Typology of Human Capital Development in EU Countries

Available online at www.sciencedirect.com ScienceDirect

Procedia Economics and Finance 23 ( 2015 ) 1641 – 1648

2nd World Conference on Business, Economics and Management

Typology of Human Capital Development in EU countries

Sigitas Vaitkevičiusa* , Rūta Čiutienėa, Evelina Meilienėa, A sta Savanevičienėa

aDepartment of Management, Kaunas University of Technology, K.Donelaicio str. 73, Kaunas LT-44029, Lithuania

Abstract

This paper proposes to reveal typology of EU countries in human capital development. On the bases of theoretical findings on human capital development human capital development index which consists out of three sub-indices: Social progress of human capital, Innovation development rate of Human capital and Potential of Human capital development had been created. The human capital development index verified using the factor analysis and reliability analysis methods, created out of 26 primary variables and data of 26 EU countries were used. The hierarchical cluster analysis of human capital development in EU countries revealed six groups of the countries having the different human capital development patterns in EU. This scientific research is financed by the Research Council of Lithuania (Institutional scientific research program „Challenges of Lithuanian economy‘s long-term competitiveness“). © 20152014 The The Authors. Authors. Published Published by by Elsevier Elsevier B.V. B.V. This is an open access article under the CC BY-NC-ND license (Selectionhttp://creativecommons.org/licenses/by-nc-nd/4.0/ and/ peer-review under responsibility of). Academic World Research and Education Center. Selection and/ peer-review under responsibility of Academic World Research and Education Center Keywords: Human capital, development, typology

1. Introduction

There are no doubts that human capital plays an important role in economic growth. Human capital is described as knowledge and skills, country’s or enterprise‘s labor force capabilities, factors that create assumptions or possibilities for people to create innovations or strive for higher productivity (Škare, 2011; Blair, 2012). According Drucker (1999) and Porter (1998) human capital not only influences the economic growth, but increases national competiveness as well. Moreover, human capital affects the growth of other kinds of the capital (Lange et al., 2006). Recently, the issues of human capital development are increasingly becoming as the most important field of scientific research. There are a number of scientific researches representing peculiarities of development of human capital in different countries (Didenko et al., 2013). However, there is still a gap of researches disclosing

* Sigitas Vaitkevičius. Tel.:+4-34-323-232. E-mail address: [email protected]

2212-5671 © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Selection and/ peer-review under responsibility of Academic World Research and Education Center doi: 10.1016/S2212-5671(15)00520-1 1642 Sigitas Vaitkevičius et al. / Procedia Economics and Finance 23 ( 2015 ) 1641 – 1648

