LT6: IV2 Sam Marden

Question 1 & 2 We estimate the following demand equation ln(packpc) = b 0 + b 1 ln(avgprs) +u What do we require for a consistent estimate of b 1 ? What is the biggest problem we are likely to face? What is the likely direction of bias? Why might Sales Tax be a suitable instrument for ln(avgprs)? Why might it not?

Question 1 & 2 We estimate the following demand equation ln(packpc) = b 0 + b 1 ln(avgprs) +u What do we require for a consistent estimate of b 1 ? Standard asummption: cov(ln(avgprs), u)=0 What is the biggest problem we are likely to face? What is the likely direction of bias? Simultaneity: price and quantity are jointly determined. Other biases exist. Why might Sales Tax be a suitable instrument for ln(avgprs)? Why might it not? It’s clearly relevant. Exogeneity? Culture? Cross-price elasticities. Sales tax in pence usually depends on pre-tax price etc.

Question 3 First Stage: What’s the interpretation? Why do I use the absolute value of sales tax? Linear regression Number of obs = 528 F( 1, 526) = Prob > F = R-squared = Root MSE = | Robust lnravgprs | Coef. Std. Err. t P>|t| [95% Conf. Interval] rstax | _cons |

. sum salestax,d salestax Percentiles Smallest 1% 0 0 5% % 0 0 Obs % Sum of Wgt % Mean Largest Std. Dev % % Variance % Skewness % Kurtosis Ln(0)=????

Question 4 & 5 (1)(2)(3)(4) VARIABLESlnpackpclnravgprslnpackpc lnravgprs-1.213*** *** (0.195) (0.319) rstax *** ( ) lnravgprshat *** (0.334) Constant10.34***4.617***9.720*** (0.935)(0.0289)(1.597)(1.528) Observations48 R-squared Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 What is the price elasticity of demand for cigarettes? What is the implications of the different estimates for public policy? What do you notice about the estimates obtained manually copared to the estimates obtained by ivregress? What do the difference in IV and OLS results tell us?

Question 6 Why should we include log per capita income? What might it have to do with the exogeneity assumption. On seeing the results what does it have to do with the exogeneity assumption? Instrumental variables (2SLS) regression Number of obs = 48 F( 2, 45) = 8.19 Prob > F = R-squared = Root MSE = | Robust lnpackpc | Coef. Std. Err. t P>|t| [95% Conf. Interval] lnravgprs | lnrincomepc | _cons | Instrumented: lnravgprs Instruments: lnrincomepc lnravgprshat

Question 7 Maybe there are unobserved variables driving the sales tax and smoking? How could panel data help?

Question 7 Maybe there are unobserved variables driving the sales tax and smoking? How could panel data help? Some of you estimated this in first differences instead of with FE and obtained different results; what’s up with that? Number of obs = 96 F( 2, 46) = Prob > F = Total (centered) SS = Centered R2 = Total (uncentered) SS = Uncentered R2 = Residual SS = Root MSE = | Robust lnpackpc | Coef. Std. Err. z P>|z| [95% Conf. Interval] lnravgprs | lnrincomepc | Underidentification test (Kleibergen-Paap rk LM statistic): Chi-sq(1) P-val =

Question 8 Would excise duty on cigarettes likely be more or less exogenous than general sales taxes?

Question 8 Would excise duty on cigarettes likely be more or less exogenous than general sales taxes? -‘More’ exogenous, because levied per-pack so dollar value of exices taxes not effected by price -‘Less’ exogenous, because more specific to cigarettes (although sales taxes often different rates for cigs) so more likely to be driven by tastes/culture/politics

LATE, ITT and TOT LATE: Local average treatment effect When we do IV we are exploiting variation in x that is caused by the instrument. This means we discover the effect of x on people whose x’s were changed – E.g. if x is education, and our instrument is living near a four year college then there are 1.People who would go to college whether they lived close to one or not 2.People who won’t go to college regardless 3.People who would only go to college if they live near one. – It is the effect of education for the third group that we discover with IV.

LATE, ITT and TOT When we run an experiment we typically randomise treatment and control groups. However, conditional on being in the treatment group, sometimes only a subset of people will actually take up the treatment. For instance suppose the treatment is random assignation of microfinance organisations to villages. Only some of the people will join the microfinance groups. This means we can talk about two effects: ITT (Intention to Treat): this is the reduced form effect of being in the treatment group. I.e. it is y T -y C TOT (Treatment on the Treated): this is the effect of the treatment on those who actually took up the treatment. – it is the LATE using assignment to treatment as an instrument