Automatic Voter Reregistration as a Housewarming Gift: Quantifying Causal Effects on Turnout Using Movers

Abstract

How much can automatic voter registration (AVR) increase turnout? Unlike in many democracies, most American voters face the additional cost of registration, resulting in potential disenfranchisement. Automatic voter registration is naturally expected to promote turnout, but its causal effects have rarely been quantified due to violations of crucial assumptions. I show that a variation of AVR that targets existing registrants as opposed to eligible nonregistrants—termed automatic reregistration (ARR)—increases turnout by 5.8 percentage points. I exploit a natural experiment in a novel administrative dataset; election officials in Orange County, California, notified existing registrants who moved within-county that their residential addresses were automatically updated. The treatment alleviated registrants of reregistration burdens, but only for those who moved before the legal cutoff date, enabling a quasi-random treatment assignment. Contrary to the popular narrative, ARR had no significant effect on the turnout of registered Democrats, but Republicans’ and nonpartisans’ turnout increased by 8.1 and 7.4 percentage points, respectively.

How much can automatic voter registration (AVR) increase turnout? In the United States, voter registration is not automatic, unlike in most democracies. Having to register to vote has been cited as a significant cost of voting that potentially disenfranchises the electorate (Ansolabehere and Konisky 2006; Braconnier, Dormagen, and Pons 2017; Highton 2004; Rosenstone and Wolfinger 1978). Per theories of political participation (Riker and Ordeshook 1968), higher costs would lower voter turnout. Naturally, AVR—defined here as an opt-out registration program—is expected to rid voters of such costs and promote voting access, leading to higher participation rates. This is an educated guess based on the successful adoption of opt-in “motor-voter” registration programs from the 1993 National Voter Registration Act (NVRA; Highton and Wolfinger 1998; Knack 1995). Now, many states are adopting statewide AVR (Merivaki and Smith 2020).

Despite its importance in theories of voting costs and voter enfranchisement—culminating in the For the People Act of 2021—a robust causal estimate of AVR’s influence on turnout in the US is surprisingly lacking. This gap is because common forms of AVR are always implemented at the state or county level, the selection effect of which makes it difficult to estimate purely causal effects. It has been linked to increased turnout (Griffin et al. 2017), but the nonrandomness of interventions jeopardizes counterfactual analyses, whether in cross-sectional or panel data. ¹

I show that a variation of AVR that targets existing registrants as opposed to eligible nonregistrants—termed automatic reregistration (ARR)—increases turnout by 5.8 percentage points. I exploit a natural experiment in a novel administrative dataset; election officials in Orange County, California, notified existing registrants who moved within the county that their residential addresses were automatically updated. This treatment alleviated the burden of having to reregister with new residential addresses, but only for those who moved before the legal cutoff date, enabling a quasi-random assignment of the treatment. I find that ARR significantly boosted the 2018 general turnout. Contrary to the popular narrative, it had no significant effect on Democrats’ turnout, whereas Republicans’ and nonpartisans’ turnout increased, respectively, by 8.1 and 7.4 percentage points.

Though ARR does not apply to nonregistrants as AVR is typically understood, this paper makes important contributions to the literature. First, I provide robust causal estimates of lowering registration burdens on turnout, a valuable observation for theories of voting costs. I use individual-level data and not jurisdiction-level aggregated data. Moreover, the empirical strategy relies on a natural experiment, where the treatment is subject to an exogenous temporal discontinuity, circumventing selection biases. This informs the debate on whether registration indeed constitutes a significant barrier to political participation, where the empirical evidence has been mixed (Martinez and Hill 1999).

Second, I quantify the effect of ARR on voter turnout for the first time, a policy implemented for decades yet never analyzed. In particular, ARR is designed to break down barriers for movers. Moving is a common life event that affects, on average, 10% of all American residents every year (Census Bureau 2022). ² Because changes in residence require reregistration to ensure that voters are assigned to correct political districts, movers face a unique burden of having to register again to maintain their voting status (Highton 2000) as well as disruptions to their habit-inducing environment (Aldrich, Montgomery, and Wood 2011). The results show that ARR is strikingly effective in reenfranchising such registrants, hinting that relief from new registration burdens is a significant factor even for existing registrants. The results also have implications for comparative research. For example, Braconnier et al. (2016) report that in France, where registration is similarly self-initiated, movers are often misregistered and do not turn out.

Finally, this paper is a counterexample to the popular idea that lowering registration costs only benefits Democrats, providing implications for current policy debates; as recently as in 2008, only 48% of registered voters supported AVR (Alvarez et al. 2011), though more recent studies show highly polarized support between Democrats and Republicans (Mann, Gronke, and Adona 2020). Although ARR is not equal to AVR, the root of the partisan debate is the same.

