Today I’m reading a few papers from NBER:
“Cognitive Economics is the economics of what is in people’s minds. It is a
vibrant area of research (much of it within Behavioral Economics, Labor
Economics and the Economics of Education) that brings into play novel types
of data—especially novel types of survey data. Such data highlight the
importance of heterogeneity across individuals and highlight thorny issues
for Welfare Economics. A key theme of Cognitive Economics is finite
cognition (often misleadingly called “bounded rationality”), which poses
theoretical challenges that call for versatile approaches. Cognitive
Economics brings a rich toolbox to the task of understanding a complex
Austerity in 2009-2013, ungated version
“The conventional wisdom is (i) that fiscal austerity was the main culprit
for the recessions experienced by many countries, especially in Europe,
since 2010 and (ii) that this round of fiscal consolidation was much more
costly than past ones. The contribution of this paper is a clarification of
the first point and, if not a clear rejection, at least it raises doubts on
I’m hoping that this paper on austerity will be a little more illumating
than the fly-by analysis I was talking about
Quick chart fight. A while back, Matt Yglesias posted this, saying that “2014
is the year American austerity came to an end”:
Econ blogger Angus argued that Yglesias is trying to re-define austerity
because we’re now seeing some decent growth. He posted the nominal graph and
quipped, “Either austerity means nominal cuts and we never had any of
it, or austerity means cuts relative to trend and we are still savagely in its
Kevin Drum says that’s bogus, because you have to look at real spending per
capita, like so:
So here’s my entry. I’m going to add two economic indicators to that same
chart: growth in real GDP per capita, and the prime-age employment-population
ratio (which I like better than unemployment):
To put growth and the E-P ratio on the same scale, I’ve arbitrarily subtracted
79%, which is about the average over the period in question. It’s the trend,
not the level, that matters.
The point, as I see it, is this: to make an argument about the “end of
austerity” and what it means, you have to look at that graph and say that the
2014 part of that chart is meaningfully different from the 2009-2013 part. If
you see that, you have better eyes than I do.
This is why people don’t trust economists or economics writers. It’s why they
shouldn’t. You can’t tell anything from that graph, and claiming you can means
you’re at best overstating your case, and at worst lying. It can be a data
point, but only as part of a larger analysis and I haven’t seen any
that I’m particularly thrilled about or ready to bank on.
In a post dramatically entitled Voter Suppression in 2014, Sean McElwee of
the think tank Demos argues that early statistics already
suggest that meaningful numbers of voters were wrongly disenfranchised. He
makes three points: first, that the number of people who cannot vote because
they committed a felony was high relative to some victory margins; second, that
states with voter ID laws saw suppressed turnout, and third, that states with
same-day registration had higher turnouts.
I want to focus on the second point there, because it’s been a hot-button issue
lately, and because I’m more skeptical than most people that voter ID makes
much of a difference. McElwee’s tries to demonstrate his point by
graphing the mean voter turnout among states in three pools: those which
require photo ID, those which require non-photo ID, and those with no ID
Mean turnout was highest in the no-ID states, and higher in the (presumably
less restrictive) non-photo ID states than in the photo ID states. Case closed,
Not exactly. To use statistics like this to make a real point, you have to
remember that you’re got an incredibly small sample size. What we really want
to know is whether the variance between groups is bigger than the variance
For example, here’s another version of that graph, but I’ve added confidence
The idea here is that, if you tell me which group a state is in, I
can be 95% sure, statistically, that the voter turnout for that state fell
between the top and bottom of the black line. You can see that there’s a lot of
overlap. A turnout of 38 percent, say, wouldn’t be out of line for any group.
Maybe we’d be better off if we didn’t look at the mean, but rather the
median—the state that ranks exactly in the middle of its group in terms of
turnout. This takes care of any outliers—observations that aren’t
characteristic of the group as a whole:
Whoops! Now the suppression story doesn’t fit at all. There’s almost no
difference between photo ID states and no-ID states, and non-photo ID states do
worse for some reason. Of course, at this point, we start to suspect that it’s
not so much a reason as chance, and other unexplained factors that affect
Heck, let’s do one more. Here’s a box plot:
The line in the middle is the mean—same as the first graph. The box
represents the middle 50 percent of the states in that group. Finally, the
lines (called “whiskers”) represent the entire range across the group, up to
one and a half times the spread of the middle 50 above and below the mean.
Here we see an important point: there are two dots in the no-ID group that are
so much higher than the rest that they fall outside that
mean-plus-one-and-a-half-times-middle-fifty range. Those dots happen to
represent Maine and Wisconsin, which had particularly high turnouts, and which
pulled the mean of the no-ID group up quite a bit. Now, looking across the
whole distribution, that data point looks a lot less compelling.
This all amounts to a huge statistical nothingburger. As more data comes out,
I’m sure more careful analyses will be run on the numbers to see whether we
think voter ID laws were important to the election. My bet’s on the null
hypothesis, but I might be wrong.
But let’s not excite ourselves about statistically meaningless charts just yet,
I use Python for almost all my data work, but both in my workplace and my field
more generally Stata dominates. People use Stata for a reason, and it provides
a far wider range of advanced statistical tools than you can find with Python
(at least so far), but I hate working in it.
I’ve always found it hard to explain to others just why I hate it so much.
You can generally get your problem solved, the help files aren’t terrible,
there’s lots of Google-able help online, you can write functions if you
want to learn how. And while I find lots of little things annoying (the way
you get variable values, for example, or the terrible do-file editor), the big
problem was the one other people didn’t understand.
Today, however, I was re-reading some pages about the Unix Philosophy, when
I saw something that hit the nail on the head. It’s Rob Pike’s Rule 5:
Rule 5. Data dominates. If you’ve chosen the right data structures and
organized things well, the algorithms will almost always be self-evident.
Data structures, not algorithms, are central to programming
Stata only has one data structure: the dataset. A dataset is a list of columns
of uniform length. You can only have one dataset open at a time.
This is the right data structure for performing the actual analysis of
data—say, a regression—and the wrong data structure for literally
everything else. The problem is, 90 percent of doing data work is cleaning,
aligning, adjusting, aggregating, disaggregating, and generally mucking around
with your source data, because source data always comes from people who hate
you. And because the data structure is wrong, you’re forced to use algorithms
that look like they come from an H.P. Lovecraft story.
Never having seen anything better, most Stata users seem to be resigned to
doing things like creating an entire column to store a single number and
writing impenetrable loops for simple tasks. Or they use sensible tools to
create their datasets (increasingly Python, but also even something like
Excel) and then use Stata just for the analysis.
The latter is my approach when I can’t avoid Stata entirely. But I’m really
looking forward to the day when I can avoid the fundamentally flawed design of
A few weeks ago, Franklin Foer wrote an article at The New Republic arguing
that Amazon is now a monopoly and therefore should be broken up. The
difference between Amazon and what we used to think of as monopolies, he says,
is that Amazon squeezes its producers, not its customers, and consumers are
complicit in the squeezing, which is just kind of assumed to be a bad thing.
Foer didn’t offer very specific recommendations, but he did point to, say AT&T
which was broken up using antitrust law in the 1970s as a good example.
“That’s silly”, I thought when I first read the piece, and I didn’t expect to
hear much more about it.
Today, however, Paul Krugman followed up with an op-ed that correctly identified
Amazon’s relationship to its producers as a monopsony, not a
monopoly, and argued that it is totally not ok, guys.
Krugman’s argument zeros in on Amazon’s fight with publisher Hachette. Hachette
won’t agree to the revenue sharing that Amazon wants, so Amazon has
disadvantaged their books.
Like Foer, Krugman calls to mind the old progressive “victories” like the
breakup of Standard Oil, saying, “The robber baron era ended when we as a
nation decided that some business tactics were out of line. And the question is
whether we want to go back on that decision.”
I think that line explains why suddenly we’re all supposed to be up in arms
about Amazon. It’s certainly not out of deep concern for book publishers.
Everyone hates book publishers, who squeeze authors as much as Amazon squeezes
them (and, interestingly, more than Amazon squeezes authors, at least at
In fact, in a sane hour, Krugman et al. would probably have no
trouble agreeing that what we’re really seeing here is publishers losing value
because what they do is not nearly as valuable when you don’t need to
physically print all your books. Certainly they would agree that, if the market
were well and truly competitive, none of the publishers would be making money
anyway because profits in a competitive market go to zero.
But Amazon is a BIG BUSINESS with MARKET POWER, and BIG BUSINESSES with
MARKET POWER are bad and exploitative in the progressive view of the world. The
breakup of Standard Oil is a part of the progressive identity the same way
that, say, the Reagan tax cuts are part of the conservative identity.
If Amazon isn’t actually hurting real people, then maybe BIG BUSINESSES with
MARKET POWER aren’t always bad. Maybe the breakup of Standard Oil wasn’t all
that huge a victory for real people after all. So it’s important to the
progressive view of the world that Amazon be perceived as hurting
Now, there’s nothing wrong with having general rules for policy, like “monopoly
is bad, let’s avoid that” or “let’s not try anything for the first time at the
national level.” They’re especially good when they’ve been learned over time.
But the hyper-dynamic technology-driven economy, where it’s has been harder and
harder to preserve market power, has presented a powerful challenge to these
old progressive beliefs, and those of us not wed to them should demand that
they prove themselves again.