Question

The variable smokes is a binary variable equal to one if a person smokes and zero otherwise. Using the data in SMOKE, we estimate a linear probability model for smokes: smokes = 0.656- 0.069log(cigpric)+ 0.012log(income)- 0.029educ+ 0.020age- 0.00026age? (0.855) (0.204) (0.026) (0.006) (0.006) (0.00006) -0.101restaurn- 0.026white (0.039) (0.052) n=807, R2=0.062 where income= annual income. cigpric=the per-pack price of cigarettes (in cents). educ=years of schooling. age=age measured in years. restaurn=a binary indicator equal to 1 if the person resides in a state with restaurant smoking restrictions, zero otherwise. white = a binary indicator equal to 1 if the respondent is white, zero otherwise. Holding other factors fixed. if education increases by four years, what happens to the estimated probability of smoking? (1 point) At what point does another year of age reduce the probability of smoking? (2 points) Interpret the coefficient on the binary variable restaurn. (1 point)

Answer 1

If the education increases by 4 years, probability of smoking changes by 4 a(smokes) = 4(-0.029) = -0.116, i.e., the probability of smoking decreases by 0.116. a (educ) = a(smokes) 0.020 – 2(0.00026)age = 0.020 – 0.00052(age), i.e., smokes increase for each a(age) additional year if 0.020 – 0.00052(age) > 0 or 0.020 > 0.00052(age) 0.020 or 0.00052 > age or 38.46 > age or age <38.46 (up to 2 decimal points) => After year 38.46 or 39, the probability of smoking decreases with additional year of age. If the state has smoking restrictions in restaurant, then the probability of smoking decreases by 0.101.