education system and familiarity with related technologies), institutional constraints that affect the overall efficiency of the economy (e.g. expropriation risk), and distortions that affect the price and incentives to invest in physical capital and KBC.<sup>58</sup> In a suggestive study, Fogli and Veldkamp (2012) explore how different social structures might affect a country’s rate of technology diffusion and progress. Based on the idea that communicable diseases and technologies spread in similar ways, through inter-personal contact, they explore an evolutionary model where limited connectivity reduces the risk of an infection entering the social group, allowing people to live longer, but also restricting the group’s exposure to new ideas, slowing technology diffusion and inhibiting growth.<sup>59</sup> The network model, which explicitly addresses the intensive margin of adoption, explains why societies with a high prevalence of contagious disease might evolve toward lowconnectivity, growth-inhibiting social institutions, and how small initial differences can produce large divergence in incomes. A main finding is that a 1-standard deviation increase in connectivity between people increases TFP by an amount equal to 23% of US productivity: these numbers imply, for example, that the difference between high-connectivity Finland and low-connectivity Ghana explains just under half of the difference in their technology diffusion rates, and just over one-third of their per capita output gap.<sup>60</sup> An explicit promotion of global collaboration via global consortia and other mechanisms to facilitate within- and cross-country connectivity and collaboration between people and enterprises that otherwise would not have interacted may be called for. It could be interpreted as tilting prevailing social structures towards global collaboration growth-enhancing social networks that speed the diffusion of new ideas and technologies, and that help better manage risks. This is especially important in environments where the risk of germ infection is now less than the risk of foregone ideas that otherwise would impede more efficient risk management (see Boxes 5 and 6).

**Box 5. Policies to spur collaborative risk-sharing for innovation: Combining technology-push with market-pull support, underpinned by open data policies.** Traditional technology-push public R&D grants to researchers at US NIH-type national research councils, universities and public labs face a number of challenges, such as allocating resources to the best researchers<sup>61</sup> , the need to reduce systematic

58 Both Comin and Hobijn (2010) and Comin and Mestieri (2010) rely on data on the diffusion of 15 important technologies in 166 countries over the last two centuries.

59 In his 1969 AEA presidential address, Kenneth Arrow observed: “While mass media play a major role in alerting people to the possibility of an innovation, it seems to be personal contact that is most relevant in leading to its adoption. Thus, the diffusion of innovation becomes a process formally akin to the spread of an infectious disease”. 60 Fogli and Veldkamp (2012), pp 31-2. Results are based on data on the prevalence of 34 diseases in 78 geopolitical regions (the countries with the highest pathogen prevalence are Brazil, China, Ghana, India and Nigeria, with the lowest include Canada, Hungary, Switzerland and Sweden), and a survey by Hofstede (2001) on national differences in cultural values reflecting degrees of collectivism (where low-connectivity societies where people from birth onwards are integrated into strong, cohesive in-groups, often extended families, with people averse to breaking those ties, and with weak or non-existent global ties are labeled collectivist, and high-connectivity societies with strong global ties are labeled individualistic; the most collectivist countries are Ecuador, Guatemala, Indonesia, Pakistan and Venezuela, while the most individualist are Australia, Canada, the Netherlands, the UK and the US).

61 Nicholson and Ioannidis (2012) ask whether the US National Institutes of Health award its grants to those most likely to make fundamental breakthroughs in their fields (based on biomedical researchers who studies received more than 1,000 citations), and find that only 40% of such high-impact primary authors who are not part of study sections (experts in the fields in question who hand out the grants) currently receive NIH grants. This finding that too many US researchers of the most innovative and influential papers in the life sciences do not receive NIH funding is supported by a second finding that study sections appear to favor work similar to that done by their 30