The NGO Project Part II: "Grants for Votes" Scheme in AZ, CO, NM, UT
How CTCL cornered the ballot harvesting market in the 4 Corner States
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
In Part I of this series, I introduced the concept of using some simple math to correlate the CTCL grants per county to voting outcomes. For a refresher please go to Part 1.
Calculation Basis
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.
D = # Democrat Votes in a year
R = # Republican Votes in a year
D/R > 1 indicates more D votes
D/R < 1 indicates more R votes
a = D/R2020 - D/R2016 (for CTCL Counties)
b = D/R2020 - D/R2016 (for NonCTCL Counties)
aAVG1 = sum(a1:an)/n
bAVG = sum(b1:bn)/n
2020 DIFF = aAVG - bAVG
+ 2020DIFF means a county is trending D
- 2020DIFF means a county is trending R
Let D' = # of D votes in a county had CTCL grants NOT been made
D' = D - (D * 2020DIFF)
D - D' = 2020 Additional D Votes because of CTCL
Anyone can try this at home with some patience and diligence.
Aggregate Analysis: AZ, CO, NM, UT
CTCL Spend
In total, $8,018,127 was spent (listed here) in all four states and is broken down as follows:
AZ - $5,182,695 (~65%)
CO - $745,754 (~9%)
NM - $1,791,077 (~22%)
UT - $295,601 (4%)
Cast Votes
For all states, 4,358,751 votes (~47%) were cast in counties with CTCL grants. 4,707,867 votes (~53%) were cast in counties with no CTCL grants. This aggregate is weighted heavily by AZ were 3,037,188 of the votes cast in CTCL counties occurred in AZ alone. In the aggregate, AZ votes account for ~ 70% of all votes cast in CTCL counties in these four states.
Based on 2016 D/R Aggregate
What is quite damning in my view is that the average 2016 D/R for counties that received grants in 2020 was 1.24 compared to 0.75 for counties that did not receive CTCL grants. In other words, the counties which “applied” (I use that term loosely) for grants had ~1.7x MORE Democrat voters as a ratio to Republicans in 2016 than Non-CTCL counties. Am I to conclude that Covid-19 was 1.7x more problematic for Democrat counties (no pun intended)?
Based on Average 2020DIFF
The average 2020DIFF for all 28 CTCL counties was 5.95% while the average for the NonCTCL counties was 4.86% which is another indicator of CTCL grants pushing D votes higher. With ~ 9MM votes at stake total in these four states, a 1% D advantage in CTCL counties adds up to theoretically ~ 90,000 votes (180,000 vote swing) using this measure. This will be broken down further below.
County Basis
Only 28 of the 141 counties (20%) in all four states received CTCL grants in 2020. Did the other 80% not apply? Of the 28, 18 (65%) had + 2020DIFF value which means that 65% had increasing Democrat turnout ratios with respect to Republicans in 2020 compared to 2016. This shows that CTCL grants turned out a higher ratio of D votes in 65% of the counties that received grants.
What perhaps is even more interesting is what happened in the remaining 10 counties that had a - 2020DIFF.
Five of them became less D but remained D counties. This means that the D/R2020 remained > 1. The other 5 were R counties that became MORE R. These 5 special counties bucked the trend so I will call them out here and highlight them in yellow below:
Chaves, NM
Eddy, NM
Hidalgo, NM
Graham, AZ
Yuma, AZ
These counties show no D gains despite CTCL spending $304,097 in these counties which was a paltry 3.7% of the total CTCL spend in all four states. Yuma accounted for $180,765 (~60%) of the total. This may lead one to believe that so little money was spent that one could count Chaves, Eddy, Hidalgo and Graham as NonCTCL counties. For the sake of this analysis, I did not ignore it, I simply assigned no D vote gains in these 5 counties.
Aggregate Analysis Summary
In summary, by my estimation, CTCL grants lead to an aggregate additional D votes of ~388,000 between all four states. The maximum potential vote swing of ~ 776,000 votes. We will now look at how those totals were arrived at.
State by State Aggregate Analysis
This is a summary of key metrics for all states.
Let’s unpack a few of these metrics
Col 1 - 2 are obvious.
Col 3 - 4 are sourced from the IRS form 990 and show the number of counties were grants were provided or not provided.
Col 5 is the % of counties receiving CTCL grants in that state.
Col 6 are the votes cast from CTCL counties.
Col 7 are the votes cast in NonCTCL counties.
Col 8 is the % of votes cast in CTCL counties.
Col 9 is the 2020DIFF for each state for CTCL counties (arithmetic average).
Col 10 is the difference in D/R between 2020 and 2016 for all counties.
