Robots and export quality

Timothy DeStefano, Jonathan Timmis 30 October 2021

a

A

An increasing number of media articles highlight how rich countries are rapidly automating and that developing countries may lose out – endangering the future of manufacturing-led development.  Much of the literature is drawn from robot adoption in rich countries and focused on the labour market impacts (Acemoglu and Restrepo 2017, Adachi et al. 2021, Deng et al. 2021).  We know surprisingly little about robot diffusion in developing countries and the impact on trade.  Recent evidence on automation and reshoring is at best mixed and finds that automation in rich countries may actually increase imports sourced from poorer countries (Artuc et al. 2018, 2019, Stapleton and Webb 2020).  

In this column we explore a new channel – whether automation leads to export quality upgrading – in both developing and developed economies.  Robots perform tasks repeatedly to the same high level of accuracy, so are commonly used in the assembly of small electronic components, precision welding of car parts, or cutting of metals.  Some types of advanced robots are able to operate within extremely accurate tolerances – for instance, those with lasers can cut to within 10 micrometres (0.01 millimetres) – or are equipped with sensors that allow the machines themselves to identify product defects. Robots may therefore reduce human error in production, increasing product quality.  

To assess this, in a new paper (DeStefano and Timmis 2021), we combine data on robot diffusion and export quality that span a broad sample of countries (28 developed and 31 developing economies) over the period from 2000 to 2015. Export quality is derived from detailed HS 10-digit US import data (following Khandelwal et al. 2013) and combined with data on robot use at the country-industry level. The extensive country coverage in our data allows us to distinguish effects in developed and developing economies, and the richness of the trade data enables us to examine which types of products are being upgraded within each country.  

Robot diffusion in global manufacturing

We find that robots are becoming an integral component of global manufacturing – in developing countries too. Today there are roughly 2.7 million robots in operation around the world, with the number of new robots installed each year more than tripling over the last decade (IFR 2020). While most of the evidence so far has focused on automation in rich countries, the increasing automation of manufacturing tasks is occurring not only in developed economies, but also in developing economies (see Figure 1). Robot use in developing countries has increased nearly 10-fold over 2000-2015.  While developed economies remain more intensive users of robotics, developing countries are catching up.  

Figure 1 Robot diffusion in the top five developed and developing economies

Notes:  Observations in the figure reflects the (log) total robot stock for each country and year, for the economies with the largest robot stock in 2015.  Note in our regression analyses, robot stock is used at the country-industry-year level. Country income status is defined by the World Bank (2000).

Robot diffusion among foreign export customers strongly predicts automation adoption at home.  To address endogeneity concerns, we instrument robot adoption of the home country-industry using robot diffusion among their foreign customers located in other world regions, using their initial foreign trade network defined in 2000. Our motivation for this instrument comes from the extensive evidence of technology spillovers from foreign multinational customers to their overseas suppliers (Javorcik 2004). We find that automation cascades from foreign customers to their suppliers.

Robot adoption and export quality upgrading

We find that robot diffusion leads to increases in the quality of exported products. Across all countries, a 10% increase in robot stock results in a 1.2% increase in quality (see Figure 2). The strongest quality gains accrue to developing economies, where a 10% increase in robot stock leads to a 2.7% gain in quality. While quality gains are still achieved through robot use in developed economies, the size of the effect is considerably less (roughly a 0.4% gain in quality from a 10% increase in robot adoption). One reason why we find a smaller effect of robots on quality in developed economies over the entire sample period is that more advanced countries were early adopters, and thus quality gains were realised early on.  

We also find some evidence of differences in quality upgrading by type of robots. Adoption of more basic robots leads to quality upgrading in developing economies, whereas some sophisticated robot applications lead to stronger quality upgrading in advanced economies. For instance, in terms of cutting machines, we find mechanical cutting robots matter more for quality upgrading in emerging economies and laser cutting robots more for high-income economies.

Figure 2 Effect of robot adoption on export quality: All countries, developing and developed

Note: The following figure illustrates the estimated coefficients in the second stage 2SLS regression of robot adoption for all countries, developing countries and developed countries. Country income status is defined by the World Bank (2000).  Controls include (log) employment and (log) real value added per worker at the country-industry-year level, as well as exporter*10-digit product and exporter*year fixed effects.  

Differential gains depending on initial quality

Within each country, robots lead to quality upgrading of initially low-quality exports, furthest from the quality frontier. This is true of both advanced and developing economies. For example, products that are initially 10% further from the quality frontier achieve a 1% faster increase in quality from a given growth in robot stock in developed economies, and a 1.3% faster increase in quality in developing countries. Since developing economies in general tend to produce lower-quality exports, this reconciles the stronger aggregate quality upgrading we observe for developing countries in Figure 2. 

The fact that robots matter more for initially lower-quality goods in both developed and developing countries suggests that robots are important for economic development. Quality improvements in production and exports help determine economic development (Hidalgo et al. 2007, Fontagne et al. 2008). Robot adoption appears to enable catch-up in export quality, given that the gains are stronger for initially poorer quality exports.  For developing countries, robot diffusion may therefore help them to overcome quality standard that firms face looking to participate in and climb the ladders of global value chains. 

Conclusion

We find that robots lead to export quality upgrading, but the gains are not spread evenly.  Much of the quality upgrading is obtained by developing countries. Within countries, we find that robots lead to upgrading for initially low-quality products – a result found for both developed and developing countries.  Since developing countries tend to export lower-quality goods, the gains from robots are larger for these types of countries.  Developing countries therefore have greater potential for quality catch-up through automating their production.

Our results have several important implications for policy. First, technology always creates winners and losers. While automation poses risks, it also presents new opportunities for developing countries through the ability to upgrade production and leverage the benefits of global value chains. Second, trade openness can help technologies like robots diffuse through supply chains and across borders.  Increased protectionism may stymie the potential for international technology diffusion and constrain the ability of firms in developing countries to upgrade production processes, move into higher value-added activities, and produce the high-quality products increasingly demanded by consumers globally. Finally, there is no ‘one size fits all’ recipe for policy targeting technology adoption. Firms and countries adopt different types of production technologies depending upon their factor endowments and incentives to do so.  Thus, encouraging only the most advanced technologies is unlikely to be appropriate, especially for developing economies.  

References

Acemoğlu, D and P Restrepo (2017), “Robots and jobs: Evidence from the US”, VoxEU.org, 10 April.

Adachi, D, D Kawaguchi and Y Saito (2021), “Robots and employment: Evidence from Japan, 1978-2017”, VoxEU.org, 09 February.

Artuc, E, P Bastos and B Rijkers (2018), “Robots, Tasks and Trade”, World Bank Policy Research Working Paper No. 8674. 

Artuc, E, L Christiaensen and H Winkler (2019), “Does Automation in Rich Countries Hurt Developing Ones? Evidence from the U.S. and Mexico”, World Bank Jobs Working Paper No. 25. 

Deng, L, V Plümpe and J Stegmaier (2021), “Robot adoption at German plants”, VoxEU.org, 16 January.

DeStefano, T and J Timmis (2021), “Robots and Export Quality”, World Bank Policy Research Working Paper No. WPS 9678.

Fontagne, L, G Gaulier and S Zignago (2008), “Quality matters: Everything is (not) made in China”, VoxEU.org, 28 March.

Hidalgo, C A, B Winger, A L Barabási and R Hausmann (2007), “The product space conditions the development of nations”, Science 317(5837): 482–487. 

International Federation of Robotics (2020), World Robotics Report. 

Javorcik, B S (2004), “Does foreign direct investment increase the productivity of domestic firms? in search of spillovers through backward linkages”, American Economic Review 94(3): 605–627. 

Khandelwal, A K, P K Schott and S J Wei (2013), “Trade liberalization and embedded institutional reform: Evidence from Chinese exporters”, American Economic Review 103(6): 2169–2195.

Stapleton, K and M Webb (2020), “Automation, trade and multinational activity: Micro evidence from Spain”, SSRN Journal.

a

A

Topics:  Development International trade Labour markets Productivity and Innovation

Tags:  robots, automation, robotisation, export quality, developing countries

Economist, Harvard Business School, Laboratory for Innovation Science Harvard

Economist, Chief Economist Office for East Asia and the Pacific, World Bank

Events

CEPR Policy Research