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As was said earlier, Moore g Viscusi uses two different data sets named NTOF and BLS. The later in the experiment, estimates the wage equation, by excluding variable compensation, which had not been done before in literature. As it was estimated, performance NTOF to his death risk was superior to the BLS's risk approach. The coefficient of the BLS death variable was always positive, and the largest t-ratio that was observed was in 1.625 and this coefficient was accepted at 5 percent level (1.645). The comparison between these two datasets was the coefficient of NTOF always positive, and t-rate was never less than 3.35, and it passes the most demanding statistical tests of significance. This finally was concluded that there had been error in the variable to the BLS data (Viscusi and Moore 1988a: 485).

In the experiment is used the price level of 1986. Estimated values ??for the BLS risk ranged from $ 2 million and from $ 5 million to $ 7 million for NTOF. Risk variable's coefficient of death-risk in the wage regression equation measures the amount of increase or decrease corresponding increases or decreases the risk which the worker meets on the job. This change is interpreted to U.S. dollars and an amount of salary that the worker requires in order to accommodate the small amount of risk (ibid: 485).

The willingness to pay will also be mentioned at this paper. The theory is described so that if the annual death increases with 1/100, 000 They assume that if the worker receives 2.5 cents for each increase in the risk, he will then require 0.025 * 2000 (annual hours) is $ 50 to accept this increase is 1/100, 000 in daily life at work. Furthermore, 100,000 workers accept a compensation equivalent to $ 5 million in salary to accept this increase in risk. But between 100,000 workers, will only one life is saved. So will these workers in total set aside $ 5 million per statistical life that is saved. Later, the authors begin by analyzing the experiment by assuming that the risk of death increases by 1/100,000. By further calculation of values ??for trade-off between death and risk, they found an estimated trade-off on 0.021667 between hourly wages and risk. By multiplying this with the 2000 annual working hours and again multiply this with 100,000 to reflect the risk variable change in 1981, estimated value per statistical life of $ 4.34 million (ibid: 486).

In conclusion it is mentioned that by using data that was provided from NIOSH, the trade-off between wages and risk expected to be almost twice as large as the estimated values ??of the BLS as was saw earlier. Finally, it is mentioned that it is expected that the previously estimated value of the life of PSID will fall about 50 percent by using the new variable from the BLS. The extra powers were errors on the data from the BLS have had major consequences on the earlier estimated levels for statistical life. This measurement error increases the coefficient of NIOSH or NTOF with more than 2. Therefore, it is such that the f本论文由英语论文网提供整理，提供论文代写，英语论文代写，代写论文，代写英语论文，代写留学生论文，代写英文论文，留学生论文代写相关核心关键词搜索。