Harnessing the Transformative Power of Data and Digitalization for Industrialization – Insights from Modern Trade Economics

By Jörg Mayer - 09 September 2021
Harnessing the Transformative Power of Data and Digitalization for Industrialization – Insights from Modern Trade Economics

Joerg Mayer argues that data governance that harnesses the increasing dependence of manufacturing on data, and innovation policies that give greater importance to indigenous innovation, augment the potential of industrialization as a development strategy in a digital era.

Harnessing the transformative power of data and digitalization has become essential for economic growth. Most debates on the value creation of data emphasize digital platforms and social media, concentrating on misuse of personal data, surveillance, algorithmic bias, economic concentration, and the rapid spread of misinformation. This emphasis primarily reflects the priorities and needs of advanced economies, though some observers recognize data as a development issue. A perspective from modern trade economics can further a debate on value creation from data that includes developmental concerns and explore how data governance and digitalization policies may support industrialization in developing countries and their gainful participation in the global digital economy.

Digitalization changes the nature of industrialization as a development strategy because robotization allows for automation and reshoring of production to developed countries, while information and telecommunication technologies (ICTs) improve the tradability and productivity of services. These features may disable traditional export-led manufacturing as a development strategy but enable a services-led development strategy. The COVID-19 crisis accentuates these changes by accelerating reshoring or shortening supply chains more generally to include only those countries whose digital infrastructure can ensure real-time transparency and increase supply-chain resilience.

So, how can developing countries continue to industrialize despite likely reduced opportunities for manufactured exports and further potential threats to industrialization from digitalization? A trade economics perspective can provide some answers, especially for middle-income countries.

Insights from recent trade economics

The trade literature on multi-product firms argues that two parameters determine firm-level productivity: a firm-level measure of ability, where products or product varieties closer to a firm’s core competence are produced more efficiently, and firm-product specific expertise, which is determined by knowledge of consumer preferences. Empirical evidence suggests that these two parameters are positively correlated, but that closeness to customer preferences plays a crucial role. A firm can offset the fixed cost of market entry and increase productivity by exporting a broad range of products if its product mix matches the required quality and revealed consumer preferences of its target market.

These findings have important implications for North-South trade relationships. Demand patterns in developed countries differ from those in developing countries because higher requirements for quality and functional sophistication are associated with higher per capita incomes, and national demand idiosyncrasies unrelated to income. Understanding these differences requires information about prevailing customer preferences and their evolution. In this respect, a local firm whose embeddedness in local markets provides low-cost knowledge about local customer preferences and whose core-competence product matches domestic demand patterns has an advantage over foreign firms, for which acquiring knowledge about demand in developing-country markets requires a systematic and costly effort. Hence, control over customer data becomes a key determinant for a developed-country firm to supply developing country markets.

Big data technologies can provide firms such information at low cost, enabling them to supply the heterogenous demand patterns in their target markets. This opportunity exists for firms from developed and developing countries alike. However, existing digital divides cause firms in developing countries to be less ready or able to access and process data on customer preferences. They also imply that data-related productivity growth and other gains accrue asymmetrically to the benefit of firms from developed countries, eroding the “natural” advantage of developing country firms from their embeddedness in local markets in the process. Accordingly, in addition to measures that bridge digital divides, a crucial element of development strategies in a digital era is data governance that regulates the use of data on customer preferences in a way that maximizes developmental impacts.

Figure 1 illustrates the importance of data on customer preferences. It depicts the markets for high, medium and low price-quality varieties of a given product from both the supply side (firm production possibility frontiers) and the demand side (consumer indifference curves). A quality-conscious customer will have a relatively steep curve, which touches the technological frontier of a producer serving top customers (red line). Northern firms will be placed best to serve quality-conscious customers because well-off customers in the South tend to aspire to the same tastes, habits and quality as consumers in the North. Price-conscious customers will have a relatively flat curve in the quality-price space, which touches the technological frontier of a producer that serves base customers (blue line). Southern firms will service price-conscious customers, given that their preferences are too far away from a Northern firm’s core product for it to serve these customers profitably. Value-for-money customers will choose varieties from producers with intermediate positions (dashed curve). Figure 1 places this market segment halfway between quality- and price-conscious customers but its actual placement will differ across countries depending on the differences in per capita incomes of the three groups of customers.

Figure 1: Price-quality combinations of product varieties for three groups of customers



The development question is under what conditions Southern firms can expand their product scope and supply value-for-money customers. To serve this middle market segment, the Northern firm incurs a fixed trade cost, while a Southern firm incurs a fixed cost regarding innovation and technology adoption required to broaden its product scope by including a higher quality variety. The size of these fixed costs are the outcome of government policies regarding data governance and innovation that determine the cost which (i) a Northern firm incurs to access data on Southern customer preferences; and (ii) a Southern firm incurs to broaden its product scope and include a higher-quality variety.

