Census Population Estimates 2020-29

[jimrtex]

These are the Rhode Island estimated components of change. Where did the extra people come from?

Year	Net	Births	Deaths	Net	International	Domestic	Migration
2010	1024	2871	2330	-37	1704	-1171	533
2011	-165	10998	9755	-112	4585	-6005	-1420
2012	1064	11006	9315	-103	4507	-5087	-580
2013	667	10873	9631	-139	3938	-4474	-536
2014	951	10726	9697	-166	3607	-3574	33
2015	375	10874	10077	-184	4654	-5027	-373
2016	930	10975	9828	-240	4242	-4436	-194
2017	-1262	10577	10043	-205	2282	-4048	-1766
2018	2784	10737	10039	-179	4631	-2522	2109
2019	-1180	10459	9996	-196	1295	-2950	-1655
2020	-1033	10447	10470	-248	1193	-2234	-1041

[jimrtex]

As expected, the July 1, 2020 county population estimates came out yesterday. Here's a map of the 7/1/19-7/1/20 county percentage population change:



As expected, the estimates used 2010 Census as base, not 2020, meaning NY's count is "only" off by 800K+. Every NYC borough supposedly lost population year-over-year. Take that with a huge grain of salt.

As always, excellent informative map, great work cinyc!


Here is some more data (in numerical form) I think you all might find interesting.

Color Scheme

Gold - South
Red - West
Green - Midwest
Blue - Northeast

Top Twenty Counties in terms of % population growth rate (2019 - 2020)

• Yakutat City and Borough, AK (+10.59%) (+61 people)
• Loving County, TX (+9.70%) (+16 people)

I remember a visit to Mentone the county seat. Many Texas counties have courthouse squares with parking on four streets around the courthouse. In Mentone, the courthouse square only had two sides. The highway along the front, and a dirt road along one side.

• Borden County, TX (+7.62%) (+50 people)

At the county seat in Gail, there is a hitching rail, though I think it is mostly ceremonial.

• Daggett County, UT (+6.43%) (+62 people)
• King County, TX (+5.99%) (+16 people)

There is a sweet picture from Guthrie CSD with the entire student body from PPK-12 on a staircase, and including the entire staff - teachers, secretary, principal, cooks, custodian, etc. The youngest children are on the ground with their sweet three-year-old smiles. The seniors at the top include some in their cheerleader uniforms. Guthrie is the HQ of the 6666 Ranch and is not related to the King Ranch.

• Comal County, TX (+5.35%) (+8,377 people)

Suburb NE of San Antonio, as Bexar is finally beginning to spill over.

• St. Johns County, FL (+5.14%) (+13,647 people)
• Kaufman County, TX (+4.94%) (+6,739 people)

Suburb east of Dallas, spillover - Dallas is slightly to the west of center.

• Rockwall County, TX (+4.79%) (+5,026 people)

Suburb (northeast) of Dallas, Spillover.

• Hays County, TX (+4.76%) (+10,965 people)

Suburb south of Austin.

• Williamson County, TX (+4.40%) (+26,032 people)

Suburb north of Austin.

• Jackson County, GA (+4.36%) (+3,184 people)
• Jasper County, SC (+4.27%) (+1,294 people)
• Adams County, ID (+4.27%) (+182 people)
• Chambers County, TX (+4.26%) (+1,864 people)

Exurb east of Houston. Growth is inhibited by Upper Galveston Bay which is in Chambers County, but Harris County includes the western shore.

• Brunswick County, NC (+4.23%) (+6,052 people)
• Pinal County, AZ (+4.16%) (+19,188 people)
• Currituck County, NC (+4.07%) (+1,136 people)
• Sumter County, FL (+3.92%) (+5,246 people)
• Washington County, UT (+3.92%) (+6,975 people)

Bottom Twenty Counties in terms of % population growth rate (2019 - 2020)

• Yazoo County, MS (-9.15%) (-2,716 people)
• Issaquena County, MS (-7.84%) (-104 people)
• Terrell County, TX (-6.65%) (-50 people)

At one time, railroad crew days were measured in miles. Sanderson is midway between San Antonio and El Paso. Crews would change in Sanderson and overnight there. Regulations in 1995 and eliminated this practice. I-10 through the Hill Country has bypassed US 90 for transcontinental travel, and even from Houston and San Antonio to Big Bend.

• Hampton County, SC (-6.54%) (-1,263 people)
• Bristol Bay Borough, AK (-6.19%) (-52 people)
• Lake and Peninsula Borough, AK (-5.69%) (-90 people)
• Ziebach County, SD (-5.18%) (-145 people)
• Alexander County, IL (-5.08%) (-294 people)
• Billings County, ND (-5.02%) (-47 people)
• Crowley County, CO (-4.89%) (-293 people)
• Lee County, AR (-4.23%) (-376 people)
• Chattahoochee County, GA (-4.00%) (-440 people)
• Bent County, CO (-3.98%) (-222 people)
• Mississippi County, MO (-3.91%) (-517 people)
• De Baca County, NM (-3.91%) (-68 people)
• McDowell County, WV (-3.89%) (-684 people)
• McPherson County, NE (-3.85%) (-19 people)
• Livingston County, MO (-3.80%) (-569 people)
• Hamilton County, KS (-3.58%) (-358 people)
• Briscoe County, TX (-3.57%) (-55 people)

Unknown. This is ranch country. I'd think that COVID-19 would show up in estimates due to decreased visits to Caprock Canyons.
[/list]

[jimrtex]

The Census Bureau had a webinar explaining their methodology for the 2021 estimates which should be released next month, but the webinar has not been posted yet.