peculiarities in Baltic region, e.g. Lithuania. This paper is concerned about different human capital development patterns in EU and Lithuania as well. Purpose of the study is to create the index of human capital development and using it to classify the EU countries. In this paper, attention will be focused upon an analysis on dimensions of human capital development. World Economic Forum (WEF) (2013) calculates Index of human capital. According WEF Human Capital Index (HCI) is a tool for understanding where countries stand today. It is a measure that government and business can use to reveal directions of workforce development. HCI includes four sub-indexes: Education, Health and Wellness, Workforce and Employment, Enabling Environment. The sub-index of Education evaluates the level of citizen’s education and readiness of labor force for future challenges. The sub-index of Health and Wellness evaluates physical and mental well– being of citizens. Four dimensions distinguished by Health and Wellness are: survival, health, well-being and services. The sub-index of Workforce and Employment includes the indices allowing assessment of experience, knowledge and their constant development of working age citizens. Workforce and Employment sub-index distinguishes dimensions of participations, talent and training. The sub-index of Enabling Environment assess assumptions of human capital development (country‘s legal system, infrastructure, cooperation, legal system and social mobility) (World Economic Forum, 2013). In 1990 the United Nations Development Programme launched the Human Development Index (HDI) (Human Development Report, 2013). The first Human Development Report introduced a measuring development by combining indicators of life expectancy, educational attainment and income into a composite HDI. HDI had been improved by disaggregation, and the Inequality-adjusted HDI (IHDI) was introduced in 2010. Over the years new measures to evaluate progress in reducing poverty and empowering women had been created. The Multidimensional Poverty Index (MPI) identifies multiple deprivations at the individual level in health, education and standard of living. Some authors highlights that MPI is extremely important for human capital development in social approach (Alkire et al., 2010; Angulo Salazar et al., 2013). The Gender Inequality Index (GII) reflects women’s disadvantage in three dimensions—reproductive health, empowerment and the labour market. Demographic change, migration and foreign direct investment influence human capital development as well (Contreras, 2013; Liudwig et al., 2012; Miyamoto, 2003; Nafukho et al., 2004). Heitor et al. (2014) analysed the impact of science policies on the process of development of human capital. Partnerships and networking between research and higher education organizations and business companies are of particular importance. It is clear that ability to use science for economic and social growth can describe human capital of country. Internationalization of science and workforce migration affects processes of human capital development. Samstad et al. (2005) examined variations in human capital development across and within individual product sectors. They proposed “management-cantered ” model of human capital development. The model is based on the relationship of markets, technology, and human capital development. Local management plays central role in this model. Labour unions and Civil Society are involved in human capital development process as well. Sakalas et al. (2013) adapted methodology of calculation of multifactor indices and constructed a complex human capital assessment system that distinguishes social, innovation, Economic Value Index, Cost Index, and Income Index. This system allows to assess the role of human capital to the growth of country‘s economic growth in complex (Sakalas et al., 2013; Liepe, 2013). A content of portfolio of human capital assessment indices has to be formed individually, depending on the context and the field of specific research (Ciegis et al., 2009) and based on economic logic. It is very important to understand that development conception covers not only economic indicators but social characteristics also (Oscan et al. 2011; Neumayer, 2001). Consequently, on the bases of theoretical findings on human capital development we decided to disclose the typology of EU countries using index which consists out of three sub-indices: Social progress of human capital, Innovation development rate of Human capital and Potential of Human capital development.

2. Methodology of the study

2.1. Study design

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The methodology of the study covers the verification of Human Capital Development index and its application for development of the typology of EU countries. The Human Capital Development index verified using the factor analysis and reliability analysis methods. The Human Capital Development index created out of 26 primary variables (Table 1). Data of 26 EU countries were used to create the index. All data were selected from Eurostat data basis. The year 2010 data was used for Human Capital Development index modeling and for the typology creation. All sub-indexes and index created first rescaling original data to the ordinal scale. Ntile method had been used for the rescale. All primary variables rescaled to 5-point scale where the 1 indicates low level and 5 indicates the high level of the primary variable. Then sub-indexes were created using factor analysis out of rescaled primary variables. The sub-indexes and index constructed using Principal Component Analysis combined with Varimax rotation method. The construct validity of the sub-indexes and index was tested by Reliability analysis. The typology of human capital development in EU countries created using hierarchical cluster analysis (Fig. 1). Three sub-indexes of Human Capital Development index were used for the classification. The SPSS 20.0 software and Excel 2010 (both licensed to the KTU) were used for data analysis.

2.2. Verification of Human Capital Development index

The Human Capital Development (HCD) index consists out of three sub-indices that describe the country's HCD pattern (Table 1): Social progress of human capital, Innovation development rate of Human capital and Potential of Human capital development. The Social progress of human capital as variable describes the achieved state of evolvement of the human capital in the country; the Innovation development rate of Human capital used to describe creativeness of the human capital in the country and the Potential of human capital development describes the prerequisites of human capital evolution. Verification of HCD index and its sub-indices revealed the high factor scores (lowest Fmin=0.543, highest Fmax =0.99) which indicating a high level of compatibility between the primary items and implicit index. Corrected Item-total correlations range more differentiated (lowest i/tt = 0.236, highest i/tt = 0.96). It means that index and sub-indices are characterized by high qualitative diversity.

