County-level USA: No Robust Relationship between Geoclimatic Variables and Cognitive Ability

Bryan J. Pesta (Cleveland State University)
John G. R. Fuerst (Cleveland State University)
Emil Kirkegaard (Ulster Institute for Social Research)


Using a sample of ~3,100 U.S. counties, we tested geoclimatic explanations for why cognitive ability varies across geography. These models posit that geoclimatic factors will strongly predict cognitive ability across geography, even when a variety of common controls appear in the regression equations. Our results generally do not support UV radiation (UVR) based or other geoclimatic models. Specifically, although UVR alone predicted cognitive ability at the U.S. county-level (β = -.33), its validity was markedly reduced in the presence of climatic and demographic covariates (β = -.16), and was reduced even further with a spatial lag (β = -.10). For climate models, average temperature remained a significant predictor in the regression equation containing a spatial lag (β = .35). However, the effect was in the wrong direction relative to typical cold weather hypotheses. Moreover, when we ran the analyses separately by race/ethnicity, no consistent pattern appeared in the models containing the spatial lag. Analyses of gap sizes across counties were also generally inconsistent with predictions from the UVR model. Instead, results seemed to provide support for compositional models.


Cognitive ability, Ultraviolet radiation, Climate; Geography

Full Text:



Lynn, R., & Vanhanen, T. (2006). IQ and Global Inequality. Augusta, GA, US: Washington Summit Publishers.

Lynn, R., & Becker, D. (2019). The Intelligence of Nations. London, GB: Ulster Institute for Social Research.

Kirkegaard, E. O. W., & Pesta, B. J. (2018). An S-factor analysis on the provinces of Vietnam: Relationships with cognitive ability, ethnicity, and latitude. Mankind Quarterly, 58(4), 562-579. doi:10.46469/mq.2018.58.4.2

McDaniel, M. A. (2006). Estimating state IQ: Measurement challenges and preliminary correlates. Intelligence, 34(6), 607-619. doi: 10.1016/j.intell.2006.08.007

Beaver, K., & Wright, J. (2011). The association between county-level IQ and county-level crime rate. Intelligence, 39(1), 22-26. doi: 10.1016/j.intell.2010.12.002

Kirkegaard, E. O. W., & Fuerst, J. (2017). Admixture in Argentina. Mankind Quarterly, 57(4), 542. doi: 10.46469/mq.2017.57.4.4

Templer, D., & Rushton, J. P. (2011). IQ, skin color, crime, HIV/AIDS, and income in 50 U.S. States. Intelligence, 39, 437-442. doi: 10.1016/j.intell.2011.08.001

Lynn, R. (2010). In Italy, north-south differences in IQ predict differences in income, education, infant mortality, stature, and literacy. Intelligence, 38, 93-100. doi: 10.1016/j.intell.2009.07.004

Reeve, C. (2009). Expanding the g nexus: Further evidence regarding the relationship among national IQ, religiosity, and national health outcomes. Intelligence, 37, 495–505. doi: 10.1016/j.intell.2009.06.003

Pesta, B. J., McDaniel, M., & Bertsch, S. (2010). Toward an index of well-being for the 50 U.S. states. Intelligence, 38, 160-168. doi: 10.1016/j.intell.2009.09.006

Lynn, R., Fuerst, J., & Kirkegaard, E. O. W. (2018). Regional differences in intelligence in 22 countries and their economic, social and demographic correlates: A review. Intelligence, 69, 24–36. doi: 10.1016/j.intell.2018.04.004

Rindermann, H., Becker, D., & Coyle, T. R. (2016). Survey of Expert Opinion on Intelligence: Causes of International Differences in Cognitive Ability Tests. Frontiers in Psychology, 7. doi: 10.3389/fpsyg.2016.00399

Van de Vliert, E., & Van Lange, P. A. M. (2020). Latitudinal gradients as scientific tools for psychologists. Current Opinion in Psychology, 32, 43-46. doi: 10.1016/j.copsyc.2019.06.018

Rindermann, H. (2018). Cognitive Capitalism: Human Capital and the Wellbeing of Nations. Cambridge University Press.

