ECONOMIC RESEARCH CENTERDISCUSSION PAPER
E-Series

No.E16-4

Gender Differences in Extended Warranty Purchases:
The Case of Electronic Products
byTakanori Adachi
Ryo Kato

July 2016

ECONOMIC RESEARCH CENTERGRADUATE SCHOOL OF ECONOMICSNAGOYA UNIVERSITY

Gender Di§erences in Extended Warranty Purchases:
The Case of Electronic Products!
Takanori Adachiy

Ryo Katoz

June 27, 2016

Abstract
Are women more likely to purchase an extended warranty? In this paper, we study
in-store sales of extended warranties for electronic products, and Önd that gender andincome matter: female consumers from low-income households are more likely to purchase an extended warranty, though there are little gender di§erences for middle- andhigh-income households. It is also found that the gender gap in extended warranty
purchases is more apparent in the case of personal computers than in the case of CRT(cathode ray tube) televisions, suggesting that gender may matter more in extended
warranty purchases for more mechanical products. We also argue that our empiricalresults may suggest that advantageous selection prevails in the extended warranty
market for personal computers.
Keywords: Gender Gap; Extended Warranties; Advantagenous Selection.
JEL classiÖcation: D40; J16; L11.

!
We are grateful to Victor Aguirregabiria, Koki Arai, Lanier Benkard, Carsten Bienz, Ambarish Chandra, Andrew Ching, Michal Fabinger, Makoto Hanazono, Mitchell Ho§man, Takahiro Hoshino, Hideshi
Itoh, Daiji Kawaguchi, Kohei Kawaguchi, Tatsuya Kikutani, Kozo Kiyota, Michael Kummer, Jun Bum
Kwon, Manabu Matsunaka, Toshiyuki Matsuura, Hitoshi Mitsuhashi, Takeshi Murooka, Ryo Nakajima,
Yuji Nakayama, Hiroshi Ohashi, Hiroyuki Okamuro, Hideo Owan, Dan Sasaki, Yasuyuki Sawada, Junichi
Suzuki, Emiko Usui, Naoki Wakamori, Yue Wang, Ryan Webb, and seminar and conference participants
at Hitotsubashi, Keio, Kyoto, Tokyo, Toronto, and the 96th meeting of the Japan Institute of Marketing
Science for helpful discussions and comments on earlier versions of this paper. We also thank the Informs
Society of Marketing Science (ISMS) (http://www.informs.org/Community/ISMS) for providing us withthe data for this study (the ISMS Durable Goods Datasets 1 and 2), and the data contributors Baohong
Sun and Scott Neslin for responding to our inquiries about the structure of the data. Adachi acknowledgesÖnancial supports from the Japan Society for the Promotion of Science (a Grant-in-Aid for ScientiÖc Research (C) (15K03425)) and from the Japan Center for Economic Research. Kato thanks a Grant-in-Aid
for JSPS Fellows (16J05118). A part of this work was started while Adachi was visiting the Departmentof Economics, University of Toronto. He thanks its hospitality and a Önancial support from the "Strategic
Young Researcher Overseas Visits Program for Accelerating Brain Circulation" by the Japan Society ofPromotion of Science. Any remaining errors are our own.
ySchool of Economics, Nagoya University, 1 Furo, Chikusa, Nagoya 464-8601, Japan. E-mail:adachi.t@soec.nagoya-u.ac.jpzGraduate School of Economics, Keio University, 2-15-45 Mita, Minato, Tokyo 108-8345, Japan. E-mail:kato.ryo@keio.jp

1

Introduction

Extended warranties (or extended service contracts) are sold by retailers as an option at
the purchase of a durable good such as an electronic product and an automobile. Whilea base warrantyóprovided (mandatory) by manufacturersóis usually valid for the Örstone year, an extended warranty provides an additional period (usually 2 to 3 years) fora repair without further charge. It is well known that electronics retail chains earn aconsiderable amount of revenues from selling extended warranties,1 and as such, they areoften criticized by the popular media. Some opponents even argue that consumers should
avoid purchasing an extended warranty at all.2
This paper studies gender di§erences in extended warranty purchases in two major
categories of electronic products: personal computers (PCs) and cathode ray tube televisions (TVs). In particular, our empirical analysis focuses on the gender gap in extended
warranty purchases considering income status. We Önd that in the case of extended warranties for personal computer purchases, female consumers in low-income households are
16 percentage points more likely to purchase an extended warranty than the male counterparts, though this number for female consumers in middle- or high-income households isonly 6 and 4 percentage points more, respectively, in comparison to men from low-income
households. However, the gender gap is less apparent in the TV category: while a female consumer in a low-income household is 7 percentage points more likely to purchasean extended warranty than the average men in the same category do, female consumers
from middle- and high- income households are 5:5 and 7:6 percentage points less likely
to purchase one than men from low-income households do. In both categories, poorer
women tend to purchase an extended warranty. While this may indicate that they are1
For example, according to Robert Bernerís article in Bloomberg Businessweek (ìThe Warranty Windfall,î December 19, 2006; http://www.businessweek.com/stories/2004-12-19/the-warranty-windfall), Circuit Cityís estimated proÖts from extended warranties in 2004 consisted of 45 percent of its total operating
proÖts. Unfortunately, the details on the proÖtability are usually unclear because companies are not obliged
to disclose information speciÖc to sales and proÖts from extended warranties.
2
For example, Consumer Reports lists up the reasons ìwhy we recommend against buying service
plansî at http://www.consumerreports.org/cro/extended-warranties/buying-guide.htm (accessed June2016). See, e.g., Pope, Ishida, Kaufman, and Langrehr (2014) on the institutional characteristics of the
U.S. extended warranty market.