It sounds like they are going to adjust the 2020 base to match the 2020 Census but continue their methodology for 2010s.

They are also going to adjust their 2010-2020 estimates to match to the 2020 census. They could apply a simple ramp, or perhaps adjust the migration numbers to match the census.

[jimrtex]

Their estimates for the northeast have baked in an algorithm for a permanent population decline that will not be adjusted even in the face of actual population data.

NY did not lose 300,000 people in a year from domestic migration, despite Cuomo. That's just the Census Bureau cleaving off numbers from the 20.2 million 2020 Apr 1 Census count to fit where the algorithm thinks it's supposed to be by now. The Census Bureau estimated a population drop for NY over the 2010/20 decade, and cutting off nearly half of the decade's gain in a year will get them back on track.

For a similar reason, the population growth estimates for TX and particularly AZ from 2020 to 2022 are exaggerated to account for their 2020 Apr 1 Census misses -- dramatically so in the case of AZ. It didn't gain a single House seat! But now AZ is the fourth fastest growing state by percentage and third in absolute count, again. Phew.

I did not detect a single factoid in your message that is true.

The 2020 estimate for New York was 19.369M.

The 2021 estimate for New York is 19.836 an increase of 500K.

The way that the Census estimates works is that they begin with the census, including age, race, and sex.

If there are 123,456 White persons who are age 23 in 2010 in a particular state, then in 2011 there will 123,456 White persons who are age 24 MINUS those who have died PLUS net domestic immigration PLUS net international immigration. Any births will produce persons between 0 and 1 YO.

Births and deaths are pretty reliable - though there is some ambiguity where to attribute some births and deaths. But migration is much harder to estimate. Domestic migration can be tracked to a certain extent using IRS and SS records, but tax returns are delayed, and there may be trouble matching others. There could be a systemic problem in matching. Perhaps people who don't move are more likely to file one year and not the next. In that case, it would appear that matchers were more likely to be movers than the overall population, but that share might be applied to the entire population.

International migration is most difficult to estimate. There are little records of persons who return to their home country. An international immigrant in 2011 might become a domestic outmover in 2015.

In the early part of the last decade, the Census Bureau revised its international migration downward. This was particularly pronounce in New York which went from a projected gain of +1 for the decade, to zero, -1, and eventually -2.

From 2010 to 2020, the Census Bureau estimated for NY 2.339M births, 1.431M deaths, for a natural increase of 784K (note that births are almost 3X natural increase).

Net international migration was 717K, and net domestic migration of -1.563M. Add these for a net migration of -835K.

Add the natural increase to net migration and you get a net change of -51K. Note in particular that all the components were much larger than the net change, and that the net international migration and net domestic migration are made up of two components each: in and out. International immigration must be necessarily greater than 717K.

Domestic migration tends to be very sloshy. The largest state-to-state flows are Texas to California and California to Texas. People move for a job or adventure, and then move back home, months, years, or decades later. Family composition changes. After a divorce one spouse along with a child move back to be closer to family. They may associate the moved to state with the breakup of the marriage. Retirees to Florida may return to New York in their later years where they can live with a child or at least nearby. At 60 or 65 they were quite able and independent. At 85 not so, particularly after the hip fracture or stroke.

In 2019, NY domestic outflow was 440K and inflow 254K, for a net 186K.

Between 2011 and 2018, net outflow increased by 125%, inflow only decreased by 10%, and outflow only increased by 21%.

It is plain goofy to think that domestic outflow was estimated at 300K vs. a real value of 0K. Instead, it appears to have increased from 200K to 300K.

This could be easily explained by outflow increasing from 450K to 500K and inflow from 250K to 200K. How many elderly Floridians would return to NYC to be placed in Cuomo-de Blasio Happy Home, where they would probably be died and buried in a mass burial pit in the Bronx? Cuomo also posted armed guards on the state borders. People were driving in on back roads around Olean with their headlights covered over at 3 AM to avoid detection. How many college freshmen would arrive on campus to online classes, particularly with tuition of many $10s of thousands. They would wait a year, or take classes from home. And how many upper classmen would not come back in the fall of 2020 or spring of 2021.

If you owned a condo in Florida and an apartment in New York, and spent winter in the south, why wouldn't you decide to stay in Florida full time? If you were forced to work from home, why would you do it from Brooklyn or Queens or Long Island. Why not move to Vermont or Maine or Florida? If you were paying enhanced unemployment, why bother actually looking for a job?

The Census Bureau has not had an opportunity to evaluate why their migration estimates were off, so they are likely using the same methodology with

In the 2020 estimates, between 2019-2020 AZ was the 2nd fastest growing by percentage and third by absolute count. Its rate of increase has declined a bit, and it was passed by Utah and Montana on percentage.

After the housing bubble domestic migration to Arizona declined. People in California who planned to cash out their home equity and retire in Arizona where they could supplement their Social Security, pension, and savings with a few $100,000, were upside down on their mortgages. They couldn't afford to move. As the housing market recovered they could escape to Arizona.

[jimrtex]

Obviously, 2021 was a very unique year and it's very unlikely that each of the trends we saw will continue onwards into the decade, much less intensify, but just to show the extent of the changes I thought extrapolating the 2021 estimates forward by ten years and then conducting a reapportionment would be interesting.

