Chapter 11 Regression with a Binary Dependent Variable

Transcription

1 Chapter 11 Regression with a Binary Dependent Variable Solutions to Empirical Exercises 1. Smkban (1) (2) (3) Linear Probability 0.078** Linear Probability 0.047** Age ** (0.0018) Age ** ( ) Hsdrop 0.323** (0.019) Hsgrad 0.233** Colsome 0.164** Colgrad 0.045** Black Hispanic 0.105** Female 0.033** Intercept (0.041) F-statistic and p-values on joint hypotheses Education indicators Significant at the 5% * or 1% ** level (0.00) Probit 0.159** (0.029) 0.035** ** ( ) 1.142** (0.072) 0.883** (0.060) 0.677** (0.061) 0.235** (0.065) (0.053) 0.338** (0.048) 0.112** (0.028) 1.735** (0.053) (0.00)

2 Solutions to Empirical Exercises in Chapter (a) Estimated probability of smoking (mean of smoker) ˆp SE ( p ˆ) All Workers No Smoking Ban Smoking Ban (b) From model (1), the difference in we a standard error of The resulting t-statistic is 8.66, so the coefficient is statistically significant. (c) From model (2) the estimated difference is 0.047, smaller than the effect in model (1). Evidently (1) suffers from omitted variable bias in (1). That is, smkban may be correlated with the education/race/gender indicators or with age. For example, workers with a college degree are more likely to work in an office with a smoking ban than high-school dropouts, and college graduates are less likely to smoke than high-school dropouts. (d) The t-statistic is 5.27, so the coefficient is statistically significant at the 1% level. (e) The F-statistic has a p-value of 0.00, so the coefficients are significant. The omitted education status is Masters degree or higher. Thus the coefficients show the increase in probability relative to someone with a postgraduate degree. For example, the coefficient on Colgrad is 0.045, so the probability of smoking for a college graduate is (4.5%) higher than for someone with a postgraduate degree. Similarly, the coefficient on HSdrop is 0.323, so the probability of smoking for a college graduate is (32.3%) higher than for someone with a postgraduate degree. Because the coefficients are all positive and get smaller as educational attainment increases, the probability of smoking falls as educational attainment increases. (f) The coefficient on Age 2 is statistically significant. This suggests a nonlinear relationship between age and the probability of smoking. The figure below shows the estimated probability for a white, non-hispanic male college graduate with no workplace smoking ban.

3 146 Stock/Watson - Introduction to Econometrics - Second Edition 2 (a) See the table above. (b) The t-statistic is 5.47, very similar to the value for the linear probability model. (c) The F-statistic is significant at the 1% level, as in the linear probability model. (d) To calculate the probabilities, take the estimation results from the probit model to calculate z ˆ, and calculate the cumulative standard normal distribution at z ˆ, i.e., Prob( smoke) =Φ ( zˆ ). The probability of Mr. A smoking without the workplace ban is and the probability of smoking with the workplace bans is Therefore the workplace bans would reduce the probability of smoking by (6.3%). (e) To calculate the probabilities, take the estimation results from the probit model to calculate z ˆ, and calculate the cumulative standard normal distribution at z ˆ, i.e., Prob( smoke) =Φ ( zˆ ). The probability of Ms. B smoking without the workplace ban is and the probability of smoking with the workplace ban is Therefore the workplace bans would reduce the probability of smoking by.033 (3.3%). (f) For Mr. A, the probability of smoking without the workplace ban is and the probability of smoking with the workplace ban is Therefore the workplace ban would have a considerable impact on the probability that Mr. A would smoke. For Ms. B, the probability of smoking without the workplace ban is and the probability of smoking with the workplace ban is In both cases the probability of smoking declines by or 4.7%. (Notice that this is given by the coefficient on smkban, 0.047, in the linear probability model.) (g) The linear probability model assumes that the marginal impact of workplace smoking bans on the probability of an individual smoking is not dependent on the other characteristics of the individual. On the other hand, the probit model s predicted marginal impact of workplace smoking bans on the probability of smoking depends on individual characteristics. Therefore, in the linear probability model, the marginal impact of workplace smoking bans is the same for Mr. A and Mr. B, although their profiles would suggest that Mr. A has a higher probability of smoking based on his characteristics. Looking at the probit model s results, the marginal impact of workplace smoking bans on the odds of smoking are different for Mr. A and Ms. B, because their different characteristics are incorporated into the impact of the laws on the probability of smoking. In this sense the probit model is likely more appropriate. Are the impacts of workplace smoking bans large in a real-world sense? Most people might believe the impacts are large. For example, in (d) the reduction on the probability is 6.3%. Applied to a large number of people, this translates into a 6.3% reduction in the number of people smoking. (h) An important concern is two-way causality. Do companies that impose a smoking ban have fewer smokers to begin with? Do smokers seek employment with employers that do not have a smoking ban? Do states with smoking bans already have more or fewer smokers than states without smoking bans?

