How much is China's coronavirus epidemic impacting its economy?
That's a question we asked in our recent review of Rob Eastaway's book, Maths on the Back of an Envelope, where the math we presented to answer it was originally done by economist David Tufte. And because that was both useful and timely math, we've built a new tool for doing it, where you can make it apply for any country undergoing an economic shock resulting from a disaster if you have its energy consumption data.
Energy consumption can be key to estimating what the economic impact of a disaster is because it will be proportionate to economic activities. If businesses and factories shut down because their employees cannot get to work following a disaster, then they will consume less energy, with the drop in power use being proportional to declines in productive activity resulting from the disaster.
In the following tool, we've used Tufte's analysis for assessing the economic impact of China's coronavirus crisis set the default values, which you would need to modify if you want to consider other disaster scenarios. It's super easy, and if you're accessing this article on a site that republishes our RSS news feed, you may need to click through to our site to access a working version. Let's get straight to it:
We've done our best to generalize the math in the tool to allow it to be applied to many post-disaster scenarios where human activities simply either vanish or are diminished for a time, while keeping it as simple as possible.
Update 5 March 2020: we've updated the default estimate for energy elasticity from 1.5 to 0.8 per Du Wei's 2016 paper to make this estimate more current.
There's a price for simple in that you may need to run the tool more than once to generate a complete estimate of the economic impact of a disaster. If your data is such that it contains both a "growing" and "stable" post-disaster impacts, for example, you may want to run both for the appropriate number of weeks that apply to each to estimate the full lost GDP, which you would obtain by adding your GDP results together.
That's pretty much all it takes to generate results form this kind of back of the envelope math. We'll close with Tufte's analysis so you can see where the default numbers in the tool came from without having to click away.
This may be the best real time estimate yet on what COVID-19 has done to the Chinese economy. China’s power plants run mostly on coal. China’s coal consumption appears to be down between 20 and 45%.
This is measured in days since the Chinese New Year, which fell on January 25 this year. So, they’re usually down for about 10 days after that, and this slowdown has stretched on for almost a month now.
To get that to GDP we need to know China’s energy elasticity. A plausible value for any country is around one, estimates from 15 years ago suggest 1.5 is more suitable for China. Here’s the back of the envelope calculation:
- Choose a round number for China’s GDP like $20,000B/yr
- Coal consumption is down 20% to 45%
- The elasticity suggests a hit of 30% to 70% for GDP
- That’s $6,000B/yr to $14,000B/yr if it’s a discrete jump. It isn’t, so looking at that typical slope showing recovery by about day 25 in most years, that slope suggests effects so far that are perhaps half of that as China built up to a sustained shortfall.
- This shortfall is new and gradual, let’s say it’s about 1/25th of a year so far (about 2 weeks). That converts to a GDP loss of between $120B and $280B so far, or –0.6% to –1.4% of annual GDP in total.
- China’s economy in 2020 is roughly the size of the U.S. economy in 2008-9. During the worst parts of that recession, the U.S. economy was off $20B in 2008 III, $85B in 2008 IV, and $45B in 2009 I.
All of these numbers are sketchy, but the suggest that the effect of COVID-19 on China over a few weeks is already comparable to what a large recession did to the U.S. in a few quarters.
Image credit: Denny Ryanto
Labels: coronavirus, economics, math, tool
Welcome to the blogosphere's toolchest! Here, unlike other blogs dedicated to analyzing current events, we create easy-to-use, simple tools to do the math related to them so you can get in on the action too! If you would like to learn more about these tools, or if you would like to contribute ideas to develop for this blog, please e-mail us at:
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