The jtrain.dta dataset contains information on job training, and other variables, at a variety of manufacturing facilities. Here is a list with brief definitions of the variables in the data. Please label your answers a., b., c., d., e.

1. year1987, 1988, or 1989

2. fcodefirm code number

3. employ# employees at plant

4. salesannual sales, $

5. avgsalaverage employee salary

6. scrapscrap rate (per 100 items)

7. reworkrework rate (per 100 items)

8. tothrstotal hours training

9. union=1 if unionized

10. grant=1 if received grant

11. totraintotal employees trained

12. hrsemptothrs/totrain

13. lscraplog(scrap)

14. lemploylog(employ)

15. lsaleslog(sales)

16. lreworklog(rework)

17. lhrsemplog(1 + hrsemp)

18. lscrap_1lagged lscrap; missing 1987

19. grant_1lagged grant; assumed 0 in 1987

20. lavgsallog(avgsal)

Use the command "xtset fcode year" to inform STATA that this is a panel dataset.

Run a pooled regression of lhrsemp on lavgsal lscrap lsales union granta.

a. Report below the coefficient and p-value for lsales.

b. All else equal, a 1% decrease of avgsal leads to a ____ change in hrsemp.

Run a fixed effects regression with the same variables from above, where the fixed effects entities are the firms. For reasons that will be apparent below, I recommend using the dummy variable technique.

c. All else equal, when a firm gets a grant this will increase the lhrsemp by ____. The confidence interval for this increase is between ____ and _____.

d. The intercept for firm number 410567is ___.

e. One of the firms has been "omitted" from the analysis due to perfect multicollinearity. Determine precisely why this happened. (Hint: it actually has to do with the Union variable.)