Table 1. Factor and Corrected item total correlation scores of Human Capital Development index

Corrected Index and sub- Item-Total indexes Variables: Factor Correlation I. Human I.1. Social progress of human capital 0.898 0.723 Capital I.2. Innovation development rate of human capital 0.913 0.749 Development I.3. Potential of human capital development 0.732 0.502 I.1.1 Demographic change of human capital 0.841 0.716 I.1.2. Human capital resistance to poverty 0.769 0.628 I.1. I.1.3. Population growth rate 0.763 0.609 I.1.4. Human development index 0.838 0.727 I.1.5. Healthiness of human capital 0.652 0.494 Total population change 0.953 0.816 I.1.1. Net migration plus statistical adjustment 0.953 0.816 People at risk of poverty or social exclusion, age 18-64 0.838 0.622 I.1.2. Cumulative people at risk of poverty rate (ISCED97 all levels) 0.805 0.571 Unemployment rate, annual average 0.838 0.622 Crude death rate 0.99 0.96 I.1.3. Suicide death rate, Total crude death rate per 100 000 persons 0.99 0.96 Under-five survival rate (per 1,000 live births) 0.833 0.542 I.1.5. Health care expenditure, % from GDP 0.705 0.39 Employment rate by age group 20-64, % 0.748 0.428 I.2.1. High-tech turnover rate 0.844 0.425 I.2. I.2.2. Learning and innovation development rate 0.844 0.425 High-tech import as a percentage of total Imports 0.953 0.816 I.2.1. High-Tech Exports, % of total exports 0.953 0.816 Participation in education and training or Life-long learning total, %, From 25 to I.2.2. 64 years 0.845 0.807 Human resources in science and technology as a share of labor force, % 0.869 0.833 1644 Sigitas Vaitkevičius et al. / Procedia Economics and Finance 23 ( 2015 ) 1641 – 1648

Scientists and Engineers, From 25-64 years, % of Total population 0.799 0.747 Official development assistance as share of gross national income, % 0.886 0.857 Outsourced employment, number of persons 0.727 0.673 Patent applications to the European Patent Office at the national level, per million of inhabitants 0.929 0.904 Level of Internet access - households, % 0.875 0.838 Information Technology Expenditure % of GDP 0.76 0.706 Total R&D expenditure, all sectors, % of GDP 0.895 0.863 Total R&D expenditure, higher education sector, % of GDP 0.769 0.715 I.3.1 Human capital formation costs 0.864 0.494 I.3. I.3.2. The attractiveness of high education 0.864 0.494 Total public expenditure on education as % of total public expenditure, for all levels of education combined 0.938 0.76 I.3.1. Spending on Human Resources, as % of GDP, for all levels of education combined 0.938 0.76 Population with tertiary education, from 15-64, 5-6 level 0.779 0.38 I.3.2. Inflow of students (ISCED 5-6) from EU-27, as % of all students in the country 0.848 0.503 Students (ISCED 5-6) enrolled in education field - as % of all students 0.543 0.236 Extraction Method: Principal Component Analysis.

KMO and Bartlett's test indicate that all sub-indices and HCD index are significant (Table 2). Other statistics indicate that the homogeneity is sufficient for development of HCD index in all stages.

