Assignment:
The following table describes variables from the height weight file we will use for this problem.
Variable Name Description
wage96 Hourly Wages in 1996
height85 Adult height (in inches) 1985
height81 Adolescent height (in inches) 1981
athlets High school athletics (1=y,0=n)
clubnum Number of club memberships in HS
siblings Number of siblings
age Age in 1996
male Male (1=y,0=n)
(a) Estimate two OLS regression models: one where adult wages are regressed on adult height, the other where adult wages are regressed on adult height and adolescent height. Discuss differences accross the two models. Explain why the coefficient on adult height changed.
(b) Notice that IQ is omitted from the model. Is this a problem? Why or why not:how do you know?
(c) Notice that eye color is omitted from the model. Is this a problem? Why or why not:how do you know?
2. Do cell phones distract drivers and cause accidents? The following problem will use the Cellphone2012homework.dta file.
(a) While we do not know how many people are using their phones while driving, we can find the number of cell phones that are activated in a state. Estimate the SLR model with traffic deaths as the dependent variable and cell phone subscriptions as the independent variable.
Briefly discuss the results. Do you think that this is an accurate measurement?
(b) Add population to the model. What happens to the coefficient on cell phone subscriptions? Why?
(c) Now, add total miles driven to the model. What happens to the coefficient on cell phone subscriptions? Why?
3. What determines how much drivers are fined if they are stopped for speeding? Even though state law sets a formula for tickets based on speed, police officers in practice often deviate from the formula. We will use the data set speedingticketstext.dta for this question. An amount for the fine is only reported in the cases where the officer decided to asses a fine.
(a) Estimate an SLR model in which ticket ammount is a function of age. Is age statistically significant? Is endogeneity possible?
(b) Estimate the model from (a), though now, also control for the miles per hour over the speed limit. Explain what happens to the coefficient on age and why.
(c) Suppose we had only the first thousand observations in the data set. Estimate the model from part (b) and report on what happens to the standard errors and statistics when we have fewer observations.