Slow technology diffusion

Proposed and experimental methods

Methods that are proposed, under trial, approved in some places, or tried and then failed. Each shows a stage label and an evidence rating. A stage label shows how far a method has progressed, not whether it works. The stage labels are explained on the technological factor page.

• Seeding new technologies through central members of social networks (Large trial, B). Information is given first to people chosen by network position rather than by extension workers. In 200 Malawian villages, giving training in pit planting (a water-saving farming method) to two highly central farmers raised adoption from 0% to about 11% over three years, and after three years there was no diffusion at all in 45% of villages where extension workers chose who to train (Beaman et al., 2021). The year 3 difference in pit planting between arms was not statistically significant.
• Temporary adoption subsidies that spread through social networks (Large trial, B). In the first randomized trial of a government input subsidy program in Africa, temporary fertilizer and seed subsidies for Mozambican maize farmers raised technology adoption and yields, effects persisted after the subsidy ended, and the authors found that "spillovers account for the vast majority of subsidy-induced gains" through farmers' contacts (Carter, Laajaj, and Yang, 2021).
• Agricultural advice by mobile phone (Large trial, B). Text messages and voice calls give farmers tailored advice at almost no marginal cost. A meta-analysis of experimental trials found that digital advice raised yields by about 4% and the odds of adopting recommended inputs by about 22%, with benefits estimated at roughly ten times costs (Fabregas, Kremer, and Schilbach, 2019). Two of the authors are linked to Precision Development, a nonprofit that delivers such advice, and the per-farmer effects are small.
• Free management consulting to spread modern business practices (Early trial, B). In a field experiment with large Indian textile firms, plants randomly given intensive consulting became about 17% more productive in the first year and opened more plants within three years, and the authors concluded that "informational barriers were the primary factor" behind the slow adoption (Bloom et al., 2013). The sample was a small number of firms in one industry.
• Not shown to help: giving universities ownership of academic inventions (Failed or reversed, B). Technology transfer offices manage university patents and licenses, and in 2003 Norway ended the "professor's privilege" so that universities instead of researchers held most rights. Entrepreneurship and patenting by university researchers then fell by about 50%, and the quality of their start-ups and patents also declined (Hvide and Jones, 2018).

Sources cited on this page

1. Comin, D., & Hobijn, B. (2010). An exploration of technology diffusion. American Economic Review, 100(5), 2031-2059. AEA B Moderate
2. Beaman, L., BenYishay, A., Magruder, J., & Mobarak, A. M. (2021). Can network theory-based targeting increase technology adoption? American Economic Review, 111(6), 1918-1943. link B Moderate
3. Bloom, N., Eifert, B., Mahajan, A., McKenzie, D., & Roberts, J. (2013). Does management matter? Evidence from India. The Quarterly Journal of Economics, 128(1), 1-51. link B Moderate
4. Carter, M., Laajaj, R., & Yang, D. (2021). Subsidies and the African Green Revolution: Direct effects and social network spillovers of randomized input subsidies in Mozambique. American Economic Journal: Applied Economics, 13(2), 206-229. link B Moderate
5. Fabregas, R., Kremer, M., & Schilbach, F. (2019). Realizing the potential of digital development: The case of agricultural advice. Science, 366(6471), eaay3038. link B Moderate
6. Hvide, H. K., & Jones, B. F. (2018). University innovation and the professor's privilege. American Economic Review, 108(7), 1860-1898. link B Moderate

Every source for this factor is listed on the technological factor page.