QUALITY, UNCERTAINTY AND THE INTERNET: THE MARKET FOR CYBER LEMONS* by John H. Huston and Roger W. Spencer* Abstract The
internet makes it easier for buyers to purchase the inability of goods from distant sellers. However, the buyer to examine the merchandise results in asymmetry of information. This paper develops a the to analyze oretical model the relationship between quality and price in a setting of asymmetrical infor
mation. In the spiritofAkerlof (1970), themodel predicts thathigherquality goods are less likely tobe in the market. Since buyers have difficulty distinguishing to accept sellers would have quality, lower prices for their highest quality items. The model is tested using data from internet coin auctions. The results show that coins that are claimed to be of higher quality are less likely to sell and when they sold
do
sell do
so at lower prices
relative
to their market
I. Introduction
value.
nals about the workings
of the new
internetmar
kets.
This paper takes a fresh look at the issues of The Internet has opened the door to development of an enormous number of innovative markets that quality, asymmetric information, and uncertainty in the context of themarket for lemons, as labeled by could not have existed as little as a decade ago. Some function to auction industrial purchasing con George Akerlof (1970) in a classic article three tracts from business to business. Others, termed decades ago. Akerlof developed a small, theoretical model that led him to conclude that bad cars buyer-biding auctions, serve to facilitate the sale of cars out of themarket and drive ("lemons") good consumer as items such art, collector pre-owned that in certain situations, there is no price at which cards, antiques, or rare coins. The firm eBay popu trades will take place. Also, in similar markets such larized the latter category, altering permanently the as medical insurance, there may be no insurance way millions of individuals buy or sell numerous sales at any price to cover older, unhealthy commodities.1 ("lemons") individuals in part because of the relat Internet markets differ from traditional markets ed principle of adverse selection. This paper builds in that reduced transactions costs make it possible on thework ofAkerlof and others to study the inter for a large number of buyers and sellers to interact net trading of rare coins. Coins are a particularly over that distance However, long distances. good market for study because of the high volume between market participants is not fully bridged by of trades,multiple levels of quality, the potential for technology. Buyers cannot hold the merchandise, to reduce information asym improved uncertainty, and they are less likely to be familiar with the ven metric information and existence of dual markets? dor. The resulting asymmetry of information may internet and dealer?for similar products. lead to sub-optimal market performance. These The use of auction markets to study the various markets, which have contributed to a more compet issues associated with the lemons problem is some itive, entrepreneurial environment across the coun what novel. Akerlof for example, observed that try,provide economists new opportunities to assess quality variance, asymmetric information, and/or associated with market fundamental principles dishonest behavior accounted for the lemons prob to few date have lem in the auto, medical insurance, and minority activity. Surprisingly, analysts published *
articles in mainstream
economics
jour
labor markets, as well as credit markets
in underde
210-999 Professors of Economics, Trinity University, San Antonio, TX 78212-7200, 210-999-8471, 7255 (Fax);
[email protected] The authors gratefully acknowledge the helpful comments and suggestions of an anonymous referee.
50
THE AMERICAN ECONOMIST
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®
veloped countries, none of which could be identi fied as auction markets. Kim (1985) and Wilson (1980) extended Akerlof's study of used cars. Cooper and Ross (1984) examined the degree to prices convey information about product quality while Landon and Smith (1998) provided estimates of the impact of current quality (and rep utation) on price. Both studies took wine markets, a non-auction market, as a point of reference (Landon
which
in considerable detail). is Feinstein, Block, and Nold (1985) who studied the impact of asymmetric infor mation on the collusive behavior of highway con struction contractors submitting rigged auction bids to governmental purchasers of their services. More and Smith (1998) An exception
typical of auction papers is the recent work of Luck ing-Reiley (1999) who examined Vickrey's (1961) revenue equivalence theorem across English, Dutch first-price, and second-price auctions. Lucking-Rei ley's article, which does not focus on lemons qual
is an important issues, ity or informational antecedent of the current paper because of its expansive use of the internet to study collector card auctions. In conjunction with the internet,Lucking
Reiley (p. 1067) noted that his approach differed from previous laboratory experiments "by auction ing real goods on a real market." Our results are also based on the auctioning of real goods on a real market. As with Akerlof, it is
determined that bad goods (or "lemon" coins, in this case) drive out good. Additionally, the study of quality as a continuum demonstrates that of the
coins which do trade, higher quality coins trade at lower prices relative to theirmarket value, a result which could be termed a "lemons" corollary. A third finding is that the internetmarket shrinks dealer spreads, with similar coins selling on the internet for only 78 percent of the dealer selling price. The lower internet price results from a combination of
reduced transactions costs and the lemons problem. This introduction is followed by the develop ment of a small, theoretical model. The coin market and related data are discussed in section III. Section IV presents the empirical results, and the conclud ing remarks are found in section V
II. Model are assumed to know the quality of the they offer. Buyers are unable to differentiate
Sellers good
Vol. 46, No.
