Journal of Risk and Uncertainty, 5:247-25 l, (1992) © 1992 Kluwer Academic Publishers

Propitious Selection in Insurance

DAVID HEMENWAY* Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02215

Key words: insurance, automobile insurance, risk, moral hazard, adverse selection

Abstract

The theory of propitious selection suggests that there are risk-avoiding personalities who both take physical precautions and buy financial security (insurance). Conversely, there are risk seekers who tend to do neither. Survey evidence is presented that is consistent with the theory. Individuals who obtain motor vehicle liability coverage are less likely than others to drink-and-drive, and are more likely to engage in health-beneficial (risk-avoiding) behaviors. Propitious selection may be a general phenomenon promoting favorable selection in many real world insurance markets.

The relationship between insurance purchase and risk-taking behavior is influenced by many factors. Two are well known and have specific names--adverse selection and moral hazard. This article presents evidence concerning a third factor that has been termed propitious selection (Hemenway, 1990).

While adverse selection and moral hazard promote unfavorable selection in insurance markets, propitious selection is a force for favorable selection. The propitious selection theory asserts that individuals are (somewhat) consistent in their taste for risk across physical and financial dimensions. Risk avoiders will tend to take physical precautions and to seek financial security. Risk seekers will tend to do neither.

Few studies have tried to correlate physical risk-taking with financial risk-taking as measured by insurance purchase (Greene 1963). A major problem is that good data to test the theory of propitious selection are not readily available. This article presents some relevant statistics concerning one line of insurance--vehicle liability coverage. The evidence is crude, but suggestive.

Each year the Roper Organization conducts a national household survey for the prop- erty/liability insurance industry, interviewing approximately 1500 adults, face-to-face, in the respondent's home. While the survey is primarily designed to determine public opinion about insurance, two of the factual questions asked in these Public Attitude Monitor surveys are the following:

*This research was supported by the Harvard Injury Control Center, funded by the Centers for Disease Control. Special thanks to Jennifer Carter, Sara Solnick, and also to Beth Sprinkel of the Insurance Research Council. Useful suggestions were received from Eric Latimer, Roger Davis, Marcello Pagano, and anon- ymous reviewers. The raw data were obtained from the Roper Center for Public Opinion Research in Storrs, Connecticut.

248 DAVID HEMENWAY

1. How many licensed vehicles are owned or leased for personal use by members of your household?

2. How many of these vehicles have liability insurance and how many have no liability insurance at all?

The 1988 survey (PAM 1988) also included this question:

3. In the past year or so, have you had the occasion to drive a motor vehicle after having a drink of alcoholic beverage?

The relationship between liability coverage and driving-after-drinking is influenced by many factors, including adverse selection, moral hazard, and propitious selection. Since vehicle liability insurance premiums are not directly affected by drinking behavior, the theory of adverse selection argues that people who drink-and-drive should be more likely to purchase the insurance. They are getting a bargain by being offered coverage at average rates. The theory of moral hazard argues that once people purchase the insurance, they are more likely to drink-and-drive. The theory of propitious selection suggests that highly risk- avoiding individuals will not drink-and-drive but will tend to purchase the insurance.

The evidence in table 1 is consistent with propitious selection. Thirty-nine percent of the uninsured drive after drinking alcohol compared to 28% of the insured (p < .05).

While the raw data were not available, certain cross-tabulations were. Household income is highly correlated with both variables, but the addition of income to the analysis should not undermine the discovered relationship. For these data, income is positively associated with both insurance coverage (p < .001) and with drinking-and-driving (p < .001) (not shown). Low-income individuals are less likely to carry liability insurance, but they are also less likely to drink-and-drive.

The Public Attitude Monitor survey (PAM 1985) gathered information not only on motor vehicle liability coverage, but also on various personal risk-seeking and risk- avoiding behaviors. Respondents were asked to rate six activities as being very typical, somewhat typical or not at all typical of their own behavior:

1. I smoke at least a pack of cigarettes a day 2. I am careful about my alcohol consumption

Table 1. Drinking and driving versus insurance

All vehicles insured One or more vehicles uninsured

Number Column % Number Column %

Both drink and drive 321 28% 41 39%

Don't drink and drive 846 72% 64 61% 1167 100% 105 100%

Notes:p < 0.05. Data from PAM 1988 questionnaire.

PROPITIOUS SELECTION IN INSURANCE 249

3. I get a medical checkup every year 4. I try to avoid food with additives or preservatives 5. I exercise at least two to three times a week 6. I am careful about my weight

If people have consistent tastes for risk, actions 2 through 6 might be positively corre- lated with each other, and all would be negatively correlated with smoking (action 1). The theory of propitious selection suggests that the risk avoiders will tend to buy insurance, while those engaging in risk-seeking activities will be less likely to purchase coverage.