This is what the electoral college would look like after such changes:

Image Link

GAIN FOUR: TX
GAIN TWO: FL, AZ
GAIN ONE: GA, NC, TN, UT, ID
LOSE ONE: PA, MN, RI
LOSE TWO: IL
LOSE FOUR: CA, NY


LAST FIFTEEN SEATS ALLOCATED:

Jimrtex: Edited to show percentage difference from change in projected apportionment. Projected gains for decade are quite iffy since they are based on estimates for 1.25 years. The rate of change can vary over a decade, particularly since they are increasingly tied to migration, and the change for the 1.25 years is an estimate not a measurement.

421. CA-47 -8.3% vs. -5.9% loses a 6th seat.
422. FL-30 7.0% vs. 9.4 costs 2nd seat.
423. TX-41 8.5% vs. 11.0% costs 3rd seat.
424. AL-7 +0.6% vs. 2.5% loses a seat.
425. NJ-12 -3.6% vs. -1.9% loses a seat.
426. WI-8 -1.0% vs. +0.3% loses a seat.
427. MI-13-3.4% vs. -2.1% loses a seat.
428. GA-15 5.5% vs. 6.7% costs an extra seat.
429. OR-6 0.8% vs. 1.7% loses a seat.
430. MA-9 -5.9% vs. 5.0% loses a seat.
431. TN-10 +7.1% vs. +7.7% costs extra seat.
432. NC-15 +8.3% vs. +8.9% costs extra seat.
433. CA-48 -6.2% vs. -5.9% loses a fifth seat.
434. AZ-11 14.3% vs. 14.8% costs second seat.
435. TX-42 TX and DE are almost tied for 435, depends on how you project change.
---
436. DE-2 TX and DE are almost tied for 435, depends on how you project change.
437. FL-31 10.3% vs. 9.4% would add a third seat.
438. PA-17 -1.1% vs. 2.4% would save seat.
439. NY-23 -13.1% vs. -13.6% would save a seat.
440. RI-2 -0.6% vs. -2.4% would save a seat.
441. CA-49 -4.2% vs. -5.9% would save a seat.
442. NV-5 12.1% vs. 10.6% gains a seat.
443. MN-8 2.2% vs. 0.1% saves seat.
444. OK-6 7.7% vs. 5.6% gains seat.
445. SC-8 13.8% vs. 11.9% gains a seat.
446. WA-11 6.0% vs. 3.5% gains a seat.
447. VA-12 3.7% vs. 1.0% gains a seat.
448. TX-43 13.4% vs. 11.0% gains a fifth seat.
449. IL-16 -5.5% vs. -8.5% saves one seat.
450. OH-16 +2.4% vs. -1.3% gains a seat.


Potential Dark Horses for a seat gain:
DE, NV, OK, SC, WA, VA, MO, IN

Potential Dark Horses for a seat loss:
MA, OR, MI, WI, NJ, AL, LA, OH

A couple of interesting changes is that Wisconsin gained a tiny bit more than Minnesota. Previously it was estimated that Minnesota would surpass Wisconsin by 2030.

The other is that Alabama is edging away from losing its 7th seat. In 2020, it was suing the Census Bureau over counting methods and was relieved to save the seat.

[jimrtex]

Their estimates for the northeast have baked in an algorithm for a permanent population decline that will not be adjusted even in the face of actual population data.

NY did not lose 300,000 people in a year from domestic migration, despite Cuomo. That's just the Census Bureau cleaving off numbers from the 20.2 million 2020 Apr 1 Census count to fit where the algorithm thinks it's supposed to be by now. The Census Bureau estimated a population drop for NY over the 2010/20 decade, and cutting off nearly half of the decade's gain in a year will get them back on track.

For a similar reason, the population growth estimates for TX and particularly AZ from 2020 to 2022 are exaggerated to account for their 2020 Apr 1 Census misses -- dramatically so in the case of AZ. It didn't gain a single House seat! But now AZ is the fourth fastest growing state by percentage and third in absolute count, again. Phew.

I did not detect a single factoid in your message that is true.

The 2020 estimate for New York was 19.369M.

The 2021 estimate for New York is 19.836 an increase of 500K.

The way that the Census estimates works is that they begin with the census, including age, race, and sex.

If there are 123,456 White persons who are age 23 in 2010 in a particular state, then in 2011 there will 123,456 White persons who are age 24 MINUS those who have died PLUS net domestic immigration PLUS net international immigration. Any births will produce persons between 0 and 1 YO.

Births and deaths are pretty reliable - though there is some ambiguity where to attribute some births and deaths. But migration is much harder to estimate. Domestic migration can be tracked to a certain extent using IRS and SS records, but tax returns are delayed, and there may be trouble matching others. There could be a systemic problem in matching. Perhaps people who don't move are more likely to file one year and not the next. In that case, it would appear that matchers were more likely to be movers than the overall population, but that share might be applied to the entire population.

International migration is most difficult to estimate. There are little records of persons who return to their home country. An international immigrant in 2011 might become a domestic outmover in 2015.

In the early part of the last decade, the Census Bureau revised its international migration downward. This was particularly pronounce in New York which went from a projected gain of +1 for the decade, to zero, -1, and eventually -2.

