Article Summary: “Long-term Barriers to the International Diffusion of Innovations” by Spolaore and Wacziarg

Title: Long-term Barriers to the International Diffusion of Innovations
Author: Enrico Spolaore and Romain Wacziarg
Scope: 4 stars
Readability: 2 stars
My personal rating: 4 stars
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Topic of article

The authors use statistical models to understand the role of culture in the diffusion of technologies over the last 500 years.

Key Take-aways

  • Face-to-face contact between people is critical to the diffusion of technology.
  • Technology diffuses much faster to cultures that are similar to the innovating society.
  • Different culture effectively acts as a barrier to the diffusion of technology.

Important Quotes from article

The main idea of this paper is twofold: (1) on average, populations that are more closely related tend to be more similar with respect to traits (habits, customs, beliefs, values, etc.) that are transmitted with variation from one generation to the next; and (2) similarity in intergenerationally transmitted traits tends to reduce the barriers to technology adoption; that is, populations that share more similar intergenerationally- transmitted traits face lower costs when imitating each other’s innovations.

We argue that long-term genealogical distance works as a barrier to the diffusion of innovations across populations.

What matters, in our model, is that random historical divergence introduces different customs, habits, and norms across populations, and that these differences, on average, tend to decrease their ability to learn from each other.

As Rogers points out, summarizing the lessons from decades of research, most people depend upon a subjective evaluation of an innovation that is conveyed to them from other individuals like themselves who have previously adopted the innovation.

Variation in diffusion in 1500:

For every measure of bilateral standardized betas, technology difference, genetic distance relative to the United Kingdom enters with a positive sign, is statistically significant at the 10% level in all cases, and at the 1% level in five of the six cases. The weakest results, again, are for agriculture, and the strongest for transportation and military technologies. The magnitude of the effects, in terms of standardized range from 10.41% (agricultural technologies) to 41.81% (transportation technologies), while the effect on the overall technological difference index is 31.63%. Thus, a standard deviation in genetic distance relative to the United Kingdom can account for about one- third of a typical difference in technology adoption between countries.

For diffusion of individual technologies in 1990s:

In every single case, the effect of relative frontier distance is positive. Additionally, in 22 of the 33 cases, the effect is statistically significantly different from zero, at least at the 10% level (in 19 of these the significance obtains at the 5% level). Here the results are particularly strong for some agricultural technologies, for most communications technologies, and for all but one of the industrial technologies. The results are weakest for medical technologies and transportation. Turning to the magnitude of the effects, for the technologies where relative frontier distance is statistically significant, the standardized betas vary between about 8% and 24%—slightly smaller than for the Comin et al. data but in the same rough order of magnitude.

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