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

A Science for Everyone

The Quality of Data

We live in a world filled with data – but a lot of the presentations are slanted.  Sometimes the slant is political, sometimes the slant is a bizarre sense of humor.  I like Wikipedia – but I don’t rely on it.  I tapped in to look for a bio on George Washington Carver, and I read the damndest story about carving peanuts into busts of our first president.  If I want satire, I’ll go to the Onion or the Babylon Bee.  Wiki is accessible, fast, and I’ll continue to use it – but I check wiki data against other sources.  Using Wiki as a reliable source of data is similar to accepting President Biden as a fact-checker.

If I want information on shootings and murders in Chicago, I start with https://heyjackass.com/  It’s reliable, but not respectable.  They even sell T-shirts.  I’d never use it in a professional article – but whoever puts the data together does a pretty good job.  For example, as I write this, heyjackass shows

Year to Date

Shot & Killed: 586

Shot & Wounded: 2843

Total Shot: 3429

Total Homicides: 619

It’s a fast source of data that usually checks out. It even goes into neighborhoods, cause of death, race and gender – well, I’d say race and sex, since it lists male and female, but I may be a bit old fashioned.  It would be nice if all the violent cities had their own heyjackass, but this one seems unique to Chicago.

Climate data – at least the sort of data that shares first and last frosts, annual precipitation, and other medians gleaned from past records – is much more available.  For years, while some stuck with the Farmers Almanac, we carried with us Climate and Man – a 1941 yearbook of Agriculture that had compilations for most of the US.  Now, I can get online to check snow depth at each snow course, NOAA offers answers to all sorts of questions.  Climate data is vastly improved – though you still need to weed through and select reliable sources.  Personally, I stick with USDA and NOAA.

It is hard to find quality data on illegal immigrants and crime.  Texas’ Department of Public Safety provides data on crimes and convictions in Texas, but other states don’t provide data of similar quality.  I’m not sure we can generalize from Texas – but better data is hard to find.

The quality of data on abortion is impressive – each state provides data in a similar form.  You can sort between states and years – there’s a requirement that data be kept and published.  Unlike crime and illegal immigrants, this data is easy to access and use.

This publication presents itself as quality data: “30 Facts You Need to Know”.

Unfortunately, the folks who put it together didn’t include the links to those 30 facts that make them easy to confirm or reject.  I really don’t know which of the “30 Facts” I should accept and which ones should be rejected.

There is a lot more data available than there was in my younger days.  But a lot of that data is still less than easily confirmed – and a lot of folks are still trying to pass opinion off as fact.

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. 

A Science for Everyone, Community

Electronic Visit to the Snow Course

It still amazes me that I can turn on the computer and, in 15 minutes, get the data that used to take a week’s work to obtain.  Of course it also amazes me that my work is so far in the past that it no longer shows up in the 30-year averages.  Still, some of that data – starting with my first run in the mid-seventies are still available:

As I look at the little squares on the left, I do see that Jay and I did measure the lowest year on this chart, back in 1977.

My closest snow courses are Stahl Peak and Grave Creek. Stahl is listed at 27.7 inches and 75% of average – but still significantly better than the 20-inches of water back in my youth.

Grave Creek is listed at 3.8 inches and 60% of average. 

Banfield Mountain shows 9.9 inches – 66% of average.  The chart shows that this is fairly close to the record low measurements.

Hawkins Lake, in the northwest corner of the county, shows 20.9 inches and 81% of the 30 year average.  The historic peaks chart shows that I measured the record low in 1977, and the snowpack is still above that.

Community, Demography

When the official data isn’t good data

As I was retiring, the American Community Survey(ACS) was replacing the long-form Census questionnaire.  There is merit to the argument that a survey can provide data that is as good as a form that one out of six people fill out – both are, after all, actually surveys.  Still, as a rural sociologist whose primary duties were rural demography, I wasn’t comfortable with the American Community Survey results – the sampling size was too small.

Now, I can access data that compiles five years worth of estimates – so here is some data on Rexford, Eureka, Fortine and Trego, by zip code, in two separate five-year conglomerates:

2012-2016Rexford
59930
Eureka
59917
Fortine
59918
Trego
59934
Total Population5664,425584562
5 to 9212823452
10 to 14353052249
Median (Average) Age58.043.650.749.2
Per capita income$22,377$18,799$21,203$25,999
2015-2019Rexford
59930
Eureka
59917
Fortine
59918
Trego
59934
Total Population6584,769747476
5 to 953212
10 to 14502163520
Median (Average) Age56.046.847.260.4
Per capita income$13,438$22,867$28,753$26,671

The American Community Survey is a well-conducted survey.  The data is correct, in both cases, within the limits of the survey.  The small samples, however, can create some large swings and make the data less useful.  I have been looking forward to reviewing the Trego data since I was selected to return the ACS survey.  Trego’s median age went up 11 years.  The population dropped by 15%.  The youth population plunged.  Meanwhile Fortine incomes increased by 36%, as Rexford plunged into the depths of poverty.  All the survey data is correct – but sampling bias, due to the small number of participants, has given us data we can’t use.

I still prefer the old, time-consuming long form results over the ACS.

Demography

Party Affiliation over Time

It seems, especially in the midst of an election year that the political parties are long established and permanent. While we don’t have an especially high rate of turnover in major political parties we do have one.