1 (Spring 2002)
51
between the quality of items less thanR units apart. Thus sellers can offer goods with quality ranging from qm, themaximum quality, to (qm- R), themin imum quality, without buyers being able to distin guish between them. To create the potential for exchange, buyers value the product more than sell ers.We assume thatwhile sellers value a product of quality q at q dollars, buyers would be willing to pay dq dollars, where d > 1. (Akerlof uses a similar
linear utility tomotivate his argument. In his model sellers value a product at x and buyers at 1.5x.) Quality (measured in dollars) is assumed to be uniformly distributed between qm-R and qm. If all goods sold, then the average quality would be (qm R + sellers with a product of qual qm)/2. However, than the price, p, will choose not to sell. ity higher
The expected quality of the goods sold is thusmid = way between qm-R andp: qe (p + qm-R)/2. This in is shown "quality supplied" Figure 1. (At any are above all and the expect sold the price goods qm ed quality is is vertical.) Buyers are qm-R/2\ supply = a to for pay p willing product of average qual dqe for q generates the "demand" for ity qe. Solving as shown in Figure 1. Setting p/d quality, qe demand equal to supply and solving for p gives an equilibrium price of
I-1 providedthat1)d > 1
3)/?>2^(l-^) This is an equilibrium in the rational expecta tions sense. At any price above /?*, the quality required by buyers to justify the price is greater than the average quality provided by sellers. At a price below /?*, the quality required by buyers to justify the price is less than the average quality provided by sellers. The first inequality stipulates that buyers value the product more than sellers do. If d falls below 1
then the demand curve rotates downward and, as a result, does not intersect supply. Since in that case, buyers value the product less than sellers, no items are exchanged. The second inequality requires that = R must be less than If R then can't buyers q^. qm, distinguish between even the very worst and very best goods.
In themodel
supply crosses demand at
Expected quality (Supply) /^
Price
^f /^
Willingness to Pay (Demand)
Om ~.-??.f'?\
d(qm-R)[_.J*/
ji ^
(qm-R)/d FIGURE
qm-R
I !
I
(gm-R).?.-y^'/
q*
qm-R/2 qjd
Quality
1.
a price of zero. This is the extreme case ofAkerlof's model inwhich theworst goods drive out all goods of higher quality. The model predicts that as long as demand inter
sects supply in its upward sloping range, that is as long as the third inequality holds, price will be less than qm.This provision is violated if either R is too small or d is too large. A smaller R implies better informed consumers who can distinguish finer gra
dations of quality. IfR < then consumers 2qm (\--j) are well enough informed to avoid the lemons prob lem, and a price above qm is generated. At the extreme, ifR = 0 customers know precisely what quality is being offered. The second and third imply that qm > 2qm (1 j). Solving for d generates the constraint that d < 2. If d is above 2, buyers value the product so much more than sellers that even at/?= qm, the price where all the sellers are willing to sell theirproducts, buy ers are still willing to buy, and thus the lemons problem doesn't come into play. If either R is too inequalities combined
small or d is too large, demand intersects supply along the vertical section above qm and the interest of informational asymmetry are ing aspects
assumed away. The alternative results discussed above are summarized inAppendix Table 1. An extension of themodel permits the inclusion of information as a continuous variable. The buy
ers' information set, I, enters themodel through R, the range of quality. Sellers are able tomarket items
with quality less than q because buyers are unable to distinguish between qm and lower qualities. Increasing the amount of information available to consumers reduces the range of marketable quali ties
and
increases
the
equilibrium
price
> 0 . In the extreme, fully informed -Jj customers know precisely the quality of the good, and R goes to 0. In that case price rises above qmand -^j
< 0,
of quality qm are sold in this market. (Each quality level would have its own unique price and market.)