NATIONAL CHANGE-OF-ADDRESS PROCESSING AND AUTOMATIC REREGISTRATION

The NVRA, while mainly about facilitating registration for eligible nonregistrants, also requires states to maintain an accurate, up-to-date voter database. For accurate list maintenance, states can use the permanent change-of-address (COA) requests submitted to the U.S. Postal Service (USPS). Individuals submit such requests so that the USPS can forward their mail to the new address while they make necessary adjustments. The USPS maintains the last 48 months’ requests, called NCOA data, which approximate 160 million COA records with accurate old and new residences and when the individual moved/made the request. These data (1) determine the ARR treatment and (2) are the primary source for this paper.

California’s law (CA Elec Code § 2225) requires the Secretary of State to match the statewide voter file to NCOA data (called NCOA processing). If existing registrants have changed addresses within the state, the Secretary will then notify relevant counties. For voters’ privacy, COA request dates are coarsened to the month of the move instead of the exact date. The Secretary’s office disseminates the data monthly, and each county makes necessary updates to its voter file.

If a county finds that a voter moved within the state, the voter file is automatically updated and a confirmation postcard is sent to the new address. If this update was a mistake, the voter could inform the Registrar using prepaid postage or phone. ³ The NVRA requires that no list maintenance is performed 90 days before Election Day, setting an exogenous cutoff.

This is an automatic reregistration for registrants who move. It clearly conveys to the affected electorate that they have been subject to ARR, ridding movers of the costs of having to reregister to vote. Figure 1 shows the mailing that is sent out to the movers in its original form.

Figure 1. NCOA Mailing of Orange County, California, Front and Back

Most importantly, if the voter does not actively refute having moved, the voter file will reflect the new address. This is true even if voters do not return an acknowledgment of residential changes—without a counteraction, the USPS information is treated as a true move. If the voter is a permanent absentee, mail ballots will be sent to the new address. If the voter has crossed precinct boundaries, the new polling place’s roster will have her name printed and not the old one.

Automatic reregistration is an extraordinarily unique and proactive measure to help movers maintain their registration, but its efficacy has never been measured. Note that Huber et al. (2021) have shown that in Wisconsin, falsely flagging voters as movers depressed turnout, especially for racial minorities. However, the situation is dramatically different in California, where (1) same-day registration and voting are allowed, (2) in-county registrants who were flagged as movers were not removed but kept as active voters, and (3) because these were in-county movers, portable registration still applied (McDonald 2008), ensuring provisional voting opportunities.

DATA AND METHOD

The NCOA-based list maintenance also allows the detection of movers within the state. ⁴ The data are built on 156 daily “snapshots” of more than 1.5 million unique voters in Orange County, California, from April 26 to December 31, 2018 (Kim, Schneider, and Alvarez 2020). Carefully documenting the changing data enables the detection of movers because registration records that are matched to USPS data will be automatically updated. Matching the voter file to USPS records allows for complete verification of a move that is not a correction of wrongly entered data. In total, there were more than 100,000 verified in-county movers between the 2016 and 2018 general elections. ⁵ The dependent variable is the 2018 general turnout.

In Orange County, an NCOA matching and ARR were performed on July 26, 2018, up to the movers who moved before June 15, 2018, as the Secretary of State’s office disseminates NCOA data in the middle of the month. This discontinuity, stemming from the 90-day requirement, creates an interesting quasi-experimental opportunity, as those who have moved in the latter half of June and beyond were not subject to ARR and those who moved in the early half of June were subject to ARR and were notified as such. ⁶ This is a natural experiment, with ARR as the treatment, and I estimate a regression model specified as

(1) $$ {\mathrm{Turnout}}_i\hskip0.35em =\hskip0.35em \alpha +\beta {\mathrm{ARR}}_i+\gamma \mathbf{X}+{\varepsilon}_i, $$

where $ i $ indexes individual registrants, $ \mathbf{X} $ contains covariates for conditional ignorability, $ \mathrm{ARR} $ is the binary treatment variable, and the average treatment effect is $ \beta $ .

The final data consist of 5,585 movers who were registered at a different address by 2016 general but filed a change of address with the USPS in June 2018. The treatment group (3,294 voters) is movers from June 1st to 14th who have not voluntarily updated their registration records until late July. If the voter has voluntarily notified the Registrar so that the voter roll’s address is already up to date, the mailing is not sent. Therefore all others, including those who moved in early June and possibly disclosed it to the Registrar before July, are put to the control group (2,291 voters). ⁷ If anything, this will estimate a lower bound of the treatment, as those who voluntarily inform the Registrar are more likely to vote.

