Intelligence, Latin America, and Human Capital

  • Carmen Flores-Mendoza
  • Rubén Ardila
  • Ricardo Rosas
  • María Emilia Lucio
  • Miguel Gallegos
  • Norma Reátegui Colareta


Intelligence research has shown that human cognitive capital is associated with the development of nations. This chapter summarizes the results presented in previous chapters in order to analyze the quality of the human capital available in the Latin American region compared to the existing human capital in a developed country (in this case Spain). Additionally, challenges and future prospects are discussed.


IQ Intelligence Human capital Latin America Spain 


  1. Anastasi, A. (1956). Intelligence and family size. Psychological Bulletin, 53, 187–209.CrossRefGoogle Scholar
  2. Angelini, A. L., Alves, I. C. B., Custódio, E. M., Duarte, W. F., & Duarte, J. L. M. (1999). Padronização brasileira das matrizes progressivas coloridas de Raven. In J. C. Raven (Ed.), Manual matrizes progressivas coloridas de Raven: Escala especial. São Paulo: Centro Editor de Testes e Pesquisas em Psicologia.Google Scholar
  3. Arias, W. L. (2014). Estilos de Aprendizaje e Inteligencia em Estudiantes Universitarios de Arequipa, Perú. Revista de Estilos de Aprendizaje, 7, 88–107.Google Scholar
  4. Bandeira, D. R., Alves, I. C. B., Giacomel, A. E., & Lorenzatto, L. (2004). Matrizes progressivas coloridas de Raven—escala especial: normas para Porto Alegre, RS. Psicologia em Estudo, 9, 479–486.CrossRefGoogle Scholar
  5. Bandeira, D. R., Costa, A., & Arteche, A. (2012). The Flynn effect in Brazil: Examining generational changes in the Draw-a-Person and in the Raven’s Coloured Progressive Matrices. Revista Latinoamericana de Psicologia, 44, 9–18.Google Scholar
  6. Barber, N. (2005). Educational and ecological correlates of IQ: A cross-national investigation. Intelligence, 33, 273–284.CrossRefGoogle Scholar
  7. Becker, G. S. (1975). Human capital: A theoretical and empirical analysis, with special reference to education (2nd ed.). New York: Columbia University Press.Google Scholar
  8. Belmont, L., & Marolla, F. A. (1973). Birth order, family size, and intelligence. Science, 182, 1096–1101.CrossRefGoogle Scholar
  9. Burhan, N. A. S., Razak, R. C., Salleh, F., & Tovar, M. E. L. (2017). The higher intelligence of the ‘creative minority’ provides the infrastructure for entrepreneurial innovation. Intelligence, 65, 93–106. CrossRefGoogle Scholar
  10. Byrns, R., & Henmon, V. A. C. (1936). Parental occupation and mental ability. Journal of Educational Psychology, 27, 284–291. CrossRefGoogle Scholar
  11. Cheng, H., & Furnham, A. (2014). The associations between parental socio-economic conditions, childhood intelligence, adult personality traits, social status and mental well-being. Social Indicators Research, 117, 653–664. CrossRefGoogle Scholar
  12. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Earlbaum.Google Scholar
  13. Colom, R., & Garcia-Lopez, O. (2002). Sex differences in fluid intelligence among high school graduates. Personality and Individual Differences, 32, 445–451.CrossRefGoogle Scholar
  14. Colom, R., Escorial, S., & Rebollo, I. (2004). Sex differences on the progressive matrices are influenced by sex differences on spatial ability. Personality and Individual Differences, 37, 1289–1293.CrossRefGoogle Scholar
  15. Dickerson, R. E. (2006). Exponential correlation of IQ and the wealth of nations. Intelligence, 34, 292–295.CrossRefGoogle Scholar
  16. Downey, D. B. (2001). Number of siblings and intellectual development. The resource dilution explanation. American Psychologist, 56, 497–504.CrossRefGoogle Scholar
  17. Downey, D. B., Powell, B., Steelman, L. C., & Pribesh, S. (1999). Much do about siblings: Change models, sibship size, and intellectual development. American Sociological Review, 64, 193–198.CrossRefGoogle Scholar
  18. Flores-Mendoza, C., Darley, M., & Fernandes, H. B. F. (2017). Cognitive sex differences in Brazil. Mankind Quarterly, 57, 34–51.Google Scholar
  19. Flores-Mendoza, C., Widaman, K.F., Bacelar, T.D., & Lele, A.J. (2014). Propriedades psicométricas do Raven Geral no contexto de Minas Gerais. Arquivos Brasileiros de Psicologia, 66, 1–16.Google Scholar