1more risk-averse than women from middle- and high-income households, it may also imply
that extend warranty purchase results from unthoughtful decision-making. In addition,
the contrast between the PC and the TV categories may suggest that gender gap may
matter more in extended warranty purchases for more mechanical products.
Furthermore, if we compare women with men in the PC sample, women are more
likely to purchase an extended warranty for each income category. There would be two
possible interpretations. The Örst one is that (i) if women are more likely to break down acomputer than men, then our empirical Önding would suggest adverse selection. However,(ii) if women are less likely to break down a computer than men, then it would suggest
advantageous selection: those who need an extended warranty less are more likely topurchase one, and vice versa (see, e.g., Einav and Finkelstein (2011) and Mohoney andWeyl (2014)). The relationship is less apparent in the TV sample, though. We argue that
our empirical results may suggest that advantageous selection prevails in the extended
warranty market for PCs.
The rest of the paper is organized as follows. The next section brieáy surveys therelated literature. Section 3 explains the data that we use for this study. We then present
empirical results and robustness checks in Section 4. Section 5 concludes.

2

Related LiteratureThis paper studies gender gap in extended warranty purchases. Our paper is related to
the following three areas: (i) empirical and experimental studies of gender di§erences inrisk attitude, (ii) empirical studies of extended warranties, and (iii) empirical studies of
insurance purchases.

2.1

Gender Di§erences in Risk Attitude

This paper empirically Önds out gender gap in extended warranty purchases. Our empiricalÖndings are in line with the existing experimental studies on gender di§erences in risk
preferences (see, e.g., excellent surveys by Croson and Gneezy (2009) and by Meyers-Levy
and Loken (2015)). Controlling for such demographics as education, age, race, and countryof origin, the existing studies in experimental economics have repeatedly found strong2

evidence that females are more risk averse than males both in the laboratory (usually,in the context of lottery choices) and in the Öeld (usually, in the context of investment
decisions).3,4 However, our empirical results imply that womenís risk attitude varies acrosstheir income status. This is also consistent with Johnson and Powellís (1994) Önding thatgender di§erences in risk attitude are quite small in the managerial subsample, while in
the nonmanagerial subsample, women exhibit more risk aversion than men. In the labormarket context, many studies Önd that women gain less from negotiation, and have lower
preferences for risk-taking (see. e.g., an excellent survey by Azmat and Petrongolo (2014)).

2.2

Empirical Studies of Extended Warranty Purchases

There is a limited number of empirical studies of extended warranties by retailers.5 Chen,
Karla, and Sun (2009) is the Örst among the recent empirical studies that focus on consumer decision-making in extended warranty purchases. Using detailed data from a retailer(including information on promotion (price promotion and advertising) and the warranty
length), Chen, Karla, and Sun (2009) consider various factors that potentially ináuence3

Based on psychological studies, the following three factors are mentioned by Croson and Gneezy (2009)as causes of gender di§erences: (i) emotions (women react to uncertain situations and adverse outcomes
more fearfully than men do), (ii) overconÖdence (men have more overconÖdence than women do), and (iii)risk as challenges or threats.
4
Relatedly, Hanaoka, Shigeoka, and Watanabe (2014) study gender di§erences in changes in risk preference by using the variation in seismic intensity the Great East Japan Earthquake (March 11, 2011). TheyÖnd that changes in risk tolerance after a natural disaster are experienced mainly by men, especially old,less educated and unmarred men. This result would suggest that menís risk preference is more subject
to changes than womenís. However, in Hanaoka, Shigeoka, and Watanabe (2014). risk attitude is notmeasured based on the actual behavior: when asked, the respondent answers as if he or she becomes
more risk tolerant, but he or she may become just insane and less sober, rather than more risk tolerant.
Although Hanaoka, Shigeoka and Watanabe (2014) also consider gambling and drinking as another measure for risk-taking behavior, this criticism still applies these behaviors may be motivated by changes in
preferences and environments that are unrelated to risk. In contrast, the present study uses a dataset on
actual insurance purchase.
5
Base warranties by manufacturers are studied by Chu and Chintagunta (2009, 2011). Chu and Chintagunta (2011) empirically compare four competing theories of warranty: (1) risk-sharing mechanism;(2) sorting mechanism (heterogeneity in risk attitude, and it is private information. second-degree pricediscrimination; (3) as a signal of product quality; and (4) as an incentive mechanism form manufacturers
to reveal and improve product quality. Chu and Chintagunta (2011) Önd supporting evidence for (1) and(2), but not for (3) or (4). In the present paper, following Chu and Chintaguntaís (2011) Öndings, we take
into account theories (1) and (2) that are behind our empirical results. On the other hand, using datafrom the US server market, Chu and Chintagunta (2009) propose and estimate a structural model of basewarranty demand and its pricing to quantify the value of base warranties, decomposing it into its insurance
value and its sorting (price discrimination) value. In particular, Chu and Chintagunta (2009) argue, basedon their counterfactual experiments, that if manufacturers are forced to provide three-month or two-year
warranties uniformly, they would also 20 percent of their proÖts in either case. This result suggests that
manufacturers considerably beneÖt from the sorting role of base warranties.

3

consumersí extended warranty purchase such as product characteristics, retailer actions,and consumer characteristics. Focusing on seven product categories (video, audio, phone,camera, computer, game, and mobile audio), Chen, Karla, and Sun (2009) estimate astructural model of binary choice (whether to purchase an extended warranty or not)based on the mean-variance utility to take into account risk aversion, and then Önd thatthe coe¢cient for the variance term is statistically signiÖcant, indicating that consumers
are indeed risk averse.
Chen, Karla and Sun (2009) also mention gender di§erences in extended warranty
purchases, Önding ìa puzzling resultî (p.620) that ì[e]verything else being equal, men are
more likely to purchaseî EWs ìthan women.î They claim that they cannot conÖrm oneof their hypotheses (H5 in p.614) that ì[w]omen are more likely to purchaseî EWs ìthan
men because they are more risk averse.î In contrast, our estimates suggest that womenare more likely to purchase an extended warranty. In addition, we consider interactions
between gender and income, while Chen, Karla and Sun (2009) do not. Regarding income,Chen, Karla and Sun (2009, p.620) conclude that ìgiven everything else equal, lowerincome consumers are more prone to buyingî EWs. They argue two possible reasons.
One is that ìlower-income consumers are more sensitive to the replacement cost.î On theother hand, we Önd that lower-income men are the least prone to purchasing EWs. Thus
gender di§erences are more apparent for lower-income consumers. Interestingly, in the
TV category, our estimates are consistent with Chen, Karla and Sun (2009): lower-income
consumers are more likely to purchase an extended warranty. However, in the PC category,higher-income consumers are more likely to purchase one.
Another recent study by Jindal (2015) provides an experimental study on extended
warranty purchase to compare alternative theories from the traditional expected utility
theory to behavioral economics.6 Jindal (2015) decomposes di§erent preferences overrisk/uncertainty into: (i) risk aversion; (ii) loss aversion (the magnitude of loss is larger
than that of gain with the same amount); (iii) nonlinear weighting of failure probabilities.
Allowing variation in failure probabilities (this is a target of estimation in Chen, Karla, andSun (2009)) and variation in repair costs, Jindal (2015) Önds that loss aversion explains
6

