- The Gentzkow-Shapiro Lab is on Github. Their RA manual is interesting and includes bits on PhD applications and a writing style guide.
- Soviet mosaics
- Greg Mankiw: “Getting People to Get Along, Even When They Disagree” (reading list)
- “The Umbrelly Man”
Jeff Attwood (co-founder of Stack Overflow): “The Existential Terror of Battle Royale”:
I would generally characterize my state of mind for the last six to eight months as … poor. Not just because of current events in the United States, though the neverending barrage of bad news weighs heavily on my mind, and I continue to be profoundly disturbed by the erosion of core values […].
In times like these, I sometimes turn to video games for escapist entertainment. One game in particular caught my attention because of its unprecedented rise in player count over the last year.
I absolutely believe that huge numbers of people will still be playing some form of this game 20 years from now.
It’s hard to explain why Battlegrounds is so compelling, but let’s start with the loneliness. […] PUBG is, in its way, the scariest zombie movie I’ve ever seen, though it lacks a single zombie. It dispenses with the pretense of a story, so you can realize much sooner that the zombies, as terrible as they may be, are nowhere as dangerous to you as your fellow man.
Battle Royale is not the game mode we wanted, it’s not the game mode we needed, it’s the game mode we all deserve. And the best part is, when we’re done playing, we can turn it off.
This animation blows my mind:
I just stared at this for 5 minutes.
It shows the real performance of the S&P 500 since 1923 and the data before that is from Robert Shiller.
Took the DLR, took the tube
Read a book nothing else to do
Read Gill Hicks she survived the bombings
Wrote a book, made a killing. (link)
- Consider an asset that’s worth 10,000 euros. For 12 periods the value of either rises by 70% or falls by 60% with equal probability. Now consider these five buckets: 1) below 6,400, 2) 6,400-12,800, 3) 12,800-19,200, 4) 19,200-25,600 or 5) above 25,600.
- The asset will be simulated for you and if you have guessed right and the asset ended up in your bucket, you get 20 euros. Else, you get nothing. Which bucket would you bet on?
My intuition would have been to go for the third bucket. But the right answer is the first bucket. The reason is that this question asks you what the most likely price for such an asset is, not for the average price. Ensthaler et al. offer the clue that one increase can’t compensate for one fall (0.4 · 1.7 = 0.68 < 1).
Let’s simulate this in R for many assets to see what this process is up to:
Next we extract some tidy statistics about our assets:
Now we can plot how prices develop over the 12 periods:
All the assets start out at 100. The mean price of assets rises, but the median falls fast.
The authors conclude that people are bad at calculating compound interest rates and that we tend to neglect skewness.
Ensthaler, L., O. Nottmeyer, C. Zankiewicz and G. Weizsäcker (2016). “Hidden Skewness: On the Difficulty of Multiplicative Compounding under Random Shocks”. Management Science. doi
We’ve updated our paper on automation patents which you can find here. In short, we identify automation patents based on their patent texts and create this new dataset:
Reading Erik Brynjolfsson and Andrew McAfee’s “Second Machine Age” three years ago got us interested in the topic. We were looking for data on how automation had advanced over time and across industries, but none of the existing proxies quite satisfied us. The idea to use patents came after reading Acemoglu et al. (2014) for a class which made us aware of patents as a data source.
We were lucky that Google provides a bulk download page for patents (also see these codes). One of the tasks that took us the longest time was to write a parser to extract and clean the text sections for all patents from the titles, abstracts and text bodies of the patents. That was tricky as those are 336 GB of text which makes storing and retrieving the documents an issue. The parsing step took a week on a server where eight cores ran in parallel.
We then trained a naive Bayes algorithm on a sample of patents that we classified ourselves (using these guidelines).
Here’s an example of such an automation patent:
So the patent with the name “Automatic Taco Machine” was invented by one Barry Brummet from California and is assigned to (owned by) Taco Bell. He applied for the patent in 1994 was granted it in 1996. It cites other patents such as this one. Using this data, we can also check who cited the “Automatic Taco Machine”. We find that up until 2010 it was cited a total of 11 times (for example by this, this and this patent).
Even in the title of the “Automatic Taco Machine” you have the word “automatic”, so this is an easy patent to classify. Other words in the patent text that were important in classifying it as automation were are “removable”, “storage”, “acceptable”, “support arm”, “assist”, “communicate”, “measures”, “processor” and 179 others.
