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. 

A Science for Everyone, Demography

Non-reproducible research

About 20 years ago, I realized that I had a fairly unique opportunity to test the hypothesis that 4-H was strongest where it was multigenerational – 4-H members grew up to be 4-H leaders, and the program was strongest where the multi-generational membership was the most common. 

I was working with 22 counties, and 4 of them had Extension secretaries with 30 or more years of experience, and full records.  Complete records is more challenging than you might think – when I worked as a County Agent, the records were in the basement, and a cracked sewer line helped me make the decision that they couldn’t be recovered.  Obviously, if I had only 4 counties out of 22, reproducing the research would be difficult at best.  On the other hand, if it didn’t get done in the next year, retirements would make it impossible to do once. 

In 1950, 18% of rural youth belonged to 4-H, with the membership plateau ending in 1976 (Putnam 2000, Bowling Alone), with a 26% decline in membership between 1950 and 1997.  And I was listening to folks who told me that the problem was a shortage of volunteer leaders.  It looked like I could find the numbers in those 4 counties with the oldest secretaries. 

I was on a roll – the secretaries showed that 151 4-H families had at least one parent who had been a 4-H member as a child, 78 families where neither parent had been a member, and the parents of 6 families could not be determined.  We defined 4-H members who had belonged to a club four years or more as persistent, and contrasted their statistics with first-year members.  None of the six families whose 4-H history couldn’t be determined had any persistent members, so the sample, while not particularly large, was clean.

Well, the stats were simple – Chi square was calculated at 45.03, the probability of the distribution occurring by chance was less than 0.001.  The data supported the hypothesis that parental involvement in 4-H (as a club member) is the greatest single predictor of member persistence in 4-H.  Two thirds of the persistent members (4 years or more) had parents who had been 4-H members in their youth, while two thirds of the first-year members had parents who had not been 4-H members.   The kids most likely to drop 4-H were kids whose parents had not been in 4-H and were not 4-H club leaders. 

The evidence was pretty solid that a multigenerational 4-H identity helped keep kids in 4-H – but it was equally solid that 4-H membership wasn’t random . . . it was hereditary, like the British nobility.    Still, making a conclusion about a national program from a sample of 334 people in 4 counties seems to be a stretch.  As I look at the Harry that was once an English prince, I wonder about researching the worldwide decline of royalty.

Non-reproducible research isn’t necessarily bad research, and it can provide some interesting conclusions – but it is better when you know it’s non-reproducible from the beginning.

Community, Demography

Economic Drivers and Housing Shortages

Back in 1960, Lincoln County was timber, mining and farming.  There was a fairly stable population, jobs were available, and both small and large ranches.  Along with the Corps of Engineers planning Libby dam, and looking at flooding the lands near the Kootenai, by the middle of the decade our communities became boomtowns, needing housing for the newcomers who would be working on the tunnel, the railroad relocation, the highway relocation, and Libby dam itself. 

I was in high school – and Dad was looking at a trailer court, pretty much where the trailer court is today in Trego.  His advantage in negotiating with the company that had the contract for the tunnel was me being in high school.  Gail Tisdell, a classmate  who worked at Lynn’s Cafe over lunch hour, kept me informed about the company honchos lunch table discussions.  Long story short, we wound up with a trailer court built pretty much to the specifications they brought in from San Mateo.  Jack Price put in a trailer court down by the substation.  Up the creek were two more: Westwood Acres and S&S.  All told, Trego went from being virtually trailer-free to about 200 spaces in the course of a year. 

Likewise, when the tunnel and the railroad relocation ended, so did the largest trailer courts.  Across the county, we had a surplus of trailer spaces that would last for decades.  Many weren’t built to those California standards – I recollect septic tanks built from laminated cull 2×4’s.  The materials were cheap, the construction sub-marginal, and a place that had once housed 50 families emptied and left to collapse.

A half-century later, watching the ads and listening to people looking for housing shows me that we’re in a new housing situation.  Kind of a 55-year cycle – in 1910, all it took to resolve a housing shortage was an axe, a crosscut saw, a froe and a chisel.  In 1965, the spirit of capitalism moved in to develop trailer courts across the county as labor boomed in construction projects.  Now, we’re looking at a county that the USDA Economic Research Service lists as Government dependent. 

It says something when your county’s largest economic drivers are federal and state government employment – the definition is that over 14% of annual earnings are from federal or state government jobs.

Additionally, the ERS lists Lincoln county as a “low employment” county – and the definition is “less than 65 percent of county residents 25-64 were employed.”  The examples are often anecdotes instead of data – a friend from my youth, who hasn’t migrated out for work, explained that his highest paid years were in the seventies.  We’ve been migrating out for work, then returning, for several generations.  It makes a difference in the statistics. 

If you note the classification as a retirement destination county – “where the number of residents age 60 and older grew by 15 percent or more between the 2000 and 2010 censuses due to net migration.”  Home building, home purchases, are a bit easier for folks who are moving in at the end of their careers.  Some of our retirees are returning.  Others, not originally from here, still have the same motivation – in both cases, “going home to a place he’d never been before” is somewhat appropriate.

Anyway, watching the rental market shows that the world has changed.  An axe, saw and froe no longer combine with a strong back and a willingness to work to build a home.  There are safer, less regulated places for venture capital than building a lot of housing rapidly.  Young families compete with retirees – and it’s a lot easier to buy or build that second or third house when you’re holding the check for the house you sold in Oregon, or Washington, or California.