Isegoria

Robin Hanson once wrote about how intelligent people tend to overestimate how smart everyone else is, and Anatoly Karlin elaborates on this, with support from PISA test scores:

Fortunately, the PISA website has sample math questions from the 2012 assessment, corresponding to each of the six different levels of difficulty, as well as statistics on the percentage of 15-16 year old students from each of the participating countries that is capable of correctly answering it.

Here is the sample question from Level 6, the hardest level:

Helen rode her bike from home to the river, which is 4 km away. It took her 9 minutes. She rode home using a shorter route of 3 km. This only took her 6 minutes.

What was Helen’s average speed, in km/h, for the trip to the river and back?

Karlin notes how few people get this right:

This problem requires a multi-step approach, an understanding of rates, and the intelligence to complete it in the correct order.

Though not especially hard, even at this level. I suspect that many of you can do it in your heads within a minute.

But a majority of all the tested teens begged to differ.

OECD average: 3% (!!). Korea: 12%, Japan: 8%, Germany: 5%. The US, Italy, Sweden, and Russia were all at 2%; the Mediterranean was at 1%.

Some countries where a big fat 100% (rounded up) were unable to do this problem: Argentina, Brazil, Chile, Colombia, Indonesia, Jordan, Kazakhstan, Malaysia, Mexico, Peru, Qatar, Tunisia, Uruguay.

The number of people at this level, the highest measured by PISA, is dwindling away into insignificance in Latin America and the Middle East.

And yet this only translates to an IQ of 120-125. We’re nowhere even near genius level yet.

This matters:

The classical definition of an economy is a system for the production and exchange of goods and services. However, I will argue that you can view it even more fundamentally as a system for generating and trading solutions to problems.

[…]

Some of these problems, such as subsistence farming and trucking, are pretty simple and can be accomplished with reasonable efficiency even by relatively dull workers. This is because problems in this “Foolproof sector” (as Garett Jones calls it) require few steps and have only a minimal threshold difficulty, so production in this sector is governed by the standard Cobb-Douglas equation. More highly skilled workers are only modestly more productive, and are thus awarded with modestly higher salaries. Labor differs by productivity, but is substitutable — one experienced waiter is worth two novice ones.

Other problems are very complex and require teams of competent workers to perform multiple complicated steps to create a successful solution. The best are paired with the best for maximum productivity. Moreover, many O-Ring problems might have a threshold limit for IQ, below which no productive work can be done on them in principle (as per the Ushakov-Kulivets model). To be commercially viable, the risk of failure on any one link of a long production chain needs to be kept low. Examples of these “O-Ring” tasks may include: Aircraft manufacturing; corporate merger planning; computer chip design; machine building; open-heart surgeries.

It is tempting to think that working at a think tank is a way to encourage possible social change, but Arnold Kling found that working in business is the activity most likely to achieve positive social change:

In 1980, I completed my Ph.D dissertation. My goal was to solve a theoretical problem in Keynesian economics, hoping to steer the profession away from the “rational expectations neoclassical” macroeconomics that was all the rage within top economics departments. The idea was to explain price stickiness as based on information problems.

Trying to solve an important theoretical problem is a terrible strategy for a dissertation. Instead, you should figure out what the departments that are hiring are looking for. What they were looking for at the time were dissertations that were based on the rational expectations approach.

For me, the result was particularly bad. A professor who interviewed me on the job market stole my idea and published it before I did, without acknowledgment. The idea had no impact on the profession. In fact, it has been periodically rediscovered since (with no credit either to me or to the man who stole it), but again with no impact. This experience has left me less than excited about the academic research process in economics as a way to generate social change through ideas.

My first job out of grad school was at the Fed. I do not recall coming up with any significant ideas when I was there.

In 1986, I started working at Freddie Mac, the mortgage giant. At the time, I thought of it as a profit-seeking enterprise, albeit with some peculiar features. One feature was that it was supposed to “serve” upward mobility in the housing market.

Another feature was that with its government guarantee, its debt costs were low, and this gave it an advantage in undertaking certain forms of financial arbitrage. I was appalled when an economist told me excitedly about an anomaly in the Eurodollar market that he thought that Freddie could and should exploit. To me, that seemed like an abuse of Freddie’s Congressional charter.

In the 2000s, long after I had left, Freddie and Fannie took on riskier borrowers in a (misguided) attempt to serve upward mobility in the housing market. They also engaged in more of what I thought of as abuses of their low-cost debt status. I ended up happy to see them shut down during the financial crisis of 2008. I think that profit-seeking and a tight relationship with the government were ultimately a bad combination.

But working at Freddie gave me the best opportunity I have ever had to produce social change. My most significant idea there was to change the underwriting process to reduce judgment and rely more on data. Instead of trying to use AI to imitate human underwriters, I pushed for using credit scores. I also promoted using the Case-Shiller method for estimating home prices in an attempt to reduce the reliance on appraisals. The goal was to reduce the cost of obtaining a mortgage loan, to reject fewer good loans, and to accept fewer bad loans.

Freddie Mac adopted the credit scoring approach in 1994. For me personally, this was more bitter than sweet. Just as the idea I was pushing for was adopted, I was treated to a humiliating demotion, and I soon left the company.

As far as social change is concerned, the move toward credit scoring dramatically changed the mortgage industry. Yes, underwriting costs fell, and decisions became more accurate, with fewer good borrowers turned down and fewer bad borrowers accepted. But it also allowed new players to enter the mortgage lending market. Some of these players developed the so-called subprime mortgage market, with mortgage securities provided by Wall Street. These new players were central actors in the financial crisis of 2008.

I am not saying that I personally brought down mortgage lending costs, or that I personally caused the financial crisis. My guess is that the move toward credit scoring was going to happen at some point, anyway, and so that my efforts accelerated the process by at most a few years. And the financial crisis had many causal elements, mostly involving the political economy of mortgage lending in the U.S. I still think that introducing credit scoring into mortgage lending was socially beneficial, at least directly. But in a complicated world, the indirect effects of actions are difficult to assess.

When I left Freddie Mac in April of 1994, I created The Homebuyer’s Fair, one of the first commercial sites on the World Wide Web. The goal was to use the Internet to disintermediate in real estate and mortgage lending. For me personally, it worked out well. But apart from any role that the site played in stimulating interest in the Web (we got tons of press in 1994-1997), I would not say that it came anywhere close to achieving any major social goals. As any number of people who have tried to get rid of the excessive costs in real estate can tell you, institutional resistance to change is strong. In principle, the Internet could have eliminated real estate commissions 25 years ago. In practice, not so much. In principle, a digital property database could eliminate the title insurance industry. In practice, the title industry’s grip on Congress is too strong.

After our web site was sold in 1999 (to a subsidiary of the National Association of Realtors(tm), ironically enough), I “retired” to a career of teaching and writing. I taught on a volunteer basis for 15 years at a local high school. I mostly taught AP economics and AP statistics. I hated the AP econ curriculum, because my experience in business had led me to believe that a lot of mainstream economics is poorly conceived. I really liked the statistics curriculum, although one of my students, an autodidact who was a follower of the rationalist community, chided me for teaching a “frequentist” rather than a Bayesian approach.

My goal was to have some long-run influence with at least a few students. I think I was somewhat successful, although by 2015 or so I was struggling with what appeared to me to be the reduced maturity of the students.

I also taught for a few years at George Mason University, at the ridiculous adjunct salary of about $1500 for a class of 100 students. And, as my wife is fond of pointing out, GMU even made me pay for parking. Again, I would have been happy to reach a small number of students with a long-term impact, but I don’t think that I did.