IDEAS home Printed from https://ideas.repec.org/p/ucd/wpaper/200931.html
   My bibliography  Save this paper

On a dubious theory of cross-country differences in intelligence

Author

Listed:
  • Kevin Denny

    (School of Economics and Geary Institute University College Dublin)

Abstract

Kanazawa (2007) offers an explanation for the variation across countries of average intelligence. It is based on the idea human intelligence is a domain specific adaptation and that both temperature and the distance from some putative point of origin are proxies for the degree of novelty that humans in a country have experienced. However the argument ignores many other considerations and is a priori weak and the data used questionable. A particular problem is that in calculating distances between countries it implicitly assumes that the earth is flat. This makes all the estimates biased and unreliable.

Suggested Citation

  • Kevin Denny, 2009. "On a dubious theory of cross-country differences in intelligence," Working Papers 200931, Geary Institute, University College Dublin.
  • Handle: RePEc:ucd:wpaper:200931
    as

    Download full text from publisher

    File URL: http://www.ucd.ie/geary/static/publications/workingpapers/gearywp200931.pdf
    File Function: First version, 2009
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Bound, John & Brown, Charles & Mathiowetz, Nancy, 2001. "Measurement error in survey data," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 59, pages 3705-3843, Elsevier.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. John Abowd & Martha Stinson, 2011. "Estimating Measurement Error in SIPP Annual Job Earnings: A Comparison of Census Bureau Survey and SSA Administrative Data," Working Papers 11-20, Center for Economic Studies, U.S. Census Bureau.
    2. Kaspar W thrich, 2013. "Set Identification of Generalized Linear Predictors in the Presence of Non-Classical Measurement Errors," Diskussionsschriften dp1304, Universitaet Bern, Departement Volkswirtschaft.
    3. Liran Einav & Ephraim Leibtag & Aviv Nevo, 2010. "Recording discrepancies in Nielsen Homescan data: Are they present and do they matter?," Quantitative Marketing and Economics (QME), Springer, vol. 8(2), pages 207-239, June.
    4. G. Miller & Yuriy Pylypchuk, 2014. "Marital Status, Spousal Characteristics, and the Use of Preventive Care," Journal of Family and Economic Issues, Springer, vol. 35(3), pages 323-338, September.
    5. Fabian T C Schmidt & Clemens M Lechner & Daniel Danner, 2020. "New wine in an old bottle? A facet-level perspective on the added value of Grit over BFI–2 Conscientiousness," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-25, February.
    6. Brent Kreider & Steven C. Hill, 2009. "Partially Identifying Treatment Effects with an Application to Covering the Uninsured," Journal of Human Resources, University of Wisconsin Press, vol. 44(2).
    7. Fernández-Kranz, Daniel & Rodríguez-Planas, Núria, 2011. "The part-time pay penalty in a segmented labor market," Labour Economics, Elsevier, vol. 18(5), pages 591-606, October.
    8. Peter Gottschalk & Minh Huynh, 2010. "Are Earnings Inequality and Mobility Overstated? The Impact of Nonclassical Measurement Error," The Review of Economics and Statistics, MIT Press, vol. 92(2), pages 302-315, May.
    9. Maclean, Johanna Catherine & Popovici, Ioana & French, Michael T., 2016. "Are natural disasters in early childhood associated with mental health and substance use disorders as an adult?," Social Science & Medicine, Elsevier, vol. 151(C), pages 78-91.
    10. Thomas Pave Sohnesen, 2019. "Are you what you consume?: Impact of food, soft drinks, and coffee on cognitive and non-cognitive test scores," WIDER Working Paper Series wp-2019-117, World Institute for Development Economic Research (UNU-WIDER).
    11. Zhiguo Xiao & Jun Shao & Mari Palta, 2010. "GMM in linear regression for longitudinal data with multiple covariates measured with error," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(5), pages 791-805.
    12. Markowitz, Sara & Nesson, Erik & Robinson, Joshua J., 2019. "The effects of employment on influenza rates," Economics & Human Biology, Elsevier, vol. 34(C), pages 286-295.
    13. Charles Courtemanche & Augustine Denteh & Rusty Tchernis, 2019. "Estimating the Associations between SNAP and Food Insecurity, Obesity, and Food Purchases with Imperfect Administrative Measures of Participation," Southern Economic Journal, John Wiley & Sons, vol. 86(1), pages 202-228, July.
    14. Dora Gicheva, 2020. "Occupational Social Value and Returns to Long Hours," Economica, London School of Economics and Political Science, vol. 87(347), pages 682-712, July.
    15. Hu, Yingyao, 2021. "Identification of Causal Models with Unobservables: A Self-Report Approach," Economics Working Paper Archive 64330, The Johns Hopkins University,Department of Economics.
    16. Ximena Quintanilla, 2011. "Did Chileans Maximize Pensions when Choosing between PAYG and DC?," Working Papers 46, Superintendencia de Pensiones, revised Sep 2011.
    17. Giovanna Culot & Matteo Podrecca & Guido Nassimbeni & Guido Orzes & Marco Sartor, 2023. "Using supply chain databases in academic research: A methodological critique," Journal of Supply Chain Management, Institute for Supply Management, vol. 59(1), pages 3-25, January.
    18. Meta Brown & Andrew F. Haughwout & Donghoon Lee & Wilbert Van der Klaauw, 2011. "Do we know what we owe? A comparison of borrower- and lender-reported consumer debt," Staff Reports 523, Federal Reserve Bank of New York.
    19. Lothar Essig & Joachim K. Winter, 2009. "Item Non-Response to Financial Questions in Household Surveys: An Experimental Study of Interviewer and Mode Effects," Fiscal Studies, Institute for Fiscal Studies, vol. 30(Special I), pages 367-390, December.
    20. Daniel S. Hamermesh, 2001. "The Changing Distribution of Job Satisfaction," Journal of Human Resources, University of Wisconsin Press, vol. 36(1), pages 1-30.

    More about this item

    Keywords

    intelligence; measurement error; international comparisons;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ucd:wpaper:200931. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Geary Tech (email available below). General contact details of provider: https://edirc.repec.org/data/geucdie.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.