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This article was downloaded by: [Academia Sinica - Taiwan], [Kong-Pin Chen]
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Asia-Pacific Journal of Accounting &
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Do consumers discount parallel
imports?
a b c
Kong-Pin Chen , Hung-pin Lai & Ya-Ting Yu
a
Research Center of Humanities and Social Sciences, Academia
Sinica, Taipei City, Taiwan
b
Department of Economics, National Chung Cheng University,
Chiayi County, Taiwan
c
Graduate Institute of Industrial Economics, National Central
University, Jhongli City, Taiwan
Published online: 16 Dec 2013.
To cite this article: Kong-Pin Chen, Hung-pin Lai & Ya-Ting Yu , Asia-Pacific Journal of Accounting
& Economics (2013): Do consumers discount parallel imports?, Asia-Pacific Journal of Accounting &
Economics, DOI: 10.1080/16081625.2014.862906
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Downloaded by [Academia Sinica - Taiwan], [Kong-Pin Chen] at 20:43 18 December 2013Asia-Pacific Journal of Accounting & Economics, 2013
http://dx.doi.org/10.1080/16081625.2014.862906
Do consumers discount parallel imports?
Kong-Pin Chena*, Hung-pin Laib and Ya-Ting Yuc
a
Research Center of Humanities and Social Sciences, Academia Sinica, Taipei City, Taiwan;
Downloaded by [Academia Sinica - Taiwan], [Kong-Pin Chen] at 20:43 18 December 2013
b
Department of Economics, National Chung Cheng University, Chiayi County, Taiwan;
c
Graduate Institute of Industrial Economics, National Central University, Jhongli City, Taiwan
(Received 20 July 2013; accepted 1 November 2013)
Using data from Taiwan’s Yahoo! auctions of Nikon cameras, this paper investigates
whether there exists any difference in transaction results between commodities which
are sold by authorized sellers and those which are parallel imports. We find that the
parallel imports and the authorized products differ not only in the warranty duration
offered by the sellers, but also in the formats in which they are listed, both of which
substantially affect trade probability and transaction price. We show that, after taking
into account the endogenous choice of listing formats and the characteristics of the
sellers and the items, an authorized product has a 7% higher trade probability than
that of a parallel import and, if there is a sale, the transaction price as a ratio of
the manufacturer’s suggested retail price is 0.093 higher. A small number of
less-experienced parallel import sellers, in an attempt to maintain a price comparable
to the authorized product, list their items in pure auction with unusually high starting
bids. This accounts for the overall lower trade probability for the parallel imports. If
we disregard these sellers who adopt pure auction, the authorized product has higher
transaction price than, but about the same trade probability as, the parallel import.
Keywords: parallel imports; authorized products; online auction; warranty duration;
listing formats
1. Introduction
The function played by warranty in a world of asymmetric information regarding
product quality has been a well-studied area in industrial economics. Emons (1989) has
nicely summarized three motives for offering warranty. The first is the insurance
motive, by which the seller shares risk with the buyer through the provision of
warranty (Heal 1977). The second motive is the signaling motive, by which sellers with
higher-quality product (which is unobservable to the consumers) offer better warranty
to signal their quality.1 Third, since offering warranty to the consumers is more costly
when product quality is lower, warranty also prevents the producer (who has the choice
of producing a low-quality good and labeling it as high quality) from cheating on
quality. This is called the incentive motive.2 Chu and Chintagunta (2011) also mention
the sorting/price-discrimination motive, by which the sellers design a menu of different
warranties for the heterogeneous consumers with different willingness to pay, and
thereby increase profit.3
Although the rationales behind the four motives are different, they all predict that
products with more warranty coverage will obtain better deals for the sellers. In reality,
*Corresponding author. Email: kongpin@gate.sinica.edu.tw
© 2013 City University of Hong Kong and National Taiwan University2 K.-P. Chen et al.
this might translate to two things. The first is that a product with better warranty can
result in a sale with higher probability. The second is that it will fetch a higher price if
there is a sale. Despite the seemingly obvious implications, the empirical evidence for
them is relatively scarce compared to the theory. This might be due to the fact that
while price data might not be difficult to collect the transaction rate is much harder to
obtain.
The ready availability of online transaction data, especially those for the online
auctions such as eBay, provides researchers interested in this issue a great opportunity
for investigation. In this paper, we use data from Taiwan’s Yahoo! auctions of Nikon
Downloaded by [Academia Sinica - Taiwan], [Kong-Pin Chen] at 20:43 18 December 2013
digital cameras for our object of study. The data also allow us to distinguish between
cameras which are from authorized dealers (called “authorized products” hereafter) and
those which are parallel imports. Cameras from the two different sources differ not only
in that the former have longer duration of warranty, but also in that the service is
provided by the official dealer for the former, while it is by the individual seller for the
latter.4 Using this data, we can investigate whether there is any difference in transaction
outcomes between the authorized products and the parallel imports. We can also
investigate whether, within the same source, longer duration of warranty results in
better transaction outcomes.
Despite their advantage in enabling us to calculate the trade probability, online
auction data also bring additional complications. First, the sellers in the online auctions
differ substantially in their reputation, experience, and other characteristics.5 All these
differences have implications for the transaction results. Second, and perhaps more
importantly, the items are listed with different formats. There are three major listing for-
mats in the online auction: the fixed-price listing (in which the sellers allow the item to
be sold only at the prices they post); the usual ascending price auction (called the “pure
auction” in this paper) which, with the proxy bid rule, is essentially a second price auc-
tion; and the buy-it-now (BIN) auction. The BIN auction is similar to a pure auction
except that the seller also posts a price (the BIN price) at which any bidder can win
the item immediately by paying that price without competing with others, if the item is
still available.6 It is well known that otherwise identical items can result in different
transaction outcomes if they are listed with different formats. For example, Hammond
(2010) shows that, other things being equal, the pure auction results in higher trade
probability but lower transaction price than the fixed-price listing. Chen, Liu, and Yu
(2013) not only confirm Hammond’s results (2010), but also show that while the BIN
auction results in similar trade probability and transaction price it has shorter sale
duration than the pure auction.