Steiner and Otter (2014) is another experimental study on extended warranties to focus on consumersí
information processing.

4

the data of stated choices signiÖcantly better than two competing theories, standard risk
aversion (corresponding to the concavity of von NeumannñMorgenstern utility function)and nonlinear probability weighting. Jindal (2015) also Önds that there is substantial
heterogeneity in loss aversion among the subjects.7,8 While we remain agnostic about
the actual psychological factors behind extended warranty purchases in this paper, ourempirical results would imply how these factors are a§ected di§erently by consumersídemographic characteristics.

2.3

Empirical Studies of Insurance Purchases

Cohen and Einav (2007) already Önd that women are more risk averse with data (froman auto insurance company in Israel) on deductible choices in auto insurance contracts.
However, their sample does not include information on individualís income (or wealth).
They use wealth from the census data by using zip code information in the original data
to match (the averaged) wealth to an individual.9 In contrast, we are able to directlyinteract between gender and income because our sample has household-level information
on both variables. In addition, the dataset that we use for this study (see the next section)
originally covers a wide variety of electronic products. Thus, we are able to study how
product novelty is related to gender di§erences in extended warranty purchase. In particular, we compare the PC category with the CRT (cathode ray tube) TV category. While
CRT television sets supposedly belonged to one of the traditional categories of electronic
products in the period of the data for this study (December 1998 to November 2004),
personal computers were probably deemed to be still relatively new electronic products
for most households. In the empirical analysis below, we Önd that gender di§erences inextended warranty purchases are more apparent in the PC category than in the TV cate7
Relatedly, by using the same dataset as in the present study (ISMS Durable Goods Dataset 1; see
the next section), Abito and Salent (2015) introduce overweighting of breakdown probabilities, instead ofstandard risk aversion, and argue that it better explains the data. Their counterfactual experiments showthat the retailerís proÖt would become lower by 80 percent if there were no overweighting of breakdown
probabilities.
8
In a di§erent vein, Wang, Ata, and Islegan (2012), by using the same data source as in the present
study, estimate a dynamic model of demand for a durable good and its extended warranty, assuming
risk-neutrality, and Önd that extended warranties are overpriced for high-end brands, and underpriced for
low-end brands.
9
Cohen and Einav (2007) do not use income information in their benchmark estimation because 20
percent of the individuals would be dropped. See Cohen and Einavís (2007, p.761) Footnote 33.

5gory. In contrast to auto insurances, contract heterogeneity (most importantly, coverage
length) would be less important in the case of extended warranties (as suggested by Chen,
Karla and Sun (2009)) because a car owner, if he or she is not extremely poor, usuallypurchases an insurance at the purchase of a car, and thus insurance providers would caremore about the variety of insurances (how long and how much the insurance covers).

3

Data

In this section, we Örst explain how we construct our samples of PC and TV transactions
from the original dataset. We then present the summary statistics of these samples.

3.1

Sample Construction

We use the INFORMS Society for Marketing Science (ISMS), Durable Goods Dataset
1 (see Ni, Neslin, and Sun (2012) for an introduction of the data).10 It is a customerlevel transaction dataset collected from 1176 stores of an anonymous national consumer
electronics retail chain in the U.S., located throughout the U.S.11 The dataset covers 6
years (December 1998 to November 2004). It randomly selects 19,936 households, and foreach randomly selected household, the transaction record is complete (at this retail chain)through the 6 years, yielding total 173,262 transactions, including extended warranty(EW) purchases.
We focus on transactions in the personal computer (PC) category (desktop and laptop) and the CRT (Cathode Ray Tube) television category (TV for short). According
to Table 2 of Ni, Neslin, and Sun (2012), the latter category has the highest number oftransactions in the ISMS Durable Goods Dataset 1, followed by the Music category, and
the PC category. However, the Music category has a tiny percentage of associated purchases of an extended warranty. This is the reason why we choose these two categories
for our study. During the sample period, LCD (Liquid Crystal Display) televisions had
not gained popularity yet, and thus their share is very small in the ISMS Durable Goods
Dataset 1. In addition, information on size is available for CRT televisions (they are
10

It is available (with charge) at https://www.informs.org/Community/ISMS/ISMS-ResearchDatasets/.
11
Unfortunately, no geographical information (such as ZIP code) for each transaction is contained.