Frost, P. (2019). The Original Industrial Revolution. Did Cold Winters Select for Cognitive Ability?. Psych, 1(1), 166-181. doi: 10.3390/psych1010012

Sternberg, R. J. (2017). An alternative interpretation of climate data: Intelligence. Behavioral and Brain Sciences, 40. doi: 10.1017/S0140525X16001102

Park, J. (2017). Hot Temperature, Human Capital and Adaptation to Climate Change. Unpublished Manuscript, Harvard University Economics Department.

Randell, H., & Gray, C. (2019). Climate change and educational attainment in the global tropics. Proceedings of the National Academy of Sciences, 116(18), 8840-8845. doi: 10.1073/pnas.1817480116

Van Lange, P. A. M., Rinderu, M. I., & Bushman, B. J. (2017). The logic of climate and culture: Future avenues for CLASH. Behavioral and Brain Sciences, 40, 42-49. doi: 10.1177/1368430217735579

Van de Vliert, E., & Kong, D. T. (2018). Cold, heat, wealth, and culture. In D. Matsumoto & H. C. Hwang (Eds.), Oxford Handbook of Culture and Psychology (2nd ed.). New York: Oxford University Press.

León, F. R. (2016). Race vis-à-vis latitude: Their influence on intelligence, infectious diseases, and income. Mankind Quarterly, 56(3), 411. doi: 10.46469/mq.2016.56.3.7

Eppig, C., Fincher, C. L., & Thornhill, R. (2011). Parasite prevalence and the distribution of intelligence among the states of the USA. Intelligence, 39(2-3), 155-160. doi: 10.1016/j.intell.2011.02.008

Varnum, M. E. (2014). Sources of regional variation in social capital in the United States: Frontiers and pathogens. Evolutionary Behavioral Sciences, 8(2), 77. doi: 10.1037/h0098950

León, F. R. (2015). The east-to-west decay of math and reading scores in the United States: A prediction from UVB radiation theory. Personality and Individual Differences, 86, 287–290. doi: 10.1016/j.paid.2015.06.028

León, F. R., & Burga-León, A. (2014). Why complex cognitive ability increases with absolute latitude. Intelligence, 46, 291–299. doi:

León, F. R., & Burga-León, A. (2015). How geography influences complex cognitive ability. Intelligence, 50, 221–227. doi: 10.1016/j.intell.2015.04.011

León, F. R., & Burga-León, A. (2018). UV Radiation Associates With State Income Through Complex Cognitive Ability in the USA. Journal of Individual Differences, 39, 18-26. doi: 10.1027/1614-0001/a000245

Fedderke, J.W., Klitgaard, R.E., MacMurray, J.P., & Napolioni, V. (2014). Diagnosing deep roots of development: Genetic, disease and environmental factors. Economic Research Southern Africa. Retrieved from

Klitgaard, R., Fedderke, J. W., & Napolioni, V. (2020). Geography, Climate, and Genes in Development Studies. The World Bank Economic Review, 34(Supplement_1), S46-S51. doi: 10.1093/wber/lhz034

Meisenberg, G. (2018). From Skin to Brain: In Search of Plausible Messengers. Mankind Quarterly, 58(4). doi: 10.46469/mq.2018.58.4.10

Sultan, S., Taimuri, U., Basnan, S. A., Ai-Orabi, W. K., Awadallah, A., Almowald, F., & Hazazi, A. (2020). Low Vitamin D and Its Association with Cognitive Impairment and Dementia. Journal of Aging Research, 2020. doi: 10.1155/2020/6097820

León, F. R. (2018a). Diminished UV radiation enhances national cognitive ability, wealth, and institutions through health and education. Personality and Individual Differences, 120, 52-57. doi: 10.1016/j.paid.2017.08.025

León, F. R. (2018b). Boundary conditions and new frontiers for Cognitive UV Radiation Theory. Mankind Quarterly, 58(4), 673-685. doi: 10.46469/mq.2018.58.4.11

