Then They Came for my Job — and No One was Left to Speak for Me

Everybody loves Martin Niemoller. Wikipedia will tell you he is famous for his “provocative poem about the cowardice of German intellectuals following the Nazis’ rise to power and subsequent purging of their chosen targets, group after group.” Here’s the original form of that poem, in case it hasn’t come across your Facebook feed lately:

First they came for the Socialists, and I did not speak out —
Because I was not a Socialist.

Then they came for the Trade Unionists and I did not speak out —
Because I was not a Trade Unionist.

Then they came for the Jews, and I did not speak out —
Because I was not a Jew.

Then they came for me — and there was no one left to speak for me.

It’s a lovely piece to quote: it reckons with the past, signals enlightenment and moral clarity, and challenges us all to live up to its ideals. You pretty much can’t help feeling a little more ethical and upright when you read it. Still, before we get too complacent, I’d like to ask you to accompany me, and Niemoller, on a short tour of recent American economic and political history. I want to see if we’ve really learned to speak up for others as well as we think we have. Humor me, you don’t really mind, do you?

First they came for the Socialists — this feels like a quaint anachronism now, but the Socialist party was highly relevant politically in the United States in the early 1900’s. The Socialists had two representatives in Congress and high water marks of 6% of the popular vote in the 1912 presidential election for socialist Eugene Debs and almost 17% in 1924 for socialist-supported progressive Robert LaFollette. (By contrast, Gary Johnson won 3% and Jill Stein won 1% of the popular vote in 2016.) Within 25 years, discredited by American politics and world events, the Socialists were more or less irrelevant in the U.S., winning less than 0.1% of the presidential vote from the 1950’s on. Some people did speak out, but the tide was against them. You and I weren’t around then, in any case.

Then they came for the Trade Unionists —  much of this happened in our lifetimes. Labor unions were highly influential in the United States for most of the 20th century, especially during what’s known as the Great Compression, the period following the New Deal reforms when income inequality declined dramatically. From Wikipedia:

This “middle class society” of relatively low level of inequality remained fairly steady for about three decades ending in early 1970s, the product of relatively high wages for the US working class and political support for income leveling government policies.

This is clear in this chart that tracks income inequality over time. This income leveling correlates well to labor union membership, which grew steadily during the New Deal, and eventually reached almost 35% — more than 1 in 3! — of salaried workers in 1954. Union membership gradually declined from that point. The best statistics start in 1983, when around 20% of the work force still belonged to unions, and they show another 50% decline to just 11% of the work force in 2015. For my generation (I am in my mid-40’s), and for the so-called new economy, I think we can say that we’ve played out Niemoller’s script pretty much as written: few of us were unionists, and few of us have paid attention as union influence declined throughout our working lives. (Uber rides are cheap and convenient, so who cares if the drivers have no rights and the company can raise its take from fares at will? I bet a hundred years ago there would have been a strike.)

Then they came for the Industrial Cities. Here I am talking about both big (Detroit, MI, population 1.85M in 1950) and small cities (Youngstown, OH, population almost 170K in 1960). Since then, both cities have shrunk by about 60%! The history of these cities (and many more like them) in the 20th century is a complex mix of economics, sociology, and racism, but here’s the world’s shortest introduction:

U.S. cities and industry in the North and Midwest grew rapidly in the late 1800’s and early 1900’s, powered by a huge wave of  European immigrants. Then World War I created a labor shortage, and blacks from the South migrated north to fill the gap. This began the Great Migration of blacks to cities in the North and (eventually) the West. Apart from a short break during the Great Depression, the migration continued for the next 40-50 years, transforming the black population from 80% rural-20% urban to the exact opposite — 80% urban-20% rural — in little more than half a century.

As they came to the white-dominated cities, incoming blacks were steered to neighborhoods with lower economic opportunity and investment, away from whites, a morally bankrupt process known as redlining. (For a history of redlining that goes beyond Wikipedia, read Ta-Nehisi Coates’s mighty essay, The Case for Reparations.) Meanwhile, whites tended to concentrate in their own segregated parts of the same cities, and eventually to move away from the cities altogether as road networks improved (white flight). Some of the industrial jobs that brought blacks to the cities went to the suburbs with the white population, some began to go overseas, and some were lost to automation, with the latter trend showing no signs of abating. In just 20 years (1967-1987), Philadelphia, New York City, Detroit, and Chicago all lost over 50% of their manufacturing jobs. Some cities managed to reorient around the technology or financial sectors (Boston, New York), while others lost significant population and income that they’ve never regained. (For more on the relationship between lost industrial jobs and inner city poverty, you can read William Julius Wilson; here is a short summary paper.)

Who spoke out for these cities, for their minority populations and for their financially starved schools? Not those whites who moved to the suburbs to get away from blacks, and not the mainstream Democratic party of the 1980’s and 90’s, which decided it needed to distance itself from inner city concerns in order to win elections. Maybe you did?

