Registering Re-Entering Citizens to Vote

Last registered on September 02, 2022

Pre-Trial

Trial Information

General Information

Title
Registering Re-Entering Citizens to Vote
RCT ID
AEARCTR-0004574
Initial registration date
August 13, 2019

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
August 13, 2019, 5:09 PM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Last updated
September 02, 2022, 12:10 PM EDT

Last updated is the most recent time when changes to the trial's registration were published.

Locations

Primary Investigator

Affiliation
MIT

Other Primary Investigator(s)

PI Affiliation
Texas A&M
PI Affiliation
Alloy
PI Affiliation
Denver University
PI Affiliation
Yale University
PI Affiliation
University of Texas

Additional Trial Information

Status
Completed
Start date
2019-08-28
End date
2021-02-01
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Millions of people in the US are eligible to vote despite past felony convictions, but their voter participation rates are extraordinarily low. Efforts to register and mobilize this population have foundered due to data limitations. In this pilot project, we will test a method for registering previously-convicted or formerly-incarcerated people to vote. We propose to use administrative data to identify and find contact information for people with past convictions, and then to send them messages encouraging them to register. This pilot study will lay the groundwork for a larger experiment. Ultimately, we plan a large randomized control trial to test the efficacy of such contacts for converting returning citizens into registered voters. This type of evidence is especially important as states consider restoring voting rights to individuals with felony convictions who remain disenfranchised.
External Link(s)

Registration Citation

Citation
Doleac, Jennifer et al. 2022. "Registering Re-Entering Citizens to Vote." AEA RCT Registry. September 02. https://doi.org/10.1257/rct.4574
Former Citation
Doleac, Jennifer et al. 2022. "Registering Re-Entering Citizens to Vote." AEA RCT Registry. September 02. https://www.socialscienceregistry.org/trials/4574/history/154939
Experimental Details

Interventions

Intervention(s)
We will contact previously-convicted people prior to fall municipal elections, offering them information about eligibility to vote and a registration form and stamped envelope to return that form to the board of elections.

We will scale up the previous pilot study for a larger trial prior to the 2020 general election. We use a between-subjects design with five groups: 1) control (uncontacted); 2) contacted with a letter from our partner organization with felony-targeted introductory language, information about eligibility, a registration form and stamped envelope; 3) treatment 2 minus the felony-targeted introductory language; 4) treatment 2 minus the registration form and envelope; 5) treatment 2 plus a stronger encouragement to register that informs recipients of the importance of voting to elect representatives who will shape civil rights and criminal justice policies.

We are also conducting a second trial focusing on individuals who live in the same zip codes as those in our main study, but who administrative records do not indicate have previous felony convictions. We can use the results of this study to compare responses in our population of interest (those with previous convictions) to those in the broader population. This trial will have two arms: 1) control (uncontacted); 2) a letter from a partner or us with information about eligibility, plus a form + stamped envelope. (treatment arm 3 from the main study).

Intervention Start Date
2019-08-28
Intervention End Date
2020-11-03

Primary Outcomes

Primary Outcomes (end points)
Voter registration (appearing on the state's public voter file) and voting in municipal elections where relevant, as well as longer-term voter participation in the next statewide election.

For the scaled up trial, we will collect the following outcome variables after each state’s voter registration deadline and after the 2020 General Election: voter registration and whether an individual casts a ballot in the election.

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We will randomly divide people in a sample of previously-convicted residents of North Carolina into two groups; the "control" group will not be contacted, while the "treatment" group will be sent a letter telling them about the voter registration process and encouraging them to register if eligible.

For the scaled-up study we will use a between-subjects design with five arms, one of which will be an (uncontacted) control group. The study will be fielded in North Carolina and Texas. We will randomize at the individual level with equal probability across the five treatment groups, blocking on state, using the "randomize" function from the "experiment" library in R. The comparison (no-record) group is randomized (individually) with equal probability into treatment and control.


Experimental Design Details
In North Carolina, people who have been convicted of felonies are eligible to vote after they have completed their sentences (including any probation or parole). North Carolina’s Department of Public Safety (DPS) provides publicly available data containing personal information for everyone who has been in NC DPS custody since 1972. We will use these data to identify people who were convicted of a felony and have completed the terms of their sentence (248,472 records as of early 2019). We will then use North Carolina voter file to identify and remove those who are already registered to vote from the data. The resulting dataset will include individuals who have completed the terms of their sentences and are, therefore, eligible to vote, but who are not registered to vote.

In our first pilot study we draw a random sample of 10,000 people from this dataset of individuals who have completed sentences and are not registered to vote. We will then contact a data vendor to find current mailing addresses based on their names and DoB; we will retain everyone matched to an address for assignment to treatment/control. Then, we will remove individuals residing in the 3rd and 9th Congressional districts. We do this because special elections are being held early in September in these districts, and we worry that our mailer could reach people in these districts after the registration deadline for these elections has passed. We will then randomly assign the remaining individuals in the sample (with valid addresses) to treatment or control conditions with equal probability.

Our treatment will consist of a letter describing the eligibility criteria for registering and voting and encouraging people to register and vote, including a blank voter registration form and a postage-paid envelope for returning it to the local elections office. We will send one letter to everyone in the treatment group; we will not contact the control group. After a pre-specified period of time, we will collect our outcome measures. We will observe whether people have registered to vote by collecting another snapshot of the North Carolina voter file and searching for them. We will also plan to collect voter turnout in the next statewide election. For both outcomes, our first analysis will be a simple difference-in-means comparison between the treatment and control groups.

