Provider-induced Asymmetric Information in the
Insurance Market
Larry Y. Tzeng*
Jennifer L. Wang**
Kili C. Wang***
Jen-Hung Wang***
Abstract
This paper examines the existence of provider-induced asymmetric information in the insurance market.
The empirical data on comprehensive automobile insurance in Taiwan provide a unique opportunity to
test our hypothesis. Consistent with this hypothesis, we find evidence that providers do induce
asymmetric information problems.
Our empirical results show that the conditional correlation
between the coverage level and the occurrence of a claim is higher for insurance policies sold through
dealer-owned agents than for those sold through other marketing channels.
Key words: asymmetric information, automobile insurance, dealer-owned agents, marketing channel.
* Professor, Finance Department, National Taiwan University
** Associate Professor, Risk Management and Insurance Department, National Chengchi University
*** Associate Professor, Risk Management and Insurance Department, Shih Chien University
**** Assistant Professor, Finance Department, Shih Hsin University
1. Introduction
Rothschild and Stiglitz (1976) pioneered the study of asymmetric information problems in the
insurance market.
In the three decades since, their work has inspired many researchers who continue
to provide ingenious theoretical findings. The theoretical papers on asymmetric information that
followed Rothschild and Stiglitz (1976) include Wilson (1977), Miyazaki (1977), Grossman (1979),
Shavell (1979), Riley (1979), Radner (1981), Holmstrom (1982), Dionne (1983), Rubinstein and Yarri
(1983), Crocker and Snow (1986), Cho and Kreps (1987), Cooper and Hayes (1987), Hellwig (1987,
1988), Arnott and Stiglitz (1988), Hosios and Peters (1989), Hoy (1989), Mookerjee and Png (1989),
and Abreu, Pearce, and Stacchetti (1990). However, until recently, relatively few empirical studies
have been devoted to this issue.
As discussed by Chiappori and Salanie (1997), data from insurance companies are well-suited for
studies of asymmetric information, because they not only record both the coverage and the claim
amounts but also provide information on many characteristics of individuals.
Some recent papers
have used empirical data in alternative insurance markets to investigate asymmetric information
problems.
In the life/health insurance market, Cawley and Philipson (1999), Cardon and Hendel
(2001), and Finkelstein and Poterba (2000) have examined the US life insurance and health insurance
markets and the UK annuity market, respectively.
At the same time, in the property/liability insurance
market, Puelz and Snow (1994), Chiappori and Salanie (2000), and Dionne, Gourieroux, and Vanasse
(2001) have studied the automobile insurance market by using data from the US, Canada, and France.
Although these studies have successfully constructed a bridge between the theoretical world and real
practices to further understand asymmetric information problems, their empirical results have not
provided consistent findings for the existence of asymmetric information in the insurance market.
In addition, most of these empirical studies have focused on asymmetric information between the
insurer and the insured.
Only a few have investigated asymmetric information caused by providers.
1
Polsky and Nicholson (2004) investigated the risk differences of enrollees and medical expenditures
between HMOs and non-HMOs; Newhouse (1996) also investigated asymmetric information problems
in these different organizations.
Without a doubt, the provider’s asymmetric information
problems—e.g., the existence of moral hazard in health insurance—have raised major concerns in real
insurance practices. However, an empirical testing of asymmetric information caused by providers
might be difficult because it requires data from both the insurance companies and the providers.
In Taiwan, it has been widely believed that comprehensive automobile insurance coverage has
long suffered from asymmetric information problems. According to Wang (2004), alternative
products could be designed to cope with these problems in this market. Thus, the comprehensive
automobile insurance coverage market meshes with our goal to determine whether asymmetric
information problems exist in the market, since it is voluntary and offers different coverage choices.
There are three different types of comprehensive coverage: A, B, and C in Taiwan. Type A covers all
kinds of collision and non-collision losses, including those caused by missiles or falling objects, fire,
explosion, windstorm, intentional body damage, malicious mischief, and any unidentified reasons other
than the exclusions in the policy. Type B covers all the areas of type A but excludes the non-collision
losses caused by intentional body damage, malicious mischief, and any unidentified reasons. Type C
covers only damage in a collision involving two or more vehicles.
Collision losses caused by hitting
other objects—such as a telephone pole, a tree, or a building—and non-collision losses that used to be
covered under types A and B are specifically excluded from type C.
In this paper, we intend to use
comprehensive automobile insurance data from the largest insurance company in Taiwan to examine
whether this problem might be induced by the providers. The data from the automobile insurance
market in Taiwan provide a unique opportunity to investigate provider-induced asymmetric information
problems, since more than 40 percent of automobile insurance policies are sold through dealer-owned
agents.
2
We first examine whether a positive relationship exists between coverage and the occurrence of a
claim.
If there are asymmetric information problems, we should observe a positive correlation
between them.1
One important conclusion of Rothschild and Stiglitz (1976) is that a separating
equilibrium could exist in the insurance market. In this case, insurance companies offer a variety of
products to attract different types of insured, since the companies may not have enough information to
identify the insured’s risk types. Thus, in this equilibrium, high-risk individuals choose
higher-coverage insurance and low-risk individuals choose lower-coverage insurance. On the other
hand, it is also well known that high insurance coverage could induce the insured’s moral hazard
problem in the insurance market.
An individual with high insurance coverage might drive less
carefully, since most of the loss would be compensated.
However, why would asymmetric
information be induced by dealer-owned agents?
