outcome of a job training experiment

c10 You need to use two data sets for this exercise, JTRAIN2.RAW and JTRAIN3.RAW. The former is the outcome of a job training experiment. The file JTRAIN3.RAW con- tains observational data, where individuals themselves largely determine whether they participate in job training. The data sets cover the same time period.

(i)       In the data set JTRAIN2.RAW, what fraction of the men received job training? What is the fraction in JTRAIN3.RAW? Why do you think there is such a big difference?

(ii)      Using JTRAIN2.RAW, run a simple regression of re78 on train. What is the estimated effect of participating in job training on real earnings?

(iii)    Now add as controls to the regression in part (ii) the variables re74re75educageblack, and hisp. Does the estimated effect of job training on re78 change much? How come? (Hint: Remember that these are experimental data.)

(iv)    Do the regressions in parts (ii) and (iii) using the data in JTRAIN3.RAW, reporting only the estimated coefficients on train, along with their statistics. What is the effect now of controlling for the extra factors, and why?

(v)     Define avgre 5 (re74 re75)/2. Find the sample averages, standard deviations,

and minimum and maximum values in the two data sets. Are these data sets representative of the same populations in 1978?

(vi)    Almost 96% of men in the data set JTRAIN2.RAW have avgre less than \$10,000.

Using only these men, run the regression

re78 on train,re74,re75,educ,age,black,hisp

and report the training estimate and its statistic. Run the same regression for JTRAIN3.RAW, using only men with avgre # 10. For the subsample of low-in- come men, how do the estimated training effects compare across the experimental and nonexperimental data sets?

(vii)   Now use each data set to run the simple regression re78 on train, but only for men who were unemployed in 1974 and 1975. How do the training estimates compare now?

(viii)  Using your findings from the previous regressions, discuss the potential impor- tance of having comparable populations underlying comparisons of experimental and nonexperimental estimates.

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