6

categorized as either 9-16 inches, 19-20, 25, 27, or 30 and larger). For each category, we
exclude transactions with no information on (i) gender of the individual who made thetransaction, (ii) his/her householdís income, or (iii) PC and EW prices. Households withmore than 4 PC/TV transactions in the data period are also excluded. Also excluded aretransactions that were Önally returned. Our Önal sample for the PC category has 2,683
PC transactions. For the TV category, there are 3,812 transactions.12 Unfortunately, we
have no information on a menu of extended warranties o§ered to a consumer when he or
she purchased a PC.13
Regarding information on the demographic characteristics of a household to which theconsumer who purchases a PC/TV belongs, Öve pieces of information are available: (1) age
of the household head, (2) whether there are children living in the household, (3) income
level of the household (nine categories; unfortunately, no dollar value is attached to eachcategorical number), (4) gender of the household head, and (5) gender of the household
member who purchased a PC. Unfortunately, we cannot include information on the age ofthe householdís age or the number of children because almost half of the transactions in
the original sample lack either piece of information. Furthermore, we have no informationon the age of the consumer who made a transaction. Thus, it is not possible to consider
an interaction between gender and age.
To augment the ISMS data, we have collected information on breakdown probabilitiesof PCs from various issues of Consumer Reports. These are rates of breakdown in Öve
years with normal usage. Consumer Reports did not publish breakdown rates for laptops
until 2004. Thus, we use these rates for the entire sample period. Although ìactualî
12

We do not distinguish onsite and online transactions because the number of online transactions is
negligible This is probably because consumers would have preferred to purchase heavy products such
PCs and TVs at onsite stores. On the other hand, Abito and Salent (2015), by using the same ISMS
Durable Goods Dataset 1, exploit the di§erences between onsite and online in frequency of extended
warranty purchase to argue that aggressive attitude by store clerks cause so many consumers to purchasean extended warranty.
13
Although Ni, Nelsen and Sun (2012, p.1009) document that ì[e]ach record includes ... informationsuch as brand purchased or returned, service contracts purchased returned, product category, price paidor refunded, length of coverage of the service contract and time and location of the transactionî (italics
added), it is veriÖed (from personal communication with Baohong Sun and Scott Neslin) that the ISMS
Durable Goods Dataset 1 does not include information on the length of coverage of extended warranties. In
the ISMS Durable Goods Dataset 2, there is information on the length of coverage of extended warranties.
However, the unit of observation is household, and hence only the average length of coverage of all extended
warranties purchased during the sample period is observed for each household. In addition, no information
on household characteristics is included in the ISMS Durable Goods Dataset 2.

7breakdown probabilities may vary across heterogenous households, we assume they arecommon for all households. As for the TV category, we directly use Wang, Ata, andIslegenís (2012) information, which bases on the December 2004 issue of Consumer Reports.
These manufacuturer-size speciÖc failure rates are converted as the rates of breakdown in
5 years with normal usage. We also use these rates in the TV category for the entire data
period.

3.2

Summary Statistics

Tables 1 and 2 show the summary statistics of the 2,683 PC transactions. Interestingly, 43
percent of all the PC transactions are accompanied by extended warranty purchase. The
percentage is higher for laptops (47.4 percent) than for desktops (41.5 percent). About
38 percent of all the PC transactions were made by female consumers. Recall that eachhousehold is assigned to one of the nine income categories. We collect the lowest 3 categories into a new ëLowí category, and so on. The past experience of PC purchases by thesame household is observed only in the data period.14 This is also true for EW purchaseexperience. About 8% of the households have purchased a PC prior to a current transaction. Among them, 14 % of the households have ever purchased an extended warranty
for a PC. Turning to the PC characteristics in Table 2, about a quarter of all the PC
transactions are laptop computers.15 On average, the laptop price is 1.8 times higher thanthe desktop price, though the EW price for a laptop is only 1.2 times higher than thatfor a desktop. Accordingly, the ratio of the EW price to the PC price for a laptop is as
0.7 times large as for a desktop. As expected, the ratio of extended warranty purchaseis higher for laptop computers (47.4 percent for laptops and 41.5 percent for desktops).
However, the average 5-year breakdown rate is lower for laptops (17.7 percent for laptops
and 18.2 percent for desktops).16[Tables 1 and 2]
14
Recall that the data is collected from only one retail chain: a household may have purchased a PC ata di§erent retail chain.
15
The share of laptop computers doubled from the Örst half of the data period to the second half: it was
16.3% in 1999-2001, and it doubled to 33.9% in 2002-2004.
16
This could partly reáect the di§erence in the manufacturersí shares between desktops and laptops.

8

Tables 3 and 4 present the summary statistics of the 3,812 TV transactions. The
overall attachment rate (warranty purchases as a percentage of television purchases) is
about 29 percent. About 37 percent of all the TV transactions were made by female
consumers. The income distribution is similar to the case of PCs (see Tables 1 and 3). A
slightly higher percent (11%) of the households have purchased a TV prior to a current
transaction in comparison to TV. Among them, about 10 % of the households have everpurchased an extended warranty for a TV. Turning to the product characteristics (Table4), we consider two TV categories by size: ì27 Inches and Smallerî and ì31 Inches andLarger.î The latter category consists of about 28 percent of all the transactions, and its
average price is as 3.1 times high as the average price of ì27 Inches and Smallerî TVs.
Accordingly, the average ratio of the EW price to the PC price is smaller for the ì31 Inches
and Largerî category (0:205=0:228 = 89:9 percent smaller). The average 5-year breakdown
rate is higher for the ì31 Inches and Largerî category (10.2 percent for 31 inch- or larger
TVs and 8.8 percent for 27 inch- or smaller TVs). The frequency of extended warranty
purchase is also higher for the ì31 Inches and Largerî category (33.6 percent for 31 inchor larger TVs and 26.8 percent for 27 Inch- or smaller TVs). This is in contrast to thecase of PCs, where laptop computers, which have lower breakdown rates than desktop
machines do, are associated with a higher average rate of extended warranty purchases.
[Tables 3 and 4]