Generally speaking, the policies sold through dealer-owned agents might include a larger
percentage of high-coverage policies; and those who purchase insurance through dealer-owned agents
might include a greater number of high-risk drivers. On the one hand, car dealerships may have an
incentive to promote higher coverage to high-risk customers, since contracts with higher coverage are
more expensive and the dealerships are rewarded with a commission that is a fixed percentage of the
insurance premium. Meanwhile, high-risk customers2 may bring them more revenues from repairing
cars because of accidents in the future. On the other hand, the high-risk insured also have an incentive
to purchase insurance through the dealer-owned agents. One reason for this is because dealer-owned
agents have stronger bargaining power enabling the high-risk insured to obtain a “better” deal on their
1
We can observe this directly from the unconditional correlation. To control the heterogeneity of the sample, in
our empirical results, we will report the conditional correlation in Table 6 after controlling for the related
variables.
2
Dealer-owned agents may understand more about the risk type of their customers than other agents or sellers of
insurance. They may have longstanding relationships with their customers from selling cars. Otherwise, they
could also predict the risk type of their customers not only from the individual characteristics of the insurance
contracts, but also by observing the customers’ preferences and needs regarding a vehicle when they choose the
vehicle.
3
contracts. This is especially the case when they consider the ongoing purchase of contracts in
subsequent years. In such cases, high-risk customers might be more likely to be involved in an accident,
and thus they should be charged a higher premium as a penalty if an accident really does occur and a
claim is made in the previous year. In practice, the subsequent contracts sold by dealer-owned agents
seldom reflect the punishments recorded in accident records3. The other reason is that dealer-owned
agents may promise to provide “better” service for the insured when they have their cars repaired.
Thus, dealer-owned agents may attract more high-risk insured to purchase high-coverage policies.
Moreover, high-coverage policies sold through the dealer-owned agents may result in more claims
for insurance companies. Repair shops owned by car dealers may have an incentive to augment the
work to increase their revenues, especially for those car owners who not only repair the cars at their
repair shops, but who also purchase insurance from them. On the one hand, they are very clear about
who has high coverage contracts and how those high coverage contracts can cover the loss from an
accident, as compared with the case of an insured who is without any dealer-owned agent standing by
him. On the other hand, only repair shops can really comprehend how damaged the car is from an
accident, and how much work is needed to restore the car. Because repairing a car is such a professional
task, if the insurance companies want to audit the claim, they should devote more efforts and funds to
this channel than to other channels. When the cost of the audit is too high to cover the benefit derived
from auditing the claim, the insurance companies will not bother to audit the claim4. This is one of the
reasons why the insurance companies will devote less effort to auditing the claims resulting from the
contracts sold by the dealer-owned agents. The other ironic reason is that insurance companies usually
have to tolerate this type of corruption between the insured and the supplier simply to avoid losing
business, since repair shops owned by car dealerships are the major distribution channels for
3
This description comes from an interview with a manager of an insurance company.
This is also true when the insurance companies underwrite. If dealer-owned agents comprehend the type of their
customer, and if they hide some of the underwriting information, the insurance companies will not necessarily
underwrite clearly and costs may exceed benefits.
4
4
automobile insurance in Taiwan.5 In some cases, insurance companies may even pay claims under
certain amounts without performing an inspection. Thus, dealer-owned agents could have both the
motive and the ability to lie and induce the over-use of car-repair expenditures from insurance claims.
According to Alger and Ma (2003), dishonest car dealers and repair shops owned by car dealerships
(i.e., the providers) always lie when the insurance contracts are not collusion-proof. Since insurance
companies may audit them less, they should be more likely to induce more augmented claims involving
higher coverage than in the case of insurance sold through other channels.
Therefore, we hypothesize that automobile insurance policies sold through dealer-owned agents
might suffer from more severe problems of asymmetric information. We expect the conditional
dependence to be greater in the group of dealer-owned agents than when other channels are involved.
We follow Chiappori and Salanie’s (2000) approach and perform a preliminary test of the conditional
dependence between the choice of coverage and the occurrence of the claim. The empirical evidence
from this methodology shows that there seems to be a higher positive conditional correlation in
insurance policies sold by the dealer-owned agents than in those sold through other marketing channels.
However, we can only compare the values of the conditional correlation coefficients for each of those
channels using this method, and we hardly perform a formal test6 to prove whether the contracts from
the dealer-owned agents suffer more severe asymmetric information problems than those from other
marketing channels.
To complete the test of our hypothesis, we also adopt a methodology similar to that of Dionne,
Gourieroux and Vanasse (2001), i.e., a two-stage method to test the conditional dependence between
the choice of coverage and the occurrence of the claim. The main benefit in this research from using
5
The main distribution intermediaries of automobile insurance in Taiwan are the direct writers and car dealers.
Car dealers write more than 40 percent of the automobile insurance policies in Taiwan. Therefore, repair shops
owned by car dealers have very strong bargaining power in claim settlements.
6
We have designed an informal test which is introduced in Section 2 as a robust test of the methodology of
Chiappori and Salanie (2000).
5
this method is that we can test whether the asymmetric information problems are more severe when
they go through dealer-owned agent channels than through other direct channels. Because the choice of
coverage and the occurrence of claims may interact with each other, we engage in two different models
in the two-stage methodology while we test for the conditional dependency. We estimate the probability
of the occurrence of a claim in the first stage, and then perform a regression on the choice of coverage
in the second stage in one of the models. Furthermore, we estimate the probability of the choice of
coverage in the first stage, and then perform a regression on the occurrence of the claim in the second
stage in the other model. In the former, we test our hypothesis through the cross term between the
dealer-owned agent dummy and the occurrence of the claim dummy in the second stage. In the latter,
we test our hypothesis through the cross term between the dealer-owned agent dummy and the choice
of coverage dummy in the second stage. Again, all of the evidence from the two-stage method,
regardless of which one is modeled, supports our hypothesis.
The remainder of this paper is organized as follows. Section 2 describes the data and the
methodology used.