4 Solutions to Empirical Exercises in Chapter Answers are provided to many of the questions using the linear probability models. You can also answer these questions using a probit or logit model. Answers are based on the following table: Dependent Variable Regressor Insured Insured Insured Healthy Healthy Healthy Any Limitation (1) (2) (3) (4) (5) (6) (7) selfemp 0.128** (0.015) 0.174** 0.210** (0.063) * age 0.010** (0.001) (0.0017) age * ( ) 0.010** ( ) (0.000) (0.000) age selfemp (0.000) deg_ged 0.151** (0.027) 0.151** (0.027) 0.045* (0.020) 0.061* (0.024) deg_hs 0.254** 0.254** 0.099** deg_ba 0.316** (0.017) 0.316** (0.017) 0.122** 0.042** deg_ma 0.335** ** 0.128** (0.015) 0.078** deg_phd 0.366** (0.026) ** (0.025) 0.138** 0.084** (0.027) deg_oth 0.288** (0.020) 0.287** (0.020) 0.115** 0.049** (0.017) familysz 0.017** 0.017** ** race_bl 0.028* 0.028* 0.022* 0.035** race_ot 0.048* (0.023) 0.048** (0.023) (0.015) reg_ne 0.037** 0.037** ** reg_mw 0.053** 0.053** reg_so male 0.037** 0.037** 0.015** (0.005) married 0.136** ** ** Intercept (0.004) 0.299** (0.054) 0.296** (0.054) 0.927** 0.953** (0.031) 0.902** (0.035) (0.044) Significant at the 5% * or 1% ** level.

5 148 Stock/Watson - Introduction to Econometrics - Second Edition (a) Probability of being insured ˆp SE ( p ˆ) All Workers Self Employed Not Self Employed The self-employed are 12.8% less likely to have health insurance. This is a large number. It is statistically significant: from (1) in the table the difference is significant at the 1% level. (b) From specification (2), the result is robust to adding additional control variables. Indeed, after controlling for other factors, the difference increases to 17.4% (c) See specification (2). There is evidence of nonlinearity (Age 2 is significant in the regression). The plot below shows the effect of Age on the probability of being insured for a self-employed white married male with a BA and a family size of four from the northeast. (The profile for others will look the same, although it will be shifted up or down.) The probability if being insured increases with Age over the range years. (d) Specification (3) adds an interaction of Age and selfemp. Its coefficient is not statistically significant, and this suggests that the effect of selfemp does not depend on Age. (Note: this answer is specific to the linear probability model. In the probit model, even without an interaction, the effect of selfemp depends on the level of the probability of being insured, and this probability depends on Age.) (e) This is investigated in specifications (4) (7). The effect of selfemp on health status or Any Limitation is small and not and statistically significant. This result obtains when the regression controls for Age or for a full set of control variables.

6 Solutions to Empirical Exercises in Chapter There are potential problems with including healthy on the right hand side of the model because of adverse selection problems. It is possible that only those less healthy individuals pursue health insurance, perhaps through their employer. This causes a selfselection problem that more healthy individuals might (a) choose to be self-employed or (b) choose not to obtain health insurance. While the evidence suggests that there might not be a strong correlation between health status and self-employment, the adverse selection concerns still exist.