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nged by the exclusion of women by 2 percent. As it was explained by Leigh differences that 13.5 percent and 2 percent occurred using 1979 data that were not published and were encoded with two and three digit industry related. He points out that this data was used by the majority in the estimation of the value of a life, and that the reason that the value of a life varies may be the differences in the data set. He also compares the values ??estimated with including of women and exclusion of them, and he found a coefficient difference about 40 to 60 percent. (ibid: 264).

 

Also, by link to tables that he had created, he points out a possible answer to question two that were posted further up. Namely women or men receiving different compensating wage. He finds only men's estimated coefficient that was significant, while the coefficient of women's death rate had large standard error. Both coefficients were estimated negative. However, with an upper limit of 95 percent confidence intervals are positive. Namely 0.0342 for the QES data and 0.0019 for the CPS data. Because of the empirical results, the possibility that women receive as much as men are ruled out (ibid: 256).

 

The third question that Leigh poses in his attempt is to determine whether the company size has something to say when one estimates the compensation reward for the risk of death. According to Garen (1985) firm size plays role in wage. The firm's size may play a role because of the two following reasons:

 

Based on epidemiological research, it emerged that the firm's size and the number of accidents is correlated very strongly. The second reason is that the department of labour has only looked at companies with more than 11 employees. This means that they have not seen the company that has 10 or less employees. Based on this, this may only reflect the size of the company only and not but the true death rates of workers are facing in the industry (ibid: 265-266).

 

He ran a simple test to see if the death rate and firm size are correlated or not. Estimated coefficient he was 0.03977 and was significant at .001 level. It indicated that company size and death rate is strongly positively correlate(ibid: 266).

 

The fourth question, as it was presented was about identifying the right industry. The question is aimed for when estimating the value of life will be affected by the choice of identification of incorrect industry. Bias from industry measurement was small (ibid: 268).

 

n the last part of his work, Leigh tries to estimate the value of life. Using the so-called data set from 1979 containing death rate, the value of lives that were presented based on this data set was very similar. Value was estimated based on the QES data set was also very similar, they had a difference of only $ 100,000, but estimates based on CPS data showed a deviation of less than $ 1,000,000. The coeffic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。