Voodoo Economics and the Minimum Wage

Quartz suggests that companies can make more money by raising their wages, focusing on the well-paid line workers at QuikTrip and the research of Zaynep Ton at MIT Sloan..  I think we should probably file this under “Wonderful if true, but pretty darn unlikely”.  I think there’s a simple way to evaluate this type of claim – if true, where are the private equity firms running LBOs of fast-food chains and raising everyone’s wages?  This argument doesn’t even have to be of the “nobody leaves $20 on the sidewalk” strength, merely that if this were a reasonable strategy someone would have tried it.  However, that’s not the only argument against it.

Let’s assume that QuickTrip does get positive ROI on the high wages – how much of the “QuickTrip effect” is compositional?  There are two possible stories of the QuikTrip effect – the first is that higher wages drive higher productivity, and the second that higher wages attract more productive employees.  The Quartz story and Zaynep Ton focus on the first story, but to me that seems less plausible than the second.  A QuikTrip job is obviously much more appealing than one at McDonald’s, and presumably the selection process is more competitive.  Even if you believe in the QuikTrip effect, and there’s no reason not to, it is almost certainly because they attract better employees than their competition.

This has important policy consequences. First, this isn’t a generalizable strategy – by definition only a few companies can be “well-paid” relative to their competition and poach the better employees. For many companies, choosing not to play this game is the smarter choice.  Secondly, we should be very dubious about common liberal claims that raising the minimum wage will be inexpensive or positive-sum.  If there’s no direct productivity effect of high wages, then raising the minimum wage is costly; it doesn’t mean that it’s a bad idea, but deluding oneself about its effect does nobody any favors.  The belief in the positive-sum minimum wage raise is probably the closest liberal equivalent to the voodoo economics of deficit-reducing tax cuts – wonderful if true, but pretty darn unlikely.

The Consequences of MH17 for Ukraine (and Russia)

In the latest escalation in Ukraine, today a civilian airliner was shot down with a surface-to-air missile over Donetsk, killing all 295 people aboard.  It’s not clear yet what happened, but the explanation seems obvious – Russia gave jumpy, poorly-trained separatists heavy anti-aircraft systems (along with, probably, some trained operators, because you can’t exactly pick those things up and figure it out).  These separatists see a blip on the radar and fire enthusiastically without realizing it’s a civilian plane.  As the narrative is pieced together, I would be very surprised if we discover otherwise.

The crisis in Ukraine has officially spiraled completely out of control.  Over the past few weeks or so, Russian involvement has become more and more overt – yesterday evidence emerged of a Russian jet shooting down a Ukrainian plane, and Russian artillery shelling the Ukrainian army.  The two countries are very close to a de facto state of war, and a de jure state of war might not be far off.  I agree with Julia Ioffe – this incident is clearly a game changer, but it’s not immediately clear how.  However, the crisis has obviously entered a more volatile and less predictable phase that should worry everyone.

Americans should reevaluate the reputation of Vladimir Putin as an evil genius; for the last six months his behavior has been reactive and panicky. First, he lost his client state in Ukraine by pushing too hard against EU association.  He successfully claimed Crimea, but seems to have cemented the dominance of the pro-Western faction in Ukraine for the foreseeable future.  Vladimir Putin might have thought his backing of separatist rebels was a clever low-cost way to encourage the new Ukrainian government to fall into line, but as fighting escalated he has lost any control he might have had over the situation.  This incident was a shocking blow to his position; meaningful EU sanctions are much more likely than yesterday.  This is all a bad thing – the combination of reactive, panicky, and backed into a corner is terrifyingly unpredictable.

In my view, this incident substantially increases the chance of an overt Russian invasion of Eastern Ukraine.  Putin has completely lost control of the irregulars he has armed, and crucially has now done so very publicly.  He must be considering whether it is possible to disarm the rebels before they do something else that will so drastically compromise Russia’s international position, the economy, and potentially even his grip on power.  Unfortunately for him, a “peacekeeping operation” will now be even more vilified internationally than if he had launched one yesterday.  He’s in a very tight spot, which should frighten everyone involved.

Also, incidentally, try and extrapolate from this incident to policy for the US.  Putin was transferring arms to well-known actors immediately on the other side of his border, with defined objectives, trained fighters, and Russian intelligence handlers heavily involved.  Do you think we will have substantially more control or influence over Syrian rebels?

Six Thoughts on Six Californias

The news is full of talk about secessionism and state-splitting now that eccentric venture capitalist Tim Draper’s “Six Californias” plan has qualified for the ballot. It would, as the name suggests, split California into six parts.  A few initial reactions, because I am befuddled and fascinated by how this would work.