  20. Flores-Mendoza, C., Widaman, K. F., Mansur-Alves, M., Silva Filho, J. H., Pasian, S., & Schlottfeldt, C. G. M. (2012). Considerations about IQ and human capital in Brazil. Temas em Psicologia (Ribeirão Preto), 20, 133–154.Google Scholar
  21. Flynn, J. R., & Rossi-Casé, L. (2012). IQ gains in Argentina between 1964 and 1998. Intelligence, 40, 145–150. CrossRefGoogle Scholar
  22. Gelade, G. A. (2008a). IQ, cultural values, and the technological achievement of nations. Intelligence, 36, 711–718.CrossRefGoogle Scholar
  23. Gelade, G. A. (2008b). The geography of IQ. Intelligence, 36, 495−501.Google Scholar
  24. Gunver, M. G., Senocak, M. S., & Vehid, S. (2017). To determine skewness, mean and deviation with a new approach on continuous data. PONTE International Journal of Sciences and Research, 73, 30–44.Google Scholar
  25. Canady, H. G. (1936). The intelligence of Negro college students and parental occupation. American Journal of Sociology, 42, 388–389. CrossRefGoogle Scholar
  26. International Labour Office. (2017). World employment and social outlook: trends 2017 (p. 2017). Geneva: ILO.Google Scholar
  27. Instituto Nacional de Estadística e Informática – INEI (2015). Perú. Síntese Estadística 2015 [Statistical Overview 2015]. Lima: INEI. Retreved from
  28. Jensen, A. R. (1998). The g factor. The science of mental ability. London: Praeger.Google Scholar
  29. Jones, G., & Schneider, W. J. (2006). Intelligence, Human capital, and economic growth: a bayesian averaging of classical estimates (BACE) Approach. Journal of Economic Growth, 11, 71–93.CrossRefGoogle Scholar
  30. Jones, G., & Schneider, W. J. (2010). IQ in the production function. Economic Inquiry, 48, 743–755.CrossRefGoogle Scholar
  31. Jordan, A. M. (1933). Parental occupations and children’s intelligence scores. Journal of Applied Psychology, 17, 103–119. CrossRefGoogle Scholar
  32. Kanazawa, S. (2006). IQ and the wealth of states. Intelligence, 34, 593–600.CrossRefGoogle Scholar
  33. Kanazawa, S. (2009a). IQ and the values of nations. Journal of Biosocial Science, 41, 537–556. CrossRefPubMedGoogle Scholar
  34. Kanazawa, S. (2009b). Why liberals and atheists are more intelligent. Social Psychology Quarterly, 73, 33–57. CrossRefGoogle Scholar
  35. Lynn, R. (2006). Race differences in intelligence. An evolutionary analysis. Washington: Summit Publishers.Google Scholar
  36. Lynn, R., & Mikk, J. (2007). National differences in intelligence and educational attainment. Intelligence, 35, 115–121.CrossRefGoogle Scholar
  37. Lynn, R. (2009). Fluid intelligence but not vocabulary has increased in Britain, 1979–2008. Intelligence, 37, 249–255.CrossRefGoogle Scholar
  38. Lynn, R. (2012). IQs predict differences in the technological development of nations from 1000 BC through 2000 AD. Intelligence, 40, 439–444. CrossRefGoogle Scholar
  39. Lynn, R., & Meisenberg, G. (2010). National IQs calculated and validated for 108 nations. Intelligence, 38, 353–360.CrossRefGoogle Scholar
  40. Lynn, R., & Vanhanen, T. (2002). IQ and the wealth of nations. London: Praeger.Google Scholar
  41. Lynn, R., & Vanhanen, T. (2006). IQ and global inequality. Washington, DC: Summit.Google Scholar
  42. Lynn, R., & Vanhanen, T. (2012). National IQs: A review of their educational, cognitive, economic, political, demographic, sociological, epidemiological, geographic and climate correlates. Intelligence, 40, 226–234.CrossRefGoogle Scholar
  43. Lynn, R., Backhoff, E., & Contreras, L. A. (2005). Ethnic and racial differences on the standard progressive matrices in Mexico. Journal of Biosocial Science, 37, 107–113. CrossRefPubMedGoogle Scholar
  44. Lynn, R., Harvey, J., & Nyborg, H. (2009). Average intelligence predicts atheism rates across 137 nations. Intelligence, 37, 11–15.CrossRefGoogle Scholar
  45. Lynn, R., Meisenberg, G., Mikk, J., & Williams, A. (2007). National IQs predict differences in scholastic achievement in 67 countries. Journal of Biosocial Science, 39, 861–874.CrossRefGoogle Scholar
  46. Mackintosh, N. J., & Bennett, E. S. (2005). What do Raven’s matrice measure? An analysis in terms of sex differences. Intelligence, 33, 663–674. CrossRefGoogle Scholar
