paper （due in 26 hours）

Q1: treatment: 14,870 control: 86,124 received and listened: 6,874

Q2:

Q3. Since the p-value is large enough for us to not reject the hypothesis that the mean of

control group and treatment group are the same for the sample characteristics, the

randomization worked well. The reason is that if the randomization was implemented

correctly, there would be no huge difference in characteristics between two groups.

Q4. The change in probability when the individual goes from not receiving the call to

receiving the call increases 1.1 percentage points and is significant. 1 percent increase in the

voting behavior is large in practical. If you call 100 hundred people, then there will be one

more voter in the selection.

Q5.

Q6. Adding the contact=1 control variable changes the coefficient of treatment effect

dramatically. This shows that being assigned to a treatment group won’t increase the voting

rate but being assigned and answered the call will increase the rate.

Adding other control variables did not change the coefficient much. They only low down the

magnitude. All of this shows that the covariates and being assigned to the treatment group are

correlated to some degree.

mean of control group

mean of treatment group

mean difference p-value

newreg 0.0481399 0.0489576 -0.00082 0.667442

age 55.79974 55.76005 0.039688 0.813659

female 0.5631061 0.5565458 0.00656 0.141801

vote00 0.7337211 0.73154 0.002181 0.578586

vote98 0.5710255 0.5741089 -0.00308 0.482903

Q7. It won’t generate the causal effect. We can see from the last table. When adding nearly all

other factors into the regression model, the treatment effect drops significantly. Individuals

are more likely to vote if they are new registers no matter whether they received an

encouraging call.