BME_16(1)_2018_maketas.indd Copyright © 2018 The Author(s). Published by VGTU Press This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons. org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. *Corresponding author. E-mail: matuzeviciute@gmail.com Business, Management and Education ISSN 2029-7491 / eISSN 2029-6169 2018 Volume 16 Issue 1: 1–12 https://doi.org/10.3846/bme.2018.66 Introduction The growing economic integration, globalization, international openness in the context of regionalization, more and more enhances the competitiveness of countries and regions. One of the most important economic policy goals in European Union is the development of na- tional competitiveness. Social and economic development of a country and its standard of living depend on the ability to identify competitiveness problems, to anticipate problem solv- ing approaches and to anticipate the extent to which such problems are solved. Low labour productivity is one of the indicators which negatively affects competitiveness of the EU coun- tries. So, this encourages increasing interest in analysing a productivity and its impact factors. The importance of human capital for labour productivity growth has been discussed very intensively during the last two decades. Authors emphasize that productivity growth can be achieved improving labours skills and knowledge, together with physical and mental health. The impact of human capital development on country productivity level is theoretically justified, but the results of empirical research vary. Differences in results may occur, as the IMPACT OF HUMAN CAPITAL DEVELOPMENT ON PRODUCTIVITY GROWTH IN EU MEMBER STATES Alma MAČIULYTĖ-ŠNIUKIENĖ1, Kristina MATUZEVIČIŪTĖ2, * 1Vilnius Gediminas Technical University, Department of Business Technologies, Sauletekio Ave. 11, Vilnius, Lithuania 2 Siauliai University, Social Innovation Research Centre of Siauliai University Research Institute, Visinskio str. 25, Siauliai, Lithuania Received 12 January 2018; accepted 06 May 2018 Abstract. In this research, we investigate the impact of human capital on labour productivity in European Union member states using panel data analysis. Results of the paper are estimated using the Pooled ordinary least squares (OLS) and Fixed effects model (FEM). The results show that hu- man capital is positively significant in improving the growth of labour productivity in the EU. Our estimates also suggest that the impact occurs after three times lags in case of education expenditure. Keywords: human capital, productivity, health care expenditure, education expenditure. JEL Classification: J24, E24, I15, I25. 2 A. Mačiulytė-Šniukienė, K. Matuzevičiūtė. Impact of human capital development on productivity ... effect may depend on the level of productivity already achieved by the country, as well as on the analysis period. The effect may not be determined due to the fact that the possible lagged effects are often not evaluated. Therefore, in order to eliminate these limitations of the previous research, we will seek to determine the effects of time lags and the achieved level of productivity. The rest of the paper is organized as follows: Section 1 presents literature review on relationship between human capital and labour productivity. Section 2 describes research variables, hypotheses and model. Section 3 presents the estimation results and summarises research findings. The last section concludes the paper. 1. Impact of human capital on labour productivity: theoretical background The concept of human capital was shaped by a change in the composition of the capital in- volved in the product development process. Potelienė and Tamašauskienė (2014) define hu- man capital as a personal knowledge, acquired skills, education, innate abilities, experience, attitudes, behaviour, intellect, creativity, entrepreneurship, motivation, innovation, insights, accumulated experience, physical, emotional and mental condition of health, energy, orien- tation in the environment, the ability to properly and timely use the knowledge and skills, and other personal characteristics that increase the productivity and income in the form of wages. It is the most developed concept of human capital, covering both human capital components and the goals and result of its development (increasing labour productivity and labour income). Based on this definition, it can be said that the concept of human capital is multidimensional and includes elements of human capital and the impact of accumulation and utilization of its resources on individual income and can be analysed in a micro and macro level. As many authors note (Drucker, 1999, Delsen & Schonewille, 1999; Chani & Shahid, 2012; Whalley & Xiliang, 2013; and others) human capital influences the country’s economic growth, labour productivity and increases national competitiveness. In the model (see Figure 1), human capital is treated as the complex of two main ele- ments: education and health, which are developed through investment in education and in the form of additional training and investment in health care. It should be noted that in all analysed researches, human capital is related to formal education and training in work (time- based learning, education level, or investment in education). Meanwhile, health as an element of human capital is ignored. According to Bloom, Canning, and Sevilla (2004), Pocas (2014) it can be stated that the impact of investment in health improvement on productivity can occur directly because a healthier person is working more productively, and also through life expectancy changes, increased population learning abilities and creativity, reduced in- come inequality, which makes it possible to accumulate more human capital resources due to higher investment in education and through active increase in the share of labour force in the population. The impact of human capital components on labour productivity occurs both directly and indirectly. The direct relationship between investment in employee training (in all forms) and experience also investment in employee health, which are measured at macro level by public and private investment in the education system and healthcare system, as well as private training costs, generates increasing labour productivity. Figure 1 presents channels of human capital impact on labour productivity. Business, Management and Education, 2018, 16(1): 1–12 3 The macro level researches on the effect of human capital on productivity can be divided into two groups: single country (Afrooz, Rahim, Noor, & Chin, 2010; Umoru & Yaqub, 2013; Backman, 2014; Arshad & Ab Malik, 2015) and cross-country (Bloom et al., 2004; Rivera & Currais, 2003; Belorgey, Lecat, & Maury, 2006; Chansarn, 2010; Jajri & Ismail, 2010; Qu & Cai, 2011; Fleisher, Hu, Li, & Kim, 2011; Goos, Konings, & Vandeweyer, 2015). Some of the recent studies (Ucbasaran, Westhead, & Wright, 2008; Chansarn, 2010; Afrooz et  al., 2010; Jajri & Ismail, 2010; Forbes, Barker, & Turner, 2010; Qadri & Waheed, 2014; Goos, Konings, & Vandeweyer, 2015; Chang, Wang, & Liu, 2016; Benos & Karagiannis, 2016; Goldin, 2016) have identified that human capital in terms of education has positive impact on labour pro- ductivity growth. While other researches by Bloom et al. (2004), Rivera and Currais (2003), Lopez-Casasnovas, Rivera, & Currais (2005), Howitt (2005), Becker (2007), Umoru and Yaqub (2013), Dillender (2016) included health aspects as well in measuring the magnitude of human capital influence on labour productivity. de la Escosura and Rosés (2010), Gano- takis (2012), Slaper, Hart, Hall, & Thompson (2011), McGuirk, Lenihan, & Hart (2015) pay attention to the innovations as a key driver of productivity. The investment in higher educa- tion enhances productivity in country (Annabi, Harvey, & Lan, 2011; Annabi, 2017). Bartel (1995) explored the impact of training of skilled workers on productivity and wage levels and found that the number of days spent in training did not have statistically significant impact on productivity. Black and Lynch (1996) found that formal education has a positive impact Development of human capital Investing in education, training and experience Development of innovation Physical capital accumulation Resource Allocation E�ectiveness Capital to labour ratio In di re ct im pa ct Indirect im pact Investing in health care Labour productivity growth D ir ec t i m pa ct D irect im pact Longer life expectancy Higher learning abilities Growing creativity Decreasing income inequality Figure 1. The model of human capital development impact on labour