World Trade, Import/Exports
BACI dataset [1] provides data on bilateral trade flows for 200
countries at the product level (5000 products). Used the
BACI_HS17_V202201.zip file, processed it for 2019 using code below. As
each country-dyad-product is processed line by line, the code creates
a relation matrix, if there is trade between country i and j its
value is added in A[i,j] for each product. First analysis simply
sums all product trades at bilateral level, to create a trade flow
number between two countries. To keep visualization simple, exports
and imports are added to each other.
With the final relation matrix, first simple counts,
import scipy.io as io
A = io.mmread("/tmp/A-final").tolil()
rows,cols = A.nonzero()
print (len(rows))
vals = np.array([A[row,col] for row,col in zip(rows,cols)])
16752
Naturally all country pairs do not trade; out of approx 400K relations we have 16K relations.
mean,std = np.mean(vals),np.std(vals)
np.round(mean/1e6,2),np.round(std/1e6,2)
Out: 1.08 10.79
Which trade links are above, below average,
hv = vals[vals < mean]
print (np.count_nonzero(hv))
hv = vals[vals > mean]
print (np.count_nonzero(hv))
hv = vals[vals > mean+4*std]
print (np.count_nonzero(hv))
15339
1413
73
Trade is highly skewed; many countries trade below average, few are above average. Some, a massive 4 sigma's away from average comprise the trading countries we hear about eveyday, US, China, Germany, etc. An interactive map of the extraordinary flows is below.
Reference
[1] BACI, International Trade Database at the Product-Level.