In Section 3, the main empirical results are presented. Section 4 concludes the
paper and provides recommendations for further research.
2. Data and Methodology
To empirically analyze the asymmetric information problems in Taiwan’s automobile insurance
market, we collected individual-level data as well as provider-level data from a large automobile
insurance company that controls over 30 percent of the market share of automobile insurance in Taiwan.
The research data included 61,642 and 64,234 observations in 1999 and 2000, respectively.
Since type C coverage was first introduced to Taiwan in 1999, we employed data only in the
policy years 1999 and 2000 in order to control for the market’s learning effect. It is easier to detect
the existence of asymmetric information in the early stages when insurance companies offer alternative
6
products to sort the insured. One possible reason why Chiappori and Salanie (2000) did not find
evidence to support the existence of asymmetric information in the French automobile insurance market
is because that market was already well-developed. The asymmetric information problem might exist
in the early stages of insurance, as described by Rothschild and Stiglitz (1976), but could be solved
prior to the mature stage, since insurance companies have many years to learn from their underwriting
results. Thus, Chiappori and Salanie’s empirical results (2000) and Rothschild and Stiglitz’s
theoretical models (1976) could be reconciled if we can find evidence to support the existence of
asymmetric information in the early stages of an emerging insurance market. We believe that the
comprehensive automobile insurance market in Taiwan provides us with a natural experiment to
investigate this proposition.
In order to conduct the empirical testing, we used two methods to examine whether providers
induce asymmetric information problems.
In this paper, the first method basically follows Chiappori
and Salanie’s (2000) empirical model to test for the conditional dependence between the choice of
coverage and the occurrence of a claim.
We separate the data into two groups: insurance contracts
sold by the dealer-owned agents and those sold through other marketing channels. For each group, we
run a pair of probit models and then test the conditional dependence. The probit models are as
follows:
Pr ob(cov erage  1)  X i  c   i , and
(1)
Pr ob(accident  1)  X i  a   i ,
(2)
where X i is the variable for the insured’s information,
 c and  a are the regressor coefficient vectors, and
 i and  i are error terms.
7
Since both types A and B cover non-collision claims and type C covers only collision claims, we
classify types A and B as high coverage and type C as low coverage.
When an individual chooses
comprehensive coverage automobile insurance of type A or B, then cov erage  1 ; otherwise
cov erage  0 .
It should be noted that we do not use all claims when defining the variable accident .
we only examine claims involving a collision with at least two cars.
Instead,
It is important to recognize that
we can observe all the claims but may not be able to observe all the car accidents. Since types A and
B have broader coverage than type C, the insured with types A or B might report more claims than
those with type C. Thus, accidents involving insured with types A or B may be more observable than
those with type C. To avoid a potential bias caused by unobservable accidents in type C, we employ
the same criteria to identify a claim for all policies, i.e., accident  1 when an individual files a claim
caused by a collision with at least two cars; otherwise accident  0 .
We further define accident  1 by using three monetary thresholds: a claim amount above
NT$0, a claim amount of more than NT$10,000 and a claim amount greater than NT$20,000.
It
should be noted that the monetary threshold may influence the existence of asymmetric information,
since insurance companies usually pay more attention to claims involving larger monetary amounts.
The estimators of  i and  i can be calculated as follows:
( X i c )
( X i c )
yi  (1  yi )
( X i  c )
(  X i  c )
( X i a )
(X i a )
,
ˆi  E ( i | zi ) 
z i  (1  z i )
( X i  a )
(  X i  a )
ˆi  E ( i | yi ) 
(3)
(4)
where  and  are the density and cumulative distribution functions of N (0,1) ; and yi and z i
represent coverage and accident, respectively.
8
To test the conditional dependence of ˆi and ̂ i , we follow Chiappori and Salanie (2000) and
use a statistic:7
n
W 
( ˆiˆi ) 2
i 1
n
 ˆ ˆ
i 1
2
i
(5)
2
i
W is distributed asymptotically as  2 (1) . We test its significance under the null hypothesis of
cov( i , i )  0 .
We predict that insurance policies purchased through the dealer-owned agents suffer more severe
asymmetric information. Hence, we intend to investigate whether the relationship between coverage
and the occurrence of an accident (  A ) is greater in the dealer-owned agent group than through the
other channels (  NA ).
However, merely comparing the value of  A and  NA does nothing to test our hypothesis. To
perform a robust analysis of Chiappori and Salanie’s method (2000), we design another test by creating
a new variable, Ŵi , which uses the estimates of the error terms ( ˆi and ̂ i ) in the pair of probit
models:
Wˆ i 
ˆiˆi
(6)
ˆi 2ˆi 2
We further let Di  1 when Wˆ i  1 , and Di  0 when Wˆ i  1 . We run another probit
regression with D as the dependent variable. The regressors here include the independent dummy
variables from the former regression model as well as a new variable, “empeno,” which denotes the
7
In Chiappori and Salanie’s (2000) empirical data set, because the difference in the lengths of policies comes
from the mismatch of the policy year and calendar year, the w-statistic in their research requires a weight. Since
our data are calculated on a policy-year basis, the w-statistic does not require the weight factor.
9
contracts sold by the car dealers. The model can be written as:
Pr ob( Di  1)   0  1  empenoi   2  carage0 i   3  carage1i   4  carage2 i
  5  carage3i   6  carage 4 i   7  carage5 i   8  carage6 i   9  carage7 i
 10  carage8 i  11  carage9 i  12  carage10 i  13  carage11i  14  sexfi
 15  marria i  16  city i  17  areani  18  areas i  19  areaeast i
(7)
  20  catpcd _ 1i   21  catpcd _ 2 i   22  tramak _ ni   23  tramak _ f i
  24  tramak _ hi   25  tramak _ t i   26  tarmak _ ci   27  age2 i   28  age3i
  29  age4 i
All the definitions of the variables that appear in the above regression are reported in Table 1.