Because of these complications specific to the online auctions, to conduct an
empirical study two empirical issues should be taken into account. First, the listing
format is an endogenous choice of the sellers, and it might be correlated with other
factors that influence transaction probability and price. Second, since we observe the
price only when there is a transaction, estimation of the trade probability and transac-
tion price equations involves sample selectivity. Empirically, the latter can be corrected
by Heckman’s sample selection model, and the former by a multinomial probit model
and, together with the Heckman model, a mixed-process model. Our estimation of the
mixed-process model shows that, consistent with theory, other things being equal, the
authorized product has both higher trade probability and transaction price (if there is a
sale). Detailed analysis of the data also shows that the sellers of the parallel imports, in
an attempt to maintain a reasonable transaction price, overwhelmingly list their items
with fixed-price, but with posted prices lower than that of the authorized product.Asia-Pacific Journal of Accounting & Economics 3
However, a minority of less-experienced parallel import sellers, in an attempt to
“signal” their quality, list the items under pure auction with unusually high starting
bids. These listings have the lowest transaction rate among all, and that is the main
reason why, overall, the trade probability of the parallel import is lower than that of the
authorized product. If we disregard these sellers’ listings, although the authorized
product still has higher transaction price, its trade probability will be similar to the
parallel import. This also implies that the advantage of the authorized product, having a
longer warranty duration and perhaps better service, is mainly reflected in higher
transaction price, rather than trade probability.
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With regard to the four motives of warranty mentioned above, since the data
contain only cameras from a single manufacturer (Nikon) which are all new, our results
have no implications for the signaling or incentive motives of warranty. Given that the
authorized product has a longer warranty duration than the parallel import, our result
supports the insurance motive. Finally, our results also support the price-discrimination
motive, as the majority of parallel import sellers (who list their items at fixed-prices)
post their prices lower than that of the authorized product in order to attract buyers
who are willing to accept lesser warranty coverage at lower prices.
2. Related literature
Compared to the enormous theoretical literature on warranty which is already discussed
in the Introduction, the empirical literature is relatively scant. Early papers have found
little evidence of the relationship between warranty and quality. For example, Priest
(1981) finds no relation between warranty duration and expected life of automobiles
and various appliances. Similarly, Gerner and Bryant (1981) find a weak link between
warranty and reliability for five products in their data. Later studies, on the other hand,
do find supporting evidence in accordance with theory. For example, both Wiener
(1985) and Kelley (1988) have identified a significant and positive relationship between
coverage of warranty and products reliability for a majority of categories of product
they choose from Consumer Reports. Using Canadian data on used cars, Soberman
(2003) finds that sellers use both base warranties and menus of warranties to simulta-
neously signal product quality and discriminate between consumers based on price.
Using US quarterly data from 1999 to 2004 of wholesale prices, retail prices and sales
in direct and indirect channels for servers, Chu and Chintagunta (2009) quantify the
value of warranties. By decomposing the value of a warranty into its insurance and
price-discrimination values, they show that manufacturers and channel intermediaries
benefit from warranty provision and price discrimination (by offering non-uniform
warranties). Customers also benefit from a menu of bundled warranties. In another
paper (Chu and Chintagunta 2011), they use data on servers and automobiles in the
USA to test for four functions of warranty in the theory: insurance, signaling, incentive,
and sorting/price-discrimination. Their empirical investigation supports the insurance
and sorting/price-discrimination theories, but finds no support for signaling and
incentive theories.
Most of the literature on parallel imports investigates how their presence affects the
price of authorized products. One theory (Malueg and Schwartz 1994) shows that,
compared to the third-degree price discrimination, uniform pricing might result in lower
global welfare, as many markets might not be served at all. But if the markets can be
separated into two groups (e.g. the authorized product and the parallel import
segments), global welfare might improve even if price is still uniform within the same4 K.-P. Chen et al.
group. Maskus and Chen (2004) develop a theory in which the manufacturer trades off
the benefit of vertical price control in the importing country with the prevention of
parallel import in the home country. Chard and Mellor (1989) and Barfield and
Groombridge (1998) discuss the free-riding problem, by which the parallel importers
free-ride on the promotional activities of the authorized dealers. Ganslandt and Muskus
(2004) construct a simple model to show that parallel imports decreases the domestic
price of authorized products, and then use the price data of 50 drugs in Sweden from
1994 to 1999 to show that the prices of drugs where there exist parallel imports are
12–19% lower than those without. Thompson (2009) collects data on 53 leading brands
Downloaded by [Academia Sinica - Taiwan], [Kong-Pin Chen] at 20:43 18 December 2013
of digital cameras from NextTag.com during February and April 2006. He shows that
the seller will lower its price when a particular model of camera has a parallel import
in the market.
All the empirical literature is concerned with transaction price. Our online auction
data, however, enable us to compare not only the price, but also the trade probability.
3. Data description
The data were collected from Taiwan’s Yahoo! auctions of Nikon SLR digital cameras
and compact digital cameras for the period from 3 April to 2 August 2008. All cameras
belonged to four series (D, L, S, and P series) within this period. Every listing was
observed from the beginning to its end. As a result, the data contain all available
information on each item listed in the period.
The information includes three basic categories: transaction information, price
information, and other information. The transaction information contains whether the
listing resulted in a sale and, if yes, under what format.7 Price information contains
starting bids, ongoing price, BIN price, and transaction price. Other information
contains listing characteristics, product characteristics, seller characteristics, and bidder
characteristics. Listing characteristics contain listing ID, the starting and ending time of
a listing, auction duration (in days), method of payment, and shipping and handling
charges. Product characteristics contain whether the product is new, product series
number, and product source, i.e. whether it is an authorized product or a parallel
import. Seller characteristics contain seller ID, time (in days) since a seller became a
Yahoo! seller, auction number, and reputation score. Finally, bidder characteristics
contain bidder ID, number and level of bids, and the identity of the highest bidder. We
deleted from our samples those listings which are second-hand, whose source cannot
be attributed, and whose manufacturer’s suggested retail prices cannot be obtained. In
total there were 840 observations remaining.
