STATA-project

EC328, Spring 2016

Major Assignment 1

Educational Attainment in Canada Using the same data as you used for Class Assignment 2. You should already have a years of schooling variable generated.

1. Tabulate your estimate of overall years of schooling against highest degree obtained - Table 1. Do the results make sense? Explain.

2. Produce a table showing the means, standard deviations, minimum and maximum of: (a) overall years of schooling; (b) age; and (c) total income for men and women separately – Table 2. Are men or women more educated on average in your sample?

Generate an age variable. generate age = 27 if AGEGRP==[value for agegroup 25-29] replace age = 32 if AGEGRP==[value for agegroup 30-34] …. etc

3. Construct a graph showing mean years of schooling by age, with separate lines for men and women – Figure 1.

i. preserve ii. collapse (mean) yrschool, by(AGEGRP SEX)

iii. tabstat yrschool if SEX==1, by(AGEGRP) iv. twoway (line yrschool AGEGRP if SEX==1, clcolor(red)) (line

yrschool AGEGRP if SEX==2, clcolor(blue) ytitle("Average years of education") xtitle("Age group"))

v. restore vi. Note: you can graph the data either in STATA or copy the results

from the table over to Excel or another spreadsheet package and use it

4. Construct a similar graph showing the percentage of the cohort that has achieved a particular highest level of education (your choice) - Figure 2

5. Briefly describe the trends you see in your graphs. 6. Run two Mincerian log income regressions using only education, experience and

experience squared, one for men and one for women. You will need to first create an (approximate) experience variable (equal to age, less years of schooling, less 6), and then experience squared

7. Run a wage regression including some additional controls (your choice). Explain why you chose these.

8. Run a wage regression that might help to identify nonlinearities in the return to education. Is there any evidence of this?

9. Report the results of the wage regressions you have done in a table similar to those in economics journal articles - Table 3

Note: your table should have 6 columns – three regressions each for men and women.

10. Interpret the coefficient on the education variable in the basic regression specification, for both men and women. What sorts of problems are there in interpreting this figure as a return to education?

11. What is your estimated effect of another year of experience? To do this, draw a graph showing the marginal effect of an additional year of experience on income (for years of experience ranging from 1 to 40 years), with separate lines for women and men

Note: you should do this in a separate spreadsheet; write out your estimating equation very clearly before you try it.

12. Interpret the coefficient on the extra variable you included in your regression. What do you think this means?

13. Was there any evidence of non-linearity in the returns to education? 14. Overall, how do your estimates look compared with other estimates of the

Mincerian log wage regression we have looked at? 15. There are arguments that this type of regression will not provide a good estimate

of the causal effects of an additional year of education on income. What is the key reason for this? What sorts of methods have applied economists used to try to estimate a causal effect of education on income? Have they shown very different results, in general? Discuss briefly.