Question: The file slid2009.csv is based on the 2009 Survey of Labour and Income Dynamics, implemented by Statistics Canada. The SLID is a representative sample of Canadian households. The file includes data on individuals living in 1-person households, between the ages of 25 and 34 , who are not currently enrolled in an educational institution. The variables in the data set are age, sex, wages_salaries, which is total annual income from wages and salaries in dollars, years_schooling, which is the total number of years that the person spent in education, and years_experience, which is the total number of years of full-time equivalent job experience.

1. Estimate a regression of wages on a female dummy variable (Hint: you will need to create this dummy variable). Interpret the results.
2. Estimate a regression of wages on number of years of schooling, number of years of experience, and a female dummy variable. How much does an additional year of schooling increase wages? What about an additional year of job experience?
3. Would you be comfortable interpreting the results above as the causal impact of effect of more education on wages? Why or why not?
4. How much of the variation in wages is explained by number of years of schooling, education, and gender?
5. Imagine that the (true) population regression function is:

Instead you estimate the following regression:

wages \(=\beta_{0}+\beta_{1}\) years_schooling \(+u\)

Under what conditions would you expect to get an unbiased (good) estimate of \(\beta_{1} ?\) Do these conditions hold in the data?

Price: $2.99

Solution: The downloadable solution consists of 3 pages
Deliverable: Word Document