California State University, FullertonECON 440ECON440 Smoking.

ECON440 Fall 2020 Binary Dependent Variable Believe it or not, workers used to be able to smoke inside office buildings. Smoking bans were introduced in several areas during the 1990s. In addition to eliminating the ...

ECON440 Fall 2020 Binary Dependent Variable Believe it or not, workers used to be able to smoke inside office buildings. Smoking bans were introduced in several areas during the 1990s. In addition to eliminating the externality of secondhand smoke, supporters of these bans argued that they would encourage smokers to quit by reducing their opportunities to smoke. In this assignment you will estimate the effect of workplace smoking bans on smoking, using data on a sample of 10,000 U.S. indoor workers from 1991 to 1993, available in the file Smoking. The data set contains information on whether individuals were or were not subject to a workplace smoking ban, whether the individuals smoked, and other individual characteristics. a. Estimate the probability of smoking for i. All workers (Table 5) Mean probability of smoking for all workers is 0.242 ii. Workers affected by workplace smoking bans (Table 5) Mean probability of smoking if smkban = 1 is 0.212 iii. Workers not affected by workplace smoking bans (Table 5) Mean probability of smoking if smkban = 0 is 0.2896 b. What is the difference in the probability of smoking between workers affected by a workplace-smoking ban and workers not affected by a workplace-smoking ban? Use a linear probability model to determine whether this difference is statistically significant. Difference of mean probability: 0.212 – 0.2896 = -0.0776 Using a linear regression model (Table 6), we find that the coefficient is -0.077558, and the difference is statistically significant (F = 78.6, p = 0.00) c. Estimate a linear probability model with smoker as the dependent variable and the following regressors: smkban, female, age, age2, hsdrop, hsgrad, colsome, colgrad, black, hispanic. Compare the estimated effect of a smoking ban from this regression with your answer from (b). Suggest a reason, based on the substance of this regression, explaining the change in the estimated effect of a smoking ban between (b) and (c). The coefficient of smkban is -0.0472 (Table 7), which is smaller than the one in (b). The result in (b) is likely to suffer from omitted variable bias, which the multiple-regression model takes into account. For example, college graduates are less likely to smoke than high school dropouts. d. Test the hypothesis that the coefficient on smkban is zero in the population version of the regression in (c) against the alternative that it is nonzero, at the 5% significance level. We can perform a t-test on this hypothesis: H0: βsmkban = 0, H1: βsmkban != 0 t = (-0.0472 – 0)/0.00897 = -5.262 Since |t| > 1.96, we can reject the null hypothesis at the 5% significance level. e. Repeat (c) using a probit model. (Table 8) t = (-.1586 – 0)/0.029 = -5.45 Since |t| > 1.96, we can reject the null hypothesis at the 5% significance level. f. Repeat (c) using a logit model. (Table 9) t = (-0.262 – 0)/0.0495 = -5.293 Since |t| > 1.96, we can reject the null hypothesis at the 5% significance level. g. i. Mr. A is white, non-Hispanic, 20 years old, and a high school dropout. Using the probit regression and assuming that Mr. A is not subject to a workplace-smoking ban, calculate the probability that Mr. A smokes. Carry out the calculation again, assuming that he is subject to a workplace-smoking ban. What is the effect of the smoking ban on the probability of smoking? 1. P(A|smok=1,smkban=0) = Φ (-1.735 + 20*0.0345 – 20^2*0.00047 + 1*1.142) = Φ(0.09) = 0.464 2. P(A|smok=1,smkban=1) = Φ (-1.735 – 1*0.1586 + 20^2*0.0345 – 20*0.00047 + 1*1.142) = Φ (-0.24) = 0. 402 The effect of the smoking ban is 0.464 – 0.402 = 0.062 = 6.2%