State of the Nation: A Critical Analysis of State-Funded Resources for Youth Civic Participation
Advisor: Van Dusen
Internship Location: Remote
Onsite Mentor: Dr. Biplav Srivastava, Professor of Computer Science, University of South Carolina
This research project aims to understand how state governments can effectively engage high schoolers and the 18-24 year-old voter demographic in sustained civic engagement through a combination of immersive initiatives and legislation. This work will get to the root of the issues blocking young individuals from involvement, compare and contrast states, and identify successes and pitfalls of current efforts. To do so, this research will first involve data analysis from publicly available datasets and be performed in two stages. To understand the impact of legislation on the voting population, a difference in difference analysis of twelve sample states with varying levels of voter turnout can show the effects of each new policy. Second, to measure interest among the nonvoting population, this project will explore the impact of optional engagement and volunteering opportunities on voluntary petition-signing, public commenting, and other official methods of outreach. To augment the analysis with qualitative insights, a final stage will involve studying existing interviews with local officials, students, and young voters, then conducting new ones. Thus, this project will provide a comprehensive and refined list of improved strategies that can truly engage the youth in civic issues.
Project Blog
Week 1: The Beginning
Hi everyone! My name is Aarohi, and I’m excited to delve into the nuanced topic of civic engagement, how state governments can influence and encourage it, and my first week’s progress on this journey.
Background
From passionately researching and delivering politically-focused speeches to feeling a sense of helplessness watching the news, I noticed a unique combination of interest and inaction that characterizes my experience with civics. For years, I observed this same perspective in my peers and corroborated it with the common statistics that surrounded me; I knew well that the 18 to 24 age demographic of voters historically has had the lowest turnout, with a little over half voting in the 2020 election. As a result, young people are often perceived as apathetic to political concerns; however, CIRCLE at Tufts hopes to discredit these beliefs, indicating through research that an uneven civic education and other barriers to participation are major contributors to these trends.
I realized then that, to navigate this complex issue, I must understand how the government can extend a hand to these age groups and inspire increased participation.
Which brings me to my research question: how can state governments engage young voters (18-24 year-olds) and high schoolers in sustained civic participation through programs and legislation?
Method
To investigate this, I’ll draw from available datasets and analyze them across two stages: the first studying the impact on voter turnout, and the second researching non-voting methods of participation.
Stage 1
In this stage, I’ll assess the impact of Same-Day Voter Registration, where people can register and vote on Election Day; Automatic Voter Registration, where eligible individuals are automatically registered when interacting with government agencies such as the DMV; access to polling sites on college campuses; and the ability to pre-register to vote. To study this, I will conduct a difference-in-differences analysis of states within groups that have similar levels of fluctuation in their voter turnout rate over time. With this similarity, I’ll be able to clearly determine how, as one or more states incorporate a new policy, their voter turnout trends shift or diverge.
Stage 2
Second, to measure interest among the nonvoting population, this project will explore the impact of optional engagement and volunteering opportunities such as being a member of a Youth Advisory Council, or abiding by a mandatory civics education requirement in high school. I'll quantify engagement as the frequency of voluntary petition-signing or submission of public comment--a direct communication to government agencies about proposed regulations or policies.
Stage 3
Finally, to augment the analysis with qualitative data, I will study existing interviews with local officials, students, and young voters, then conduct new ones.
Thus, this project will provide a comprehensive and refined list of improved strategies that can aid states with low engagement and truly include the youth in civic issues.
Week 1
To kick off the first stage, I’ve gathered voter turnout data from KFF, an independent information organization. I compiled the information on voter turnout of 18-24 year olds as a share of their voting population in every national and midterm election from 2014 to 2024. Then, I calculated the coefficient of variation for each state's turnout over this period of time, categorizing them as Low, Medium, and High Volatility states. Within these categories, I aimed to find the best matches by determining the similarities of these states’ trends to each other through calculating their Euclidean Distance to each other. With these two metrics, I organized the most similar states in groups of three. In the next steps, I’ll be assessing any differences in behavior after the introduction of Same-Day Voter Registration.
Week 2: Same-Day Success?
Welcome back everyone!
Last week, we discussed Stage 1 of my methodology, where I assess the impact of legislation on voter turnout. In pursuing this goal, I start by comparing states’ turnout trends to each other before and after one adopts the policy of Same Day Voter Registration (SDVR).
What is Same Day Voter Registration?
While registration to vote is a requirement to participate with a deadline before Election Day, SDVR allows eligible voters to register and cast their ballot on the same day. Whether that’s only during Election Day, the early voting period alone, or both, depends on the state. As of October, 2024, twenty-three states and Washington D.C have adopted this legislation across a several decades-long timeframe; Wyoming, Washington, and Wisconsin implemented it in the 1970s, while Colorado and Idaho did in 2022 and 2023 respectively.
Current Literature
Same Day Voter Registration has historically elicited mixed reactions. Proponents applaud it for accommodating voters who face accessibility barriers, such as transportation or scheduling costs, and those who wish to get involved very close to the election. Others share concerns about administrative disarray and increases in uninformed or rushed decision-making on the voters’ parts.
Studies on the subject point to hopeful results, where SDVR successfully encourages voter participation. In a study by Grumbach and Hill, published in the Chicago Journals, young voters (aged 18-24) suffer from the usual registration rules since they move frequently yet do not interact with government agencies regularly to update registration information. And according to their Differences-in-differences analysis, SDVR increases turnout among young voters by 3.1 to 7.3 percentage points.
Week 2
Entering the week with this context, I aimed to identify states that would implement Same Day Voter Registration within my range of 2014 to 2024, since my voter turnout data encompasses this time frame. Then, I identify three control states whose voter turnout rates fluctuate similarly before SDVR was implemented, and finally, compare the level of change in their voter turnout afterwards.
Eight states had adopted this policy within my required timeline: New York, Virginia, Nevada, New Mexico, Maryland, Michigan, Utah, and Washington.
Using the strategy tested and refined in Week 1, I grouped similar control states by first calculating their coefficient of variation to categorize them as Low, Medium, and High Volatility states. Within these groups, I found the Euclidean distance of their data to each other, identifying the top 3 best matches to the state with SDVR.
Finally, I calculated the Euclidean distance post-enactment of Same Day Voter Registration, to measure divergence by the increase in the distance value.
This increase occurred for nearly every state in comparison to each of the three control states, with the exceptions of: Virginia to its first and third closest control, and New York to its second and third closest controls.
Week 3: A Party Problem
This week, I prepare to replicate my methodology for the next legislative variable: Automatic Voter Registration, a policy with similar intentions to Same-Day Voter Registration, boosting voting and increasing accessibility. But before I seek these results, I aimed to research a major challenge to my findings and how I could correct for it.
Political Parties & The Youth Vote
Does the political party in charge influence efforts to appeal to the youth vote? The Democratic and Republican parties have historically prioritized it differently according to their reach with these age groups; In 2024, around two-thirds of 18-24 year-old voters aligned themselves to the Democratic Party, indicating a clear advantage gained by the youth vote for Democrats (Pew). As a result, great divides have formed on the opinions surrounding early voting or decreasing registration requirements. Where 84% of Democrats and Democratic-leaners agree with the concept of Automatic Voter Registration, only 38% of Republican and Republican-leaning responders agree (Pew, 2021).
So time and time again, we see legislation specifically catering to the youth vote and needs during elections. The Youth Voting Rights Act, sponsored by the Democratic Sen. Warren and Sen. Williams, exemplifies this as it proposes pre-registration and on-campus polling sites among other additions. And as Sen. Warren was quoted claiming Republicans responsible for voter suppression laws that "silence youth voices," the issue of target demographic reflects in policy-making and both parties' agendas.
Correcting This Influence
So how do I ensure that the changes I notice are from the laws themselves and their effects over time, not simply due to the political party in charge? First, the current method of comparing a treatment state to a control group helps, and can be improved by creating a treatment group of similar states as well. The treatment states would all have to adopt the policy simultaneously, offering a more limited view to compare my previous results to, rather than a new method to rely on.
As a secondary analysis after checking for the effects of legislation, I can additionally monitor campaign spending and how it is directed to young voters. In this way I can assess if it differs between parties, and identify periods of constant campaigning to use for my studies (rather than times with more fluctuation).
AVR Data Collection
Once again, I'll be referring to the National Conference of State Legislatures for their data on the Automatic Voter Registration laws in each state. With each state implementing AVR in a much more recent range of the past 2 decades, comparing a treatment group to a control group is significantly more practical.
