In this problem set we use data from the NLS young men cohort, collected by David Card and used in his influential paper “Using Geographic Variation in College Prox imity to Estimate the Return to Schooling”. The dataset (Card.rda) is provided to you (import it using the ‘load’ function). Carefully read the paper Card (1995).
. Describe and summarize the data. Some important things to check out are the age distribution and the distribution of key variables.
. Run a regression of log wage on education.
. Add potential experience and it’s square to the regression. How does the coefficient on education change? Why does the coefficient change in that direction?
. Add the “iq” variable to the regression. Notice how the “ed76” coefficient changes (why?).
. Add the kww variable. This is a test score from a non-cognitive survey examining how much young men know about the workforce. Notice how the “ed76” coefficient changes and how the IQ coefficient changes. Briefly explain.
. Use nearc4 to instrument for education in predicting log wages. What are the income returns to education according to this regression (they will be large)?
. Add controls for locational dummies to the IV. The locational dummies are 9 region dummies and an indicator for smsa: reg66l-reg668 smsa66r. Note how the coefficient on education changes as a result of controlling for location. How does adding locational dummies change the assumption necessary for the nearc4 instrument to be valid? Is nearc4 a plausible instrument in this context?
. Industry is a major determinant of wages. Should we control for industry in trying to determine the return to education? Why or why not?
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