  47. Marincovich, R. I., Sparosvich, E. F., Santana, M. C. D., Game, J. H., Gómez, C. C., & Marincovich, D. I. (2000). Estudio de la capacidad intelectual (Test de Matrices Progresivas de Raven) em escolares chilenos de 5 a 18 años. Antecedentes generales, normas y recomendaciones. Revista de Psicología General y Aplicada, 53, 5–30.Google Scholar
  48. Millones, D. M., Flores-Mendoza, C., & Rivalles, R. M. (2015). Intelligence in Peru: Students’ results in Raven and its relationship to SES. Intelligence, 51, 71–78.CrossRefGoogle Scholar
  49. Neisser, U., Boodoo, G., Bouchard Jr., T. J., Boykin, A. W., Brody, N., Ceci, S. J., … Urbina, S. (1996). Intelligence: Knowns and unknowns American Psychologist, 51, 77–101.Google Scholar
  50. Nisbett, R. E., Aronson, J., Blair, C., Dickens, W., Flynn, J., Halpern, D. F., & Turkheimer, E. (2012). Intelligence. New Findings and Theoretical Developments. American Psychologist, 67, 130–159. CrossRefPubMedPubMedCentralGoogle Scholar
  51. Nista, M. T. F., & Ibarra, S. M. M. (2014). Datos Normativos de las Matrices Progresivas Coloreadas em niños indígenas yaquis. Anuario de Psicologia, 44, 373–385.Google Scholar
  52. Obydenkova, A. V., & Salahodjaev, R. (2017). Government size, intelligence and life satisfaction. Intelligence, 61, 85–91. CrossRefGoogle Scholar
  53. OECD. (2016). The survey of adult skills: reader’s companion, Oecd skills studies (2nd ed.). Paris: OECD. CrossRefGoogle Scholar
  54. Quiroz, R., Chávez, W., & Holgado, M. (1998). Baremos para la escala especial de las Matrices Progesivas de J.C. Raven en niños de educación primaria de la ciudad del Cusco. SITUA, Año VI, 11. Retrieved from Scholar
  55. Raven, J., Raven, J. C., & Court, J. H. (2000). Manual for Raven’s progressive matrices and vocabulary scales. Section 3: The standard progressive matrices. Oxford, England: Oxford Psychologists Press.Google Scholar
  56. Reeve, C. L. (2009). Expanding the g-nexus: Further evidence regarding the relations among national IQ, religiosity and national health outcome. Intelligence, 37, 495–505.CrossRefGoogle Scholar
  57. Rindermann, H., & te Nijenhuis, J. (2012). Intelligence in Bali—A case study on estimating mean IQ for a population using various corrections based on theory and empirical findings. Intelligence, 40, 395–400. CrossRefGoogle Scholar
  58. Rindermann, H. (2007). The g-factor of international cognitive ability comparisons: the homogeneity of results in PISA, TIMSS, PIRLS and IQ-tests across nations. European Journal of Personality, 21, 667–706.CrossRefGoogle Scholar
  59. Rindermann, H. (2012). Intellectual classes, technological progress and economic development: The rise of cognitive capitalism. Personality and Individual Differences, 53, 108–113. CrossRefGoogle Scholar
  60. Rushton, J.P., & Čvorovic, J. (2009). Data on the raven’s standard progressive matrices from four serbian samples. Personality and Individual Differences, 46, 483–486.CrossRefGoogle Scholar
  61. Rindermann, H., & Meisenberg, G. (2009). Relevance of education and intelligence at the national level for health: The case of HIV and AIDS. Intelligence, 37, 383–395.CrossRefGoogle Scholar
  62. Rindermann, H., & Thompson, J. (2011). Cognitive capitalism: The effect of cognitive ability on wealth, as mediated through scientific achievement and economic freedom. Psychological Science, 22, 754–763.CrossRefGoogle Scholar
  63. Rodgers, J. L., Cleveland, H. H., van den Oord, E., & Rowe, D. C. (2000). Resolving the debate over birth order, family size, and intelligence. American Psychologist, 55, 599—61.CrossRefGoogle Scholar
  64. Rushton, J. P., & Templer, D. I. (2009). National differences in intelligence, crime, income, and skin color. Intelligence, 37, 341–346.CrossRefGoogle Scholar
  65. Salahodjaev, R., & Azam, S. (2015). IQ and the weight of nations. Personality and Individual Differences, 87, 105–109. CrossRefGoogle Scholar
  66. Salahodjaev, R. (2016). Intelligence and deforestation: International data. Forest Policy and Economics, 63, 20–27. CrossRefGoogle Scholar
