66
in risk on comprehensive coverage.
117
Table 5 shows that the differences among neighborhood income groups were
much smaller than those among racial and ethnic groups. The one substantial difference
in risk was that customers in low-income neighborhoods pose a 16% higher risk for
comprehensive coverage. Again, this may in part be due to the lack of an effective
geographic risk measure for comprehensive coverage.
118
These results show that there were substantial differences in the average risk of
consumers in different racial and ethnic groups for all four major automobile insurance
coverages.
119
For property damage liability coverage, Asians were the only group with
117
The geographic risk measure in the FTC database is based on property damage liability claims, which
result from accidents. The estimated effect of the geographic risk measure is much smaller in the
comprehensive coverage risk models than in the models for the other coverages, suggesting that it is a poor
control for geographic variation in comprehensive coverage risk. According to the Bureau of Justice
Statistics, African Americans and Hispanics are much more likely to be victims of automobile theft (a risk
covered by comprehensive coverage) than non-Hispanic whites. See Bureau of Justice Statistics file
cv0516.csv, available at www.ojp.usdoj.gov/bjs/pub/sheets/cvus/2005/cv0516.csv
; Bureau of Justice
Statistics file cv0517.csv, available at www.ojp.usdoj.gov/bjs/pub/sheets/cvus/2005/cv0517.csv. In the
absence of a good measure of the geographic variation in comprehensive coverage risk, race, ethnicity, and
neighborhood income are likely picking up some of that variation in risk (e.g., they may be acting as a
proxy for other characteristics of neighborhoods that affect comprehensive coverage risk). Additional
support for this hypothesis was found by estimating separate risk and severity models that included race,
ethnicity, and income controls. In those models, race, ethnicity, and income affected only frequency in the
property damage liability, bodily injury liability, and collision coverage models. In the comprehensive
coverage model, race, ethnicity, and income were strongly related to claim severity. This is consistent with
those variables being related to the likelihood of theft claims.
118
Id.
119
We found similar patterns when we used loss ratios as the measure of relative risk, instead of the direct
results of the risk models. The loss ratio is the ratio of payments companies made on claims divided by
premiums customers paid in. Using loss ratios, therefore, shows whether customers in different racial and
ethnic groups generated greater or lesser total payouts on claims, on average, than predicted by the
companies, as reflected in the premiums the customers were charged. Loss ratios were fairly similar across
groups for property damage liability coverage, with Hispanics and Asians generating somewhat more
claims relative to premiums than African Americans and non-Hispanic whites. For bodily injury liability
coverage, collision coverage, and comprehensive coverage, African Americans and Hispanics generated
higher claims relative to premiums than did non-Hispanic whites. The same was true for Asians for
collision coverage, although Asians had a substantially smaller loss ratio for comprehensive coverage than
did any other group. For example, the loss ratios of African Americans and Hispanics for collision
coverage were 83.9% and 85.6%, respectively, for Asians 78.2%, and for non-Hispanic whites the loss ratio
was 63.3%. Unlike in our risk models, the coverage with the largest differences across groups was bodily
injury liability coverage, as opposed to comprehensive coverage. This again suggests that part of the
reason we find such large differences in risk across groups for comprehensive coverage in our models is the
lack of a geographic risk measure that is specific to risk on comprehensive coverage. For the four
(continued)