Economics Paper 10 Pages(Due In 24 Hours)

1

Brandon Lang

Econ 104

Prof. Dobkin

1/31/18

Paper One

Abstract

This paper attempts to analyze the effect of sending eligible voters in Iowa a pre-recorded phone

call message telling them to vote in an upcoming election in 2002 to find if their likelihood of

voting is changed. To study the effects of the call, two approaches are used: one in which the

treatment group is assigned by subjects simply being sent the message with the control group

not be sent it, and the second in which the treatment group is assigned by subjects listening to

the message to completion and the control group not listening to the message. All kinds of data

are pooled together to help isolate the effect of the treatment when producing regression

estimates. Although the listening to completion treatment shows more of an effect on subjects’

likelihood of voting, both methods produce murky results as they are both subject to bias.

Introduction

Data

Methods

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vote02 = B0+B1treat_real+u

Results

Using the treatment variable:

Table 1

untreated treated difference mean sd mean sd b p vote02 0.59 0.49 0.61 0.49 -0.01** (0.01) contact 0.00 0.00 0.46 0.50 -0.46*** (0.00) newreg 0.05 0.21 0.05 0.22 -0.00 (0.57) busy 0.00 0.00 0.03 0.17 -0.03*** (0.00) age 55.80 18.95 55.78 18.82 0.01 (0.94) female 0.56 0.50 0.56 0.50 0.00 (0.35) main voter: turnout in 01

0.73 0.44 0.73 0.44 0.00 (0.67)

vote98 0.57 0.49 0.57 0.49 -0.00 (0.67) county 59.70 30.64 59.55 30.70 0.16 (0.56) Observations 85931 15000 100931

Standard errors in parentheses

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

This table shows the comparison of the means, standard deviations, and p-values of the

differences between the treatment and control groups. Results were successfully randomized, as

is indicated by the means for almost all of the sample characteristics being nearly or completely

identical between the treated and untreated groups, with the only exceptions being “contact”

and “busy” due to the untreated group not having received calls.

The difference in voting rates for the treatment and control groups is .0120234, or an increase

1.2 percentage points in the likelihood of voting. It is statistically significant as the t-stat (2.77) is

higher than the t-critical (1.960) and the p-value is very small (0.006). Even though it is statistically

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significant, the actual practical effect is very small.

Table 2

(1) (2) (3) (4) (5) (6) (7) VARIABLES vote02 vote02 vote02 vote02 vote02 vote02 vote02 treat_real 0.0120**

* 0.012** *

0.013** *

0.012** *

0.012** *

0.012** *

0.012** *

(0.00434 )

(0.004) (0.004) (0.004) (0.004) (0.004) (0.004)

newreg

- 0.315** *

0.105** *

0.160** *

0.173** *

0.160** *

0.160** *

(0.007) (0.007) (0.006) (0.006) (0.007) (0.007)

main voter: turnout in 01

0.582** *

0.443** *

0.442** *

0.400** *

0.400** *

(0.003) (0.003) (0.003) (0.004) (0.004) vote98

0.274** *

0.257** *

0.274** *

0.275** *

(0.003) (0.003) (0.003) (0.003) age

0.001** *

0.002** *

0.002** *

(0.000) (0.000) (0.000) female

- 0.023** *

- 0.023** *

(0.003) (0.003) busy

-0.016 (0.020)

Constant 0.594*** 0.609** *

0.162** *

0.104** *

0.039** *

0.065** *

0.065** *

(0.00167 )

(0.002) (0.003) (0.003) (0.005) (0.005) (0.005)

Observations 100,931 100,931 100,931 100,931 100,931 98,327 98,327 R-squared 0.000 0.019 0.260 0.318 0.321 0.304 0.304

Standard errors in parentheses

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

This table shows the effects of regressing the effect of the treatment on the likelihood of a subject

voting in 2002. Covariates are added progressively to reduce bias and make more accurate

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estimates as to the effect of the treatment variable. Added covariates include whether or not a

subject is a newly registered voter (newreg), whether or not they voted in the 2000 election

(vote00), whether or not they voted in the 1998 election (vote98), the average age of the subjects

(age), whether or not the subject is female (female), and if the subject receiving the message was

busy when called (busy).

