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

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

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

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

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’

Data Survey of attitudes to the Australian smash repair industry (IMRAS Consulting) 1997-1999 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)

Descriptive Data

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Smash Repair Costs ($) Repairs ($) Residuals

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)

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.

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)

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”

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 - 0.145 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)

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.

Finish Bibliography: Arrow, K. J., 1963. Uncertainty and the welfare economics of medical care. The American Economic Review LIII (5), 941–973. Dionne, G., 2013. 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. 811-824 Pauly, M. V., 1968. The economics of moral hazard: Comment. The American Economic Review 58 (3), 531–537.

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).

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.