## Talkin’ Bout the Weather Some More

One of my current web addictions (1) is the Contra Costa County Flood Control and Water Conservation District  rain gauges page:

The Flood Control district maintains 29 automated rain gauges scattered around the 804 square miles of Contra Costa County. This table is automatically updated at the top of the hour, and a quarter after and a quarter til.

I’ve put together a little Google sheet that does a little math, where I can grab the data off this page and paste it in to get some percentages, totals and averages:

Now, if you’re a math guy, and especially a science guy, this little snippet should make your head explode – so, so wrong! Doing totals, percentages and averages by gauge makes complete sense (however limited its use), but doing so *across* gauges?!? Huh?

(Here’s where I expose – to perhaps well-deserved ridicule – how a science-loving non-scientist goes about analysing some data. The key step for me is, as always, philosophical: what am I looking at? What can it tell me in theory? What does it tell me in practice? These are questions that must be answered before you even bother to look at the numbers. Failure to do so is by far the most common technical failure in the Science! news I read: the writer doesn’t know what he’s looking at, doesn’t know the limits of what it can tell him, and then doesn’t understand what it is actually telling him. Stupidity and/or dishonesty is the dominant non-technical problem.)

The sneaky-bad part is that, until you think about it, it sort of makes sense: aren’t I getting an average for rainfall across Contra Costa County? No, I am not – the best I’m getting is the average of a bunch of point samples that are related in a manner that is not clearly understood.

First off, to think that an average of the gauges tells you something about rainfall in general over the area throughout which the gauges are deployed is making some assumptions. These 29 rain gauges represent, at best, a few square feet of the 804 square miles of CCC. Well? Are we supposing that these gauges are representative (whatever that might mean) of the other 803.9999 square miles? Why would we think that? What would we mean by it?

Why are there 29 gauges?  Why not just use one? More obviously, why are the totals at each gauge so different? Season total averages run from 11 inches up to 33 inches, and this year the differences in actual rainfall are at least as pronounced.

Contra Costa County is made up of at least 3 pretty distinct areas: The west-facing slopes of the Richmond/El Cerrito hills and the flats between them and the Bay, extensive hilly areas with a couple of hilly interior valleys punctuated by a big mountain (about 2/3 of the total area), and some flats on the delta to the far east.

Close to the center of this map is Mount Diablo (DBL 22). This year, Mount Diablo has gotten over 51 inches of rain, which is, according to my fun little spread sheet, 186%  of season average – and we’ve got a couple months more to go.

Immediately to the north of Mount Diablo are two gauges – the Concord Pavilion (CCP 43) and Kregor Peak (KGR 38). These two gauges are among the 5 remaining gauges that have not yet reached their season average total so far. In fact, while Mount Diablo is almost 2 feet of rain over for the season so far, these two are about 3 and a half and 5 and a half inches under. The other three gauges that have not hit their seasonal average total yet are much closer, and might hit them with the storms coming this weekend.

How could this happen? Two gauges within a couple of miles of Mount Diablo are not even getting average rainfall, while the mountain stands to get twice its average.

Consider this current predicted rain map:

Note Hawaii at the bottom center. That long line of rain from Hawaii to California is pretty much what the weather people call an atmospheric river – a Pinapple Express. This one, which blew through our neighborhood early this morning, was nothing like the size of the last couple. That stuff out to the east looks a bit more exciting. Zoomed in a little:

That thing that looks like a swirl? It is. When it reaches California in the wee hours of Friday, the rain will be pushed from south to north along the stronger, leading edge.

Speculating here: This puts (CCP 43) and (KGR 38) in Mount Diablo’s rain shadow. Gauges just south of Mount Diablo are all above average; the two directly north of it are below.

In a more typical Northern California rain year (2) the storms come down from the Gulf of Alaska, maybe or maybe not picking up some tropical moisture, and hit pretty much directly west to east. (CCP 43) and (KGR 38) would, in such cases, not be in the rain shadow of Mount Diablo, and might therefore get more rain, comparatively, to years like the one we’re having now (3). Thus, the season averages don’t really tell us what to expect. They are useless, really for predictions, as what they tell you is more like what a blended picture of two or more (you can have both Gulf of Alaska storms and some tropical stuff in the same year, for example) mechanisms by which California gets rain and snow.

So, what am I getting if I average Mount Diablo with Concord Pavilion and Kregor Peak? Should I take the average of only 2 out of 3? Add some more gauges? It will make a difference. Fundamentally, there’s nothing magic about these 29 gauges or about the number 29 – we could add or subtract gauges to the mix, or even double count some we think particularly important or ‘representative’. There’s nothing to stop us, it might even make sense, under certain assumptions.

