Statistician Reveals How Pennsylvania Democrats Used Fake Voter Registration “Birthdays” to Commit Voter Fraud

 This guest post is the third installment of Revolver News’s ongoing series on election fraud. Revolver News is applying a rigorous, statistically informed approach to investigating fraud in the 2020 United States presidential election. We encourage the reader to consult Part 1 and Part 2 of the series, as well as our summary of another major statistical analysis.

Guest Post by Carl Bell

Identifying Voter Fraud through Suspicious Birthday Distributions in Pennsylvania Voter Registration Data, and the Effect on the 2020 Pennsylvania Presidential Election

Short Summary:

We construct a new metric of potential voter fraud using suspicious distributions of birthdays in Pennsylvania voter registration data. The basic idea is that people picking fake birthdays will make predictable non-random choices, like picking round numbers for days of the month, and not knowing what true birth month distributions look like. 

Under this metric, a number of counties in Pennsylvania have extremely unlikely distributions of voter birthdays. Seven counties representing almost 1.4 million votes total (Northumberland, Delaware, Montgomery, Lawrence, Dauphin, LeHigh, and Luzerne) have suspicious birthdays above the 99.5th percentile of plausible distributions, even when using conservative assumptions about what these distributions should look like.

These suspicious birthdays also matter significantly for election outcomes. While there are suspicious counties that vote Republican overall, in general more suspicious birthdays in a county are strongly associated with a larger Biden vote share, and a higher Biden vote share relative to all Democrat presidential candidates since 2000. More suspicious birthdays are also associated with a higher vote share for Jorgensen relative to Trump (consistent with a fraud scheme aiming to get Biden high but not “too high”, while simultaneously giving as few votes to Trump as possible). 

Our key insight is that someone making up fake birthdays for voter registrations is unlikely to be able to do so in a truly random manner. Instead, we identify several likely hallmarks of fake birthdays:

-They are likely to excessively cluster on round number days of the month (1, 10, 15, 20, 30, 31), since people generally overweight round numbers. 

-They are likely to excessively cluster on January and December for the same reason.

-They are likely to excessively cluster on months of the year which in general have few birthdays in overall demographic data (i.e. fake birthdays will be drawn roughly evenly across months, subject to the round number effect above, while true birthdays tend to cluster more in certain months like July and August, and less in months like February and November).

We call these “suspicious birthdays” — individually any one person can easily have any of the traits above, but having too many overall in a county suggests that fake birthdays have been added to the pool. We take these three measures of suspicious birthdays, and evaluate them against a combination of two types of benchmarks of what might be expected in the absence of fraud. These are designed to ensure that any unusual patterns are not coming from other reasons (e.g. births generally avoiding holidays, or people generally having sex more at certain times of the year):

https://www.revolver.news/2020/12/pennsylvania-election-fraud-exposed-by-suspicious-birthdays/