productivity (made by authors) 4 A. Mačiulytė-Šniukienė, K. Matuzevičiūtė. Impact of human capital development on productivity ... on productivity, while other types of training, such as computer training, have no statistically significant impact. In many researches (Appleton, Hoddinott & Mackinnon, 1996; Nielsen & Westergard-Nielsen, 2001; Teal, 2001; Whaba, 2000; Keswell & Poswell 2004; Burger & Teal, 2014) it was stated that education has a significant direct positive effect. Other authors (Delsen & Schonewille, 1999; Polasek & Schwarz-Bauer, 2011; Yunus, Said, & Hook, 2014) found that the level of education does not have statistically significant effect on productivity. After analysing empirical research on the impact of human capital on productivity, it can be stated that the results of the research depend on the research sample, the analysis period, the level of research, the methods, the measurement of human capital and other parameters. The impact of human capital on productivity has a significant impact not in all countries. 2. Research methodology and data In various scientific works, the development of human capital is measured by different indi- cators. In our research we have used education expenditure and health care expenditure to measure the development of human capital. The health expenditure, as an indicator of the human capital development, was used in the studies Tompa (2002), Reino, Kiander, and Matti (2006), Panopoulou and Pantelidis (2012), Razmi, Abbasian, and Mohammadi (2012), Eneji, Dickson, and Onabe (2013), Hartwig (2015), etc. Education expenditure as an indicator of human capital development used Annabi et  al. (2011), Farzanegan (2011), Olimpia (2012), Panopoulou and Pantelidis (2012), Hartwig (2015), Annabi (2017), etc. This confirms the ac- ceptability of the use of selected indicators. In the paper, we used added value per employee as a standard indicator to reflect the productivity. Multiple regression analysis was used to investigate the effect of human capital develop- ment on productivity. Regression analysis may use time series, cross sectional and panel data (Asteriou, 2009). In this study we used panel data. According Hsiao (2003), because of the higher degrees of freedom, the higher volatility and the lower amount of collaterals, the panel data models are more reliable than time series or cross sections. They enable to include in model a larger number of independent variables at a relatively short time; to include fictitious (pseudo) variables; allows to control the heterogeneity of variables, as well as check lagged effects. Using panel data, three regressive models are essentially applied: Pooled ordinary least squares (OLS), Fixed effects model (FEM) and Random effects model (REM). Because of the high number of variables used and the small number of objects, the regression analysis of random effects methods cannot be applied, so the analysis is done using two methods: fixed effects and least squares. Gwartney et  al. (2006) recommends use data of the longest possible period in order to minimize the impact of business cycles, the impact of various external shocks on economic indicators. We used the data of EU member states (except Luxembourg and Croatia) for 1995–2015 period from World Bank data base. The beginning of the period was determined by the fact that The World Bank (and other) database published statistics of new EU member states (joining in 2004) only since 1995. Data for 2016 is not yet published. Luxembourg was eliminated from the sample due to the excessive gap from other countries, Croatia – due to the lack of data. Business, Management and Education, 2018, 16(1): 1–12 5 In the first stage EU member states were assigned to clusters characterised by relative- ly high (RHP) and relatively low (RLP) productivity. Referring to Everitt, Landau, Leese, and Stahl (2001), for this assignment we used cluster analysis. We assigned countries in a way that differences in terms of productivity among them would be smaller within the cluster than between the clusters. In the second stage, we aim to identify when, if any, the impact of human capital de- velopment on productivity occurs and for how long it lasts. Here we are testing the first hypothesis: H1 – Development of human capital positively affects