Table 2. Human Capital Development and its sub-indices homogeneity verification results

KMO and Bartlett's Test Extraction Sums Bartlett's Test of Sphericity of Squared Reliability Statistics Inter-Item Correlations Variables Approx. Loadings, % of Cronbach's N of KMO Chi-Square df Sig. Variance Alpha Items Mean Minimum Maximum I. 0.64 29.6 3 0 72.5 0.81 3 0.58 0.46 0.79 I.1. 0.69 52.2 10 0 60.2 0.83 5 0.50 0.19 0.67 I.1.1. 0.50 25.7 1 0 90.8 0.90 2 0.82 0.82 0.82 I.1.2. 0.70 18.2 3 0 68.5 0.76 3 0.53 0.51 0.57 I.1.3. 0.50 57.4 1 0 98.0 0.98 2 0.96 0.96 0.96 I.1.5. 0.61 8.9 3 0.031 58.4 0.64 3 0.37 0.26 0.46 I.2. 0.50 4.7 1 0.03 71.3 0.59 2 0.43 0.43 0.43 I.2.1. 0.50 25.7 1 0 90.8 0.90 2 0.82 0.82 0.82 I.2.2. 0.86 190.0 45 0 70.2 0.95 10 0.67 0.37 0.89 I.3. 0.50 6.0 1 0.014 74.7 0.65 2 0.49 0.49 0.49 I.3.1. 0.50 18.5 1 0 88.0 0.86 2 0.76 0.76 0.76 I.3.2. 0.54 8.0 3 0.045 54.0 0.56 3 0.30 0.13 0.48

Results of HCD index verification indicate that the HCD index is informative and can be used for classification of Human Capital Development in 26 selected EU countries.

2.3. Methodology for Modeling of Human Capital Development Typology

The hierarchical cluster was used for modeling of HCD. Cases clustered using the cluster method: Between- groups linkage; Measure: interval: Squared Euclidian distance. Values transformed by case using Z-scores standardize. Methodology of hierarchical cluster allows classifying the countries according their development pattern. Using this methodology the size of the country does not affect the results, so in cluster models presented countries are classified by similarity of their Human capital development model (see Fig 1-7).

3. Findings and Results

The hierarchical cluster analysis of HCD in EU countries initially revealed seven groups of the countries having the different human capital development patterns in EU (see Fig 1). Due to similarity of patterns in six and seven clusters the countries are grouped together. The HCD patterns of grouped countries for all six clusters are presented in graphs (see Fig 2-7). Sigitas Vaitkevičius et al. / Procedia Economics and Finance 23 ( 2015 ) 1641 – 1648 1645

Fig. 1. Human capital development model of EU countries

The first group includes three EU countries that can be described as countries with high social progress of human capital and low potential of human capital development (see Fig 2). In these countries the social progress of human capital and innovation development rate of human capital evolve faster comparing to the potential of human capital development. In this group of the countries the innovation development rate of human capital tend to be less than social progress of human capital. These countries can be characterized as currently having the high quality of human capital, the enough high innovativeness of human capital but do not currently investing into the increase of human capital development.

1,5 1 Romania 1 0,5 0,5

0 Malta 0 scale scale -0,5 z- -0,5 Netherlands z- Germany -1 Italy -1

-1,5 Finland -1,5 France

Czech Republic

Austria Human capital Human Human capital Human I.2. Innovation I.2. Innovation human capital human capital development rate of development rate of capital development capital capital development capital I.1. Social progress of progress Social I.1. I.1. Social progressof I.3 Potential of Human of Potential I.3 I.3 Potential of Human of Potential I.3 Fig. 2. Cluster of countries with relatively high social progress of human Fig.3.Cluster of countries with relatively high innovation capital and relatively low potential of human capital development development rate and social progress of human capital

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The second group comprises six countries with high innovation development rate and social progress of human capital (see Fig 3). In these countries as in previous the social progress of human capital and innovation development rate of human capital evolve faster than potential of human capital development. Difference only is that in these countries Innovation development rate of human capital tend to be higher than social progress of human capital. These countries can be characterized as currently having the high innovativeness of human capital, the enough high quality of human capital but do not investing into the increase of human capital development. The third group covers five countries with relatively high social progress of human capital and relatively low innovation development rate of human capital (see Fig 4). These countries same as previous has high social progress of human capital but in contrast of them they have relatively low innovation development rate of human capital. These countries can be characterized as: having the high quality of human capital; the low innovativeness of human capital; and, except the Greece, do not investing into the increase of human capital development.