Nope, what my averages across gauges tells me is not that we’re 130% of season average rainfall so far in Contra Costa County. What it tells me is that the average across the gauges is 130% of the average of the total season rainfall for each gauge – and that is all. Which is not all that helpful, and is only interesting in a vaguely cabalistic sort of way.

The point, if any, is that sometimes what may look like reasonable numbers to look at do not, in fact, tell you much. And that I’m a LITTLE bit obsessive on occasion. In a fun way! Really!

1. Other web addictions include: boat building (the 1337 woodworking skillz and empirical engineering fascinate me. Lapstrake for the win!), Sci Fi short films (there are a million of these, some quite good)  and primitive iron smelting (there’s a band out there named Bog Iron Bloom – wish I’da thought of that!). In my fantasy world, I’d dig my own bog iron, smelt it in a clay brick furnace, hammer it into an axe and iron nails, chop down some oak and build a Viking long ship – and make a Sci Fi short film about it! I’d need to find some people who don’t get sea sick to sail it for me, but I’m imagining that’s the least of the problems with this plan.
2. This is when the discussion gets weird: our entire sample size upon which we base our assumption of ‘typical’ is only about 150 years long, and only a fraction of that has anything like the widespread measure-taking we use now. The oldest CCC Water District gauge dates back to only 1937; most are either from the 1970s  – or since 2000. What would be an appropriate timeframe? 10,000 years? 100,000? Why or why not? Certainly, based on physical evidence, (and there are more recent updates that show even more variation I can’t seem to lay my hands on at the moment)  over 10,000 years, the averages would be different – and over 100,000 years, the median prediction would be: much colder, with a chance of more snow.
3. If in fact we have more than one year like this – so far, I’ve only heard things like a 1 in 25 year, but the year isn’t over yet. This seems to me to be a very unusual year, one not captured well by rain gauges such as those discussed above. How many rain and snow gauges are there in the 6,000+ square mile drainage of the Feather River? Because the Oroville Dam is almost 50 years old – and this is the first time the emergency spillway has been used. And there’s more rain on the way, and a massive snowpack to melt. In other words, are we really capturing the full extent of this precipitation year? The physical evidence – reservoirs around the state at or near capacity with a couple months of rain still to go – suggests we’re not.

## The Mystery of Workforce Participation

I’m willing to bet that workforce participation in hunter-gatherer societies is darn near 100% for the key demographic of men aged 24 – 54. Ya know? So, if all we want is high workforce participation, all we’d have to do is return to a hunter-gatherer economy.

Despite the great progress toward a return to just such an impoverished, backward economy made here in recent years(1), I’m thinking there’s more to it, for example, how many of us are willing to tolerate living in a less than total employment economy for benefits such as cell phones, indoor plumbing and hospitals.

Yet, invariably, any report of a decrease in the percentage of the adult working age population in the workforce is greeted as bad news. This one, for example. And, I hasten to add, it may be. But it might not be.

The implied judgement: Higher and increasing workforce participation = good; lower and falling workforce participation = bad. Is this true? Or only sort of true within a certain range and for certain people?

Wouldn’t it be nice if the percentage of working aged people who no longer worked because they didn’t have to were routinely reported?  Sure, one imagines that many of the people not in the workforce would like to have a good paying job. One also imagines that most of the working age people in or out of the workforce would love to be in a position that they didn’t have to work unless they wanted to. That’s me, for sure – I’d retire in a heartbeat if I could, if, somehow, a few million bucks fell in my lap. Why not? I have plenty to keep me busy and entertained and useful every waking hour. Imagine all the essential blogging I’d do! Or not.

Starting 0ver 20 years ago, it became quite possible, even common, for some mid-level guy or gal in the tech field to get some stock options or make a few astute stock purchases and then, a few years later, find themselves sitting on millions in assets. A lot of those folks looked in the mirror one morning, and thought: I don’t actually have to go to work anymore. And some of those soon didn’t. And more still do every day.

Rather than being some sort of problem, this is in fact the outcome of a free market most to be desired from an individual’s perspective. If I had a few million in the bank-equivelent, think of all the good I could do! Think of all the time I’d have!