For the scaled up 2020 trial:
We focus on North Carolina and Texas due to the states’ felony disenfranchisement policies, accessible administrative data, and the availability of potential research partners. In North Carolina and Texas, people who have been convicted of felonies are eligible to vote after they have completed their sentences (including any probation or parole). We use publicly available conviction records in both states to identify people with felony convictions who are eligible to vote.

We used the following steps to obtain our sample in North Carolina:
We downloaded datasets from NC DPS on July 19, 2020. We began with the dataset of all offenders (specifically, “Offender profile”; OFNT3AA1.dat; 1,205,971 rows) and then joined the inmate and supervision profile tables. We then dropped records of individuals that met the following criteria: Still incarcerated, On parole, Died in the system, Not convicted of a felony, Still under supervision (parole, probation), Possible Absconders/ escapees / out of state.

We then removed obvious duplicate records using full name and date of birth (many of these were “duplicates” because they lacked first or last names or both), anyone under the age of 70, individuals with no name, and non-US citizens. The final set of 289,772 records represents what we believe to be the pool of individuals who have felony convictions, have completed all terms of their sentence, and who are eligible but have not registered to vote in North Carolina. From this set we removed all observations used in previous pilots of the study. This left us with 136,268 observations, which we sent to our data firm to find mailing addresses. The firm sent back 35,249 observations that had mailing addresses.

We followed a similar procedure in Texas. We requested and received the Conviction Database from the Texas Department of Public Safety. We drop all misdemeanor charges from the data. We remove duplicate observations, including only each individual’s most recent experience with the system, and then focus on those observations for which we can identify an end date to their prison, parole, and/or probation and for which those dates are not at some point in the future. Finally, we remove any individuals who are under 18 years of age or over 100 years of age, look for and drop any remaining duplicates or observations with supervision end dates in the future, and remove any observations for which the last interaction with the Department of Public Safety was a deferral. The final data set of individuals eligible to be in this trial includes 1,429,264 observations. We sent a random set of 1,000,000 of these observations to a voter-data firm. This data firm matched the observations we sent them to the Texas voter file. The dataset they sent back showed that 776,575 from our list were not on the state’s voter file, and, therefore, not registered to vote: these unregistered people were retained for the study. We then dropped from the data any individuals over the age of 70 in order to match our data from North Carolina, which left 625,412 observations.

Finally, we pulled a random sample of 250,000 observations to send to our data firm (along with the North Carolina data) to find mailing addresses. The firm was able to match 163,160 of these observations to addresses and sent us a random sample of 89,751 of those matched addresses. The observations with matched addresses from North Carolina (35,249) combined with the observations with matched addresses from Texas gives us a total sample size of 125,000 for the main study.

Our data firm also supplied us with a list of 37,000 unregistered North Carolina residents for our second study. In this study we will test the effectiveness of treatment three on individuals who live in similar neighborhoods to the people in our main study, but do not appear to have been convicted of or incarcerated for a felony (given the information we have available to us). These records were generated by drawing all non-registered voters known to our data firm from a set of six zip codes in North Carolina: these were the six most common residential zip codes among our main-study sample. Then, the data firm suppressed any records that appeared on our main-study list, to avoid overlap, and provided us with a random sample of 37,000 of the resulting addresses. We then checked the resulting list against our records from previous pilot studies to ensure that no one in the comparison-group sample was known to have been in DPS custody for a felony, and discarded several hundred observations based on name and address similarities to people that had been in our pilot study samples. This process resulted in a comparison-group dataset of 35,708 people that would be assigned to treatment or control.




Randomization Method
Randomization done in office by a computer.
Randomization Unit
individual

For the scaled-up trial: individual, blocking on state

For the 2022 midterm extension: individual, blocking on state; and in Texas, blocking on race and county
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
8,621 people for whom we have addresses in the initial sample.

Across the two trials all conditions and control groups, we expect a total sample size of about 160,703 individuals in the 2020 trial.

Sample size: planned number of observations
same as clusters (individual randomization)
Sample size (or number of clusters) by treatment arms
people will be randomized with equal probability into treatment/control conditions (so n/2).

People will be randomized with equal probability into treatment/control conditions, blocking on state, for the 2020 expansion.

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
With respect to power, we have performed power calculations aimed at testing the sample size needed to identify differences between treatment arms. We assumed a baseline control-group registration rate of 2%, which was the average registration rate across all three of our pilot studies. We believe that in our main study, the sample size of 125,000 people, with 25,000 in control, should allow us to detect a minimum treatment effect of .28 percentage points. Comparing the control to any one of the treatment arms (each of which included 25,000 people) will allow us to detect a minimum treatment effect of .35 percentage points. These minimal detectable effects are substantially smaller than the effects we saw in our pilot studies. In our second study, with a total of 35,708 individuals equally split between treatment and control, we should be able to detect a minimum treatment effect of .41 percentage points.
IRB

Institutional Review Boards (IRBs)

IRB Name
MIT COUHES
IRB Approval Date
2019-07-19
IRB Approval Number
1907914986

Post-Trial

Post Trial Information

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials