Matosinhos
The sea trade port in Matosinhos, Portugal. (Image: qwitka via Unsplash)

Identifying opportunities for export-driven industrialization

Big data can help detecting high-potential export products to support diversification.

By Wim Naudé and Martin Cameron

The diversification of exports along the extensive margin—exporting new products and exporting products to new destinations—can contribute to a country’s industrialization and growth. As Harvard economist Ricardo Hausmann phrased it: “what you export matters”.1 For many small open economies this may matter even more post-COVID-19. In our recent work we illustrate this further by closely examining the case of Portugal, a small open economy located at the periphery of the European Union. Like in many other countries, COVID-19 has caused a huge demand-side shock in Portugal: private consumption is expected to have dropped by 8 per cent in 2020, and investment spending by 11 per cent. The government has only been able to provide a relatively small fiscal stimulus for households and firms affected by lockdowns, and remains hamstrung due to the country’s debt almost exceeding 130 per cent of gross domestic product (GDP) at the end of 2020. With consumers, businesses and government pulling out of spending, this leaves foreign demand as the only potential source of growth. We ask then whether Portugal can take advantage of foreign demand to boost export growth in the wake of the crisis, and if so, how?

The resilience of merchandise trade

Some may immediately object, pointing out that all countries have been affected in a similar fashion by COVID-19, and that international trade all but collapsed during the height of the pandemic, with long delays at ports and logistics disruptions. This is only partly true – global trade did contract markedly in 2020. What is, however, also true and not always fully appreciated is that global trade in physical products has fully recovered and even expanded after the initial COVID-19 shock. Consider, for example, the RWI/ISL Container Throughput Index, which reflects the number of containers being shipped through the world’s major ports (figure below). A 14-year high was reached in October 2020, after the Index had dipped significantly in early 2020. Even that slump was not as substantial as the one in 2008-2009, during the global financial crisis.

RWI/ISL Container Throughput Index (2007-2020)

Note: The RWI/ISL Index is based on container handling data collected from 91 ports around the world. These ports account for the majority of the world’s container handling (60 per cent). With globally traded goods being mainly transported by container vessels, this index can be used as an early indicator of developments in international trade. The RWI/ISL Index uses a base year of 2008, and figures are seasonally adjusted.

January 2020 captures Chinese New Year and beginning of COVID-19 impacts.

Source: Authors’ compilation on data from the Institute of Shipping Economics and Logistics.

While service trade remains subdued and tourism and the travel industry are operating severely below capacity, merchandise trade has been an important source of resilience for many countries. While shorter term logistical hiccups did occur globally, most countries and service providers have managed to adapt to the new operating circumstances. This lesson is unlikely to be lost on many countries, which will likely seek to reinvigorate their industrial policies post-pandemic, aiming to diversify their production structures into manufacturing goods that a person can “drop on their feet”.

Export diversification after COVID-19

There are four major reasons why a diversification of export growth will be pursued following the pandemic: For one, export-driven industrialization will not only raise aggregate demand while it is sluggish, but will also boost the productivity and innovation of firms that export through what is known as the market-size effect. This is especially key for economies with limited domestic market potential.

Secondly, export-driven industrialization enhances domestic firm entry and entrepreneurship. This is known as an “induced” competition effect – the confirmation of the existence of more and better export opportunities signals that a larger market is available to, e.g. Portuguese firms, and hence stimulates market entry at the domestic level.

Third, diversification into new export products and new markets can help improve the economy’s resilience and provide insurance against future shocks, including future pandemics, given that these are likely in future due to continued changes in land-use patterns and climate change. This aspect should also be considered by partner countries (importers) from a supply source diversification perspective to mitigate potential future supply disruptions.

Fourth, the exports of highly successful exporting countries tend to follow a ‘power-law’. That is, these countries tend to have highly concentrated exports. William Easterly et al (2009) attribute this to “big hits.”2 According to their model of exporting, most countries only export a few products to a very limited number of destinations, with most export success stories being reflected in scoring one “big hit” in terms of a product’s destination. Making “big hits” requires export diversification as a form of experimentation and learning as well as a dose of luck to discover a particular niche – akin to the entrepreneurial knowledge-spillover mechanism described by Ricardo Hausmann and Dani Rodrik3.

Overcoming informational frictions

The fundamental challenge is identifying opportunities for the diversification of exports and new export destinations. This is a question that has confronted policymakers and scholars for some time. How can opportunities for “big hits” be identified?

Before the advent of Big Data, many economists were pessimistic that governments could actually accumulate the necessary information and preferred leaving the discovery of export opportunities to entrepreneurs. While the latter remains crucial, the ability of governments to sensibly and objectively guide entrepreneurs—based on big data analytics—has gained traction. Such a wealth of information can help reduce informational frictions, which may be particularly important for growing exports at the extensive margin, i.e. for exporting new products and exporting to new export destinations, not only by helping match individual exporting / importing firms, but to generally expand export possibilities or opportunities that the country could tap into.

In this respect, an intriguing perspective is provided by the “balls-and-bins” model of trade.4 In this model, export products are akin to balls and export destinations are akin to bins. Thus, the total product-destination combinations that can be filled at any point in time depends on the number of products traded (balls) and the number of countries involved in that trade (bins). From the exporting country’s perspective, say of Portugal, some bins (export destinations) are empty, while some bins contain more balls (export products) than others. One of the insights from simulations using the balls-and-bins model is that the number of firms that will export different goods (balls) depends on the number of available bins (export destinations).