We test whether  1 is significantly positive and use it as evidence to support the existence of
provider-induced asymmetric information. Indeed, Equation (7) is a robustness check of whether the
results from the previous model still hold after controlling for other variables.
The second method is similar to Dionne, Gourieroux and Vanasse’s (2001) model. We test the
asymmetric information problems in two stages. Because the two decision variables, the choice of
coverage and the occurrence of claims, may interact with each other, we build two different models. In
one model, we estimate the occurrence of a claim using a probit regression in the first stage:
Prob(accident i  1 X 1i )  ( X 1i  )
(8)
where the definition of accident is consistent with our first method in equation (2). X 1i is the same
as X i in equations (1) and (2), too. In the second stage, we regress the choice of coverage by means
of the following probit regression:
P r o (bc o ev r a gi e 1 a c icˆd e ni ,t a c c i d ei ,na tc c i d ei n D
t i , X 2i )
 ( 1a c icˆd e ni t  2 a c c i d ei 
n t 3 a c c i d ei n D
t i  X 2i  4 )
(9)
where the definition of cov erage is also consistent with that in equation (1) using the first method.
10
acciˆdent is the estimator from the first-stage estimation. Di  1 when the contract is sold by the
dealer-owned agent, otherwise Di  0 . X 2 i includes all variables in X 1i except for the area
dummy variables ( arean , areas , areaeast ) and city variables ( city )8. We test whether the
conditional dependency between the occurrence of a claim and the choice of coverage is more severe in
the channel of dealer-owned agents based on whether the coefficient  3 is significantly positive.
In the other model, we estimate the choice of coverage by means of a probit regression in the first
stage:
Prob (cov eragei  1 X 3i )  ( X 3i )
(10)
where the definition of cov erage is the same as before. X 3i is the same as X 2 i in equation (9).
In the second stage, we regress the occurrence of the claim by means of the following probit regression:
P r o (ba c c i d ei n t1 c o veˆr a gi e, c o ve r a gi ,ec o ve r a gi e Di , X 4i )
 (  5 c o veˆr a gi e  6 c o ve r a gi e  7 c o ve r a gi e Di  X 4i  8 )
(11)
where cov êrage is the estimator from the estimation of equation (10) in the first stage. The
definitions of accident and Di are both the same as before, and X 4 i is the same as X 1i in
equation (8). We test whether the conditional dependency between the choice of coverage and the
occurrence of a claim is more severe in the channel of dealer-owned agents based on whether the
coefficient  7 is significantly positive.
(Insert Table 1 Here)
8
The reason why the area dummy variables and city variables are included in the regression which estimates the
occurrence of a claim, but are not included in the regression which estimates the choice of coverage, is that the
location factors could really affect the occurrence of an accident. However, the insurance companies in Taiwan
still do not take them into consideration when they calculate the premium for the contracts until then. So, the
choice of coverage will not be affected by them.
11
3. Empirical Results
The summary statistics for all the variables are displayed in Tables 2 to 5.
From Tables 2 to 5,
which report the statistics for the data, we observe that the means of the claim amounts and the
coverage levels from the dealer-owned agents are much greater than those from other marketing
channels in both years.
(Insert Table 2-5 Here)
The empirical results based on our first method are the same as in Chiappori and Salanie (2000)
and are displayed in Table 6.
In a way that is consistent with our hypothesis, we find that the
correlation coefficients (  ) are all positive.
Furthermore, the statistics ( W ) show that the correlation
between the choice of coverage and the occurrence of a claim are significantly different from zero.
The above empirical evidence demonstrates that there do exist asymmetric information problems in the
insurance market.
Furthermore, we find that the correlation coefficients (  ) decrease with respect to
an increase in the monetary threshold. This result is predictable, since the insurer will pay more
attention to auditing a claim with a larger monetary amount or to underwriting an insured who may
produce a larger claim.
(Insert Table 6 Here)
While Chiappori and Salanie (2000) and Dionne, Gourieroux, and Vanasse (2001) have found no
evidence to support the existence of asymmetric information using data from the Canadian and French
insurance markets, our results do find evidence to support the existence of such asymmetric information
12
in the Taiwanese insurance market.
It should be noted that our results actually do not refute, but rather
supplement, the literature in explaining why asymmetric information exists in some insurance markets
but not in others.
Underwriting systems can serve as an essential tool for insurance companies to overcome
asymmetric information problems.
For newly-written business, insurance companies may not have
enough information related to the insured’s risk, as suggested by Rothschild and Stiglitz (1976).
Insurance companies should thus collect more useful information year by year and use the data to
classify the insured. Eventually, insurance companies may learn to adopt various tools to control the
asymmetric information problems well enough for the statistical results to reject their existence, as
found by Chiappori and Salanie (2000) and Dionne, Gourieroux, and Vanasse (2001). However, it
may take years for insurance companies to establish such effective underwriting systems.
Compared
to the insurance markets in Canada and France, the insurance market in Taiwan is still in an
emerging-market stage. Specifically, some critical underwriting factors used in well-established
insurance markets have not been employed in the Taiwanese insurance market. For example, driving
records are not yet used for underwriting and pricing because insurance companies do not have access
to the database of driving records. Another important issue of concern is that the provider’s moral
hazard may make it more difficult for insurance companies to implement an effective underwriting
system.
High-level executives reveal that insurance companies in Taiwan frequently give the insured
credit for their experience rating but waive the penalties in relation to experience rating due to both
marketing competition and pressure from the dealer-owned agents.
