The NGO Project: CTCL 50 State Wrap Up Analysis
Voter Influence Peddling
This is the last in a series of articles on all 50 states that attempted to ask and answer the same question.
Question: Is CTCL misrepresenting the information on the IRS Form 990 stating that the PURPOSE of the grants were to help support the SAFE administration of public elections during the Covid-19 pandemic?
This series of 50+ articles (linked below in References) started with the intent to write only 2: Georgia and Michigan. Then it evolved to look at all of the swing states. Finally, as some modest amount of interest grew in the topic from the readers, I expanded it. When I hit 22 states left to analyze, I decided to complete the entire country. This has taken almost 2 months to complete.
With all the important priorities out there to #FIX2020, I wonder if it is really worth the effort, looking back at the past…..
But, along the way #2000MULES was released so that was encouraging factor in deciding to finish this given the impact NGO’s have on our elections. This applies to D’s and R’s by the way. NGO’s are used for both parties.
The analysis is my own.
While I do the best I can to double and triple check everything, I don’t have a QA department to hand this off to. That is why I have always recommended you try this at home for your own state. The data is all publicly available and I explain my methodology below.
This is also provided as a public service, I have not charged a dime for it.
You can skip Calculation Basis and still understand the Analysis below.
Let’s walk through methodology before looking at the results. The math is simple. You don’t need to understand the math to read this article or necessarily agree with it.
It is a lens to examine data, that is all.
Roll up grant value to the County level first ( this is tedious work) from the revised IRS 990 form issued in January 2022.
D = # Democrat Votes in a year per county in 2016 and 2020
R = # Republican Votes in a year per county in 2016 and 2020
O = # Other Votes in a year per county in 2016 and 2020
T = D + R + O in 2016 and 2020
D/R per county
D/R > 1 indicates more D votes
D/R < 1 indicates more R votes
a = D/R2020 - D/R2016 (for each CTCL County)
b = D/R2020 - D/R2016 (for each NonCTCL County)
+ a or b means a county is trending D
- a or b means a county is trending R
n1 = sum of total votes CTCL counties in a state
n2 = sum of total votes NonCTCL counties in a state
w1 = total vote per county / n1 for each county for CTCL counties
w2 = total vote per county / n2 for each county for NonCTCL counties
2020DIFF Contribution per CTCL County = w1 * a
2020DIFF Contribution per NonCTCL County = w2 * b
aAVG = sum all (w1*a)
bAVG = sum all (w2*b)
2020DIFF = aAVG - bAVG
+ 2020DIFF means D vote harvesting which is only applied to Counties who have [+a] value. I “conservatively” assumed that -2020DIFF counties had no D vote harvesting. You might say this is overly conservative, and you might be right.
Let D' = # of D votes in a county had CTCL grants NOT been made
D' = D - (D * 2020DIFF)
D - D' = 2020 Additional D Votes harvested because of CTCL
Anyone can try this at home with some patience and diligence.
There are many ways to analyze data from ~3,100 counties. I will present three of them.
Approximately 50% of the CTCL grants (~$160MM) were distributed within only 28 counties (less than 1% of counties in the US by count). This is an average of $5.7MM per county.
This had the potential to impact up to 21.5MM votes (~13% of all votes cast in the US) in this 28 county group alone.
To say it a different way, 50% of the grants were spent on 13% of the voters.
The non-weighted average 2020 D/R vote cast ratio was 2.38 in these 28 counties indicating heavy D bias.
$7.41 per cast vote (all parties) was spent in these 28 counties in total.
The bottom 28 counties on the other hand in terms of grants were valued at only ~$111,000 and had a total vote value of 166,462 votes or $0.67 per vote. The 2020 D/R ratio was 0.398. This means these are heavy R counties.
How fair is that?
This was an attempt to look at where, potentially, the most corruption and influence occurred based on 3 parameters.
CTCL money was distributed somewhere in a county. This is important because in my analysis, I rolled up all contributions to the county entity even though in some cases, the grants were made to the municipality.
The $/Cast Vote (all parties) was greater than $5. I simply picked that number. The range for all counties was $0.03 to $161.
The County 2020DIFF was greater than 5% or .050 for CTCL counties. This means that in 2020, there were 5% more D’s voting in the county. This says nothing about if the county was won by a D or an R, it is simply a ratio indicating a shift.
If your County name made it on all three lists above, it will be listed below. I consider these hot spots for CTCL vote harvesting. Simple right? There are 55 of these counties.
The states represented on the list include: AL (1), AZ (2), FL (1), GA (13!), IA (2), KS (1), KY (7), ME (3), MI (1), MO (3), MS (3), MT (4), NC (2), NJ (2), PA (3), SC (2), SD (2), TX (1) and WI (2).
If your state and county are on this list above, you have problems. GA, you have more serious problems than any other state by far. (I did not ignore smaller counties with lower vote totals if they fit the criteria.)
In a small handful of cases, the D gains are actually 0 and there is a reason for that based on how I structured the model to be very “conservative”. Those details are explained in the state articles, not here.
The total CTCL grants in these 55 counties is ~96MM and the total votes harvested based on this model is ~522,000 votes.
This means that most astonishingly, the $/Harvested Vote factor overall is 96MM/522,000 = $183/vote!
The last observation is very simple. What are the counites that based on my model, had greater than 20,000 D votes harvested due to CTCL. ~60MM was spent on these counties to harvest 3MM votes, that is $20/harvested vote in this group.
Here is an excerpt form the CTCL IRS 990 for Washington DC.
Given all of the above and the 50+ articles I have written, if you believe these grants were used “To support the safe administration of public elections during the Covid-19 pandemic”, as stated on the IRS 990, please unsubscribe. I can’t help you.
For everyone else, I would recommend you read the article on your state linked below and take action by reviewing the appropriate County or City budgets to trace the how the grants where used.
CTCL IRS Form 990 (revised form from Jan 2022 used)
Telegram - https://t.me/electiondataanalyzer
Truth - @ElectionDataAnalyzer
The math here is simple, try this on your own. It is a model to look for trends, not an exact science.