Then they came for the Heartland. Or perhaps for the U.S. manufacturing economy, depending on whether you prefer to view things geographically or economically. This has been the subject of much debate since the election, because Trump’s margin of victory came from three “Rust Belt” states (PA, MI, WI) that he had been expected to lose, and because it’s believed that his margin of victory in those states came from dissatisfied white working class voters either seeking change or lashing out either at bullying elites or at bullied minorities or immigrants. (This is probably a lousy explanation of voter dynamics in Pennsylvania.) Frustratingly, these discussions have devolved into arguing over whether these voters are motivated by economic or social-cultural factors (as though it were easy to separate the two) and whether they deserve sympathy (as though we’ve never seen anyone bully others while being bullied at the same time).

No matter how you view the politics here, we should be able to come to some kind of agreement on the economic facts. Here’s a recent article suggesting that the Rust Belt is not a struggling region, because other parts of the country are worse off: the states we are talking about are all in the middle third of U.S. states by median income. Sure, they’re not Connecticut or California, but they’re not Arkansas or Mississippi either. This is true enough as far as it goes, but it fails to consider decline. As a quick back-of-the-envelope exercise, I pulled up the income data and sorted the states top to bottom by median income as of 2015 and as of 1995. Here’s what I found:

  • The state with the biggest decline in ranking over the last 20 years (from #5 to #28)? Wisconsin.
  • The state with the second biggest decline (from #14 to #31)? Michigan.
  • Five of the ten states with the biggest drop in ranking form a contiguous region at the heart of the Rust Belt: Wisconsin, Michigan, Illinois, Indiana, and Ohio. (There are no other clusters; the other five states are completely non-contiguous.)

At this point, these Rust Belt jobs are more likely lost to automation than to other states or countries (though there is some of the latter) or incoming immigrants from lower-income countries (virtually none coming into the Midwest). So it’s hardly clear what to do. Still, there’s not much sympathy for these people’s plight on my Facebook feed, where most people (1) have college or graduate degrees and professional occupations, (2) live in desirable areas, and (3) are frustrated and terrified, as I am, to see Trump in power.

Then they came for — OK, who’s next? Are you? What job will you have in twenty years, or what job will your children have, when:

  • Economic opportunity appears to be declining, not increasing, across generations.
  • The technology sector, which is not very labor intensive, and also finds it easy to move jobs around or offshore, is coming to dominate the (non-service) economy. (Just to give you an idea, Facebook is the 7th most valuable company in the world and employs only 13,000 people, a fraction of the workforce of the industrial behemoths of 50 years ago, whose employees numbered in the hundreds of thousands.)
  • Automation is increasing, and potentially applies to more and more jobs, both in and out of the tech sector, as technology itself becomes ever more capable.
  • The gig economy is rising, and there doesn’t appear to be an obvious limit on what kind of jobs can be gig-ized. (Gig workers have even less leverage than long-term employees.)
  • The financial industry, which has grown rapidly over the last several decades, provides relatively little economic value relative to the human capital invested in it, and might be ripe for contraction. You could speculate that the same might be true of marketing as well (another industry where too many of the well-educated settle), or at least that it could be done more cheaply. If our news stories can be replaced by crap written on the cheap in Eastern Europe, why not the ads?

Shifting into my ominous Rod Serling voice: with the manufacturing economy gone, both in the cities and out, with more and more people shifted into low-wage service jobs, with labor as powerless as it seems it’s ever been, with our economy sliced up into strata, and the humans in each one of those strata cut away in turn — who will be left to speak for you when they come for you? And how will you feel with Niemoller’s warning, turned into prophecy now, running on a loop over and over, not just on your Facebook feed but inside your head?

The Models Were Telling Us Trump Could Win

Nate Silver got the election right.

Modeling this election was never about win probabilities (i.e., saying that Clinton is 98% likely to win, or 71% likely to win, or whatever). It was about finding a way to convey meaningful information about uncertainty and about what could happen. And, despite the not-so-great headline, this article by Nate Silver does a pretty impressive job.

First, let’s have a look at what not to do. This article by Sam Wang (Princeton Election Consortium) explains how you end up with a win probability of 98-99% for Clinton. First, he aggregates the state polls, and figures that if they’re right on average, then Clinton wins easily (with over 300 electoral votes I believe). Then he looks for a way to model the uncertainty. He asks, reasonably: what happens if the polls are all off by a given amount? And he answers the question, again reasonably: if Trump overperforms his polls by 2.6%, the election becomes a toss-up. If he overperforms by more, he’s likely to win.

But then you have to ask: how much could the polls be off by? And this is where Wang goes horribly wrong.