From 2010 to 2020, the Census Bureau estimated for NY 2.339M births, 1.431M deaths, for a natural increase of 784K (note that births are almost 3X natural increase).

Net international migration was 717K, and net domestic migration of -1.563M. Add these for a net migration of -835K.

Add the natural increase to net migration and you get a net change of -51K. Note in particular that all the components were much larger than the net change, and that the net international migration and net domestic migration are made up of two components each: in and out. International immigration must be necessarily greater than 717K.

Domestic migration tends to be very sloshy. The largest state-to-state flows are Texas to California and California to Texas. People move for a job or adventure, and then move back home, months, years, or decades later. Family composition changes. After a divorce one spouse along with a child move back to be closer to family. They may associate the moved to state with the breakup of the marriage. Retirees to Florida may return to New York in their later years where they can live with a child or at least nearby. At 60 or 65 they were quite able and independent. At 85 not so, particularly after the hip fracture or stroke.

In 2019, NY domestic outflow was 440K and inflow 254K, for a net 186K.

Between 2011 and 2018, net outflow increased by 125%, inflow only decreased by 10%, and outflow only increased by 21%.

It is plain goofy to think that domestic outflow was estimated at 300K vs. a real value of 0K. Instead, it appears to have increased from 200K to 300K.

This could be easily explained by outflow increasing from 450K to 500K and inflow from 250K to 200K. How many elderly Floridians would return to NYC to be placed in Cuomo-de Blasio Happy Home, where they would probably be died and buried in a mass burial pit in the Bronx? Cuomo also posted armed guards on the state borders. People were driving in on back roads around Olean with their headlights covered over at 3 AM to avoid detection. How many college freshmen would arrive on campus to online classes, particularly with tuition of many $10s of thousands. They would wait a year, or take classes from home. And how many upper classmen would not come back in the fall of 2020 or spring of 2021.

If you owned a condo in Florida and an apartment in New York, and spent winter in the south, why wouldn't you decide to stay in Florida full time? If you were forced to work from home, why would you do it from Brooklyn or Queens or Long Island. Why not move to Vermont or Maine or Florida? If you were paying enhanced unemployment, why bother actually looking for a job?

The Census Bureau has not had an opportunity to evaluate why their migration estimates were off, so they are likely using the same methodology with

In the 2020 estimates, between 2019-2020 AZ was the 2nd fastest growing by percentage and third by absolute count. Its rate of increase has declined a bit, and it was passed by Utah and Montana on percentage.

After the housing bubble domestic migration to Arizona declined. People in California who planned to cash out their home equity and retire in Arizona where they could supplement their Social Security, pension, and savings with a few $100,000, were upside down on their mortgages. They couldn't afford to move. As the housing market recovered they could escape to Arizona.

Nice to see that you agree with me not only that Cuomo was not an effective governor, but also that the Census Bureau's method for estimating the largest component of population movement is also the most tending to subjectivity. Using mail forwarding data compiled by the USPS is adequate (except when people don't forward their mailing address, but that seems accounted for), but using data from Redfin and Van Allen Lines is dubious (the latter specializes in relocations by managers and executives). But I take exception to the bolded part: I suggested no such thing. I said their methodology at least outside the sunbelt was flawed, in that the CB vintage estimate exaggerates outflows while suppressing inflows.

Their model never predicted NY reaching 20 million in the first place, now in a year it falls hugely from it -- that matter was addressed in an earlier reply. Their estimate was wrong by 600,000+ in NYC -- only around 30 cities in the US have a larger population than that miss. They also predicted lurid declines in Cook County when informed residents at the time knew the population declines of IL, while widespread, were heavily in Downstate.

You yourself were adamant about MN losing a congressional seat this redistricting.

I surmised that a 1.6% drop in a state population in a single year is a Hurricane (Katrina?) level catastrophe that I did not see evident in NY even considering the COVID fatalities in Apr-June. In other words, an artifact. But besides the bolded part which is a strawman that I take exception with, I see that you agree with me in general, so I guess thanks for the reinforcement.

The Census Bureau does not use data from Redfin or Van Allen Lines for estimating domestic migration. They apparently do look at USPS forwarding data as a check on their domestic migration estimates which are based on administrative records from the SSA and IRS (the Census Bureau receives data from these agencies that has been stripped of identifying information (e.g. "person" who filed 1040 from a NYC zip code in 2019 and from a Florida zip code in 2020). From this they have to extrapolate race, sex, age, dependents.

We do know that the Census Bureau estimates of domestic migration are consistent with ACS estimates of interstate movement (the ACS asks where the respondent lived one year previously).

We do know that net domestic migration from NY was increasing toward the end of the decade, due to an increase in outflow and a simultaneous decrease in inflow.

By 2018, about 2.3% of New Yorkers were moving to other states annually. This was partially balanced by about 1.3% moving from other states.

This produced a net domestic outflow of about 1.0% annually.

It is not unreasonable that this domestic outflow increased in 2020 and the first half of 2021. People want to get out of NYC, and fewer want to get in. Workers in Manhattan are working virtually from home. This kills restaurants in Manhattan. If you live in Queens and your coworker lives in New Jersey, you aren't going to meet for lunch. You are going to rummage through the pantry. Broadway has been shut down. Less work for hotel staff, and aspiring actors are not going to move to the city.

If you are getting enhanced unemployment, you could move elsewhere and work and get a job. How likely is it that the city or state government is going to check up on you? NYC/S are not exactly models of non-corruption.

