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using this method, the estimated value of a statistical life of $4 million and statistical damage was estimated at $5,400. These amounts were estimated according to him was very similar to the numbers that had been estimated previously by others using the same data set (ibid: 12).

 

But as it was discussed earlier by Garen 1988, he applied econometric techniques to eliminate bias in the OLS estimated risk premium. Using variables that affect wages, such as experience, education, employment period, racial unionism and occupation were included in the risk equation. He points out that it is difficult to find any proxies for the level of risk aversion. However, since the measures are available to measure respondents living style, and it is believed that it is negatively correlated with risk aversion. It may be noted that such measurements are, for example, the number of dependents, value of the house if it is owned by the respondent and marital status. Also, variables that could indicate that the respondent was disabled and for how long, while the respondent's spouse education and dummy variable indicating whether the spouse was employed or not. This would affect household production function and the willingness of safety against income (ibid: 13).

 

Garen 1988 summarized the new results in a new and separate table. The variables that were associated with higher income were negatively associated with fatal and non fatal risk. And this was somewhat expected, since the safety is a normal good. So the individuals with higher qualities that lead to higher incomes choose safer jobs. The demand for safety increases with income based on the negative and significantly correlated non-labour income. It turned out that marriage was positively related to risk, but it was contrary with risk aversion. Ownership of houses and having children was small related (ibid: 13).

 

As it is appears from the empirical evidence, the endogenous causes can lead to bias estimates of the wage premium for risk of death and injury. Garen 19988 identified two sources that lead to bias estimation. One was that those with unobserved earnings capacity are likely to weigh safe job and a bias induced by this which is declining in the relationship between wages and risk. The second reason for the bias estimation was discovered that unobserved heterogeneity to the job of risk reward, which may cause bias in both directions (ibid: 15-16).

 

Viscusi and Moore (1988a)

 

Also Viscusi and Moore (1988a) attempted to estimate the value of a statistical life based on data that was provided by the institution for health and work supervision based on U.S. labour market. The main objective of this research was to find an instrument with the hope of estimating the value of life while using this, minimize serious injuries or deaths in the U.S. market by initiating policies with the help of this instrument.

 

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