The Republicans became the Democratic Republicans, which eventually become the Democrats (an extremely brief summary of a rather lengthy chunk of American History). The Republican party, as we know it today, actually came out of the Whig party (well, a splinter faction, sort of. No one said politics was straight forward).

At any rate, political parties are not constant and neither is their membership. Gallup has a nice collection of data on party affiliation that we referenced last week in things that make surveys hard. Since they’ve provided it as a table, here is the graph:

Party Affiliation over time; data from Gallup

Looking at Gallup’s data, we can make several observations. Since 2004, the general trend has been an increase in people identifying as Independents, and a decrease in both Republicans and Democrats. We also notice that declines in either major party tend to coincide with increases in Independents.

The top of the graph is 50%, and while none of our three categories make it that high, Independents come the closest (highest percent independents was 47%, which occurred both in October of 2013 and October of 2014).

A “Zoomed in” version of the previous graph, from 2012 on

Taking a closer look at things (note that I’ve changed the vertical scale as well, the bottom is now 15%) from 2012 on, we can see a large drop in both Republicans and Democrats in 2013 that has a corresponding rise in independents. 2018 had a decline in Independents that mirrors a rise in Democrats.

The difficulty with examining trends is “How far do we have to zoom out?” Over a large amount of time, it’s difficult to see the impacts of smaller events but easier to examine long term trends. Another consideration is that what looks like a clear trend on the small scale may not reflect the trend in the long term.

Political polling doesn’t give us all that much long term data. Do we have enough to make predictions from? Well, the people making predictions certainly seem to think so!

Community, Demography

If LCHS District were a County

After the article on searching Lincoln County data, the question came in: “What if North Lincoln County was its own county?”  The answer is available, but it takes the sort of personality that enjoys digging through data.  Here’s a few facts that would describe the thought experiment that would be county 57.

County 57, sharing boundaries with the Lincoln County High School District, would rank 31st in population of Montana’s 57 counties (6,260 residents).  The remainder of Lincoln County would drop from the tenth largest population (19,980) to twelfth (12,694 residents). Data from 2010 Census and, of course, will likely change with completion of the 2020 Census. 

Looking at market and taxable valuations we find that High School District 13 (LCHS) has a market value set at $1,202,098,056 and taxable valuation set at $17,042,130.  By my count, and this is the type of sort where it is easy to miss something, County 57 would have market and taxable valuations larger than 17 Montana counties. 

The same source shows that Lincoln County’s market valuation is $2,741,812,498 and taxable valuation is $37,491,358.  44 Montana counties show lower market values than Lincoln County – not a great variance from the population rank.  County 57 would be 22nd in market valuation.  The remainder of County 56 – consisting of Troy and Libby High School districts, would be 21st.

A more detailed study might include roads, total area, access to county services and a number of other items.  For a simple, “what if” analysis, looking at population and market value seems adequate.

Demography

Talking about Risk

A headline read, “What if we could live for a million years?”  My casual answer was “not a chance.”  Even if we could eliminate aging and disease, we still have to consider risk.  The National Safety Council reports 1.25 deaths for every 100 million miles of highway travel.  Assume 10,000 miles per year of highway travel, in the first thousand years of life, you’re up to 10 million miles.  That turns out to be a 1 out of 8 chance of dying – just from highway transportation.  The National Geographic gives odds of being struck by lightning as 1 in 700,000 each year, and one in 3,000 over a lifetime.  Increase the lifetime to that same 1,000 years, and the odds go up.  We live in a risk society, and often ignore that fact. 

I know a guy who survived a grizzly mauling, and I’ve known one who died from wasp stings.  Even in Trego, I encounter more wasps than bears.  CDC stats show 89 people who died from bee stings in 2017.  It isn’t a large risk – but 80% of the fatalities are male.  Bee stings are a greater risk to the male half of the population. 

Just about every endeavor includes misunderstood risks.  In 2018, the police fatality rate was 13.7 per 100,000 (bureau of labor and statistics).  CDC shows the 2017 fatality rate for farmers was 20.4 per 100,000.  BLS shows a rate of 132.7 per 100,000 loggers in 2015.  CDC shows bartenders have a higher on job fatality rate than police.  In some places policing is a relatively risky job – but in farm country and logging country it seems fairly safe.  Here’s the link to more data if this interests you.

When we started looking a Covid-19, we didn’t have the statistical data to calculate risk.  Now, we do have some data – and we’re seeing disagreements, mostly because folks see risks differently.  As I write, RealClearPolitics shows 5,746,272 confirmed cases in the US, and 177,424 deaths, yielding a 3.09% confirmed case fatality rate.  Still, that number goes from 8.25% in New Jersey to 0.79% in Utah.  We have solid numbers – but not numbers that allow us to calculate risk.  When we look at the charts that include age, we can begin to calculate risk – 70 is higher risk than 40.  80 is even higher.  New York numbers suggest that dense populations and public transportation increase risk – but not enough to calculate. 

The numbers let us develop ordinal data about covid risk – ranking things as more or less, rather than develop statistical data.  At 70, I’m at more risk than someone who is 50. The age data is good enough to develop some statistics – but the comorbidities that make it covid fatal mask that. Riding mass transportation is more dangerous than driving a car alone.  The virus doesn’t differentiate between bars and churches – open spaces are safer than crowded enclosed spaces.  So far we know that Covid is a bit more infectious than flu, and less infectious than measles.  Ordinal data. Buzz Hollander, a physician in Hawaii, has a good, readable article on covid at this site