1. Centre for the Business and Economics of Health (CBEH)
Empirical tests for consumer-push and producer-pull ex post moral hazard in a market for automobile insurance
David Rowell, Son Nghiem & Luke Connelly

2. OUTLINE Aim Definitions of moral hazard Empirical models Data Results
Empirical tests for Consumer-push & Producer-pull ex post moral hazard
Definitions of moral hazard
Ex ante moral hazard
Ex post moral hazard
Health Insurance
Automobile insurance
Empirical models
Consumer-push
Producer-pull
Data
Results
Discussion

3. Types of Moral Hazard Moral hazard
Occurs when some component of the agent’s behaviour (policyholder), which is unobservable to the principal (insurer) is material to the outcome.
Ex ante moral hazard
Occurs before the fact, insurance may increase the probability of an RTC
Ex post moral Hazard
Occurs after the fact, insurance may increase the cost of vehicle repair

4. Temporal Relationship
Moral hazard
Occurs when some component of the agent’s behaviour (policyholder), which is unobservable to the principal (insurer) is material to the outcome.
Ex ante moral hazard
Occurs before the fact, insurance may increase the probability of an RTC
Ex post moral Hazard
Occurs after the fact, insurance may increase the cost of vehicle repair

5. Hypotheses Ex post moral hazard:
Following an insured RTC both the vehicle owner and the smash repairer have an incentive to engage in behavior that is not observable to the insurer, which can increase the total cost of the repair
Consumer-push:
The vehicle owner has an incentive to exaggerate the extent of the damage and claim for extra repairs
Producer-pull:
The smash repairer has an incentive to charge a premium for ‘insurance-jobs’

6. Data
Survey of attitudes to the Australian smash repair industry (IMRAS Consulting)
4,006 households
Response rate 17%
994 road traffic crashes (RTCs)
Data
Repairs ($)
Insurance status (0/1)
Vehicle characteristics
Make, model, value, age
Driver characteristics:
Gender, age postcode, income, licensure
Smash severity:
Parts repaired
Road services that attended RTC (i.e., Ambulance, Police & Tow truck)

7. Descriptive Data

8. ….continued

9. Smash Repair Costs ($)
Repairs ($)
Residuals

10. Linear Models 𝑙𝑛𝐶= 𝛼 0 + 𝛼 1 𝐼+ 𝛼 2 𝐕+ 𝛼 3 𝐒+ 𝛼 4 𝑀+ 𝜀 𝑖
Ex post moral hazard:* Consumer-push: Producer-pull:
𝑙𝑛𝐶= 𝛼 0 + 𝛼 1 𝐼+ 𝛼 2 𝐕+ 𝛼 3 𝐒+ 𝛼 4 𝑀+ 𝜀 𝑖
eq. 1
𝐷= 𝛽 0 + 𝛽 1 𝐼+ 𝛽 2 𝐕+ 𝛽 3 𝐒+ 𝛽 4 𝑀+ 𝜀 𝑖
eq. 2
These linear models assume no income effects. ?
𝑙𝑛𝐶= 𝛾 0 + 𝛾 1 𝐼+ 𝛾 2 𝐕+ 𝛾 3 𝐒+ 𝛾 4 𝑀+𝛾 5 𝐷+ 𝜀 𝑖
eq. 3
lnC
= Log of repair costs I
= Comprehensive Insurance (0/1) V
= Vehicle characteristics S
= Smash severity D
= Damaged parts M
= Male (0/1)

11. Results Ex post Consumer-push Producer-pull log Costs Eq. 1
Parts Eq. 2
Accessories Eq. 3
log Costs Eq. 4
Coef.
p-value
Insured (0/1)
0.30
0.04
0.09
0.20
0.22
0.02
0.26
0.05
RTC severity
Damaged parts -
0.23
<0.01
Ambulance (0/1)
0.87
0.18
0.03
0.89
-0.09
0.80
Tow Truck (0/1)
0.73
0.01
0.38
0.39
0.53
0.07
Police (0/1)
0.46
0.21
0.33
Towed away (0/1)
0.77
0.17
0.27
0.62
0.12
Vehicle characteristics
Value ($)
2.35E-05
2.51E-07
0.96
-6.25E-06
0.31
2.18E-05
Value squared ($)
-3.66E-11
4.73E-12
0.67
1.69E-11
0.19
-3.84E-11
Age of vehicle (years)
-0.01
0.24
0.13
-0.02
Gender (0/1)
0.90
0.00
0.99
-0.06
0.44
Constant
6.38
0.68
5.93 R2
0.06
0.35
Vehicle make: (Audi, BMW, Chrysler/Jeep, Daewoo, Daihatsu, Holden/GMH, Honda, Hyundai, Jaguar, Kia, Land Rover, Mazda, Mercedes, Mitsubishi, Nissan, Peugeot, Renault, Rover, Saab, Seat, Subaru, Suzuki, Toyota, VW & Volvo) estimated but not reported.

