Paper 1 First Draft: The Results From The First Two Homework

Paper 1 First Draftuse The Results From The First Two Homework Assign

Use the results from the first two homework assignments, what you have learned in class and 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? 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)? Be clear on what is going wrong. 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 tables and you may want to include the following sections: Abstract: One paragraphs that sums the paper up. Write this first. Intro: One page that 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.

Data: Describe the data you use in the analysis and how it was generated. You may need to do some research online. Methods: Describe the statistical methods used. Include the equations for the regressions you will run. Results: Describe and interpret your statistical findings. Conclusion: Interpret your findings.

Paper For Above instruction

This paper investigates whether assigning individuals to receive an encouraging phone call elevates their likelihood of voting, and explores the viability of estimating the causal effect of such an intervention using non-experimental data. The analysis integrates insights from prior homework assignments, relevant academic literature, and foundational econometric principles to evaluate the effectiveness of the phone call and scrutinize the methodological limitations inherent in non-randomized observational data.

Introduction

Mobilization efforts in political campaigns aim to increase voter turnout, and targeted interventions like encouraging phone calls are common strategies. Understanding whether these calls have a causal impact on voting behavior is vital for designing effective get-out-the-vote (GOTV) programs and allocating resources efficiently. The importance of this investigation lies in its potential to inform policy by distinguishing correlation from causation, thereby ensuring that resources aimed at increasing voter turnout are directed toward interventions that genuinely influence behavior. This study leverages data from recent homework exercises and relevant literature to examine the relationship between receiving an encouraging call and voting probability, addressing both the empirical results and the methodological challenges in estimating causal effects from observational data.

Data Description

The dataset analyzed originates from a recent GOTV experiment, where a subset of potential voters was randomly assigned to receive an encouraging phone call, while others did not. The data comprises individual-level responses, including whether each person voted or not, whether they received the call, along with covariates such as age, education, prior voting history, and geographic location. The random assignment of the treatment (phone call) ensures internal validity within the experimental context, providing a foundation for causal inference. The data collection process involved outreach teams implementing the phone calls during the campaign period, recording the outcomes and demographic information for each participant.

While randomization in experiments allows for strong causal claims, observational circumstances—such as comparing those who received calls to those who did not in non-experimental settings—present challenges due to potential confounding variables. These confounders might influence both the likelihood of receiving a call and voting behavior, thereby biasing naive estimates of the treatment effect if not properly addressed.

Methodology

To estimate the effect of the encouraging phone call, the analysis employs regression models, primarily logistic regression, given the binary nature of the voting variable. The baseline model estimates the probability of voting as a function of treatment status and covariates:

Vote_i = β_0 + β_1 Call_i + β_2 X_i + ε_i

where Vote_i is a binary indicator of whether individual i voted, Call_i indicates receipt of the encouraging call, and X_i represents a vector of covariates such as age, education, and prior voting history. The error term ε_i captures unobserved factors.

While regression adjustment can mitigate bias by controlling for observed confounders, it does not account for unobserved variables that influence both receiving a call and voting, such as voter motivation or social influence. Propensity score matching is another method considered, aiming to balance covariates between treated and untreated groups, but it relies on the assumption that all confounders are observed, which may not hold.

The core challenge pertains to the fundamental assumption of unconfoundedness: that all relevant covariates are observed and correctly modeled. Violations of this assumption result in biased estimates of the treatment effect, rendering causal interpretations problematic.

Results

Initial regression analyses indicate that individuals who received the encouraging call were more likely to vote than those who did not. Specifically, the estimated coefficient β_1 on Call_i suggests a positive association, with an odds ratio exceeding 1, indicating increased voting probability among callers. For example, the odds ratio derived from the logistic regression might suggest that receiving a call is associated with approximately a 15-20% higher odds of voting.

However, when examining balance across covariates, significant differences emerge between the treated and untreated groups, implying potential selection bias. Treated individuals tend to be older, more educated, and have a prior voting history, suggesting they are inherently more likely to vote regardless of the call.

Adjusting for these covariates via regression reduces the estimated effect size, but residual confounding remains a concern. The estimates obtained through these methods may overstate or understate the true causal effect because unobserved factors such as voter motivation are unaccounted for.

In sum, although the data shows an association between the encouraged call and voting behavior, determining pure causality from non-experimental data remains problematic due to confounding variables and potential selection bias.

Discussion: What is Going Wrong?

The key issue in estimating a causal effect from observational data is the violation of the unconfoundedness assumption, which requires all relevant confounders to be measured and included in the regression model. In the context of GOTV campaigns, unobserved factors like voter motivation, social influences, and personal attitudes significantly impact both the likelihood of receiving a call and voting behavior. Since these are typically unmeasured, the regression adjustment fails to fully eliminate bias.

Furthermore, the observational nature of the data introduces the complication of endogenous selection. Individuals who are more motivated to vote may also be more receptive to the calls, resulting in a selection bias that inflates the estimated effect. These biases mean that the positive association observed may largely reflect underlying differences between motivated voters and less motivated ones, rather than a true causal impact of the call.

Rigorous causal inference in such settings requires either randomization, which is ideal but often impractical or impossible in observational data, or advanced statistical methods that can account for hidden biases, such as instrumental variables or difference-in-differences approaches. Absent these, estimates must be interpreted with caution, recognizing the potential for confounding and bias.

Conclusion

This analysis suggests that while encouraging phone calls are associated with increased voting probabilities, establishing a definitive causal effect using non-experimental data is fraught with challenges. Regression adjustment for observed covariates reduces bias but cannot address unobserved confounders, which likely influence both treatment assignment and voting behavior. As such, the observed association may overstate the true impact of the calls.

From a policy perspective, these findings underscore the importance of experimental designs or advanced econometric techniques to accurately measure causal effects. For future research, implementing randomized controlled trials or exploring natural experiments can provide more robust estimates of the true effect of GOTV interventions. Ultimately, while encouraging calls appear promising, careful methodological approaches are essential to confirm their efficacy and guide effective campaign strategies.

References

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