People who move between New York and Florida seasonally likely consider New York to be home. But if they did not return to New York in 2020 aren't they now Florida residents? Previously they moved twice a year, but the census counted that as zero.

Incidentally, the increase in domestic outflow was mainly to New Jersey and Pennsylvania. This was a reversal of the early 2010 movement into cities.

To/From(net) NJ went from 40/41(-1) in 2011, to 50/35(+15) in 2015, to 59/32(+27) in 2019.

For PA this was 29/26(+3), 33/25(+8), and 47/22(+25)

Another data point. In 2019, NY was the number 11 destination for Puerto Ricans.

We know that the natural increase has been dropping. The initial baby boomers are now 76 and reaching an age where they are dying in larger numbers, and the birth rate has been declining. With any net measurement, it can collapse much faster than births and deaths.


I dpn't see anything wrong with the Census Bureau's model. Each year a person will get a year older or die. If they survive they will either move or stay put.

Where they can go wrong is in estimating the number of persons moving, or being born or dying.

There can also be an error in their initial conditions. They (and you) assume that both the 2010 and 2020 census were accurate. They populated their model based on the 2010 census. But what if they missed 200,000 (or some other number) of persons in 2010, and their change estimates were perfect. Then their estimates would have been 200,000 greater throughout the decade (with small adjustments based on age, race distribution, etc.). Their estimates would show a small decline over the decade (the estimated cumulative change only went negative in 2020).

New York may be particularly hard to count since there are many immigrants, some whose presence is illegal. If your cousin entered on a 6-month tourist visa three years ago, are you going to report him to the (census) authorities, particularly if he has been working off the books, and his long-term presence in your apartment violates your rental agreement?

Similarly, do we know that the 2020 Census was accurate? There was a particular effort to get New Yorkers to respond. Alabama was expected to lose a seat, and had even sued over it. But you may recall that Alabama had an exceptional early response, particularly compared to other Southern states. So get out the response may actually work.

The 2020 Census Bureau relied more on administrative records. If someone promptly replied, their administrative records might have matched, but they were never checked - or if they were it was during a test to see if they conformed. If someone did not respond to numerous letters over several months, did not answer the door, why do we assume that anyone lives there, or if they do that administrative records are still reliable?

Birth and death records are probably the most accurate. Domestic migration matches the ACS.

The wild card is international migration. The Census Bureau dramatically decreased the estimates for New York and New Jersey based on much lower net international migration. There was less change for states where international migration was much more dominated by Latin America. The Census Bureau explanation was that they had data that supported more international emigration, that more persons were returning to their home country. But what if that adjustment was wrong?

There may also be an issue of confirmation bias. If you wanted to believe that the number of unauthorized aliens was steady, if you could "show" significant emigration it would support your belief.

1.5% decline is not Katrina-like, and really is not such a huge number. We have ACS data showing 2.3% domestic out migration in 2018. It is certainly plausible that this continued to increase, while domestic in-migration dropped. International immigration may have also dropped, and natural change decreased nationwide, not just New York.

The Census Bureau switched their estimate base to the 2020 Census and estimated increased loss for New York, continuing a 10-year trend where the second derivative of population was negative.

Will the 1.5% decline over the next decade? Who knows. The Census Bureau is not projecting it to. We (on this board) have traditionally projected estimates from the Census forward to the next census to guess at future apportionment change.

[jimrtex]

When will county level estimates be released?

March 2022 (*subject to change)

[jimrtex]

Census' 2021 incorporated place estimates are out. They're not pretty, especially for NYC.
https://www.census.gov/newsroom/press-releases/2022/fastest-growing-cities-population-estimates.html

My yearly interactive map is here:
https://cinycmaps.com/index.php/population-change/110-2021-incorporated-place-and-remainder-population-change-map

It is interesting that they included housing counts. The new urban area definition is based on housing units, rather than population. This is because they now fudge population figures, particularly for small areas, while the housing units count is correct (a casual observer can count how many houses there are on a block, and there really are not privacy issues in revealing how many apartments are in a block). The use of housing units will also permit urban areas to be updated during the decade.

Based on the April 2020 to July 2021 change, Houston will pass Chicago in about 15 years (2036).

[jimrtex]

https://twitter.com/mcpli/status/1605959620123447307
https://twitter.com/mcpli/status/1605959697911119877
2nd seat Delaware if trends continue

Wow!

Idaho has already gained enough for a 3rd seat?!

Also, that projection would be a net shift of 11 districts (roughly equivalent to the entire population of Virginia!) and 11 EV from clearly D leaning states to clearly R leaning states in 2031.  D’s were extremely lucky the census happened when it did.  At this point, I will be surprised if there isn’t a Dem EC/PV advantage in 2028.

Idaho and Texas likely gained a seat by 2021. Arizona and Florida by this year.

California and Minnesota lost a seat by 2021. Illinois and New York by this year.

