paper （due in 26 hours）

 Paper 1: First DraftPrompt

Use the results from the first two assignments, what you have learned in class,and what you have learned from reading the papers and textbook for the course to answer the following questions. 

•Did  assigning  a  person  to  get  an  encouraging  phone  call  increase  their  probability  of voting? 

AND

•Can we  get  an estimate  of the causal effect of  getting a call encouraging  you to vote on your  probability  of  voting  with  non-experimental  data  by  using  regression  to  adjust  for differences between people who got a call and those that didn’t (why or why not)?

[NOTA BENE: This is a paper about whether or not a non-experimental research design can be used to generate credible causal estimates. This is done by comparing and contrasting experimental estimates  and non-experimentalregression  adjustment  estimates. The  context has  to  do  with voting,but the focus of the paper should be empirical designs, notvoting.]

Structure of Paper

You can use the papers on the reading list as a guide for what a paper should look like. You can include your tables in the body of the paper or put them at the end of the paper. Your paper should be 10 pages long without counting tablesand you willwant to include the following sections:

1.Title: Your paper should have an informative title.

2.Abstract: One paragraphs that sums up the key elements of the paper. Write this first.

3.Introduction: 1-1.5pagesthat sums the paper up with more detail than the abstract. Should tell the  reader  why  the  questions  the  paper  answers  are  important  and  cover  data,  econometric methods, results,and conclusion.

4.Data: Describe the data you use in the analysis and how it was generated. You may need to do some research online.

5.Empirical Methods:  Describe  the  statistical  methods  used.  Include  the  equations  for  the regressions  you will run.Describe each part of the regression and what the coefficients will reveal. Describe the assumptions under which the results will generate causal effects.

6.Results: Describe and interpret your statistical findings.Be detailed.Discuss the robustness of your estimates.

7.Conclusion: Interpret your findingsand how they answer the motivating questions. 

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.

(1)

(2)

(3)

(4)

(5)

(6)

(7)

VARIABLES

vote02

vote02
vote02
vote02
vote02
vote02
vote02

1.treat_real

0.0466**

-0.165***

-0.161***

-0.119***

-0.0916***

-0.0434

-0.0580**

(0.0182)

(0.0235)

(0.0238)

(0.0242)

(0.0249)

(0.0280)

(0.0290)

1.contact

0.470***

0.466***

0.379***

0.319***

0.253***

0.280***

(0.0340)

(0.0344)

(0.0351)

(0.0359)

(0.0401)

(0.0414)

newreg

-1.356***

-1.019***

-1.009***

0.716***

0.917***

(0.0323)

(0.0332)

(0.0336)

(0.0383)

(0.0387)

age

0.0218***

0.0244***

0.0184***

0.00848***

(0.000363)

(0.000378)

(0.000422)

(0.000451)

female

-0.112***

-0.133***

-0.138***

(0.0137)

(0.0153)

(0.0159)

1.vote00

2.473***

1.971***

(0.0203)

(0.0214)

1.vote98

1.357***

(0.0173)

Constant

0.381***

0.381***

0.446***

-0.774***

-0.776***

-2.379***

-2.187***

(0.00694)

(0.00694)

(0.00714)

(0.0212)

(0.0225)

(0.0301)

(0.0306)

Observations

100,994

100,994
100,994
100,994

98,310

98,310
98,310

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

mean of not answering	mean of answering	mean difference	p-value
newreg	0.0536518	0.0434972	0.0101546	0.0042193
age	53.52564	58.35918	-4.833542	5.50E-55
female	0.5480307	0.5662047	-0.018174	0.0280706
vote00	0.6898449	0.7800407	-0.090196	2.45E-35
vote98	0.5427714	0.6105615	-0.06779	7.18E-17

Q2. No. Table 1 does not suggest that the control group will provide a good counterfactual for the treatment group’s voting potential outcomes. Because table 1 shows the difference between treatment and comparison group is significant. It does not say anything with the control group.

However, it might prove the counterfactual to some degree if we treat people in treatment group who didn’t receive phone call as control group.

Q3. Yes, it is different. The mean of not answering in table 1 is not much different from the previous table but the mean of answering and difference is significantly different. The p-value is much smaller.

The reason is that answering the phone call will change voters’ behavior. The experiment might be successful.

Q4.

Q5. Adding covariates has an effect on the treatment effect because in increases the significance of getting a convincing variance in the experiment. Besides, on the relationship between the covariates, it indicates an increase in the precision of the estimates. On the outcome, it creates a predictive equation to use in the prediction of the coefficients being estimated. Lastly, on the treatment, the effect is that it is likely to have a positive outcome in general when applied in the right way.

Q6. a) Adding covariates in an RCT design increases the precision of the estimates hence improving on its significance and the regression adjustment design produces highly predictive results.

b) the magnitude and statistical significance of the point estimates for the two designs result in a positive and statistically significant because of the strong covariates added.

c) The two tables differ because of the possibility of there being a bias in the results. More so, in Table, the level of bias is low compared to Table 1.

Q7. Adding covariates to the regression in Table 2 reduced the bias because it caused the estimates to become more similar to the correct casual estimate. I think that it did because of the significance of the added covariates implying that the estimates achieved a better predictive value of the outcome. Additionally, the variable that reduced the bias the most is vote00 because it was increasing the precision of the regression equation and model.

Q8. I think that not all bias in the estimates was eliminated by adding the covariates to the regression because some of the estimates were not statistically significant hence the inability to eliminate bias in general.