Abstract
This paper establishes a robust relationship between idea flows across countries, as captured by book translations, and two measures of population relatedness. I argue that linguistic distance imposes a cost on idea flows, whereas genetic distance captures an incentive to communicate when dissimilar countries have more to learn from each other. Consistent with this hypothesis, I find that linguistic distance is negatively associated with book translations, whereas genetic distance is positively associated with book translations after conditioning on linguistic and geographic distance. In particular, the benchmark estimate indicates that a one standard deviation increase in linguistic distance reduces book translations by 12%, while a one standard deviation increase in genetic distance increases book translations by 10%.
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Notes
Evidence of a diversity-driven communication incentive is also consistent with the idea that innovation is borne out of isolation (Ashraf et al. 2010), where genetic dissimilarity implies a long history of isolation and consequently a larger set of non-overlapping ideas.
In this paper, I abstract from a discussion of the direct and barrier effects of culture. Harutyunyan and Özak (2017) highlight the difficulty of disentangling direct and barrier effects and provide evidence of observational equivalence between the two.
See Ashraf and Galor (2018) for a survey of related work.
A printed copy of a translated text is a rival good because my purchasing of that book inhibits another’s purchase of it. But the translation itself is non-rival because my purchase of the book does not diminish the use of that translation for future copies of the book. This also implies a translation is disembodied in the sense that it is not physically contained as a tangible good.
An in-depth report on legal deposit legislation was drafted by UNESCO in 2000, which provides a guideline of how this legislation is formed. See http://archive.ifla.org/VII/s1/gnl/legaldep1.htm.
See Table B3 in the online Appendix for a complete list of observations by translating country.
Subjects are classified according to the Universal Decimal Classification system, including [1] history, geography and biography, [2] law, social sciences, and education, [3] literature, [4] philosophy and psychology, [5] religion and theology, [6] natural and exact sciences, [7] applied sciences, [8] and arts, games, and sports.
The Ethnologue is a comprehensive database cataloguing all of the world’s 7,097 known living languages. It is regarded as the most comprehensive source of its kind. See http://www.ethnologue.com.
Because genes are selectively neutral they are independent of natural selection, implying that all conclusions of this paper do not speak to a hierarchy of genetic traits and should not be interpreted this way.
It is redundant to denote the origin language since by assumption each origin language is matched to an origin country as previously noted.
The baseline sample differs because some covariates are not available for every country in each year.
Table A12 in the online Appendix includes the covariate estimates.
This alternative assignment rule comes at a cost: synthetic language-country calculations require available data for every country in which the translating and origin language is spoken. Consequently, I cannot construct a weighted average with global coverage for every language group because I lack country-level data for a number of countries.
Comparing the coefficients for genetic distance across specifications (1) and (2) yields a \(\chi ^2 = 0.92\), implying that I cannot reject the null hypothesis of statistical equivalence.
Table A11 in the online Appendix reports the regression coefficients.
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A special thanks to Nippe Lagerlöf for his detailed feedback, in addition to three anonymous referees. Thanks to Tasso Adamopoulos, Greg Casey, Jeff Chan, Avi Cohen, Oded Galor, Ingo Isphording, and Jeff Quattrociocchi, as well as seminar participants at the Capri Summer School in Economic Growth and York University, for helpful comments. I also thank Ingo Isphording and Sebastian Otten for providing me with the linguistic distance data used in an early draft of this paper, and the Social Science and Humanities Research Council of Canada for financial support (Grant # 752-2014-2195).
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Dickens, A. Population relatedness and cross-country idea flows: evidence from book translations. J Econ Growth 23, 367–386 (2018). https://doi.org/10.1007/s10887-018-9158-2
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DOI: https://doi.org/10.1007/s10887-018-9158-2