Why Do Election Results Change After Election Day? the “Blue Shift” in California Elections

Why Do Election Results Change After Election Day? The “Blue Shift” in California Elections Yimeng Li1, Michelle Hyun1, and R. Michael Alvarez1 California Institute of Technology June 23, 2021 1 California Institute of Technology, Division of the Humanities and Social Sciences, 1200 E. California Blvd., Pasadena, CA 91125, USA. Emails: [email protected], [email protected], and [email protected]. Corresponding author: Yimeng Li, California Institute of Technology, Division of the Humanities and Social Sciences, 1200 E. California Blvd., Pasadena, CA 91125, USA. Email: [email protected]. Replication materials for this paper are located at https://rmichaelalvarez.github.io/PRQ-Blue-Shift/ 1 Abstract The counting of votes in contemporary American elections is usually not completed on Election Night. There has been an increasing tendency for vote shares to shift toward Democratic candidates after Election Day in general elections (Foley and Stewart III, 2020), in particular in recent U.S. elections. Leveraging important snapshots of precinct-level election returns and precinct-level demographic and political composition from Orange County, California, we conduct the first full-fledged analysis of the potential drivers of vote share shifts. Utilizing an original large-scale post-election survey and unique snapshots of individual-level administrative records, we also provide the first analysis of the characteristics of voters whose ballots were tallied later versus earlier in the process. Far from being anomalous, our results indicate that the shifts are consistent with underlying precinct voter compositions and the order of precinct and mail ballot processing at the individual level in accordance with election administration practices. We find the same driving forces in North Carolina and Colorado, and discuss the consequences of the “Blue Shift” for public concerns about election integrity as states push policy changes regarding access to voting by mail. Keywords: American elections, “Blue Shift,” voting by mail 2 Introduction On Election night, November 6, 2018, Republican candidate Young Kim thought she had won California’s 39th U.S. House election. She had a lead of three percentage points over her Democratic opponent Gil Cisneros with over 150,000 votes counted. But weeks after Election Day, once all of the ballots in the 39th Congressional race from Orange, Los Angeles, and San Bernardino Counties were verified and tabulated, Kim learned that the outcome had flipped parties and that Cisneros was the winner. Young Kim is not the first candidate to assume that the reported election results on Election Night (or those reported early in the morning of the day after the election) are an accurate indication of the final outcome of the race. She is also not alone in witnessing that the vote shares, and even the House seats, slipped away during days after the election. Nationwide, in the U.S. House elections in 2018, vote shares shifted toward Democratic candidates in 156 congressional districts — with a magnitude of 2% or more in 63 districts — and toward Republican candidates in just 34 seats, as shown in Figure 1.1 Six Democratic candidates ultimately won while trailing the morning after Election Day (five out of 30 toss-up seats labeled by the New York Times on a five-point competitiveness scale), and three more barely lost (by less than 0.1%) after shifts in the vote shares in their favor. As increasing numbers of ballots are tabulated in many jurisdictions, the vote margins in recent elections are often observed to shift towards favoring Democratic candidates, which researchers have called the “Blue Shift” (Foley and Stewart III, 2020). Candidates, like Young Kim, and their supporters, may wonder how an election can flip from one party’s candidate to another party’s candidate if not for voter fraud. Young Kim, and Mimi Walters in California’s 45th congressional district race in Orange County — another candidate witnessing the vote share shifting toward her Democratic opponent — both alleged voter fraud is 3 behind the vote share shifts.2 Walters told supporters in an email that she needed donations to stop Democrats “from overturning the will of the voters.” Kim, meanwhile, said that all the uncounted ballots must “nearly match” the results of those already tallied and “[a]nything falling significantly outside of those percentages could reflect foul play.” Voters who cast ballots to losing candidates are already more likely to believe votes were counted improperly at local and national levels (Sances and Stewart III, 2015), and we believe seeing election results “flip” is likely to exacerbate the problem.3 Without a good understanding of why the “Blue Shift” occurs, stakeholders and voters will raise questions regarding election integrity and subsequently lose confidence in the legitimacy of elections.4 When elections are close, a phenomenon like the electoral “Blue Shift” may lead some to question the integrity of the American election system. In the spotlight are voting by mail and provisional voting, since regular polling place ballots are almost always counted on Election Night. House of Representatives Speaker Paul Ryan commented on California, where voting by mail was prevalent in November 2018, “California just defies logic to me,” and “[w]e were only down 26 seats the night of the election, and three weeks later we lost basically every contested California race. This 5 election system they have, I can’t begin to understand what ballot harvesting is.” As votes began to be tallied during the 2020 Presidential election, shifting reported vote totals in many battleground states led to erroneous claims of voting fraud, unfortunately raising concerns about election integrity. For example, in Pennsylvania, the very early vote totals reported before 10 pm Eastern trended in favor of Biden, but after about 11 pm Eastern when about 40% of the vote had been tallied, Trump began to take the lead.6 From there, Trump’s lead grew steadily, but by the time about 75% of the ballots were tabulated in the morning of November 4, Biden began to close the gap, and Biden eventually won Pennsylvania the state by about 80,000 votes. During this period of shifting vote margins, Trump repeatedly took to social media, implying that these changing vote 4 totals were somehow due to voter fraud, claims that were repeatedly struck down by the courts as having no factual support. Despite the lack of evidence, these allegations received a great deal of attention on social media and in various media outlets, and these episodes may lead to a loss of confidence in the integrity of elections in the United States in the minds of some citizens and voters. As states push policy changes regarding access to voting by mail after the 2020 Presidential election, a thorough analysis of the “Blue Shift” is crucial to seeing the whole picture of the partisan consequences, or the lack thereof, of voting by mail, a point we will return in the discussion section. Despite media coverage and questions raised by stakeholders regarding this phenomenon, shifts in vote shares after Election Day have received scarce scholarly attention. In this paper, we study the phenomenon in depth using unique granular data from Orange County, California — a large, diverse county with 1.5 million registered voters and several closely contested races. We first document the vote share shifts toward Democratic candidates in the county across congressional districts and precincts. We then establish the positive correlation between the magnitude of shifts in vote shares and the volume of mail ballots received close to or on Election Day and provisional ballots, two types of ballots typically counted after Election Day. We test hypotheses regarding the association between the vote share shifts and precinct demographic and political composition. We find, among other results, precincts with a higher proportion of young voters and non-white voters had a larger “Blue Shift.” To understand the patterns we observe across precincts, we test our hypotheses about the association between voter characteristics and casting types of ballots that are counted later in the process using survey data and individual-level administrative records. Our results indicate that voters who are young, non-white, not registered with the two major parties, or are voting for the first time are more likely to cast such ballots. Finally, to explore the generalizability of our findings to other counties and states, we examine data from North Carolina and Colorado. Results from the analyses strongly suggest the presence of the same underlying drivers. 5 We make two major contributions. First, cross-state analyses in previous studies feature only a small number of observations and covariates and suffer from confounders such as election laws that are left unaccounted for. Leveraging snapshots of precinct-level election returns as ballots were tallied and information on precinct-level demographics and registration characteristics, we conduct the first full- fledged precinct-level analysis that includes more than a thousand precincts in a large and diverse county. As a result, our analysis has an order of magnitude greater statistical power in testing our hypotheses relative to past studies, while keeping any variables concerning election administration practices constant. Second, utilizing snapshots of individual-level administrative records, we provide an individual-level analysis of the characteristics of voters whose

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