Next, we checked where every patent is likely to be used (not invented). For this, we used Brian Silverman’s concordance tables. For our example, we find:
We then matched our industries to US commuting zones (with the CBP) and get the picture at the top of this blog post. It shows the number of automation patents that can be used by a single worker in each of these commuting zones. In the earlier years, the rust belt saw a lot of automation patents, but this has become much more spread out and diffuse over the years.
Our empirical results are the following:
- Between 1976 and 2014, about 2 million automation patents (out of 5 million patents in total) were granted.
- The share of innovation concerned with automation rose from 25% in 1976 to 67% in 2014.
- There was more investment in robots and computers in industries with more automation patents. Those were also industries where in 1960 more people worked in routine tasks.
- Local labor markets (commuting zones) in the US where more new automation patents could be used experienced increases in employment.
- Automation led to a loss in manufacturing employment, but this was more than compensated by a rise in service sector employment.
If you’ve become curious about the paper, you can find it here.
Acemoglu, D., U. Akcigit and M. A. Celik (2015). “Young, Restless and Creative: Openness to Disruption and Creative Innovations”. NBER Working Paper No. 19894.
Autor, D., D. Dorn, G. H. Hanson, G. Pisano and P. Shu (2016). “Foreign Competition and Domestic Innovation: Evidence from U.S. Patents”. NBER Working Paper No. 22879.
Bell, A., R. Chetty, X. Jaravel, N. Petkova and J. V. Reenen (2017). “Who Becomes an Inventor in America? The Importance of Exposure to Innovation”. NBER Working Paper No. 24062.
Bessen, J. and R. Hunt (2007). “An Empirical Look at Software Patents”. Journal of Economics and Management Strategy, 16(1): 157–189.
Brynjolfsson, E. and A. McAfee (2014). “Second Machine Age”. Norton & Company.
Silverman, B. S. (2002). Technological Resources and the Logic of Corporate Diversification. Routledge.
A Fine Theorem on “Resetting the Urban Network”, by Michaels and Rauch (2017):
So today, let’s discuss a new paper by Michaels and Rauch which uses a fantastic historical case to investigate this debate: the rise and fall of the Roman Empire.
The Romans famously conquered Gaul – today’s France – under Caesar, and Britain in stages up through Hadrian (and yes, Mary Beard’s SPQR is worthwhile summer reading; the fact that Nassim Taleb and her do not get along makes it even more self-recommending!).
“How Reggaeton Became a Global Phenomenon on Spotify”. Contains a kind of event study on what happens to the number of listeners when an influential playlist includes a song.
Chris Blattman: “Why I am not blogging anymore”
Philip Guo: “Programming as a Professor”
I enjoyed this: “How obsessive artists colorize old photos”
I’m currently working with macroeconomic forecasts by banks and research institutes (CEF). When I checked out inflation expectations, the following figure caught my attention:
The black solid line is quarterly Japanese consumer price inflation (CPI). The gray areas are two standard errors above and below the mean forecast. (Forecaster dispersion is a reasonable proxy of how uncertain forecasters are.) I overlaid the 12-month ahead forecast with the subsequent realizations.
The jump in inflation expectations in 2013 (Abe won in December 2012) is quite striking. And that effect seems to have abated somewhat since then.
Bachmann, R., S. Elstner and E. R. Sims (2013). “Uncertainty and Economic Activity: Evidence from Business Survey Data”. American Economic Journal: Macroeconomics, 5(2): 217-49.
- Check out Eric’s new blog
A Fine Theorem: Two New Papers on Militarized Police:
… [T]he pure empirical estimates, that militarization reduces crime without any objectively measured cost in terms of civic unhappiness, are quite mind-blowing in terms of changing my own priors.
- Julia Galef, “How I think about free speech: Four categories”
- Bitcoin mine in Inner Mongolia
New NBER working paper by Stefania Albanesi, Giacomo De Giorgi and Jaromir Nosal:
Our analysis suggests a reassessment of the role of growth in the supply of subprime credit in the 2001-2006 housing boom and in the 2007-2009 financial crisis. We find that most of the increase in mortgage debt during the boom and of mortgage delinquencies during the crisis is driven by mid to high credit score borrowers, and it is these borrowers who disproportionately default on their mortgages during the crisis. The growth in defaults is mostly accounted for by real estate investors.
- Rob Hyndman: “Finding distinct rows of a tibble”