Academic journal article Journal of Risk and Insurance

An Analysis of Complaint Data in the Automobile Insurance Industry

Academic journal article Journal of Risk and Insurance

An Analysis of Complaint Data in the Automobile Insurance Industry

Article excerpt

An Analysis of Complaint Data in the Automobile Insurance Industry


Cross-firm policy service quality differences are investigated in the automobile

insurance market through empirical testing of firm-specific complaint data. The data

used here are publicly available complaint ratios which are collected by regulators in

some states to assist consumers in service quality discrimination. Previous studies have

shown independent agency firms to have larger expense ratios than direct writers, but

empirical evidence here does not suggest that direct writers provide lower service quality

than do independent agency companies. The evidence suggests that firms specializing in

high risk drivers receive relatively more complaints.

Evidence of cross-firm price variation for homogeneous automobile insurance contracts has been documented by Jung (1978), Dahlby and West (1986), and Berger, Kleindorfer and Kunreuther (1989). Smallwood (1975) suggests that service quality differences across insurers may explain price dispersion.(1) Investigation of quality differences across insurers has been limited, largely due to difficulty in identifying empirical measures of service quality.(2)

This study uses state insurance department firm complaint ratios from California, Illinois, and New York, as an empirical measure of firm service quality. The California ratio equals the number of complaints received per 1,000 covered automobiles, and the Illinois and New York ratios equal the number of complaints received divided by written premiums. A relatively large complaint ratio suggests that the insurer provides poor service quality.

The complaint ratio is an imperfect measure of firm service quality, since complaints result from disappointed expectations of the insured which may or may not be strictly due to poor service. However, the complaint ratio is what state regulators collect and use to measure consumer satisfaction with policy service quality, and use of the ratio here allows a first step toward empirical investigation of possible cross-firm quality differences. Following survey results in Consumer Reports (1988) and a Gallup poll conducted for Best's Review (1989), results here suggest that service quality differences exist across automobile insurers.

The Economics of Quality Determination

Evidence supports characterization of the property-liability insurance market as competitive.(3) In a competitive market, buyers make price-quality tradeoffs when choosing insurance coverage. Buyers purchase coverage based on expected full price, which equals premium plus complaint price, where complaint price is the loss in value to the insured of not receiving expected service.

Based on work by DeVany and Savings (1983), an economic model describes firm service quality determination in a competitive market. Full price is specified as follows:

P = p* + [Alpha] and [Alpha] = [Alpha] (v, o, c), where P is full price, p* is the premium, and [Alpha] is the complaint cost to the insured. Complaint cost can include implicit and nonpecuniary costs to the insured of not receiving good service. The variable, [Alpha], is a function of v, the marginal value of policy service to the insured; o, the total output of service provided to all policyholders; and c, the capacity of the firm to provide service.

The function [Alpha] is assumed to be increasing in v since expected complaint cost increases with the insured's marginal value of customer service. The function [Alpha] may be increasing in o and decreasing in c: if an insurer with fixed service capacity provides increased output of policy service, increased complaint costs may result. That is, if the firm's resources are limited and the frequency and severity of claims increase, the quality of service may decrease, leading to increased complaint costs. …

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