Thus, in an emerging market, such as the comprehensive automobile insurance market in Taiwan,
insurance companies may either not have adopted some important underwriting factors or may lack the
discipline to do so. Therefore, we can observe the existence of asymmetric information in the
Taiwanese insurance market, even though Chiappori and Salanie (2000) and Dionne, Gourieroux, and
13
Vanasse (2001) could not find such evidence in the Canadian and French insurance markets.
Indeed,
the imperfections in the underwriting systems of insurance companies may also be the critical reason
why we observe the existence of provider-induced asymmetric information.
In addition, the correlations between coverage and claims in automobile insurance sold by
dealer-owned agents versus non-dealer-owned agents are shown in Table 7. From Table 7, we find
that
 A is generally higher than  NA in almost all cases.9 The evidence shows that providers
may contribute at least partially to the asymmetric information problems in the market; and that the
asymmetric information problems in insurance written by the dealer-owned agents are more severe than
those written through other marketing channels.
(Insert Table 7 Here)
It is interesting to further point out that the differences in  A and  NA also decrease with
respect to an increase in the monetary threshold. This evidence coincides with our inferences
regarding the auditing and underwriting tendencies of insurance companies. The insurance companies
might be less willing to audit or underwrite when the costs exceed the benefits of doing so. Thus they
will apply less stringent auditing and underwriting to insurance policies sold through dealer-owned
agents.
However, for claims—as well as potential claims—involving larger monetary amounts, the
benefits may exceed the costs of auditing and underwriting, and the insurer will therefore employ more
stringent criteria regardless of whether or not the policy is written through dealer-owned agents, since
no insurer will tolerate any hidden actions by the provider that damage the insurer’s profits. Thus, we
might observe that the provider-induced asymmetric information is reduced in the case of larger claims.
9
 A   NA
in the three groups— claim
 0 , claim  10000 , and claim  20000 --are, respectively,
0.09, 0.07, and 0.06 in 1999 and 0.09, 0.07, and 0.03 in 2000.
14
The results of our robustness check for the method that is the same as that adopted by Chiappori
and Salanie (2000) are reported in Table 8. Table 8 displays the coefficients of the dependent
variables of the probit regression involving Equation (7). The conclusions of these analyses are
similar to those of our previous analysis. In terms of the year panels and the threshold claim amounts,
the coefficients of the dealer-owned agents (empeno) are generally positive and significantly different
from zero.
(Insert Table 8 Here)
The empirical results using our second method which is similar to the approach adopted by
Dionne, Gourieroux and Vanasse (2001) are displayed in Table 9. All the outcomes in Table 9 are
consistent with the results from our first method. Based on the coefficients of  2 and  6 , which are
significantly positive, we can confirm that the asymmetric information problems exist in the
comprehensive automobile insurance market in Taiwan. The coefficients for  3 and  7 are
significantly positive which means that the positively conditional dependency between the choice of
coverage and the occurrence of a claim is higher in the case of the policies written through
dealer-owned agents than through other means. This evidence supports our hypothesis, too. We also
control for the threshold of the amounts claimed in the empirical tests, the results of which appear in
Table 9. Again, we find that the larger the amount of the claim, the less severe the asymmetric
information problems will be. This is especially true when policies are sold through dealer-owned
agents. These outcomes can be explained by the extent to which the stringency of the auditing or
underwriting varies with the amount of the claim.
(Insert Table 9 Here)
15
4. Conclusion
In this paper we examine the existence of provider-induced asymmetric information in the
insurance market. The data for comprehensive automobile insurance in Taiwan provide a unique
opportunity to test our hypothesis. Because dealer-owned agents could induce both adverse selection
and moral hazard problems in the automobile insurance market, the asymmetric information problems
arising where policies are sold through the dealer-owned agents might be more severe than those
arising because of policies sold through other marketing channels.
From our empirical results based
on Chiappori and Salanie’s (2000) method, the correlation coefficient between the coverage and claims
in relation to insurance written through dealer-owned agents (  A ) is generally higher than that written
through the other channels (  NA ). The robustness check of this method also demonstrates that
insurance through dealer-owned agents carries a significantly high correlation between the coverage
and the claim.
Other empirical results of ours that are similar to those of Dionne, Gourieroux and
Vanasse’s (2001) model that also supports the view that the conditional dependency between the
coverage and the claims is more significantly positive when the policies are written through the
channels of the dealer-owned agents. This can be proved from the evidence that  3 and  7 are both
significantly positive. In general, our empirical findings are consistent with our hypothesis that
providers could induce asymmetric information problem. Furthermore, asymmetric information
problems involving policies written through dealer-owned agents are more severe than those concerned
with policies written through other marketing channels.
While Chiappori and Salanie (2000) and Dionne, Gourieroux, and Vanasse (2001) have found no
evidence in the data based on the Canadian and French insurance markets, our results support the
existence of asymmetric information in the Taiwanese automobile insurance market.
In an emerging
insurance market, such as the comprehensive automobile insurance market in Taiwan, the existence of
asymmetric information problems may result from either imperfections in the underwriting or pricing
16
systems or an inability to implement effective underwriting systems.
In addition, provider-induced
asymmetric information problems may make it more difficult for insurance companies to implement
effective underwriting systems, since car dealers control the major marketing channels for the
comprehensive automobile insurance market in Taiwan. Thus, the imperfections in the underwriting
systems of insurance companies might also be the critical reason why we observe the existence of
provider-induced asymmetric information. Our results definitely demonstrate the need for further
studies on asymmetric information problems in different markets as well as in different countries.
