Emil Ole William Kirkegaard

Genetic variation plays a significant role in shaping socioeconomic status. Conventional census racial categories (Black, White, Asian, Hispanic) obscure substantial variation among ancestry groups. Surnames reflect ancestral lineage and genetic heritage, enabling finer measurement. The authors combined 23andMe genetic ancestry data on surnames with surnames yielded from government financial records, criminal databases, Wikipedia entries, and physician licensure records. They found that genetic ancestry strongly predicted surname-level outcomes across all metrics, with substantial differences in social standing among ethnic groups. Ashkenazi Jews and Indians ranked highest; British/Irish and Iberian ranked lowest among Europeans; highly selected immigrant groups such as Arabs and Iranians scored above Europeans.

Using surname distributions as proxies for historical migration, the authors study how ancestral geography relates to contemporary educational outcomes across Italian provinces and 2,244 municipalities. A directional TF-IDF framework quantifies the prevalence of Southern-origin surnames. Regions with higher Southern surname shares perform systematically worse on standardized assessments, net of economic controls. At the provincial level, Southern surname prevalence strongly predicts scores ( β = −0.815 to −1.028, p < 0.001); municipal effects are attenuated yet significant ( β = −0.220 to −0.485, p < 0.01). Latitude strongly predicts performance at both levels. The authors introduce surname-weighted provincial scores for Mathematical Olympiad outcomes, attributing performance to paternal ancestral provinces. In this setting, latitude significantly predicts surname-weighted scores ( β = 0.460, p < 0.001), with a role for GDP ( β = 0.311, p < 0.015). Results suggest persistent regional differences shaped by migration, cultural transmission, ancestry, and socioeconomic stratification.

The authors surveyed a nationally representative sample of 478 Americans on Prolific to assess the stereotypes about national intelligence, and found that these correlated at .78 with measured national IQ s; although the aggregate ratings are accurate, it’s not clear how people determine the average levels of intelligence in a country, whether it’s based on regional stereotypes, economic development, or other methods. Left wingers were slightly less accurate in their ratings of the intelligence of nations (r = −0.21), even after covariates such as age, gender, and race were controlled for. Both race and political views seem to be related to the perception of the intelligence of certain nations; Black people assign much higher ratings to the intelligence of African nations than Whites, and left wingers tend to give lower ratings to the average levels of intelligence of Russians, Israelis, and Americans. Some of the analyses in this article were preregistered.