The following covariates are controlled: number of times moved in 24 months (once, twice, or three times), distance from home to polls (Dyck and Gimpel 2005; McNulty, Dowling, and Ariotti 2009), permanent absentee voting status (Southwell and Burchett 2000), age, inferred gender (Blevins and Mullen 2015), inferred race (Imai and Khanna 2016), partisan affiliation, 2016 general turnout history, census block-level median household income of both old and new residences, whether the voter was born abroad (e.g., a naturalized citizen), the congressional district of the new home, and increasing degree of informational cost depending on how voters’ political districts changed. ⁸ Congressional districts were controlled in the model to account for some hotly contested House races, potentially driving higher turnout. The replication data is available at Kim 2022.

I present linear probability model results with a binary treatment specification and robust standard errors for clarity of the coefficient interpretations. The logistic regression results’ average marginal effects are identical to the LPM estimates. Almost all Kolmogorov–Smirnov tests of the pretreatment variables pass, showing a well-balanced dataset—see Table 1. ⁹

Table 1. Covariate Summary Statistics and Kolmogorov–Smirnoff Tests

AUTOMATIC REREGISTRATION INCREASES TURNOUT

Figure 2 shows the regression result in the first column. ¹⁰ Automatic reregistration increases turnout for movers by 5.8 percentage points (95% CI: $ \Big[0.0329 $ , $ 0.0823\Big] $ ). To put this in comparison with nonmovers, 70% of registrants who did not move in the last 24 months voted, whereas 64.0% of the treatment group voted, up 5.9% from 58.1% of the control group.

Figure 2. The Effect of Automatic Reregistration and Placebo Tests

Note: Average turnout: treated group (64.0%), control group (58.1%), and stayers (70.0%).

The estimated treatment effect is very large. To put this in context, take the landmark results of get-out-the-vote (GOTV) mailing analysis in Gerber, Green, and Larimer (2008). Showing households that voting is public information and their own records resulted in a 4.9 percentage-point increase in turnout. Threatening to expose voting records to neighbors resulted in an 8.1 percentage-point increase. Many GOTV devices are insignificant or low in effect sizes.

For robustness, I perform placebo tests for cases that should theoretically yield null results. An intuitive test for sample self-selection is using turnout of previous elections. Because residential changes occurred after the 2016 general election, ARR should not affect the turnout of previous elections. The placebo tests all pass for 2016. ¹¹

So why is the effect so large? First, ARR was an official, prepaid postage mailing from election administrators and not a GOTV mailing from third-party civic organizations. Mann and Bryant (2020) show that even a simple postcard from official election administrators can encourage voter registration and turnout (2 percentage points), without any legal or administrative process changes—an official “nudge.” Malhotra, Michelson, and Valenzuela (2012) also show that although third-party emails made no difference in turnout, emails from official sources increased turnout.

In addition, Orange County in 2018 was highly contentious. One district that had a Republican representative for thirty-five years now elected a Democrat. Voters may have been more responsive to stimuli related to the general election. However, note that the congressional districts of new residences are already controlled for in the model.

One could say that movers are, in general, a more peripheral set of voters, albeit temporarily. Many papers such as Highton (2000) and Hansen (2016) have demonstrated that disruptions of social environments can depress turnout for movers, and Highton and Wolfinger (2001) shows that residential stability slowly increases turnout. But also note that Highton (2000) argues that the major reason that movers have lower turnout is because of the reregistration burden. Therefore, from a policy perspective, this is an ideal setting to test the effect of resolving registration burdens. Considering that the sample is limited to in-county movers, likely less affected by moving than out-of-county or out-of-state movers, the estimated effect is extraordinary.

Finally, one could also note that because the information costs of learning new political contexts are lower for in-county movers, they may show a differentially large treatment effect. If further subset by disruptions to political environments, although the effect is null for those with minimal disruptions (i.e., within-precinct movers), it is also null for those with significant disruptions such as changes in congressional districts (see Online Appendix G). This rules out the possibility that ARR is entirely driven by nudging, but the effect may be smaller for out-of-county or out-of-state movers.

It is true that, unlike motor-voter type AVR, the ARR is not on nonregistrants but rather existing registrants who have been disrupted. For nonregistrants, automatic registration after interacting with the USPS may not boost turnout as much—these are a more peripheral population who have so far not been convinced by other registration drives such as interactions with the Department of Motor Vehicles. But as aforementioned, the effect is underestimated by including voluntary disclosers in the control group. Thus, the true effect size might be even larger so that even for nonregistrants we may find some effects.