Table 1 reports the auction results by product source. We can see that 69% of the
items are the authorized products and 31% are parallel imports. Since products in
different series differ substantially in price, direct comparison of transaction prices
between authorized products and parallel imports is meaningless. Therefore, in order to
compare the transaction prices for products in different series, we first compute the total
price (TP) as the sum of an item’s transaction price (if there is a sale) plus its shipping
and handling charges, and divide it by its manufacturer’s suggested retail price (MSRP).
We call this the total price ratio (TPR), which we use as a measure to compare prices
of cameras in different models and series.8 In general, MSRP is substantially greater
than the transaction price, and is the upper bound for the retail and transaction prices.
Therefore, the higher the value of TPR, the closer is the product’s transaction price to
its upper-bound. We can see from the first panel in Table 1 that the mean total priceAsia-Pacific Journal of Accounting & Economics 5
Table 1. Results of auctions by product source.
Auction results All sample Parallel import Authorized product
Sale rate 0.17 0.16 0.18
(0.38) (0.16) (0.38)
[0.00; 1.00] [0.00; 1.00] [0.00; 1.00]
Total price (NT$) 7004 6836 7072
(2767) (3307) (2532)
[1690; 23000] [3790; 23000] [1690; 12875]
Total price ratio 0.68 0.63 0.70
Downloaded by [Academia Sinica - Taiwan], [Kong-Pin Chen] at 20:43 18 December 2013
(0.16) (0.13) (0.17)
[0.16; 1.01] [0.46; 0.94] [0.16; 1.01]
Observations 840 261 579
Notes: (a) Standard errors are in parentheses. Brackets for the minimum value and the maximum value. (b)
Total price equals transaction price plus shipping and handling charges. (c) Dividing the total price of a listing
(if there is a sale) by its manufacturer’s suggested retail price to be its total price ratio.
ratio is about 0.68, meaning that the price suggested by the manufacturer is about 30%
higher than the price at which the camera is generally sold. The average sale rate and
total price ratio for the authorized product (parallel import) are 18% (16%) and 0.70
(0.63), respectively. The authorized product does seem to have both higher trade
probability and transaction price.9
Table 2 shows that there are 120 sellers in our data in all. Among them, 89 (74%)
list only authorized products, 18 (15%) list only parallel imports, and 13 (11%) list both.
When we look into more details about the sellers’ listings, we find that 53 of the sellers
had only one listing during our study period, with the great majority (87% = 46/53)
listing the authorized products. A total of 67 had two or more listings during our study
period, and 64% (43/67) of them listed only authorized products while 19% (13/67)
listed both authorized products and parallel imports.
Authorized products and parallel imports differ not only in their source, but also in
corresponding service and warranty duration. The official dealer runs many service
centers, so the buyers of authorized products can easily get their cameras serviced at
various locations. Buyers of parallel imports, however, can only get their cameras
Table 2. Number of sellers by product source.
Source Number of Sellers Percentage
Authorized products 89 74
Parallel imports 18 15
Both authorized products and parallel imports 13 11
Total 120 100
Inventory size equals 1
Authorized products 46 38
Parallel imports 7 6
Subtotal 53 44
Inventory size greater than 1
Authorized products 43 36
Parallel imports 11 9
Both authorized products and parallel imports 13 11
Subtotal 67 566 K.-P. Chen et al.
serviced by the individual seller. In general, a buyer will consider the service from the
official dealer more trustworthy than that from the individual seller. Therefore, the two
types of warranty, official and unofficial, even with the same warranty duration, are
treated differently in this study. As can be seen from Table 3, the majority of the
authorized products offer 18 months of warranty, while that for the parallel imports is
12 months. Therefore, we take 18 (12) months as the base warranty duration of the
authorized product (parallel import) and define EXWAR_AP (EXWAR_PI ) as the
extended warranty duration against the base warranty duration in the unit of 6 months.10
For example, if an authorized product has a warranty period of 12 (24) months, then
its extended warranty, EXWAR_AP, is −1 (1). If a parallel import has a warranty period
Downloaded by [Academia Sinica - Taiwan], [Kong-Pin Chen] at 20:43 18 December 2013
of 18 (0) months, its value of EXWAR_PI is 1 (−2). Complete definitions and summary
statistics of variables used in the paper are in Table 4.
4. Empirical model
We first investigate the difference between the trade probabilities of authorized products
and parallel imports by estimating the following equation:
TRADEi ¼ a0 þ a1 BINi þ a2 FPi þ a3 APi þ a4 STARTBIDi þ a5 Xi þ ei ;
TRADEi ¼ 1fTRADEi [ 0g; ð1Þ
where 1. denotes the indicator function.
The dependent variable in Equation (1), TRADE, equals 1 if the listed item ends
with a trade, and 0 if not. For the dependent variables, the dummy variable BIN
indicates whether the seller chooses a buy-it-now format; the dummy FP indicates
whether the seller chooses a fixed-price format. The key variable in our model is AP. It
is a dummy variable which is equal to 1 if the item is an authorized product; and 0 if
it is a parallel import. This dummy variable is used to examine whether the trade
probability for the authorized product is higher than that for the parallel import. The
vector X contains all the control variables that appear in both trade and price equations
(the latter is to be specified shortly). It includes seller’s reputation (POSFB), posted
duration (POSTDUR), three product dummy (GROUP 1, GROUP 2, and GROUP 3),
the interaction term between warranty and product source, and the number of potential
bidders (BUYERNO). Empirically, (1) can be estimated by the probit model under the
iid
assumption that ei N ð0; 1Þ.
Many studies on the impact of a seller’s reputation on the trade probability and
transaction price of the internet auctions indicate that it has a positive effect.11 Given
this, we use the positive ratio (POSFB), defined as the ratio of a seller’s positive
feedback to his total feedback, as a proxy of the seller’s reputation in our regression.
Because a longer duration is likely to attract more bidders to submit bids, we include
Table 3. Warranty period by product source.
