force behind the large decline that I find in the prices of Chrysler vehicles.

Along with Hortac¸su et al. (2010), my findings help to clar-ify the driving force behind the empirical results of Opler and Titman (1994) that highly leveraged firms lose market share during industry downturns. These losses could be explained by changes in the financially distressed firm’s pricing and ad-vertising behavior (supply-side explanations) or by changes in consumers’ purchasing behavior (demand-side explanations). Section6.6provides evidence against supply-side explanations and supports the interpretation that consumers reduce their will-ingness to pay for the goods of a financially distressed company. These changes in consumer perception complicate a financially distressed firm’s ability to ameliorate financial difficulty with internal funding (Chevalier and Scharfstein1996; Hendel1996) or by poaching a rival’s customers (Busse2002). This new link

between financial markets and the demand side of product mar-kets should be incorporated into future work on appropriate pricing strategies in times of financial distress.

ACKNOWLEDGMENTS

I thank two referees, the associate editor, and the editor for suggestions; Charles Knoeber, Thayer Morrill, Denis Pel-letier, Sang Soo Park, Mark Walker, and especially Stephen Margolis for discussions; seminar participants at North Carolina State University, the 10th World Congress of the Econometric Society, the 2010 International Industrial Organization Confer-ence, and the 2010 Western Economic Association Conference for comments; the staffs at the Duke and North Carolina State University Libraries for help in locating the data; the Edwin Gill Research Grant fund for financial support; and Jennifer Maki for excellent research assistance.

[Received December 2011. Revised February 2013.]

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Gambar

Table 1. Timeline of the Chrysler Loan Guarantee Act period
Table 2. Distribution of used-vehicle prices over time
Table 3. Fuel economy by manufacturer/segment
Figure 2. (a) 1976 Model year average used-vehicle prices by manufacturer. (b) 1977 Model year average used-vehicle prices by manufacturer.
+5

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