4

Empirical Results

Recall that the price of an extended warranty is observed only if it is purchased. Thus,
the price of an extended warranty for an observation with no EW purchase is imputedby a random draw from the normal distribution, where the mean and the variance arecomputed vis-a-vis a manufacturer-year-portability pair. This process can be regarded
as a stochastic regression imputation assuming ìmissing at random.î Since in our casewhether information is missing depends on observed components, namely, it is caused
only by the purchase or non-purchase of EW, we can assume missing at random as a
missing mechanism. Hence, this procedure provides us valid estimators in our empiricalanalysis (Enders, 2010). We do not think that the extended warranty price is a§ected by9

a consumerís demographic characteristics. This is especially true if the retailer in the datao§ers one-price plans only. However, if it o§ers menus with di§erent prices, sorting might
be a§ected by a consumerís demographic characteristics. Even if so, the e§ect would not
be large.17
Table 5 shows the probit estimates of the average/marginal e§ects in extended warranty purchases for the PC sample, and Table 6 shows the estimates for the TV sample. In
the Örst column (SpeciÖcation (1)), we focus on only two variables (except year dummies):
natural log of the ratio of the extended warranty price to the product price, and the dummy
for laptop. The price ratio has a negative e§ect on the extended warranty purchase, and
it is 0.1% statistically signiÖcant. This is true for both PC and TV samples. At the mean,
the e§ect of a 1% change in the price ratio in the PC category is as twice large as that in
the TV category. Note that we cannot straightforwardly compare the average/marginale§ects across groups in the probit regression because the magnitude of the coe¢cients area§ected by unobserved factors (Train, 2009). However, summary statistics in Tables 1 and3 indicates that the demographic characteristics of the PC sample and the TV sample arequite similar. Therefore, the problem of scale parameter di§erence would be much smallerin our case. In all the speciÖcations that we consider, the price e§ect is 0.1-1 % statisticallysigniÖcant. Next, SpeciÖcation (2) introduces manufacturer-portability speciÖc breakdown
rates, whereas in SpeciÖcation (3), manufacturer Öxed e§ects are considered instead. In
both PC and TV samples, Akaikeís information criterion is lower when manufacturer Öxede§ects are considered. Thus, we use manufacturer Öxed e§ects when we take into accountconsumer characteristics below.
Now, SpeciÖcation (4) considers gender. Its e§ect is positive for both categories,
and is 1% statistically signiÖcant for the PC sample. When we additionally consider
income in SpeciÖcation (5), the e§ect of gender is still statistically signiÖcant in the PC
sample, whereas neither (low nor high) income category has a statistically signiÖcant e§ect.
However, in the TV sample, the opposite is true: while gender has still no statisticallysigniÖcant e§ect, both income categories have statistically signiÖcant e§ects. The direction
17

Chen, Karla, and Sun (2009, p.615) state that ì[s]ome retailers, such as Circuit City and Wal-Mart,o§er consumers a menu of ESCs (Extended Service Contracts) that vary in length and price, whereas
others, such as Best Buy and Target, provide only one plan.î Unfortunately, the identity of the electronics
retailer is not disclosed in the ISMS Durable Goods Datasets 1 and 2.

10

is positive for the low-income category, whereas it is negative for the high-income category.
Finally, we consider an interaction of gender and income in SpeciÖcation (6). The basecategory is Male " (Income = Low). It is shown that female consumers from low-income
households are 17 percentage points more likely to purchase an extended warranty thanthe male counterparts are. This e§ect is 1% statistically signiÖcant. However, this number
for female consumers in the middle-income and the high-income categories is only 6 and
4 percentage points more than the base group, respectively, and there is no statisticalsigniÖcance. In contrast, in the TV sample, there is no such an apparent contrast between
men and women from low-income households. While females from low-income households
are still more likely to purchase an extended warranty than the male counterparts are, thedi§erence is only 7 percentage points, and it is 5% statistically signiÖcant. Thus, genderdi§erences are more apparent in the PC sample than in the TV sample. In the latter
category, income appears more responsible: as SpeciÖcation (5) indicates, consumers from
low-income households are more likely to purchase an extended warranty than those frommiddle-income households. The opposite is true from comparison between high-income
and middle-income households. Particularly, as SpeciÖcation (6) shows, men from highincome households are the least likely to purchase an extended warranty for a TV.
As a robustness check, Table 7 considers detailed information on consumer characteristics. Unfortunately, information on the age and the gender of the household head is
missing for some observations. Columns 1 and 2 take into account the age of the household head and the gender dummy for the household head. Comparing the gender e§ect inColumn 1 in Table 7 to SpeciÖcation (5) in Table 5, one Önds that statistical signiÖcancein the PC sample is now lost in this new speciÖcation, and moreover, the sign is reversed.
This is because variables ìFemaleî and ìFemale Household Headî are highly correlated:the correlation coe¢cient is 0.9853. This implies that if a woman purchases a PC, it almost
means that she is a household head. As expected, ìFemale Household Headî positively
contributes to the likelihood of extended warranty purchase. Regarding the TV sample, itappears that the inclusion of the two variables causes only small changes in the estimatede§ects for gender and income (compare SpeciÖcation (5) in Table 6 and Column 2 in Table7), although the correlation coe¢cient for ìFemaleî and ìFemale Household Headî is also
as high as 0.9894. Apparently, the estimate for gender becomes unstable. Columns 3 and114 instead consider the presence of children in a household. Somewhat interestingly, theestimated e§ect for the children presence dummy is negative for both PC and TV samples,
contrary to our intuition that the presence of children would make the household more
willing to purchase an extended warranty. Unfortunately, statistical signiÖcance is lostfor the gender dummy in the PC sample, and for the income dummies in the TV sample
presumably because of reduction in the sample size.
[Table 7]
Lastly, if we go back to Table 5 and compare women with men in the PC sample,
women are more likely to purchase an extended warranty for each income category. There
would be two possible interpretations. The Örst one is that (i) if women are more likely
to break down a computer than men, then our empirical Önding would suggest adverse
selection. However, (ii) if women are less likely to break down a computer than men,then it would suggest advantageous selection: those who need an extended warranty lessare more likely to purchase one, and vice versa. The relationship is less apparent inTable 6 for the TV sample, though. To investigate this issue more, Table 8 considers
how gender matters to cognition regarding extended warranty purchase, and shows theestimates of the average/marginal e§ects when past product purchase in the data period
is considered. To mitigate the left-censored problem (i.e., purchase experience before thesample period is not observed), we use the transactions in the latter 3 years of the data
period (2002, 2003, and 2004) for estimation. In both PC and TV samples, past purchaseexperience has a negative e§ect on extended warranty purchase. However, if we interact
past purchase experience with gender, the e§ects are di§erent across the two samples:while female consumers have an additional negative e§ect in the PC sample, those in
the TV sample have a reverse positive e§ect.18 Thus, while women are, ceteris paribus,
more likely to purchase an extended warranty for a more mechanical product (compare
SpeciÖcations (4) and (5) in Table 5 and in Table 6), they may change attitude towardextended warranty purchase based on past experience to a greater extent than men do for
18

Unfortunately, we are not able to consider the e§ects of past EW purchase because in both samples,
all of those who have purchased an EW in the past also purchase an EW in the current transaction, andhence there is no variation. Interestingly, the majority is men in both samples (69% in the PC sample,
and 60% in the TV sample).