only goods
empirical model will be estimated in two stages. First, it is necessary tomodel whether or not an item sells as a function of q and I. For those The
items that sell, a second set of relationships will be estimated modeling the sales price as a function of q and I.
52
THE AMERICAN ECONOMIST
=
1 if the coin is uncirculated of MS63 and 0 otherwise. quality
III. Data
G63
The data consist of the results from 225 Morgan Type (Liberty Head) dollar auctions carried out on
G64
eBay.com during mid-May 2000.2 A single type of coin was chosen to minimize the variation in the product. Coins were chosen because their attributes can be well described with a small number of vari the lemons principle concerns items
ables. Since
omitted from the market, the auction format was selected because of the rare chance to observe items which do not sell due to an excessively high mini
mum bid set by the seller.3 quality qm is the maximum
the buyer could obtain. In our sample this is the quality claimed by the seller. Sellers could be offering a lower quality but it is unlikely theywould provide a product even better than advertised. Two measures
of q
are test
ed; is the retail price for the coin if it had the quality claimed by the seller. Since in the model qm ismeasured in dollars, market value is the 1.MARKET
logical gauge of quality. The data for this variable come from Coin Universe Price Guide forMay 18, 2000 (http://www.coin-universe.com). They are the average dealer asking prices for coins of a specified
year, mint and grade. 2. The grade of the coin, G_. Coin collectors on and dealers place grades their coins reflect
ing the quality of the coin. These grades vary from Poor-1 "worn so smooth it is barely iden tifiable as to type" up toMS-70, a "mint-state"
coin?"an absolutely perfect uncirculated coin" (Ruddy 1995, pp. 8-9).4 Mint state coins are graded from 60 to 70.
=
1 if the coin is uncirculated of MS64 quality and 0 otherwise. = 1 if the coin is uncirculated of MS65 quality and 0 otherwise.
G65
=
1 if the coin is uncirculated of MS66 0 otherwise. and quality
G66
= 1 if the coin is uncirculated of MS67 quality and 0 otherwise.
G67
Coins with a zero for all of the above dummies are those graded VF, very fine, the lowest grade of those coins in this study. The buyers' information set, I, contains:
= 1 if the coin is certified by one of the coin of the services. Because major grading a in coin's difficulty grade, par determining CERT
ticularly for less experienced collectors, firms began offering grading services. For a fee these firmswill have a team of expert graders establish the grade of the coin. The coin and a ticket specifying its grade are then sealed in a
to guarantee that the coin plastic "slab" remains in the stated condition. This third party grading should be valuable information for buyers. = 1 if the coin is OTHER graded by a less well known grading service. In addition to the well-known grading services such as NCG, ANACS and PCGS there are other less well known grading services. It is anticipated that the information value of these services will be lower.
The very low quality coins are rarely collected or exchanged. The coins in our study ranged fromVF,
= 1 if a PICTURE picture is included with the description of the product.
"very fine,"
EBay calculates a seller's rating based on the number of transactions the seller has had and the proportion of buyers who gave the seller a positive evaluation following a trans
(lowest quality)
to MS-67
(highest
quality). GEF
=
1 if the coin
is "extra fine" and 0
otherwise.
=
GAU
1 if the coin is "about uncirculated"
and 0 otherwise. G60
=
1 if the coin is uncirculated of MS60
quality and 0 otherwise. G62
=
1 if the coin is uncirculated of MS62
quality and 0 otherwise.
Vol. 46, No. 1 (Spring2002)
action. They then give sellers stars of various colors based on the seller's rating. A seller with a rating less than 10 has no star, 10-99 a yellow star, 100-499 a turquoise star etc. SELL = 0 if the seller is in the lowest range (no star), 1 if the seller is in the next highest range, 2 if the seller is in the category above that etc.5
53
IV. Results
Only
fails to explain
PICTURE
any variation
in
sales.