The data from this survey largely support the theory. Responses to the smoking, alcohol, medical checkup, and food questions were significantly correlated with each other, with the expected signs (not shown).

As further predicted, these four variables were also associated with insurance pur- chase. In table 2, the column figures contrast insured versus uninsured motorists in terms of cigarette consumption, regular exercise, yearly medical checkups, etc. Compared to the uninsured, a higher percentage of insured motorists appear to be risk avoiders. For example, 69% of the insured said smoking a pack a day was not at all typical of them, compared to 56% of the uninsured (p < .001); 48% of the insured receive yearly medi- cal checkups compared to 37% of the uninsured (p < .01).

The data were also modeled using multivariate logistic regression. Because of high multicollinearity, one approach we took was to create a single risk-avoidance variable. An individual received one point each for being careful about alcohol consumption, avoiding additives in food, having yearly medical checkups, and claiming that smoking a pack a day was not at all typical. The variable was thus a whole number with values of 0 through 4. Additional independent dummy variables were age (30 and over), sex (fe- male), education (college degree), homeownership (yes), and household income (over $20,000). The dependent variable was belonging to a household that has no licensed,

Table 2. Contrasting insured versus uninsured motorists (auto insurance liability coverage)

All vehicles insured One or more uninsured Significance n = 1107 n = 189 chi-squared

Cigarettes (No) 69% 56% .001 Alcohol (careful) 53% 45% .04 Medical checkups (yearly) 48% 37% .01

Food additives (avoid) 23% 17% .07 Exercise (regular) 37% 40% N.S. Weight (careful) 39% 37% N.S.

Age (30 or older) 73% 61% .001 Sex (female) 53% 46% .11 Education (college degree) 18% 13% .10 Homeowner (yes) 70% 53% .001 Income ($25,000+) 66% 56% .03

Note: N.S. = not significant. Data from 1985 PAM questionnaire.

250 DAVID HEMENWAY

uninsured motor vehicles. Because a large number of people did not answer the income question, we ran the regression with and without that variable. The coefficients of all variables had the expected sign, and risk avoidance was highly significant (table 3). The results are similar for other approaches (not shown), such as giving the risk-avoidance variable only two values (1 = following at least two of the four risk avoidance behaviors; 0 = following fewer than two) or including the four risk-avoidance dummy variables in the multiple regression and testing them for significance as a group.

The evidence presented has many limitations. It deals only with motor vehicle liability insurance, and that coverage is often mandated by individual states, though many mo- torists do not obey the law (All-Industry Research Advisory Council, 1989). It relies on self-reporting rather than actual observations of behavior. The variables are not ideal: a "yes" to drinking-and-driving may just mean having one glass of wine before driving a mile home; the insurance question asked about uninsured but not underinsured vehicles. The risk-behavior questions refer to the respondent, but the uninsured vehicle question applies to the entire household. And there are various possible confounders we could not account for.

Perhaps the most important missing variable is price. While automobile insurers do not predominantly rely on feature rating to identify risk-seeking individuals, they do experience rate. Premiums typically rise for drivers who receive traffic citations or are found at fault in reported collisions. Merit rating thus eventually penalizes many risk- taking motorists. Their failure to purchase insurance may be as much a matter of price as of taste. An ideal data set would include information on actual insurance prices faced by the individual motorist.

Our findings are consistent with the theory of propitious selection, but because of data limitations, the results are suggestive rather than conclusive. If propitious selection ex- ists, it has a favorable impact on insurers, and may also benefit society by counteracting the potentially market-unraveling effects of adverse selection. However, were propitious selection to dominate, in some sense the "wrong" people would be buying insurance

Table 3. Predictors of households with no uninsured licensed vehicles (logistic regression results)

N = 1267 N = 1009

Coefficient Significance Coefficient Significance

Risk-avoidance activities .24 .01 .34 .001 Age (30 or older) .48 .01 .41 .05

Sex (female) .17 N.S. .15 N.S. Education (college degree) .16 N.S. .28 N.S. Homeowner (yes = 1) .54 .01 .33 .09 lncome ($20,000+) .30 .12 Constant .63 .01 .39 .10

Note: N.S. = not significant. Data from 1985 PAM Questionnaire.

PROPITIOUS SELECTION IN INSURANCE 251

protection. Individuals who are most prone to sustain injury or loss would be among the least likely to be covered by insurance.

References

All Industry Research Advisory Council. (1989). Uninsured Motorists. Oak Brook, I L: AI RAC. Greene, Mark. (1963). "Attitudes Toward Risk and a Theory of Insurance Competition," Journal ofblsurance

30, 165-182. Hemenway, David. (1990). "Propitious Selection," Quarterly Journal of Economics 105, t063-1069. PAM. (1985; 1988). Public Attitude Monitor. Annual Survey by the Roper Organization for the All-Industry

Research Advisory Council. Oak Brook, IL.: AIRAC