A Science for Everyone, Community, Demography

Our Communities by ACS Numbers

I listened to a comment about the median household income in Trego – and defaulted to my professional statement before retirement – “That’s American Community Survey data, and it’s not very good for small communities.”  When I checked it, the $36,458 median household income for Trego translates as “somewhere between $27,478 and $45,438.  ACS data has its uses, but it has to be used with a lot of caution.

So here’s a little ACS data on our communities – you can check for margin of error (MOE) here.   I wouldn’t recommend using any of the numbers without reviewing MOE – but just sharing the data shows the variance.  It’s safe to admit that my household was one selected for the ACS. With two retirees at home, I didn’t hurt Trego’s school enrollment rate, I raised the percentage of bachelors degree or above, kept the employment rate down, and raised the median age.

Trego CDPFortine CDPEureka CCDRexford Town
Population5153176,47078
Median Age60.527.950.153.3
Median Household Income$36,458$68,036$40,827$30,481
Bachelor’s Degree or more26.10%19.20%22.40%0.00%
Veterans6.80%16.20%12.90%16.80%
Poverty9.50%5.20%20.40%23.60%
School Enrollment97.80%72.30%81.90%100%
Employment Rate40.20%59.50%38.30%20.60%
Housing Units2831773,71673
Occupied Housing Units2371442,79646
Disabilities31.10%18.80%26.70%65.90%
Children under 189.30%32.50%22.10%13%

It looks like the Fortine sample drew some younger respondents.  Eureka CCD with a larger population and larger sample is probably closer to correct, and the town of Rexford data is probably close to useless because the small sample size almost guarantees sampling bias

Community, Demography

Trego and the American Community Survey

Montana’s American Community Survey is composed from the final interviews conducted with 10,138 households in the state.  Since Montana has 519,935 households, the chance of any household being in the final interview is 10,138 out of 519,935 = 0.0195, right around 2% of the population is included in the survey.  Since Tregp shows 295 households, we can guess that our community data is assembled from somewhere around 6 completed interviews.

This table about sample sizes is from kenpro.org

As you will note, a population of 250 calls for 152 samples, and that the break is in the lower right corner – while a sample of 382 covers a population of 75,000, 384 is good for a million.  I’m not going into details about sampling – this is a blog, not a stats class. If enough people ask for the stats instruction, I’ll do another article.  Suffice to say, we can expect the numbers on the ACS to be pretty vague in small communities. So let’s look at the ACS data: data.census.gov

The top of the page shows:

Total Population                                           515

Median Household Income                          $36,458

Bachelor’s Degree or Higher                       26.1%

Employment Rate                                        40.2%

Total Housing Units                                      283

Without Health Care Coverage                   9.0%

Total Households                                         249

As we go further down the page, we start to encounter the variance – the range the number represents.  That 515 population is taken from the decennial census not the ACS. 

Median Age:    60.5 +/- 3.2 years 

16.8% of Trego folks speak a language other than English at home – plus or minus 15.4% so that’s somewhere between 6 and 166.  Probably not a particularly useful piece of information.

That Median Household Income turns out to be plus or minus $8,980: the number can be as much as 24.6% off either way.  It can be as low as $27,478 or as high as $45,438.  As we move into the full chart on that number, we see that the number of households lists the margin of error as plus or minus 67.  Could be as low as 182 or as high as 316. 

That 26.1% of Trego residents with a bachelor’s degree or more has a 12% margin of error – it could be as low as 14.1% or as high as 38.1%,  It shows 8.1% of our residents holding graduate or professional degrees, but doesn’t give a margin of error there.

The school board will be pleased to know that 97.8% (plus or minus 6.6%) of our kids are enrolled in Kindergarten to 12th grade.  Might even surprise the County Superintendent.  Pretty sure some kids out there are home-schooled.

That 40.2% employment rate (+/- 12.9%) looks low – but I guess it fits right in with a median age over 60 and 31.1% (+/-9.4%) disability. 

And finally, there are 48 women 15 to 50 years old – but the margin of error is 38, so it translates to somewhere between 10 and 86.

The ACS data is good – but the sample for Trego was small, and not checking the limitations lets us make blunders.

Community, Demography

4% Growth for County 57

The 2020 Census numbers have been released, and we’re looking at data we can begin to use.  I’m hoping to get the data at a school district level later on – but for now, we have county level data, CCD level data, and Census tract level data.

First – Lincoln County’s population dropped by a tenth of a percent.  Second, the population in the Libby CCD dropped by 1.2% (now 9,772), population in the Troy CCD dropped by 3.9% now 3,435), and population in the Eureka CCD increased by 4.0% (now 6,470).   North County is now officially 89 residents less than a third of the county’s population.  3,435 of the people represented by the Troy Commissioner reside in the Troy CCD, while 3,124 reside in the Libby CCD.  This is a trend worth watching.

Housing data is available at the county level – and it may give us some insight on rentals in the area.  Housing units in Lincoln County decreased by 4.0% – occupied housing units increased by 0.5%, and unoccupied housing units decreased by 19.6%. 

In County 57 – the Eureka CCD – housing unit numbers are:

 2020 #2020 %2010 #2010 %Change
Total Housing Units3,716 3,771 -1.5%
Occupied2,79675.2%2,69271.4%3.9%
Vacant92024.8%1,07928.6%-14.7%

All these statistics are in comparison with the 2010 Census. 

It’s going to be fun as future releases will show even more usable data.

A Science for Everyone, Community, Demography

Measuring Migration

When you work with Census data, migration numbers can be very precise – but the 10 years between each Census often make the data obsolete.  As demographers, we had to find ways to work around that – and U-Haul had the websites that let me better understand and explain migration.

For example, if I price renting a 15’ truck in Bakersfield, California, heading to Eureka, Montana, I get a price quote of $5,173 today.  On the other hand, it costs $1,109 to rent the same truck in Eureka and drive it to Bakersfield.

If I want to see beautiful Bend, Oregon in the rear view mirror of my 15’ rental truck, the website tells me the trip to Eureka, MT is $3,052.  Renting the same truck in Eureka, to go to Bend is only $654.

I didn’t learn to abuse U-Haul’s website in a classroom – I got the general idea while riding a bus seated alongside a very successful retarded guy.  He made a living riding the bus – back then there was a pass that was good for six months travel in country – and then driving a car or small truck back to Denver.  He may not have completed high school – but he gave me the foundation of a method to quantify migration.  Obviously, Bakersfield and Bend have more people trying to leave, and Eureka has more inmigration. 

If we look at the trip from Minneapolis to Eureka, it’s $1,703.  Eureka to Minneapolis is $1,362.  Park City, Utah showed up as $990 to Eureka, while Eureka to Park City was $495. 

It provides a better feel for migration in central locations like Park City – where you can go in any direction.  You can’t go north from Eureka in this time of Covid – and you can’t drive west from California.  Still, it gives data in something resembling a ratio – the challenge the rental truck industry has is getting the trucks from destination locations (inmigration) back to the places they came from (outmigration) without hiring my friend with the Greyhound pass.

TaxFoundation.org gives last year’s data, and it is massaged and compiled from more moving companies.  Guess what?  The top destination state (inmigration) is Idaho – and Idaho has a lot of similarities to western Montana.  Oregon was a destination state – and still needs the rental trucks from Eureka to keep things going.  I think the last person renting a truck to leave New Jersey might want to turn the lights off as he or she pays the last toll to drive out.

I’ve rented U-Haul trucks a couple of times – but the company has provided me a lot of comparative data on migration during my career.  It’s still science, and it’s still numbers driven. 

Demography, Recipes

Fruit Soup

For many years, the Census differentiated between Germans and Germans from Russia.  While there were significant historical differences between the two groups, by the time I was doing the demographic work for South Dakota, the largest difference I could see was the menu.  This recipe, for Plumemoos, a fruit soup served cold, is a hot weather dish passed to us from the Germans from Russia.