The uncertainty here is virtually impossible to model statistically. US presidential elections don’t happen that often, so there’s not much direct history, plus the challenges of polling are changing dramatically as fewer and fewer people are reachable via listed phone numbers. Wang does say that in the last three elections, the polls have been off by 1.3% (Bush 2004), 1.2% (Obama 2008), and 2.3% (Obama 2012). So polls being off by 2.6% doesn’t seem crazy at all.

For some inexplicable reason, however, Wang ignores what is right in front of his nose, picks a tiny standard error parameter out of the air, plugs it into his model, and basically says: well, the polls are very unlikely to be off by very much, so Clinton is 98-99% likely to win.

Always be wary of models, especially models of human behavior, that give probabilities of 98-99%. Always ask yourself: am I anywhere near 98-99% sure that my model is complete and accurate? If not, STOP, cross out your probabilities because they are meaningless, and start again.

How do you come up with a meaningful forecast, though? Once you accept that there’s genuine uncertainty in the most important parameter in your model, and that trying to assign a probability is likely to range from meaningless to flat-out wrong, how do you proceed?

Well, let’s look at what Silver does in this article. Instead of trying to estimate the volatility as Wang does (and as Silver also does on the front page of his web site, people just can’t help themselves), he gives a careful analysis of some possible specific scenarios. What are some good scenarios to pick? Well, maybe we should look at recent cases of when nationwide polls have been off. OK, can you think of any good examples? Hmm, I don’t know, maybe…

brexit-headlines

Aiiieeee!!!!

Look at the numbers in that Sun cover. Brexit (Leave) won by 4%, while the polls before the election were essentially tied, with Remain perhaps enjoying a slight lead. That’s a polling error of at least 4%. And the US poll numbers are very clear: if Trump overperforms his polls by 4%, he wins easily.

In financial modeling, where you often don’t have enough relevant history to build a good probabilistic model, this technique — pick some scenarios that seem important, play them through your model, and look at the outcomes — is called stress testing. Silver’s article does a really, really good job of it. He doesn’t pretend to know what’s going to happen (we can’t all be Michael Moore, you know), but he plays out the possibilities, makes the risks transparent, and puts you in a position to evaluate them. That is how you’re supposed to analyze situations with inherent uncertainty. And with the inherent uncertainty in our world increasing, to say the least, it’s a way of thinking that we all better start becoming really familiar with.

The models were plain as day. What the numbers were telling us was that if the polls were right, Clinton would win easily, but if they were underestimating Trump’s support by anywhere near a Brexit-like margin, Trump would win easily. Shouldn’t that have been the headline? Wouldn’t you have liked to have known that? Isn’t it way more informative than saying that Clinton is 98% or 71% likely to win based on some parameter someone plucked out of thin air?

We should have been going into this election terrified.

Still Dancing

“As long as the music is playing, you’ve got to get up and dance. We’re still dancing.” These words were spoken by Citigroup CEO Charles Prince in July 2007, shortly before the start of the financial crisis. He was talking about Citi continuing to lend and lend (especially for the sake of financing leveraged buyouts) in spite of fears that reduced liquidity (coming, for example, from much lower valuations of subprime securities and other securities on Citi’s balance sheet) would leave the bank significantly exposed.

Prince would eventually be derided both for his stewardship of Citi and for his commentary on dancing, which became a summation of the financial sector’s attitudes and behavior leading up to the crisis. But as an observation of social myopia, his words seem pretty spot-on. When we start to see a future crisis looming, we usually realize, on some level, that we’ll need to adjust our day-to-day behavior to prepare for the storm, or to head it off — to trade short term gratification (making money, having fun, whatever) for long-term sustainability. The question is when. When we’re by ourselves, making decisions independently, we usually do a decent job of timing the shift. If you find out that your roof is starting to leak, you’ll probably fix it before water floods your house. But when we’re in large groups, we’re guided by the behavior of others. And if we don’t see anyone else preparing for the crisis, most of us are reluctant to be the first ones to act.

If I were a better person, I would keep Prince in mind every time I hear (almost daily, now) about irreversible climate change, the most obvious and important example of a looming crisis that we’re collectively ignoring. But apparently I am still too myopic for that. Instead, his words occurred to me yesterday as I thought about the Super Bowl. At this point, we’re all aware that football, as currently played, sometimes results in repeated concussions, brain injury, and shortened lifespans — that, when we watch a game, we might literally be watching some of the players, perhaps even the very best ones, killing themselves out there. Already, many parents are discouraging or forbidding their kids from playing. You might think that, with the game heading for what feels like a long-term crisis, perhaps starting to lose its moral acceptability, there might be less business as usual — that some of us would look to the future and stop watching.

But — the Super Bowl feels like an important shared experience. And sometimes it’s really fun. For now the music is still playing, and we’re still dancing.