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Exporter Dynamics Database version 2.0: What does it reveal about the trade collapse?

Ana Fernandes's picture

The recent global financial crisis was closely followed by a trade collapse. Global trade plunged by 23% in 2008-2009. Despite a rebound in 2010-2011, trade growth has been almost stagnant ever since and is predicted by the WTO in its April 7 2016 press release to remain sluggish, a grim outlook compared to the expansions in pre-crisis times (Constantinescu et al., 2015).  What were the underlying micro sources of this trade collapse: were exporters’ ability to participate in foreign markets or their pace of growth most hurt? Evidence from high-income countries shows that declines in the intensive margin—average exporter size—explain most of the decline in global trade, compared to the fall in the extensive margin—the number of exporters. But what about developing countries? 

Download and query Exporter Dynamics Database indicators

The recently released Exporter Dynamics Database (EDD) version 2.0 with its indicators on both margins of trade at a micro level for 70 countries (of which 56 developing countries) can help answer this question. The EDD can be downloaded in bulk from the World Bank Microdata catalog and now it is also available for customized queries in the World Bank Databank. The EDD indicators for developing countries show that a decline in the average size of exporters was the key factor behind the decline in total exports resulting from the global financial crisis. 

Studying the micro patterns behind the trade collapse is just one of the many issues that can be explored using the EDD indicators. Other issues range from the role of non-tariff measures (such as product standards) or trade logistics reforms for exporter entry, survival, and diversification to the micro patterns behind the trade creation and diversion ensuing from preferential trade agreements. Fundamental issues such as the role of trade margins for development can also be examined and a recent study shows that stronger exporter growth is the main factor explaining why richer countries export more (Fernandes et al., 2016).

The EDD is the only database where researchers, analysts and practitioners can find more than 100 indicators on basic characteristics of exporters, their concentration/diversification, their dynamics, all calculated based on exporter-level customs data for 70 countries over the 1997-2014 period and at 7 different disaggregation levels. The analytical and descriptive opportunities using the EDD are immense – just think of delving into the micro patterns behind any trade flow previously known in aggregate.  

Let us take Bangladesh’s total exports of $30.1 billion in 2014. The EDD indicators at the country-year level show that those exports were made by 7,937 exporters of which 24% were new exporters.
Now consider Bangladesh’s top export, apparel products (HS 2-digit 61), of $11.7 billion in 2014. The EDD indicators at the country-HS 2-digit-year level show they were accounted for by 3,670 exporters of which 25% were newly exporting this product. “Harmonized System” or HS codes are a standard way of classifying trade data.

If we take a geographic perspective, Bangladesh’s top 3 destination markets in 2014 were the United States ($5.4 billion), Germany ($4.7 billion) and Great Britain ($3 billion). The EDD indicators at the country-destination-year level show how many exporters served each of those markets: 2,020 sold to the United States, 1,733 sold to Germany, and 1,784 sold to Great Britain.

Finally, the diagram below shows what can be learned by digging deeper and combining geography and exported products - looking at EDD indicators at the country-HS 2-digit-destination-year level.

Proportion of apparel exporters in Bangladesh that were new entrants in the top 3 destination markets


Source: based on the Exporter Dynamics Database version 2.0 – indicators at country-HS 2-digit product-destination year level.

We look forward to seeing the ways in which you will use the Exporter Dynamics Database at its 7 disaggregation levels. Please leave any comments, suggestions, and findings in the comment box below.


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