Consistent with the above insights on big hits and bins-and-balls, we developed a data-intensive methodology that “opens” more export bins for firms by reducing some of the informational frictions – and raising the likelihood of discovering a ‘big hit’ in export markets. We illustrate the usefulness of this approach by demonstrating how it was recently applied to the case of Portugal.

Finding most-promising export destinations

We first describe our modelling approach5. Our basic aim is to bridge the information gap mentioned above and to contribute to the identification of export opportunities based on a process of ‘filtering’ the “big data” on trade that is available from the UN COMTRADE database6, and refined in the CEPII BACI data set7. The challenge big data and the large number of potential combinations poses is addressed by reducing the potential set of options (balls and bins) that need to be selected with well-researched filters. Our approach takes all possible worldwide product and market (country) combinations into consideration, using four major filter categories consisting of various sub-filters which are consecutively applied. The approach systematically eliminates less-promising markets until those with the greatest prospects of success are revealed.

These algorithmic filters can be categorized in general terms. First, broad general market potential as reflected in size of the economy, growth and political and commercial risk are considered. When this filter was applied to the case of Portugal, we identified 637,678 product-by-country flows (“balls-and-bins”), i.e. 5,200 of the 6,374 traded Harmonized System (HS) products in the database. Second, product-country market potential characteristics, such as short-term and long-term import growth and relative import market size were taken into account. After applying this filter to the case of Portugal, we identified 257,335 product-by-country flows (balls-and-bins). Third, product-country market access conditions, including factors such as market concentration and accessibility were factored in. Subsequently, we identified 147,205 product-by-country flows. Last, outcomes were categorized based on the revealed comparative advantage8 (RCA), relative trade advantage (RTA) and ‘home market’ and ‘target market’ product-level trade characteristics. At the end, we identified 46,813 overall realistic export opportunities, consisting of 1,279 traded products across 128 countries, as potential target markets. 

New export opportunities for Portugal

By applying this model9, we find that there are significant opportunities for Portugal to diversify its exports along the extensive margin, despite the COVID-19 pandemic. Of the overall 46,813 product-country opportunities identified, around 45,250 are “new” export opportunities at the extensive margin for markets – in other words, markets that Portugal is not yet exporting the particular products to, or has so far only been a relatively small supplier (5 per cent or less) of the particular products into these markets. For example, there is ample opportunity for Portugal to export products that it is already good at exporting, such as machinery and equipment, motor vehicles and parts and wearing apparel to markets such as the United States, Germany, China, United Kingdom, France and Japan. From an extensive margin perspective with respect to products, examples identified include the manufacturing of macaroni, noodles, couscous and similar farinaceous products, the manufacturing of regulating or controlling instruments, and apparatus and machinery for filtering or purifying water.

Moving forward 

In the post-COVID-19 world, many countries will resort to industrial policies to strengthen their domestic manufacturing sector, and will try to achieve this through the diversification of exports. The challenge is how to overcome informational frictions to identify realistic opportunities and narrow down the field for entrepreneurs to discover opportunities – even perhaps discovering new “big hits” in existing or new export markets. In this article, drawing on recent results for Portugal, we demonstrated how the data in the “big” UN COMTRADE and BACI databases, combined with relevant ancillary indicators, can be algorithmically filtered to uncover new product-combinations for exporters.

  • Wim Naudé is Professor of Economics at the Cork University Business School, University College Cork, Ireland.
  • Martin Cameron is Managing Director of Trade Research Advisory (Pty) Ltd in Pretoria, South Africa.

Disclaimer: The views expressed in this article are those of the authors based on their experience and on prior research and do not necessarily reflect the views of UNIDO (read more).

References

  1. Hausmann, Ricardo; Hwang, Jason and Rodrik, Dani. (2007) What you export matters. Journal of Economic Growth 121–25.
  2. Easterly, William; Reshef, Ariell and Schwenkenberg, Julia M. (2009) The Power of Exports. World Bank Policy Research Working Paper No. 5081. Washington, D.C.: World Bank.
  3. Hausmann, Ricardo and Rodrik, Dani. (2003) Economic Development as Self-Discovery. Journal of Development Economics 72(2), 603-633.
  4. Armenter, Roc and Koren, Miklós. (2014) A Balls-and-Bins Model of Trade. American Economic Review 104 (7), 2127-2151.
  5. This is based on the pioneering work of Cuyvers, Ludo; De Pelsmacker, Patrick; Rayp, Glenn and Roozen, Irene T.M. (1995) A decision support model for the planning and assessment of export promotion activities by government export promotion institutions—The Belgian case. International Journal of Research in Marketing 12(2), 173-186.
  6. United Nations. (2021) UN Comtrade Database.
  7. CEPII. (2021) BACI.
  8. For this purpose, we used an RCA >= 0.8, not the typical theoretical value of RCA >=1, since we wanted to include some potential ‘less mature’ export products.
  9. Naudé, Wim and Cameron, Martin. (2021) Export-Led Growth after COVID-19: The Case of Portugal. Notas Económicas (forthcoming).

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