Should we trust the voter rolls? Hell no, eight states have more registered voters than people eligible to vote

In the previous post, I presented evidence that more people voted in 2020 than the total number of registered voters. One criticism is that we should not rely on surveys but government data to estimate the total number of registered voters. 

Using this data on state counts of registered voters and this website that calculates the total number of people in a state who are eligible to register, I constructed the table shown below that displays the number of registered voters as a percent of those eligible to vote. 

You can see that eight states have more registered voters than people eligible to vote, clear evidence that the voter rolls are wildly inaccurate. Another ten states have 95-99% of all eligible people registered which seems highly implausible. Does it make sense that 19 out of 20 eligible people are currently registered? Many states have a serious problem.

As we move down the table, the numbers get more and more plausible. Eight out of ten of all eligible people currently registered? Okay, maybe, but only four states are below that cutoff. 

It seems clear that the rolls in many of these states are larded up with outdated registrations, and the Current Population Survey (CPS) is a more trustworthy way to get an estimate of current registrations. And according to my analysis with CPS data, more people voted in 2020 than the total number of registered voters. 

Analyzing the Current Population Survey, more than 100% of registered voters voted this year

Using the Current Population Survey (CPS), there is evidence of widespread 2020 election fraud. The CPS is a monthly survey of around 60,000 U.S. households that is conducted by the Census Bureau. The survey is used by the Bureau of Labor Statistics to estimate the unemployment rate, and the Census uses it to estimate population characteristics in between decennial censuses. In other words, it's a good survey.

The CPS asks people if they are currently registered to vote. Let's use its estimates to calculate the percent of registered voters who voted in presidential elections since 1960, the first year CPS asked a question about voter registration. I'll use the numbers provided here.

Percent of registered voters who voted

1960   107.8

1964     95.1

1968     89.4

1972     79.8

1976     77.7

1980     76.5

1984     74.6

1988     74.5

1992     71.2

1996     65.9

2000     67.5

2004     70.0

2008     89.8

2012     84.3

2016     86.8

While most years seem perfectly reasonable, notice how the 1960 election--the election stolen from Nixon--yields an impossible number. 95.1% voting in 1964 seems fishy, too. Election experts say that over 90% is a red flag for fraud. 

CPS has not provided 2020 numbers yet. The most recent is 2018, and it estimated 153.1 million registered voters with a margin of error at about 750,000.  If we average increases in registered voters since 1980. that is 1,027,000 per year, or 2,054,000 for two years. Perhaps 2018-2020 saw a much better than average increase. Let's be generous and say 5 million new people got registered during that time. That puts us at 158.1 million registered in 2020. The problem is, 158.4 million voted for president in 2020 according to Ballotpedia. That gives us an estimate of 100.2% of registered voters voting for president in 2020. Impossible.

A response to this is that we can simply use state counts of registered voters, and the World Population Review totals those to be 214 million. The problem with using that approach is that voter rolls are notoriously larded up with outdated or improper registrations: dead people (who ever calls the officials to let them know Granny just died), people who have moved away, etc. There are many sources of error. For example, recently in California, non-citizens were being registered to vote when they got their driver's licenses. Government seems curiously lax on cleaning up voter rolls. 

The CPS is considered a gold standard of surveys, and you shouldn't have undercounts due to people legitimately being registered but being shy about admitting it. If anything, it might be like responses about voting where people want to look good and say they voted when they did not. Registering to vote is what good citizens do.  

The 1960-2016 CPS results shown above seem valid with the clear exception of 1960, the first year the CPS asked about voter registration, and the year when an election was stolen. I'm sure methodological refinements have been made over the past 60 years. 

For 2020 to have the voting rate we saw in 2016--86.8% of those registered voting--there should be 182.5 million people currently registered. The CPS will be off by a little but not almost 25 million voters. All this suggests funny business in 2020. 

UPDATE: I show here that voter rolls are unreliable for this analysis.

Why do people from large families earn less income?

 According to conventional economic theory, growing up in a large family predicts less income as an adult because parents were unable to invest as much in each child. By contrast, genetic theory would predict that that family size would not matter for how much income you earn as an adult; rather, income would be predicted by one's IQ. Let's test these two competing hypotheses using General Social Survey data. 