productivity in countries with relatively low, as well as with relatively high level of productivity, but it takes time for this effect to occur. On the basis of initial testing, the six-year period chosen to examine the impact of human capital on productivity. Model realizing FE method: ln(VApei,t)=α+δ8td2002t+…+δ21td2015t+βln(HelthEXPtoti,t)+ β1ln(HelthEXPtoti,t-1)+β2ln(HelthEXPtoti,t-2)+β3ln(HelthEXPtoti,t-3)+ β4ln(HelthEXPtoti,t-4)+β5ln(HelthEXPtoti,t-5)+β6ln(HelthEXPtoti,t-6)+ λln(EducEXPtoti,t)+λ1ln(EducEXPtoti,t-1)+λ2ln(EducEXPtoti,t-2)+ λ3ln(EducEXPtoti,t-3)+λ4ln(EducEXPtoti,t-4)+λ5ln(EducEXPtoti,t-5)+ λ6ln(EducEXPtoti,t-6)+vi,t. (1) Model realizing OLS method: Δln(VApei,t)=α+δ9td2003t+…+δ21td2015t+βΔln(HelthEXPtoti,t)+ β1Δln(HelthEXPtoti,t-1)+β2Δln(HelthEXPtoti,t-2)+β3Δln(HelthEXPtoti,t-3)+ β4Δln(HelthEXPtoti,t-4)+β5Δln(HelthEXPtoti,t-5)+β6Δln(HelthEXPtoti,t-6)+ λΔln(EducEXPtoti,t)+λ1Δln(EducEXPtoti,t-1)+λ2Δln(EducEXPtoti,t-2)+ λ3Δln(EducEXPtoti,t-3)+λ4Δln(EducEXPtoti,t-4)+λ5Δln(EducEXPtoti,t-5)+ λ6Δln(EducEXPtoti,t-6)+vi,t, (2) where: – Dependent variable Vapei,t – value added per employee in the country i in the period t; – Independed variable: HelthEXPtoti,t – total health expenditure (USD) in the country i in the period t; EducEXPtoti,t – total education expenditure (USD) in the country i in the period t; –– β – the coefficient, which reflect impact of health expenditure on productivity in current time; –– β1 ... β6 – the coefficients, which reflect impact of health expenditure on productivity after one to six years; –– λ – the coefficient, which reflect impact of education expenditure on productivity in current time; –– λ 1 ... λ 6 – the coefficients, which reflect impact of health expenditure on productivity after one to six year. H1 hypothesis approval conditions: β>0, p>0,05; λ>0, p>0,05; at least one β1...6>0, p<0,05; at least one λ1...6>0, p<0,05. 6 A. Mačiulytė-Šniukienė, K. Matuzevičiūtė. Impact of human capital development on productivity ... In the third step we explore the impact of human capital development on productivity growth through productivity level. Based on theoretical assumptions and retrospective studies, we are testing hypothesis: H2  – the quantitatively higher human capital impact on productivity is manifested in the countries with relatively low productivity. Two econometric models were conducted for hypothesis testing. In econometric models we used cluster of RLP countries as the base category that will be the starting point for impact assessments. In this group, the estimated impact coefficient will be evaluated directly, and for the RHP countries cluster, the coefficients determined will show the difference from the base category. It should be noted that, we have chosen a time lags taking into account the results obtained in the second stage of the research. Model realizing FE method: ln(VApei,t)=α+δ6td2000t+…+δ21td2015t+β21ln(HelthEXPtoti,t-2)+ β22ln(HelthEXPtoti,t-2)·RHPi,t+β31ln(HelthEXPtoti,t-3)+ β32ln(HelthEXPtoti,t-3)·RHPi,t+λ31ln(EducEXPtoti,t-3)+ λ32ln(EducEXPtoti,t-3)·RHPi,t+λ41ln(EducEXPtoti,t-4)+ λ42ln(EducEXPtoti,t-4)· RHPi,t+vi,t. (3) Model realizing OLS method: Δln(VApei,t)=α+δ7td2001t+…+δ21td2015t+β21Δln(HelthEXPtoti,t-2)+ β22Δln(HelthEXPtoti,t-2)·RHPi,t+β31Δln(HelthEXPtoti,t-3)+ +β32Δln(HelthEXPtoti,t-3)·RH Pi,t+λ31Δln(EducEXPtoti,t-3)+ λ32Δln(EducEXPtoti,t-3)·RHPi,t+λ41Δln(EducEXPtoti,t-4)+ λ42Δln(EducEXPtoti,t-4)·RHPi,t+vi,t, (4) where: – Dependent variable Vapei,t – value added per employee in the country i in the period t; – Independed variable: HelthEXPtoti,t – total health expenditure (USD) in the country i in the period t; EducEXPtoti,t – total education expenditure (USD) in the country i in the period t; – β21 – the coefficient reflecting the impact of health expenditure on productivity in the base group of countries (RLP) in the second year after the costs incurred; – β22 – the coefficient reflecting the difference between the impact of health expenditure on productivity in the RHP countries relative to the RLP countries, in the second year after the costs incurred; – β31 – the coefficient reflecting the impact of health expenditure on productivity in the base group of countries (RLP) in the third year after the costs incurred; – β32 – the coefficient reflecting the difference