The transformational power of data has brought to bear a new era of analytics, and with it has come a revolution in the way we eat, work, travel, and socialise. But when it comes to global trade – which underpins every aspect of our day-to-day lives – we still can’t reliably answer the question of “Who trades what with whom?”. This is an inexcusable state of affairs in today’s world of big data, artificial intelligence and machine learning, as Dr Rebecca Harding, CEO of Coriolis Technologies, explains.
By analysing just a small sliver of the around 2.5 quintillion bytes of data created by everything from mobile phones to smart city infrastructure and credit cards every day, businesses and governments can quantify and gain actionable insights on everything from shopping habits to healthcare, public transportation and education – in real time.
Not so for global trade. Policymakers and companies alike currently find themselves trying to make sense of lagged and fragmented data points in order to glean a bigger picture, which means they are in danger of making decisions without a reliable evidence base.
This situation would be bad enough in a benign international trading environment. When taken against the current backdrop of a global trade war and a populism-driven shift away from multilateralism, however, its rectification becomes an urgent necessity.
If trade institutions are changing, then politicians, negotiators and businesses alike need accurate and timely data in order to be able to assess the impact of, for example, higher tariffs, greater non-tariff barriers, regulatory changes and the reconfiguration of trade agreements. This requires accurate, timely and consistent data that can cater for the complexities of global supply chains across multiple borders and multiple components.
Nowhere is this clearer than in the United Kingdom as it prepares to leave the European Union. The UK government’s EU Exit: Long-term economic analysis research paper, upon which parliament will base its assumptions on the long-term economic and fiscal impact of the country’s exit from the 28-member bloc, relies upon data from the Global Trade Analysis Project (GTAP), one of the most widely used computable general equilibrium models internationally for trade analysis. So far, so good. However, the GTAP data, which looks at bilateral trade flows through costs, distance, production and consumption patterns, was last updated in May 2015 – over a year before the Brexit vote took place.
The risks associated with developing policy impact assessments based on four-year-old data are glaringly obvious.
Lagged data isn’t the only issue. Gaps and discrepancies abound across all publicly available datasets. For example, Germany reports twice the amount of service exports to the UK as the UK says it imports from Germany according to TradeMap, which uses United Nations service trade data. This is similarly the case for France and Italy. This suggests that the UK may be miscalculating the size of its trade surplus in services – which is worrying, given that trade in services represents 48% of all trade in the UK.
The UK is far from alone in suffering from inadequate trade data. Japan, for instance, says it imports 21% more from China than China says it exports to Japan. Kenya, meanwhile, has not reported any trade data at all since 2013.
The accepted methodology for dealing with this has been variously developed between the OECD and the World Bank, taking one country’s exports in relation to another country’s imports in order to yield a divergent, but potentially more reliable picture of the trade flow itself. But the gaps remain despite improvements in trade data collection techniques and tightening reporting standards, as countries report in different sector codes, in different currencies and at different times. This leaves both the public and the private sector to fill in the blanks and hope for the best when trying to predict the future of trade.
In today’s age of information, however, models no longer need to make assumptions about how the world looks. They can instead be based on real time data. Using AI mirroring techniques on a multi-dimensional basis – a system that must handle more than 3TB of data at any one point in time – Coriolis Technologies ensures that not only are trade flows between countries equal, but so too are trade flows between countries and their partners by sector or product. For goods, this approach yields a difference between what is published in publicly available data and the cleaned and harmonised data of a relatively consistent 11-12% of world trade since 1996. In other words, there is 11-12% more trade happening than what has been captured by the patchy, inconsistent data the world is using at present.
The tools are now available to answer the question: “Who trades what with whom?”. Putting them to use will transform how policy is formulated, how the impact of the changing trade landscape is assessed, and how trade is financed by banks. The imperative for harnessing data in trade has never been stronger, and a failure to do so will have dramatic consequences.
About Coriolis Technologies
Coriolis Technologies is the world’s leading source of data and analytics in the trade and trade finance space.
Coriolis Technologies’ mission is to harness the power of world-class data in order to address broken trade and the broken politics and finance that goes with it.
With leading-edge technological solutions and an experienced team of economics, risk, finance, strategy and investment professionals, Coriolis Technologies turns billions of unconnected datapoints into accurate insight, analysis and solutions for exporters, policymakers, investors, banks, analysts and governments around the world.