Warranty period (Month) Authorized product Parallel import
0 – 76
12 2 184
18 465 1
24 112 –
Average warranty period (month) 18.96 8.61
Observations 579 261Asia-Pacific Journal of Accounting & Economics 7
Table 4. Definitions of variables and summary statistics.
Std.
Variables Definition Mean Dev.
TRADE A dummy variable equals 1 if there is a trade; and 0 0.17 0.38
otherwise.
TP Total Price: transaction price plus average shipping and 7004.38 2767.21
handling charges.
P Transaction price. 6853.49 2765.97
MSRP Manufacturer’s suggested retail price. 10822.86 4469.55
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TPR TP/MSRP. 0.68 0.16
STARTBID Starting bid set by seller. 7670.60 3655.18
POSFB Ratio of the seller’s positive feedback to total feedback. 0.98 0.11
POSTDUR Total days of duration set by the seller. 9.78 1.58
AP A dummy variable indicating the source which is 1(0) if it 0.69 0.46
is authorized product of the item, (parallel import).
EXWAR_AP Extended warranty period for authorized product; if the 0.19 0.41
duration of warranty coverage is 12(24), then its extended
warranty (in units of six months) is −1(1).
EXWAR_PI Extended warranty period for parallel import. If the duration −0.58 0.91
of warranty coverage is 0(18), then its extended warranty
(in units of six months) is −2(1).
GROUP 1 A dummy variable equal to 1 if the item is in lowest price 0.33 0.49
range; 0 otherwise.
GROUP 2 A dummy variable equal to 1 if the item is in medium price 0.40 0.49
range; 0 otherwise.
GROUP 3 A dummy variable equal to 1 if the item is in highest price 0.27 0.44
range; 0 otherwise.
BUYERNO Number of observed bidders in 20 other auctions in our 0.47 0.75
sample with closest beginning time
INVENTORY Total inventory of seller during study period. 20.05 13.93
EXEPR Total number of products sold by seller in the past two 1060.33 1878.57
years.
Notes: For some listings, the sellers offered different options to ship the items but with different charges, so
we take the average shipping and handling costs for these listing.
the posted duration POSTDUR as a regressor. The number of potential bidders for a
listing obviously affects its transaction results. For example, when the camera maker
has an intense advertisement campaign, demand is higher for items listed during that
period, and there should be more potential bidders. Competition then becomes more
intense, which affects transaction outcomes. Nevertheless, the number of potential
bidders is unobservable because not every potential bidder submits a bid or makes a
buy-it-now purchase. We can only observe the number of bidders who actually place a
bid and/or make a buy-it-now purchase. To account for this problem, we follow
Yin (2007) to construct a proxy variable BUYERNO which measures the number of
potential bidders. Specially, BUYERNO is the average number of observed bidders in
20 other listings with the closest beginning time in our sample. These three variables
above should have positive effects on the trade probability.
Since cameras in different price ranges might value warranty differently, we also
separate all cameras into three groups: the low-price, medium-price, and high-price
groups, based on the manufacturer’s suggested retail prices.12 The sample mean of TPR
for each group and product source is summarized in Table 5. The authorized products
in general have higher TPRs than the parallel imports. Also, since the consumers might8 K.-P. Chen et al.
Table 5. The sample mean of total price ratio by product source and group.
Authorized product Parallel import
GROUP 1 0.760 0.723
(0.121) (0.117)
[42] [11]
GROUP 2 0.586 0.552
(0.148) (0.100)
[37] [20]
GROUP 3 0.778 0.677
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(0.190) (0.107)
[25] [11]
Total 0.702 0.630
(0.172) (0.129)
[104] [42]
Notes: Standard errors are in parentheses. Brackets for the number of observations.
value warranty of the authorized product differently to that of the parallel import, we
use the interaction terms AP × EXWAR_AP and PI × EXWAR_PI to capture the effect
of warranty for products with difference sources.13 Finally, since the level of the
starting bid (STARTBID) affects whether a bidder will place a bid, we also include it in
the trade probability regression.
As mentioned, the literature has shown that the listing format significantly
influences the transaction outcomes.14 We therefore must control for its influence in our
regressions. Since there are three listing formats in an online auction – the fixed-price
listing, the pure auction, and the buy-it-now auction – we use two dummy variables to
examine the influence of the format, BIN and FP. BIN is a dummy variable which is
equal to 1 if the item is listed in a BIN auction; and 0 otherwise. Similarly, FP is a
dummy variable which equals 1 if the item is a fixed-price listing; and 0 otherwise.
Since the seller’s choice of formats might be correlated with certain unobserved
characteristics which affect either trade probability or transaction price, the empirical
model should take into account the endogeneity of the seller’s choice of listing formats.
In order to correct for the possible estimation bias arising from this problem, we use
the multinomial probit model to endogenize the seller’s choice of the BIN and
fixed-price formats (leaving the pure auction as the base). Chen et al. (2013) have
shown that one of the main determinants of a seller’s decision to adopt BIN is the
seller’s experience. Hammond (2010) has also shown that one of the determinants of a
seller’s decision to adopt a fixed-price listing is the size of inventory. The reason is as
follows. One main function of the BIN option identified in the literature is that it
reduces the transaction risk for both the buyer and seller.15 However, Yahoo! charges
sellers for posting BIN, and posting the wrong BIN price might actually decrease the
seller’s profit. Therefore, a more-experienced seller is more likely to figure out the opti-
mal buy-it-now price and is more likely to post the buy-it-now option. We thus expect
a positive relationship between a seller’s experience and his tendency to adopt BIN. In
the paper, we use the number of items sold by the seller in the past two years (EXPER)
as a measure of his experience. Moreover, Hammond (2010), citing the theory of Harris
and Raviv (1981) that capacity constraint is an important factor influencing the relative
advantage between the fixed-price sale and the auction, has identified the size of inven-
tory as a determinant for the seller to adopt the fixed-price listing. Another explanation
for this is that if a seller has large inventory, then using pure auction is costly, as heAsia-Pacific Journal of Accounting & Economics 9
needs one auction webpage for each item. Fixed-price listing, however, has the
advantage of listing all items on a single webpage, with available quantity reduced by
1 every time an item is sold. We then hypothesize that the seller with larger inventory
is more likely to adopt the fixed-price listing. From the insights of these papers, we
should expect inventory and experience to be sufficiently correlated with a seller’s
listing-format choice.