  1. The water politics of Six Californias are horrifying.  Specifically, Central California would have a stranglehold over the water to the coasts, and control of the water supply is actually one of the key objectives of Central California Republicans today.  Will they try and starve the coasts to keep cheap water for farming?
  2. You need the approval of the California State Legislature and the US Congress, according to the Constitution.  Good luck with that.
  3. This would be a meaningful handicap to Democratic Presidential candidates, but a gigantic boon for the Democratic position in the Senate.
  4. There’s no conceivable way this won’t devastate rural California’s economy (particularly the new states of Central California and Jefferson) which are currently heavily subsidized by the coast.
  5. It seems like this would create a truly gigantic mess for existing contracts and legal actions governed by California law, especially long-term ones that would reasonably stretch beyond whatever adjustment period is going to go on the ballot.
  6. This does provide an intriguing possibility to sweep out some of the junk in the California state constitution and law, since presumably each new state will write their own constitutions. If the new states rely less on initiatives, that could ultimately provide for more stable governance in The Region Formerly Known as California.

I find it very hard to believe that this will pass (but will readjust my priors if it starts polling at 70%), so this is mostly academic.  As an academic matter, I am incredibly curious!

The Worthlessness of Psychological Testing

So, the Myers-Briggs psychological profile test is worthless.  You’ve probably encountered this before: take a bunch of profiling questions and it’ll spit out whether you’re Introverted/Extroverted, Intuitive/Sensing, Thinking/Feeling, and Perceiving/Judging (16 total profiles).  It’s plagued by measurement issues, including the fact that the constituent categories are based on nothing at all and the fact that measurement error is remarkably high – as many as 50% of people get different results when they take the test multiple times.  Furthermore, and this is the real kicker, it has zero predictive power in predicting people’s happiness, situational comfort, job success, or any other tangible outcomes.

So measuring latent variables is hard, and it’s actually particularly difficult in psychology.  A latent variable is one that can’t be observed, but can be inferred from other things.  A simple example is generosity – you can’t exactly measure how charitable a person or society is in their heart of hearts, but you can measure how much they volunteer or donate to charity and use that to make inferences about their level of generosity.   Measuring these latent variables is a key social science problem, and one on which people spend a great deal of time. 

There are two reason why psych latent variable measurement is particularly tricky – we don’t really know what the variables mean, and we don’t really know what the key variables are.  The first is simply that it’s difficult to cleanly define introversion/extroversion in a way that doesn’t rely heavily on pre-existing notions that emerge from…where?  Probably from pre-existing notions, which is indeed where Jung derived his categories.  This is troublesome, because it means we’re to some degree testing for things defined however we want and introduces a degree of circular logic.  The second concern is more diffuse – how do we know that introversion/extroversion is a key component of personality?  How specifically do we know that it’s more important than, say, general degree of anxiety or like/dislike of peanut butter?  It may seem more important, but…um…why?  Even if Jung’s four axes were scientifically derived and correctly measured, there’s no obvious reason to believe these four axes are the central components of personality.

The problems of deriving measurements for psychology suggest that it might be a better fit for different techniques.  Psych testing is a classic example of “supervised learning” – we define outcomes, see how people match up to them, and use that info to derive predictions about how new people will match up to them.  That in turn drives the test.  But the problems with that are large, as I detailed above – unsupervised learning might be a better fit.  This would include techniques such as clustering, wherein you give people a bunch of questions and use an algorithm to see whether there are natural lines of division in the data rather than specify beforehand what questions are important.  That in turn would allow you to infer what exactly are the crucial components of personality – though it wouldn’t necessarily help with defining what exactly it is you’re measuring, it is a clear step forward.

A lot of social science problems are not obviously well-suited for unsupervised learning, but this one seems to be.

Social Science: The Roman Frontier

There’s a big focus in academic humanities these days on ‘digital humanities’.  This can include a lot of vaguely silly stuff, like doing word counts in great works of literature in attempts to make literary analysis more sophisticated.  However, there’s also much more interesting work going on, particularly in economic history.  A traditional problem for application of social science methods to historical question is the scarcity of data, because hard data in an easy-to-digest format is pretty rare.  However, determined researchers can apply some new tools and some hard thinking in order to find out quite a lot.

ORBIS is one of the coolest projects I’ve seen in the “digital humanities” world.  It’s a reconstruction of the travel network across the Roman world.  The researchers, Walter Schiedel and Elijah Meeks, have gone to great lengths to reconstruct the methods of travel across the empire.  The topographic map is just the starting point – they’ve included information on highways, travel modes, and even seasonal weather and wind conditions for information on seasonal changes in travel times.  They’ve even incorporated historical records on Roman-era prices so that you can see the inflation-adjusted cost of various travel options, selecting for the fastest or the cheapest trip. 