17
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20
Table 1
Definitions of the Variables
Variable
coverage(
Definition
y)
accident( z )
a dummy variable that equals 1 when an individual chooses a type A or B policy, otherwise it equals 0
a dummy variable that equals 1 when an individual’s claim is caused by a collision and the claim amount
is above the threshold amount, otherwise it equals 0
carage0
a dummy variable that equals 1 when the car is new, otherwise it equals 0
carage1
a dummy variable that equals 1 when the car is one year old, otherwise it equals 0
carage2
a dummy variable that equals 1 when the car is two years old, otherwise it equals 0
carage3
a dummy variable that equals 1 when the car is three years old, otherwise it equals 0
carage4
a dummy variable that equals 1 when the car is four years old, otherwise it equals 0
carage5
a dummy variable that equals 1 when the car is five years old, otherwise it equals 0
carage6
a dummy variable that equals 1 when the car is six years old, otherwise it equals 0
carage7
a dummy variable that equals 1 when the car is seven years old, otherwise it equals 0
carage8
a dummy variable that equals 1 when the car is eight years old, otherwise it equals 0
carage9
a dummy variable that equals 1 when the car is nine years old, otherwise it equals 0
carage10
a dummy variable that equals 1 when the car is ten years old, otherwise it equals 0
carage11
a dummy variable that equals 1 when the car is eleven years old, otherwise it equals 0
sexf
a dummy variable that equals 1 when the owner of the car is female, otherwise it equals 0
married
a dummy variable that equals 1 when the owner of car is married, otherwise it equals 0
city
a dummy variable that equals 1 when the owner of the car lives in a city, otherwise it equals 0
arean
a dummy variable that equals 1 when the car is registered in the north of Taiwan, otherwise it equals 0
areas
a dummy variable that equals 1 when the car is registered in the south of Taiwan, otherwise it equals 0
areaeast
a dummy variable that equals 1 when the car is registered in the east of Taiwan, otherwise it equals 0
catpcd_1
a dummy variable that equals 1 when the car is a sedan and is for non-commercial or for long-term rental
purposes, otherwise it equals 0
catpcd_2
a dummy variable that equals 1 when the car is a small freight-truck and is for non-commercial purposes
or for business use, otherwise it equals 0
tramak_i
i=n,f,h,t,c, a dummy variable that equals 1when the trademark of the car is the assigned brand, otherwise
it equals 0
age2
a dummy variable that equals 1 when the insured is between the ages of 30 and 25, otherwise it equals 0
age3
a dummy variable that equals 1when the insured is between the ages of 60 and 30, otherwise it equals 0
age4
a dummy variable that equals 1 when the insured is over the age of
21
60, otherwise it equals 0
Table 2
Summary Statistics for Data on Dealer-owned Agents in 1999
Variable
N
Mean
Std Dev
Minimum
Maximum
accident
21615
0.329216
0.469939
0
1.000000
coverage
21615
0.684201
0.464844
0
1.000000
carage0
21615
0.715244
0.451309
0
1.000000
carage1
21615
0.163081
0.369448
0
1.000000
carage2
21615
0.066343
0.248886
0
1.000000
carage3
21615
0.026186
0.159690
0
1.000000
carage4
21615
0.012399
0.110660
0
1.000000
carage5
21615
0.007865
0.088337
0
1.000000
carage6
21615
0.004673
0.068199
0
1.000000
carage7
21615
0.002082
0.045581
0
1.000000
carage8
21615
0.000879
0.029636
0
1.000000
carage9
21615
0.000139
0.011781
0
1.000000
carage10
21615
0.000278
0.016659
0
1.000000
carage11
21615
0.000139
0.011781
0
1.000000
sexf
21615
0.690955
0.462110
0
1.000000
married
21615
0.452324
0.497733
0
1.000000
city
21615
0.483506
0.499740
0
1.000000
arean
21615
0.418136
0.493264
0
1.000000
areas
21615
0.277076
0.447565
0
1.000000
areaeast
21615
0.028221
0.165608
0
1.000000
catpcd_1
21615
0.976313
0.152076
0
1.000000
catpcd_2
21615
0.023687
0.152076
0
1.000000
tramak_n
21615
0.084201
0.277695
0
1.000000
tramak_f
21615
0.112052
0.315438
0
1.000000
tramak_h
21615
0.07814
0.268398
0
1.000000
tramak_t
21615
0.56049
0.496339
0
1.000000
tramak_c
21615
0.092297
0.289452
0
1.000000
age2
21615
0.109785
0.312629
0
1.000000
age3
21615
0.834744
0.371420
0
1.000000
age4
21615
0.018367
0.134277
0
1.000000
22
Table 3
Summary Statistics for Data on Non-dealer-owned Agents in 1999
Variable
N
Mean
Std Dev
Minimum
Maximum
accident
40027
0.219677
0.414033
0
1.000000
coverage
40027
0.537837
0.498573
0
1.000000
carage0
40027
0.266120
0.441934
0
