1. Discussions on “Decomposing Automobile Insurance Policy Buying Behavior – Evidence of Adverse Selection” by Chu-Shiu Li, Chwen-Chi Liu and Jia-Hsing Yeh Tong Yu University of Rhode Island tongyu@uri.edu ARIA, August 6, 2007

2. Summary Issue –To present evidence on the presence of adverse selection –More specifically, to see if there is an positive relation between risk and insurance purchase Data –Coverage and claim information of Taiwan auto insurance in years 2002 and 2003, facilitating two sets of analyses: High-coverage policy (comprehensive policy) versus low-coverage policy (collision only) – both without deductible Policy without deductible versus policy having deductible

3. Summary Testable Conditions –A positive link between insurance claims and subsequent coverage –A negative link between insurance claims and subsequent deductible choice Specific Cases favoring Adverse Selection –1. L in year t, no loss, L in year t+1 –2. H in year t, no loss, L in year t+1 –3. L in year t, loss, H in year t+1 –4. H in year t, loss, H in year t+1

4. Summary Results –T 6 – Prob(LC in 03|LC in 02) is positively related to the No_Claim dummy of 2002 (NoClaim_02) –T 7 – Prob(LC in 03|HC in 02) is negatively related to NoClaim_02 –T 8 – Prob(HD in 03|HD in 02) is positively related to NoClaim_02 –T 9 – Prob(HD in 03|LD in 02) is negatively related to NoClaim_02 –Results are obtained after controlling for some characteristics of insured and auto, e.g., age, gender, car age, expected losses of a policyholder, etc Carefully describe the procedure to compute expected loss, e.g., E[NoClaim_02]

5. Minor Suggestions Also look at the group having high coverage in 2003 Perform an unconditional test examining coverage choice and prior-year claim experience –Need discuss the benefit of decomposing year t insured type –Compare the results across various groups

6. Major Issue Risk ≠ Loss Experience

7. Major Issue Risk ≠ Loss Experience Loss experience is not private information to policyholder. It is available to insurers as well Hard to conclude the finding is supportive to adverse selection Test against alternative hypotheses: learning and habit persistence

8. Direct Test on Adverse Selection Develop a model to compute the price of each insurance contract in year t+1 Look at insurance purchase in the over- and under-price groups respectively Underlying assumption: Risk is quantifiable Feasible??

9. Solution 1 – Estimate Risk Get claim information for more years. Say 5 years, L 1, L 2, L 3, L 4, and L 5. Test Prob(C 2 |C 1 ) as a function of insured’s subsequent loss experience L i Underlying assumption: Insurers have better information on their own future losses than insurers

10. Solution II – Get around Risk Identify insured factors potentially correlated with insured’s AS incentive but uncorrelated with insurance price, e.g., income, education Test if the loss and coverage relationship differs across insured groups with different values of insured characteristics Specifically, interact loss experience with some of the control variables used in the regressions

11. Conclusions Smart idea, neat data, good potential The authors need to differentiate adverse selection from competing hypotheses Risk ≠ Loss Experience