The addition of more covariates reduces bias and increases the precision of a model’s prediction,

as the inclusion of more variables that are strong predictors of the outcome reduces dilution in

the regression. This indicates that the treatment effect on the likelihood of voting is understated

without the inclusion of the other covariates.

Using the contact variable:

Table 3

Standard errors in parentheses

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

Table 4

NoContact Contact Difference mean sd mean sd b p vote02 0.59 0.49 0.67 0.47 -0.08*** (0.00) age 55.59 18.93 58.57 18.74 -2.98*** (0.00) female 0.56 0.50 0.58 0.49 -0.02* (0.01) newreg 0.05 0.22 0.04 0.21 0.00 (0.10) Vote98 0.57 0.50 0.61 0.49 -0.04*** (0.00) main voter: turno~01

0.73 0.44 0.77 0.42 -0.04*** (0.00)

Observations 94021 6910 100931

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(1) (2) (3) (4) (5) (6) VARIABLES vote02 vote02 vote02 vote02 vote02 vote02 contact 0.0768*** 0.060*** 0.052*** 0.052*** 0.046*** 0.040***

(0.00611) (0.006) (0.006) (0.006) (0.005) (0.005) age

0.006*** 0.006*** 0.006*** 0.002*** 0.002***

(0.000) (0.000) (0.000) (0.000) (0.000)

female

-0.029*** -0.029*** -0.023*** -0.023*** (0.003) (0.003) (0.003) (0.003)

newreg

-0.221*** -0.047*** 0.159*** (0.007) (0.007) (0.007)

vote98

0.421*** 0.274*** (0.003) (0.003)

main voter: turnout in 01

0.399***

(0.004)

Constant 0.591*** 0.272*** 0.279*** 0.316*** 0.271*** 0.065*** (0.00160) (0.005) (0.005) (0.005) (0.005) (0.005)

Observations 100,931 100,931 98,327 98,327 98,327 98,327 R-squared 0.002 0.050 0.058 0.067 0.219 0.304

Standard errors in parentheses

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

Conclusion

Although It is apparent that the control group in Table 3 will not provide a good counter factual

for the treatment group earnings outcomes as the results in every category are very different in

most categories except for female. This indicates that the observed groups aren’t similar enough

and that bias is likely present. The fact that this is no longer completely randomly assigned but

also has an opt-in element is not accounted for and likely contributes to the different outcomes.

When focusing on the effects of subjects simply receiving the treatment, the means of the

treated and control groups were nearly identical, but this table shows greater differences

between the groups which are unlikely to simply be the result of random chance. A potentially

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major contributing difference may be that the kinds of people who would pick up the phone to

listen to the message to completion are different from those who wouldn’t, with one potential

difference being that those who opt-in could be unemployed. This could mean that subjects who

listen to the message are likely to be either school-aged young people or elderly retired people.

The addition of covariates reduced the bias given that as more variables were added, the upward

bias of contact was diminished. The covariate which reduced the bias the most was age,

decreasing the effect of contact on the outcome by 1.8%. A probable reason for this could be

that, as speculated previously, a significant portion of the treatment group could be retirees. The

mean age of the treatment group being about 58 years old indicates that this is probable. As this

population is likely able to spend more time at home on account of being unemployed, it’s

reasonable to say that they have more free time to be at home to pick up the phone when called

and listen to the message to completion, potentially increasing their likelihood of voting.

Table 1 shows that the treatment and control groups have different means which indicates that

they aren’t similar enough to draw accurate conclusions about the treatment’s effects without

bias. Due to the contact variable being opt-in, the randomness of the experiment was diminished.

Due to the it being indicated that age has a large effect on contact, it is possible that older people

are more likely to vote generally, so even though they may listen to the message to completion,

they may have intended to go out to vote before even receiving it, making the treatment

pointless and giving it an upward bias in its effect on the outcome. The absence of other variables

which may effect subjects’ likelihood of voting in the election very likely causes further upward

bias for the treatment.

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