A. Factors Affecting Sales Results for the regression determining the fac tors affecting the likelihood of an item selling are 1. The dependent variable is presented in Table SOLD which equals one if the item sold and 0 otherwise. Prediction success is frequently used as a gauge of thefit of a probit model.6 The model cor rectly predicts 70% of the outcomes. The coefficient on MARKET is negative and at As the 5% level. significant expected, higher quality items are less likely to sell. The coefficient
for the "about uncirculated," GAU, grade is nega tive and significant, implying that this grade is less likely to sell than the lower very fine grade. The coefficients on the "mint state" grades are negative and generally become larger and more significant as the grade rises. The coefficient on G66 is insignifi cant due to a very large from the small number of sample. Three of the four statistically significant at
standard error stemming coins of this grade in our information variables are the 10% or better level.
As expected, certified coins are more likely to sell. Oddly, the coefficient on OTHER is negative and barely significant at the 10% level.7 Not only are the less well-known certifying firms not as valuable to buyers, they actually reduce the likeli hood of a sale. However, this result is not very robust. In alternative specifications, the coefficient on OTHER slips below the threshold for statistical significance. Even more surprising is the negative
coefficient on SELL, the level of the seller's expe rience. Apparently, the more experienced seller is less likely to have an item sell. Novice buyers may
be wary of dealing with more experienced sellers. It is also likely that themore experienced sellers place higher minimum bids on their items, thus reducing the odds of a sale.
B. Factors AffectingPrice The model predicts that in a market with asym metric information, price will be less than the qm. In
TABLE 1 Dependent Variable
Probit Results SOLD = 1 if the coin is sold, 0 otherwise
+ SOLD = (3,+ ^MARKET + $?ERT + $4OTHER + (3//C+ $6SELL + $7GEF + fifiAU+ (39G60 _(310G62 VARIABLE NAMES
+
p?G63
MARKET CERT OTHER PICTURE SELL GEF GAU G60 G62 G63 G64 G65 G66
+ + + P14G66 p12G64 p13G65 e_ ASYMPTOTIC ESTIMATED T-RATIO COEFFICIENT
+
-2.085** 2.014** -1.746* -0.819 -3.495*** 0.010 -3.282*** -0.828 -0.801 -1.828* -2.387** -2.451** -0.0391
-0.002 0.495 -0.775 -0.199 -0.317 0.005 -1.306 -0.372 -0.425 -0.771 -0.982 -1.130 -6.084
_CONSTANT_1.923_4.210***_
N = 225 OBSERVATIONS RATIO TEST = 53.7780 LIKELIHOOD MADDALA R-SQUARE 0.2126 *= significance at 10% level ** =
*** =
significance
at 5%
level
significance
at 1%
level
54
THE AMERICAN ECONOMIST
TABLE 2 Price Equation Dependent Variable: Price
VARIABLE
PRICE = 0, + ^MARKET + e ESTIMATED
NAME_COEFFICIENT_124 0.866 MARKET -57.32 CONSTANT
T-RATIO DF 19.11*** -1.61
= 0.748 R-SQUARE = 0.746 R-SQUARE ADJUSTED = N 125OBSERVATIONS *= significance at 10% level ** = significance at 5% level *** = significance at 1% level
who are not fully informed and thus discount the claim of increased quality. To facilitate the comparison of prices charged by dealers and prices charged by internet sellers, the ratio of internet price to dealer price is of quality and regressed against our measures were made to the other information. Two changes set of explanatory variables before estimating the were col price-ratio equation. The coin grades to lapsed into two ranges; about uncirculated
MS62 (GAU62) andMS63 toMS67 (G6367), as
this sample the mean ratio of price to market is .775. Internet price and dealer asking = are .86), but a dol highly correlated (p price lar's increase in dealer price does not, on aver value
increase in internet price. A the of price on MARKET, simple regression serves to the demonstrate dealer price, point. Table 2 contains the results with price as the age, lead to a dollar's
dependent variable. We confirm that price rises with quality, but as expected the coefficient is less than one. Sellers increasing the quality of a good by one dollar receive an increased price of 87 cents. This result is consistent with consumers
the reduced number of observations made it dif ficult to get reliable results for the individual grades. Information concerning the experience of the buyer was also added. Other studies have found that the likelihood of a winner's curse is reduced for better informed and more experi enced bidders. The variable BUY is a measure of