12

mechanical product such as a PC. However, women may be more conservative than men
are for a less mechanical product such as a TV. In other words, if we interpret this as
womenís perception about breakdown being more unstable for PCs, it would suggest that
advantageous selection, rather than adverse selection, prevails in the market of extended
warranties for PCs (at least in comparison to the market of TV extended warranties).
However, if the estimates suggest that women are more adaptable in recognizing breakdown
probabilities for mechanical products, it may be just the case that women are more likely
to purchase an extended warranty than men are because they are more risk-averse.
[Table 8]5Concluding Remarks

This paper studies to what extent an individualís demographic characteristics matter tohis or her decision on extended warranty purchase. We Önd that female consumers from
low-income households are more likely to purchase one. It is important to investigate
what lies behind this result. Is it because female consumers from low-income households
are most risk-averse? Is it because they are most likely to have misconceptions about
breakdown? Although we remain agnostic about these ìdeepî determinants in the present
paper, mainly due to the data limitation, it is interesting and important to study how
gender matters to these determinants.
It is also important to model interactions in the extended warranty market. This
market is considered as an aftermarket. As Farrell (2008) argues, it may be the case that
extended warranty prices are ìhiddenî, and they are revealed when consumers are charged.
A retailer behaves as a ìde facto monopolistî when selling an extended warranty for adurable good if it is extremely costly to search for extended warranties before shopping,
and a consumer has to decide whether to buy one at the purchase of the durable good(see. e.g., Cutler and Zeckhauster (2004), Ellison (2005) and Gabaix and Laibson (2006)).
This may be related to ìoverinsuranceî as Cutler and Zeckhauser (2004, p.28) and Sydnor(2010) point out. In particular, if the average woman has less probability of PC breakdown
than the average man does, then advantageous selection may be prevailing in the market

13

of extended warranties for PCs in the sense that individuals who need less to be insured
are more likely to purchase an insurance.19
Then, what are the e§ects of introducing competition in this market of de facto
monopoly? In July 2013, (then) the UKís O¢ce of Fair Trading (OFT)20 launched a website called ìCompare Extended Warrantiesî21 in cooperation with participating extended
warranty providers. With the mandatory participation of two big retailers (Argos andDixons) in the UK (due to a legal agreement), it covers 75 percent of extended warranty
sales, and shoppers can use it free to compare prices of various types of extended warranties. The OFTís aim of creating the website is exactly to combat this situation of defacto monopoly. Then, the following questions naturally arise. How much will consumer
welfare improve if the extended warranty price approaches to the ìfairî price (i.e., themarginal cost) after competition in extended warranty sales by retailers and third-party
extended warranty providers is introduced? Is it a signiÖcant change? Turning to thesellersí side, how much proÖts will retailers still obtain after competition is introduced?
Is their loss signiÖcantly large? These and other interesting questions are left for future
research.

References
Abito, Jose Miguel, and Yuval Salent. 2015. ìWhy are ProÖt Margins So High in the
Extended Warranties Market?î Unpublished manuscript.
Azmat, Ghazala, and Barbara Petrongolo. 2014. ìGender and the Labor Market: What
Have We Learned from Field and Lab Experiments? î IZA Discussion Paper, No. 8135.
Chen, Tao, Ajay Karla, and Baohong Sun. 2009. ìWhy Do Consumers Buy Extended
Service Contracts?î Journal of Consumer Research, 36(4): 611-623.
19

In contract to many cases of insurance contracts, retailer chains selling extended warranties do not
usually keep information on actual breakdown accidents because technically extended warranty contracts
are underwritten by third-party insurance agents. Thus, we need separate information sources to know
the likelihood of womenís breaking down a computer. However, we must note that a home PC or TV is
jointly used in a household: it can be the case that a female spouse purchases a PC or TV and its extended
warranty and a male spouse breaks it down.
20
The OFT closed on March 31, 2014, splitting its responsibilities into a number of di§erent organizations. The main body was succeeded on April 1, 2014, by the Competition & Markets Authority.
21
Its URL is http://www.compareextendedwarranties.co.uk/ (accessed June 2016).

14

Chu, Junhong, and Pradeep K. Chintagunta. 2009. ìQuantifying the Economic Value ofWarranties in the U.S. Server Market.î Marketing Science, 28(1): 99-121.
ó, and ó. 2011. ìAn Empirical Test of Warranty Theories in the U.S. Computer Serverand Automobile Markets.î Journal of Marketing, 75(1): 75-92.
Cohen, Alma, and Liran Einav. 2007. ìEstimating Risk Preferences from Deductible
Choice.î American Economic Review, 97(3): 745-788.
Croson, Rachel, and Uri Gneezy. 2009. ìGender Di§erences in Preferences.î Journal of
Economic Literature, 47(2): 448-474.
Cutler, David M., and Richard Zeckhauser. 2004. ìExtending the Theory to Meet thePractice of Insurance.î in Richard Herring and Robert E. Litan (editors), BrookingsWharton Papers on Financial Services: 2004, Brookings Institution Press.
Enders, Craing K. 2010. Applied Missing Data Analysis. Guilford Press.
Einav, Liran, and Amy Finkelstein. 2011. ìSelection in Insurance Markets: Theory and
Empirics in Pictures.î Journal of Economic Perspectives, 25(1): 115-138.
Ellison, Glenn. 2005. ìA Model of Add-On Pricing.î Quarterly Journal of Economics,
120(2): 585-637.
Farrell, Joseph. 2008. ìSome Welfare Analytics of Aftermarkets.î Unpublished manuscript.
Gabaix, Xavier, and David Laibson. 2006. ìShrouded Attributes, Consumer Myopia,
and Information Suppression in Competitive Markets.î Quarterly Journal of Economics,
121(2): 505-540.
Hanaoka, Chie, Hitoshi Shigeoka, and Yasutora Watanabe. 2014. ìDo Risk Preferences
Change? Evidence from Panel Data Before and After the Great East Japan Earthquake.î
Unpublished manuscript.
Johnson, J.E.V., and P.L. Powell. 1994. ìDecision Making, Risk and Gender: Are Managers Di§erent?î British Journal of Management, 5(2): 123-138.