1,5 1,5 1 1

0,5 0,5 0 Spain 0 scale scale z- -0,5 z- -0,5 Slovenia Cyprus -1 -1 -1,5 Slovakia -1,5 Bulgaria Portugal Belgium Greece I.1. Social I.1. Social I.1. progress of progress of development development human capital human capital Human capital Human Human capital Human I.3 Potential of Potential I.3 I.3 Potential of Potential I.3 I.2. Innovation I.2. Innovation development rate development rate of Human capital of Human capital Fig. 4. Cluster of countries with relatively high social progress of human Fig. 5. Cluster of countries with dominated potential of human capital and relatively low innovation development rate of human capital capital development

The forth group includes three countries with dominated potential of human capital development (see Fig 5). The Cyprus, Bulgaria and Belgium comparing to previously described countries has different pattern of HCD. These countries can be characterized as: having the lower quality of human capital and the lower innovativeness of human capital comparing to the currently increasing speed of human capital development. The fifth group comprises five countries with relatively high potential of human capital development and relatively low social progress of human capital (see Fig 6). These five countries have some similarities in development pattern comparing to the forth group of the countries. The difference is only that the fifth group of countries tends to have higher innovation development rate of human capital comparing to the Social progress of human capital while the forth group tend to have the innovation development rate of human capital lower.

1,5 1,5 1 1

0,5 0,5 United 0 Poland 0 Kingdom scale scale z- z- -0,5 -0,5 Luxembourg Hungary -1 -1 -1,5 Lithuania -1,5 Sweden Latvia

Estonia Ireland development development Human capital Human capital Human I.3 Potential of Potential I.3 of Potential I.3 Human capital Human capital Human I.2. Innovation I.2. Innovation of human capital human of capital human of I.1. Social progress I.1. Social progress development rate of development rate of Fig. 6. Cluster of countries with relatively high potential of human capital Fig. 7. Cluster of countries with relatively high innovation development and relatively low social progress of human capital development rate of human capital and relatively low social progress of human capital

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The sixth group includes the four countries with relatively high innovation development rate of human capital and relatively low social progress of human capital (see Fig 7). These countries are similar by the high innovation development rate of human capital to the second group of the countries, but by the social progress of human capital they have same pattern as fifth group of countries. Some differences in this group can be seen in the tendency of the Potential of human capital development. Here the Sweden and Ireland tend to have the positive development while the United Kingdom and Hungary - the negative. We decided to merge them into one cluster due to the fact that the difference of potential of human capital development between these countries did not exceed one standard deviation. Summarizing can be stated that using this methodology became possible to combine the 26 EU countries by their pattern of human capital development. The methodology allowed extracting at least six types of countries. Inside of each type the countries are homogenous by the pattern of HCD and across the types the countries are heterogeneous by the HCD pattern.

4. Conclusions and Recommendations

The paper provides an approach to the development of EU countries typology in human capital development. A central finding is that the index of human capital development had been created. It covers 26 primary variables and 26 EU countries. This index allows understanding the evolvement of human capital development in and across the countries and make possible to find out why the countries at the same time being so different has the same HCD pattern. The hierarchical cluster analysis of human capital development in EU countries revealed six groups of the countries having the different human capital development patterns in EU: x The first group includes three EU countries, which can be described as countries with high social progress of human capital and low potential of human capital development. x The second group comprises six countries with high innovation development rate and social progress of human capital. x The third group covers five countries with relatively high social progress of human capital and relatively low innovation development rate of human capital. x The forth group includes three countries with dominated potential of human capital development. x The fifth group comprises five countries with relatively high potential of human capital development and relatively low social progress of human capital. x The sixth group includes the four countries with relatively high innovation development rate of human capital and relatively low social progress of human capital. Finally, our findings discover new fields of scientific researches. It is necessary to answer to the question why some countries being different has same human capital development pattern.

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