between the impact of health expenditure on productivity in the RHP countries relative to the RLP countries, in the third year after the costs incurred; – λ31 – the coefficient reflecting the impact of education expenditure on productivity in the base group of countries (RLP) in the third year after the costs incurred; – λ32 – the coefficient reflecting the difference between the impact of education expen- diture on productivity in the RHP countries relative to the RLP countries, in the third year after the costs incurred; – λ41 – the coefficient reflecting the impact of education expenditure on productivity in the base group of countries (RLP) in the fourth year after the costs incurred; Business, Management and Education, 2018, 16(1): 1–12 7 – λ42 – he coefficient reflecting the difference between the impact of education expendi- ture on productivity in the RHP countries relative to the RLP countries, in the fourth year after the costs incurred. Hypothesis approval conditions: β21, β31, λ31, λ41 >0, p<0,05; β22, β32, λ32, λ42 >0 or <0, p<0,05. 3. Estimation results and discussion As it was explained above, in the first stage of estimation EU member states (except Lux- embourg and Croatia) were divided into relative high productivity (RHP) and relative low productivity (RLP) clusters (see Table 1). Table 1. EU member states by productivity level (results of cluster analysis) RHP cluster Ireland, Austria, Belgium, Denmark, Spain, Finland, France, United Kingdom, Italy, Netherlands, Sweden, Germany. RLP cluster Bulgaria, Cyprus, Czech Republic, Estonia, Greece, Latvia, Lithuania, Poland, Malta, Portugal, Romania, Slovakia, Slovenia, Hungary. In the second study stage, effect of all countries education and health expenditures on pro- ductivity were examined in order to determine the duration of impact and confirm or deny H1 hypothesis. To achieve this goal regression analysis was used: fixed effects and ordinary least squares models (equations 1 and 2). The results of the research indicate that statistically significant positive effect of health ex- penditure on productivity is manifested in the second and third years after the costs incurred. Statistically significant positive effect of education expenditure on productivity is manifested in the third and fourth years after the costs incurred. This result was obtained by using both regression analysis methods (FE and OLS), which confirms the reliability of the results obtained. These estimation results are in line to prove first hypothesis. It is not surprising, that it takes time for health and education expenditures to transit into higher productivity. According to the results of estimation, it can be argued that 1% increase the total health expenditure, in two statistically significant years of impact, productivity would increase on average by 0.26% (based on the FE model) to 0.31%. (based on the OLS model). The 1% increase in education expenditure, over a period of statistically significant impact, would result an increase in productivity by an average of 0.18%. (based on the FE model) to 0.23% (based on the FE model). Therefore, estimations additionally disclosed that increase of health expenditure influences productivity more comparing with increase of education expenditure. These results are surprising. In traditional point of view education is more important for productivity improvement comparing with society health. Nevertheless, such result could occur due to the high level of education achieved by EU countries. In the third study stage, we have investigated differences in human capital impact on productivity in EU relatively high and relatively low productivity clusters. The estimation was conducted taking into account that, statistically significant positive impact of human capital on productivity is manifested after time lag. Estimation results are presented in table 2 (3 ir 4 equations). 