Impact of seat belt use on driving behavior

Economic Inquiry, Oct, 1992 by Harinder Singh, Mark Thayer

(2) -|D.sub.e~ = |P.sub.e~L P|L.sub.e~.

The relationship between individual and exogenous safety measures can be determined by treating equation (2) as an implicit function and using the implicit function rule to solve for de/dg.

(3) de/dg = (|D.sub.eg~ |P.sub.eg~L |P.sub.e~|L.sub.g~ |P.sub.g~|L.sub.e~ P|L.sub.eg~) / (-|D.sub.ee~ - |P.sub.ee~L - 2|P.sub.e~|L.sub.e~ - P|L.sub.ee~)

Following Blomquist |1986~, de/dg |is less than~ 0 if individual and exogenous actions are assumed to be substitutes in the production and loss functions. This is the formula for compensating behavior. However, the sign of de/dg is indeterminate if e and g are assumed to be complementary in reducing risk and loss. In this case compensating behavior does not always occur and will depend on the extent that complementarity produces additional safety that offsets the extra disutility of any exogenous safety measure.

The manner in which individuals treat individual and exogenous safety measures is an empirical question. A priori one would expect that relatively risk averse individuals would demonstrate the least amount of compensating behavior. That is, exogenous safety measures might not be offset by reductions in individual carefulness. Likewise, risk preferrers or risk lovers would be relatively strong candidates for compensating behavior. In the next section we present the data and empirical model that allows this hypothesis to be tested.

III. DATA SPECIFICS AND EMPIRICAL MODEL

The data was collected through a mail survey sent to residents in six San Francisco Bay area counties: Alameda, Contra Costa, Marin, San Francisco, San Mateo and Santa Clara. Potential respondents were selected at random from a master data tape of homeowners. We employed the Dillman |1978~ Total Design Method (TDM) in order to maximize the response rate. This procedure requires complete personalization of the correspondence and multiple attempts to convince respondents to participate in the survey.

Approximately 3,000 surveys were distributed and 1,092 surveys were eventually returned. The response rate was 37.13 percent. This is considered a satisfactory response rate for a mail survey that was extensive, provided no compensation for respondents, and did not utilize all of the TDM approach (we did not follow our mail correspondence with a telephone call due to limited funds).(2)

The survey obtained information concerning seat belt usage, years of seat belt use, number of moving violations over the previous three years, risk preferences, and several control variables including income, sex, age, and education. A detailed definition of each variable is provided in Table I.

Four variables require further discussion. First, the dependent variable in the empirical analysis is the number of moving violations the respondent has received in the previous three years.(3) Moving violations include exceeding posted speed limits, failure to stop at stop sign or traffic light, reckless driving, etc. Second, the seat belt use variable is used to test the compensating-behavior hypothesis by examining its relationship to the number of moving violations. Third, the independent variable for years of seat belt use allows a determination of the magnitude of learning concerning compensating behavior. Finally, the risk index variable, formed by summing the responses to questions concerning six different revealed preferences about risk behavior (presence of smoke alarm, burglar alarm, car alarm, earthquake home insurance, emergency equipment, and emergency food items), is used to measure relative risk aversion.(4) That is, if the individual's risk index is close to the maximum value (6) then he/she is considered relatively risk averse. On the other hand, if the individuals' risk index is near zero then this individual is considered a risk lover since relatively few precautions are taken.