Respondents were asked how much income they earned in the past year, and they were also given a ten-word vocabulary quiz, which makes a decent proxy for IQ (N = 19,902).

This table shows the estimates for a regression model that includes personal income in constant dollars as the dependent variable and the number of siblings as the predictor. You can see that each additional sibling results in a predicted reduction in one's income of $1,139.  (I believe these are 1986 dollars.)

Looking at this table above, we can see that IQ is positively related to income, and the beta indicates that the relationship is of considerable magnitude. It is predicted that each additional IQ point will result in an additional $423 in income. 

The unstandardized coefficient for number of siblings has dropped from $1,139 down to only $586. In other words, much of the reason why a large family predicts a smaller income is due to the correlation between having many siblings and having a lower IQ. This finding supports, to some extent, the genetic hypothesis. On the other hand, we see that the sibling coefficient is still statistically significant, so even after controlling the influence of IQ, the number of siblings is still negatively correlated with income. The economic hypothesis appears to have something to it. 

Race trumps: Race, not social class, predicts 2016 voting

You often see the argument that the central political divide is social class, not race and ethnicity. If this were true, then we should see it in voting patterns. 

The General Social Survey asked participants who they voted for in 2016, and they also asked about annual income and race. The results below are estimates from a logistic regression model that predicts 2016 voting for President with income and race. The three racial categories are white, black, and other race. Whites are the reference category and so are omitted from the model (sample size = 1,360).

From the p-values (probability) you can see that, once you adjust for race, REALINC (inflation-adjusted income) does not significantly predict who you voted for in 2016. By contrast, blacks and people of other non-white races were less likely than whites to vote for Trump, regardless of one's social class. 

Race trumps.  

Who are the racists? Part 2

The last post was incomplete because we were unable to see the percentages of whites who could be considered racist. The General Social Survey did not label most of the numbers, but let's assume the 6 means slightly racist, 7 is somewhat racist, and 8 is considerably racist. 

You can see that 14.5% of white dropouts are at least somewhat racist. Compared this to those with graduate degrees: only 3%. So, as I wrote in the last post, anti-black racism is concentrated among white dropouts. Cold feelings towards a race of people is not good, but these are people at the bottom of society. They don't have the power to deny someone an education or a good job. They are nobodies, and yet who do elites love to hate more? 

Who are the racists? The General Social Survey knows all.

America's elites seem obsessed with the country's "racists" and seem to treat them like they are the National Socialist German Workers' Party that is going to power at any moment, if they're not already in power with Trump running the show. 

But exactly who are these racists, and why are they so scary?  The General Social Survey asked whites how warmly they feel toward blacks. This is one of the few decent questions asked by social scientists to get at anti-black racism. If you say you feel coldly toward blacks as a group, it's reasonable to call that racist.

The table below shows mean coldness for highest academic degree earned. The coolness scales ranges from "very warm" (1) to "very cool" (9). So whites overall feel warmly toward blacks. But notice how there is a pretty big gap between those with advanced degrees and those who dropped out of high school. The difference is over one-half a standard deviation--a sizeable gap. 

So, coolness toward blacks tends to be concentrated among lowest status whites. Progressives are supposed to have compassion and to seek for understanding of the negative qualities of the people on the bottom rungs of society. But not for racists. What do these dregs of society deserve? They deserve to be expunged.

 

2016-18 GSS data: Atheism and agnosticism are dysgenic

 Using 2018 General Social Survey (GSS) data, I looked at the relationship between confidence in the existence of God and IQ as measured by a vocabulary quiz:

The IQ average for atheists is over 6 points higher than the mean for those who know God exists.  Agnostics, in fact, have the highest average with 103.9. 

Now let's look at fertility by confidence in God for those who are old enough to have completed their family size but are not so old they're really from another generation. Let's choose 45 to 64:

We see that atheists average 1.2 children, while the mean for those who know God exists is 2.1. So believers have families that are 1.8 times bigger than those of atheists.  The believer/agnostic difference is similar.  

We know that general intelligence is highly heritable, so smart people tend to have smart kids. Atheists and agnostics have fewer kids, so, at least under current conditions, skepticism is dysgenic.