The NGO Project Part XXIX: CTCL in CA
Eureka
Introduction
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?
Answer: It appears so given the results of both aggregate, state and county by county analysis as we will see below. It appears this is a quantifiable democrat ballot harvesting operation.
Background
This is Part XXIV in The NGO Project series which examines the role NGOs had in determinative outcomes in the 2020 Presidential Election. In prior articles, I focused on the effect the Center for Technology and Civic Life (CTCL) had on AZ, CO, CT, GA, IA, IL, ME, ME, MI, MN, NC, NH, NJ, NE, NM, NY, OH, PA, RI, TN, TX, UT, VT, VA, WA and WI.
This article will solely focus on CTCL in California (CA).
Calculation Basis
The calculation basis was previously explained in detail here. In this article, I do make one adjustment and that is to calculate the 2020DIFF factor by weighted average rather than arithmetic average.
Analysis
27 of 58 CA counties (47%) received CTCL grants.
Total votes cast in CTCL counties were ~ 11,701,906 (65%) and NonCTCL counties was ~ 5,810,354 (35%). To state it a different way, on a per county basis, CTCL had the opportunity to influence 65% of CA voters.
The total amount of grants to CA was ~ $21,801,120 and the value of individual grants ranged from ~ $5,400 to $8.3MM.
This table includes the top 10 CTCL grants by county. 8 out of 10 of them have -2020DIFF county values. This is unprecedented in my analysis so far.
$19,206,703 of the grants (90%) were focused in the 10 counties above. The $/vote spent by CTCL in these five counties range from $1.57/vote to $2.48/vote (all parties). The vote totals in these 10 counties account for ~58% of the votes in CA. 45% of that was in Los Angeles County.
To state that a different way, 90% of the grants were spent on 58% of the total votes cast in CA. Is that fair if this was all about a Plandemic?
The average 2016 D/R ratio for CTCL Counties was 1.917 (not weighted). The average 2016 D/R ratio for NonCTCL Counties was 1.347 (not weighted). This means that CTCL grants were provided to more D leaning counties. The top 10 counties in terms grants had a average 2016 D/R ratio of 4.317….big time D areas for sure. This is ~3.5x the NonCTCL county average in 2016. More bias in favor of D.
To continue on this track, if you look at all the counties in 2016 that had a D/R ratio of less than one (R leaning counties), there were 25 (43%) counties. In total, they received ~ $1,427,000 in grants in 2020. This is a stingy ~ 6% of the total 2020 CTCL grants in CA. These counties contributed ~1,409,000 votes (all parties) in 2016 which is 8% of the vote total.
To put it a different way, 6% of the 2020 CTCL grants went to counties where 8% of the votes were cast in 2016 in NonCTCL counties.
The top R leaning counties in 2016 that received CTCL money in 2020…..
Do these facts alone confirm or disapprove my thesis that the grants were NOT used for public safety?
2020DIFF Calculated with Weighted Average
For this analysis, I used a slightly different way to calculate the 2020DIFF using a weighted average based on total votes in a county. This is what it looks like.
w = Total County Vote / Total State Vote
a = D/R2020 - D/R2016 (for CTCL Counties)
a' = a * w (per county)
2020DIFF = sum(a'1:a'n)
This method in theory permits a better correlation for D vote harvesting because it is weighted for counties with higher vote totals.
California blew up the model in 2020 (along with Washington by the way).
The 2020DIFF for CTCL counties is an astonishing -0.341 and for NonCTCL counties it is -0.067. This means that the CTCL D vote harvesting factor in CTCL entities is -0.341-(-0.067) = -0.274 or ~ -27%….a negative value.
This means a D vote harvesting cannot be calculated in CA. Why is this? Going back to the table at the top, 8 of 10 of the top CTCL counties trended -2020DIFF meaning more R’s came out for Trump in 2020 than in 2016 as a ratio of D/R. 46% of them are negative! This explains why the model broke.
These are all of the CTCL counties. [edited]
Noteworthy R Stalwarts
These are all of the R stalwarts in terms of -2020DIFF which indicates higher R turnout as a ratio to D between 2016 and 2020.
Look at some of the names on this list….Los Angeles, San Francisco…etc. R voters came out for Trump in 2020.
Conclusion
CTCL issued ~$22MM grants in CA. It was not possible to calculate a D vote harvesting factor for this state. R voters turned out for Trump in 2020 in traditionally D heavy counties.
All Counties
References
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.
Any earlier version of this article I had pasted in the wrong list for CTCL counties. Corrected.
A Zoom conference and/or Live YouTube stream (with Chat Enabled) by-state would be the icing on the cake. It would further cement reality, forcing others to open their eyes to what happen. A video library could have a huge effect nationally especially if it generates television commentary/guest appearance. It also, perhaps more importantly, exposes the left-favoring CTCL organization for who they truly are since they were the ones who had the final say as to how the funding pie portions would be sliced. Outstanding reporting and dedication to United States of America.