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Human Capital and Innovation—Basic Concepts, Measures, and Interdependencies

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Innovation, Human Capital and Trade Competitiveness

Abstract

With the acceleration of globalization processes, human capital stock and quality as well as invention and innovations have become particularly important. The greater mobility of capital on an international scale has led to a situation in which its allocation in a given country is increasingly determined by the quality of labor, in addition to such aspects as the price of labor, the tax system, and the quality of physical infrastructure and institutions. There are a few reasons for this. Production departments based on low-qualified labor force have been moved to areas where the labor costs are the lowest. The competition between the OECD (Organisation for Economic Co-operation and Development) countries and the dynamically developing countries, such as Chile, India, or Brazil, starts to concern the location of the technologically advanced production as well as allocation of research and development (R&D) works and R&D centers. The important elements of that competition are people, their knowledge, creativity, and the ability to convert these resources into innovation. For that reason, as the analysis of various theoretical approaches set out in the preceding section indicates, human capital and innovation are becoming increasingly critical for shaping the competitive advantage of countries and boosting benefits achieved in international exchange. The model approach to that kind of correlation as well as the empirical verification of its direction and strength requires supplementation of previous theoretical deliberations regarding the nature of international competitiveness with selected theoretical strands relating to human capital and innovation. This section is thus an attempt to integrate terms such as human capital and innovation based on the concept of innovation systems, as well as to present and select measures adequate for analyzing these complex and overlapping phenomena.

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Notes

  1. 1.

    “A man educated at the expense of much labor and time to any of those employments which require extraordinary dexterity and skill, may be compared to one of those expensive machines. The work which he learns to perform, it must be expected, over and above the usual wages of common labor, will replace to him the whole expense of his education, with at least the ordinary profits of an equally valuable capital.” (Smith 1776, p. 118).

  2. 2.

    After Schultz (1961, p. 3).

  3. 3.

    “Adam Smith boldly included all of the acquired and useful abilities of all of the inhabitants of a country as a part of capital” (Schultz 1961, p. 2).

  4. 4.

    Respectively: Program for International Student Assessment (PISA), International Adult Literacy Test (IALS) and Trends in International Mathematics and Science Study (TIMSS). Cf. further in this subsection.

  5. 5.

    Quote after Kiker (1966, p. 483).

  6. 6.

    First edition in 1927.

  7. 7.

    Quote after Kiker (1966, p. 484).

  8. 8.

    Data on wages in developing countries are very often unavailable or may contain an error in estimation. Until the mid-twentieth century, the data on wages were not a part of statistics of many developed countries.

  9. 9.

    Designation changed. Full description of the method for all age groups is contained in Fraumeni, (2011, p. 3–5).

  10. 10.

    Hence, it is not a standardized ‘non-numéraire’ index; the numéraire is a wage of an unqualified worker.

  11. 11.

    The wage of an unqualified worker is estimated with the use of a power function with an exponent which is the constant from Mincer’s wage regression, calculated separately for each country in each year of the studied period of time.

  12. 12.

    http://www.pisa.oecd.org/

  13. 13.

    See Woessmann (2003), p. 243. By definition, the quality of education is taken into account in the approach based on tests’ scores.

  14. 14.

    Definition after UNESCO (1993).

  15. 15.

    Depending on the country and customary or legal regulations, ‘schooling obligation’ might be only a matter of customary habits and might be not supported by any legal sanction.

  16. 16.

    Cf. also Psacharopoulos and Arriagada (1992), where authors repeated the studies using updated data.

  17. 17.

    http://databank.worldbank.org/ddp/home.do, Education Statistics base, series with the prefix “Barro-Lee.”

  18. 18.

    Kyraciou denotes L as \(\overline{S}(MEAN)\).

  19. 19.

    Quote after Nehru et al. (1993).

  20. 20.

    Problems connected with records and calculation of this share are discussed in further detail in Nehru et al. (1993, p. 5).

  21. 21.

    All quoted studies assumed the continuity of education and mortality from the general population θ a , characteristic of age a at which—in accordance with the law or social norms—an individual should attend grade g at level P.

  22. 22.

    http://www.pisa.oecd.org/

  23. 23.

    http://www.statcan.gc.ca/bsolc/olc-cel/olc-cel?catno=89-588-X

  24. 24.

    http://nces.ed.gov/timss/

  25. 25.

    The National Center for Education Statistics, a unit of the U.S. Education Department.

  26. 26.

    International Association for the Evaluation of Educational Achievement, http://www.iea.nl. Assessments of mathematical competences were conducted in the years 1963–1964 (11 countries) and 1980–1982 (17 countries). Assessments of competences in science were conducted in 1966–1973 (17 countries) and 1983–1986 (23 countries). Cf., e.g., Hanushek and Kim (1995).

  27. 27.

    International Assessment of Educational Progress, an organization that conducts international competence tests based on the NAEP methodology.

  28. 28.

    It was not possible to obtain the technical documentation of the methodology from the authors.

  29. 29.

    Per capita.

  30. 30.

    The full list of indicators is contained in the supplement A to the study (Messinis, Ahmed 2009).

  31. 31.

    In 2035 (Ederer et al. 2007).

  32. 32.

    http://databank.worldbank.org/ddp/home.do, Education Statistics base, series with the prefix “Barro-Lee”.

  33. 33.

    Cf. theories analyzed in Sect. 1.

  34. 34.

    For more on this subject, see Weresa (2012).

  35. 35.

    The analysis and its results as well as empirical studies related to this issue are the subject of a separate monograph—cf. Weresa (2012).

  36. 36.

    Discussed in more detail: Weresa (2012).

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Correspondence to Ziemowit Czajkowski or Arkadiusz Michał Kowalski .

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Czajkowski, Z., Kowalski, A., Michorowska, B., Weresa, M. (2014). Human Capital and Innovation—Basic Concepts, Measures, and Interdependencies. In: Weresa, M. (eds) Innovation, Human Capital and Trade Competitiveness. Innovation, Technology, and Knowledge Management. Springer, Cham. https://doi.org/10.1007/978-3-319-02072-3_2

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