As there are three possible choices for format adoption, we use the multinomial
probit model to model the seller’s choice of format. Let CHOICEi ðqÞ denote the
unobserved value for choosing the format q, where q = 0, 1, 2 corresponds to pure
auction, BIN auction, and fixed-price listing, respectively. The empirical model for a
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seller’s choice can be specified as
CHOICEi ðqÞ ¼ bq0 þ bq1 EXPERi þ bq2 INVENTORYi þ bq3 APi þ bq4 STARTBIDi
þ bq5 Xi þ uq;i ; for q ¼ 1; 2 and CHOICEi ð0Þ ¼ 0: ð2Þ
Let CHOICEi be the observed choice of seller’s format, then
CHOICEi ¼ q; if CHOICEi ðqÞ ¼ maxfCHOICEi ð0Þ; CHOICEi ð1Þ; CHOICEi ð2Þg:
Equations (1) and (2) form a mixed-process model, which can be estimated by the
maximum likelihood method.
Finally, as the transaction price is observed only for items which are sold, the
estimation will be biased if the transaction price is estimated using only the sample that
results in sales. Here, the estimation bias is mainly due to the correlation between the
transaction price and trade probability. At the same time, in order to deal with the
endogeneity of listing formats in the trade probability equation, we use the model
suggested by Wooldridge (2002, p. 567) and include the two variables (EXPER,
INVENTORY ) for (BIN, FP); then the selection Equation (1) becomes
TRADEi ¼ c0 þ c1 EXPERi þ c2 INVENTORYi þ c3 APi þ c4 STARTBIDi þ c5 Xi þ vi ;
TRADEi ¼ 1fTRADEi [ 0g; ð3Þ
The price equation can then be written as
TPRi ¼ k0 þ k1 BINi þ k2 FPi þ k3 APi þ k4 Xi þ xi ; if TRADEi ¼ 1: (4)
Note that TPR is defined as the sum of transaction price and shipping and handling
charges, divided by the manufacturer’s suggested retail price, as the winner pays not
only the transaction price, but also the shipping and handling charges. Also note that
the price equation does not contain the variable STARTBID, as it influences only the
trade probability, but not the transaction price if there is a sale (Livingston 2005). In
summary, we use Heckman’s selection model (Equations (3) and (4)) to correct for
possible sample selection bias, and the multinomial probit model (Equation (2)) to
endogenize the seller’s choice of listing formats. Equations (2)–(4) form a
mixed-process model and are jointly estimated by the maximum likelihood method
under the multivariate normal distribution assumption.
5. Regression results
The regression results for the trade probability, including Equations (1) and (2), are
reported in Table 6, and the joint estimation results for trade probability and transaction10 K.-P. Chen et al.
price, including Equations (2)–(4), are summarized in Table 7. Both regressions have
considered the endogeneity of the seller’s listing-format choice.16 The marginal effects
of the trade probability are reported in Table 8.
A few findings in Tables 6 and 8 are worth mentioning. First, choosing the BIN
auction has about a 39.1% greater chance to reach a sale than the pure auction, other
things being equal. Second, a more-experienced seller is more likely to adopt the BIN
format, which is consistent with the finding in Chen et al. (2013). Third, a seller with a
larger inventory is more likely to adopt the fixed-price listing, which is also consistent
with the empirical result in Hammond (2010).17
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Table 6. The regression results of trade probability.
Dependent variables TRADE BIN FP
BIN 1.184 ***
– –
(0.251)
FP 0.659 – –
(0.536)
STARTBID (÷1000) −0.163*** 0.067** 0.065*
(0.044) (0.029) (0.037)
POSFB 0.731 −4.412*** 4.411
(0.667) (1.344) (9.391)
POSDUR −0.057* 0.070 −0.003
(0.031) (0.076) (0.049)
AP 0.470* 2.075*** −1.153***
(0.262) (0.653) (0.385)
GROUP 2 0.189 −0.313 −0.163
(0.139) (0.263) (0.198)
GROUP 3 0.557*** −0.872*** −0.550**
(0.199) (0.330) (0.263)
AP × EXWAR_AP 0.016 −2.033** −0.371**
(0.272) (0.954) (1.562)
PI × EXWAR_PI −0.213* −0.125 0.228
(0.118) (0.461) (0.164)
BUYERNO 0.079 0.528*** −0.134
(0.062) (0.124) (0.121)
INVENTORY – −0.038*** 0.025***
(0.014) (0.009)
EXPER (÷1000) – 0.745*** 0.191
(0.080) (0.161)
CONSTANT (−0.976) – −3.786
(0.793) – (9.491)
σ4 1.365*** – –
(0.386)
ρ13 −0.583*** – –
(0.168)
ρ14 −0.366 – –
(0.365)
ρ34 0.102 – –
(0.607)
Notes: (a) The numbers in parentheses are robust standard errors. *, **, and *** indicate the 10, 5, and 1%
levels of significance, respectively. (b) σ4 is the sample standard deviation of the error in multinomial choice;
ρ denotes the correlation coefficient between errors in the structure estimation; the subscript “1” denotes the
trade equations, and “3” and “4” represent the BIN and FP equations.Asia-Pacific Journal of Accounting & Economics 11
Table 7. The regression results of transaction price and trade probability.