Col 11 is the estimated D gains per state due to the presence of CTCL.
The most important metrics to elaborate on are columns 5 and 9.
% CTCL Counties (col 5)
There is potentially research to be done here that will likely require a FOIA’s. The range of counties receiving grants in a state is 7-60% across all four states. Why is there such a large spread? Did CTCL target certain counties and deemed others not important (safe from Covid)? Did counties that “applied” for grants have money in their budget that permitted them to apply and the others did not? Did CTCL give money to certain counties so that they could apply? Why did certain counties choose not to apply? How many counties were “sold” on the idea of accepting CTCL grants?Is this really about administering safe elections or something else entirely? What did the CTCL counties report in their budget expenditures versus what was provided by CTCL in the IRS 990?
I can’t address all of these questions here but perhaps with FOIA’s you can.
2020DIFF (col 9)
If I was an investor in “election racketeering”, 2020DIFF would be THE key metric to determine the return on my investment. Did all that money I gave away in the end actually create more D votes compared to R votes. In all four states, the answer is YES.
This might be tough to get your mind wrapped around. 2020DIFF is simply a percentage which indicates a shift in the ratio of D/R between 2020 and 2016 for CTCL counties. 2020DIFF only applies to CTCL counties. It has no impact on NonCTCL counties in this analysis.
For example, in NM the D/R ratio for all CTCL counties increased by ~ 5% on average. In other words, had CTCL not been present one could reasonably expect there to be 5% less D votes and as many as 5% more R votes creating a 10% swing.
In CO astonishingly, the value is a whopping 22%. However in CO, CTCL grants were only distributed to 5 counties (8%). Still, it is a useful insight for CO.
DEM Gains (col 11)
Now that we understand how to calculate 2020DIFF factor for each state overall, which is an average of the individual county 2020DIFF, an estimation of the D votes contributed by the presence of CTCL can be made. The calculation is explained above in the “Calculation Basis” section above. The total D increase by state are:
AZ ~ 267,937
CO ~ 115,908
NM ~ 1,065
UT ~ 3,436
Total ~ 388,345 votes
Keep in mind, these D votes may not have voted at all OR they may have voted R. We will never know. Is it exactly 388,345 votes? Of course not, but that is not the point. This approach is about stepping back and taking a 100,000 foot perspective to look for trends.
Clearly, there is a significant bias in the amount of D vote harvesting that occurred in CTCL counties in 2020. That is the main point.
County Analysis for AZ, CO, NM, UT
Arizona CTCL Counties (9 of 15)
As you can observe in the tables below, CTCL counties do not always have a county + 2020DIFF value. This means that despite CLCT grants, the county trended somewhat more R in the ratio in 2020. These - 2020DIFF counties are lesser in number in the aggregate across every state. Maricopa, Pima and Coconino counties are the top three vote contributors in the state (+2020DIFF). The CTCL grant $ per drop box is also presented in the last column as a point of reference to note the wide range of possibilities in a state. In AZ the $ / dropbox ranged from $794-$59,870. This range is worth investigating further.
Colorado CTCL Counties (5 of 64)
CTCL did not provide grants to many counties in CO but where they did, it appears to have made a HUGE impact. All counties except Yuma had +DIFF2020 values higher than 31%. The $ / dropbox maintained a tighter range here of ~ $1,300-$11,000.
New Mexico CTCL Counties (12 of 33)
I am presenting both CTCL and Non CTCL counties in the table below for New Mexico because it was unique from the perspective of the high percentage of counties who had - 2020DIFF values despite CTCL grants. This would indicate a strong tendency towards R votes that might have been suppressed by the presence of CTCL in that county. However, because the 2020DIFF value is an arithmetic average for the entire state, D gains were simply zero’d out. The $ / dropbox values ranged from $952-$46,891 .
Utah CTCL Counties (2 of 29)
It seems CTCL was not much interested in UT (lucky YOU!) in terms of the number of counties but where they did interject themselves, they made a significant impact with a swing of ~ 90,000 D votes.
Conclusion
In summary
47% of the votes cast in AZ, CO, NM and UT were in CTCL counties
The 2016 D/R ratio for counties who received CTCL grants in 2020 was 1.7x HIGHER than NonCTCL counties indicating a bias to D leaning counties
AZ 2020DIFF was 7.9% (~ 267,937 D votes)
CO 2020DIFF was 22.3% (~ 115,908 D votes)
NM 2020DIFF was 5.4% (~ 1,065 D votes)
UT 2020 DIFF was 3.7% (~ 3,436 D votes)
Total D votes gained in CTCL counties ~ 388,000
References
The author will be looking at weighted average in future analysis.