1.000000
carage1
40027
0.293102
0.455191
0
1.000000
carage2
40027
0.187523
0.390336
0
1.000000
carage3
40027
0.101806
0.302397
0
1.000000
carage4
40027
0.069028
0.253506
0
1.000000
carage5
40027
0.042596
0.201948
0
1.000000
carage6
40027
0.021486
0.144998
0
1.000000
carage7
40027
0.010493
0.101897
0
1.000000
carage8
40027
0.003972
0.062902
0
1.000000
carage9
40027
0.001949
0.044101
0
1.000000
carage10
40027
0.000999
0.031597
0
1.000000
carage11
40027
0.000150
0.012243
0
1.000000
sexf
40027
0.595773
0.490748
0
1.000000
married
40027
0.602368
0.489415
0
1.000000
city
40027
0.538661
0.498509
0
1.000000
arean
40027
0.488545
0.499875
0
1.000000
areas
40027
0.270717
0.444336
0
1.000000
areaeast
40027
0.040997
0.198287
0
1.000000
catpcd_1
40027
0.969321
0.172450
0
1.000000
catpcd_2
40027
0.025408
0.157362
0
1.000000
tramak_n
40027
0.183576
0.387143
0
1.000000
tramak_f
40027
0.157119
0.363917
0
1.000000
tramak_h
40027
0.083369
0.276442
0
1.000000
tramak_t
40027
0.210533
0.407692
0
1.000000
tramak_c
40027
0.128988
0.335191
0
1.000000
age2
40027
0.119344
0.324197
0
1.000000
age3
40027
0.818548
0.385398
0
1.000000
age4
40027
0.021461
0.144915
0
1.000000
23
Table 4
Summary Statistics for Data on Dealer-owned Agents in 2000
Variable
N
Mean
Std Dev
Minimum
Maximum
accident
21533
0.414527
0.492652
0
1.000000
coverage
21533
0.767798
0.422247
0
1.000000
carage0
21533
0.700274
0.458149
0
1.000000
carage1
21533
0.146612
0.353727
0
1.000000
carage2
21533
0.074583
0.262724
0
1.000000
carage3
21533
0.037199
0.189253
0
1.000000
carage4
21533
0.020620
0.142110
0
1.000000
carage5
21533
0.009381
0.096402
0
1.000000
carage6
21533
0.005526
0.074136
0
1.000000
carage7
21533
0.003390
0.058128
0
1.000000
carage8
21533
0.001393
0.037301
0
1.000000
carage9
21533
0.000060
0.024564
0
1.000000
carage10
21533
0.000139
0.011803
0
1.000000
carage11
21533
0.000279
0.016691
0
1.000000
sexf
21533
0.722612
0.447720
0
1.000000
married
21533
0.551804
0.497321
0
1.000000
city
21533
0.512237
0.499862
0
1.000000
arean
21533
0.412065
0.492218
0
1.000000
areas
21533
0.289648
0.453610
0
1.000000
areaeast
21533
0.028886
0.167490
0
1.000000
catpcd_1
21533
0.980495
0.138295
0
1.000000
catpcd_2
21533
0.019505
0.138295
0
1.000000
tramak_n
21533
0.031579
0.174882
0
1.000000
tramak_f
21533
0.114290
0.318170
0
1.000000
tramak_h
21533
0.090094
0.286323
0
1.000000
tramak_t
21533
0.643710
0.478914
0
1.000000
tramak_c
21533
0.022291
0.147633
0
1.000000
age2
21533
0.095528
0.293949
0
1.000000
age3
21533
0.876701
0.328788
0
1.000000
age4
21533
0.014350
0.118932
0
1.000000
24
Table 5
Summary Statistics for Data on Non-dealer-owned Agents in 2000
Variable
N
Mean
Std Dev
Minimum
Maximum
accident
42701
0.230323
0.421044
0
1.000000
coverage
42701
0.522798
0.499486
0
1.000000
carage0
42701
0.225405
0.417853
0
1.000000
carage1
42701
0.239222
0.426613
0
1.000000
carage2
42701
0.204866
0.403609
0
1.000000
carage3
42701
0.135454
0.342211
0
1.000000
carage4
42701
0.080794
0.272522
0
1.000000
carage5
42701
0.054636
0.227271
0
1.000000
carage6
42701
0.032482
0.177278
0
1.000000
carage7
42701
0.014777
0.120661
0
1.000000
carage8
42701
0.007494
0.086244
0
1.000000
carage9
42701
0.002787
0.052717
0
1.000000
carage10
42701
0.001124
0.033509
0
1.000000
carage11
42701
0.000679
0.026052
0
1.000000
sexf
42701
0.618393
0.485787
0
1.000000
married
42701
0.703496
0.456721
0
1.000000
city
42701
0.546990
0.497793
0
1.000000
arean
42701
0.505140
0.499979
0
1.000000
areas
42701
0.256411
0.436656
0
1.000000
areaeast
42701
0.042973
0.202799
0
1.000000
catpcd_1
42701
0.971125
0.167458
0
1.000000
catpcd_2
42701
0.024215
0.153718
0
1.000000
tramak_n
42701
0.178520
0.382955
0
1.000000
tramak_f
42701
0.142549
0.349617
0
1.000000
tramak_h
42701
0.089272
0.285139
0
1.000000
tramak_t
42701
0.230018
0.420849
0
1.000000
tramak_c
42701
0.124400
0.330041
0
1.000000
age2
42701
0.104260
0.305601
0
1.000000
age3
42701
0.862673
0.344196
0
1.000000
age4
42701
0.019180
0.137159
0
1.000000
25
Table 6
Conditional Correlation Between Coverage and Claims in 1999 and 2000
Year 1999
claim  0
claim  10000
claim  20000
Year 2000
claim  0
claim  10000
claim  20000

W
0.2786***
130.919***
0.2490***
1117.44***
0.1336***
3652.82***

W
0.3793***
98.5284***
0.3406***
411.491***
0.1266***
5029.32***
Note: The significance level of 99% is denoted by ***
The significance level of 95% is denoted by **
The significance level of 90% is denoted by *
26
Table 7
Correlation Between Coverage and Claims in Automobile Insurance Sold
by Dealer-owned Agents Versus Non-dealer-owned Agents