Now think of a nation with a growing number of such people, people who are attached in some sense to their money because they did, in some sense, earn it. What a wonderful place that would be! (That’s also the nightmare of statists everywhere, but that’s another story.)  If you were married or the adult child of such an one, you, too, might be able to not work if you didn’t want to – how cool is that? Soon, we’d have a growing pool of people with resources and time. Sure, I suppose, some waste it. But many would not – I fervently believe I would not. The possibilities of local action to make life better are endless!

So, while I’d readily believe that such people make up a small percentage of the decrease in workforce participation, should we not at least break them out of the total? Should we not celebrate them at least as much as we lament those who’d like a job but can’t get one?

On a more serious level, this equating work with prosperity and, ultimately, with personal goodness itself (the hoary Protestant Work Ethic) is merely an example of how economic reporting, reflecting economic teaching, makes things much simpler and black-and-white than they really are (2). Is growing manufacturing output a good or bad thing? How about a falling average workweek? Growing GDP? Falling consumer debt? Are these things, in and of themselves, good or bad? How can you tell? There are situations were they might be good or bad or indifferent. They might be good for some people, bad for others, and indifferent to others or on the whole. And there are plenty of  other cases like this as well. At the very least, the standard disclaimer should say something like: Within a certain range, all other things being equal. Note: all other things are never equal.

Right?

1. Just think of the low carbon footprint! I quiver!
2. And that’s even before you reach the Marxist/Bernie level of willful stupid. Nope, here I’m talking about economics as understood by people at least trying to make sense.

## Venn-ever You’re Ready to Get Started & Other Ephemera

Saw someplace a Venn diagram showing the overlap between people who hold that A: businesses are insatiably greedy and will do whatever makes them the most money, and B: businesses promote less qualified men and oppress qualified women as a result of institutional misogyny. I have generalized the point being made below:

So, here’s the game: list any two positions which are logically incompatible but are held simultaneously by the same people. Then weep as you contemplate the above diagram.

I’ll start:

A: Business control the government – it’s bought and paid for;

Comments: So, if business owns the government, wouldn’t it make more sense for Richy McRichperson to simply tell his minions to change the tax laws? This disconnect also leads to the spectacle of Presidents and candidates accusing businesses of the sin of doing what the laws incent them to do as if the government was somehow not responsible for writing those laws in the first place.

Next:

A: I am Woman, hear me roar!

B: Asking women to testify in campus rape hearings would be too retraumatizing! They have suffered enough!

Maybe one more, in case I’m not offending enough people:

A: Gender is a social construct.

B. If my little boy likes dolls, I’m duty bound to get him a sex change operation and only an unenlightened meanie would object.

You?

On a related topic, here in California, politicians and activists are going to dislocate their shoulders if they don’t ease up on patting themselves on the back for two ‘achievements’: the statewide \$15/hr minimum wage, and the San Francisco mandatory 6 weeks of paid parental leave for both men and women. Only a really mean Neanderthal (hot off conking his future mate on the head and dragging her to his cave, no doubt) would be so insensitive to point out what this logically motivates businesses to do:

1. Stop hiring people for jobs that aren’t worth more than \$15/hour to me, the business owner. Since there’s also a ton of overhead involved in hiring anybody, getting those jobs done has got to be worth considerably more than \$15/hour to me, the business owner, before I’ll risk hiring.
2. If I’m doing business in San Francisco, I’ve got to not only factor in all of the above, bit add the very real possibility that, if I hire a young person of reproductive age, I might also have to figure he or she is going to cost me 12% more (that’s 6 weeks out of a typical 50 week work year) than someone I’m sure won’t be taking that leave, even if I can miraculously get by for 6 weeks without having to hire a temp or replacement.

What San Francisco will end up with is nothing but big businesses – you know, those evil, selfish dudes who make those obscene profits from which they can pay for these political whims. Smaller, less profitable (less greedy, right?) companies will be forced to leave.

All in all, the message to businesses is clear: Stay out of California. If you must be there, keep as much of your operations out of the state as possible. If you must be in San Francisco, keep your operations to a minimum, and only hire potentially fertile people if you’re one of those evil companies Sanders wants to take over.

That all this is perfectly clear, predictable and logical only means that more people will jump into the orange area of the above graph, where they will not have to think about it. And then, they’ll blame mean businesses when it doesn’t work.

## Politics of Professions: More Fun With Partisan Graphs

Maybe you’ve seen this very cool graph showing the “politics of professions”?

Here’s a screen grab of a small part of it:

Below these amusing comparisons is a set of little colored circles for each considered profession that you can click on and expand:

These are really fun, and largely confirm a number of biases. Here, we see that the Media and Academia are leftist strongholds, or that most people who can think shun the right – take your pick.

