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The Alarming Problems Caused By Highly Misleading Global Trade Data

The author's first job in economic research was the nightmarish task of finding a way of measuring corruption on behalf of a country client in the Middle East.

World Economics
10 May 2019

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It was explained that I should start by researching something simple - like exports of German cars to the country under investigation, as listed in German export documents. And compare the results with imports of German cars as documented by the Middle Eastern country.

The results were spectacular. Over 10 times more cars were exported from Germany according to German data, than were supposedly imported according to Middle Eastern data, over the same period.

Something wrong somewhere! The project was hurriedly shelved.

This exercise took place over 45 years ago, and such data might be expected to have changed for the better. But recent evidence suggests that raw trade data may be even more misleading today.

A paper by two academics at the University of Amsterdam (“Globalisation and the growing defects of international economic statistics", by Lucas Linsi and Daniel K. Mugge, published in the Review of International Political Economy, in January 2019) suggests that American authorities claim that US imports from France exceed US exports to France by some $14bn, while French sources indicate the difference to be less than $4bn. OK, not a factor of 10 as in the ancient example above, but a massive discrepancy, and one between the data produced by two developed countries that spend large amounts on producing economic data, not data differences between one developed country and one with poor quality national statistics.

Linsi and Mugge cite other examples. They show that major differences of this sort are not cherry picked outliers. They are the rule rather than the exception.

The Apple example of a product designed in California, sourced in many different countries and assembled in China (and therefore counted in trade data as 100% Chinese, despite a very small percentage of the net value deriving from China), is well known (see Sturgess "Trade data - use with care".) But even bigger general problems are highlighted by Linsi & Mugge, which are not widely appreciated (and certainly not by President Trump). Their conclusion is "…the de-nationalisation of economic production and consumption and the growing complexity and opacity of corporate and financial structures have not only impaired progress towards the harmonisation of statistical standards. Much more fundamentally, they have undermined the validity and hence usefulness of the statistics constructs themselves”.

Their appraisal of the potential harm such data can cause is extremely worrying. They note that rising current account deficits are taken as unmistakable signs of economic troubles, although the reality may be grounded in nothing more than mismeasurement. That mismeasurement can distort policy analyses and produce misguided policy responses. That poor data can turn mismeasurements into erroneous investment decisions. And that credit rating agencies can build skewed country risk assessments on the basis of such data that can distort government's access to global capital markets.

Linsi & Mugg's paper is a carefully considered analysis of great importance. It should be required reading for anyone using trade data.

Unfortunately the probable reality is that populist politicians will continue to use these numbers for their own purposes, as President Trump does today, and as a result many sub optimal or poor decisions will be taken by companies and governments. In the case of the current US - China dispute, there is clearly the danger of a full scale trade war resulting, quite possibly from the inappropriate use of data that are potentially wildly misleading.

Far too few economists and politicians will try to understand the murky reality behind these increasingly unreliable data.