Thanks for reading!
Week 4: Automatic Advantage
Hi everyone!
It's time to discuss the second policy under review for impact on voter turnout: Automatic Voter Registration. Let's get some context on the subject.
What is Automatic Voter Registration?
We've discussed how registration is often a barrier to voting, and AVR is just another way to streamline the process. It allows eligible voters can become registered when interacting with certain government agencies, like the DMV, and their information will pass on to election officials as necessary. 24 states and Washington D.C. have adopted this legislation in some form. The main two types are front-end opt-out and back-end opt-out.
Front-end opt-out: Whether the voter is asked to "continue" to register or "decline" to register, the choice is presented to them on a screen at the government agency.
Back-end opt-out: While interacting with said agency, the voter will provide all necessary registration information, later receiving a post-transaction mailer that they will be registered unless they respond and decline.
So, two different approaches, with the intention to reduce time costs and inform people on the official steps leading up to voting.
Current Literature
Like Same-Day Voter Registration, many credit this policy with diversifying a state's voting population. In 2019, Oregon governor Katie Brown views the success of AVR as a direct factor in the increase of people of color registered to vote. And when it comes to the youth vote, a working paper from 2024 finds that the prescence of AVR increased voting turnout for those aged 18-24 by 3.2% (Christy, Hankins, et al.).
On the note of bureaucratic efficiency, the practice has been studied to reduce confusion and delays, both due to human error in paper forms, and also the fact that voter registration does not update when a voter moves (a fact many learn too late).
Progress
This week, I replicated my methodology from Week 2 used for SDVR, finding treatment states that adopted AVR in the 2014 to 2024 range and comparing them to their three control states. These control states were determined by their Euclidean distance -- the smaller it is, the more similar their fluctuations in turnout were.
The color-coding below for the pre-AVR distance of the first control state means:
Green: Within 0 and 0.05 -- a very strong match
Yellow: Within 0.05 and 0.1 -- a fairly strong match
Red: Greater than or equal to 0.1 -- a weak match
We know their distance, but not the direction, so to understand if their subsequent difference in path is positive or negative, I take the average turnout of treated state and the 3 control states to find their difference: the net impact value.
An increase in turnout (as shown in green) means their divergence is positive, and a decrease in turnout (red) means their divergence is negative.
Key Takeaways
First, some states have been ruled out for weak matches. As a potential solution, I'll look for turnout data older than 2014 for a better range.
Second, AVR seems to overwhelmingly increase turnout rather than decrease, but it's important to note that the decrease could either mean a real decrease in turnout, or a failure to keep pace with the control group. This should be further studied.
At the moment, I'm re-evaluating Delaware's data to derive the net impact value.
I'm in the process of repeating this last step of finding the direction for SDVR, and will add it to next week's update!
Week 5: Seeing the Big Picture
Hi everyone!
By now, we've reached the end of my planned methodology for analyzing Same-Day and Automatic Voter Registration. And while these results contain some answers, they highlight important considerations and next steps that I'll be investigating while moving forward with the variables I'm testing.
Visualizing Impact
When researching the best ways to graph the true effects of both SDVR and AVR, I found event study plots as the best visualization.
The y-axis represents the gap in voter turnout between treated states (those with the legislation) and control states (those that never implemented it). If the legislation had an effect, the gap would increase, but whether that was in a positive or negative direction depends, as we discussed in greater detail last week.
All states are organized so that their different enactment years align into one relative event time, the red line drawn to divide the graph into "before legislation" and "after legislation" periods.
The shaded area is the confidence interval, a visualization of how precise points are, with narrower areas corresponding to higher precision.
Key Characteristics
In that section before the red line, we're looking for low fluctuation close to the x-axis, because the treatment and control states should be fairly similar at this point.
Ideally, CI should be narrower to indicate that the information is reliable and more certain. Also, if it crosses 0, the data isn't statistically significant.
The line's positive slope means an increase in turnout, so combined with a CI above 0, the legislation can be proven to increase turnout.
AVR
Overall, the flatter line pre-enactment shows that the control and treatment states fluctuated similarly. But the wide CI shows an uncertainty about the results, perhaps due to a wide variety of behaviors in each state. And after the enactment, in the first 2 years and the 7th, the CI is clearly above 0 and the upward slope shows increase in turnout post AVR. The drop approaching year 5 shows some decreasing turnout, but crossing 0 makes the causation uncertain, pointing to other factors affecting turnout.