Dependent variables TPR TRADE BIN FP
***
BIN 0.318
(0.038)
FP 0.144*** – – –
(0.040)
STARTBID (÷1000) – −0.171*** 0.067** 0.055*
(0.052) (0.029) (0.030)
POSFB 0.077 −1.129 −6.158*** 3.987
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(0.128) (0.862) (1.399) (7.044)
POSDUR −0.0003 −0.056* 0.090 0.010
(0.006) (0.033) (0.090) (0.605)
AP 0.093*** 0.338** 3.375*** −1.291**
(0.036) (0.170) (0.723) (0.154)
GROUP 2 −0.182*** 0.158 −0.112 −0.120
(0.026) (0.145) (0.291) (0.191)
GROUP 3 −0.074** 0.560** −0.662* −0.447**
(0.031) (0.230) (0.355) (1.720)
AP × EXWAR_AP 0.081** −0.296 −1.510*** −3.036*
(0.040) (0.205) (0.481) (0.179)
PI × EXWAR_PI −0.033* −0.222* −0.527 0.183
(0.020) (0.116) (0.599) (0.183)
BUYERNO −0.013 0.151** 0.618*** −0.141
(0.017) (0.060) (0.116) (0.117)
INVENTORY – −0.001 −0.049*** (0.014)
EXPER (÷1000) – 0.128*** 0.750*** 0.143
(0.027) (0.091) (0.123)
CONSTANT 0.358** 1.278 – −3.571
(0.153) (0.950) (6.975)
σ4 1.142** – – –
(0.517)
ρ12 0.739*** – – –
(0.121)
ρ13 −0.347** – – –
(0.138)
ρ14 −0.451*** – – –
(0.154)
ρ23 0.046 – – –
(0.134)
ρ24 0.039 – – –
(0.078)
ρ34 −0.162 – – –
(0.385)
Notes: (a) The numbers in parentheses are robust standard errors. *, **, and *** indicate the 10, 5, and 1%
levels of significance, respectively. (b) σ’s are the sample standard deviations of the errors in the
mixed-process model; and ρ’s denote the correlation coefficients between errors in the structure estimation.
The subscripts “1”, “2”, “3”, and “4” correspond to the price equation, trade probability, the listing formats,
BIN and FP, respectively.
Table 7 shows the joint estimation results of the transaction price and trade
probability. The estimated coefficients for the trade probability, BIN, and FP equations
in Table 6 are quite similar to those in Table 7. However, the coefficient of
INVENTORY in Table 7 is not significant, which may be due to the loss of estimation
efficiency.18 We can see from the third column of Table 7 that, after controlling for the12 K.-P. Chen et al.
Table 8. The marginal effects for trade probability.
Table 6 Table 7
Dependent variables TRADE TRADE
BIN 0.391*** –
(0.098)
FP 0.166 –
(0.147)
STARTBID (÷1000) −0.040*** −0.038***
(0.009) (0.010)
−0.252
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POSFB 0.180
0(0.161) (0.194)
POSDUR −0.014* −0.013*
(0.008) (0.007)
AP 0.106* 0.070**
(0.059) (0.032)
GROUP 2 0.048 0.036
(0.035) (0.145)
GROUP 3 0.154*** 0.142**
(0.058) (0.062)
AP × EXWAR_AP 0.004 −0.066
(0.067) (0.045)
PI × EXWAR_PI −0.053* −0.050*
(0.031) (0.027)
BUYERNO 0.020 0.034**
(0.016) (0.013)
INVENTORY – −0.0003
(0.0009)
EXPER (÷1000) – 0.029***
(0.006)
Notes: (a) The numbers in parentheses are standard errors. *, **, and *** indicate the 10, 5, and 1% levels of
significance, respectively. (b) Marginal effects are shown in each trade equation (probit model).
difference in duration of extended warranty and format of listing, the authorized
product has a higher trade probability than the parallel import (at 5% significance
level). However, warranty duration has no effect on the trade probability for the
authorized product, and a negative effect on that for the parallel import. As can be seen
from the last column of Table 8, other things being equal, the authorized product has
about a 7% greater chance to reach a sale than the parallel import. Also significant is
that the cameras in the high price range have a 14.2% greater chance of reaching a sale
than those in the low price range.
As to the transaction price, the second column of Table 7 shows that, after
controlling the extended warranty duration and the listing format, the authorized
product has a higher transaction price than that of the parallel import. Specifically, other
things being equal, the value of the total price ratio of the authorized product is 0.093
higher than that for the parallel import. As an example, a camera with 18 months of
warranty from the authorized dealer with an MSRP of NT$7000 (which is the average
MSRP in our data) will fetch a price NT$651 higher than the parallel import. With a
7% advantage in trade probability (Table 8), the expected revenue of listing this camera
will be NT$594 higher than that of an identical parallel import.19 Similar to the case
for trade probability, the duration of warranty period has a positive effect on transaction
price for the authorized product while it has a negative effect on the trade probabilityAsia-Pacific Journal of Accounting & Economics 13
for the parallel import. Moreover, cameras of the highest price range are sold at a
greater price discount than those in the lower price range.
The results above show that authorized products do enjoy an advantage, both in
trade probability and transaction price, over the parallel imports. But what determines
the specific difference in transaction price and trade probability in the results? Why, for
example, do the bidders not lower their bids to increase the trade probability for the
parallel imports, or bid higher to further decrease its trade probability? It seems that, if
the bidders value the parallel imports less than the authorized products, they can bid at
a lower price so that the trade probability will increase to a level comparable to that for
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the authorized product. However, this is not the case. Looking further into the data, we
find that an overwhelming majority of parallel imports are posted with fixed price (212
out of 261), with only 48 items listed in a pure auction. It appears that there are two
types of sellers for the parallel imports. The first type of sellers is more experienced,
with an average value of EXPER of 1012. Knowing that the bidders are only willing to
pay for the parallel imports at a discount, they overwhelmingly list the items at low
fixed prices.20 As a result, their transaction rate (0.17) is even higher than that for the
authorized products which are also listed at a fixed price (0.14) (See Table 9). The
second type of sellers is less experienced, with an average EXPER of 559. In an
attempt to “signal” their quality, they adopt the pure auction and set a starting bid at a
level comparable to the authorized product.21 As a result of this, the trade probability
for these parallel imports in pure auction is the lowest among all.
