Using the data in WAGE1.RAW gives the estimated equation. log(wage 284 + 092 edu
ID: 1131324 • Letter: U
Question
Using the data in WAGE1.RAW gives the estimated equation. log(wage 284 + 092 educ +0041 exper 022 tenure (.104) 007) 0017) 003) n = 526, R2-316. Where standard errors appear in parentheses below the estimated coefficients. wage: hourly earnings in dollar educ: years of education exper: years of workforce experience tenure: years with current employer 1) Based on a simple rule of thumb, are these explanatory variables statistically significant at 5% significant level? Provide your evidence. 2) If now we measure the education in 'season' instead of 'year', will the coefficient educ and R2 change? Is it possible that the t-test becomes insignificant? a) Now we asd owper s gete oln qions log(wage)-0.198+0.085 educ+ 0.0329 exper+0.0208 tenure+0,.0001 exper whether exper has increasing or diminishing marginal effect on log(wage)?Explanation / Answer
1) Here we will use the 2-t rule of thumb. That is if the t stat is greater than 2 then the coefficients are significant. In this case the t stats for educ, exper and tenure are 13.14, 2.41 and 7.33. These are calculated as t=coeff/standard error. Thus as all the t stats are greater than 2, all the coefficients are thus significant at the 5% level of significance.
2) This will have no impact on the results as we are in effect scaling the results by dividing the year and measuring by season. The nature of the data is not changing. Thus the end results will not change.
3) The net effect on log wage will be almost entirely due to the exper variable as the net effect is 0.0329. The effect of the exper squared variable is negligible. There will thus be increasing marginal effect on log wage but only marginally so.
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