Example: Race, Redlining and Automobile Insurance Prices

In an article with this title, Harrington and Niehaus (1998) investigated whether insurance companies engaged in (racial) discriminatory behavior, often known as redlining. Racial discrimination is illegal and insurance companies may not use race in determining prices. The term redlining refers to the practice of drawing red lines on a map to indicate areas that insurers will not serve, areas typically containing a high proportion of minorities.

To investigate whether or not there exists racial discrimination in insurance pricing, Harrington and Niehaus gathered private passenger premiums and claims data from the Missouri Department of Insurance for the period 1988-1992. Although insurance companies do not keep race/ethnicity information in their premiums and claims data, such information is available at the zip code level from the US Census Bureau. By aggregating premiums and claims up to the zip code level, Harrington and Niehaus were able to assess whether areas with a higher percentage of blacks paid more for insurance (PCTBLACK).

A widely used pricing measure is the loss ratio, defined to be the ratio of claims to premiums. This measures insurers’ profitably; if racial discrimination exists in pricing, one would expect to see a low loss ratio in areas with a high proportion of minorities. Harrington and Niehaus used this as the dependent variable, after taking logarithms to address the skewness in the loss ratio distribution.

Harrington and Niehaus (1998) studied 270 zip codes surrounding six major cities in Missouri where there were large concentrations of minorities. Table 6.1 reports findings from comprehensive coverage although the authors also investigated collision and liability coverage. In addition to the primary variable of interest, PCTBLACK, a few control variables relating to age distribution (PCT1824 and PCT55P), marital status (MARRIED), population (ln TOTPOP) and income (PCTUNEMP) were introduced. Policy size was measured indirectly through an average car value (ln AVCARV).

Table 6.1 reports that only policy size and population are statistically significant determinants of loss ratios. In fact, the coefficient associated with PCTBLACK has a positive sign, indicating that premiums are lower in areas with high concentrations of minorities (although, not significant). In an efficient insurance market, we would expect prices to be closely aligned with claims and that few broad patterns exist.

begin{matrix}
begin{array}{c}
text{Table 6.1 Loss Ratio Regression Results}
end{array}\small
begin{array}{ll|rr} hline
& & text{Regression} & t- \
text{Variable} & text{Description} & text{Coefficient} & text{Statistic} \ hline
text{Intercept} & text{} & 1.98 & 2.73 \
text{PCTBLACK} & text{Proportion of population black} & 0.11 & 0.63 \
text{ln TOTPOP} & text{Logarithmic total population} & -0.1 & -4.43 \
text{PCT1824} & text{Percent of population between 18 and 24} & -0.23 & -0.50 \
text{PCT55UP} & text{Percent of population 55 or older} & -0.47 & -1.76 \
text{MARRIED} & text{Percent of population married} & -0.32 & -0.90 \
text{PCTUNEMP} & text{Percent of population unemployed} & 0.11 & 0.10 \
text{ln AVCARV} & text{Logarithmic average car value insured} & -0.87 & -3.26 \
{R_a^2} & {~~~~~~~~~0.11} & & \ hline
end{array}\scriptsize
begin{array}{c}
textit{Source}: text{Harrington and Niehaus (1998)} \
end{array}
end{matrix}

Certainly, the findings of Harrington and Niehaus (1998) are inconsistent with the hypothesis of racial discrimination in pricing. Establishing a lack of statistical significance is typically more difficult than establishing significance. In the paper by Harrington and Niehaus (1998), there are many alternative model specifications that assess the robustness of their findings to different variable selection procedures and different data subsets. Table 6.1 reports coefficient estimators and standard errors calculate using weighted least squares, with population size as weights. The authors also ran (ordinary) least squares, with robust standard errors, achieving similar results.

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