A look ahead to the 2020 reapportionment (2019 Census estimates)

I wrote a diary last year estimating the results of the 2020 reapportionment using the 2018 Census population estimates. Now that the Christmas holidays are over (and with a bit of procrastination), I decided to reprise that diary with the newly-released 2019 population estimates and some fun new tools I didn’t have last time.

How the estimates work:

Like last year, I used a few different methods to project 2020 state populations using growth rates calculated from the 2010-2019 year-over-year estimates (which you can find here or here if you’re interested):

Equal Weight Arithmetic Average: Assumes each state will grow at the simple mean of its 2010-2019 year-over-year growth. This estimate is pretty conservative; it will help suppress outlier years, but if a state is seeing a genuine trend in growth rates, it will miss that as well as random variability.

Assumes each state will grow at the simple mean of its 2010-2019 year-over-year growth. This estimate is pretty conservative; it will help suppress outlier years, but if a state is seeing a genuine trend in growth rates, it will miss that as well as random variability. Last Year Only: 2019-2020 growth is the same as 2018-2019 growth. Takes more account of recent growth, but is more vulnerable to being wrong if 2018-2019 growth is an outlier or the population estimates themselves are off. (Note: if you’d like to calculate using your own weighting scheme, see here for the spreadsheet I used; you can put in your own weights in the upper-right-hand corner of the Dashboard tab)

2019-2020 growth is the same as 2018-2019 growth. Takes more account of recent growth, but is more vulnerable to being wrong if 2018-2019 growth is an outlier or the population estimates themselves are off. (Note: if you’d like to calculate using your own weighting scheme, see here for the spreadsheet I used; you can put in your own weights in the upper-right-hand corner of the Dashboard tab) Geometric Average: This is technically more accurate for calculating means of percentage change, but in practice here will be pretty similar to the Equal Weight Arithmetic Average.

This is technically more accurate for calculating means of percentage change, but in practice here will be pretty similar to the Equal Weight Arithmetic Average. Linear Trend: This is the first method to take into account genuine trends in growth rates over time (which a few states exhibit: New York, Illinois, and Louisiana, for example, show a fairly solid downward trend in growth rates over the decade, while Idaho is on an upward trend).

This is the first method to take into account genuine trends in growth rates over time (which a few states exhibit: New York, Illinois, and Louisiana, for example, show a fairly solid downward trend in growth rates over the decade, while Idaho is on an upward trend). Monte Carlo Simulation: This method tries to account for uncertainty in future growth rates. I used each state’s past growth rates to randomly generate a 2019-2020 growth rate for that state, which I then used to estimate 2020 population and calculate the reapportionment. Repeating this step 10,000 times gives a pretty good estimate of the range of plausible outcomes and their likelihood (in technical terms, I assumed the year-over-year change in each state’s growth rate was normally distributed with mean and standard deviation of the mean and standard deviation of that state’s year-over-year change in growth rates since 2010)

I then plugged these results into a spreadsheet (which you can play around with here) that reapportions seats based on the population estimate using the Census’ reapportionment algorithm to get change in number of seats.

Now that’s out of the way, let’s get on to the results!

The estimates:

As you might expect this close to the Census, there’s pretty close agreement between the various scenarios:

Likewise, the Monte Carlo estimates suggest only a few seats are still in doubt:

This table shows the estimated likelihood a state has the noted seat change. So, there’s roughly a 65% chance Alabama loses one seat, and a 35% chance it sees no change. Likewise, New York has around a 1/3 chance of losing 2 seats, 2/3 chance of losing 1.

Unfortunately, the program I used to make this table doesn’t seem to allow me to round the likelihoods to avoid false precision, but keep in mind that there is margin of error.

For those who like maps:

This is the most likely scenario. All of the averages produce it and the Monte Carlo simulation gives it a roughly 55% chance.

New York loses 2 seats instead of 1, with its second loss keeping Alabama from losing 1. Probably the second most likely scenario. The Monte Carlo simulation gives it 25%, though none of the deterministic scenarios result in this.

New York again loses 2 but its second keeps Minnesota from losing 1 instead of Alabama. The other candidate for second-most likely. The Linear Trend results in this, the Monte Carlo gives it less than 10%.

Finally, here’s a few results the Monte Carlo gives less than 5%; pretty unlikely, but perhaps worth a mention:

Alabama avoids losing 1 by taking Florida’s second gain.

