Thursday, March 12, 2020

Can Trade with China Predict COVID-19 Cases? Part 2

In my last post I discussed whether exports from china can explain current COVID-19 rates. There were some criticism with the analysis.

The first was that I assumed some sort of causal relationship . I probably shouldn't have said whether trade can 'explain' COVID-19 rates. What I meant whether trade can 'predict' the differences in COVID -19 rates.  The mechanism itself seems obvious - increased contact should increase probability of spreading diseases. Of course $ amount of trade is imperfect - I expect $1million of food exports to have more of a chance to have diseases than $1 million of phone exports - but it seems like a decent proxy for 'connectivity'.

The biggest critique was that I didn't take into account population sizes. I thought that was a decent critique so have run results again and reproduced similar charts as before. Feel free to comment.

Below is a histogram of log per capital COVID-19 cases. Seems far more symmetric taking into account population sizes.

Below is scatterplot of log(covid-19 cases / population) ~ log(chinese exports) for each country. Looks like a fairly strong relationship. There are several small country outliers that reduces r^2 of regression to ~ .24. This is an increase in r^2 relative to not taking into account population.

I ran a similar model of a random forest with all sectors of trade. Below is predicted vs actual plot. 

The relationship now is ~ .55 r^2 which shows is an increase in what was previously posted and the univariate regression above. Iran is still the largest outlier. Bahrain and Iceland are also now outliers.

Below is an updated importance plot. Nickel and sunflower seeds are still highly predictive of COVID-19 cases. I'm sure many of these correlations (not causations!) are geographic in nature. But some exports may be more likely to carry diseases like 'articles of gut'. I think the best way to treat the below graph is a rough exploratory device not meant to draw grand conclusions.

The conclusions don't dramatically change form previous analysis. If anything the results have gotten stronger.


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