León, F. R., & Hassall, C. (2017). UV radiation is associated with latitudinal trends in cognitive ability of White children in the USA. Journal of Individual Differences, 38(3), 155-162. doi: 10.1027/1614-0001/a000232

León, F. R., & Antonelli-Ponti, M. (2018). UV radiation theory and the Lynn (2010): Italian debate. Mankind Quarterly, 58(4), 621-649. doi: 10.46469/mq.2018.58.4.7

León, F. R., & Avilés, E. (2016). How altitude above sea level affects intelligence. Intelligence, 58, 33-41. doi: 10.1016/j.intell.2016.06.001

Gelade, G. A. (2008). The geography of IQ. Intelligence, 36(6), 495–501. doi: 10.1016/j.intell.2008.01.004

Hassall, C., & Sherratt, T. N. (2011). Statistical inference and spatial patterns in correlates of IQ. Intelligence, 39(5), 303–310. doi: 10.1016/j.intell.2011.05.001

Bromham, L., Hua, X., Cardillo, M., Schneemann, H., & Greenhill, S. J. (2018). Parasites and politics: Why cross-cultural studies must control for relatedness, proximity and covariation. Royal Society Open Science, 5(8), 181100. doi: 10.1098/rsos.181100

Robinson, W. S. (1950). Ecological correlations and individual behavior. American Sociological Review, 15(195), 351-57. doi:

Kirkegaard, E. O. W., & Pesta, B. J. (2019). Smart and Poor, or Rich and Dull? A US County-Level Analysis of the Relationship between IQ and Presidential-Election Voting Behavior. Mankind Quarterly, 60, 243-255. doi: 10.46469/mq.2019.60.2.7

Pesta, B. J., & Poznanski, P. J. (2014). Only in America: Cold Winters Theory, race, IQ and well-being. Intelligence, 46, 271-274. doi: 10.1016/j.intell.2014.07.009

Luoto, S. (2019). An updated theoretical framework for human sexual selection: From ecology, genetics, and life history to extended phenotypes. Adaptive Human Behavior and Physiology, 5(1), 48-102. doi: 10.1007/s40750-018-0103-6

Fischer, D. H. (1991). Albion’s Seed: Four British Folkways in America (Reprint edition). New York, NY: Oxford Univ Press.

Woodard, C. (2012). American Nations: A History of the Eleven Rival Regional Cultures of North America (Reprint edition). New York, N.Y.: Penguin Books.

Becker, D., Rindermann, H., & Coyle, T. R. (2013). 2013 survey of expert opinion on intelligence. Presented at the 14th Annual Meeting of the International Society for Intelligence Research, Melbourne, Australia, December 2013.

Parvane, N. R., Tadepalli, S., Singh, P., Qian, A., Joshi, R., Kandala, H., ... & Cheriyath, P. (2018). Prevalence of vitamin D deficiency and associated risk factors in the US population (2011-2012). Cureus, 10(6). doi: 10.7759/cureus.2741

Tang, H., Quertermous, T., Rodriguez, B., Kardia, S. L., Zhu, X., Brown, A., ... & Schork, N. J. (2005). Genetic structure, self-identified race/ethnicity, and confounding in case-control association studies. The American Journal of Human Genetics, 76(2), 268-275. doi: 10.1086/427888

Fang, H., Hui, Q., Lynch, J., Honerlaw, J., Assimes, T. L., Huang, J., ... & DuVall, S. L. (2019). Harmonizing genetic ancestry and self-identified race/ethnicity in genome-wide association studies. The American Journal of Human Genetics, 105(4), 763-772. doi: 10.1016/j.ajhg.2019.08.012

LeSage, J., & Pace, R. K. (2009). Introduction to Spatial Econometrics. Chapman and Hall/CRC.

Corrado, L., & Fingleton, B. (2012). Where is the economics in spatial econometrics?. Journal of Regional Science, 52(2), 210-239. doi: 10.1111/j.1467-9787.2011.00726.x

Reardon, S. F., Ho, A. D., Shear, B. R., Fahle, E. M., Kalogrides, D., & DiSalvo, R. (2018). Stanford Education Data Archive (Version 2.1). Accessed at:

Fahle, E. M., Shear, B. R., Kalogrides, D., Reardon, S. F., Chavez, B., & Ho, A. D. (2019). Stanford Education Data Archive Technical Documentation Version 3.0 July 2019.

