Getting things done: startup edition

Publicly available startup data includes firms that exist just as online profiles. So, maybe these firms will do their product some other time or they will disappear. It's better to exclude such startups from stats and look at who survives.

Funding is a good filter here. Getting seed funding means a startup at least has a team and idea. But over the years, the fraction of series A deals decreases:

Source 

If a smaller fraction of startups gets next-stage funding, it means that fewer startups survive after getting seed money. This survival rate indicates how well startups get prepared for doing business. The fewer firms lost on the way, the lower risks investors bear.

The major startup nations from CrunchBase:

China and Israel do well here. The US makes other countries look like dwarfs on charts, so it has a separate graph:

About 80% of startups live their first to fifth funding stage. Having more stages isn't that common. By the later stages, a startup either becomes a company with more conventional funding (revenue, bank loans, bonds, public equity), or gets acquired by another company, or disappears.

Replication files: https://github.com/antontarasenko/blog-replication-files/tree/master/2014-09/08_cb_funding_stage

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.)

Investing and failures in startups

The efficient market hypothesis got a bad press after 2008. Not surprisingly. It's a half-truth. For instance, what Robert Shiller identified as genuine mispricing Robert Lucas called a minor deviation. Also, the hypothesis has many interpretations, and here's one of them.

(data link)

On the left we have the mean of money that startups received over their lifetime. On the right is a rude measure of risk: the ratio of acquisitions to closed companies in the respective market. So, enterprise software has three successful acquisitions per one failure. I dropped "operating" startups because it's difficult to interpret their success.

The graph is interesting because clean tech gets much funding but has one acquisition per two failures. Analytics gets small funds (not so sexiest as it was called?), but gives very stable outcomes. These two are exceptions because in general funding match the risk measure. And so in other markets: it's enough for one product (like housing) to have abnormal pricing for the entire market to be under risk.

That is an attempt to make complex things embarrassingly simple, of course. For example, some may insist that average funding is a measure of capital intensity, not of competition among investors. Or what we should honestly calculate returns, as was done here. But it all seems to be half-truths, including this piece. We have to keep watching.