But, alas! What you think you’re seeing – what the title “Which way does your occupation lean?” suggests you are seeing – you are not seeing. Once again, just a tiny amount of curiosity and research reveals that this lovely, award-winning graph doesn’t say what it claims to say. And it’s the usual suspects that render this graph, like so many before it, largely meaningless:

Method. Well? How did the authors arrive at these numbers? Nowhere stated – which, to put it mildly, is not a good sign.

Data source. Where did these numbers come from? As highlighted in the title to the first screengrab above, “campaign contribution data from the FEC”. Googling that directs one to the FEC website. There’s a lot of stuff on that site, I don’t have all day to sift through it, so here are some preliminary considerations:

1. Are we only considering those who have made political contributions in some manner to one of the two major parties? And in a manner that requires the FEC keep track of it? This means if I’m a mathematician, for example, who votes consistently for one party or the other but can’t bring myself to part with any cash to fund them, I don’t count? Isn’t that *most* people, or at least a huge percentage of people?
2. What period is covered? The database runs from 1997 to approximately the present. The same person – the Donald, for example – might change who and what he supports over time. Sticking with this example, he might be a staunch and generous Hillary supporter right up until he decides he needs to be a Republican President. Is this accounted for? How?
3. An awful lot of people in business will give to *both* parties.(a) If I’m an investment banker, I might largely vote Republican but *always* contribute to Democratic causes – it’s just simple prudence. It’s just business. Investment Bankers, according to this graphic, are split right down the middle – I strongly suspect that this is not the result of a neat and fundamental split in partisan loyalties right down the middle as it is a result of a sort of natural selection: investment bankers would be idiots NOT to make sure that candidates from BOTH parties get a cut. Is this addressed, somehow, in the graphic?
4. If I give \$100 to Democratic causes and \$1,000,000 to Republican causes, is that accounted for?

Classifying Professions. Who determines the list of professions? Who decides where the line falls between, say, a mathematician and a statistician and a data analyst?

1. Many people have multiple professions, or their work straddles a number of professional classifications. (Am I a strategic planner? A sales manager? A market researcher? A content creator? A pricing analyst? A product manager? In my current job, the answer is ‘Yes’.)  Is that addressed? How?
2. Are these claims verified? In other words, does the person donating to a partisan cause get to just say what their profession is? If I say I’m a rocket scientist, do I get any push back?

This is similar to the problem of asking people in exit polls to say what their highest level of education is – the guy with the degree from a barber college can say he is a college grad, while the gal who dropped out in the 6th year of a nuclear physics degree might say she isn’t. Even if they answer truthfully (and what would make them?) the answers are not giving you a true picture: that college grads (assumed against most of the evidence to be the smart people) voted for X, while those with no college degree (assumed to be the dumb people – you know, like Gates, Jobs, Wozniak)  voted against it. So if I ask you your profession, and you once had a bit part in Galaxy Quest but now wait tables and live with your mother, you put ‘actor’ as your profession, right? Or if I got a government grant once to try to sell small towns on putting in solar panels, I’m an entrepreneur, right? Me, I’ve helped people in far-away places figure out the math involved in leasing an airplane – so I’m an international financier, right?

It’s fun to imagine that this set of little graphs tells us, for example, that 81% of roofers are Republicans while 80% of philanthropists are Democrats. But that is not what it is telling us. What it is telling us, most likely, is that 80% of the donations by count (not amount) made by people who a) made political donations tracked by the FEC since 1997,  and b) self-reported that they were philanthropists went to candidates/PACs/causes/whatever that the FEC classified as Democratic.

Maybe. The lack of any explanation other than the general statement that the data came from the FEC inclines me to assume the most simple approach was used: add ’em up by claimed profession across all the years, divide the Democratic total by the gross total to get a percent, slap a graph on the numbers, and voila! You have a hot steaming pile posing as information. Could be wrong, but don’t know how I’d find out.

a. If you think that one of the major purposes of all this FEC disclosure isn’t so that the elected officials can check up on to whom they owe favors and to whom smackdowns, you need to read up on how, say, LBJ operated. He *always* kept score, and knew *exactly* what he owed and was owed from everybody he worked with, down to cub reporters. The essential part of implementing Callicles’ model of political virtue (Punish your enemies, reward your friends, indulge your every whim) is knowing who is a political enemy and who a political friend. That, and, I suppose, cultivating memorable whims.