Policy implications

Policymakers confronting data governance face two trade-offs. First, they need to balance the potential costs of governance of cross-border data flows and data localization for e-commerce and services exports against the benefits in terms of data privacy, cybersecurity and national security. The second trade-off regards industrialization strategies. A focus on inclusion in GVCs and export-oriented industrialization may require free cross-border data flows and no localization requirements to allow for real-time information emphasizing logistical bottlenecks and disruptions along the supply chain. By contrast, more domestically oriented industrialization calls for reinforced governance of data that tracks domestic customer preferences. Hence, this second trade-off depends on the chosen industrialization strategy: export-oriented industrialization with tight inclusion in GVCs relies on different types of data than more domestically oriented industrialization. This means that free cross-border flow of data required for global supply-chain management and data localization requirements for data required for domestically oriented industrialization are not mutually exclusive. A tiered approach with different rules for different data categories could accommodate related differences in data requirements.

Focusing on the domestically oriented part of industrialization, development-oriented data governance may require that data on domestic customer preferences be used only by domestic firms. Accordingly, such data would be classified “important” and allowed to be stored and processed abroad, e.g. by providers of cloud and online processing services, only upon discretionary decision by a competent authority, where the risk of such data leaking to unauthorized users would be a major determinant.  Data localization requirements and the availability of a national digital and data infrastructure will facilitate access of domestic firms to data on domestic customer preferences if storage in the cloud and processing through specialized software providers carries too high a risk of data leakages. Building domestic digital and data infrastructure is costly and will need to be assessed against the benefits from using cheaper cloud services and the associated risks of data leakages. However, the high energy consumption of data centres may give developing countries with access to cheap renewable energy a comparative advantage and make providers of cloud services locate data centres in developing countries. Depending on legal arrangements, developing country hosts could use part of them under their national jurisdiction.

The relative importance of the export-oriented and the more domestically oriented parts in such a re-balanced industrialization strategy will depend on country-specific circumstances. Following such a strategy nonetheless implies significant uncertainty, including because of the dearth of knowledge on the benefits from big data for industrialization. Virtually all existing evidence on the benefits of big data for development relates to humanitarian areas or the optimal provision of utilities (e.g. energy) and public transport.

Another source of uncertainty concerns the implications of such a strategy for innovation policies. Developing countries have traditionally targeted improved access to international sources of technology and knowledge, combined with improved local capabilities required to identify appropriate technology transfer mechanisms and facilitate the absorption and adaptation of imported technology. Only more recently have their innovation policies given greater attention to indigenous strengths and independent local sources of product and marketing innovation. These efforts have been based on mixed evidence of technology transfer and knowledge spillovers from MNE-subsidiaries and built on progress on innovation capabilities.

Uncertainty surrounding the achievability and viability of such innovation processes will be high and risk holding back related investment. Industrial policy can reduce this uncertainty by providing fiscal incentives and ensuring access to required digital technology, as well as by increasing demand for domestic innovation by (i) regulating competition (and hence the level of demand enjoyed by individual firms); (ii) determining the number of licenses for certain activities or by imposing certain industry standards; or (iii) concentrating support to innovation in certain areas, or through tax incentives and subsidies that stimulate innovation from domestic firms.


The strategy outlined in this column could prove one way to address the conundrum of how to advance industrialization in a world with dimmed prospects for traditional export-oriented manufacturing but new opportunities arising from digitalization.

Countries that choose the outlined response could start with small but well-informed steps to test data and innovation policies, including in industrial parks, before embarking on large-scale policy implementation. Such a choice could pave the way for a mixed strategy within the same country. It would combine remaining and upgrading in existing supply chains, including through supply-chain-digitalization, with fostering own innovation-driven growth and enhanced use of data.

Some developing countries may lack the human and financial resources for the investment required to engage in the increased complexity of industrialization. But even these countries, and irrespective of the response to digitalization that policymakers eventually choose, need to be aware of the value of their data, because once foregone, gainful access to these data will be difficult to recover.



Jörg Mayer is a Senior Economist at the United Nations Conference on Trade and Development

Disclaimer: The opinions expressed are solely those of the author and do not necessarily reflect the views of UNCTAD or its member States.

This Opinion piece draws on the author’s article “Development strategies for middle-income countries in a digital world—Insights from modern trade economics”, published in The World Economy.

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