SUBGROUP ANALYSES BY PARTY

Here I discuss subgroup analyses by party affiliation (Figure 3). Although the treatment is not blocked by subgroups, as convenience voting measures are often quoted as benefiting registered Democrats, it is still worth performing analyses by party registration.

Figure 3. ARR Effects by Party

Note: Average turnout: treated Democrats (68.8%), control-group Democrats (67.4%), stayer Democrats (72.4%), treated Republicans (66.8%), control-group Republicans (58.6%), stayer Republicans (77.4%), treated other voters (56.8%), control-group other voters (49.1%), stayer other voters (59.3%).

In particular, national AVR reform is a major part of For the People Act of 2021. In this sweeping bill, automatic registration is defined as a “system that registers an individual to vote in elections for Federal office in a State, if eligible, by electronically transferring the information necessary for registration from government agencies to election officials of the State so that, unless the individual affirmatively declines to be registered, the individual will be registered to vote in such elections.” Although not explicitly specified, ARR is also applicable by this definition because the USPS is a government agency.

The results are in Figure 3. ¹² For Democrats, ARR shows no effect statistically different from zero (95% CI: [ $ -0.0226 $ , $ 0.0615 $ ]). The effects are strong for the rest. The point estimate is 8.1 percentage points for Republicans (95% CI: [ $ 0.0376 $ , $ 0.1250 $ ]) and 7.4 percentage points for registrants with third-party or no partisan affiliations (95% CI: [ $ 0.0315 $ , $ 0.1175 $ ]). The placebo tests all pass.

Although it remains to be seen whether we will see similar effects in counties other than this traditional conservative bastion, this is one piece of evidence that ARR, a close cousin of AVR, is not uniformly beneficial to the Democratic party. Indeed, given that the distribution of parties was almost uniform in the dataset—32.4% Democrats and 32.8% Republicans—why the treatment had no effect for Democrats is puzzling.

One possibility is that conditional on being a Democrat, voters choose more to be permanent absentees. In the test (full) dataset, 69.7% (67.0%) of Democrats and 64.2% (65.0%) of Republicans were permanent absentee. So, ARR does indeed have a greater effect on those who are not permanent absentees (8.9 percentage points) compared with those who are (4.1 percentage points), again passing all placebo tests (see Online Appendix E). But although the differential adoption rate of absentee voting is likely a factor, it is still not enough to fully explain the partisan gap.

Another possibility is that base mobilization for Democrats was much stronger—that they voted regardless of the treatment status, stimulated by the Trump presidency. This possibility is more difficult to test but seems unlikely because, overall, Republicans turned out more (76.7%, conditional on registration) than Democrats (72.1%).

A final possibility is that many voters who registered as “No Partisan Preference” options are more Democratic leaning. If only voters with no partisan preferences are considered, the effect estimate is 8.1% (95% CI: [ $ 0.0376 $ , $ 0.1250 $ ]). So although including them could shift the treatment effect upward, it still seems that Republicans benefited more from ARR in Orange County.

DISCUSSION

This paper exploited a natural experiment to robustly test the causal effects of ARR, which, unlike AVR, is not for nonregistrants. However, ARR still applies to an important group of existing registrants facing additional burdens of reregistration after changing residences.

I find that ARR increases turnout by 5.8 percentage points for in-county movers of Orange County, California, where some had the benefit of ARR and some did not. Moreover, the common rhetoric that convenience voting measures disproportionately benefit Democrats turned out to be false in this case, as Democrats had a small and statistically insignificant increase in turnout, whereas both Republicans and third-party/nonpartisans saw considerable boosts.

The results are promising. Although currently unique to California, ARR was designed for higher turnout and better voter list maintenance, and it is already part of the NVRA. The implementation is relatively simple, and it is not inherently favorable toward Democrats—both in nature and effect. From a public policy perspective, it is an encouraging confirmation that variations of AVR can help voters easily take part in the political process.

The results address important literature on political participation and voter disenfranchisement—ARR does increase turnout per predictions of the theory of voting costs. Moreover, the estimated effect size is large. In addition, in line with other literature that debunks common myths about partisan effects of voter participation (Shaw and Petrocik 2020), I show that ARR does not always benefit Democrats more than it does Republicans.

If ARR is to be implemented nationwide, it will still be vital to systematically assess whether it merely increases the turnout of those already participating at higher rates. If the latter, more public discussions will be necessary about how election administration affects representation.