Transcript Driver Negligence vs. Odometer Miles

Why Low Credit Scores
Predict More Auto Liability
Claims: Two Theories
Patrick Butler*
American Risk & Insurance Association
August 7, 2007
*National Organization for Women
[email protected]
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#774.7804
Policy dilemma: get “A” w/o getting “B”
A. Mandatory liability insurance
– Public demands it
– Insurers oppose it because of “B”
B. Price regulation
– To keep mandatory insurance affordable
– Recent example is initiatives to ban or regulate
credit score (CS) pricing
– In response auto insurers commissioned a
large study completed in 2003
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Figure 1. Liability Claims vs. Credit Scores
(from Miller & Smith 2003)
+90%
-25%
 Claims per 100 car years: +90% to -25% of average
 1.90 / 0.75 = 2.5 times
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Theory 1
“More driver negligence”
• Basis for theory
– Each liability claim requires a negligent
act by insured car’s driver
• Logic
– Cars of low CS drivers average more
liability claims
– Therefore these drivers are more
negligent
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Theory 1 (continued)
Explanations
• Miller & Smith 2003 (insurance industry
explanation)
“[Credit-based] scores seem to provide an objective means
of measuring personal responsibility and its effect on
insurance losses.”
• Brockett et al. 2005 (1st academic study)
– Presented at WRIEC, Salt Lake City
– Published 2007
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Theory 1 (continued)
biological explanation
Brockett & Golden 2007 (J. Risk & Insurance)
• Title (underline added):
“Biological and Psychobehavioral Correlates of Credit
Scores and Automobile Insurance Losses: Toward an
Explication of Why Credit Scoring Works”
• Conclude that the research examined by their study
“suggests that the discussed individualized biological and
psychobehavioral correlates provide a connection between
credit scores and automobile insurance losses.“
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Model for Theory 1 is the biological explanation for
correlation with driver age
Involvement Rate =
Accidents per 1,000,000 miles
(from Williams 1999)
Driver Age
 Similar age effects confirmed per worker hour of exposure
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Theory 1
Unaddressed Matters
• Claims & accidents are referenced to driver- and caryears, but individual annual-miles exposures vary widely
• A conflict with risk aversion theory
– Greater financial constraint predicts more risk
aversion, i.e., less negligence
• Insurers report that lower CS also predicts more
uninsured motorist (UM) claims.
– But a UM claim requires non negligence by the
insured car’s driver
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CS Theory 2
“More miles per insured car year”
• UM & Liability Claims must correlate (+):
– The more miles a group of cars averages, the more
claims per 100 car years the group produces
• more negligence claims, and also
• more non-negligence (UM) claims
• This means that the cars of financiallyconstrained drivers must be averaging
more miles. Why so?
• But first some basics:
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Basics for Theory 2
• Given – Accidents are a cost of car
operating
• Given – Premiums are a cost of car
owning
• But is the range in annual miles enough to
explain the 2.5 times CS variable range in
claims per 100 car years?
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Fig. 2. Yes. Household Cars Distributed by
Odometer Miles Shows Range
20%
18%
Mean = 11,801 miles
Median = 9,448 miles
N = 32,153 cars
N = 32,153 cars
16%
WOMEN DRIVERS
AVG = 10,143 MILES
14%
MEN DRIVERS
AVG = 16,553 MILES
12%
10%
Cars
8%
6%
4%
2%
Annual Miles (000)
>30-32
>28-30
>26-28
>24-26
>22-24
>20-22
>18-20
>16-18
>14-16
>12-14
>8-10
>6-8
>10-12
1.0% = 0
miles
>4-6
>2-4
>0-2
0%
4.4% > 32K miles
• Subgroups by car age, and by driver sex and age (despite
difference in averages) span entire annual-miles range
• So cars within insurance classes span low- to high-miles 11
Figure 3 (Recasting of Figure 2)
 The CS range in claims: 2.5 times = 15,000 mi / 6,000 mi
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Figure 4. Why miles must be individually measured
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14%
> 15,000 mi.
0.9
0.8
10+ years old
8,758 mi. avg*
0.7
from 1995 NPTS
Quantiles
0.6
0.5
0-2 years old
16,092 mi. avg*
0.4
0.3
0.2
0.1
* Owners' est'd
annual miles
13%
< 6,000 mi.
0
0
5
10
15
20
Annualized Odometer Miles -
25
30
Thousands
 Avg miles explain why new cars average more liability claims than old cars
 But distribution shows many new cars are driven fewer miles than old cars
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Logic of Theory 2 illustrated
(Hard but sure way to economize)
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Theory 2 fits other predictors
1.
2.
3.
4.
Zip code income (-)
Education & occupation (-)
No prior insurance (+)
Installment plan (+)
X. Any marker of tight budgets predicts more
liability claims per 100 car years
Therefore, the highest per-car premiums are
charged to those who can least afford them 15
Theory 2 recommends
• Informed by Theory 2, the strong public demand for
enforcing mandatory liability insurance could be
accompanied by
– A strong demand that automobile insurers provide the
audited odometer-mile exposure unit as an option
– At cents-per-odometer-mile class prices this option
would constitute a free-market remedy for the upward
cost-price spiral that the traditional car-year exposure
unit sets off for groups of economizing drivers
– With this option drivers could car pool or take the bus to
save on insurance while keeping their own cars insured
and available for use
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Table 2. What’s at stake – the challenge for both theories
e. g., Albany, NY: lowest and highest quoted for one car class profile
(same driver age, record, sex, and marital status).
Insurance Company
Premium
ALLSTATE PROP & CAS INS CO
258
GEICO
318
PROGRESSIVE NORTHEASTERN
326
STATE FARM MUT AUTO INS CO
375
GEICO IND CO
492
METROPOLITAN GRP P&C INS CO
641
AUTOONE INS CO
854
ALLSTATE IND CO
1,136
Range: $1,136 / $258 = 4.4 times
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Conclusion – Research question
Which policy response to the dilemma
“free-market vs. affordability vs. mandatory insurance”
• Theory 1, some issues
– Identify the negligent driver groups on an
accidents-per-1,000,000-mile basis
– Find incentives to reduce negligence per-mile
• Theory 2, some issues (discuss in Q & A)
– Federal surveys show average miles per car year
decreases moderately as household income decreases
– Some high premiums for adult-driver-class cars may
reflect not only higher miles per car, but also the higher
per 1,000,000 mile accident involvement rates of
“undisclosed” young and old drivers sharing the cars
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