I differ from Li's projections for 2030:

Texas +4 (up +1 from 2021)
Florida +4 (up +2 from 2021)
Arizona +1 (down -1 from 2021)
Delaware +1 (NC)
Georgia +1 (NC)
Idaho +1 (NC)
North Carolina +1 (NC)
Tennessee +1 (NC)
Utah +1 (NC)

California -5 (down -1, i.e. greater loss, from 2021)
New York -3 (up -1, i.e. less loss, from 2021)
Illinois -2 (NC)
Minnesota -1 (NC)
Pennsylvania -1 (NC)
Rhode Island -1 (NC)
Oregon -1 (-1 loss from 2021)
Wisconsin -1 (-1 loss from 2021)

By Year:

2001: +Idaho(3), + Texas(39), -California(51), -Minnesota(7)
2002: +Florida(29), -Illinois(16)
2003: +Arizona(10), -New York(25)
2004: +Texas(40), +Utah(5), -California(50), -Rhode Island(1)
2005: +Florida(30), -Oregon(5)
2006: +Georgia(15), +Texas(41), -California(49), -New York(24)
2007:
2008: +Florida(31), +North Carolina(15), +Tennessee(10), -California(48), Illinois(15), -Pennsylvania(16)
2009: +Texas(42), -New York(23)
2010: +Delaware(2), +Florida(32), -California(47), -Wisconsin(7)

[jimrtex]

Like I did in previous years, I decided to see what a Wyoming Rule house size would look like with the new population estimates. The house membership would expand to 572 seats, an increase of 137 over its current size:

(States in red gain 1 seat, blue gains 2 seats, green gains 3 seats, yellow gains 4 seats, brown gains 5 seats, orange gains 6 or more, and gray means no change)

Relative to last year, the house would decrease by 1 seat, with Texas, Florida, and Arizona gaining a seat each, New York and Pennsylvania losing one each and California losing 2 seats.

Wyoming gained slightly on Vermont, but based on current rates of growth the Wyoming Rule is good for decades. Wyoming is volatile because of the importance of energy to the state's economy. If the Front Range ever spills over into Cheyenne that could tip the balance.

[jimrtex]

2020 is Apportionment was to fractional representation (but using Huntington-Hill). 2030 is projection based on compounding projecting estimates from 2020 to 2022 (2-1/4 years) forward to 2030. Change is difference between 2020 and 2030. App is projected 2030 apportionment. Ch. is change in apportionment. Margin is population change in 1000s needed to lose/gain another seat. Growth is projected change. Rate is projected annual growth rate. New is growth rate for remainder of decade to effect apportionment.

For example, Alabama barely and somewhat surprisingly kept its 7th seat (it was above 6.5). But now its growth rate is above the national growth rate (0.25% per year).
Another way to look at Change is that if trends would continue for another 72 or so years, Alabama would gain an 8th seat (7.5 - 6.709)/0.110 * 10 = 71.9. Alabama is projected to gain 226K between 2020 and 2030, this would give it a 150K margin on retaining its 7th seat. The projected annual growth rate for Alabama is 0.44%. If it slowed to 0.07% for the remainder of the decade it would be in danger of losing its 7th seat.


State               2020    2030  Change App Ch.  Margin  Growth    Rate     New
Alabama            6.598   6.709   0.110   7   =    -150     226   0.44%   0.07%
Alaska             1.083   1.061  -0.022   1   =     382       1   0.01%   5.57%
Arizona            9.378  10.361   0.983  10  +1     157     970   1.28%   1.53%
Arkansas           3.975   4.065   0.090   4   =     364     154   0.50%   1.92%
California        51.779  47.565  -4.214  47  -5     165   -2212  -0.57%  -0.52%
Colorado           7.577   7.756   0.178   8   =    -184     300   0.51%   0.11%
Connecticut        4.748   4.737  -0.011   5   =    -177      91   0.25%  -0.38%
Delaware           1.389   1.516   0.126   2  +1     -10     133   1.27%   1.15%
Florida           28.209  31.683   3.474  32  +4     -73    3323   1.45%   1.41%
Georgia           14.036  14.833   0.797  15  +1    -229     922   0.83%   0.57%
Hawaii             1.970   1.840  -0.130   2   =    -276     -66  -0.46%  -3.27%
Idaho              2.460   3.007   0.547   3  +1     408     488   2.38%   4.53%
Illinois          16.786  15.069  -1.717  15  -2     409    -993  -0.80%  -0.37%
Indiana            8.900   8.933   0.033   9   =    -321     214   0.31%  -0.30%
Iowa               4.208   4.153  -0.054   4   =     294      45   0.14%   1.27%
Kansas             3.880   3.773  -0.107   4   =    -208      -3  -0.01%  -0.96%
Kentucky           5.922   5.800  -0.122   6   =    -224      29   0.06%  -0.59%
Louisiana          6.120   5.585  -0.535   6   =     -54    -293  -0.65%  -0.81%
Maine              1.853   1.936   0.083   2   =    -354     105   0.75%  -2.78%
Maryland           8.105   7.816  -0.288   8   =    -232     -56  -0.09%  -0.59%
Massachusetts      9.219   8.704  -0.516   9   =    -141    -211  -0.30%  -0.57%
Michigan          13.206  12.608  -0.598  13   =     -57    -191  -0.19%  -0.26%
Minnesota          7.490   7.349  -0.140   7  -1     153      48   0.08%   0.42%
Mississippi        3.910   3.689  -0.221   4   =    -142     -93  -0.32%  -0.97%
Missouri           8.076   7.990  -0.086   8   =    -368     103   0.17%  -0.61%
Montana            1.505   1.690   0.185   2   =    -154     183   1.57%  -0.11%
Nebraska           2.617   2.585  -0.032   3   =     -62      29   0.15%  -0.27%
Nevada             4.096   4.416   0.320   4   =      87     339   1.04%   1.37%
New Hampshire      1.872   1.924   0.052   2   =    -345      80   0.57%  -2.87%
New Jersey        12.174  11.693  -0.481  12   =    -126    -121  -0.13%  -0.31%
New Mexico         2.818   2.721  -0.097   3   =    -171     -19  -0.09%  -1.18%
New York          26.459  22.908  -3.550  23  -3    -270   -2227  -1.16%  -1.35%
North Carolina    13.680  14.845   1.165  15  +1    -238    1204   1.10%   0.83%
North Dakota       1.136   1.112  -0.024   1   =     336       1   0.01%   4.75%
Ohio              15.460  14.800  -0.660  15   =    -203    -192  -0.16%  -0.39%
Oklahoma           5.209   5.420   0.211   5   =      88     276   0.68%   0.94%
Oregon             5.571   5.439  -0.133   5  -1      73      13   0.03%   0.25%
Pennsylvania      17.035  16.403  -0.632  16  -1     151    -136  -0.10%   0.05%
Rhode Island       1.522   1.466  -0.056   1  -1      35     -16  -0.15%   0.26%
South Carolina     6.721   7.521   0.800   7   =      17     771   1.41%   1.45%
South Dakota       1.264   1.362   0.098   1   =     122     108   1.15%   2.67%
Tennessee          9.064   9.643   0.579  10  +1     -91     647   0.90%   0.74%
Texas             38.170  42.416   4.246  42  +4     260    4140   1.34%   1.44%
Utah               4.313   4.850   0.536   5  +1    -266     514   1.47%   0.52%
Vermont            0.979   0.979   0.000   1   =     455      18   0.28%   7.29%
Virginia          11.314  11.308  -0.006  11   =     203     235   0.27%   0.56%
Washington        10.103  10.295   0.192  10   =     209     364   0.46%   0.80%
West Virginia      2.402   2.239  -0.163   2   =     221     -81  -0.46%   1.11%
Wisconsin          7.734   7.520  -0.214   7  -1      18      -5  -0.01%   0.03%
Wyoming            0.906   0.911   0.005   1   =     519      20   0.35%   8.78%