8 A. Mačiulytė-Šniukienė, K. Matuzevičiūtė. Impact of human capital development on productivity ... Before discussing the results of the research, first of all it should be noted that realized FE and OLS models differ in reliability. This conclusion is based on information criteria. The lower value of Schwarz, Akaike and Hannan-Quinn criteria means higher reliability (Konishi & Kitagawa, 2008). So, based on the obtained values, it is concluded that the results of the realization of OLS models are more reliable. For this reason, when assessing the impact of human capital development on productivity in RHP and RLP clusters, discussion of findings are based on the OLS model results. Table 2. Research findings on the impact of the human capital development on labour productivity in European Union countries clusters Coefficients 1 and 2 equations Coefficients 3 and 4 equations FE OLS FE OLS α 0,724* 0,030*** α –0,365 –0,053*** δ8 0,049*** δ6 –0,079*** – δ9 0,103*** 0,036*** δ7 –0,060** 0,074*** … … δ20 0,009 –0,076*** δ20 0,236*** –0,002*** δ21 0,067*** –0,017 δ21 0,262*** 0,104*** β –0,008 0,039 β21 0,148*** 0,181** β1 0,013 0,033 β22 –0,122** –0,190** β2 0,135*** 0,181*** β31 0,131** 0,159** β3 0,127*** 0,130*** β32 –0,135** –0,133** β4 0,048 0,025 λ31 0,110** 0,093** β5 0,016 0,045 λ32 –0,125*** –0,051** β6 0,025 0,029 λ41 0,102*** 0,082*** λ 0,001 0,003 λ42 –0,089** –0,091** λ1 0,050 0,019 λ2 0,022 0,008 λ3 0,108*** 0,093*** λ4 0,121*** 0,083** λ5 0,055 –0,007 λ6 0,009 0,012 n 390 364 n 442 416 R2kor. 0,793 0,682 R2kor. 0,488 0,579 DW 1,771 1,867 DW 0,403 1,825 Information criteria Schwarz Akaike Hannan-Quinn –1137 –1334 –1257 –1113 –1210 –1171 Schwarz Akaike Hannan-Quinn –556 –762 –680 –1075 –1176 –1136 Note: * indicates significance at the 10 percent level; ** indicates significance at the 5 percent level; *** indicates significance at the 1 percent level. Business, Management and Education, 2018, 16(1): 1–12 9 Results of regression analysis show, that higher education and health expenditure posi- tively correlate with productivity in both clusters. However, differences of human capital impact on productivity between clusters are statistically significant. It is seen that impact of education and health expenditure on productivity is bigger in countries with relatively low productivity level compared with countries that have relatively high productivity level. This evidence is in line with H2 hypothesis. Estimation results show that the increase in total health expenditure by 1% determines the average increase in productivity by 0.34% in relatively low productivity countries and 0.17% in relatively high productivity countries during a statistically significant period of impact. After increase in total education expenditure by 1%, productivity increases by 0.18% in relatively low productivity countries and 0.03% in relatively high productivity countries in two years of a statistically significant period of impact. It is likely that EU countries with relatively low productivity can increase productivity investing in education and health care more comparing with relatively low productivity coun- tries because RHP countries have exhausted their productivity potential. Over 1995–2015 period, productivity in RLP countries on average increased by 52%, while in RHP countries – by 237%. Over the analysis period, the value added per employee increased in Lithuania (489%), Latvia (435%) and Romania (426%). At the same time, it has grown in Germany (15%), UK (30%) and Finland (34%). Conclusions Theoretical analysis reveals that results of empirical research vary according to analysis pe- riod and primary productivity level. Low labour productivity is one of the main indicators decreasing EU competitiveness. So it is important to analyse the factors influencing labour productivity. As it was show in theoretical analysis human capital is treated as education and health and it influences country’s economic growth productivity and national competitiveness. Results of examining the impact of human capital development, expressed by health and education expenditure, on EU member states productivity revealed positive and significant impact. This effect occurs with two years lag and lasts for two years in case of health expen- diture, and after three times lag and also lasts for two years in case of education expenditure. This allows us to argue that human capital is one of source of productivity growth. It was also found that impact of human capital development on productivity is bigger in countries with relatively low productivity level compared with countries that have relatively high productivity level. 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