This could be a great resource for social scientists or for historians looking to apply social science methods to historical problems.  If you’re interested in systematically studying the effects of, say, Roman administrative quality on local economic outcomes this data set is invaluable.  This data would allow the use of what’s called an “instrumental variable” study, which is a method for studying effects that can’t be easily untangled from causes.  Local administrative quality and economic productivity are a good example; neither is really exogenous to the other. However, you can get around this by using a third variable, an “instrument”, that is exogenous to both but only affects the treatment.  Travel time from Rome is perfect for this – it definitely has an effect on local administrative quality, but doesn’t have an obvious impact on local productivity.  This allows you to back out the effect of administrative quality on local productivity, which is a question that’s otherwise very difficult to answer.

Of course, that relies on similarly high-quality data on all three variables of interest.  That could be difficult to come by, which just goes to show the difficulty of doing empirical social science on historical topics.  However, ORBIS is an incredible step in the right direction.  It’s very cool what these researchers have done, and I hope to see more work like this in the future!

The Positive Feedback Loop of Urbanization

There is something of a debate about why people make more money in cities, and whether at the individual level it’s wisest to make more money in a higher-cost city or less money in a lower-cost city.  Emily Badger has an excellent piece on economist Rebecca Diamond’s work on the growing educational/economic divide across American cities.  Diamond found (unsurprisingly, if you’ve ever compared Boston and Detroit) that places with more college graduates are expensive, but tend to be nicer and to offer higher-paying jobs.  In short, even after you account for the higher cost of living big, well-developed cities tend to come out ahead.  That doesn’t surprise me, but this did – places with higher concentrations of college graduates tend to pay college graduates more!

This suggests that the urbanist case is actually right – that people are more productive in cities than rural areas.  There are two countervailing forces that could act on the wages of highly-skilled workers in areas with many of them, greater supply and greater productivity.  We should expect to see lower wages for college grads in cities with lots of them, and the fact that the opposite holds true suggests that there are in fact quite substantial productivity benefits gained by embedding in a local economy with more specialization and more opportunities to apply specialized skills.

Urbanization is a positive feedback loop of productivity.  Urban workers produce more – while they have to pay more in housing costs, there is a positive net social benefit that increases the more people take up the opportunity.  This is actually the opposite of a collective action problem, a situation where everyone is incentivized to take actions that make everyone else better off.  Even better, this generates surplus income that can be taxed to make rural residents better-off, something the state does now through taxing income and spending on services/infrastructure in rural areas.  The main thing standing in its way is structural constraint, namely the limited housing available in the densest and richest urban areas.

Arguably, by preventing development rich urban landholders are extracting rents from the rest of the country.  Certainly from the rest of their states.

The Goodhart’s Law Problem with Public Loan Guarantees

As a way to encourage private investment in clean energy research, the Department of Energy has extended loan guarantees to many private companies involved in renewable energy development.  While conservatives seem to believe that they are all Solyndras, in fact the portfolio is doing remarkably well.  And that’s a problem.  As Michael Grunwald says, the whole point of this program is to bankroll promising technology that offers high rewards, but is too risky for the private sector to invest in.  If almost all the loan recipients are paying it back, that means the government is not only not investing in promising-enough technologies, but is actually crowding out private investment in the sector.

The political economy of public-private research partnerships are less promising than they seem initially.  They are justified as offering lower costs than direct government sponsorship by risk-sharing, which is hypothetically true.  However, it carries with it the inherent risk of Goodhardt’s Law – when a measure becomes a target, it ceases to be a good measure.  The key measurement here is default rate – the government can hypothetically monitor the riskiness of its program by monitoring the default rate and making adjustments accordingly.  However, the default rate in a loan guarantee program is actually the only visible metric available, and it is a natural rule of organizations that you manage to what you can measure.  Even without the political pressure applied, it seems like one would naturally expect these types of programs to be managed to generate maximum return of capital rather than maximum investment in promising technology.

There are better ways to structure public investment into research.  The simplest is to fund public research, which has been used plenty successfully in the past.  There’s no monetary recovery, but it’s also not set up in such a way as to encourage it – and if the money is well-directed, the social benefit can far outweigh the accounting cost.  There are also tax incentives for R&D, which are a bit less well-directed towards basic innovation but can be relatively cheap.  There are public innovation prizes, which are likely underutilized and are a generally neat solution.  But none of these face the perverse incentives of public loan guarantees.  They’re a clunky policy tool that emerge from the contradictory desire to keep the government out of something while still using policy to drive it.

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