The literature has consistently shown that pure auction yields a higher trade
probability than the fixed-price listing.22 However, because of the reason above, the
trade probability for the parallel imports reverses this regularity (0.13 for the pure
auction vs. 0.17 for the fixed-price listing).23 Also note that, by posting high starting
bids, these parallel imports listed in pure auctions do result in a TPR (0.67) similar to
the overall authorized products TPR (0.68). It appears that the parallel imports sellers
who adopt the pure auctions attempt to maintain a price comparable to the authorized
product at the cost of low trade probability. In fact, the sellers who adopt the pure
auctions with high starting bids are the main reason why our regression shows that
overall the parallel import has a lower trade probability than the authorized product.24
In summary, the higher price and trade probability of the authorized product appear
to be driven by two factors. The first is that, on the average, the authorized product has
10 more months of warranty coverage. The second is the choice of listing formats. An
overwhelming majority of the parallel imports are listed with relatively low fixed
prices, which results in a trade probability comparable to that of the authorized
products listed with same format. However, a small number of inexperienced sellers of
parallel imports, in an attempt to sustain a price comparable to the authorized product,
Table 9. The sample mean of auction results, starting bid and posted price by product source
and listing format.
TPR Sale rate STARTBID/MSRP Posted Price/MSRP
Authorized product
Pure auction 0.63 0.18 0.68 –
Fixed-price listing 0.70 0.14 – 0.81
Parallel import
Pure auction 0.67 0.13 0.67 –
Fixed-price listing 0.62 0.17 – 0.6414 K.-P. Chen et al.
list their item in pure auctions with unusually high starting bids. This results in the
lowest trade probability of all, and is the main reason why the parallel import overall
has lower trade probability than the authorized product. If we do not consider those
parallel imports sellers who adopt pure auction, the authorized product has only higher
transaction price, but not trade probability.
It might be instructive to compare the results in Tables 6 and 7 to the case when
neither the endogeneity of listing formats nor the sample selection problem is consid-
ered, and the case when only the sample selection problem is considered. Table A1 of
the Appendix 1 summarizes the results for the single trade probability equation and the
single price equation without considering the any of two problems above (the first two
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columns), and the joint results for the trade probability and price equations when only
the sample selection problem is corrected (M0, M1, and M2). M0 is the model which
includes only BIN and FP in the TRADE equation; M1 includes all of BIN, FP,
EXPER, and INVENTORY; and M2 includes only EXPER and INVENTORY. The values
of the inverse Mills Ratio for three models are all significant at 1% level, implying that
the correction of the sample selection problem is important. Moreover, we apply the
LR (likelihood ratio) test and the BIC (Bayesian Information Criterion) model selection
criterion to help deciding which model is the better fit. Both the LR test and BIC con-
sistently suggest that, among the three models, M2 is the better fit.25 Despite this, by
comparing Tables 6 and 7 with M2 in Table A1, we can still see clearly that there are
substantial differences in estimation results if the endogeneity problem is not controlled
for, even if we correct for the sample selection problem.26 Specifically, for the trade
probability, the coefficients of BIN and FP are significantly underestimated when the
endogeneity of listing formats is not considered. For the price equation, we find less-
significant parameters, and many parameters are underestimated when the issue of sam-
ple selection is ignored in the estimation. The differences between the estimated coeffi-
cients in Tables A1, 6, and 7 show that the potential estimation bias due to ignoring
either the endogeneity or the sample selection problem is serious. All in all, the model
which considers both the endogeneity of listing formats and the sample selection
problem seems to be more appropriate.
6. Conclusion
In this paper, we use data from Taiwan’s Yahoo! auctions of Nikon cameras to
investigate the influences of product source and warranty on the results of transactions.
We show that the authorized product has both higher trade probability and transaction
price than the parallel import. An overwhelming majority of parallel import sellers list
their items with relatively low posted prices, resulting in higher trade probability than
the authorized product. However, a small number of less-experienced sellers of parallel
imports who list their items in pure auctions with unusually high starting bids is the
main reason why, overall, the parallel imports have lower trade probability. Therefore, if
we do not consider these parallel imports sellers who adopt pure auction, the authorized
product has higher transaction price than, but about the same trade probability, as the
parallel import. Our result confirms the conventional wisdom that people purchase the
parallel imports because they can buy them at a discount to the authorized products. In
fact, because of its lower price, the trade probability of the parallel imports is even
greater than that for the authorized products in the fixed-price listings.
As regards to the four functions of warranty discussed in the Introduction, the
signaling and incentive motives are not relevant to our data as the items are all newAsia-Pacific Journal of Accounting & Economics 15
Nikon cameras. Our results support the insurance motive: the authorized product,
having 10 more months of warranty coverage, does have advantages in both trade
probabilities and, when there is a sale, higher transaction prices. Our result also
supports the sorting/price-discrimination motive, as those who list the parallel imports
in the fixed-prices format post lower prices to ensure a trade probability similar to the
authorized products.
Our data are on a product of not only one type (the camera) but also the same
brand. Therefore, we cannot claim that similar findings will also hold in general. Future
empirical research might greatly improve our understanding of the function played by
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warranty by collecting online transaction data of more diverse commodities, rather than
a single commodity of the same brand.
Acknowledgement
We thank Ching-I Huang, Chang-Ching Lin, and Chia-Hui Lu, an anonymous referee,
participants of the 2013 APJAE Symposium in the City University of Hong Kong, seminar
participants in the Center of Institution Behavior Studies, and in particular James Rauch for
helpful comments and suggestions. Financial supports from Taiwan’s National Science Council
(NSC100-2410-H-001-012-MY3) is gratefully acknowledged.
Notes
1. Spence (1977), Grossman (1981), Gal-Or (1989) and Lutz (1989).
2. Cooper and Ross (1985), Emons (1988), Lutz (1989), and Mann and Wissink (1990).
3. See Kubo (1986) and Matthews and Moore (1987).
4. In Taiwan, the official dealer for Nikon cameras is Lin Trading Co., Ltd., which provides
the product support to the consumers at various locations.
5. For example, some are professional sellers and some are individuals; some have a diversity
of commodities in store, and some only have camera-related items.
