|Title||Intensive Spanish-Language GOTV: Voter Mobilization prior to the 2018 Midterm Elections|
|C1 Background and Explanation of Rationale||It is often argued that low turnout among less acculturated (Spanish-dominant) Latinos reflects a vicious circle: political campaigns are often unwilling to spend resources to reach out to people who are very unlikely to vote, and this inattention leaves these voters disengaged. The aim of this study is to demonstrate the extent to which same-language mobilization efforts can break this cycle by promoting substantially higher turnout. We work with a group of Spanish-speaking students to reach out to likely Spanish-speaking voters, in the hopes of increasing their turnout on November 6th 2018.|
|C2 What are the hypotheses to be tested?||
The principal hypothesis is that those randomly targeted for same-language mobilization will vote at higher rates than their control group counterparts.
We also will follow-up on the groups that were randomized in previous elections to see if there are enduring effects.
In order to assess spillover effects, we will assess the voter turnout of others living at targeted addresses relative to the control group.
|C3 How will these hypotheses be tested? *||
Experiments are conducted in the following locations.
We selected another 180 to assign to our treatment group, while the other 1,708 were left to the control group.
The outreach efforts include 18 research assistants who are Spanish-speaking. Each assistant received a random set of 10 individuals selected into the treatment group. Each assistant was instructed to reach out to each individual via text messaging in Spanish. These messages were designed to mobilize these individuals to vote in the November 6th 2018 elections. Research assistants all followed a common script of opening messages and replies to responses from voters.
Data and Outcome Measures
Voter turnout will be assessed by obtaining updated voter files and calculating turnout rates for the randomly assigned treatment and control groups.
Method for Estimating Average Treatment Effects
We will report 95% confidence intervals for the average treatment effect, using a margin of error equal to the estimated standard error multiplied by the appropriate critical value from the t-distribution. This estimate will be generated by regressing turnout on treatment assignment, using the covariates listed below to generate more precise estimates.
To assess robustness, we will also report a simple regression result with only block and treatment indicator, omitting prior turnout statistics. We expect these results to be similar but less precisely estimated due to the exclusion of prognostic covariates. When interpreting the results, we will rely primarily on the covariate-adjusted estimates.
Covariates to use in Regression Adjustment
We plan to include several covariates in our estimation in order to produce a more precise estimate of the treatment effect of Spanish-language GOTV texting efforts. These covariates will be age as well as lagged versions of our dependent variables. We will include as covariates:
• Voter turnout in the 2016, 2017, and 2018 (primary) elections.
|C4 Country||United States|
|C5 Scale (# of Units)||180 (treatment) + 1,708 (control)|
|C6 Was a power analysis conducted prior to data collection?||No|
|C7 Has this research received Insitutional Review Board (IRB) or ethics committee approval?||Yes|
|C8 IRB Number||n/a|
|C9 Date of IRB Approval||10/11/2018|
|C10 Will the intervention be implemented by the researcher or a third party?||Research Assistants/Organization members|
|C11 Did any of the research team receive remuneration from the implementing agency for taking part in this research?||No|
|C12 If relevant, is there an advance agreement with the implementation group that all results can be published?||Yes|
|C13 JEL Classification(s)||not provided by authors|