[jimrtex]

2020 is Apportionment was to fractional representation (but using Huntington-Hill). 2030 is projection based on compounding projecting estimates from 2020 to 2022 (2-1/4 years) forward to 2030. Change is difference between 2020 and 2030. App is projected 2030 apportionment. Ch. is change in apportionment. Margin is population change in 1000s needed to lose/gain another seat. Growth is projected change. Rate is projected annual growth rate. New is growth rate for remainder of decade to effect apportionment.

For example, Alabama barely and somewhat surprisingly kept its 7th seat (it was above 6.5). But now its growth rate is above the national growth rate (0.25% per year).
Another way to look at Change is that if trends would continue for another 72 or so years, Alabama would gain an 8th seat (7.5 - 6.709)/0.110 * 10 = 71.9. Alabama is projected to gain 226K between 2020 and 2030, this would give it a 150K margin on retaining its 7th seat. The projected annual growth rate for Alabama is 0.44%. If it slowed to 0.07% for the remainder of the decade it would be in danger of losing its 7th seat.


State               2020    2030  Change App Ch.  Margin  Growth    Rate     New
Alabama            6.598   6.709   0.110   7   =    -150     226   0.44%   0.07%
Alaska             1.083   1.061  -0.022   1   =     382       1   0.01%   5.57%
Arizona            9.378  10.361   0.983  10  +1     157     970   1.28%   1.53%
Arkansas           3.975   4.065   0.090   4   =     364     154   0.50%   1.92%
California        51.779  47.565  -4.214  47  -5     165   -2212  -0.57%  -0.52%
Colorado           7.577   7.756   0.178   8   =    -184     300   0.51%   0.11%
Connecticut        4.748   4.737  -0.011   5   =    -177      91   0.25%  -0.38%
Delaware           1.389   1.516   0.126   2  +1     -10     133   1.27%   1.15%
Florida           28.209  31.683   3.474  32  +4     -73    3323   1.45%   1.41%
Georgia           14.036  14.833   0.797  15  +1    -229     922   0.83%   0.57%
Hawaii             1.970   1.840  -0.130   2   =    -276     -66  -0.46%  -3.27%
Idaho              2.460   3.007   0.547   3  +1     408     488   2.38%   4.53%
Illinois          16.786  15.069  -1.717  15  -2     409    -993  -0.80%  -0.37%
Indiana            8.900   8.933   0.033   9   =    -321     214   0.31%  -0.30%
Iowa               4.208   4.153  -0.054   4   =     294      45   0.14%   1.27%
Kansas             3.880   3.773  -0.107   4   =    -208      -3  -0.01%  -0.96%
Kentucky           5.922   5.800  -0.122   6   =    -224      29   0.06%  -0.59%
Louisiana          6.120   5.585  -0.535   6   =     -54    -293  -0.65%  -0.81%
Maine              1.853   1.936   0.083   2   =    -354     105   0.75%  -2.78%
Maryland           8.105   7.816  -0.288   8   =    -232     -56  -0.09%  -0.59%
Massachusetts      9.219   8.704  -0.516   9   =    -141    -211  -0.30%  -0.57%
Michigan          13.206  12.608  -0.598  13   =     -57    -191  -0.19%  -0.26%
Minnesota          7.490   7.349  -0.140   7  -1     153      48   0.08%   0.42%
Mississippi        3.910   3.689  -0.221   4   =    -142     -93  -0.32%  -0.97%
Missouri           8.076   7.990  -0.086   8   =    -368     103   0.17%  -0.61%
Montana            1.505   1.690   0.185   2   =    -154     183   1.57%  -0.11%
Nebraska           2.617   2.585  -0.032   3   =     -62      29   0.15%  -0.27%
Nevada             4.096   4.416   0.320   4   =      87     339   1.04%   1.37%
New Hampshire      1.872   1.924   0.052   2   =    -345      80   0.57%  -2.87%
New Jersey        12.174  11.693  -0.481  12   =    -126    -121  -0.13%  -0.31%
New Mexico         2.818   2.721  -0.097   3   =    -171     -19  -0.09%  -1.18%
New York          26.459  22.908  -3.550  23  -3    -270   -2227  -1.16%  -1.35%
North Carolina    13.680  14.845   1.165  15  +1    -238    1204   1.10%   0.83%
North Dakota       1.136   1.112  -0.024   1   =     336       1   0.01%   4.75%
Ohio              15.460  14.800  -0.660  15   =    -203    -192  -0.16%  -0.39%
Oklahoma           5.209   5.420   0.211   5   =      88     276   0.68%   0.94%
Oregon             5.571   5.439  -0.133   5  -1      73      13   0.03%   0.25%