and is formed the buyer's level of experience as same for the variable the methodology using SELL.8 The ratio of price/market is regressed against the other independent variables in Table 3. Once again certification is shown to have a positive is now effect on price. The variable PICTURE
significant at the 10 percent level suggesting that the picture increases the sales price. (It is perhaps not surprising that picture has so little impact on price and ability to sell. The differences in char acteristics of the mint-state coins are so minute
TABLE 3 Dependent
Price Ratio Equation Variable: (PR = Internet Price/Dealer Price)
+ (3//C+ |37SELL+ fifiUY+ e PR = ^ + fifERT + MOTHER + $?A U62 + + (35G6367 T-RATIO
ESTIMATED
VARIABLE
_NAME_COEFFICIENT_118 2.601*** 0.158 CERT -0.160 -1.290 OTHER GAU62 -0.008 -0.120 -4.090***
-0.302 G6367
PICTURE 0.095 SELL -0.018 BUY0.014
significance
Vol. 46, No.
at 1%
level
1 (Spring 2002)
-0.859 0.425
_CONSTANT_0.814_9.325***_
= 0.247 R-SQUARE = 0.201 ADJUSTED R-SQUARE N = 125OBSERVATIONS *= significance at 10% level ** = significance at 5% level *** =
1.671*
55
DF_
that pictures are often incapable of capturing them (Ruddy 1995, p. 22).) The most highly graded coins have lower prices relative to their market value. This is consistent with information asymmetry leading to lower demand for high quality items. The significant coefficient on CERT raises the question of whether the increased certainty in leads certified coins to have a buyers minds function. To examine this regression we reestimate the function with dis hypothesis tinct coefficients for certified coins. Each vari able in the regression is multiplied times CERT and the products are added to the regression.9 unique
Table
4 presents the results of this regression. test of the hypothesis that the Employing aWald on are all the certified variables coefficients equal Thus,
to zero generates a test statistic of 20.16.10 we can reject at the 1 percent level the
that certified and uncertified coins hypothesis share the same regression coefficients. PICTURE is positive and significant at the 10% level for uncertified coins but not for certified coins.11 Finally, higher quality uncertified coins sell at a larger discount tomarket price while higher qual ity certified coins do not. (The positive coeffi cient on C6367*CERT nearly cancels out the
the negative coefficient for G6367.) Apparently informational asymmetry for uncertified coins is greater, thus generating a larger lemons problem
for this group. The price spread in the retail coin market appears to be quite large. The prices at which dealers buy coins are less than half the prices at which they sell coins.12 As noted above, the prices paid by the internet buyers in our sample were
78% of dealer prices. This suggests that if instead of selling directly to buyers, coins were sold by dealers in internet auctions, the price margin would be significantly reduced. While the above results imply the presence of an asymmetric
information problem for the internet, the results do not fully explain the differences between retail and internet prices. For uncertified coins, those most likely to suffer from a lemons problem, the
average price in our sample is 24.8% below deal er prices. For certified coins, those that cus tomers should be most confident in, the average price is 20.0% below retail. (Certified coins with pictures were still 19.2% below retail.) These results suggest that while the information asym metry which discourages buyers is an important
for low internet prices relative to explanation dealer prices, other factors such as the reduction
TABLE 4 Certified versus Uncertified Coins = Internet Price/Dealer Price) Dependent Variable: (PR
+ ^SELL + $fiUY + PR = 0, + (32G6367 $5PIC + hfERT + 87G6367*CEffr+ \SELL*CERT + S9BUY*CERT VARIABLE
+
SWPIC*CERT ESTIMATED
+ e
_NAME_COEFFICIENT_116 G6367 -0.381
PICTURE G6367*CERT SELL*CERT BUY*CERT PIC*CERT = 0.293 R-SQUARE = 0.238 ADJUSTED R-SQUARE N = 125OBSERVATIONS *= significance at 10% level ** = significance at 5% level ***
=
significance
at 1%
SELL BUY
0.013 0.024
CERT
0.101
T-RATIO -6.353***
DF_ 0.438 0.632 1.713* 0.471 2.459**
0.105 0.326 -0.060-1.354 -0.052 -0.658 0.014
0.083 _CONSTANT_0.736_7.536***_
level
56
THE AMERICAN ECONOMIST
in transactions costs for internet deals account for the bulk of the differential.