Alabama avoids losing 1 by taking Montana’s second seat.

Marginal Seats:

Here’s a chart of the marginal seats for each deterministic scenario (producing something like this for the Monte Carlo simulations is probably beyond the capabilities of the software I had access to). In each scenario these were the last 15 seats to be awarded (in green) and the 15 that most narrowly missed out on being awarded (in red).

Changes since last year’s estimates:

In last year’s estimates, MT-02 (usually coming at the expense of CA-53) was one of the most uncertain seats. The two more aggressive models had Montana gaining a seat and California losing one, while the more conservative models had California barely holding on. But from 2018-2019 Montana’s growth held steady, while California hit its lowest growth rate this decade (and, given the state’s history, likely its lowest growth in many decades), and now all four models have Montana gaining one and California losing one, and the Monte Carlo simulation finds both results to be almost certain. I wouldn’t consider MT-02 solid yet – the seat is still one of the last to be awarded, and Montana isn’t growing as fast as it was in the middle of the decade – but it’s a lot more solid than last year.

The other most uncertain seats last year were MN-08 and NY-26. This one hasn’t shifted as much as MT-02 and CA-53 – both Minnesota and New York had a bad 2018 with marked declines in growth (Minnesota went from .72% in 2017-2018 to .60% in 2018-2019 while New York went from -.30% to -.39%) but New York’s decline was around what last year’s model expected while Minnesota’s was considerably worse, so NY-26 now looks overall more likely than not to beat out MN-08 (and if NY-26 doesn’t make it, there’s a good chance it falls to AL-07 instead. Speaking of which:)

Though I listed it as a potential upset last year, AL-07’s prospects were looking pretty bleak. It was close to being awarded, but there were still always two or three seats in line ahead of it. But unlike poor Ohio (in fact, OH-16 rather remarkably stayed in the exact same marginal seat ranking in all 4 scenarios), Alabama did get a small boost in growth over the past year (.27% to .32%), which bumped this seat up to being 436 or 437 in the rankings. It’s certainly not in the clear – Alabama would probably need another burst of growth or declines in some other states to make it over the line – but Alabama keeping its seventh seat is now the most likely deviation from the most likely scenario in the Monte Carlo simulation.

Florida had a notably worse year than Texas last year (though both are still among the fastest-growing states in the country and both are off their mid-decade peak). Both are still quite solid to gain 2 and 3 seats, respectively, but if there is an upset miss among a large, fast-growing Sunbelt state in 2020, it now looks more likely to be FL-29 than TX-39 (but again, both seats look very likely to be awarded).

Overall results:

The seats most clearly still up in the air are NY-26, AL-07, and MN-08, with FL-29, MT-02, and perhaps even TX-39 slightly uncertain as well. Unsurprisingly, the general trend away from purple-to-blue but mostly red-trending Midwest and northeast states towards bluish-to-red but mostly blue-trending southern and western states continues. Red states gain a few electoral votes on net, but that could well be obviated during the 2020s by blue trends in Arizona and Texas, and by 2030 it might be more accurate to say that purple states gained at the expense of both blue and (to a lesser extent) red states.

Here’s how the results break down by my subjective judgment of state leans and trends:

Partisanship: Net Change: States: Safe R 0 or -1 Alabama, Montana, West Virginia Likely R +2 Ohio, Texas Lean R +2 Arizona, North Carolina Tossup 0 Florida, Michigan, Pennsylvania Lean D 0 or +1 Colorado, Minnesota Likely D +1 Oregon Safe D -4 or -5 California, Illinois, New York, Rhode Island

Trend: Net Change: States: Strongly R -4 Michigan, Ohio, Rhode Island, West Virginia Slightly R 0 to 2 Alabama, Florida, Minnesota, Montana, Pennsylvania None 0 or 1 North Carolina, New York, Oregon Slightly D 0 Colorado, Illinois Strongly D +3 Arizona, California, Texas

While these changes would be a boost to Republicans (at least in the short term), they’re not big enough to affect any but very close presidential elections (for example, everyone’s favorite 269-269 split would give Trump around 275 EVs under this configuration). They certainly wouldn’t have flipped any recent elections, though they would, especially in 2000 and 2004, give the Republican candidate shorter and more varied paths to victory.