            Plumemoos

2 qt      water
1 c.      sugar
1 c.      seedless raisins
1 c.      dried prunes
1          29-oz can of peaches
1          cinnamon stick
1          package red jello
1 qt.     Purple grape juice

Cook dried fruit, sugar and cinnamon stick til fruit is tender.  Add jello to hot soup and stir to dissolve – this will color and thicken the soup when it has cooled.   When cooled, add grape juice to taste.  Serve cold – a wonderful, soothing soup for a hot summer day.

Community, Demography

What is a Farm

A dozen years ago, I wrote “What is a Farm” and now I have one.

The bottom line that defines a farm is production.  “The current definition, first used for the 1974 census, is any place from which $1,000 or more of agricultural products were produced and sold, or normally would have been sold, during the census year. (1992 Census of Agriculture).”  It’s kind of fun to be able to quote myself, and find that the commentary is still accurate 12 years later.

This July, I harvested 275 little round bales of grass hay, and stored them in the log shed.  I figure if I sell them at $4 each, the place makes the minimum to be a farm.  Logically, that makes me a farmer, for the first time in my life.  I remember seeing a neighbor in Ag Hall when I worked for Extension – and commenting to Todd that he was the first farmer I had seen in that building . . . to be fair, I hadn’t worked in Ag Hall all that long.  Now that I’m a farmer I do have to sell those cute little bales to actually qualify.

Since I’ve already done the research, I can help others determine if they also qualify: “The definition also makes it easy to be a “small farmer”: if a family has a couple dozen hens and eats organic eggs from its own free-range chickens, the family probably produces enough to be living on a farm. Similarly, a two-Holstein-steer feedlot with all purchased feed can meet the definition of a farm. Obviously, a large hog confinement facility is a farm, even if it lacks plows and fields.” 

This table shows how the government’s definition of a farm has changed over time:

Demography

Alumni Magazines

As a young man, MSU’s alumni magazine occasionally brought information about classmates, but was by and large an irrelevant publication.  Adding a couple more degrees brought more alumni magazines – and the deaths column became something I watch more.  Not sure why – perhaps to make sure I’m not there.

Today, STATE listed Jeeta Kant and Bob Mendelsohn.  I met Jeeta when she was unable to get into the sociology Master’s program, and couldn’t understand why her 35 year-old bachelors in Soc didn’t punch all the buttons – she had good grades, but lacked the research.  A colleague in geography looked at the books she had done on Hutterite colonies, and in 2008 she completed her MS in geography on the topic.  After that, she worked on a research project in the civil engineering department, on edible and usable plants on the Pine Ridge, completing her Ph.D. in 2013, at the age of 66.  She spent a few years as a postdoc researcher before retiring.  Jeeta didn’t have a conventional academic career, but she did show that age isn’t an insurmountable handicap, and combining a research career with social security isn’t impossible.  

Bob Mendelsohn’s specialty was deviance – and it always struck me as a bit strange that our deviance prof was the closest to the norm.  I mean, the guy was married to his high school girlfriend, from 1967 until he went away this May.  He retired in 2008, and spent several hours telling me of his return to studying his Judaism.  He was challenged by the thought of giving up deli ham sandwiches – hopefully keeping kosher came easier as he moved to the east coast.  I’ll remember a Jewish researcher who loved the green and red decorations, and the music of Christmas.  Totally different upbringings – but a good friend who left the world a better place for having been here.

A Science for Everyone, Demography

IQ Testing Government Officials

Donald Trump described himself as a “stable genius.”  Joe Biden challenged another old man to an IQ test competition.  These are things that never happened with George Bush, and I scoured the internet for reliable IQ numbers on politicians.  I learned that a US government official IQ tested a group of German military and political leaders.  So near as I can tell, the only data available on the intelligence of government officials came from the Nuremberg trials after World War II.  An American psychologist, Gustave Gilbert tested the 21 former Nazi officials with an early Wechsler IQ test, with the following results:

Position HeldIQ
Schacht, HjalmarMinister of Economics143
Seyss-Inquart, Arthur Reichkommisar of Netherlands141
Dönitz, KarlAdmiral138
Göring, HermannChancellor138
Papen, Franz vonChancellor134
Raeder, ErichGrand Admiral134
Frank, HansGovernor of Poland130
Fritzsche, HansDirector of Propaganda130
Schirach, Baldur vonHitler Youth Leader130
Keitel, WilhelmField Marshall129
Ribbentrop, Joachim vonMinister of Foreign Affairs129
Speer, AlbertMinister of Armaments128
Rosenberg, AlfredMinister of Occupied Territories 127
Jodl, AlfredColonel General127
Neurath, Konstantin vonMinister Foreign Affairs125
Frick, WilhelmMinister of Interior124
Funk, WaltherEconomics Minister124
Hess, RudolfDeputy Fuhrer (until 1941)120
Sauckel, FritzHead Labor Deployment118
Kaltenbrunner, ErnstSS, Head of Security113
Streicher, JuliusNewspaper Publisher106

All were above average – most, excepting the publisher of the party newspaper and the head of security (Streicher and Kaltenbrunner) above the “normal range” of intelligence.  The only thing I can generalize from the sample is that you don’t have to be dumb to be a nazi, and that isn’t a conclusion I like.

There’s a chart at IQ Comparison that shows the probability of each score.  For example, Julius Streicher, with an IQ of 106, almost made it into the top third of the population.  Kaltenbrunner, at 113, scored in the top fifth.  Hermann Goring, at 138, was statistically the sharpest knife in a drawer with 177 others.  Hjalmar Schacht, with an IQ of 143 was one out of 278 . . . and he was acquitted of all charges at Nuremberg. 

There is a clickbait series on US presidential IQ scores – complete to two decimal points, and it looks unreliable to me – so this seems to be the best available data.  I suspect we could develop some pretty good estimates on recent presidents, if we had their ASVAB or college placement scores – but most of our presidents preceded IQ tests.  In 1916, Terman set the minimum standard for genius at 140 . . . so Trump may well have scored above that – basically, the probability in the general population is 1 in 261.  Biden probably did have a better than 50-50 chance of beating a random 83-year-old in an IQ test.  I’ve seen Einstein listed at 160 – a one in 31,560 probability.

In a nation of 330 million, we have about as many smart people as dumb ones – and, if we extrapolate from the Nurenberg IQ tests, we have some equally bright people in politics, and bright politicians can do some really dumb things.

A Science for Everyone, Demography

An IQ Too Low for the Military

Jordan Peterson has a brief video on youtube describing the IQ cutoff the US military uses in recruitment. (Jordan Peterson | The Most Terrifying IQ Statistic)  He explains that the army doesn’t recruit for people who score below 83 because they can’t be trained. 

I think he has simplified the explanation – the ASVAB is the military test.  While it is not technically an IQ test, it correlates closely.  I’m not about to fact-check Jordan Peterson on a technicality.  He explains that 10% of the population have an IQ below 83, and the chart shows that 11.5% of the population score 82 or below.  Definitely close enough for a short lecture.

I think back among my students, and recall asking the slowest veteran I ever had in a class what he did in the army.  He replied he had been a gama goat driver.  The photo suggests that he probably had skills that would transfer to operating a rubber tired skidder – but probably lacked the forest experience.  My experience tells me that he would have been a good, reliable tail chainman on a survey party – but even at that time, electronic measuring devices were replacing the chains.

All told, I think I understand why Jordan Peterson called it “The most terrifying IQ statistic.”  If he was close to correct – and I suspect he was – we’re looking at somewhere around one person in nine that can’t be trained to perform a minimum military job adequately.  I suspect the civilian world isn’t any more merciful.  Years ago, I had the privilege of knowing Doug.  The army had released him because of a low score – whether IQ or ASVAB makes no difference.  He was in his fifties, and remembered vividly the date when he learned he wasn’t good enough.  He made a living as a ranch hand, mostly working cattle, haying and fixing or building fences . . . he was conscientious and reliable at handloading ammunition, and a cautious, safe driver.  As I watched Peterson’s video, I realized how few jobs there are for folks like Doug.   There was a place for Doug in north central Montana, but few areas have that opportunity.  Doug couldn’t have made it in the urban technical world.   Anything that finds one person in nine untrainable is a terrifying statistic.