Overview: Was the Wealth of Nations Determined in 1000 B.C.?

To the extent that history is discussed at all in economic development, it is usually either the divergence associated with the Industrial Revolution or the effects of colonial regimes. Is it possible that precolonial, pre-industrial history also matters significantly for today's national economic development? The authors find that technology adoption circa 1500 A.D., prior to the era of colonization and extensive European contacts, predicts approximately 50 percent of cross-country differences in both current per capital income and technology in a large cross-section of countries. When exploring the causes of this extreme persistence in technology, they find evidence in favor of the importance of the effect of current adoption on subsequent adoption as the main driver. This leaves a limited role to country-specific factors such as institutions, geography, or genes to explain the persistence of technology. Key concepts include:

• Precolonial, pre-industrial differences have striking predictive power for the pattern of both per capital incomes and technology adoption across nations that can be observed today.
• Technology is very persistent both within countries and sectors. Adoption dynamics vis-à-vis country-specific factors such as institutions, geography, or genes appear to be the mechanism behind such persistence.

AUTHOR ABSTRACT

We assemble a dataset on technology adoption in 1000 B.C., 0 A.D., and 1500 A.D. for the predecessors to today's nation states. We find that this very old history of technology adoption is surprisingly significant for today's national development outcomes. Our strong and robust results are for 1500 A.D. determining per capita income today. We find technological persistence across long epochs: from 1000 B.C. to 0 A.D., from 0 A.D. to 1500 A.D., and from 1500 A.D. to the present. Although the data allow only some suggestive tests of rival hypotheses to explain long-run technological persistence, we find the evidence to be most consistent with a model of endogenous technology adoption where the cost of adopting new technologies declines sufficiently with the current level of adoption. The evidence is less consistent with a dominant role for population as predicted by the semi-endogenous growth models or for country-level factors like culture, genes or institutions.