15

Jindal, Pranav. 2015. ìRisk Preferences and Demand Drivers of Extended Warrantiesî
Marketing Science, 34(1): 39-58.
Mahoney, Neale, and E. Glen Weyl. 2014. ìImperfect Competition in Selection Markets.î
Unpublished manuscript.
Meyers-Levy, Joan, and Barbara Loken. 2015. ìRevisiting Gender Di§erences: What We
Know and What Lies Ahead.î Journal of Consumer Psychology, 25(1), 129-149.
Ni, Jian, Scott A. Neslin, and Baohong Sun. 2012. ìThe ISMS Durable Goods Data Sets.î
Marketing Science, 31(6): 1008-1013.
Pope, Nat, Chiharu Ishida, Peter Kaufman, and Frederick W. Langrehr. 2014. ìExtended
Warranties in the U.S. Marketplace: A Strategy for E§ective Regulation.î Journal of
Insurance Regulation, 33(3), 1-20.
Sydnor, Justin. 2010. ì(Over)insuring Modest Risks.î American Economic Journal: Applied Economics, 2(4): 177-199.
Steiner, Michael, and Thomas Otter. 2014. ìDurable Product Purchase Decisions: Ináuences of Retailer Warranty Extension Information Sequencing.î Unpublished manuscript.
Train, Kenneth E. 2009. Discrete Choice Methods with Simulation. Cambridge University
Press.
Wang, Jingqi, Baris Ata, and Ozge Islegen. 2012. ìWarranty Pricing with Product Failuresand Forward Looking Consumers: An Empirical Approach.î Unpublished manuscript.

16

Table 1. Summary Statistics (PCs)Mean Std. Dev.
Consumer Characteristics
Female0:379
0:485
Age of Household Head
49:01
14:36
Female Household Head
0:375
0:484
Presence of Children
0:686
0:464
Household Income
Low
Middle
High

Obs.
2683
2582
2312
1397

0:184
0:417
0:398

0:388
0:493
0:490

2683
2683
2683

Past PC Purchases(in a household)

0:079

0:270

2683

Past EW Purchases(conditional on Past PC
Purchases = 1)

0:136

0:344

213

Note: All variables except ìAge of Household Headî are 0-1 dummies.

Table 2. Summary Statistics (PCs; continued)
Mean
Std. Dev. 5th % Median
PC Characteristics
Laptop (dummy)
0:252
0:434
0
0

95th %

Obs.

1

2683

Price
(Desktop)(Laptop)901:88754:82
1337:62

407:06
297:24
375:56

408:34
390:19835:65

835:65
699:991299:881671:30
1309:402036:84

2683
2006
677

Breakdown Rate (in 5 years)(Desktop)(Laptop)

0:180
0:182
0:177

0:014
0:014
0:011

0:15
0:15
0:16

0:19
0:19
0:18

0:19
0:19
0:19

2683
2006
677

Extended Warranty
Purchase (dummy)(Desktop)(Laptop)

0:430
0:415
0:474

0:495
0:493
0:500

0
0
0

0
0
0

1
1
1

2683
2006
677

Price
(Desktop)(Laptop)225:47
213:45
256:62

68:34
60:81
76:56

129:98127:03
145:88

209:98
202:96
250:69349:99339:29358:87

1153
832
321

Ratio of EW Priceto PC Price(Desktop)(Laptop)

0:259

0:088

0:143

0:250

0:400

1153

0:283
0:196

0:086
0:055

0:182
0:108

0:267
0:197

0:421
0:285

832
321

Note: Prices are measured in 1999 dollars.

17

Table 3. Summary Statistics (TVs)
Mean Std. Dev.
Consumer Characteristics
Female0:365
0:481
Age of Household Head
51:56
15:49
Female Household Head
0:354
0:478
Presence of Children
0:622
0:485
Household Income
Low
Middle
High

Obs.
3812
3653
3299
1912

0:190
0:403
0:406

0:393
0:491
0:491

3812
3812
3812

Past TV Purchases(in a household)

0:106

0:308

3812

Past EW Purchases(conditional on Past TV
Purchases = 1)

0:099

0:299

405

Note: All variables except ìAge of Household Headî are 0-1 dummies.

Table 4. Summary Statistics (TVs; continued)
Mean Std. Dev. 5th % Median
TV Characteristics
31 Inches and Larger (dummy) 0:278
0:448
0
0

95th %

Obs.

1

3812

Price
(27 Inches and Smaller)(31 Inches and Larger)

391:11
245:60769:38

364:70133:31
484:31

92:84
84:82
329:87

278:54
221:03
603:521114:03
510:67
1856:99

3812
2753
1059

Breakdown Rate (in 5 years)(27 Inches and Smaller)(31 Inches and Larger)

0:092
0:088
0:102

0:027
0:018
0:041

0:066
0:066
0:066

0:091
0:093
0:086

0:132
0:128
0:185

3812
2753
1059

Extended Warranty
Purchase (dummy)(27 Inches and Smaller)(31 Inches and Larger)

0:287
0:268
0:336

0:452
0:443
0:473

0
0
0

0
0
0

1
1
1

3812
2753
1059

Price
(27 Inches and Smaller)(31 Inches and Larger)

89:29
56:72
156:70

68:92
33:10
74:69

27:85
24:24
74:27

66:30
47:12
148:55

250:69109:99334:25

1093
737
356

Ratio of EW Priceto TV Price(27 Inches and Smaller)(31 Inches and Larger)

0:220

0:104

0:113

0:192

0:400

1093

0:228
0:205

0:111
0:086

0:119
0:090

0:200
0:188

0:417
0:360

737
356

Note: Prices are measured in 1999 dollars.