major coin grading service enhances the likelihood of a sale by way of improved information to the
V. Conclusion Rapid advances in internet commodity trading have not only fostered a renewed, competitive entrepreneurial sprit across multiple markets, but
have also provided economists with fresh opportu nities to examine basic tenets of market operation. One of these is the lemons principle as described by George Akerlof (1970). Akerlof's model showed that since firms cannot charge a premium for high quality products when consumers lack information about the product, only products of low quality ("lemons") will trade. A small, theoretical model is developed in this in relating quality or paper following Ackerlof value to price in a setting of asymmetrical informa tion. The model predicts that, as with Akerlof, high er quality goods above a certain value will not be sold in themarket. Since buyers have difficulty dis tinguishing quality, sellers would have to accept lower prices for their higher quality items. to information is the increase Improved expected likelihood of a sale and the equilibrium price. The empirical evidence indicates that higher on measures two based of quality, are coins, quality
Vol. 46,No. 1 (Spring2002)
less likely to sell than lower quality coins. Buyers are clearly aware that sellers could be offering a lower quality coin than claimed. Certification by a
57
potential buyers. Of the coins which do sell, price rises with qual ity but only by 87 cents for each dollar of claimed
quality improvement, suggesting that consumers discount seller claims of increased quality. Also, the more highly graded coins fetch lower prices relative to theirmarket value, a result that could be termed of coin pictures corollary." Display
a "lemons
increases the price only slightly. Seller experience seems to enhance the value of uncertified coins but negatively affects the price of certified coins. The empirical evidence also suggests a greater informa
tional asymmetry for uncertified than certified coins, resulting in a more substantial lemons prob lem for the uncertified coins.
the internet coin market appears to cut substantially into the wide profit margins enjoyed Finally,
by coin dealers. Prices paid by internet buyers are only 78% of dealer prices. Both lower transactions costs (supply) and a "lemons sensitive" set of buy
ers (demand) probably contribute to lower internet
prices.
APPENDIX TABLE 1 Alternative Equilibria Condition
and Disequilibria
Result
Is Price
Condition 1: d > 1, otherwise buyers value product less / ^^v than sellers.The figure to the rightshows thecase where / ^s^ d?
ph I
S|/?
^m \_AA
of S). The figure to the right shows that this happens / y when thekink in S occurs to the rightofD so thatq I d / A
/\/\
\
m / / ! !
\
2so D ismore steeply sloped thanS. / / \ \
^
j
1 Pnce \/
occurson thevertical (i.e.,equilibrium part | completely
Jy^^ s^
\
^ q--"'?
Condition 3: R > 2^(1 -?) Otherwise, buyers are suf- V ficiently well informed to avoid the lemons problem
******
(q*rR)/2
q?-R
;_
qm-R/2
Quality
S -_ -X
qm_._.._/
~/\ \ X
!
| \/j qjd
58
Quality
qJ-R/2
THE AMERICAN ECONOMIST
Notes 1. The New that U.S.
York Times (June 2, 2000) reported consumer online auction sales are to rise to $6.4 billion in 2000 from $3 expected billion in 1999. EBay, with 12.6 million regis tered users, controls more than 90% of themar ket.
auctions are a form of Vickrey second auction. Bidders enter a maximum bid price and eBay's software automatically out bids oth ers up to that figure. Thus the winning buyer pays a price just above the second highest bid. 3. In some cases there is also a "reserve price" in addition to a minimum bid. If the high bidder
2. EBay
does not bid over the reservation price, the sell er does not have to sell. 4. There are "proof coins prepared by the mint with collectors inmind. These were excluded from the study. 5. Experimentation with both the raw seller score
and dummies for the various levels produced similar results. 6. The observation is predicted to be zero if the estimated probability is less than .5 and is pre dicted to be one if the probability is greater than or equal to .5. Hensher and Johnson (1981, p. 54) develop
a related measure
of predictive
success.