12. Summary of Results Ex-post moral hazard:
Insurance associated with a 30% increase in the cost of RTC repair
Consumer-push:
Insurance has no statistically significant effect on # parts replaced
Insurance has a “small” on number of accessories replaced
Accessories are a sub-set Parts
Producer-push:
Controlling for Parts in (eq. 3) made no substantial difference to the effect Insurance had on cost of repairs
0.30 (0.05) verses 0.26(p=0.04)

13. Discussion: No income effect ?
Pauly (1968)
In a response to Uncertainty and the welfare economics of medical care (Arrow 1963), Pauly (1968) provides an empirical estimate of ex post moral hazard in market for medical insurance that assumed no income effect.
Dionne (2013 p.431)
“The main difficulty in isolating the ex post moral hazard effect in different levels of insurance coverage is separating the effects of price and income variations from the effects of asymmetric information. Contrary to what is often stated in the literature (especially that of health insurance), not every variation in consumption following a variation in insurance coverage can be tied to ex post moral hazard”

14. Hicksian Decomposition –Health Insurance
Nyman (1999) used Slutsky’s equation with estimates from RAND HIE to estimate a compensated price elasticity that controlled for income elasticity of demand
𝜁=𝜂+𝜖𝛼
Concludes:
Welfare loss due to ex post moral hazard was 17% points less when a pure price effect is estimated 𝜁
= Hicksian (compensated) price elasticity 𝜂
= Marshallian (uncompensated) price elasticity
- 0.18 𝜖
= Income elasticity of demand
0.22 𝛼
= Proportion of household income spent on healthcare
0.16
In response to Pauly (1968)

15. Hicksian Decomposition –Auto Insurance
Income elasticity (ϵ)
log-log coefficient (𝛼 5 ) is interpreted as an income elasticity
Proportion of income spent on RTCs (α)
≈ Average premium Average household income = $445 $59,332
Negligible difference between ζ & η, therefore can disregard income effect.
RNC(2018)
Nyman(1999)
Source ζ
-0.339
-0.145
𝜁=𝜂+𝜖𝛼 η
-0.34**
-0.18
α1 (eq. 1) ϵ
0.15*
0.22
α5 (eq. 4) α
0.008
0.16
IMRAS dataset
***, **, * denotes 0.01, 0.05 & 0.1 levels of statistical significance, respectively.
𝑙𝑛𝐶= 𝛼 0 + 𝛼 1 𝐼+ 𝛼 2 𝐕+ 𝛼 3 𝐒+ 𝛼 4 M+ 𝛼 5 𝑙𝑛𝑌+ 𝜀 𝑖
eq. 4
Where Y is approximated by mid-points for 7 income bands.

16. Finish
Bibliography:
Arrow, K. J., Uncertainty and the welfare economics of medical care. The American Economic Review LIII (5), 941–973.
Dionne, G., The empirical measure of information problems with emphasis on insurance fraud and dynamic data. In: Dionne, G. (Ed.), Handbook of Insurance, 2nd Edition. Springer, New York, Ch. 15.
Nyman, J.A., (1999) “The economics of moral hazard revisited” Journal of Health Economics 18(6) pp
Pauly, M. V., The economics of moral hazard: Comment. The American Economic Review 58 (3), 531–537.

17. Reviewer’s Comment
In this paper the author proposes empirical tests to separate consumer-push from producer-pull moral hazard. The test is based on the type of activity the participants may generate. He assumes that asking for particular damage repairs is a consumer-push activity while varying the cost of a repair is a producer-pull activity which is totally arbitrary. It is clear that if a consumer asks for an unnecessary repair he will also increase the cost of the claim. But my main concern is with equation (1). Estimating this equation is not a test for ex-post moral hazard. The insurance variable can be significant without asymmetric information. This is the role of insurance to provide better services at higher cost if an accident occurs. So to test for ex-post moral hazard the author must separate the variation in cost that is due to asymmetric information from that related to full information in presence of insurance. There is a large literature on ex-post moral hazard that the author must read. See for example, the surveys of Dionne and Picard in the Handbook of Insurance (2013).

18. Results: Income as Categorical Variable
Ex Post
Consumer-push
Producer-pull
Log of repair costs
Costs Eq.1
Parts Eq.2
Accessories Eq.3
Costs Eq.4
Coef.
p-value
Insurance (0/1)
0.37
0.02
0.17
0.03
0.22
0.31
0.04
RTC Severity
Damage (# parts)
n.a.
0.24
< 0.01
Ambulance (0/1)
0.54
0.26
0.12
0.57
0.30
Tow truck (0/1)
1.00
0.39
0.79
Police (0/1)
0.40
0.25
0.01
0.27
0.05
0.19
Towed away (0/1)
0.49
0.06
0.20
0.23
0.29
Vehicle Characteristics
Value vehicle
0.00
0.10
0.86
0.45
0.08
Value vehicle 2
0.58
Age car (yrs.)
-0.02
0.28
-0.01
0.18
0.33
Driver Characteristics
Male (0/1)
0.88
-0.03
0.66
-0.07
0.68
< $20,000
0.97
-0.08
0.69
0.84
$20,000-$39,999
0.70
0.93
-0.11
0.60
0.50
$40,000-$59,999
0.14
0.61
0.92
0.71
0.21
0.42
$60,000-$79,999
0.11
0.73
-0.16
-0.44
0.47
$80,000-$99,999
0.34
-0.04
0.41
$100,000-$149,999
0.78
0.75
-0.15
0.63
0.72
>$150,000
0.91
Constant
6.72
0.64
6.90
Vehicle make: (Audi, BMW, Chrysler/Jeep, Daewoo, Daihatsu, Holden/GMH, Honda, Hyundai, Jaguar, Kia, Land Rover, Mazda, Mercedes, Mitsubishi, Nissan, Peugeot, Renault, Rover, Saab, Seat, Subaru, Suzuki, Toyota, VW & Volvo) estimated but not reported.