SDVR
Again, the line is fairly straight with a narrow CI, a good indication of closeness before SDVR. But afterwards, the overlaps of CI where it touches and crosses 0 makes the result not statistically significant. At year 3 and 5 onwards, turnout increases more conclusively.
Takeaways
First, I may identify states that fluctuate in extreme ways and remove them to narrow the CI.
Second, I'll try to factor out the states that have poor control state matches; as discussed last week, some states' distance scores with their "closest match" are too large to really be considered a good control, which could be throwing off the results.
Overall, this points me in the direction of finding more years of turnout data. Working in the timeframe of 2014 to 2024 could be making it harder to find true close matches between states (by using distances derived in a short period), and displaying unreliable trends over just a few years. Finding this older data has proven challenging, but may be necessary.
Week 6: Campus Community
Hey everyone!
What I've learned so far is that, when it comes to the final act of voting at the polls, location is everything. A lack of accessibility or transportation costs can deter too many interested individuals. Young individuals often cite inconvenient location as a barrier to voting (CIRCLE, 2019). And as the Fair Elections Center points out, college campus polling sites are an opportunity to fix this, yet in 2020, 74% of campuses did not have any in-person voting options.
In Week 6, I worked to study how the availability of on-campus polling sites impacted voter turnout. As I began, I realized how different it was from the Same-Day and Automatic Voter Registration legislation I studied beforehand. First, the National Conference of State Legislatures was my primary source for SDVR and AVR data, with well-organized start times for legislation and descriptions of their nuances, whether they changed over the years, and more. Finding exact start years for on-campus poll sites was much harder, and the process drew from multiple sources. Second, instead of comparing whole states to each other, I'm comparing similar colleges (one treatment and one control).
Methodology
While researching, I found fewer real papers on this subject, but to help me narrow down my method, I read what factors make a college most similar to each other. These factors include their type (public, private, community college, etc.), tuition cost, highest degree awarded, and student-faculty ratio. For the most part, I was able to find this data from a source called College Scorecard. Next, I began grouping similar colleges by clustering them, a method based in the same principles of calculating each point's distance to each other.
As of now, I've determined similar colleges in the states without AVR or SDVR at any time (to control for the effects of those legislation). However, adding the data for when each college began on-campus polling was more of a manual process I was only able to do for a few states, and instead of a clear start date, the source gave a two year range (ex. between 2025 and 2026). Now, I'm trying to find a better solution to the issue and be able to draw comparisons for more colleges in more states.
Week 8: - Education is Everything
Hey everyone!
Last week we talked about on-campus polling sites. This week, I wanted to share an update on the next steps of my research and Week 8 featured a pivot and a new plan.
Public Comment & Petition
First, I had planned to study the age groups submitting public comments or signing petitions. Public commenting is an action citizens can take where they respond to new legislation detailed by their state governments, pointing out improvements or concerns that can lead to real action. Petition-signing was something I was looking to measure from sources like online websites like Change.org, more than official government forums.
Unfortunately, most of this data hides the writers' ages, and in the case of location/state data, petition websites may be more hidden than public commenting sites. These two variables were my ways of measuring "interest." Therefore, I'll first still draw the insights I can on petition-signing per state or county to get a sense of civically-active states. Then, for public commenting, one website used often by these agencies called Public Input seems to have more readily available demographic data. In addition to my plans of measuring civics education requirements and youth advisory councils (a future blog post!), this pivot can still reveal the insights I need.
Civics Education
To generate interest early, historical and political knowledge becomes a huge factor, which can be taught at any point in the K-12 curriculum. At school, apart from home or any limits of social media and local news, students often learn the basics of their political system and where they fit. While a course is one way to teach, some states will give a civics test.
This test can be unique or the USCIS test, the citizenship test that immigrants take in order to naturalize as American citizens. When researchers raised doubts that the average American student could pass it, it became an easy benchmark for civic awareness.
But across the nation, there's a lot of variety in state requirements. 10 states require neither a course nor test of any kind, 13 states opt for a half or full year course instead of a test, and some states offer the test but don't require students to pass.