7. The importance of certification by reputable firms with regard to internet activities was highlighted in a recent arrangement worked out between Saturn Auto and eBay. Saturn, the small GM car company with a reputation for honest dealing, plans to perform a 135-point inspection of a seller's vehicle for about $100, permitting the seller to link the inspection report to the vehicle's eBay auction site. The program, which should be available at all 433 Saturn locations by year-end 2000, is seen by eBay as a way to improve the quality of infor
available to potential used-car buyers, which in turn should increase eBay internet car sales (Williams, 2000, p. 20). Table 1 model results suggest that auto sales will indeed be enhanced by certification by a well known "grading" agency such as Saturn, but would be
mation
negatively affected by information provided by a lesser known firm such as a local garage. That eBay realizes the significance of quality information and honest dealings to its sales is
Vol. 46,No. 1 (Spring2002)
59
revealed in the following: "But eBay's profit depends on people trusting the site?trusting that sellers are really offering what they claim to offer; trusting that buyers will pay up; and trusting that the bidding is really being done by legitimate bidders. General distrust of giving credit-card numbers online delayed the growth of electronic commerce in thefirst few years of
the internet.However, sales have taken off after numerous securitymeasures have been institut ed, such as encrypting vital data like credit-card numbers so they can't fall easily into thewrong hands."
(Carlton and Bensinger, 2000, pp. BI,
B4). 8. Coins
that did not sell were omitted. In addition five other observations were removed. In one case a negative buyer rating was reported. Four others were outliers with studentized residuals
above 3. were insignificant in 9. OTHER and GAU62 Table 3 and were not included in this regres sion. SELL and BUY were included in the hopes that they had different effects for certi fied and uncertified coins. 10. Referring to the equation in Table 4, this is a = = = = = 0. testof86 S7 S8 59 Sl0 11. For certified coins this is a test of the sum of the + coefficients on PIC and PIC*CERT, 05 810. error on The large standard 8 prevents the sum of the two from being significant. 12. By our calculations, the average dealer buying price is 46.2% of market value (dealer asking price) forMorgan Dollars. Data for the dealer's bid price come from the Official Blue Book of United States Coins 2000 Handbook of United States Coins.
References 1970. "The Market for Akerlof, George A., "Lemons": Quality Uncertainty and theMarket Mechanism." Quarterly Journal of Economics 84, 488-500. Carlton, Jim and Ken Bensinger, 2000. "Phony Bids Put eBay on Defensive." Wall Street Jour nal 24, May, pp. BI and B4. and Thomas W. Ross, 1984. Cooper, Russell, and Asymmetric "Prices, Product Qualities Information: The Competitive Case." Review of Economic Studies 51, 197-207.
Feinstein, Jonathan S., Michael K. Block, and Fred eric C. Nold, 1985. "Asymmetric Information
and Collusive Behavior in Auction Markets." The American Economic Review 75, 441-60. Hensher, D.A. and Johnson, L.W., 1981. Applied
Discrete Choice Modeling, JohnWiley & Sons, New York. Kim, Jae-Cheol, 1985. "The Market for "Lemons" Reconsidered: A Model of theUsed Car Market with Asymmetric Information." The American Economic Review 75, 836-43. Smith, 1998. "Quality Expectations, Reputation, and Prices." Southern Economic Journal 64, 628-^-7.
Landon,
Stuart, and Constance
E.
Lucking-Reiley, David, 1999. "Using Field Experi ments toTest Equivalence Between Auction For
on the Internet." American mats: Magic nomic Review 89, 1063-80.
Eco
Ruddy, James F, 1995. Photo grade: A Photograph icGrading Encyclopedia for United States Coins 18th Edition, Bowers
and Merena Galleries,
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