6. Unlike in eBay, the BIN option in Yahoo! is permanent. Even if some bidder places a bid,
the BIN option stays until the end of the auction.
7. An item listed with a BIN option can be sold by either BIN being exercised or a
competitive winning bid.
8. The most convincing and straightforward comparison between the authorized product and
the parallel import without defining the TPR, as pointed out by the referee, is to compare
the transaction results model-wise. That is, first separate all items into groups so that the
items in the same group are of identical model. Then compare the price and trade
probability of the authorized product and the parallel import within the same group, with
other characteristics controlled. This comparison, unfortunately, turns out to be impossible
for our data. There are a total of 27 models in our data, among which seven had only
authorized products up for sale and two had only parallel imports. For the models which
both on offer, many had an insufficient number of either the parallel import or the
authorized product to enable us to conduct a meaningful statistical comparison.
9. When we refer to the sample, the term “trade probability” actually means the “fraction
sold,” and we will therefore use it interchangeably with “sale rate.” Trade probability,
however, means the probability that an item is sold when we refer to the regression results.
10. Table 3 also explains why we need to define extended warranty for the authorized product
and the parallel import separately, rather than a single variable for extended warranty. There
is no authorized product that comes with no warranty, and only two have a warranty
duration of 12 months. On the other hand, there is no parallel import having 24 months of
warranty, and only one with 18 months. The distributions of warranty duration are too
skewed (and are skewed at opposite tails) to define a simple overall warranty duration
variable.
11. See Bajari and Hortaçsu (2003), Houser and Wooders (2006), and Li, Srinivasan, and Sun
(2009).16 K.-P. Chen et al.
12. If the camera’s MSRP falls between NT$4990 and NT$8900, it is in GROUP 1; if it falls
between NT$9900 and NT$11,900, it belongs to GROUP 2; and it belongs to GROUP 3 if
its MSRP is greater than NT$12,900.
13. PI is a dummy variable which equals 1 if the product is a parallel import, and 0 otherwise.
In fact, PI = 1 − AP. As is pointed out by the referee, service of the authorized products is
provided by Lin Trading Co., which has stores in various locations, while that of the paral-
lel imports is only by the seller. Even with identical extended duration, consumers might
value the authorized product’s warranty and the parallel import’s warranty differently.
Therefore, we use the interaction terms to measure their effect separately.
14. Hammond (2010), Chen, Liu, and Yu (2013) and Einav et al. (2013).
15. See Hidvégi, Wang, and Whinston (2006), Mathews and Katzman (2006), Reynolds and
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Wooders (2009), and Chen et al. (2013).
16. The values of the endogeneity test suggest that considering the endogeneity of listing-for-
mat choice is more appropriate. ( χ2 = 21.61, p-value = 0.00 for the trade probability equa-
tion; χ2 = 145.36, p-value = 0.00 for the transaction price equation, respectively).
Moreover, following Hammond’s (2010) suggestion of using the rank statistic proposed by
Kleibergen and Paap (2006), we reject the null of an underidentified model ( χ2 = 3.94,
p-value = 0.047), which indicates that the variables (EXPER, INVENTORY) are sufficiently
correlated with the listing formats.
17. A case can be made that the variables EXPER and INVENTORY cannot be excluded from
the TRADE equation and are therefore not valid instruments. Buyers may infer that sellers
with greater experience or inventory have better after-sale services or are more likely to sur-
vive in the future, making them more confident to purchase from these sellers. Fortunately,
our main results are in Table 7, where we do not rely on exclusion of EXPER and INVEN-
TORY from the TRADE equation.
18. In Table 7, there are 15 more parameters to be estimated than those in Table 6, given the
same number of observations.
19. Let r denote the probability of sale and p denote the price. Then, the expected revenue
(ER) from listing a product is rp. The change of the expected revenues from PI to AP is
ΔER = r(PI)[ p(AP) − p( PI)] + [r(AP ) − r(PI )] p(PI) + [r(AP) − r(PI )][ p(AP) − p(PI )]. By
rearranging the terms, we may rewrite it as ΔER = [r(AP) − r(PI )] p(AP) + r(PI )[ p(AP) − p
(PI)]. According to the last column of Table 8, the marginal difference in trade probability
is 7%, which implies r(AP) − r(PI) = 7%. The result in the second column in Table 7 sug-
gests that the price difference between the sold AP and PI items is 651(=7000 × 0.093)
given the MSRP is NT$7000. We then have p(AP) − p(PI) = 651 and p(AP) = 7000. We
use the sale rate of PI, listed in Table 1, to represent the probability of sale for the PI item,
r(PI) = 0.16. Therefore, ΔER = 0.07 × 7000 + 0.16 × 651 = 594.16. The difference of the
expected revenues between AP and PI is therefore NT$594. We thank the Editor James
Rauch for formulating the appropriate comparison.
20. The average ratio of posted price to MSRP in the fixed-price listings is 0.64 for the parallel
imports, and is 0.81 for the authorized products. See Table 9.
21. The ratios of starting bid to MSRP in the pure auctions are 0.68 and 0.67 for the authorized
product and parallel import, respectively. Note that for the parallel imports, the ratio for
starting bid to MSRP in the pure auction is even greater than the ratio of posted price to
MSRP in the fixed-price listing.
22. Hammond (2010), Chen, Liu, and Yu (2013), and Einav et al. (2013).
23. Note that the sale rates for the authorized product in the pure auction and the fixed-price
listing in Table 9 are 0.18 and 0.14, respectively, consistent with the literature.
24. We can see from Table 9 that the sale rate for the parallel import is even greater than that
for the authorized product for the fixed-price listings.
25. Please see Table A1 for the values of the likelihood ratios and the BICs. Another reason
that it might not be appropriate to include BIN and EXPER in the same regression is that,
with a correlation coefficient of 0.5070 they are too closely correlated.
26. Correspondingly, the values of endogeneity test (in footnote 16) show that the model con-
sidering the endogeneity of listing-format choice is more appropriate.You can also read