Pennsylvania      17.035  16.403  -0.632  16  -1     151    -136  -0.10%   0.05%
Rhode Island       1.522   1.466  -0.056   1  -1      35     -16  -0.15%   0.26%
South Carolina     6.721   7.521   0.800   7   =      17     771   1.41%   1.45%
South Dakota       1.264   1.362   0.098   1   =     122     108   1.15%   2.67%
Tennessee          9.064   9.643   0.579  10  +1     -91     647   0.90%   0.74%
Texas             38.170  42.416   4.246  42  +4     260    4140   1.34%   1.44%
Utah               4.313   4.850   0.536   5  +1    -266     514   1.47%   0.52%
Vermont            0.979   0.979   0.000   1   =     455      18   0.28%   7.29%
Virginia          11.314  11.308  -0.006  11   =     203     235   0.27%   0.56%
Washington        10.103  10.295   0.192  10   =     209     364   0.46%   0.80%
West Virginia      2.402   2.239  -0.163   2   =     221     -81  -0.46%   1.11%
Wisconsin          7.734   7.520  -0.214   7  -1      18      -5  -0.01%   0.03%
Wyoming            0.906   0.911   0.005   1   =     519      20   0.35%   8.78%

Alabama is slowly pulling away from losing its seventh seat. It has a fairly substantial cushion for its 7th. Note it is possible/likely that the national growth rate will increase.

Alaska is stagnant. It can't lose its single seat, but it may never gain a second.

Arizona is gaining about 1 seat (0.983) per decade. It was close to the 10th seat at the Census, and is estimated to have gained the 10th by July 2022. It will spend the rest of the decade solidifying the 10th and then working towards the 11th. A slight uptick in growth might secure the 11th.

Arkansas is a very solid 4.

California has had a 500K loss since the Census and is projected to lose 2.2M by 2030. If international immigration recovers, California will retain one or more seats. Before 2020 international immigration was hiding the domestic out migration.

Colorado gained its 8th seat in 2020 and is now solidifying that gain.

Connecticut is solid at 5, unless the national growth rate recovers.

Delaware may barely gain its 2nd seat by 2030. This would be the first and only time since 1810 that Delaware had more than one representative. From 1812-1820, Delaware elected both representatives at large, so this would be the first-ever congressional districting in Delaware. Note that Delaware's margin is quite small. It could be like Montana in the past where it was close to a second, but not quite for several decades.

Florida growth rate is about 3.5 representatives per decade. If Florida gains a fourth, it will be the 435th overall.

Georgia is gaining most of a seat per decade. Georgia is projected to surpass Ohio by 2030, but may be nicked by North Carolina. The long-time grouping of Pennsylvania, Illinois, and Ohio in 5th-7th, is about to be joined by North Carolina and Georgia.

Hawaii is slowly drifting towards losing a second district, but would not be at risk until around 2060.

Idaho has the largest congressional districts with over 900,000 persons, and barely missed out on a third. It would have gained a third by 2021. As the fastest growing state since 2020 it will have a solid third in 2030, with a 4th a bare possibility by 2040.

Illinois could lose two seats this decade as well as the next.

Indiana, Iowa, Kansas, and Kentucky could be steady at 9, 4, 4, and 6 seats for decades.

Louisiana is projected that its 6th seat is 431st nationally. Continued dropping would cost it a seat by 2040.

[jimrtex]

Interesting estimates for cities have been released: https://www.census.gov/newsroom/press-releases/2023/subcounty-metro-micro-estimates.html

No idea why Santa Cruz, CA is growing so much compared to other cities in California

It has to be the campus reopening.

Yes:

2020: 62,885
2021: 54,941
2022: 61,800

Whitman County, WA was the fastest growing county between 2021 and 2022. Whitman County is home of Pullman and Washington State.

Other cities in California with a decline of 3% from 2020 to 2021, and a 3% increase from 2021 to 2022 are:

Arcata (Cal Poly Humboldt)
Avenal (large prison with about 1/3 of population)
Berkeley (UC-Berkeley)