Rindermann, H., & Thompson, J. (2013). Ability rise in NAEP and narrowing ethnic gaps?. Intelligence, 41(6), 821-831. doi: 10.1016/j.intell.2013.06.016

McLaughlin, D. H., Bandeira de Mello, V., Blankenship, C., Chaney, K., Esra, P., & Hikawa, H. (2008a). Comparison between NAEP and state reading assessment results: 2003 (NCES 2008-474). Washington, DC: US Department of Education, Institute of Education Sciences. National Center for Education Statistics.

McLaughlin, D.H., Bandeira de Mello, V., Blankenship, C., Chaney, K., Esra, P., Hikawa, H., ... & Wolman, M. (2008b). Comparison between NAEP and State Mathematics Assessment Results: 2003. Volume 1. Research and Development Report. NCES 2008-475. National Center for Education Statistics.

Linn, R. L., McLaughlin, D., & Thissen, D. (2009). Utility and Validity of NAEP Linking Efforts. American Institutes for Research.

Stancavage, F. B., & Bohrnstedt, G. W. (2013). Examining the Content and Context of the Common Core State Standards: A First Look at Implications for the National Assessment of Educational Progress. American Institutes for Research.

Bandeira de Mello, V., Rahman, T., Fox, M.A., and Ji, C.S. (2019). Mapping State Proficiency Standards Onto NAEP Scales: Results From the 2017 NAEP Reading and Mathematics Assessments (NCES 2019-040). U.S. Department of Education, Washington, DC: Institute of Education Sciences, National Center for Education Statistics.

Reardon, S. F., Kalogrides, D., & Ho, A. D. (2019). Validation methods for aggregate-level test scale linking: A case study mapping school district test score distributions to a common scale. Journal of Educational and Behavioral Statistics, 1076998619874089.

National Cancer Institute (N.D.). Geographic Information Systems and Science for Cancer Control: County Level UV Exposure Data for the Continental United States. Retrieved July 01, 2019, from:

Centers for Disease Control and Prevention (N.D.). North America Land Data Assimilation System (NLDAS) Daily Air Temperatures and Heat Index (1979-2011) Request. Retrieved July 01, 2019, from:

Cabeza de Baca, T., & Figueredo, A. J. (2014). The cognitive ecology of Mexico: Climatic and socio-cultural effects on life history strategies and cognitive abilities. Intelligence, 47(1), 63-71. doi: 10.1016/j.intell.2014.08.007

Van de Vliert, E., & Van Lange, P. A. (2019). Latitudinal psychology: an ecological perspective on creativity, aggression, happiness, and beyond. Perspectives on Psychological Science, 14(5), 860-884. doi: 10.1177/1745691619858067

Kirkegaard, E. O. W. (2016). Inequality across US counties: An S factor analysis. Open Quantitative Sociology & Political Science. doi: 10.26775/OQSPS.2016.05.23

Parsons, T., & Platt, G. M. (1975/2013). The American University. Harvard University Press.

Drobne, S., & Lakner, M. (2018). The Influence of the Zonation Effect on a System of Hierarchical Functional Regions. Business Systems Research Journal, 9(2), 45-54. doi: 10.2478/bsrj-2018-0018

Harrell, F. E. (2019). rms: Regression Modeling Strategies (Version 5.1-3.1). Retrieved from

Fuerst, J., & Kirkegaard, E. O. (2016). The genealogy of differences in the Americas. Mankind Quarterly, 56(3), 425. doi: 10.46469/mq.2016.56.3.9

Wong, D. W. (2004). The modifiable areal unit problem (MAUP). In WorldMinds: Geographical Perspectives on 100 Problems (pp. 571-575). Springer, Dordrecht. doi: 10.1007/978-1-4020-2352-1_93



  • There are currently no refbacks.
Copyright © 2021 Bryan J. Pesta, John G. R. Fuerst, Emil Kirkegaard Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.