Panel A : Year 1999
Dealer-owned Agent
A
WA
 NA
WNA
correlation coefficient
statistic-W
correlation coefficient
statistic-W
0.30118***
claim  0
0.26814***
claim  10000
0.17076***
claim  20000
Panel B : Year 2000
382.932***
0.21408***
3.31494*
2193.68***
0.19695***
52.4969***
5659.77***
0.10586***
418.631***
Dealer-owned Agent
claim  0
claim  10000
claim  20000
Non-dealer-owned Agent
Non-dealer-owned Agent
A
WA
 NA
WNA
correlation coefficient
statistic-W
correlation coefficient
statistic-W
0.35402***
10.8775***
0.26318***
30.949***
0.30582***
2085.74***
0.23057***
29.2463***
0.11482***
9921.98***
0.08706***
169.926***
Note: The significance level of 99% is denoted by ***
The significance level of 95% is denoted by **
The significance level of 90% is denoted by *
27
Table 8
Robust Analysis of the Correlation Between Coverage and Claims in Automobile
Insurance Sold by Dealer-owned Agents Versus Non-dealer-owned Agents
Panel A : Year 1999
Intercept
-0.2950
claim  0
claim  10000 -0.6403***
claim  20000 -1.4290***
carage5
-0.1695
claim  0
claim  10000 0.0517
claim  20000 0.6294***
sexf
-0.0265**
claim  0
claim  10000 -0.0818***
claim  20000 -0.0891***
catpcd_2
0.0396
claim  0
claim  10000 0.3155***
claim  20000 0.7511***
age3
-0.1402***
claim  0
claim  10000 0.1512***
claim  20000 -0.1485***
empeno
carage0
carage1
carage2
carage3
carage4
0.0236**
0.0754
0.1679
0.1586
0.0886
0.0267
0.0386***
0.1199
0.2045
0.2220
0.1849
0.0836
0.1520***
0.3662*
0.6645***
0.7105***
0.6571***
0.5256***
carage6
carage7
carage8
carage9
carage10
carage11
-0.0949
-0.2395
-0.2513
-0.2350
-0.0820
-0.7982*
-0.0256
-0.0735
-0.1512
-0.2272
-0.1695
-0.1217
0.4833**
0.4829**
0.2844
0.5883**
0.1552
-0.2128
married
city
arean
areas
areaeast
catpcd_1
0.0404***
0.0777***
-0.0191
0.0878***
-0.1208***
0.6272***
0.0014
0.0731***
0.0321**
0.0533***
-0.0695**
0.9388***
-0.0252**
-0.0041
0.0350***
-0.0395***
-0.1228***
1.3422***
tramak_n
tramak_f
tramak_h
tramak_t
tramak_c
age2
0.2024***
0.0309*
0.0066
0.0118
0.1456***
-0.0016
0.1112***
0.0262
0.0226
0.0096
0.1334***
0.0581*
0.2763***
-0.1312***
0.0327
-0.0979***
0.1171***
0.0720**
age4
-0.2236***
-0.2837***
-0.2358***
28
Table 8 (Cont.)
Panel B : Year 2000
Intercept
empeno
carage0
carage1
carage2
carage3
`
0.0070
-0.1589
0.0702
0.0877
0.1012
-0.0281
0.0279**
-0.2273
-0.0539
0.0960
0.1005
-0.0789
0.1907***
0.0640
0.3933
0.6969*
0.6140*
0.4730
carage5
carage6
carage7
carage8
carage9
carage10
carage11
-0.1785
claim  0
claim  10000 -0.1084
claim  20000 0.5431
-0.2331
-0.0588
-0.3614
-0.2412
-0.2462
-0.9864**
-0.0841
-0.1207
-0.1324
-0.4437
-0.0513
-0.2299
0.5268
0.2713
0.3053
-0.1552
0.1859
-0.2673
married
city
arean
areas
areaeast
catpcd_1
-0.0046
0.0027
-0.1217***
-0.0297**
-0.1895***
0.4935***
-0.0241*
-0.0008
-0.0516***
0.0247*
-0.0822***
0.7366***
-0.1111***
0.0198*
0.1391***
0.0706***
0.0105
1.2216***
tramak_n
tramak_f
tramak_h
tramak_t
tramak_c
age2
0.1953***
0.1320***
0.0986***
0.0450***
0.0441**
-0.0353
0.0827***
0.0751***
0.0635***
0.0460***
0.0897***
-0.0514
-0.0519***
-0.1859***
-0.1506***
-0.1322***
-0.1796***
-0.1227***
0.2074
claim  0
claim  10000 -0.1169
claim  20000 -0.9124**
sexf
-0.0449***
claim  0
claim  10000 -0.0186*
claim  20000 -0.0846***
catpcd_2
-0.0859
claim  0
claim  10000 0.1249
claim  20000 0.8146***
age3
-0.2067***
claim  0
claim  10000 -0.1512***
claim  20000 -0.3045***
age4
-0.2102***
-0.1717***
-0.3746***
Note: The significance level of 99% is denoted by ***
The significance level of 95% is denoted by **
The significance level of 90% is denoted by *
29
Table 9
Coefficients of the Correlation Between Coverage and Claims in Automobile Insurance
Using the Two-stage Method for Equations (9) and (11)
Coefficients
First stage: estimate occurrence of claim
First stage: estimate choice of coverage
Second stage: regress on choice of coverage
Second stage: regress on occurrence of claim
1
3
2
5
6
7
Year 1999
claim  0
claim  10000
claim  20000
-1.4968***
0.6036***
0.3556***
-1.0592
0.6077***
0.2400***
-1.6535***
0.7022***
0.3159***
-0.8765
0.6819***
0.1922***
2.4143***
0.6613***
0.1868***
-0.4220
0.5833***
0.0971***
Year 2000
claim  0
claim  10000
claim  20000
-1.5402***
0.7308***
0.4450***
0.1561
0.7502***
0.3081***
-1.7469***
0.8456***
0.4161***
0.4298
0.8363***
0.2905***
-1.8007***
0.7958***
0.2990***
0.5523
0.7178***
0.1682***
Note: The significance level of 99% is denoted by ***
The significance level of 95% is denoted by **
The significance level of 90% is denoted by *
30