Unger et al. (2011), amongst others, claim that no official measure of human capital has yet developed. Likewise, Cohen and Soto argue that there is lack of a clear measurement of human capital, and consider “conceptually, there has not been a clear-cut definition on how human capital should be represented” (2007, p. 52). Interestingly, Soboleva’s (2010) account of the paradoxes involved in measuring human capital takes accounts of many definitions; some of these accentuate the market nature of human capital, but contain little on its sources. According to Soboleva (2010), such sources may include the productive skills, talents, and knowledge that permit an individual to earn an income. It is difficult to identify all of the influences on human capital as a complex economic and social phenomenon, whose evolution is marked by both endogenous and exogenous factors (Popescu and Diaconu 2008).
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Knowledge and skills form part of the description of human capital: as such, they can be used as methods of measurement. These skills include formal knowledge and general aptitudes acquired either on the job or throughout the individual’s life, often unobserved skills/knowledge (Portela 2001). A measurement of labour quality reflects the skills, know-how and abilities brought by the individual to the organisation (Mostert 2007; Edvinsson 1996; Santos-Rodrigues et al 2010). The OECD (2010) makes the interesting point that linking skills to innovation is difficult; this has led to a lack of clarity in defining and measuring the connections between innovation and human capital. This point is pivotal in the rationale for the current research.
As mentioned earlier, education and training has long been used as a proxy to measure human capital (Cohen and Soto 2007). The level and type of formal education, or number of years of schooling, form the bases of most research assessing return on investment in human capital (e.g. Mincer 1993; 1997; Schiuma and Lerro 2008; Cohan and Soto 2007; McCann and Simonon 2005).
While it is clear from the literature that education plays a key role in relation to human capital, scholars are uncertain about how to measure education in this context (Mulligan and Sala-I-Martin 2000). The average number of years’ schooling is not necessarily a good measure, as it assumes that different levels of education in all categories are standardised for workers, and that time spent in education is proportionate to an individual’s productivity; for example, a person with twenty years’ schooling is twenty times more productive than a person with one years’ education (Mulligan and Sala-I-Martin 2000). Mulligan and Sala-I-Martin suggest that schooling is not a good measure of human capital because “one year of schooling is assumed to deliver the same increase in skill always and everywhere… regardless of the field of study and the quality of the teachers, and the education infrastructure” (2000, p. 216).
Before delving into the emerging debate on human capital (at the micro- level), it is useful to understand how the concept is accounted for at the level
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of the whole economy. In studies that measure human capital at the macro level, the use of indices is common. The next section explains two such indices.
3.6.1 Measuring national/regional levels of human capital
For the most part, human capital is measured in aggregate on the national level, for example, the Lisbon Council’s The European Human Capital Index (Ederer 2006). Human capital is measured by this means in order to ascertain the level of development of Europe’s overall human capital. The Index ranks 13 countries across the European Union and observes their ability to develop and grow human capital (Ederer 2006). It defines human capital as the cost (in euros) of formal and informal education, multiplied by the number of people living in each country. The Index defines four types of human capital for the participating countries in order to analyse their collective contribution to the wealth of European citizens. The four types of human capital defined are as follows:
Human capital endowment (the cost of all types of education and training);
Human capital utilisation (measured by human capital as a proportion of the overall population of a country);
Human capital productivity (derived by dividing each gross domestic product by all the human capital employed in a country);
Demography and employment (referring to existing economic, demographic and migration trends in order to estimate employment in the year 2030).
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The 2006 European Human Capital Index report ranks Sweden first, with an overall score of eight, followed by Denmark with a score of 14. Ireland ranked joint seventh with France on a score of 30, indicating a large disparity between countries’ respective human capital.
A similar human capital index has been devised to measure regional innovation performance in the United States at county and state level (Slaper et al 2011). Human capital contributes 30 percent as one of four indicators (the others being economic dynamics 30%, productivity and employment 30%, and economic well-being 10%). The human capital measure assesses the extent to which the population and labour force are able to engage in innovative activities (Slaper et al 2011). Regions displaying higher levels of human capital are those with greater knowledge levels, measured by educational attainment; a larger, younger workforce; and a larger number of innovation-related occupations relative to the overall workforce (technology-based knowledge occupations).
Both of these indices described above, again reiterate the central role played by human capital in innovative activity for growth at a macro level. However, though useful at the regional/economy level, the indices do not address the unique needs of individual firms and the innovative elements of the person. Before addressing the emerging debate in respect to human capital and innovation it is necessary to highlight the contribution of education and training (the traditional measures of human capital).
As outlined previously, human capital is traditionally measured by educational attainment or years of schooling; these can be viewed as a type of credential that indicates a greater innate productivity (Ucbasaran et al 2007). Hofheinz (2009) states that educational attainment is a way of assessing levels of skills in a workforce, ‘higher skills’ meaning tertiary attainment or equivalent, and ‘medium skills’ meaning attainment of secondary or equivalent education; he finds that in all instances, the employment, earning potential and prospects for individuals with further training is are greater than for those with low skills (Hofheinz 2009). Higher education and in-firm training have been found to be positively
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related to innovation, and to be the most significant determinant of a firm’s absorptive capability (Rodriguez et al 2008). However, this finding is contradicted in part by Vinding’s (2006) study of 1544 Danish firms, in which higher education among employees, while positively correlated with ability to innovate was negatively correlated with innovative imitation. Vinding’s (2006) study also found that managers’ work experience was negatively associated with innovation in science-based and ICT type firms. This study confirms in part Schneider et al.’s statement that “highly educated employees are not necessarily positively related to firms’ ability to innovate” (2010, p. 186). Such findings further strengthen the call for a more holistic measure of human capital for which the current research contributes.
According to Lundvall and Johnson (1994), higher education impacts on innovation in two ways: firstly, graduates can invent and develop new technologies; and secondly, more highly educated graduates can exploit technological progress. Interestingly, entrepreneurs’ general human capital (a term discussed earlier) involves the level of education attained by an individual before becoming self-employed, and is considered to be very important for the productivity, profitability and growth of the entrepreneur’s firm (Ganotakis 2012; Bosma et al 2004). The level of education of the entrepreneur also contributes to their absorptive capacity or learning ability, and to the ability to identify other entrepreneurial opportunities (Ganotakis 2012; Uchasaran et al 2007). Ganotakis (2012) found that the human capital of a firm’s founders (in the form of specific education and experience including managerial, commercial and technical experience) contributed positively to the firm’s performance and survival.
A national-level analysis of education reveals that developing and developed economies have very different measures of human capital. For example, developing economies aspire to increase the numeracy and literacy skills of their people on a continuous basis (Soboleva 2010). This may explain Romer’s statement about endogenous growth, “that low levels of human capital may help explain why growth is not observed in underdeveloped economies…” (1990, p. S99). Developed economies, on
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the other hand, have set ambitious targets, for instance, in Ireland there is a drive to increase the number of PhD graduates, particularly in the science, and ICT sectors (Forfás 2009). The number of skilled workers in developed economies has escalated, and a recently discernible skill bias has been attributed to this increase (Arvanitis and Loukis 2009)23.
While educational attainment and years of schooling have been highly developed in the literature as objective tools to measure human capital, the same cannot be said for the subjective aspects of human capital (e.g., the tacit elements that differentiate a firm and give rise to competitive advantage). As indicated by the review of the literature so far, it is becoming increasingly difficult to ignore the emerging debate on the role of human capital for firm-level innovation. There is an emerging debate in the literature on these more innovative elements which are central to the current research. The penultimate section of this chapter provides details of these developments.
3.7 An emerging debate on the contributions of human capital to