Inside look at a bubble? China’s ed-tech startup funding over the years

A lot of money has been thrown around China’s ed-tech startup scene over the past few years. Some have even suggested it’s been big enough to create a global bubble in the ed-tech industry.

I wanted to see what the numbers suggested, so I crunched some numbers from the largest Chinese startup database: ITjuzi. I thought looking at what round of funding startups from each year were able to reach might reveal some interesting insights that other data sets – like the total amount of money invested each year – don’t show.

To be clear, ITjuzi is not a perfect or wholly complete database. And the further back in it you go, the more startups’ funding situations are marked as “unknown,” which makes the data a little tough to parse. Still, it’s the biggest database I’m aware of that dates back beyond 2012, so it’s the best source of information we have for a big-picture look at ed-tech startup funding in China.

First, the big picture. Here’s the total number of ed-tech startups founded in China since 2012. The dotted line is a trendline. The 2016 number is a projected figure based on the number of startups ITjuzi currently reports having been founded in 2016 (19).

Now let’s take a look at the raw data. The chart below shows the highest level of funding attained by startups founded in each year as a percent of the total number of startups founded that year. So, for example, according to the chart, 43 percent of the ed-tech startups founded in 2015 have thus far received no funding. 35 percent of the ed-tech startups founded in 2015 raised an angel round, but nothing more significant.

The big problem with the above chart, of course, is that it’s difficult to interpret because the further back in time you go, the more startups are labeled with the “unknown” funding status in ITjuzi’s database. And unfortunately, looking at the “unknown” startups one by one doesn’t reveal any strong patterns – many of them have gone out of business, but some are still in operation, and it’s impossible to know whether any of them might have raised funding without publicizing it.

As a result, the best option seems to be to just ignore these “unknown” startups entirely and examine the data that is there. Removing the unknowns from the list and then recalculating the percentages accordingly results in a chart that looks like this: