Technology in Both Lagging and Leading Regions

Latin America were started by immigrants (Maloney and Zambrano forthcoming). More recently, the high-tech clusters in Cork, Limerick, and Galway, Ireland; Chennai, India; and Taiwan, China, were all started by bringing home the diaspora from places like Silicon Valley. The Japanese Meiji miracle was kick-started by Satsuma students who had gone abroad to acquire expertise (see Cirera and Maloney 2017).

Foreign direct investment and GVCs offer another way of bringing advanced knowledge to regions if actively engaged. A study of the drivers of technological transfer in Brazil, Senegal, and Vietnam (Cirera, Comin, and Cruz, forthcoming) finds that exposure to multinationals proved an important source of ideas, and the probability of being exposed to these ideas was higher in more technologically sophisticated regions (figure 8.4, panel a). Even with exposure to multinationals, as panel b of figure 8.4 indicates, whatever led to actual adoption of technologies also varies by region—suggesting, again, important missing complements in lagging regions.

Establishing universities has also been as an important source of knowledge transfer. The land grant college system in the United States played a huge role in the transfer of new agricultural and mechanical technologies, particularly in the South. However, Kantor and Whalley (2019) find that the impact of these colleges has fallen over

FIGURE 8.4 Lagging Regions Are Less Likely to Be Exposed to Multinational Corporations, and Such Exposure Is Associated with Better Adoption of Technology in Both Lagging and Leading Regions

a. Exposure to information about technology through multinationals b. Exposure to multinationals and technology adoption

Predicted probability of exposure 35

30

25

20

Laggard Leading Multinational buyers or supplies 2.5

Predicted probability of exposure 2.0

1.5

No Yes No Yes Laggard Leading Multinational buyers or suppliers of general business functions

Source: Cirera, Comin, and Cruz, forthcoming. Note: Leading regions are defined as those above the median average productivity. The technology adoption measure is regressed on the firm’s exposure to multinationals controlling for sector, size, country, and regions. All estimates are weighted by sampling and country weights. The tick-marks around the point estimates represent the 95 percent confidence intervals. These estimates are based on a sample of firms from Brazil, Senegal, and Vietnam.

time—suggesting that proximity has become less important as telephones, automobiles, and other technologies have facilitated information flow.

However, as regions advance, universities, think tanks, and other knowledge-related institutions become more central to growth. When they function well, these institutions support the experimentation that facilitates the introduction of new products or processes to the region or, in some cases, innovations that are new to the world. Increasing the often-poor performance along these dimensions is often considered to be one dimension of regional support policies, although arguably with an inappropriate emphasis on high-level research and development (R&D) activity. There is a clear market failure in what is called the nonappropriability of knowledge: that is, the inability of firms to protect an innovation from imitation. Firms may underinvest in innovative activity because they are generally unable to capture the social benefits that arise from increasing the stock of knowledge either by invention or “discovering” existing ideas, and thus fostering further innovation.

In advanced regions, a credible patenting regime, tax write-offs, R&D subsidies, or matching grants can redress this gap between social and private benefit. However, in lagging regions, a suite of other complementary factors also must be in place, such as human and managerial capital, financial markets, or a welcoming enabling environment (see Cirera and Maloney 2017). A low level of innovation in a region may simply reflect the low returns to investment such as R&D resulting from an absence of these factors—and have nothing to do with classical innovation-related market failures (see Goñi and Maloney 2017). This suggests that simple policies to subsidize R&D in poorer regions with the idea of stimulating their rapid convergence through local innovation are likely to be pushing on a string. From a national growth perspective, scarce innovation resources ideally would be channeled to regions with the highest potential rate of return, which is unlikely to be a lagging region. Hence, the appropriate “innovation” support policy will depend highly on what type of region is contemplated. In lagging rural regions, the appropriate policy is likely to be basic agricultural R&D combined with a host of complementary policies in extension, human capital development, finance, and marketing, especially in the context of GVCs, as contemplated by Fuglie et al. (2019). Areas with a nascent manufacturing base may emphasize investing in managerial and technological upgrading programs, as contemplated by Baron, Kantor, and Whalley (2018) and Bartik (2020).

As an example of regional innovation policy, the European Union’s Smart Specialization Strategy has been seen as a way of building capabilities and promoting competitiveness more equally across a disparate set of regions, for more spatially balanced economic development. EU experiences confirm the importance of horizontal fundamentals at the national level, but also the presence of these foundations and specific capabilities and advantages at the local level (such as agglomeration economies, connectivity to international markets, skills, access to quality services, and natural advantages).3 As annex 8A suggests, the rationale for using public money for several