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Technology in 1500 and genetic diversity

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Abstract

The study of the deeply rooted determinants of development, technology and innovation is a very recent strand of the literature. This paper investigates the relationship between technology in 1500 and the ancestral genetic diversity of populations. It shows a strong hump-shaped relationship between genetic diversity and technological developments in 1500. This means that some of the technological achievements may stem from the genetic diversity mostly determined more than a millennium before. Results are robust to the introduction of several controls and to IV estimation. Moreover, our results highlight that agriculture and communication were the technologies most influenced by genetic diversity.

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Notes

  1. The influence of genetic diversity on ethnolinguistic fractionalization has been studied by Ahlerup and Olsson (2012).

  2. Most of the work of Diego Comin and co-authors is supported by the database CHAT—Cross-country Historical Adoption of Technology—(Comin and Hobjin 2009), which contains more than 100 technologies for more than 150 countries since 1800.

  3. The paper was a lead article in American Economic Review and was the Science’s editor choice in September 2012.

  4. The largest list of countries used (106) is detailed in the “Appendix”. Detailed lists of countries for each regression are available upon request.

  5. The database is available directly from the journal website at https://www.aeaweb.org/articles?id=10.1257/mac.2.3.65 or from Diego Comin’s website at http://www.dartmouth.edu/dcomin/indexdatasets.html.

  6. “Appendix A.2” in their paper.

  7. This is compared with regressions in “Appendix A.3” in Ashraf and Galor (2013).

  8. This is compared with regressions in “Appendix A.4” in Ashraf and Galor (2013).

  9. Regressions with specific technologies’ types as dependent variables are not reported here for space considerations but are available upon request.

  10. Note that this is not the terrestrial distance to Addis Ababa used to constructed the predicted genetic diversity measure.

  11. Correlations between predicted genetic diversity (and predicted genetic diversity squared) and distance to London (and squared) are higher than 90% (p value of 0.000) and correlations with aerial distance to Addis Ababa are higher than 88% (p value of 0.000). However, none of these distances are related in a hump-shaped relationship with technological adoption (see Table 9). Moreover, London is near the Orcadian ancestral route from Istanbul to northern Europe (as illustrated in Fig. 2 in Ashraf and Galor 2013). In some of the regressions, geodesic centroid latitude were also included as instrument. It has a correlation of more than 30% (p value of 0.000) with predicted genetic diversity and is not considered as a direct determinant of technological adoption.

  12. 2SLS Regressions with specific technologies’ types as dependent variables are not presented in the paper due to space considerations but are available upon request.

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Authors and Affiliations

  1. Departamento de Gestão e Economia and CEFAGE-UBI, Universidade da Beira Interior, 6200-001, Covilhã, Portugal

    Tiago Neves Sequeira & Marcelo Santos

Authors
  1. Tiago Neves Sequeira
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  2. Marcelo Santos
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Correspondence to Tiago Neves Sequeira.

Additional information

We gratefully acknowledge financial support from FCT—Fundação para a Ciência e a Tecnologia (Science and Technology Foundation), through project PTDC/EGE_ECO/112499/2009 and from FCT - Fundação para a Ciência e a Tecnologia (Science and Technology Foundation) and FEDER/COMPETE, through grant UID/ECO/04007/2013 (POCI-01-0145-FEDER-007659).

Appendix A

Appendix A

1.1 A.1 Robustness to alternative distances and log–log specification

See Tables 9 and 10.

Table 9 Robustness to alternative distances
Full size table
Table 10 Robustness to log–log specification
Full size table

1.2 A.2 list of countries—largest (106) sample

Afghanistan; Angola; Argentina; Australia; Austria; Belgium; Benin; Burkina Faso; Bangladesh; Bosnia And Herzegovina; Belize; Bolivia; Brazil; Botswana; Central African Republic; Canada; Switzerland; Chile; China; Cte D’Ivoire; Cameroon; Congo, Rep.; Colombia; Costa Rica; Cuba; Germany; Denmark; Algeria; Ecuador; Egypt, Arab Rep.; Spain; Ethiopia; Finland; France; Gabon; Ghana; Guinea; Guinea-Bissau; Greece; Guatemala; Guyana; Honduras; Hungary; Indonesia; India; Ireland; Iran, Islamic Rep.; Iraq; Italy; Japan; Kenya; Cambodia; Lao Pdr; Liberia; Libya; Lesotho; Lithuania; Morocco; Madagascar; Mexico; Mali; Myanmar; Mongolia; Mauritania; Malaysia; Namibia; Niger; Nigeria; Nicaragua; Netherlands; Norway; Nepal; New Zealand; Pakistan; Panama; Peru; Philippines; Papua New Guinea; Poland; Portugal; Paraguay; Romania; Russian Federation; Saudi Arabia; Sudan; Senegal; Sierra Leone; El Salvador; Sweden; Syrian Arab Republic; Chad; Thailand; Tunisia; Turkey; Tanzania; Uganda; Ukraine; Uruguay; United States; Uzbekistan; Venezuela; Vietnam; South Africa; Congo, Dem. Rep.; Zambia; Zimbabwe.

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Sequeira, T.N., Santos, M. Technology in 1500 and genetic diversity. Empir Econ 56, 1145–1165 (2019). https://doi.org/10.1007/s00181-017-1391-6

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