This week, I compiled the data on civics education requirements in different states and charted it. I compared it to the states with the highest to lowest average voter turnout from 2014 to 2024, hoping to point out a correlationary relationship.
Week 9: Second Stages
Hi everyone!
This week, my priority continues to be wrapping up my work with civics education requirements and on-campus polling. Results and progress on this front will come next week two, as I wrap up with analysis that keeps high-schoolers and non-voting methods of participation in mind.
Civics Education
To study civics education, I chose to see if there was a correlation between states' requirements and their average voter turnout over the timeframe of 2014 to 2024. To do this, I first compiled the data from a study by Craiutu and Ngalande titled "State Civics Requirements In 2024." This article detailed the states that participated in each combination of civics course and test (ex. no course and no test, no course and test not required to pass, etc.)
Then, I found the average voter turnout for each state and created a heatmap showing, as a scale which combinations corresponded to the least and highest voter turnout.
On-Campus Polling
The states selected for this study appeared neither in SDVR or AVR lists, to remove possibility of impact from these legislations. As manually entering the data proved to be the only option, I began for the first three states on the list. Using the College Scorecard dataset, which contains comprehensive details on each college in the U.S., I clustered similar colleges by factors like tuition, acceptance rate, and type (private, for-profit, non-profit, etc.).
From these clusters, I paired the most similar colleges within the state, where one college was a treatment university found in the list offering on-campus polling sites, and the other is a control. Finally, I affirmed each college's validity to belong within their own clusters by calculating their silhouette score (colorcoded indicating green as very reliable, yellow as fairly reliable, and red is unreliable).
For the next week, I'll be tackling the second step to this analysis: comparing the number of registered voters in each college in the pair. To do this, I'll be accessing the college's NSLVE reports. While some are easily found online, and colleges who participate are found on a website called All In Challenge, that website often allows colleges to hide their reports from public view. So, for those that I cannot find online, I'll be reaching out to All In representatives for the reports, as suggested on the website.
Week 10: Forward Steps
Hi everyone!
This week, I got to make major improvements to my process for studying on-campus polling sites that will help me wrap up this stage.
As we discussed last week, my two main sources for this section of the project were Voter Friendly Campus and College Scorecard. The first offers a list of the voter-friendly campuses per state that offer a polling site in two-school-year intervals. The states I chose, which never had Same Day or Automatic Voter Registration, were Arizona, Florida, Kansas, Kentucky, Missouri, and North Carolina. College Scorecard offers comprehensive information on each U.S. college, which I used to determine the most similar pairs of control and treatment colleges.
Now, I increased the number of parameters used to judge whether two colleges were similar, now being able to determine the size of colleges (to avoid comparing very big colleges to small ones) and separate community colleges from 4-year institutions.
When making the new pairs, I included both of their individual silhoutte scores (determining how well they belonged in their own college groups) and their match distance (how closely the two colleges were related to each other). This two-method approach ensures I picked the best pairs. From there, I chose the best from each state.
Now the challenge was one that I had discussed last week, finding the voter registration counts within each college. It's the best way to see real effects of polling sites. To do this, I was searching the internet for each college's NSLVE reports, where they disclose information like this.
And while a lot of these pairs can't be counted because their information isn't online, the ones that can will be the specific case studies I'll focus on in my final analysis. It seems like an inevitable limitation of studying on-campus polling sites.
So while finding the data for this wasn't as straightforward as some other variables I studied, I'm glad I can still move forward.
Week 11: Finish Line
Hello everyone!
We've reached the end of this project, and it's been an incredible journey. I wanted to use this blog post to publish some final results and share what worked and what didn't.
First, the final event study plots for AVR and SDR:
While the results cannot be called statistically significant, there is a definite increase in turnout associated with both policies.
Next, the analysis for civic education requirements:
Again, overlapping error bars means the data isn't showing a statistically significant trend, but in certain years, no test and no course seem to be better.
Finally, the comparison of on-campus polling sites was ultimately difficult to complete due to inaccessible data. With most of the NSLVE reports gone from major data sites because of an ongoing investigation, finding reports for each year and each college in these ideal pairs was very difficult. The ones I could find, the pairs would have yielded no real data since those colleges introduced polling sites only a few years ago.
Overall, educational requirements aren't enough to spur interest, and theoretical programs aren't nearly as effective as those that deal with the reality of voting.
I hope you enjoyed learning with me!