Everybody else is doing it

Sometimes your associates will say “Everybody else is doing it.” This rationale is almost always a bad one if it is the main justification for a business action. It is totally unacceptable when evaluating a moral decision. Whenever somebody offers that phrase as a rationale, in effect they are saying that they can’t come up with a good reason. If anyone gives this explanation, tell them to try using it with a reporter or a judge and see how far it gets them.

Warren Buffett, Berkshire Hathaway Letters to Shareholders

Learning to Learn from Indonesia

The World Bank publishes its 2015 development report. Behavioral economics, which the report is about, already earned a Nobel and best-seller positions for popular books on topic. The Bank now politely reminds that nudges matter for public policies.

One literally illustrative case from the chapter on productivity:


Which is about this:

Seaweed farmers in Indonesia, for example, had no problem noticing that the spacing between pods determined the amount of seaweed they could grow, and they could accurately report the spacing on their own lines. They failed to notice, however, that the length of the pod also mattered; they did not even know the lengths of the pods that they used, even though farmers had an average of 18 years of experience and harvested multiple crop cycles per year and thus had plenty of opportunities for learning by doing.

Even when randomized controlled trials on their own plots demonstrated the importance of both length and spacing—at least for researchers analyzing the data—the farmers did not notice the relationship between length and yields simply from looking at their yields in the experimental plots. Only after researchers presented them with data from the trials on their own plots that explicitly pointed out the relationship between pod size and revenues did farmers begin to change their production method and vary the length of the pods.

At this point, some think “Oh, those stupid Indonesian farmers! This never happens to me.” Well, it does. Lawrence Summers suggests a good example: the airport elevator that takes more time to fix than the Empire State building to build.

Apart from the decline of social trust in the United States (which is Summers’ main point), slow construction has some behavioral roots. Why doesn’t the owner fix his elevator faster? The losses from this elevator are not on his books—they’re opportunity costs that few care about. If thousands of people have to make their route two minutes longer each day while the elevator stands still, the owner doesn’t notice the losses either. The people do, and not Harvard professors alone. Customers are less satisfied with service (walking around the place is a service, too) and less likely to leave their money around. It boomerangs on the elevator owner through the long chain of revenues and rents from airport shops.

Like the Indonesian farmers, managers would notice this “only after researchers presented them with data from the trials on their own plots airports that explicitly pointed out the relationship between” time to fix an elevator and revenue from operations.

And guess what? Managers wouldn’t agree to establish a proper trial to find this out empirically. It requires some elevators to work and some not to work in a random order—a thing totally unacceptable to managers. After all, they realize that dysfunctional infrastructure isn’t that great.

A single dysfunctional elevator ain’t great either. The math is embarrassingly simple. If ten elevators reduce revenues by 10%, then one elevator reduces revenues by 1% (actually, more than that; people don’t care much if nothing works, but they still notice those small flaws if things run somewhat smoothly).

If elevators still seem to be a small issue, big construction projects don’t. Repairing a road takes time but contractors rarely use this time wisely. Why should they? A daily 8-hour shift is a cheap way to complete the works in one year. Three shifts could do it in six months, but margins would be smaller. Meanwhile, drivers spend another six months in jams around roadblocks.

A private contractor need not to care about drivers, but government must. Naturally, by paying contractor more for completing the project fast. It may mean more taxes or debt; but if drivers realize how much slow roadworks add to communing, they would pay to avoid this waste.

Again, like the Indonesian farmers and airport managers, drivers rarely draw the connection between “downsizing the government” and personal time lost in jams. First, politicians rarely puts things this way; second, people routinely underestimate opportunity costs compared to direct expenses. So, they choose fewer taxes and more jams.

Where does behavioral economics lead to? Teaching yourself biases is an option. But it probably won’t help with elevators, bridges, and roads. These are problems that deserve routine attention, which governments may provide. If governments prevent crimes, shouldn’t they take control over things that kill our time?

Startups across countries

A few plots in addition to yesterday’s post on startups.

Startups and economic development

Sources: CruchBase.com dataset and Penn World Table 7.0.

That’s not a bad fit for relations between startups and GDP. The number of startups in the dataset seems to be a good indicator of entrepreneurial activity in general.

Startup nation

Here’s an illustration for Dan Senor and Saul Singer’s thesis about Startup Nation:
Israel has relatively more startups than the US. Tel Aviv and Silicon Valley drive the numbers for their countries, so it’s not exactly a nation-wide phenomenon. You call the book Startup City, though the result is no less impressive.

Web data and language barriers

Like other sources based on voluntary reporting, CruchBase may have data biased on one or another way. For example, it may underrepresent countries, in which English is not a major language. And we expect a bias in favor of bigger firms. And here’s the case:

China and Russia indeed either have bigger startups on average or just underreport to CrunchBase. The latter is the case because these are exactly two major countries that stand behind a language firewall. They have their own Facebooks, Twitters, and Amazons. So, we expect them to be less active on CruchBase. More so:

The surprising break after the 90th percentile separate countries into two groups. What are the groups? Look here:

(US and UK are excluded to make the graph readable. 100+ startup countries included.)

Group 1 are countries with < 0.02 startups per 1,000 inhabitants and Group 2 are the rest. And in result Group 2 contains countries with an explicitly high role of English language. So, the break indeed looks like a language thing.

Nevertheless, language per se is not a big factor in development, so it doesn’t bias the data on GDP in a systematic way. (You can also control the very first plot for the percentage of English-speaking population.)