18

Table 5. Estimates of the Average/Marginal E§ects in EW Purchases (PCs)SpeciÖcation
Dep var: EW Purchase
(1)
(2)
(3)
(4)
(5)
Consumer Characteristics
Female0:0537!!
0:0516!!
(0:0194)

Income = Low(6)(0:0194)

0:0102(0:0263)

Income = High

#0:0349(0:0210)

Product Characteristicsln(EW Price/PC Price)Notebook#0:131!!!
(0:0257)#0:132!!!

#0:105!!!
(0:0262)#0:109!!!
(0:0263)#0:110!!!
(0:0263)#0:111!!!

0:00589

0:0104#0:0249#0:0248#0:0203#0:0168(0:0242)

Breakdown Rate

(0:0257)(0:0243)(0:0269)(0:0268)(0:0269)(0:0262)(0:0269)

0:0133(0:0072)

Interactions
Female " (Income = Low)

0:164!!!
(0:0242)

Female " (Income = Middle)(0:0370)

0:0666

Female " (Income = High)(0:0386)

Male " (Income = Middle)(0:0345)

Male " (Income = High)(0:0345)

0:04390:0517
0:0070

Fixed E§ects
Year
Manufacturer

Yes
No

Yes
No

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Log Likelihood
AIC
N#1811:03638:02683#1809:3
3636:6
2683#1794:3
3614:62683#1790:5
3609:02690#1788:5
3609:02683#1784:5
3605:02683

Notes: Standard errors are in parentheses. * p < 0.05, ** p < 0.01, *** p < 0.001. AIC stands
for Akaikeís information criterion.

19

Table 6. Estimates of the Average/Marginal E§ects in EW Purchases (TVs)
SpeciÖcation
Dep var: EW Purchase
(1)
(2)
(3)
(4)
(5)
Consumer Characteristics
Female0:02850:0209(0:0150)(6)(0:0150)

Income = Low

0:0807!!!

Income = High

#0:0384!

(0:0196)(0:0162)

Product Characteristicsln(EW Price/TV Price)
31 Inches and Larger#0:0605!!!

#0:0542!!!

#0:0387!!

#0:0392!!

#0:0391!!

#0:0394!!

0:0621

0:0742

0:04400:0444

0:04780:0475!!

(0:0137)!!!
(0:0160)(0:0139)!!!
(0:0164)(0:0143)!!
(0:0168)(0:0143)!!
(0:0168)(0:0142)!!
(0:0167)(0:0141)(0:0167)#0:00870!!

Breakdown Rate

(0:0028)

Interactions
Female " (Income = Low)

0:0686!

Female " (Income = Middle)#0:0547

Female " (Income = High)#0:0762!!

Male " (Income = Middle)#0:0494

Male " (Income = High)#0:0972!!!

(0:0319)(0:0288)(0:0294)(0:0259)(0:0260)

Fixed E§ects
Year
Manufacturer

Yes
No

Yes
No

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Log Likelihood
AIC
N#2263:1
4542:23812#2258:2
4534:33812#2237:64515:1
3812#2235:7
4513:53812#2218:04482:03812#2216:3
4482:63812

Notes: Standard errors are in parentheses. * p < 0.05, ** p < 0.01, *** p < 0.001. AIC stands
for Akaikeís information criterion.

20

Table 7. Estimates of the Average/Marginal E§ects in EW Purchases(with detailed consumer characteristics)
Sample " SpeciÖcation
Dep var: EW PurchasePC (7)
TV (7)PC (8)TV (8)
Consumer Characteristics
Female#0:0203
0:169
0:02900:0421!
(0:1433)(0:1255)(0:0271)

0:05280:0791!!!

0:0674#0:0376(0:0228)#0:0399!

Age of Household Head

0:00023

0:00016

Female Household Head

0:0805#0:142

Income = Low
Income = High

(0:0300)(0:0007)(0:1434)(0:0220)(0:0176)!!

(0:0302)#0:0420(0:0285)#0:0409#0:0499(0:0282)#0:0504!

(0:0223)(0:1257)(0:0211)#0:108!!!

#0:0436!!
(0:0160)#0:0887!
(0:0370)#0:0495!

#0:0250-

0:0015-(0:0284)

Notebook

0:0228(0:0393)(0:0007)

Presence of Children
Product Characteristicsln(EW Price/TV Price)(0:0212)(0:0383)(0:0296)!!

(0:0210)31 Inches and Larger-

0:0525-

0:0515!

Fixed E§ects
Year
Manufacturer

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Log Likelihood
AIC
N#1490:8
3017:62235#1837:63725:13169#928:4
1890:81397#1092:3
2232:5
1912(0:0182)(0:0232)

Notes: Standard errors are in parentheses. * p < 0.05, ** p < 0.01, *** p < 0.001.
AIC stands for Akaikeís information criterion.

21

Table 8. Estimates of the Average/Marginal E§ects
in EW Purchases (in the latter 3 years)
SampleDep var: EW PurchasePC
TV
Consumer Characteristics
Female0:0557
0:0266(0:0292)(0:0221)

Income = Low#0:0062

0:0724!!

Income = High

#0:0323(0:0292)#0:0166

Past Product Purchases#0:135!!
(0:0496)#0:0939!

Past Product Purchases " Female#0:0364(0:0895)(0:0632)#0:108!!
(0:0372)#0:070!!!

0:0214-31 Inches and Larger-

0:0487!

Fixed E§ects
Year
Manufacturer

Yes
Yes

Yes
Yes

Log Likelihood
AIC
N#903:91837:71364#1183:9
2411:92018(0:0373)

Product Characteristicsln(EW Price/TV Price)Notebook(0:0349)(0:0277)(0:0224)(0:0368)

0:0366(0:0193)(0:0231)

Notes: Standard errors are in parentheses. * p < 0.05, ** p < 0.01, *** p < 0.001.
AIC stands for Akaikeís information criterion.

22