# Linear regression model for log wages on education

Estimate a linear regression model for log wages on education, experience, and experience squared. Report regression coefficients and standard errors. Also report the R2and the estimate of the standard deviation of the random error.Predict the effect on average log earnings of increasing everybody’s education level byone year.Hint: If the regression model islog(wage)i = β0 + β1 × educi + β2 × experi + β3 × exper2i + εi.then the effect of increasing education level of individual i by one year isθi = β1 − β2 − β3 · (2 · experi − 1)because one year additional education implies one year less work experience. The average affect is the average of this.After you defined this, compare the task to that in lecture 5 where we consider thepartial effect in a quadratic model. This is not necessary to solve this problem, butjust a reminder to re-check the answer after you have studied lecture 5.Can you obtain the above effect by running a regression with a redefined set of covariates? How? Hint: redefined means that the new covariates are functions of theregressors in the regression model of the first part of this assignment.Assume that the error term in the regression has a normal distribution. Predict theeffect on the average level of earnings of the following policy: increase the level ofeducation for those who currently have education below 12 years of education to 12,and leave the level of education for others unchanged. Hint: Use the formula for themean of the lognormal distribution.

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Linear regression model for log wages on education 