  67. Schultz, T. W. (1971). Investment in human capital: The role of education and of research. New York: Free Press.Google Scholar
  68. Schweizer, K., Goldhammer, F., Rauch, W., & Moosbrugger, H. (2007). On the validity of Raven’s matrices test: Does spatial ability contribute to performance? Personality and Individual Differences, 43, 1998–2010. CrossRefGoogle Scholar
  69. Shatz, S. M. (2008). State IQ and fertility in the United States. Mankind Quarterly, 49, 38–49.Google Scholar
  70. Stolarski, M., Jasielska, D., & Zajenkowski, M. (2015). Are all smart nations happier? Country aggregate IQ predicts happiness, but the relationship is moderated by individualism–collectivism. Intelligence, 50, 153–158. CrossRefGoogle Scholar
  71. te Nijenhuis, J., de Jong, M., Evers, A., & van der Flier, H. (2004). Are cognitive differences between immigrant and majority groups diminishing? European Journal of Personality, 18, 405–434. CrossRefGoogle Scholar
  72. te Nijenhuis, J., Tolboom, E., Resing, W., & Bleichrodt, N. (2004). Does cultural background influence the intellectual performance of children from immigrant groups? European Journal of Psychological Assessment, 20, 10-26. Doi:
  73. te Nijenhuis, J., Willigers, D., Dragt, J., & van der Filer, H. (2016). The effects of language bias and cultural bias estimated using the method of correlated vectors on a large database of IQ comparisons between native Dutch and ethnic minority immigrants from non-Western countries. Intelligence, 54, 117–135.CrossRefGoogle Scholar
  74. Van der Ven, A. H. G. S., & Ellis, J. L. (2000). A Rasch analysis of Raven’s standard progressive matrices. Personality and Individual Differences, 29, 45–64. CrossRefGoogle Scholar
  75. Vásquez, A. D. (2014). Estudio Psicométrico del test de Matrices Progresivas de Raven a Colores en estudiantes de primaria de Lima Metropolitana. Revista de Investigación em Psiologia, 5, 43–54.CrossRefGoogle Scholar
  76. Vittorio, D., & Ostuni, N. (2013). The burden of disease and the IQ of nations. Intelligence, 28, 109–118. CrossRefGoogle Scholar
  77. Wai, J., Lubinski, D., & Benbow, C. P. (2009). Spatial ability for STEM domains: Aligning over 50 years of cumulative psychological knowledge solidifies its importance. Journal of Educational Psychology, 101, 817–835. CrossRefGoogle Scholar
  78. Wänström, L., & Wegmann, B. (2017). Effect of sibship size on intelligence, school performance and adult income: Some evidence from Swedish data. Intelligence, 62, 1–11. CrossRefGoogle Scholar
  79. Whetzel, D. L., & McDaniel, M. A. (2006). Prediction of national wealth. Intelligence, 4, 449–458.CrossRefGoogle Scholar
  80. Wicherts, J.M. (2009). The impact of papers published in Intelligence 1977-2007 and an overview of the citation classics. Intelligence, 37, 443–446.CrossRefGoogle Scholar
  81. Wicherts, J. M., Dolan, C. V., & van der Maas, H. L. J. (2010). The dangers of unsystematic selection methods and the representativeness of 46 samples of African test-takers. Intelligence, 38, 30–37.CrossRefGoogle Scholar
  82. World Economic Forum—WEF (2015). The Human Capital Report.Google Scholar
  83. World Economic Forum—WEF (2016). The future of jobs. Employment, skills, and workforce strategy for the fourth industrial revolution.Google Scholar
  84. Zajonc, R. B. (2001). The family dynamics of intellectual development. American Psychologist, 56, 490–496.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Carmen Flores-Mendoza
    • 1
  • Rubén Ardila
    • 2
  • Ricardo Rosas
    • 3
  • María Emilia Lucio
    • 4
  • Miguel Gallegos
    • 5
  • Norma Reátegui Colareta
    • 6
  1. 1.Department of PsychologyFederal University of Minas Gerais, Psychology InstituteBelo HorizonteBrazil
  2. 2.Department of PsychologyNational University of ColombiaBogotaColombia
  3. 3.School of PsychologyPontifical Catholic University of ChileSantiagoChile
  4. 4.Mental Health and Diagnosis Program Faculty of PsychologyNational Autonomous University of MexicoMexico CityMexico
  5. 5.Faculty of PsychologyNational University of RosarioRosarioArgentina
  6. 6.Faculty of HumanitiesSan Ignacio de Loyola UniversityLimaPeru

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