Chicago Insurance Redlining Example Were insurance companies in

Chicago Insurance Redlining Example Were insurance companies in Chicago denying insurance in neighborhoods based on race?

The background In some US cities, services such as insurance are denied based on race This is sometimes called “redlining.” For insurance, many states have a “FAIR” plan available, for (and limited to) those who cannot obtain insurance in the regular market. So an area with high numbers of FAIR plan policies is an area where it is hard to get insurance in the regular market.

The data (for 47 zip codes near Chicago) involact # of new FAIR plan policies and renewals per 100 housing units race % minority theft theft per 1000 population fire fires per 100 housing units income median family income in 1000s

First, some description Descriptive statistics for the variables Box plots Histograms Matrix plots etc.

Descriptive Statistics: race, fire, theft, age, involact, income Variable N N* Mean SE Mean StDev Minimum Q1 Median Q3 race 47 0 34.99 4.75 32.59 1.00 3.10 24.50 59.80 fire 47 0 12.28 1.36 9.30 2.00 5.60 10.40 16.50 theft 47 0 32.36 3.25 22.29 3.00 22.00 29.00 39.00 age 47 0 60.33 3.29 22.57 2.00 48.00 65.00 78.10 involact 47 0 0.6149 0.0925 0.6338 0.0000 0.0000 0.4000 0.9000 income 47 0 10.696 0.402 2.754 5.583 8.330 10.694 12.102 Variable Maximum race 99.70 fire 39.70 theft 147.00 age 90.10 involact 2.2000 income 21.480

Histogram of race, fire, theft, age, involact, income race 16 fire 16 12 Frequency theft 20 12 15 8 8 4 4 5 0 0 0 0 20 40 60 80 100 10 0 10 age 20 30 40 involact 0 16 7.5 12 12 5.0 8 8 2.5 4 4 0.0 0 0 20 40 60 80 0.0 0.5 1.0 1.5 2.0 60 90 120 150 income 16 10.0 0 30 8 12 16 20

Boxplot of race, fire, theft, age, involact, income race fire 100 40 160 75 30 120 50 20 80 25 10 40 0 0 0 age 80 2.0 60 1.5 40 1.0 20 0.5 0 0.0 involact theft income 20 15 10 5

Matrix Plot of race, fire, theft, . vs race, fire, theft, . 0 20 40 0 50 100 6 12 18 race 100 50 0 fire 40 20 0 theft 160 80 0 age 100 50 0 involact 2 1 income 0 18 12 6 0 50 race 100 0 fire 80 theft 160 0 age 1 involact 2 income

Simple linear regression model Fit a model with involact as the response and race as the predictor A strong positive relationship gives some evidence for redlining

Fitted Line Plot involact 0.1292 0.01388 race 2.5 S R-Sq R-Sq(adj) involact 2.0 1.5 1.0 0.5 0.0 0 20 40 60 race 80 100 0.448832 50.9% 49.9%

What’s next The matrix plot showed that race is correlated with other predictors, e.g., income, fire, etc. So it’s possible that these are the important factors in influencing involact Next the full model is fit

The regression equation is involact - 0.609 0.00913 race 0.0388 fire - 0.0103 theft 0.00827 age 0.0245 income Predictor Coef SE Coef T P -0.6090 0.4953 -1.23 0.226 race 0.009133 0.002316 3.94 0.000 fire 0.038817 0.008436 4.60 0.000 -0.010298 0.002853 -3.61 0.001 0.008271 0.002782 2.97 0.005 0.02450 0.03170 0.77 0.444 Constant theft age income

S 0.335126 R-Sq 75.1% R-Sq(adj) 72.0% Analysis of Variance Source DF SS MS F P 5 13.8749 2.7750 24.71 0.000 Residual Error 41 4.6047 0.1123 Total 46 18.4796 Regression

What have we learned? Race is still highly significant (t 3.94, p-value 0) in the full model Income is not significant (this isn’t surprising, since race and income are highly correlated).

Diagnostics Some plots are next. Uninteresting (good!) We’ll ignore more substantial diagnostics such as looking at leverage and influence, although these should be done.

Residual Plots for involact Versus Fits 99 1.0 90 0.5 Residual Percent Normal Probability Plot 50 10 1 -1.0 -0.5 0.0 Residual 0.5 0.0 -0.5 -1.0 1.0 0.0 16 1.0 12 0.5 8 2.0 0.0 -0.5 4 0 1.5 Versus Order Residual Frequency Histogram 0.5 1.0 Fitted Value -0.8 -0.4 0.0 Residual 0.4 0.8 -1.0 1 5 10 15 20 25 30 35 Observation Order 40 45

Model selection Response is involact Vars 1 2 3 4 5 R-Sq 50.9 63.0 69.3 74.7 75.1 R-Sq(adj) 49.9 61.3 67.2 72.3 72.0 Mallows Cp 37.7 19.8 11.5 4.6 6.0 S 0.44883 0.39406 0.36310 0.33352 0.33513 r a c e X X X X X i t n f h c i e a o r f g m e t e e X X X X X X X X X X